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Genetic and Environmental Risk Factors for Psoriatic Arthritis among patients with Psoriasis
by
Lihi Eder
A thesis submitted in conformity with the requirements for the degree of PhD
Institute for Medical Science University of Toronto
© Copyright by Lihi Eder 2011
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Genetic and Environmental Risk Factors for Psoriatic Arthritis among Patients with Psoriasis
Lihi Eder
PhD
Institute of Medical Science University of Toronto
2011
Abstract
Aim: Most of the patients with Psoriatic Arthritis (PsA) develop arthritis following the onset
of psoriasis. The aim of the project is to identify genetic and environmental risk factors for
PsA among psoriasis patients.
Methods: PsA and psoriasis patients from two prospective cohorts were analyzed. The
incidence of PsA among a prospective cohort of psoriasis patients was assessed. The
distribution of Human Leukocyte Antigen (HLA) alleles and Killer Cell Immunoglobulin
like Receptors (KIRs) and their combinations was compared between PsA, psoriasis and
healthy controls. In addition, the association between a wide range of environmental
exposures and PsA was evaluated by comparing the frequencies of exposed individuals
among patients with recent onset PsA and psoriasis. The association between smoking and
PsA was further investigated. The prevalence of smoking was in PsA, psoriasis and the
general population. The interaction between HLA-C*06 and smoking was also tested.
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Results: The genetic analysis revealed several HLA-B alleles and HLA haplotypes that are
associated with PsA compared to psoriasis and can potentially serve as independent markers
for PsA. Furthermore, several combinations of KIR genes and their respective HLA ligands
were also found to be associated with PsA compared to psoriasis. The incidence of PsA
among psoriasis patients was found to be higher than previously reported and its rate was
constant over time. HLA-C*06 was associated with increased interval between psoriasis
onset and PsA. Several environmental factors including occupational exposures, infections,
injuries and smoking were associated with development of PsA. The prevalence of smoking
was decreased among PsA patients compared to psoriasis. The interaction between HLA-
C*06 and smoking was found to be significant.
Conclusions: Genetic and environmental factors are associated with the development of PsA
in patients with psoriasis. These factors may serve as specific markers to identify psoriasis
patients at increased risk for PsA.
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Acknowledgments
I would like to express my gratitude and appreciation to Dr. Dafna Gladman,
my thesis supervisor for introducing me to the great world of research. Her
leadership, support, hard work, and scholarship have set an example I hope to
match some day. She is a true role model to me as a researcher, physician and
as a person.
I would like to thank the other members of my committee, Dr. Cheryl Rosen,
for the insights and patience particularly in reviewing my thesis, and Dr.
Shelley Bull for her direction and guidance in analyzing the data and insightful
comments.
Gratitude is also expressed to all of the people whom without their help this
project would not have been possible. I would like to thank Dr. Vinod Chandran
who helped me understand better the world of genetic epidemiology. My
sincere thanks also go to Sutha Shanmugarajah for her immeasurable help in
recruiting patients and managing the data for this project. I would also like to
thank Fawnda Pellet and Remy Pollock for their hard work in the laboratory
performing the HLA and KIR genotyping. I wish to thank Dr. Richard Cook,
Hua Shen and Arane Thavaneswaran for the meaningful analysis and guidance
in performing the statistical analysis of this project. Special thanks to Anne
MacKinnon for her help “behind the scene” in getting things done. I would like
to thank the Abbott PsA Fellowship and the Canadian Arthritis Network for
their financial support.
Last but not least, I would like to thank my family, Ben, Adi and Shani Eder,
who joined me on this journey. Without their encouragement and understanding
it would have been impossible for me to finish this work.
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Table of Contents
Abstract ..................................................................................................................................... ii
Table of Contents ...................................................................................................................... v
List of Tables .......................................................................................................................... vii
List of Figures ........................................................................................................................... x
List of Appendices ................................................................................................................... xi
Chapter 1. Literature Review ........................................................................................ 1
1.1. The Phenotype - Psoriasis....................................................................................... 2
1.2. The Phenotype – Psoriatic Arthritis ........................................................................ 5
1.3. The Etiology of Psoriasis and PsA ......................................................................... 9
1.4. Environmental risk factors for Psoriasis and PsA ................................................ 11
1.5. Genetics of Psoriasis and PsA .............................................................................. 16
Chapter 2. Hypothesis and Aims ................................................................................. 31
2.1. Rationale ............................................................................................................... 32
2.2. Hypothesis ............................................................................................................ 33
2.3. Aims...................................................................................................................... 33
Chapter 3. Study population and data collection......................................................... 34
3.1. Study population ................................................................................................... 35
3.2. Data collection ...................................................................................................... 37
Chapter 4. The incidence of arthritis in a prospective cohort of psoriasis patients ..... 39
4.1. Background ........................................................................................................... 40
4.2. Methods ................................................................................................................ 41
4.3. Results .................................................................................................................. 44
4.4. Discussion ............................................................................................................. 49
Chapter 5. HLA risk alleles for PsA among psoriasis patients ................................... 51
5.1. Background ........................................................................................................... 52
5.2. Methods ................................................................................................................ 54
5.3. Population stratification ........................................................................................ 61
5.4. Results .................................................................................................................. 64
5.5. Discussion ............................................................................................................. 91
Chapter 6. The effect of HLA risk alleles on the rate of development of PsA among psoriasis patients ................................................................................................................. 96
vi
6.1. Background ........................................................................................................... 97
6.2. Methods ................................................................................................................ 98
6.3. Results ................................................................................................................ 100
6.4. Discussion ........................................................................................................... 106
Chapter 7. The association of KIRs and their HLA ligands with PsA ...................... 108
7.1. Background ......................................................................................................... 109
7.2. Methods .............................................................................................................. 112
7.3. Results ................................................................................................................ 116
7.4. Discussion ........................................................................................................... 124
Chapter 8. The association between environmental factors and onset of PsA in patients with psoriasis ....................................................................................................... 128
8.1. Background ......................................................................................................... 129
8.2. Methods .............................................................................................................. 130
8.2.1. Study population ............................................................................................. 130
8.2.2. Data collection ................................................................................................ 130
8.2.3. Statistical analysis ........................................................................................... 132
8.3. Results ................................................................................................................ 133
8.3.1. Study population characteristics ..................................................................... 133
8.3.2. Proportion of exposure to the different factors ............................................... 133
8.3.3. Smoking and alcohol consumption ................................................................. 134
8.4. Discussion ........................................................................................................... 141
Chapter 9. Smoking is inversely associated with development of psoriatic arthritis among psoriasis patients ................................................................................................... 145
9.1. Background ......................................................................................................... 146
9.2. Methods .............................................................................................................. 147
9.3. Results ................................................................................................................ 151
9.4. Discussion ........................................................................................................... 158
Chapter 10. General Discussion .................................................................................. 163
10.1. Conclusions ..................................................................................................... 171
10.2. Future directions ............................................................................................. 172
References ......................................................................................................................... 174
Appendices ........................................................................................................................ 196
Appendix 1 - Questionnaire for the assessment of exposure to environmental factors 196
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List of Tables
Table 4.1 - Characteristics of the study population and the incident cases of PsA ................ 48
Table 5.1 - Population stratification within the Caucasian study sample ............................... 62
Table 5.2 - Demographic and clinical characteristics of the study population ...................... 64
Table 5.3 - HLA-A allele distribution – Psoriatic disease vs. Controls .................................. 67
Table 5.4 - HLA-B allele distribution – Psoriatic disease vs. Controls .................................. 68
Table 5.5 - HLA-C allele distribution – Psoriatic disease vs. Controls .................................. 69
Table 5.6 - HLA-DRB1 allele distribution – Psoriatic Disease vs. Controls ......................... 69
Table 5.7 - HLA-DQ allele distribution – Psoriatic Disease vs. Controls .............................. 70
Table 5.8 - The association of HLA alleles and PsD compared to healthy controls using
multivariate logistic regression analysis ................................................................................. 71
Table 5.9 - HLA-A allele distribution – PsA vs. Psoriasis ..................................................... 73
Table 5.10 - HLA-B allele distribution – PsA vs. Psoriasis ................................................... 74
Table 5.11 - HLA-C allele distribution – PsA vs. Psoriasis ................................................... 75
Table 5.12 - HLA-DRB1 allele distribution – PsA vs. Psoriasis ............................................ 75
Table 5.13 - HLA-DQ allele distribution – PsA vs. Psoriasis ................................................ 76
Table 5.14 - Odds ratios comparing HLA allele frequencies in PsA to psoriasis using logistic
regression analysis .................................................................................................................. 77
Table 5.15 - Odds ratios comparing HLA-C*06 allele frequencies in PsA to psoriasis by age
at onset of psoriasis ................................................................................................................. 78
Table 5.16 - Odds ratios comparing HLA allele frequencies in Axial PsA vs. psoriasis using
logistic regression analysis ..................................................................................................... 80
Table 5.17 - Odds ratios comparing HLA allele frequencies in peripheral PsA vs. psoriasis
using logistic regression analysis ............................................................................................ 81
Table 5.18 - Odds ratios comparing extended HLA haplotype frequencies in PsD to healthy
controls using logistic regression analysis .............................................................................. 83
Table 5.19 - Linkage Disequilibrium of selected HLA-B and –C alleles ............................... 85
Table 5.20 - Odds ratios comparing HLA-B/HLA-C haplotype frequencies in PsA to
psoriasis using logistic regression analysis ............................................................................ 86
Table 5.21 - Detailed family structure of the study population .............................................. 88
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Table 5.22 - Family based association test Affected (PsA probands) - Unaffected (psoriasis)
sib-pairs and Trios................................................................................................................... 89
Table 5.23 - Family based association test Affected (PsD)-Unaffected (Healthy controls) sib-
pairs and Trios......................................................................................................................... 90
Table 6.1 - Characteristics of the study population .............................................................. 102
Table 6.2 - The association between HLA alleles and the risk of PsA in analysis of time from
onset of psoriasis by parametric proportional hazard model ................................................ 104
Table 6.3 - The association between HLA alleles and risk of PsA in analysis of time from
birth by parametric proportional hazard model .................................................................... 105
Table 7.1 - KIR ligand specificities ...................................................................................... 110
Table 7.2 - KIR gene distribution – Psoriatic disease vs. Healthy controls .......................... 119
Table 7.3 - KIR gene distribution – PsA vs. Psoriasis .......................................................... 119
Table 7.4 - The frequency of HLA-C group 1/HLA-C group 2 alleles in PsA, psoriasis and
healthy controls ..................................................................................................................... 120
Table 7.5 – The association between combinations of KIR2D/HLA based on activation
model and PsA vs. Psoriasis ................................................................................................. 121
Table 7.6 - The frequencies of KIR2DS2 and KIR2DL2 in PsA and psoriasis.................... 121
Table 7.7 - The association between combinations of KIR2D/HLA based on activation model
and PsD vs. Controls ............................................................................................................. 122
Table 7.8 - The frequency of KIR3DS1 in PsA and psoriasis .............................................. 122
Table 7.9 - The association between combinations of KIR3D/HLA based on activation model
and PsA vs. Psoriasis ............................................................................................................ 123
Table 8.1 - Clinical characteristics of the study population.................................................. 137
Table 8.2 – The association between environmental exposures and PsA............................. 138
Table 8.3 – The Association between female hormonal exposures and PsA ....................... 139
Table 8.4 - Full regression model adjusted for age, sex, education level, duration and severity
of psoriasis ............................................................................................................................ 140
Table 9.1 – Demographic and clinical characteristics of the study population .................... 154
Table 9.2 – Smoking characteristics among psoriasis and PsA patients .............................. 155
Table 9.3 – Smoking status at the time of the diagnosis by matched pairs .......................... 155
ix
Table 9.4 – The association between smoking status and PsA compared to psoriasis alone by
logistic regression analysis ................................................................................................... 156
x
List of Figures
Figure 1.1 - Genes within the MHC region ............................................................................ 24
Figure 4.1 - Follow-up summary of the study population ...................................................... 46
Figure 4.2 - PsA incidence over time estimated with a Kaplan-Meir curve from time of first
clinic visit to diagnosis of PsA................................................................................................ 47
Figure 6.1- The probability of developing PsA among psoriasis patients from the onset of
psoriasis assuming an exponential model. ............................................................................ 103
Figure 7.1 - Illustration of the structure of the different type of KIRs ................................. 110
Figure 7.2 - The 10 most frequent KIR genotypes in PsA, psoriasis and healthy controls .. 120
Figure 9.1 - Smoking status among PsA and psoriasis patients by HLA-C*06 status ........ 110
xi
List of Appendices
Appendix 1 - Questionnaire for the assessment of exposure to environmental factors ........... 206
xii
List of abbreviations
AS - Ankylosing Spondylitis
BSA - Body Surface Area
CASPAR – Classification of Psoriatic Arthritis
CDSN - Corneodesmosin
CI – Confidence Interval
DMARDs - Disease Modifying Anti-Rheumatic Drugs
FDR - False Discovery Rate
GWAS - Genome Wide Association Study
HLA - Human Leukocyte Antigen
IBD – Inflammatory Bowel Disease
KIR - Killer Immunoglobulin-like Receptor
LR – Likelihood Ratio
LD - Linkage Disequilibrium
mSASSS - modified Stoke Ankylosing Spondylitis Spine Score
mNAPSI - modified Nail Psoriasis Severity Index
NK - Natural Killer
NSAIDs - Non-Steroidal Anti-Inflammatory Drugs
OR - Odds Ratio
PsA – Psoriatic Arthritis
PASI – Psoriasis Area and Severity Index
PSORS - PSORiasis Susceptibility locus
PsD - Psoriatic Disease
ReA - Reactive arthritis
SNP - Single Nucleotide Polymorphisms
SpA – Spondyloarthropathies
SPR – Standardized Prevalence Ratio
TNF - Tumor Necrosis Factor
ToPAS - Toronto Psoriatic Arthritis Screen
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Chapter 1. Literature Review
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1.1. The Phenotype - Psoriasis
Psoriasis is a common skin disease affecting 1-3% of the population [1-3]. The antiquity of the
disease dates back to biblical times. It has long been recognized that most cases of biblical leprosy
were actually psoriasis [4]. Lepers were considered unclean and were subject to regulation and
segregation from the normal population. Only in the 18th century a detailed description that
distinguished these skin lesions by the British dermatologist Robert Willan, ended hundreds of
years of confusion and laid the foundation for establishing psoriasis as an independent disease [5].
1.1.1. Epidemiology of psoriasis
Psoriasis affects men and women equally, and is seen in all races. Although psoriasis can
begin at any age, there seem to be two peaks in onset: one between ages 20 and 30 and
another between ages 50 and 60 [6]. Psoriasis has been described as two types depending
on the age of onset. Patients with early-onset or Type I psoriasis (before the age of 40) tend
to have more severe disease with a familial history. Patients with late-onset or Type II
psoriasis (after the age of 40) tend to have a milder disease [7].
1.1.2. Clinical manifestations of psoriasis
Psoriasis is usually manifested as raised, erythematous plaques with adherent silvery scales.
It is usually easily recognized, but atypical or non-classic forms are more difficult to
identify. There are several clinical types of psoriasis; the most common one is chronic
plaque psoriasis or psoriasis vulgaris that affects 85-90% of all patients with the disease.
This type usually presents in young adults with symmetrically distributed plaques involving
the scalp, extensor elbows, knees, and back. Other types include flexural psoriasis, guttate
psoriasis, pustular psoriasis and erythroderma [8]. Approximately 40% of patients with
psoriasis have nail lesions. One of the typical nail abnormalities in psoriasis is pitting,
consisting of a few to multiple tiny pits scattered over the nail plate. The pits reflect
abnormal nail plate growth resulting from psoriatic involvement of the nail matrix. These
changes produce friable areas of nail plate that erode away with normal friction. Another
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typical psoriatic nail lesion is onycholysis, that occurs as a result of a separation of the nail
plate from its underlying attachment to the nail bed [6].
1.1.3. Diagnosis of psoriasis
Although the differential diagnosis of psoriasis is broad, a skin biopsy is rarely needed. The
diagnosis is usually made by history and physical examination. There are no laboratory
tests that confirm or exclude the diagnosis. A detailed physical examination should focus
on typical sites of involvement such as knees and elbows with a special attention to subtle
findings in the scalp, umbilicus, intergluteal cleft, and nails.
1.1.4. Clinical course and co-morbidities of psoriasis
Psoriasis tends to be a chronic disease. However, its course is unpredictable. There may be
marked variability in severity over time, and remissions at some stage are seen in up to
40% of cases [9]. Although generally not life threatening, psoriasis may be associated with
important morbidity and disability. It can range from a very mild disease with few small
hidden plaques that do not interfere with daily life to severe wide-spread skin lesions that
may lead to disability and poor quality of life. Patients with psoriasis, like those with other
major medical disorders, have reduced levels of employment as well as decreased quality
of life [10, 11].
Psoriasis is also associated with other co-morbidities including obesity and other related
metabolic abnormalities such as diabetes mellitus and dyslipidemia [12, 13]. These in turn
lead to increased cardiovascular morbidity and mortality [14]. In addition, approximately
30% of the patients with psoriasis develop an inflammatory arthritis termed Psoriatic
Arthritis (PsA) [15].
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1.1.5. Measurement tools in Psoriasis
The Psoriasis Area and Severity Index (PASI) is the most widely used tool for the
measurement of severity of psoriasis. PASI combines the assessment of the severity
of lesions and the area affected into a single score in the range of 0 (no disease) to 72
(maximal disease) [16]. Another measurement tool for psoriasis severity is the Body
Surface Area (BSA) which is an estimate of the percentage of the body surface affected by
psoriasis. Patients with severe psoriasis are those with BSA>10% or PASI>10 [17].
1.1.6. Treatment of psoriasis
Treatment in psoriasis can generally be categorized into topical and systemic therapies. The
treatment choice is dictated by the severity, type, and location of psoriasis. Patients with
mild psoriasis can usually be managed with topical agents including: corticosteroids, tar,
retinoids and vitamin D derivates. Moderate to severe psoriasis requires phototherapy or
systemic therapies such as methotrexate, retinoids, cyclosporine or the biologic immune
modifying agents including alefacept, the anti- Tumor Necrosis Factor (TNF) agents or
anti-IL12/23 monoclonal antibody (ustekinumab) [18].
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1.2. The Phenotype – Psoriatic Arthritis
Psoriatic arthritis has been defined as an inflammatory arthritis associated with psoriasis,
usually seronegative for rheumatoid factor [19]. The association between psoriasis and
arthritis was first described in 1818 by the French physician, Baron Jean Louis Alibert [20].
However, only in 1964 did the American Rheumatology Association recognize PsA as a
unique disease entity that is separate from rheumatoid arthritis [21]. Furthermore, only in
2006, were classification criteria for PsA developed and allowed a better definition of cases
for research purposes [22].
Psoriatic arthritis is classified among the seronegative spondyloarthropathies (SpA). This
term refers to a family of diseases that share certain clinical features. The most
distinguishing features are inflammation of the axial joints, asymmetric oligoarthritis, and
enthesitis (inflammation at sites of ligamentous or tendon attachment to bone). Additional
features are genital and skin lesions, eye and bowel inflammation, an association with
preceding or ongoing infectious disorders, and a strong association with the Human
Leukocyte Antigen (HLA) -B*27. The SpA group consists of the following disorders:
Ankylosing Spondylitis (AS), Reactive arthritis (ReA) , PsA , Undifferentiated
spondyloarthritis, SpA associated with Inflammatory Bowel Disease (IBD) and Juvenile
onset spondyloarthritis [23].
1.2.1. Epidemiology of PsA
The most recent estimate of the prevalence of PsA in North America is 0.25% (95% CI:
0.18%, 0.31%) [24]. The reported incidence of PsA in the general population ranges from
3.4-8 per 100,000 [25-28]. PsA has been reported in 7-42% of patients with psoriasis, with
a recent estimate being approximately 30% [15, 29, 30]. The marked variability in reported
prevalence and incidence rates is probably related to different definitions of the disease as
well as variable sources of populations. Approximately 67% of the patients develop
psoriasis before arthritis and in 16% arthritis and psoriasis present within 12 month of each
other [31]. There is limited information about the incidence of PsA among patients with
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psoriasis. A retrospective study from Germany reported that the cumulative incidence of
PsA among psoriasis patients reached 20.5% after 30 years from the diagnosis of psoriasis
[32]. Another retrospective study from Rochester, Minnesota has found a lower cumulative
incidence of 3.1% cases of PsA among psoriasis patients after 10 years from the onset of
the skin disease [33]. No study to date has prospectively assessed the incidence of PsA
among psoriasis patients. The prevalence of the disease is equal among males and females.
PsA usually occurs in the third or fourth decade of life [15]. There is very little information
about racial and ethnic associations as most epidemiological studies has been performed in
Caucasians.
1.2.2. Clinical manifestations
Five patterns of PsA have been described: the symmetric polyarticular pattern being the
most common [34], distal arthritis that involves the distal interphalangeal joints,
asymmetric oligoarthritis in which less than 5 joints are affected, arthritis mutilans that is
characterized by deforming and destructive arthritis, and spondyloarthritis that includes
sacroiliitis and spondylitis [35]. Some of the patients present with more than one pattern or
change their pattern during the course of their disease. Another common feature of PsA is
enthesitis, an inflammation at the site of tendon insertion into the bone, that often affects
the Achilles tendon, plantar fascia and pelvis bones [36]. Dactylitis is characterized by
diffuse swelling of the entire finger or toe; it affects about half of the patients and is
associated with radiographic joint damage [37]. Similarly to the other
spondyloarthropathies, PsA is also associated with inflammation in other extra-articular
sites including the eye (uveitis) and the gastrointestinal tract (inflammatory bowel disease).
1.2.3. Relationship between skin and joint disease
PsA may be considered as a disease within a disease as most of the patients with PsA also
have psoriasis, although according to the new classification criteria, patients with PsA do
not have to have established psoriasis [22]. Most of the patients develop PsA after or at the
7
same time as the skin disease, however, 15% of patients with PsA present with arthritis
before psoriasis [35]. Since most patients develop PsA after the onset of psoriasis, the skin
disease serves as a marker for the development of PsA. The paradigm that patients with
severe psoriasis are the ones who develop arthritis is controversial. Several studies reported
a higher prevalence of PsA among patients with severe psoriasis [24, 38]. However, the
fact that PsA can present before psoriasis as well as recent observations of no relationship
between the severity of the skin and joint manifestations [39, 40], suggest there is no direct
link between psoriasis severity and arthritis. Nail lesions are more common among PsA
patients than in psoriasis and the presence of nail lesions has been suggested as marker of
increased risk for PsA among psoriasis patients [41, 42].
1.2.4. Diagnosis of PsA
The diagnosis of PsA is based on a typical combination of clinical, laboratory and
radiographic findings. The ClASsification of Psoriatic ARthritis (CASPAR) criteria are a
set classification criteria for PsA that were published in 2006. They may be used for
diagnosis and allow a uniform definition of cases for research purposes. The CASPAR
criteria showed high sensitivity and specificity for PsA (91.4% and 98.7%, respectively)
[22].
The CASPAR criteria consist of the following terms:
Required: The presence of an inflammatory arthritis, enthesitis, or spondylitis.
Plus 3 points from the following:
1. Skin psoriasis (present) (2 points), previously present by history (1 point), or a
family history of psoriasis (1 point)
2. Psoriatic nail lesions (1 point)
3. Dactylitis (1 point)
4. Negative rheumatoid factor (1 point)
8
5. Juxta-articular bone formation on radiographs (1 point)
1.2.5. Clinical course of PsA
In most patients PsA runs a course of a chronic, progressive disease, although some
patients can achieve a complete remission. In our cohort, 17.6% of the patients achieved a
remission, however, periods of remission lasted on average 2.6 years and most patients
experienced a relapse [43]. PsA is more severe than previously thought [44, 45]. It can lead
to severe joint damage and disability that are comparable to those that occur in rheumatoid
arthritis [46].Patients with PsA demonstrate clinical and radiographic progression in the
course of follow-up [47, 48] and have an increased mortality risk compared to the general
population, although this risk has decreased over the past two decades [49, 50]. In addition
to the disability that is related to the joint disease, PsA patients can suffer from the same
co-morbidities as psoriasis patients [51]. Thus, PsA poses a major health burden, in
addition to that caused by psoriasis alone.
1.2.6. Treatment of PsA
The treatment in PsA is aimed at controlling both skin and joint inflammation in order to
reduce the symptoms and to prevent joint damage. Treatment usually begins with Non-
Steroidal Anti-Inflammatory Drugs (NSAIDs) that can control mild arthritis, enthesitis and
spondylitis. Second line therapies are indicated when arthritis does not respond to NSAIDs.
Many of the Disease Modifying Anti-Rheumatic Drugs (DMARDs), such as methotrexate,
leflunomide, sulfasalazine, cyclosporine and azathioprine, were “borrowed” from
rheumatoid arthritis for the treatment of peripheral arthritis in PsA. Although there are
limited clinical trials that evaluated their efficacy in PsA, they are often effective in
controlling articular symptoms. The new targeted biologic therapies particularly the anti-
TNF agents, are the most effective treatments currently available to control all aspects of
the disease and to prevent the progression of joint damage [52].
9
1.3. The Etiology of Psoriasis and PsA
1.3.1. Immunologic mechanisms in Psoriasis and PsA
The role of immunologic mechanisms is suggested by the inflammatory response in the
psoriatic skin lesions and by the synovial lesions, which are at times indistinguishable from
rheumatoid arthritis [53]. T cells and pro-inflammatory cytokines have important roles in
the pathogenesis of both diseases. CD8+ T cells clones from the synovial fluid and skin
lesions of patients expand in a presumptive autoantigen-driven manner [54-56]. Patients
with PsA have a pattern of pro-inflammatory cytokines in the synovium, particularly from
the Th1 milieu including: TNF-alpha, IL-1, IL-6, IL-8 and more [57-59]. The effectiveness
of T cell-targeting and cytokine modulating treatments including the anti TNF-α agents,
alefacept and efalizumab in psoriasis and PsA support the important role of T cells in the
pathogenesis of these conditions. The innate immune system provides an early response
mechanism against external threats, mainly infectious agents and includes dendritic cells,
keratinocytes, macrophages and Natural Killer (NK) cells. Dysregulation of the innate
immune system is thought to play a role in the pathogenesis of psoriasis and PsA [60].
Activation of dendritic cells through antimicrobial peptides leads to an increased
production of interferon alpha which is a major inducer of psoriasis. This mechanism
provides a potential explanation for the link between environmental triggers that may break
the immunologic tolerance and lead to psoriasis and PsA [61].
Natural Killer cells and NK-T cells, which are part of the innate immune system, have been
implicated in the pathogenesis of psoriasis [62] and PsA [63-65]. Both NK and NK-T cells
have been described in increased numbers in psoriatic plaques [66] and in synovial tissues
from PsA patients [63]. NK cell activity is partially controlled through interaction between
Killer Immunoglobulin-like Receptors (KIRs) on NK cells and their respective HLA class I
ligands [67]. It has been proposed that direct activation of NK cells bearing KIRs for MHC
class I may play a role in the pathogenesis of PsA [68, 69].
The concept of synovio-entheseal complex is a proposed theory that can provide a link
between mechanical stress and the development of PsA. The synovium provides
nourishment and lubrication to the entheseal fibrocartilage. During mechanical stress and
10
injury to the enthesis, the associated inflammatory reaction would be manifested in the
juxtaposed synovium. The damaged tissue triggers the previously discussed immune
mechanisms [70]. Microanatomical studies provide support for the theory by demonstrating
an anatomical connection of the nail and the enthesis, that may explain the observation that
psoriatic nail lesions are associated with higher risk of developing PsA [71, 72].
11
1.4. Environmental risk factors for Psoriasis and PsA
Psoriasis and PsA are considered complex diseases in which the interaction between
genetic and environmental risk factors is thought to play a major role [73]. Genetic factors
cannot solely account for all cases. One of the suggested pathogenic models for PsA is that
psoriasis patients who carry susceptibility genes for arthritis develop PsA after being
exposed to triggering environmental factors [74]. These environmental factors are largely
unknown.
Several environmental factors have been associated with psoriasis including: infections,
particularly streptococcal pharyngitis, smoking, trauma, stressful life events, obesity and
certain medications [75]. Only a limited number of studies have investigated environmental
risk factors for PsA. Pattison et al. compared the prevalence of environmental exposures
among 98 British PsA and 163 psoriasis patients over a window of exposure that ranged
from 5 to 10 years prior to the onset of arthritis. Information about environmental
exposures was collected through questionnaires. In their study, physical trauma, rubella
vaccination, oral ulcers and moving house were found to be associated with PsA [76].
Another study by Thumboo et al. used an administrative database from Rochester,
Minnesota and retrospectively evaluated exposure to several environmental risk factors
among 60 PsA and 120 psoriasis patients [77]. They did not define a specific window of
exposure. In that study, pregnancy was found to be protective of PsA, while steroid use was
associated with higher risk of the disease.
Several case reports and case series have highlighted the role of physical trauma as a
potential triggering environmental exposure for PsA. In these reports, patients with
psoriasis developed arthritis following local trauma, such as injury, surgery or fracture [78-
80]. The pathogenic process that may lead from local trauma to joint inflammation in PsA
is unclear. However, the hypothesis that these two events are linked is supported by a
similar process in the skin that was termed the “Koebner phenomenon”, in which local skin
trauma induces certain skin lesions including psoriasis. This process has been reported in
24-52% of psoriasis patients [81-83]. Many different injuries can induce Koebner response
including: insect bites, burns, laceration, venipuncture among others. The time period from
12
injury to psoriasis is diverse; in general the interval ranges between 10 and 20 days [84].
Koebnerization can trigger a cascade of events that switch the feature of the disease, from
localized to generalized psoriasis. Psoriasis patients who carry the HLA-C*06 allele have a
higher incidence of Koebnerization. Local trauma to the skin induces release of
proliferation factors and proliferation of inflammatory cells that initiate an inflammatory
process. Similar events may take place in a joint following local trauma and was termed
“Deep Koebner phenomenon” [85].
Reactive arthritis is an episode of an acute articular and periarticular sterile inflammation
occurring in a genetically predisposed individual secondary to a primary infection
elsewhere in the body [86]. Urogenital and gastrointestinal infections are well established
triggering events for ReA [87]. Reactive arthritis occurs in 1% to 4% of patients with
preceding bacterial gastrointestinal infections with enterobacteria or urogenital infections
with Chlamydia trachomatis, however, this rate increases to 20% to 30% in patients
infected with one of these bacteria who are positive for HLA-B*27 [86]. Reactive arthritis
belongs to the SpA group and has some common features with PsA including: similar
pattern of joint involvement, psoriasiform skin lesions, enthesitis, eye involvement and an
association with the HLA-B*27 allele.
Infections also play a role in the pathogenesis of psoriasis. Streptococcal infection is a
common trigger for guttate psoriasis particularly in children and young adults [88] and the
reported incidence of streptococcal infections preceding this type of psoriasis ranges
between 56% and 97% [89]. Chronic plaque psoriasis is also exacerbated after such
infections and psoriasis patients were found to have a ten-fold higher frequency of
streptococcal throat infections than age-matched household controls [90]. Psoriatic lesional
T cells are oligoclonal, and T cells recognizing determinants common to streptococcal M-
protein and keratin have been detected in patients’ blood [91]. It has been suggested that
the association between streptococcal infection and psoriasis may reflect an autoimmune
process that is triggered by the presence of a superantigen, possibly the streptococcal M-
protein. However, the role of autoimmunity in the pathogenesis of psoriasis remains
controversial. The association between streptococcal infection and PsA was suggested by
elevated levels of streptococcal RNA in PsA peripheral blood [92, 93]. However, although
13
the immunoreactivity to streptococcal antigens is accepted, it is unclear if the infection
triggers PsA or if the breakage of skin barrier because of psoriasis leads to streptococcal
exposure and finally to a form of reactive arthritis.
Infection with Human Immunodeficiency Virus (HIV) is also associated with the
development of psoriasis and PsA particularly in HIV endemic populations [94, 95]. It has
been reported that in Sub-Saharan Africa the great majority of PsA patients were HIV
positive [96]. This association may hint at a viral trigger of PsA or that HIV increases the
risk for other infections that may trigger PsA.
Obesity is associated with psoriasis. Multiple studies have demonstrated that patients with
psoriasis are more frequently overweight or obese compared with patients without psoriasis
[97-99] .Obesity occurs prior to the onset of psoriasis and is now thought to be a risk factor
for the development of the disease. A large prospective cohort from the US demonstrated a
‘dose-response’ relationship for obesity on the risk of developing incident psoriasis [100].
Obesity (BMI≥30) compared with normal body weight was associated with two-fold
increased risk for psoriasis onset. Obesity is also associated with more severe psoriasis
[101] and poor response to therapy [102]. A recent case-control study from Utah
demonstrated an association between obesity and PsA. The results showed that obesity at
age 18 was associated with an increased risk and an earlier onset of PsA among psoriasis
patients [103], however the study was based on a recall weight and height at age 18 and
was not a prospective study.
The role of smoking as a risk factor for psoriasis is well established. The first studies drew
attention to the linkage between smoking and palmo-plantar pustular psoriasis [104]. Since
then several case-control studies have established the association between smoking and
psoriasis vulgaris [105-108], the most common type of psoriasis. The most conclusive
epidemiological evidence for the causative role of smoking in the pathogenesis of psoriasis
was provided by the Nurses’ Health Study II that showed, for the first time, a strong
association between smoking and incident psoriasis in a large population-based prospective
cohort study [109]. In this study the relative risk of psoriasis was 1.8 for current smokers
and 1.4 for past smokers. The dose-effect relation between smoking intensity and psoriasis
14
risk also supports its etiological role. Smoking has also been associated with more severe
psoriasis and poor response to treatment [110, 111]. The independent association between
smoking and psoriasis remained significant even after adjustment for another potential
confounder, excessive alcohol consumption, that is also increased among psoriasis patients
[112, 113]. Rakkhit et al. found a temporal association between psoriasis, PsA and
smoking [114]. They reported that the duration of time from the onset of psoriasis to
development of PsA decreases with a history of smoking prior to psoriasis onset and
increases with smoking after psoriasis onset.
Lastly, stressful life events are thought to have an effect on the course of psoriasis. In a
case-control study, psoriasis patients were more likely to experience a stressful event that
preceded the onset and the exacerbation of their disease compared to patients with other
skin disorders [115]. There is some evidence that psychological stress may modulate
immune function in humans and experimental animals [116]. It has been reported that
stress induced anxiety is related to T helper type 1 response. A study that applied
psychological stress prior to immunization suggested that stress exerts an adjuvant effect on
dendritic cells by promoting enhanced migration to lymph nodes and resulting in increased
antigen-specific T cell responses [117]. This effect may be mediated by the release of
norepinephrine from the sympathetic nervous system.
1.4.1. Limitations of previous studies of environmental risk factors in
PsA
There were only two studies that assessed environmental exposures among PsA and
psoriasis patients and one that compared PsA to rheumatoid arthritis. These studies were
limited in several aspects [76, 77, 118].First, due to their small sample size these studies
were underpowered to detect important associations. Secondly, the use of an administrative
database as a source of information [77] may have led to an underestimation of events that
are often not coded or are coded incorrectly. Lastly, as PsA is a common condition among
psoriasis patients, a potential misclassification of cases and controls might have occurred
15
since psoriasis patients were not assessed to rule out the presence of inflammatory arthritis
[76, 77]. It has been shown that PsA is underestimated among psoriasis patients, as
approximately 18% of the psoriasis patients that were found to have inflammatory arthritis
were unaware of their condition and had never been seen by a rheumatologist [119]. The
problem of misclassification of patients and controls may significantly affect the results in
case-control studies where the outcome (PsA in this case) is not rare among the control
group. Misclassification may decrease the power of the study to detect significant
associations
16
1.5. Genetics of Psoriasis and PsA
1.5.1. The Approach to identification of genetic determinants of a disease
A series of steps is required for the identification of susceptibility genes for a disease [120].
The first step involves determination of a familial aggregation. Segregation analysis is the
next step, which examines the patterns of disease in families and determines if the patterns
are indicative of traditional genetic inheritance models (such as autosomal dominant or
recessive) or are more consistent with multi-factorial (polygenic and environmental)
models [121]. Finally, once enough evidence of a genetic component in the cause of the
disease has been obtained, the next step is to locate and identify any causative gene [122].
Two basic study designs are used to identify susceptibility loci: linkage analysis and
association studies. Genetic linkage analysis relies on the tendency for short haplotypes to
pass intact, without recombination events, to the next generation within families. A genetic
marker that passes down though a family accompanied by the disease of interest, suggests
that the susceptibility gene for the disease is located close to that marker [123, 124]. An
association analysis is based on a putative functional genetic variant in the
pathophysiologic mechanism of the disease of interest. Association studies look for a
significant increase or decrease in frequency of a marker allele, genotype or haplotype
within a disease trait than would be expected by chance if there were no association
between markers and phenotype. This method can be applied to data obtained from families
or populations of healthy individuals and patients with the disease of interest [125, 126].
For complex diseases, segregation and linkage analyses have not proven to be generally
useful [127, 128]mainly due to low statistical power and the need to recruit large samples.
Therefore, for these diseases, once familial aggregation is demonstrated, it may be
appropriate to proceed with association studies, either candidate gene or genome-wide, to
determine susceptibility loci/genes. Once a gene is identified, functional studies in
biological systems are required to characterize the gene.
17
1.5.2. Familial aggregation of Psoriasis and PsA
A study on familial aggregation is the first step in pursuing a possible genetic etiology to a
disease. Familial aggregation is assessed by the recurrence risk ratio (λ). The recurrence
risk ratio (λR) is defined as the prevalence of the disease in relatives of type R of affected
cases divided by the prevalence in the general population [129-131].
Family and twin studies have clearly demonstrated that psoriasis has a strong genetic basis.
Two large scale epidemiological studies revealed a substantially higher incidence of
psoriasis in relatives of patients with psoriasis compared to the general population. The
recurrence risk for affected siblings (λs) was estimated to be between 4 and 10 [132-134].
Twin studies reveal a concordance rate for monozygotic twins to be between 62 to 70%
compared to 21 to 23% for dizygotic twins [135-137]. A segregation study concluded that a
polygenic or a multifactorial pattern is the most likely mode of inheritance [138, 139].
The genetics of PsA has been much less investigated compared to that of psoriasis.
However, epidemiological studies have implicated a strong genetic basis to PsA. Four
family studies have investigated the recurrence risk of PsA within families. The first study
that demonstrated familial aggregation of PsA was published in 1973 and found that the
prevalence of PsA among first-degree relatives of probands with PsA was 5.5% compared
to the estimated prevalence in the UK population of 0.1%. The calculated recurrence risk
ratio in first degree relatives (λ1) was 55, compared with estimates ranging from 5–10 in
cutaneous psoriasis [140]. Another study from the UK, reported a PsA prevalence of 14.3%
among siblings and recurrence risk ratio (λs) of 47 (based on estimated PsA prevalence of
0.3% in the general population) [141]. Recently, the risk ratio for PsA was estimated using
the Icelandic genealogical database. First degree relatives to fourth-degree relatives of
patients with PsA had risk ratios of 39, 12, 3.6 and 2.3, respectively (all P-values < 0.0001),
reflecting a strong familial aggregation, whereas the fifth-degree relatives had an RR of 1.2
(P = 0.236) [142]. Finally, our PsA cohort was assessed for familial aggregation. All
18
available first degree relatives of 100 PsA patients were assessed for evidence of PsA and
psoriasis. Among the 289 first degree relatives that were evaluated, 7.6% had PsA leading
to a calculated recurrence risk ratio in first degree relatives (λ1) of 30.4 [143]. There is
only one twin study in PsA from Denmark that evaluated 36 complete twins that included
one proband with PsA. In this study no difference was found in the concordance rate
between mono- and dizygotic twins [144]. However, this study may have been
underpowered to detect a genetic effect.
1.5.3. Mode of inheritance
Psoriasis and PsA are consistent with a multifactorial pattern of inheritance [145]. Several
studies have suggested an autosomal dominant [146] and autosomal recessive pattern [147]
of inheritance for psoriasis, however, even in these studies; a multifactorial model could
not be ruled out.
Genomic imprinting, a non-Mendelian mode of transmission, has been suggested to play a
role in the inheritance of psoriasis and PsA [148, 149]. Genomic imprinting refers to an
epigenetic effect that causes differential expression of a gene depending on the sex of the
transmitting parent. The imprinting process allows gene expression from only the
maternally or paternally derived chromosome [150]. A family study from the Faroe Island
reported a higher penetrance of psoriasis if the father was affected or a presumed gene
carrier [151].
A similar phenomenon has also been reported in PsA patients. In our cohort, the proportion
of PsA patients with an affected father was significantly higher than the expected
proportion (0.65 vs. 0.5 p=0.001) [148]. These findings were supported by a linkage study
in PsA that noted a significant linkage on chromosome 16q only after conditioning for
paternal transmission [152]. Thus, there are some evidence that this epigenetic
phenomenon may play a role in the inheritance pattern of psoriasis and PsA.
19
1.5.4. Genetic model for the relationship between PsA and Psoriasis
There is no doubt that there is a close relationship between psoriasis and PsA however the
nature of the relationship is not completely clear. Several models have been suggested to
describe the genetic and clinical association between psoriasis and PsA [153].
The first model suggests that PsA and psoriasis should be viewed as two distinct conditions
with different risk factors with the psoriasiform skin manifestation common to each. The
model explains the families with large numbers of psoriasis cases without any PsA, on the
other hand families with cases of both PsA and psoriasis or only PsA. This model predicts
that case series ascertained by their psoriasis will show genetic heterogeneity. However
case series ascertained by their PsA will share certain susceptibility genes and all PsA will
be genetically distinct from psoriasis.
The second model is based on the hypothesis that the genetic determinants of the skin and
the joint diseases are independent. However, the presence of one of these factors leads to a
lower threshold for the development of the other. This model explains the presence of
undifferentiated spondyloarthritis which is very similar to PsA but does not involve the
skin. This model predicts that case series ascertained by their psoriasis will be genetically
heterogeneous and those ascertained by the presence of PsA will be homogeneous and
share only some of the genes with psoriasis.
The third model views PsA as a “disease within a disease” with psoriasis as the parent
disease. PsA is considered a more severe phenotype of psoriasis that occurs due to a greater
number of susceptibility genes or environmental factors. The additional genes or
environmental risk factors on the background of psoriasis will lead to development of PsA.
This model predicts that case series ascertained by psoriasis will be genetically
homogenous and that case series ascertained by PsA will completely overlap those of
psoriasis apart from several additional distinct genes.
20
1.5.5. Gene identification studies - Linkage analysis studies
Genetic linkage analysis can be used to identify regions of the genome that contain genes
that predispose to disease. The aim of this method is to isolate the disease gene by its
chromosomal location without any prior knowledge of the position or function of the gene.
Linkage analysis requires collection of families with multiple affected individuals. There
are two methods for linkage analysis: parametric (model-based) linkage analysis and non-
parametric linkage analysis. Parametric linkage analysis requires constructing a model to
explain the inheritance of a disease in the pedigrees and then estimating the recombination
fraction for a given pedigree. For multifactorial diseases, where several genes (and
environmental factors) might contribute to disease risk, there is no clear mode of
inheritance [123]. Methods to investigate linkage have therefore been developed that do not
require specification of a disease model. Such methods are referred to as non-parametric, or
model-free. In this method, affected sibling pairs are assessed for allele sharing. The
premise for the non-parametric method is the fact that in the presence of linkage between a
marker and disease, sets of relatives who share the same disease status are more likely to
share alleles at the marker locus than the value of 50% that would be predicted by chance
[154].
1.5.6. Linkage analysis in psoriasis
Both parametric and non-parametric methods have been used to investigate the genetics of
psoriasis. The psoriasis susceptibility loci that have been mapped using linkage methods
include: PSORiasis Susceptibility locus (PSORS)1 on 6p21.3 [151, 152], PSORS2 on 17q
[155, 156],PSORS3 on 4q [157], PSORS4 on 1q21 [158], PSORS5 on 3q21 [159],
PSORS6 on 19p [160], PSORS7 on 1p [161], PSORS8 on 16q [162], PSORS9 on 4q [163]
and PSORS10 on 18p11 [164]. By far the strongest association is with a locus within the
MHC on chromosome 6p21 (PSORS1). Additional putative psoriasis candidate loci have
been reported on 16q and 20p [165]. The loci on 6p and 17q have been replicated with
independent linkage studies and a meta-analysis of previous studies has found an increased
21
allele sharing for 16q [155, 163, 165]. However, the rest of the loci were found to be
difficult to replicate.
With regard to PsA, however, only one genome-wide linkage scan has been conducted
[152]. This study identified a locus on 16q close to the PSORS8 locus identified for
psoriasis, but only when conditioned on paternal inheritance. Thus, it is unclear whether
there are distinct susceptibility loci for psoriasis and PsA. Maximum LOD score (MLS)
analysis of affected sibling pairs yielded allele sharing of 60% for markers within the MHC
region. These results emphasize the importance of the MHC region as a candidate
susceptibility region in psoriasis. In that study, evidence of allele sharing was also found on
16q and 10q22-q23.
1.5.7. Gene identification studies - Association Studies
Genetic association studies aim to detect associations between one or more genetic
polymorphisms and a trait. Traditionally, linkage analysis studies were performed for
coarse mapping as they have a limited genetic resolution of 1 cM. Association studies were
the next step for fine mapping. However, association studies have several advantages over
linkage studies. No assumption about the mode of inheritance is required, they have greater
power to detect small effects and they do not require large affected families. Therefore,
association studies are often used initially for the investigation of complex diseases [126].
There are two basic approaches. The first one is to determine the association between a
candidate gene and a trait. This approach is based on prior knowledge from linkage studies
or the biology of the disease. The other approach is to perform “hypothesis free” Genome
Wide Association Study (GWAS), where thousands of genetic markers, single nucleotide
polymorphisms (SNPs), throughout the genome are tested individually for their association
with the disease.
The association between genotype and phenotype can be explained either by direct
biological action of the polymorphism or by allelic association between the marker and a
susceptibility gene. The term Linkage Disequilibrium (LD) is used to refer to allelic
association between linked loci. Linkage disequilibrium is the hallmark of association
22
studies. The assumption is that the genetic marker studied is close enough to the actual
disease gene. This will result in an allelic association at the population level. The
magnitude of LD is affected by many factors, but the most important one is the
physical/genetic distance between the disease gene and the marker allele; the closer they
are the stronger the LD [125]. The MHC region on chromosome 6 is an example to a region
that contains a large number of genes that are in strong LD.
1.5.8. Candidate genes within MHC region (chromosome 6p)
1.5.8.1. Human Leukocyte Antigen in Psoriasis
Numerous case-control association studies and later GWAS have found a strong
association between psoriasis and the MHC region [166-170]. The strongest association has
been found with a 300kb-segment in the MHC-I region on chromosome 6p21.3 known as
PSORS1 [171, 172]. This region contains HLA genes that are associated with autoimmune
diseases. Case-control studies identified the class I antigens HLA-B*13, B*17 and its split
B*57, C*06, C*07 as associated with psoriasis [168, 173-176]. Several studies
demonstrated an association with the class II antigens, HLA-DRB1*04 and HLA-
DRB1*07 [167, 177]. The largest and most consistently reported association is with HLA-
C*06, with a relative risk of 22. The presence of HLA-C*0602 is associated with an earlier
onset and more severe psoriasis [178, 179]. The association with HLA-B and the Class II
antigens, HLA-DR, was later determined to be due to extended haplotypes and LD with
HLA-C [180]. Several studies have shown that the strongest link with psoriasis is with the
57.1 ancestral haplotype (C*06-B*57-DRB1*07-DQ*03). These findings are consistent
with the association of individual components of this haplotype with psoriasis [181, 182].
Since this region is in strong LD, the true risk allele has been difficult to determine.
Candidate genes just telomeric to HLA-C, such as CDSN and HCR, seemed like good
candidate genes since they are expressed in the skin [183, 184]. However, none of these
candidate genes were consistently associated with psoriasis independently of HLA-C [185,
186]. In order to determine the psoriasis susceptibility locus within the PSORS1 region, a
23
study that involved sequencing the putative 300-kb risk segment of PSORS1 from just
telomeric to HLA-B to beyond CDSN thus including HLA-C was conducted. After
sequencing this segment in 2 risk and 5 non-risk chromosomes, then examining
recombinant haplotypes retaining HLA-C*06 but lacking risk alleles in CDSN, the authors
concluded that HLA-C*06 is the PSORS1 risk variant that confers susceptibility to
psoriasis [180]. Two GWAS among Caucasians and Chinese psoriasis patients confirmed
previous findings. In these studies, by far, the most significant associations were of SNPs
that were in tight LD with HLA-C*0602 [170, 187].
1.5.8.2. Human Leukocyte Antigen in PsA
Case-control studies identified the HLA region as containing potential susceptibility loci
for PsA. HLA-B*13, B*17 and its split B*57 and C*06 are associated with psoriasis across
various population [41, 188-192]. While HLA-C*06 is also increased in PsA patients
compared to the general population, this association is stronger with psoriasis itself than
with PsA [193]. Several studies reported an association between HLA-B*13, B*57,
DRB1*07 and PsA. However, these results are most likely secondary to the presence of
these alleles on the recognized ancestral haplotypes: AH.13 and AH.57 both contain HLA-
C*06 [181, 182]. HLA B*27 and B*07 have been specifically associated with PsA in case-
control studies that compared patients with PsA to psoriasis [41]. Several of the HLA
antigens have been related to specific manifestations of PsA. HLA-B*27 is more common
among PsA patients with axial disease while B*38 and B*39 are increased among those
with peripheral polyarthritis [41, 189, 192]. Within the HLA Class I region the
associations are stronger with HLA-B than HLA-C alleles [194]. It has been assumed that
associations with HLA-C alleles are related to the skin disease and are not a specific
marker for the joint disease. Among PsA patients, HLA-C*06 is associated only with early
onset psoriasis and no association was observed with PsA patients with late onset psoriasis
[193]. HLA-DRB1*04 was reported to confer a risk of PsA [189, 195, 196], but several
investigators found no such associations [41, 197]. The role of HLA genes in susceptibility
to PsA was demonstrated when increased sharing of HLA haplotypes was documented
among sib pairs concordant for PsA but not among those concordant for psoriasis only
[198]. Since almost all patients with PsA have psoriasis, it is unclear whether the HLA
24
associations described above are related to psoriasis, PsA, or both. The HLA alleles that
may be specific to PsA are HLA-B*27 and possibly B*07, B*38 and B*39.
1.5.9. Other candidate genes within MHC region in psoriasis
The MHC region contains more than 160 genes that have strong LD and span over 4
megabases. Many of the genes within this area are implicated in the pathogenesis of
autoimmune diseases including: HLA, TNF, C4 and others (Figure 1.1) [199].
Figure 1.1 - Genes within the MHC region
Since the PSORS1 region is included in the MHC region, several candidate genes in the
proximal MHC class I region have been assessed for their association with psoriasis. The
25
following genes have been found to be associated with psoriasis: HLA-B [200], HLA-C
[180, 201, 202], PSORS1C3 [201, 203], OTF3 [204], HCR [205, 206], SEEK1[207],
corneodesmosin (CDSN) [203, 208, 209] and TNF-α [210-212]. Since all of these genes
have a potential role in the pathogenesis of the disease, the precise identity of the PSORS1
determinant has not been determined due to the strong LD within this region. A detailed
analysis of genomic DNA sequences and recombinant haplotypes strongly suggested that
HLA-C*0602 is the disease allele at PSORS1 [180]. In a recent detailed analysis of GWAS
data, two additional SNPs within the MHC region were found to be significantly associated
with psoriasis after adjusting for HLA-C*0602. One SNP was located within c6orf10, a
potential downstream effecter of TNF-alpha, and the other SNP located between HLA-B
and MHC class I Chain- related sequence A (MICA) locus [170].
1.5.10. Other candidate genes within MHC region in PsA
The MHC region has also been investigated for susceptibility loci to PsA. Unlike psoriasis,
CDSN was not associated with PsA [213]. The region 100 Kb centromeric to the HLA-B
locus has been associated with PsA [214-216]. The MICA gene, located in this region, is a
candidate susceptibility gene for PsA due to its functional relevance, being involved in
activation of NK cells. In a Spanish population, a MICA allele with 9 GCT repeats (A9)
corresponding to MICA*002 was associated with PsA, but not with psoriasis [214],
independently of HLA-C*06. Similar associations have been shown in Jewish [215] and
Croatian [217] patients. However, other studies could not replicate these findings [218].
We have recently compared the distribution of 55 MICA alleles among patients with
psoriasis, PsA and healthy controls. The results showed that most MICA allele associations
with psoriasis and PsA were dependent on LD with HLA-B and HLA-C risk alleles.
Independent of HLA, only homozygosity for MICA*00801 increased the risk of
developing PsA [219].
TNF-α is increased in the skin and synovial tissue of patients with PsA, and the
inflammation in both sites is dramatically reduced following treatment with anti-TNF-α
agents [220-222]. The TNF-α gene lies just centromeric to MICA. Associations have been
26
reported with TNF-α -308 polymorphism in the promoter region of the gene and PsA [210,
212]. A meta-analysis confirmed an association between TNF-α -238 polymorphism and
PsA [223]. Thus it appears that the susceptibility locus for PsA lies more centromeric than
that of psoriasis, closer to HLA-B, MICA and TNF.
1.5.11. Candidate genes outside the MHC region
1.5.11.1. KIR genes on 19q and their interaction with HLA
NK cells play an essential role in innate immunity, particularly against tumors and viral
infections [67]. Dysregulation of the innate immune system has been involved in the
development of psoriasis and PsA. NK cells accumulate in active psoriatic skin plaques
[63] and inflamed synovium in PsA [60, 61] and loss of their inhibition may exacerbate
damage to the tissue. The activity of NK cells is determined by a balance between
activating and inhibitory signals transmitted by a range of receptors including KIRs,
Leukocyte immunoglobulin like receptor and the CD94/NKG2 family of receptors
expressed on the surface of NK cells [224]. KIRs recognize HLA class I molecules and due
to the association of the latter with psoriasis and PsA, KIRs have been investigated as
potential candidate susceptibility loci.
The KIR gene cluster spans approximately 150 kb in the leukocyte receptor complex on
chromosome 19q13. The KIR region is highly diverse; different KIR haplotypes exhibit
differences in both gene content and allelic polymorphism. The KIR gene family currently
consists of 15 genes. A particular KIR haplotype encodes a distinct set of receptors for an
individual’s NK cells. Each NK cell has a combination of inhibitory and activating
receptors that interact with certain HLA alleles to result in an immune response. KIRs are
classified into activating and inhibitory receptors. The engagement of a long-tailed
cytoplasmic KIR (L) with HLA class I allotypes transmits an inhibitory signal while the
short-tailed KIR (S) leads to an activating signal. KIRs are also categorized on the basis of
external immunoglobulin-like domain into 2 groups (2D or 3D) [225]. The specificity of
the different KIRs to HLA class I molecules is determined by their extracellular structure.
KIR2DL2 and 2DL3 bind to HLA-C group 2 molecules that contain the amino acids Asn77
and Lys80, while KIR2DL1 binds to HLA-C group 1 molecules that contain the amino
27
acids Ser77 and Asn80. KIR3DL1 binds the HLA-Bα1 helix around residues 76-80 with
specificity for all Bw4 alleles containing isoleucin at heavy chain residue 80. Studies have
shown that binding of inhibitory KIRs to specific HLA molecules correlates well with their
ability to inhibit NK cytolysis of target cells bearing those HLA allotypes. Ligands for the
activating KIRs are largely unknown despite possessing similar extracellular regions,
although it has been suggested that they recognize the same HLA molecules that are
recognized by their corresponding inhibitory KIRs [68, 226].
Overall, this system provides a mechanism by which an individual’s HLA repertoire may
directly influence the type and extent of the immune response in the context of a particular
KIR haplotype. Thus, the biologic interaction between KIRs and their specific HLA ligands
may predispose to the development of psoriasis and PsA. The interaction between HLA and
KIR genes may explain the differential susceptibility to these diseases.
Several studies have shown that the presence of genes encoding activating KIRs (KIR2DS1
and/or KIR2DS2) is associated with PsA, particularly in the absence of HLA-C alleles that
encode corresponding ligands for the inhibitory KIRs (KIR2DL1 and KIR3DL2/3) [227,
228]. Furthermore, KIR2DS1 but not KIR2DS2 was associated with psoriasis in Japanese
and Polish patients [229, 230] and among a group of North American patients a decrease in
KIR2DS1 in psoriasis, but an increase in patients with PsA has been noted [231]. It is
unclear whether these findings represent an association that is independent of the skin
disease, since PsA patients were not compared directly to those with psoriasis.
Additional KIR – HLA interactions may play a role in the pathogenesis of PsA. The
inhibitory receptor, KIR3DL1, binds HLA-B molecules with Bw4 motif [232]. The
activating receptor, KIR3DS1, shares approximately 97% sequence similarity in their extra-
cellular domain with KIR3DL1, suggesting that they may share similar ligands [233]. A
study from Spain have found that the inhibitory KIR3DL1 allele was decreased while the
activating KIR3DS1 allele was increased in AS patients compared to HLA-B*27 positive
healthy controls, suggesting that the interaction between the activating receptor and its
specific ligand may increase the susceptibility to the disease [234]. However, another large
study among AS patients was unable to replicate these findings [235]. The interaction
28
between KIR3DS1 and its putative ligand HLA-Bw4 leads to activation of NK cells, which
may be beneficial for the clearance of viral infections however may also lead to the
development of autoimmune diseases. It has been shown that the combination of KIR3DS1
and HLA-Bw4 was associated with slower progression from HIV infection to AIDS, lower
viral load and protection against opportunistic infections [236]. HIV infection has been
linked to increased risk for developing psoriasis and PsA [95]. The association between
KIR3DL1/KIR3DS1 and PsA has not been assessed in the past.
1.5.12. Other candidate genes
A number of candidate genes outside chromosome 6p have been associated with psoriasis
including IL-10 on 1q31–q32, IL-12B/p40 subunit on 5q31.1–q33.1, IL-19/IL-20/IL-24 on
1q32, IL-23R on 1p31.3, IRF2 on 4q35.1, MGST2 on 4q28.3, PTPN22 on 1p13.3–p13.1,
RAPTOR on 17q25, SLC12A8 on 3q21, SLC9A3R1/NAT9 on 17q25.1 and SUMO4 on
6q25.1 [62]. Polymorphisms of interferon regulatory factor (IRF5) interacting with Class I
MHC genes were found to be associated with psoriasis [237]. Candidate genes outside 6p
have also been evaluated for a possible association with PsA. CARD 15 (NOD2) on 16q
and several SNPs from the IL-1 cluster were associated with PsA patients from
Newfoundland, however these associations have not been replicated in other populations
[238-243]. Additionally, an IL-23 receptor polymorphism on chromosome 1p31 was
associated with PsA [244]. However, this locus was also associated with psoriasis in
several studies [245-247] and does not confer an independent risk for arthritis [248].
Recently, polymorphisms within IL-13 gene on chromosome 5q31 have been associated
with PsA [249]. This association was independent of psoriasis and was replicated in two
additional populations [250, 251]. Therefore, IL-13 gene polymorphism may be one of the
first specific genetic markers for PsA that does not confer a risk for psoriasis.
29
1.5.13. Genome-wide Association studies
Several GWAS among Caucasian and Chinese psoriasis patients have been performed in
recent years. In all of the studies the strongest associations were with SNPs within the
PSORS1 region. Several of these SNPs were in strong LD with known HLA-C*0602,
confirming its role as the major susceptibility genetic marker for psoriasis. A recent GWAS
from the UK found an interaction between HLA-C and ERAP1 loci. ERAP1 is involved in
MHC class I peptide processing. ERAP1 polymorphisms were associated with
susceptibility for psoriasis only among patients carrying the HLA-C risk allele [252].
Additional novel associations from GWAS provide an insight into several important
pathogenic pathways in psoriasis. Susceptibility loci for psoriasis included genes from the
NFkB signaling pathway: TNFAIP3, TNIP1, NFKBIA, REL [246, 252, 253], genes from
the Th17 signaling pathway: IL-23A, IL-23R, IL-12B, TYK2 and TRAF3IP2 [187, 245,
246, 252-254], and genes from the epidermal differentiation complex (PSORS4): LCE3B,
LCE3D and LCE3C [187, 246, 253, 254]. A recent GWAS among PsA patients from
Germany identified HLA-C, IL-12B and TRAF3IP2 as susceptibility loci for PsA
compared to healthy controls [255]. However, all these loci have already been identified as
susceptibility genes for psoriasis and therefore cannot be considered as independent
markers for PsA. Another GWAS among PsA patients from North America is underway.
The density of markers used in GWAS will however not be able to distinguish between the
various genes in the MHC region as this region has high gene density and LD and would
therefore require a much higher density of markers to distinguish the various genes
associated with disease susceptibility.
1.5.14. Limitations of previous genetic studies
The great challenge in investigating the genetics of PsA is the ability to differentiate the
genes that confer an independent risk for the joint disease from those that are associated
with the skin disease. Studies for that purpose require a large sample size, careful
characterization of clinical phenotype and appropriate controls.
30
Most of the previous genetic studies in PsA included only small numbers of participants
and therefore were underpowered to detect significant associations. In addition, many of
the published genetic studies in PsA have used only healthy individuals as controls;
therefore it is difficult to conclude whether the reported associations are related to the skin
or the joint manifestation of PsA. Furthermore, those studies that have used patients with
psoriasis as controls do not provide details about whether the presence of inflammatory
arthritis was ruled out. PsA is often under diagnosed among psoriasis patients. It has been
shown that up to 18% of the psoriasis patients that are thought to have only skin disease,
actually also suffer from inflammatory arthritis after careful rheumatologic evaluation
[119]. Therefore, misclassification of cases and controls can be a major problem in genetic
studies in PsA and psoriasis if a proper rheumatologic evaluation is not performed. Lastly,
the problem of a clear definition of phenotype is complicated by the fact that until 2006,
there were no accepted classification criteria for PsA [22] and previous genetic studies in
PsA have used different definitions of phenotypes. All of these factors support the need for
further studies of the genetics of PsA.
In summary, psoriasis and PsA have substantive genetic determinants. Some susceptibility
genes are probably to be shared by psoriasis and PsA; however, it is likely that there are
some distinct genes that confer an independent risk for PsA. In PsA, several candidate
genes have been replicated in studies of disease susceptibility, most of them within the
MHC region. Past genetic studies in PsA have been limited by their small sample size,
potential misclassification of cases and controls, lack of appropriate control groups and
lack of uniform definition of phenotype.
31
Chapter 2. Hypothesis and Aims
32
2.1. Rationale
The nature of relationship between PsA and psoriasis is not completely clear. In one model,
since most patients with PsA develop psoriasis at some point in their disease, PsA can be
viewed as a “disease within a disease”. In this model, PsA is considered a more severe
phenotype of psoriasis that occurs due to a greater number of susceptibility genes or
environmental factors. The additional genes or environmental risk factors on the background
of psoriasis will lead to development of PsA. This model predicts that case series ascertained
by psoriasis will be genetically homogenous and that case series ascertained by PsA will
completely overlap those of psoriasis apart from several additional distinct genes. Although
other models have been suggested to describe the relation between psoriasis and PsA, the fact
that most of the susceptibility genes for psoriasis were also found to be associated with PsA
supports this model.
Approximately 85% of the patients develop PsA after or at the same time as the skin disease
[35]. Therefore, psoriasis serves as a marker for the development of PsA. Currently, there is
limited information about the epidemiology, clinical, environmental and genetic risk factors
for PsA among patients with psoriasis. The few previous epidemiological studies were
limited by their small sample size and potential misclassification of cases and controls and
lack of uniform definition of phenotype. Furthermore, studies to date have looked either at
psoriasis cohorts or at PsA cohorts, but there has been no systematic evaluation of psoriasis
patients without arthritis and patients with PsA from the same ethnic and geographic
background. The lack of direct comparison between PsA and psoriasis patients does not
allow conclusions as to whether the reported associations were related to the skin or the joint
disease. Therefore, the inclusion of both groups in one study will allow identifying markers
for PsA among psoriasis patients.
The HLA genes are associated with numerous immune-mediated diseases including psoriasis
and spondyloarthritis. GWAS among psoriasis patients have shown that by far the most
significant associations are located in this region. KIR genes have also been associated with
SpA and PsA in the past and their binding to HLA genes provide a compelling functional
33
mechanism for this association. Therefore in this thesis I have decided to focus on these two
genetic regions and to assess their association with PsA.
2.2. Hypothesis
I hypothesize that genetic factors in the MHC region, most likely HLA genes on
chromosome 6p, and KIR genes on chromosome 19q, predispose patients with psoriasis to
develop PsA. Genes that differentiate patients with PsA from those with psoriasis alone serve
as markers associated with the development of PsA in patients with psoriasis.
In addition, I hypothesize that environmental factors play a role in the pathogenesis of PsA
among psoriasis patients and their interaction with susceptibility genes for PsA may lead to
the development of the disease among psoriasis patients.
2.3. Aims
1) To identify HLA alleles that confer a risk for PsA among patients with psoriasis. (Chapter
5)
2) To determine the association between KIR genes and PsA and to assess the joint statistical
association of KIR genes and their corresponding HLA alleles with PsA. (Chapter 7)
3) To identify environmental risk factors for PsA among patients with psoriasis. (Chapter 8)
4) To determine whether there is an interaction between the selected environmental risk
factors and genetic markers for PsA in the susceptibility for PsA. (Chapter 9)
5) To determine the rate of PsA among psoriasis patients and whether it is affected by HLA
risk alleles. (Chapter 4, 6)
34
Chapter 3. Study population and data collection
35
3.1. Study population
3.1.1. Group 1- Psoriasis without arthritis
This group includes individuals from a recently established, ongoing prospective cohort of
adult psoriasis patients without arthritis. The cohort forms the basis of a long term
prospective study that aims to assess genetic and clinical risk factors for the development of
inflammatory arthritis among patients with psoriasis. All potential study subjects have a
diagnosis of psoriasis confirmed by a dermatologist. The sources of recruitment are varied
and include patients with a range of psoriasis types and severity. Patients were mainly
recruited from dermatology clinics and phototherapy centers in the Greater Toronto Area and
also from family practice clinics and through advertisement in flyers posted in several
hospitals and local media. By approaching patients with wide range of disease severity we
aimed to minimize a selection bias of patients with more severe psoriasis. The cohort
includes different ethnic groups although most of the patients (86%) are Caucasians.
The inclusion criteria required a diagnosis of psoriasis confirmed by a dermatologist. The
exclusion criteria were the presence of inflammatory arthritis or spondylitis in the past or at
the time of the assessment. All subjects were evaluated by experienced rheumatologists from
the PsA research team to exclude inflammatory arthritis and spondylitis before enrollment.
Subjects were interviewed and examined according to a standardized protocol similar to that
used in the PsA clinic [35]. Information about demographics, co-morbidities and
rheumatologic symptoms was collected. Each subject underwent a comprehensive
musculoskeletal examination that included joint assessment for tenderness, swelling and
deformities, evaluation for the presence of enthesitis, tendonitis and dactylitis, and
assessment for restriction of movement in the spine. If there were definite clinical findings of
inflammatory arthritis, enthesitis or spondylitis, the patient was excluded from the study. In
cases of doubt, imaging studies, including radiographs, ultrasound or MRI, were performed
as indicated to investigate the nature of the abnormality. Patients thus diagnosed with
psoriasis that have no current evidence or past history of PsA were eligible for the study. If a
non-inflammatory condition, such as osteoarthritis, was diagnosed, the subject was included
36
in the study. This process ensured that all participants do not have clinical inflammatory
arthritis at enrollment. The recruitment to the cohort started in January 2006. Thus far, 564
psoriasis patients were screened by a rheumatologist and to date (March 2011), 499 patients
are included in the cohort. Following the screening assessment, 65 individuals were found
ineligible to participate in the study, 34 of them due to PsA.
3.1.1.1. Follow-up in the Psoriasis cohort
All study participants are re-assessed annually according to the same protocol regardless of
their symptoms. Patients who fail to come to the yearly assessment are requested by
telephone and by mail to complete the Toronto Psoriatic Arthritis Screen (ToPAS)
questionnaire, a screening questionnaire designed to detect PsA among patients with
psoriasis as well as the general population. The ToPAS has been validated in different
populations of patients with skin conditions other than psoriasis, PsA and healthy subjects
[256]. Subjects scoring ≥8 points on the ToPAS screen were requested to come to the clinic
for an assessment.
The diagnosis of inflammatory arthritis or spondylitis was determined by the PsA research
team that included several experienced rheumatologists, after reviewing the clinical,
laboratory and imaging data. Patients were classified as having PsA if they fulfilled the
CASPAR criteria [22]. Patients diagnosed with PsA were considered to have developed the
outcome of interest. For the purpose of analysis in the case-control study designs these
incident cases were included in the PsA group.
3.1.2. Group 2 - Patients with PsA
This group includes adult prevalent PsA patients. All patients are from the University of
Toronto Psoriatic Arthritis clinic database, which currently (March 2011) numbers 1143
patients. The PsA clinic was established in 1978 as part of an ongoing prospective study.
Patients are registered in the clinic if they are diagnosed as having PsA. The patients in the
37
PsA clinic represent a wide spectrum of disease that is related to the broad referral base of
the clinic. The clinic serves as a primary, secondary and tertiary referral centre for PsA
patients from the Greater Toronto Area and Southern Ontario. Furthermore, patients are
followed regularly, irrespective of their disease activity. Many of the patients were referred
from dermatology clinics for the assessment of PsA, thus minimizing ascertainment bias
between the PsA and the psoriasis groups, since most of the psoriasis patients were also
recruited from dermatology clinics.
Since classification criteria for PsA were not available until 2006, the diagnosis of PsA was
based on the presence of psoriasis and inflammatory arthritis and exclusion of other types of
arthritis. However, 98% of the patients in the clinic satisfy the CASPAR criteria for
classification of PsA. The patients are reviewed at initial clinic entry and at 6-12 month
intervals according to a standard protocol [35] using validated clinical and radiographic
measurement tools.
3.1.3. Group 3-Healthy Controls
Control DNA was obtained from 707 healthy Caucasian volunteers, cadaveric organ donors
from laboratories at the University Health Network (usually young previously healthy
individuals appropriate for organ donation) and from a commercial biobank. Blood samples
were used for DNA typing however no additional clinical information is available.
3.2. Data collection
The following information was collected as part of the standardized protocol for each subject
in groups 1 and 2: demographic information, race and ethnicity (based on self-report),
country of origin, year of diagnosis of psoriasis and PsA, family history of psoriasis, PsA and
other SpA, alcohol and smoking habits, co-morbidities and medication history. Additional
information was collected through physical examination: tender and swollen joint count,
number of dactylitic digits, active entheseal sites, clinically damaged joint counts and
measurements of spinal mobility. An “actively inflamed” joint was defined as the presence of
38
stress pain and/or effusion. Enthesitis was defined as tenderness at sites of insertion of
tendons and ligaments into bones (initially including only the Achilles tendon and plantar
fascia and since 2006 according to the SPARCC enthesitis index [257]). Dactylitis was
defined as the swelling of a whole digit with tenderness. Clinically damaged joint was
defined as the presence of limitation of range of movement of >20% of the range not related
to the presence of joint effusion, presence of joint deformity, subluxation, loosening, or
ankylosis. Measurements of spinal mobility were made according to a standard protocol. At
the lumbar spine these included forward flexion (modified Schober's test), lateral flexion
(Domjan and INSPIRE methods) [257]. Chest expansion, cervical rotation and Occiput-to-
wall distance were also measured. Psoriasis activity was determined using the PASI score
[16]and modified Nail Psoriasis Severity Index (mNAPSI) [258] which are validated tools
for assessment of severity of skin and nail involvement, respectively. Information about
concomitant use of DMARDs or biologic medications was also collected.
Radiographic damage was assessed according to a modification of the Steinbrocker method
[259]. Radiographic lumbar and cervical spine damage was scored according to the modified
Stokes Ankylosing Spondylitis Spine Score(mSASSS) [260]. Sacroiliitis was defined
according to the New York criteria [261].
All information is tracked and on a computerized database.
39
Chapter 4. The incidence of arthritis in a prospective cohort of psoriasis patients
40
4.1. Background
The estimated prevalence of inflammatory arthritis among patients with psoriasis has varied
from 6% to 42% [31]. Approximately 67% of the patients develop psoriasis before arthritis
and in 16% arthritis and psoriasis present within 12 month of each other [31]. There is
limited information about the incidence of PsA among patients with psoriasis. A
retrospective study from Germany reported that the cumulative incidence of PsA among
psoriasis patients reached 20.5% after 30 years from the diagnosis of psoriasis [32]. Another
retrospective study from Rochester, Minnesota has found a lower cumulative incidence of
3.1% cases of PsA among psoriasis patients after 10 years from the onset of the skin disease
[33]. A prospective study is considered the preferred study design to determine the incidence
of a disease; however, no study to date has prospectively assessed the incidence of PsA
among psoriasis patients.
Our group has recently established a cohort of patients with psoriasis uncomplicated by
arthritis. The patients are followed prospectively to detect patients that develop inflammatory
arthritis. This report summarizes the results of the first four years of follow-up and reports
the cumulative incidence of PsA.
41
4.2. Methods
4.2.1. Setting
The Toronto Psoriasis Cohort was established in 2006 and enrolls psoriasis patients without
inflammatory arthritis or spondylitis. The cohort forms the basis of a long term prospective
study that aims to assess genetic, clinical and environmental risk factors for inflammatory
arthritis among psoriasis patients. The prospective design of the study provides the
opportunity to determine the incidence of inflammatory arthritis in psoriasis patients. The
recruitment for this ongoing study commenced in January 2006.
4.2.2. Patients and Assessments
A detailed description of patients’ recruitment and assessment is provided in the Methods
section (Chapter 3.1 – Study population – Group 1 psoriasis without arthritis). Briefly, all
potential study subjects have a diagnosis of psoriasis confirmed by a dermatologist. The
sources of recruitment are varied and include patients with a range of psoriasis types and
severity. Patients are mainly recruited from dermatology clinics and phototherapy centers in
the Greater Toronto Area and also from family practice clinics and through advertisement in
flyers posted in several hospitals and local media. The exclusion criteria are the presence of
inflammatory arthritis or spondylitis in the past or at the time of the assessment. All subjects
are evaluated by rheumatologists from the PsA research program to exclude inflammatory
arthritis and spondylitis before enrollment. If there are clinical findings of inflammatory
arthritis, enthesitis or spondylitis, the patient is excluded from the study. Where the diagnosis
of PsA is not clear, imaging studies are performed as indicated to investigate the nature of the
abnormality. If a non-inflammatory condition, such as osteoarthritis, is diagnosed, the subject
was included in the study. This process ensured that no participants have clinical
inflammatory arthritis at enrollment.
42
All study participants are then re-assessed annually regardless of their symptoms. Patients
who failed to come to the yearly assessment were contacted and requested to fill out the
ToPAS questionnaire [256], a screening questionnaire designed to detect PsA among patients
with psoriasis. Subjects scoring ≥8 points on the ToPAS screen were requested to come to
the clinic for an assessment.
4.2.3. Case definition
The diagnosis of inflammatory arthritis or spondylitis was determined by the PsA research
team that included several experienced rheumatologists, after reviewing the clinical,
laboratory and imaging data. Patients were classified as having PsA if they fulfilled the
CASPAR criteria [22]. Patients diagnosed with PsA were considered to have developed the
outcome of interest. Their years at risk were the time since entry into the cohort to the
diagnosis of PsA. Patients not found to have developed PsA were considered at risk from
entry into the cohort to the last contact.
4.2.4. Statistical Analysis
Descriptive statistics were computed with continuous variables summarized by their means
and standard deviations and categorical variables summarized by proportions.
The person-years at risk was calculated as the time between enrollment date and last
assessment date (in subjects not developing PsA) or the date of PsA diagnosis, whichever
came first. This duration was used to estimate the cumulative incidence of PsA among
patients with psoriasis. Some patients did not have any follow-up visits after their initial
screening visit and therefore did not contribute to follow-up time. However, on the
assumption that these patients would have contacted the clinic if they developed symptoms
of arthritis, we performed a secondary analysis that included these patients, assuming that
they did not develop PsA by December 31, 2009.
43
There is a common notion among PsA researchers that the greatest risk of developing the
disease is during the first years following the onset of psoriasis. We have used two
parametric models to evaluate whether the risk of developing PsA, as modeled by the hazard
function, changes over time. A time homogeneous (exponential) model, that assumes a
constant hazard function, was used to estimate the rate of PsA among psoriasis patients. This
model was compared to a Weibull model that assumes a trend in hazard function. The lack of
a significant difference between the two models, as assessed by the likelihood ratio (LR) test,
was taken as lack of evidence for time-dependent rate. A Kaplan-Meier curve was used to
obtain a nonparametric estimate of the cumulative incidence of PsA over time.
44
4.3. Results
The outcome of the 313 patients that were recruited from January 2006 until December 2008
is summarized in Figure 4.1. The sources of recruitment were largely from dermatology
clinics and phototherapy centers (289 patients (92.3%). The rest were recruited through local
advertisement (20 patients (6.4%)) and from a general rheumatology clinic (4 patients
(1.3%)). The patients recruited from the rheumatology clinic had been referred for an
assessment of musculoskeletal pain; however they were found to have non-inflammatory
articular problems and therefore were included in the cohort. In total, 10 subjects developed
PsA during 4 years of the prospective study follow-up; 253 participants had at least one
follow-up visit and were free of inflammatory arthritis. Twenty patients filled out the ToPAS
questionnaire. None of them scored 8 or more, therefore, they were all considered as not
having PsA at that time point.
Overall, 50 subjects did not have any follow-up assessment. 17 declined to return for a
follow up visit or to fill out the ToPAS questionnaire. We were unable to contact 30, two
moved from the region and one patient had died. This cohort had a total of 534.5 person-
years of follow-up with a mean of 1.71+1.01 years per person. Among the 10 incident cases,
the duration of time from enrolment to development of PsA ranged from 4 to 42 months,
with 3 patients that developed PsA within the first year of follow-up.
4.3.1. Incidence of PsA among patients with psoriasis
The annual incidence rate was 1.87 [95% confidence interval (CI) (0.71-3.03)] PsA cases per
100 psoriasis patients when patients with no follow-up visits were assumed not to have
developed PsA by December 31, 2009. The incidence rate increased to 2.53 (95% CI 0.96,
4.1) when only the 253 participants with at least one follow-up assessment were included. A
Kaplan-Meier curve and the estimated cumulative probability of developing PsA among the
study participants are shown in Figure 4.1. The distribution of the time to the development of
PsA was fit with an exponential model, which has a constant hazard rate. Tests for trend did
45
not suggest a departure from the constant hazard, so there was insufficient evidence to claim
a trend in the risk of disease.
4.3.2. Characteristics of the PsA cases
Ten patients developed new onset PsA during the study follow-up period. The characteristics
of the study population and the incident cases of PsA are presented in Table 4.1. The mean
age at onset of psoriasis and arthritis were 34.6±14.2, 52.2±8.8 years, respectively. Four of
the patients were male, 6 had a family history of psoriasis; however, none reported a family
history of PsA. The most common type of psoriasis was chronic plaque psoriasis (8 patients)
followed by guttate psoriasis (2 patients). The mean PASI score at presentation of arthritis
was 6.7 and 6 patients had psoriatic nail lesions.
At the time of the diagnosis, 8 patients had oligoarthritis and 2 had polyarthritis, and 3
patients had additional evidence of spondylitis. All of the patients were symptomatic at the
time of the diagnosis of PsA. The median number of actively inflamed joints was 3 (range 0-
27) with 1 swollen joint (range 0-3). Three patients had evidence of enthesitis, the most
common site being the plantar fascia and none had dactylitis. Only one patient had evidence
of a clinically damaged joint (1 joint). All of the patients were seronegative for rheumatoid
factor. Only three patients had elevated inflammatory markers, either ESR or CRP. Two
patients had evidence of definite radiographic erosions with a total modified Steinbrocker
score of 2 and 13, respectively. Two patients had grade 3 bilateral sacroiliitis and none had
evidence of syndesmophytes (mSASSS score was 0). These two patients did not have back
pain nor any limitation of spinal mobility at baseline assessment, therefore, no radiographic
studies were performed. Furthermore, none of these patients had back pain at the time of the
diagnosis of PsA and they were diagnosed based on findings of peripheral oligoarthritis.
46
Figure 4.1 - Follow-up summary of the study population
(January 2006 – December 2009)
Free of arthritis at last assessment: 253 patients
Developed PsA:
10 patients
No Follow-up visit:
50 patients
313 psoriasis patients recruited from
Jan. 2006-Dec. 2008
20 patients were free of arthritis
based on the ToPAS questionnaire - Declined to return to visit or to fill-out ToPAS: 17 patients - Unable to contact: 30 patients - Moved from the region: 2 patients - Died: 1 patient
47
Figure 4.2 - PsA incidence over time estimated with a Kaplan-Meir (KM) curve (95% CI) from time of first clinic visit to diagnosis of PsA, along with an estimated cumulative incidence curve based on an exponential model (Exp).
48
Table 4.1 - Characteristics of the study population and the incident cases of PsA
Variable Psoriasis
(N=313)
Incident cases of PsA
(N=10)
Age (Mean ± SD) Sex: Male (%) Ethnicity: Caucasian (%) Family history of psoriasis (%) Family history of PsA (%) Duration of psoriasis (Years) Psoriasis type
- Chronic plaque psoriasis - Guttate - Pustular - Flexural
PASI score (Mean ± SD) Psoriatic nail lesion (%) Current use of medications DMARDs Biologic agents
46.5±12.8 183 (58.5%) 269 (86.2%) 127 (40.6%)
7 (2.2%) 15.8±14.0
270 (86.5%) 48 (15.4%) 13 (4.1%) 9 (2.9%) 5.4±5.7
249 (81%)
21 (18.8%)
4 (4.1%)
50.4±9.9 4 (40%) 9 (90%) 6 (60%) 0 (0%)
17.6 ±10.3
8 (80%) 2 (20%)
- -
6.7±6.5 6 (60%)
2 (20%)
0 (0%)
PsA – Psoriatic Arthritis, PASI – Psoriasis Area and Severity Index, DMARDs – Disease Modifying Anti Rheumatic Drugs.
49
4.4. Discussion
The study presents results from an ongoing prospective longitudinal cohort of psoriasis
patients. To my knowledge, this is the first study that has prospectively assessed the
incidence of PsA among psoriasis patients. After 4 years of follow-up, the annual incidence
rate was 1.87 cases among 100 psoriasis patients.
In contrast to the prevailing notion that the greatest risk of developing PsA is during the first
years following the onset of psoriasis, the present report and another retrospective cohort
study from Germany have shown that the rate of development of PsA among psoriasis patient
is constant and unrelated to the duration of psoriasis [32]. Although several epidemiologic
studies have assessed the incidence of PsA in the general population, there is limited
information about the incidence of PsA among psoriasis patients. In a population based
retrospective study that used medical records to confirm the diagnosis of PsA, Wilson et al.,
from Rochester, estimated that the cumulative incidence of PsA was 3.1% after 10 years
from the onset of psoriasis and 5.1% after 20 years [33]. Several reasons, other than
geographic and ethnic differences, may explain the differences in the cumulative incidence
between our study and those of the group from Rochester. First, Wilson et al. identified cases
based on a retrospective review of computerized medical records, while in our study each
subject was examined prospectively by a rheumatologist. Underestimation and
misclassification of cases are major threats in epidemiological studies that rely on
computerized data registries, particularly since widely accepted classification criteria for PsA
were unavailable until recently.
Furthermore, underestimation of PsA among psoriasis patients may be even more
pronounced. In a German study, approximately 18% of the psoriasis patients that were found
to have inflammatory arthritis were unaware of their condition and had never seen by a
rheumatologist [119]. A referral bias may also account for the difference, since by relying
on medical records some of the mild cases of arthritis may not come to medical attention.
The prospective design of the study and careful assessment of each subject allowed us to
diagnose early and milder cases that potentially could have escaped medical attention.
50
Finally, the differences in the source populations of psoriasis patients may explain some of
the differences. The present study included mainly patients with moderate to severe psoriasis
from dermatology clinics, while the Rochester study assessed patients with a wider range of
psoriasis severities. Several studies in the past suggested that the severity of psoriasis is
associated with higher risk of developing PsA [24, 262]. A recently published study from
Europe that assessed the presence of PsA among psoriasis patients attending dermatology
clinics reported a cumulative incidence of PsA that was closer to that found in this study
(20.5% of patients developed PsA after 30 years from the diagnosis of psoriasis) [32].
In this study, most of the patients had oligoarthritis at the time of the diagnosis and only a
few had developed clinical or radiographic joint damage, which indicates an early diagnosis
of the disease. These findings are in accordance with Kane et al. that have found that 27% of
129 PsA patients assessed within 5 months of onset of symptoms had joint erosions [263].
The course of these patients with mild forms of arthritis cannot be predicted, although our
previous studies suggest that male patients with a smaller number of involved joints fare
better [43].
In summary, the results of 4 years of follow-up of a newly established cohort of psoriasis
patients without arthritis were described. The annual incidence rate of arthritis among the
cohort population was 1.87 cases among 100 psoriasis patients. These figures suggest that the
incidence rate of PsA may be higher than previously reported, particularly among patients
with moderate to severe psoriasis.
51
Chapter 5. HLA risk alleles for PsA among psoriasis patients
52
5.1. Background
PsA can be considered as a “disease within a disease”, as almost all patients with PsA will
develop psoriasis at some point in their lifetime. The strong familial aggregation, as
demonstrated by high recurrence rate of PsA and psoriasis in relatives supports the genetic
basis of these diseases [264].
Candidate gene and genome wide studies have shown a strong association between psoriasis
and the MHC region [166-170] particularly with a 300kb-segment in the MHC class I region
on chromosome 6p21.3 known as PSORS1 [171, 172]. Within this region the largest and
most consistently reported association is with the HLA-C*06 [180] allele that is linked to
earlier onset and more severe psoriasis [178, 179].
The MHC region, particularly HLA genes, has also been associated with PsA [41, 188-192].
While HLA-C*06 is more frequent in PsA patients compared to the general population, this
association is stronger with psoriasis than with PsA [193]. Several studies reported an
association between HLA-B*13, B*57 and DRB1*07 and PsA. However, these results are
most likely secondary to the presence of these alleles on the recognized ancestral haplotypes:
AH.13 and AH.57, both contain HLA-C*06 [181, 182]. In a case-control study that
compared PsA to psoriasis, HLA-B*27 and B*07 frequencies were higher in PsA [41]. HLA-
DRB1*04 was reported to confer a risk for PsA [189, 195, 196], but several investigators
found no such association [41, 197]. Evidence for the role of HLA in susceptibility to PsA
has also been demonstrated by increased sharing of HLA haplotypes among sib pairs
concordant for PsA but not those concordant for psoriasis [198].
The great challenge in investigating the genetics of PsA is related to the difficulty in
differentiating the genes that confer risk for the joint disease from those associated with the
cutaneous disease. Many previous genetic studies in PsA have used only healthy individuals
as controls, preventing the determination of whether any significant association was related
to cutaneous disease or to joint manifestations of PsA. Additional limitations of previous
studies include small sample sizes, potential misclassification of cases and controls and the
lack of uniform classification criteria for the clinical definition of the PsA phenotype.
53
Genes that differentiate patients with PsA from those with psoriasis alone may serve as
markers for the development of PsA in patients with psoriasis. In this study I aimed to
identify HLA alleles that confer a risk for PsA in psoriasis patients.
54
5.2. Methods
5.2.1. Study population
In this population-based case-control study three groups of individuals from the same
geographic region were compared. All participants were Caucasians (by self-report) from the
Greater Toronto Area.
The PsA group included 712 adult PsA patients recruited from the University of Toronto PsA
cohort. All patients were carefully phenotyped by a rheumatologist and satisfied the
CASPAR criteria for the classification of PsA [22].
The psoriasis group included 335 psoriasis patients who were recruited from a prospective
cohort of psoriasis patients. These patients had psoriasis confirmed by a dermatologist and
were assessed by a rheumatologist to rule out inflammatory arthritis. Therefore at the time of
analysis, all psoriasis patients were free of arthritis.
Healthy Controls DNA was available from the laboratory control biobank as well as from
ethnically matched unrelated healthy volunteers from the Toronto area.
The groups of PsA and psoriasis patients were combined to create a Psoriatic Disease group
(PsD) that was compared to the healthy control group.
A population based case-control study design is the most commonly used method for
investigating susceptibility genes of a disease. The main advantages of this method are the
lower costs compared to a cohort study and the fact that the predictor variable (the gene
effect) is not subjected to a recall bias. Furthermore, the method is generally considered to
have more power to identifying susceptibility genes for a disease compared to a family-based
case-control study and other types of study designs. A potential limitation of a case-control
design, that considers psoriasis patients as controls, is that the outcome of interest (PsA) may
not be rare among the “controls”. This may result in misclassification of cases and controls as
a proportion of the controls may become cases in the future. Despite the previously described
limitation I have used the case-control study design as the primary method to identify
55
susceptibility genes for PsA among psoriasis patients. Other types of analyses will be used to
determine the associations that will be identified in the population based case-control study.
5.2.2. HLA typing
DNA was extracted from peripheral blood using a modified salting out procedure (Gentra®
Puregene® Blood Kit). Extracted genomic DNA was amplified by PCR using locus specific
primers for each of the HLA-A, -B, -C, -DR and DQ loci. PCR amplicons were identified by
Sequence Specific Oligonucleotide (SSO) probes using the reverse line blot technique.
(RELITM SSO HLA typing kits) [265]. Ambiguous results were resolved using Sequence
Specific Primers (PCR-SSP).
5.2.3. Sample size
Power calculations show that a study comparing 712 PsA patients to 335 psoriasis patients
has >80% power to detect an odds ratio of 2.5, 2.0, 1.7 and 1.6, for the frequencies of the risk
allele of 0.05, 0.1, 0.2 and 0.3, respectively (p<0.01). Power calculations were performed
using PS Power and Sample Size Calculations version 3.0 [266].
5.2.4. Statistical methods
5.2.4.1. Single locus analysis
Since HLA antigens are expressed in a co-dominant manner, the frequency of each of the
HLA alleles was used for comparisons. Since the study was underpowered to detect the
association of rare alleles, only those with a frequency of > 1% in the study population were
included in the analysis. Overall, the presence of each of 105 HLA alleles at 5 HLA loci was
tested, of these, 11 rare alleles were excluded. A Likelihood Ratio (LR) test was used to
assess the differences in allelic distribution between the PsA vs. psoriasis and PsD vs. healthy
controls. To avoid false positive results due to multiple testing (type I error) the False
56
Discovery Rate (FDR) approach was employed [267] to account for multiple testing. FDR is
the expected proportion of false discoveries among all discoveries, limiting false positives to
a specific proportion. This method is less conservative than the Bonferroni correction and
increases the power to detect significant associations [268]. Each HLA locus was assessed
separately. Significance criteria of p < 0.01 (p=0.05 divided by the number of tested HLA
loci (5)) were applied.
5.2.4.2. Multi-locus analysis
Following the initial comparisons of allele frequencies, multivariate logistic regression
analysis was used to identify key genetic differences between PsA and psoriasis while
accounting for LD between loci and dependence among allele frequencies within a locus. A
targeted approach was employed to select variables for inclusion in the full regression model.
Previous literature showed that alleles with the strongest PsA associations are within the
HLA-B and –C loci [41, 188-192]. Therefore, in a screening step, a separate regression
model for each of these two HLA loci was constructed by backward selection. In the second
step, a single full logistic regression model was constructed for both loci. This model
included the following alleles: HLA-B*27, B*57, B*08, B*38, C*01, C*02, C*06, C*07,
C*12 and DRB1*07. A stepwise logistic regression was used to identify HLA alleles
occurring more or less frequently in PsA compared to psoriasis patients. Allelic associations
that were considered statistically significant were retained in the multivariate regression if the
p-value from the 2-sided Wald-test was less than 0.05. Statistical analysis was performed
using SAS 9.2© statistical software.
5.2.4.3. The association between HLA and sub-phenotypes of PsA
As in many complex diseases, there are large variations in the phenotype of PsA. It has been
shown that refining the phenotype may strengthen genetic associations by decreasing the
heterogeneity of the cases [269]. Furthermore, different mechanisms may lead to the
development of different phenotypes, as in the case of seropositive vs. seronegative
57
rheumatoid arthritis [270]. To facilitate the identification of susceptibility genes in complex
diseases such as PsA, one approach is to split the disorder based on different phenotypes.
This approach is one of the preferred methods to reduce genetic heterogeneity. The goal is to
split the disorder into subgroups based on clinical characteristics known to differentiate the
disorder into more homogenous groups. These subgroups are then analyzed separately for
association with candidate genes. For this study the PsA group was subdivided into axial and
peripheral PsA and each group was then compared separately to patients with psoriasis. This
classification has been shown in the past to be associated with different HLA alleles in PsA
[41, 189, 192]. Axial PsA was defined by radiological evidence of either bilateral at least
grade 2 sacroiliitis or unilateral grade 3 or 4 sacroiliitis at any point during the follow-up,
with or without peripheral arthritis. Peripheral PsA was defined on the basis of clinical and
radiographic evidence of peripheral joint involvement and no evidence of axial involvement
as defined above. Patients with unilateral sacroiliitis grade 2 were excluded from the
analysis. A similar statistical approach as used for the multi-locus analysis was employed to
identify the association of HLA alleles with subtypes of PsA.
Since HLA-C*06 is strongly associated with age at onset of psoriasis [9], the PsA and
psoriasis groups were sub-divided into patients with early and late onset psoriasis (early
onset: age ≤ 40, late onset: age > 40). The frequencies of HLA-C*06 were compared in these
sub-groups using the LR test.
Calculation of Positive Predictive Values (PPV) of HLA risk alleles
Sensitivity, specificity were calculated for each of the identified HLA risk alleles. Assuming
a 30% prevalence of PsA among psoriasis patients, the calculated specificity and sensitivity
were used to calculate the PPV of the respective HLA alleles to assess their in identifying
PsA patients among patients with psoriasis.
58
5.2.4.4. Haplotype Analysis
A haplotype is a combination of alleles at multiple loci on a single chromosome. Based on
the initial single locus analysis of our HLA data in psoriasis and PsA patients it appears that
several HLA-B and HLA-C alleles confer a risk for PsA among psoriasis patients. However,
due to the strong LD between the alleles in the different HLA loci it is difficult to conclude
which one imparts the primary association. Haplotype analysis has several advantages over a
single locus analysis. It is considered to be more powerful to detect significant association
compared to allele or genotype associations since haplotypes might include two or more
causative sites whose combined effect is measurable, particularly if they show synergistic
interaction [125]. Another reason is that fewer tests need to be performed. Lastly, the
causative locus may reside between the two tested loci; however, it is less likely in this case,
since there is no known gene that lies between HLA-B and HLA-C.
Haplotype analysis was performed only for HLA-C and HLA-B alleles as these loci have
shown the strongest association with PsA in the single locus analysis. Haplotype information
was inferred among PsA and psoriasis patients using the Expectation-Maximization (EM)
algorithm (SAS-Genetics©) [271] generating Maximum Likelihood Estimates given a multi-
locus sample of HLA-C and HLA-B genotypes [272-274]. Because pedigree data was
unavailable for most patients and each individual linkage phase is unknown, there might have
been two pairs of haplotypes which were consistent with the observed genotype. Posterior
probabilities of all possible haplotypes for an individual, conditional on the observed
genotypes, were estimated using EM algorithm implemented in SAS-Genetics using a
maximal number of 20 iteration and convergence criteria of 0.00001. The resulting posterior
probabilities made it possible to test the haplotype-disease association while accounting for
the uncertainly in haplotype estimation. Because of the imprecision involved in estimating
the effects of low-frequency haplotypes, only those with an estimated frequency of more than
1% in the study population were included in the analysis. Stepwise logistic regression was
used to identify haplotypes occurring at higher or lower frequencies in PsA compared to
psoriasis and PsD compared to healthy controls. Haplotype associations were considered
statistically significant if the p-value from the 2-sided Wald-test was less than 0.05. Linkage
disequilibrium between HLA-C and HLA-B alleles was calculated using Lowenstein D’
59
statistics. D’ weights the contribution of specific allele pairs to the LD using the product of
their allelic frequencies. The measure is normalized to fall between 0 and 1, with higher
values indicating a stronger LD contribution.
5.2.5. The association between HLA alleles and PsA - Family based association study
HLA alleles that were identified in the population based study as conferring a risk for PsA
were further assessed in a family based association study. The advantage of this type of study
design is that it avoids population stratification bias which can be a major threat to the
validity of any population based genetic study.
5.2.5.1. Study population and methods
PsA and psoriasis probands and their 1st degree family members (parents and siblings) were
included in this analysis. Altogether, 178 PsA probands, 30 psoriasis probands and 561 first
degree family members belonging to 208 families were analyzed.
The PsA probands were part of the University of Toronto PsA cohort. One hundred of them
were recruited consecutively to participate in a previous study that assessed familial
aggregation of PsA. The remaining patients participated in a linkage study and were recruited
due to a positive family history of psoriasis and PsA. The psoriasis probands were part of the
psoriasis cohort and were recruited consecutively. All family members were evaluated for the
presence of psoriasis and inflammatory arthritis using a screening questionnaire, physical
examination by a rheumatologist and additional tests if clinically indicated as described in
details in the Methods section. All family members were genotyped for all HLA loci as
described previously in section 5.2.2 of this chapter.
60
5.2.5.2. Definitions of traits
In the population based study three groups were assessed: PsA, psoriasis, and healthy
individuals. Since the Family Based Association Test (FBAT) method allows direct
comparison of only two traits the data were analyzed in two ways.
1. In order to compare PsA and psoriasis, PsA patients were coded as affected and
psoriasis as unaffected. Healthy siblings were excluded from the analysis. Since the disease
status of the parents is not relevant for calculation of the FBAT test statistics they were
included in the analysis irrespective of their trait.
2. In order to compare PsD and healthy controls, all available information was included.
PsA and psoriasis probands and siblings were coded as affected. Healthy siblings were coded
as unaffected. In this analysis probands with PsD were compared to their healthy siblings.
5.2.5.3. Statistical analysis - Family based association test
Only HLA alleles that were identified as associated with PsA in the population based study
(HLA- B*27, B*38, B*08, B*39, C*01, C*02, C*06, C*07 and C*12) were analyzed. FBAT
version 2.0.3 software was used for the analysis [275]. FBAT uses a unified approach to
family-based tests of association, introduced by Rabinowitz and Laird [276]. The method is
based on the original Transmission Disequilibrium Test (TDT) method [277] in which
alleles transmitted to affected offspring are compared with the expected distribution of alleles
among offspring under Mendelian transmission. The FBAT test statistic is based on the
distribution of the offspring genotypes conditional on any trait information and on the
parental genotypes. If one of the parental genotypes in a trio is not observed, the test statistic
is conditioned on the sufficient statistics for the offspring distribution. FBAT handles
pedigrees by breaking each pedigree into all possible nuclear families, and evaluating their
contribution to the test statistic independently.
A family based test statistic (Z value) and p value were calculated. Z of more than 0 indicates
over-transmission of the allele, while Z of less than 0 indicates under-transmission. An
additive model was specified. The test statistic was computed using the empirical variance (-
61
e) [278]. This option is used since we were testing for an association in an area of known
linkage with multiple sibs in a family.
The power of a family based study to detect an association is related in part to the number of
informative families. FBAT requires at least one heterozygote parent per family; otherwise
the family cannot be used, since it is not possible to determine which of the two alleles was
transmitted. Such families are called non-informative families. In this study, the number of
families that contributed to a specific allele test depended on whether the allele of interest
was observed in the family. Since each HLA locus is multiallelic, the frequency of each
allele in the study population was relatively low resulting in a relatively low number of
informative families for each allele.
5.3. Population stratification
Population stratification refers to a situation in which cases and controls differ with respect to
their ethnic background or another variable that may have resulted in a pattern of nonrandom
mating [279]. There are two basic conditions that must be met before population stratification
can be considered a threat to internal validity. The first condition is the presence of
significant population subgroup differences with respect to the genetic variable in question
within the sample of a particular study (i.e., the frequency of the tested allele must vary
across population subgroups within the study sample). The second condition is that there
must be significant differences across the same population subgroups with respect to the
outcome variable (i.e., the population subgroups that differ in allele frequency must also
differ significantly with respect to the outcome variable).
In the present study the likelihood that population stratification poses a major threat to
internal validity is low for several reasons:
1. The study was limited to Caucasians from the same geographic area.
2. In moderate scale studies that assess candidate genes; the effect of population
stratification is not large unless subjects are sampled from a few very distinct strata
[280]. Within the Caucasian population in the Greater Toronto Area there are
62
multiple subpopulations. Information about grandparental country of origin was
available for most participants and stratification within Caucasians was compared
based on categorization of country of origin to: North and South Europe, North
America and Others (mostly from Australia and South Africa). There were no
significant differences in the distribution of the sub-populations between PsA and
psoriasis patients (Table 5.1).
Table 5.1 - Population stratification within the Caucasian study sample
Grandparental country of origin PsA (N=529) Psoriasis (N=328)
North Europe 377 (71.2%)
215 (65.6%)
South Europe 106 (20%) 65 (19.8%)
North America 37 (7%) 35 (10.6%)
Other
9 (1.8%) 13 (4%)
3. Several studies have questioned the magnitude of the effect of population
stratification in moderate scale studies [281, 282]. Khlat et al. performed a simulation
study that quantified the bias and the probability of false association (Type I error) in
different scenarios of population sub-structures. They concluded that in moderate
sample size, such as in the current study, the magnitude of bias is small when the
differences in the allele frequency in question between the sub-populations do not
exceed 20% [283]. The difference in HLA allelic distribution within European
subpopulations is not that large. For example, the prevalence of HLA-C*06 among
Caucasians from European origin ranges from 4.6% to 11.5% [284].
63
4. Population stratification does not threaten the validity of family study designs since
these studies use within family information. Therefore, in the present study
significant associations were further assessed in a family based design.
In summary, in this study several measures have been taken to minimize the effect of
population stratification bias. Furthermore, based on a previous simulation study the
sample size and the population frequencies of the tested alleles indicate that the bias,
if present at all, is negligible. Therefore, population stratification bias does not pose a
threat to the validity of the present study.
64
5.4. Results Demographic and clinical characteristics of the study population are presented in Table 5.2.
The psoriasis patients were slightly older than the PsA (46.3 vs. 42.4 years, p<0.001).
Unexpectedly, the PsA patients had more severe psoriasis as measured by maximal PASI
score in the first 3 years of follow-up (8.7 vs. 6.1, p<0.001). This finding may be partially
explained by the fact that psoriasis patients who were recruited from phototherapy centers
were evaluated subsequent to their phototherapy treatments that most likely improved their
psoriasis. There was no difference in the frequency of family history of psoriasis, however,
more patients in the PsA group reported a family history of PsA, AS or Inflammatory Bowel
Disease.
Table 5.2 - Demographic and clinical characteristics of the study population
PsA
(N=712)
Psoriasis
(N=335)
Controls
(N=713)
P value
Age Sex: Male (%)
42.4±12.7 402 (58.2%)
46.3±13.3 188 (55.9%)
43.6±17.2 283 (50.7%)
<0.001 0.49
Age at diagnosis of psoriasis 27.9±14.4 29.8±16.7 0.06 Age at diagnosis of PsA 36.3±13
Duration of Psoriasis 14.4±12.5 16.4±14.5 0.02 Duration of PsA Early onset Psoriasis (age at onset<40 years) Psoriasis type -Chronic Plaque psoriasis (%)
PASI (Max. in 3 years) Severe Psoriasis (PASI>10) Psoriatic nail lesions (%) Family history of: - psoriasis (%) - PsA (%)
- Ankylosing Spondylitis (%)
- IBD (%)
6.1±8.0 547 (79.6%)
604 (90.3%) 8.7±9.9
192 (26.9%) 533 (82.9%)
329 (44.8%) 73 (10.6%) 12 (1.7%) 28 (6%)
--- 250 (74.8%)
288 (86.6%) 6.1±5.1
51 (15.2%) 169 (51.6%)
144 (43.4%)
11 (3.3%) 1 (0.2%)
24 (3.5%)
0.09
0.09 <0.001 <0.001 <0.001
0.18
<0.001 0.02 0.04
65
5.4.1. The association between HLA alleles and Psoriatic Disease
Allele frequencies in the Psoriatic disease (PsD) group were compared to those in the healthy
control group to identify susceptibility alleles that confer a risk for PsD. The results are
presented in Tables 5.3-5.7. FDR adjusted P values were not calculated for rare alleles.
Significant associations are bolded (FDR – p <0.01).
The following HLA alleles were associated with PsD compared to healthy controls: HLA-
A*03 (OR 0.66, p=0.0003, FDR-p=0.004), HLA-B*07 (OR 0.57, p=3x10-6, FDR-p<0.0001),
HLA-B*13 (OR 2.16, p=0.0001, FDR-p=0.0004), HLA-B*27 (OR 2.15, p=3x10-6, FDR-
p<0.0001), HLA-B*38 (OR 5.30, p=9x10-13, FDR-p<0.0001), HLA-B*51 (OR 0.50,
p=0.0001, FDR-p=0.0004), HLA-B*57 (OR 2.19, p=2x10-7, FDR-p<0.0001), HLA-B*60
(OR 0.54, p=0.0006, FDR-p=0.002), HLA-C*03 (OR 0.67, p=0.001, FDR-p=0.003), HLA-
C*06 (OR 2.18, p=7x10-12, FDR-p<0.0001), HLA-C*07 (OR 0.61, p=4x10-7, FDR-
p<0.0001), HLA-C*12 (OR 2.1, p=2x10-7, FDR-p<0.0001), HLA-C*15 (OR 0.48, p=0.002,
FDR-p=0.005), HLA-DRB1*15 (OR 0.66, p=0.0003, FDR-p=0.004), HLA-DQB1*0303
(OR 1.61, p=0.001, FDR-p=0.007) and HLA-DQB1*0602 (OR 0.64, p=8x10-5, FDR-
p=0.001).
The frequencies of HLA-B*27 and HLA-C*06 were compared between the group of
psoriasis alone and healthy controls. There was no difference in the frequency of HLA-B*27
between psoriasis patients and healthy controls (4.5% vs. 7.2%, p=0.09, respectively), while
the association between HLA-C*06 and psoriasis was stronger for the psoriasis alone groups
vs. controls (44.2% vs. 18.1%, p=3x10-19, respectively).
5.4.2. The association between HLA alleles and PsD compared to Healthy controls – Multivariate analysis
Due to the strong LD between the alleles at the different HLA loci, multivariate logistic
regression analysis was employed to identify the key HLA associations. The model included
all HLA alleles that were found to be significantly associated with PsD in the univariate
analysis. The results are presented in Table 5.8. The following alleles were more frequent in
66
the PsD group compared to healthy controls: HLA-B*27 (OR 2.36, p<0.0001), HLA-B*38
(OR 6.01, p<0.0001) and HLA-C*06 (OR 2.23, p<0.0001). While the following alleles were
less frequent in the PsD group compared to healthy controls: HLA-A*03 (OR 0.72, p=0.005),
HLA-B*51 (OR 0.55, p=0.001), HLA-B*60 (OR 0.59, p=0.006) and HLA-DQ*0602 (OR
0.71, p=0.007).
67
^HLA-A*28 includes HLA-A*68 and A*69, PsD - Psoriatic Disease, CI - Confidence Interval, OR - Odds Ratio
Table 5.3 - HLA-A allele distribution – Psoriatic disease vs. Controls
Allele PsD (N=1047) Control (N=712) OR 95% CI P value FDR-p
A*01 357
(34.1%) 226 (31.7%) 1.11 0.9-1.4 0.3 0.46 A*02 589 (56.3%) 355 (49.8%) 1.29 1.1-1.6 0.008 0.03
A*03
210 (20.1%) 195 (27.4%) 0.66 0.5-0.8 0.0003 0.004
A*11 116 (11.1%) 78 (10.9%) 1.01 0.7-1.4 0.93 0.93
A*23 23 (2.2%) 30 (4.2%) 0.51 0.3-0.9 0.01 0.04
A*24 158 (15.1%) 90 (12.6%) 1.22 0.9-1.6 0.14 0.28
A*25 33 (3.2%) 28 (3.9%) 0.79 0.5-1.3 0.37 0.51
A*26 86 (8.2%) 36 (5.0%) 1.68 1.1-2.5 0.01 0.04
A*28 59 (5.6%) 61 (8.6%) 0.64 0.3-4.1 0.02 0.05
A*29 70 (6.7%) 69 (9.6%) 0.67 0.5-0.9 0.02 0.05
A*31 49 (4.7%) 34 (4.7%) 0.98 0.6-1.5 0.92 0.93
A*32 62 (5.9%) 44 (6.1%) 0.95 0.6-1.4 0.82 0.93
A*33 36 (3.4%) 18 (2.5%) 1.37 0.8-2.4 0.27 0.46
A*66 A*68
9 (0.9%) 8 (1.1%) 0.76
0.3-2.0 0.58 0.73
68
PsD - Psoriatic Disease, CI - Confidence Interval, OR - Odds Ratio
Table 5.4 - HLA-B allele distribution – Psoriatic disease vs. Controls
Allele PsD (N=1047) Control (N=713) OR 95% CI P value FDR-p
B*07
176 (16.8%) 185 (25.9%) 0.57 0.4-0.7 3x10-6 <0.0001 B*08 212 (20.2%) 173 (24.3%) 0.79 0.6-1.0 0.05 0.1
B*13 102 (9.7%) 34 (4.8%) 2.16 1.4-3.2 0.0001 0.0004
B*14 89 (8.5%) 61 (8.6%) 0.99 0.7-1.4 0.96 0.96 B*18 B*
76 (7.3%) 57 (8.0%) 0.90 0.6-1.3 0.56 0.64
B*27 152 (14.5%) 52 (7.3%) 2.15 1.6-3.0 3x10-6 <0.0001
B*35 148 (14.1%) 122 (17.1%) 0.79 0.6-1.0 0.09 0.16
B*37 33 (3.2%) 23 (3.2%) 0.98 0.6-1.7 0.93 0.96
B*38 125 (11.9%) 18 (2.5%) 5.30 3.2-8.7 9x10-13 <0.0001
B*39 60 (5.7%) 22 (3.1%) 1.90 1.2-3.1 0.009 0.02
B*41 23 (2.1%) 10 (1.4%) 1.58 0.7-3.3 0.23 0.36
B*42 0 (0%) 2 (0.3%) 0.08
B*44 233 (22.3%) 185 (25.9%) 0.82 0.7-1.0 0.07 0.14
B*45 8 (0.8%) 7 (0.9%) 0.78 0.3-2.2 0.63
B*47 7 (0.7%) 4 (0.6%) 1.20 0.3-4.1 0.76
B*48 1 (0.1%) 1 (0.1%) 0.68 0.04-10.9 0.78
B*49 15 (1.4%) 23 (3.2%) 0.44 0.2-0.8 0.01 0.03
B*50 30 (2.9%) 9 (1.3%) 2.31 1.1-4.8 0.02 0.05
B*51 60 (5.7%) 77 (10.8%) 0.50 0.4-0.7 0.0001 0.0004
B*52 19 (1.8%) 17 (2.4%) 0.76 0.4-1.5 0.41 0.54
B*53 9 (0.9%) 2 (0.3%) 3.10 0.7-14.3 0.13
B*55 30 (2.9%) 24 (3.3%) 0.85 0.5-1.5 0.54 0.64
B*56 12 (1.2%) 5 (0.7%) 1.65 0.6-4.7 0.34
B*57 176 (16.8%) 60 (8.4%) 2.19 1.6-3.0 2x10-7 <0.0001
B*58 12 (1.2%) 11 (1.5%) 0.73 0.3-1.7 0.47 0.59
B*60 60 (5.7%) 72 (10.1%) 0.54 0.4-0.8 0.0006 0.002
B*61 20 (1.9%) 20 (2.8%) 0.67 0.4-1.3 0.22 0.32
B*62 76 (7.3%) 67 (9.4%) 0.75 0.5-1.1 0.11 0.18
B*63 19 (1.8%) 7 (1.0%) 1.86 0.8-4.5 0.15 0.34
B*70 16 (1.5%) 10 (1.4%) 1.10 0.5-2.4 0.83 0.91 B*71 B*73
1 0
(0.1%) (0%)
0 1
(0 %) (0.1%)
0.41 0.22
69
PsD - Psoriatic Disease, CI - Confidence Interval, OR - Odds Ratio
Table 5.5 - HLA-C allele distribution – Psoriatic disease vs. Controls
Allele PsD (N=1044) Control (N=708) OR 95% CI P value FDR-p
C*01 91 (8.7%) 42 (5.9%) 1.51 1.0-2.2 0.03 0.05 C*02 109 (10.4%) 49 (6.9%) 1.56 1.1-2.2 0.01 0.02 C*03
166 (15.9%) 156 (22.0%) 0.67 0.5-0.9 0.001 0.003
C*04 176 (16.9%) 145 (20.5%) 0.78 0.6-1.0 0.05 0.08
C*05 158 (15.1%) 101 (14.3%) 1.07 0.8-1.4 0.61 0.66
C*06 342 (32.8%) 129 (18.2%) 2.18 1.7-2.7 7x10-12 <0.0001
C*07 461 (44.2%) 402 (56.8%) 0.61 0.5-0.7 4x10-7 <0.0001
C*08 89 (8.5%) 61 (8.6%) 0.99 0.7-1.4 0.94 0.94
C*12 203 (19.4%) 73 (10.3%) 2.10 1.6-2.8 2x10-7 <0.0001
C*15 32 (3.1%) 43 (6.1%) 0.48 0.3-0.8 0.002 0.005
C*16 67 (6.4%) 61 (8.6%) 0.73 0.5-1.0 0.08 0.11
C*17 23 (2.2%) 10 (1.4%) 1.57 0.7-3.3 0.23 0.27
C*18 4 (0.4%) 1 (0.1%) 2.72 0.3-24.3 0.35
Table 5.6 - HLA-DRB1 allele distribution – Psoriatic Disease vs. Controls
Allele PsD (N=1047) Control (N=711) OR 95% CI P value FDR-p
DRB1*01
250 (23.9%) 132 (18.6%) 1.36 1.1-1.7 0.008 0.03 DRB1*02
0 (0%) 2 (0.3%) 0.09 DRB1*03
222 (21.2%) 184 (25.8%) 0.77 0.6-1.0 0.02 0.05
DRB1*04
307 (29.3%) 226 (31.8%) 0.89 0.7-1.1 0.27 0.43
DRB1*07
354 (33.8%) 199 (28.0%) 1.31 1.1-1.6 0.01 0.03
DRB1*08 B*
56 (5.3%) 36 (5.1%) 1.06 0.7-1.6 0.8 0.8
DRB1*09
15 (1.4%) 14 (2.0%) 0.72 0.3-1.5 0.38 0.54
DRB1*10
15 (1.4%) 13 (1.8%) 0.78 0.4-1.7 0.51 0.62
DRB1*11
135 (12.9%) 126 (17.7%) 0.69 0.5-0.9 0.005 0.03
DRB1*12
50 (4.8%) 21 (2.9%) 1.65 1.0-2.9 0.06 0.13
DRB1*13
207 (19.8%) 149 (20.9%) 0.92 0.7-1.8 0.54 0.62
DRB1*14
67 (6.4%) 41 (5.8%) 1.13 0.7-1.7 0.58 0.62
DRB1*15
201 (19.2%) 189 (26.6%) 0.66 0.5-0.8 0.0003 0.004
DRB1*16
50 (4.8%) 24 (3.3%) 1.44 0.9-2.4 0.15 0.27
70
PsD - Psoriatic Disease, CI - Confidence Interval, OR - Odds Ratio
Table 5.7 - HLA-DQ allele distribution – Psoriatic Disease vs. Controls
Allele PsD (N=1045) Control (N=711) OR 95% CI P value FDR - p
DQB1*02
434 (41.5%) 303 (42.3%) 0.96 0.8-1.2 0.69 0.79 DQB1*05
0 (0%) 1 (0.1%) 0.22 DQB1*0301
307 (29.5%) 237 (33.2%) 0.83 0.7-1.0 0.08 0.26
DQB1*0302
205 (19.6%) 146 (20.5%) 0.94 0.7-1.2 0.63 0.79
DQB1*0303 B*
161 (15.5%) 72 (10.3%) 1.61 1.2-2.2 0.001 0.007
DQB1*0304
4 (0.4%) 1 (0.1%) 2.73 0.3-24.5 0.35 DQB1*0305
6 (0.6%) 1 (0.1%) 4.10 0.5-34.1 0.16 DQB1*0401
2 (0.2%) 1 (0.1%) 1.36 0.1-15.0 0.08 DQB1*0402
49 (4.7%) 33 (4.6%) 1.01 0.6-1.6 0.96 0.96
DQB1*0502
53 (5.1%) 25 (3.5%) 1.47 0.9-2.4 0.12 0.31
DQB1*0503
64 (6.1%) 39 (5.4%) 1.12 0.7-1.7 0.57 0.79
DQB1*0504
0 (0%) 3 (0.4%) 0.04 DQB1*0601
22 (2.1%) 18 (2.5%) 0.83 0.4-1.6 0.57 0.79
DQB1*0602
178 (17%) 176 (24.7%) 0.64 0.5-0.8 8x10-5 0.001
DQB1*0603
123 (11.8%) 72 (10.1%) 1.18 0.9-1.6 0.28 0.52
DQB1*0604
55 (5.3%) 49 (6.9%) 0.75 0.5-1.1 0.16 0.34
DQB1*0605
2 (0.2%) 6 (0.8%) 0.23 0.04-1.1 0.05 DQB1*0609 DQB1*0501
12 253
(1.2%) (24.2%)
7 143
(1.0%) (20.1%)
1.17 1.26
0.5-3.0 1.0-1.6
0.73 0.04
0.79 0.17
71
Table 5.8 - The association of HLA alleles and PsD (N=1042) compared to healthy
controls (N=704) using multivariate logistic regression analysis
Univariate analysis Multivariate analysis (reduced model)
Allele OR 95% CI P value OR 95% CI P Value
HLA-A*03
HLA-B*07
HLA-B*13
0.66
0.57
2.14
0.5-0.83
0.45-0.73
1.43-3.19
0.0003
<0.0001
0.0002
0.72 0.57-0.91 0.005
HLA-B*27 2.12 1.52-2.96 <0.0001 2.36 1.67-3.33 <0.0001
HLA-B*38 5.51 3.28-9.23 <0.0001 6.01 3.56-10.15 <0.0001
HLA-B*51
HLA-B*57
0.49
2.17
0.35-0.71
1.59-2.95
0.0001
<0.0001
0.55 0.39-0.81 0.001
HLA-B*60 0.53 0.37-0.77 0.0007 0.59 0.41-0.86 0.006
HLA-C*06
HLA-C*07
HLA_C*12
HLA-C*15
2.18
0.59
2.12
0.49
1.73-2.74
0.49-0.72
1.59-2.83
0.31-0.78
<0.0001
<0.0001
<0.0001
0.003
2.23 1.76-2.84 <0.0001
HLA_DR*15
HLA_DQ*0303
0.65
1.62
0.52-0.82
1.20-2.18
0.0003
0.002
HLA-DQ*0602 0.62 0.49-0.78 <0.0001 0.71 0.55-0.91 0.007
PsD - Psoriatic Disease, CI - Confidence Interval, OR - Odds Ratio
72
5.4.3. The association between HLA alleles and PsA compared to psoriasis
Allele frequencies in the PsA group were compared to those in the psoriasis group to identify
susceptibility alleles that confer a risk for PsA. The results are presented in Tables 5.9-5.13.
The following HLA alleles were more frequent in PsA compared to psoriasis: HLA-B*27
(OR 5.07, p=2x10-10, FDR-p<0.0001), HLA-C*01 (OR 2.55, p=0.0009, FDR-p=0.0036) and
HLA-C*02 (OR 2.41, p=0.0005, FDR-p=0.003), while HLA-C*06 (OR 0.48, p=8x10-8,
FDR-p<0.0001), HLA-B*57 (OR 0.58, p=0.001, FDR-p=0.01) and HLA-DRB1*07 (OR
0.62, p=0.0004, FDR-p=0.005) were less frequent in PsA compared to psoriasis.
5.4.4. Association between HLA alleles and PsA – Multivariate analysis
In a multivariate regression analysis the following alleles were more frequent in PsA
compared to psoriasis: HLA-B*27 (OR 5.17, p<0.0001), HLA-B*08 (OR 1.61, p=0.009) and
HLA-B*38 (OR 1.65, p=0.026) (Table 5.14). Two of these alleles were also more frequent in
PsA compared to healthy controls (single locus analysis): HLA-B*27 (OR 3.05, p=2x10-11,
FDR-p<0.0001) and HLA-B*38 (OR 5.9, p=5x10-14, FDR-p<0.0001). In contrast, HLA-
C*06 was less frequent in the PsA group compared to psoriasis (OR 0.58, p=0.0002), but
more frequent in the PsA group compared to the healthy controls (single locus analysis) (OR
1.71, p=2x10-5, FDR-p<0.0001). The remaining HLA alleles were not found to be associated
with PsA in the multivariate regression, suggesting that they were part of extended
haplotypes with the more strongly associated alleles.
73
^ HLA-A*28 includes also HLA-A*68, *A69. PsA - Psoriatic Arthritis, CI - Confidence Interval, OR - Odds Ratio
Table 5.9 - HLA-A allele distribution – PsA vs. Psoriasis
Allele PsA (N=712) Psoriasis (N=334) OR 95% CI P value FDR- p
A*01 233 (32.7%) 124 (37.1%) 0.82 0.6-1.1 0.16 0.65 A*02 404 (56.7%) 185 (56.5%) 1.05 0.8-1.4 0.69 0.88
A*03
141 (19.8%) 69 (20.6%) 0.95 0.7-1.3 0.74 0.88
A*11 84 (11.8%) 32 (9.6%) 1.26 0.8-1.9 0.28 0.65
A*23 15 (2.1%) 8 (2.4%) 0.87 0.4-2.1 0.76 0.89
A*24 117 (16.4%) 41 (12.2%) 1.41 1.0-2.1 0.08 0.65
A*25 19 (2.6%) 14 (4.2%) 0.63 0.3-1.3 0.19 0.65 0.65 A*26 63 (8.8%) 23 (6.9%) 1.31 0.8-2.2 0.28 0.65
A*28^ 39 (5.5%) 21 (6.3%) 0.97 0.6-1.7 0.53 0.88
A*29 49 (6.9%) 21 (6.3%) 1.10 0.6-1.9 0.72 0.88
A*31 33 (4.6%) 16 (4.6%) 0.98 0.5-1.8 0.92 0.92
A*32 41 (5.8%) 21 (6.3%) 0.91 0.5-1.6 0.73 0.88
A*33 20 (2.8%) 16 (4.8%) 0.57 0.3-1.1 0.10 0.65
A*34
1 (0.1%) 0 (0%) 0.49 A*36 1 (0.1%) 0 (0%) 0.49 A*66 A*68
6 (0.8%) 3 (1.1%) 0.93 0.2-3.7 0.92 0.92
74
Table 5.10 - HLA-B allele distribution – PsA vs. Psoriasis
Allele PsA (N=712) Psoriasis (N=335) OR 95% CI P value FDR – p
B*07
119 (16.7%) 57 (17.1%) 0.97 0.7-1.4 0.89 0.95 B*08 159 (22.2%) 54 (16.1%) 1.48 1.1-2.1 0.02 0.12
B*13 58 (8.1%) 44 (13.1%) 0.58 0.4-0.9 0.01 0.08
B*14 53 (7.4%) 36 (10.8%) 0.66 0.4-1.0 0.07 0.24
B*18 B*
58 (8.1%) 18 (5.4%) 1.55 0.9-2.7 0.1 0.27
B*27 137 (19.2%) 15 (4.5%) 5.07 2.9-8.8 2*10-10 <0.0001
B*35 95 (13.3%) 31 (9.3%) 0.82 0.6-1.2 0.28 0.51
B*37 18 (2.5%) 15 (4.5%) 0.55 0.3-1.1 0.09 0.27
B*38 94 (13.2%) 31 (9.3%) 1.48 1.0-2.3 0.07 0.24
B*39 44 (6.2%) 16 (4.8%) 1.31 0.7-2.4 0.36 0.51
B*41 14 (1.9%) 9 (2.6%) 0.72 0.3-1.7 0.45 0.57
B*42 0 (0%) 2 (0.3%) 0.16 B*44 152 (21.3%) 81 (24.2%) 0.85 0.6-1.2 0.29 0.51
B*45 4 (0.6%) 4 (1.2%) 0.46 0.1-1.9 0.29 0.51
B*47 4 (0.6%) 3 (0.9%) 0.62 0.1-2.8 0.53 B*48 1 (0.1%) 0 (0%) 0.49 B*49 8 (1.1%) 7 (2.1%) 0.53 0.2-1.5 0.22 0.51
B*50 20 (2.8%) 10 (3%) 0.94 0.4-2.0 0.87 0.94
B*51 38 (5.3%) 22 (6.6%) 0.80 0.5-1.4 0.42 0.56
B*52 15 (2.1%) 4 (1.2%) 1.77 0.6-5.4 0.30 0.51
B*53 8 (1.1%) 1 (0.3%) 3.79 0.5-30.3 0.17 B*55 20 (2.8%) 10 (3%) 0.93 0.4-2.0 0.87 0.94
B*56 7 (1.0%) 5 (1.5%) 0.65 0.2-2.1 0.46 B*57 102 (14.3%) 75 (22.4%) 0.58 0.4-0.8 0.001 0.01
B*58 8 (1.1%) 4 (1.2%) 0.93 0.3-3.1 0.91 0.94
B*60 44 (6.2%) 16 (4.8%) 1.31 0.7-2.4 0.36 0.51
B*61 16 (2.2%) 4 (1.2%) 1.89 0.6-5.7 0.24 0.51
B*62 48 (6.4%) 28 (8.4%) 0.79 0.5-1.3 0.34 0.51
B*63 13 (1.8%) 6 (1.7%) 1.01 0.4-2.7 0.97 0.97
B*70
8 (1.1%) 9 (2.7%) 0.41 0.1-1.0 0.06 0.24
PsA - Psoriatic Arthritis, CI - Confidence Interval, OR - Odds Ratio
75
Table 5.11 - HLA-C allele distribution – PsA vs. Psoriasis
Allele PsA (N=710) Psoriasis (N=334) OR 95% CI P value FDR-p
C*01 76 (10.7%) 15 (4.5%) 2.55 1.4-4.5 0.0009 0.0036 C*02 90 (12.7%) 19 (5.7%) 2.41 1.4-4.0 0.0006 0.003
C*03
112 (15.8%) 54 (16.2%) 0.97 0.7-1.7 0.87 0.88
C*04 115 (16.2%) 61 (18.3%) 0.86 0.6-1.2 0.41 0.63
C*05 111 (15.6%) 47 (14.1%) 1.13 0.8-1.7 0.51 0.65
C*06 195 (27.5%) 147 (44%) 0.48 0.4-0.6 8*10-8 <0.0001
C*07 330 (46.5%) 131 (39.2%) 1.34 1.0-1.8 0.03 0.09
C*08 53 (7.6%) 36 (10.8%) 0.66 0.4-1.0 0.07 0.16
C*12 147 (20.7%) 56 (16.8%) 1.29 0.9-1.8 0.13 0.28
C*15 21 (2.9%) 11 (3.3%) 0.89 0.4-1.9 0.76 0.84
C*16 41 (5.8%) 26 (7.8%) 0.72 0.4-1.2 0.21 0.37
C*17 15 (2.1%) 8 (2.4%) 0.87 0.4-2.1 0.77 0.84
C*18 3 (0.4%) 1 (0.3%) 1.41 0.1-13.6 0.76
Table 5.12 - HLA-DRB1 allele distribution – PsA vs. Psoriasis
Allele PsA (N=712) Psoriasis (N=335) OR 95% CI P value FDR -p
DRB1*01
182 (25.5%) 68 (20.3%) 1.34 1.0-1.9 0.06 0.19 DRB1*03
167 (23.4%) 55 (16.4%) 1.56 1.1-2.2 0.009 0.06 DRB1*04
211 (29.6%) 96 (28.6%) 1.04 0.8-1.4 0.75 0.82 DRB1*07
216 (30.3%) 139 (41.5%) 0.62 0.5-0.8 0.0004 0.005 DRB1*08 B*
35 (4.9%) 21 (6.3%) 0.77 0.4-1.3 0.36 0.78 DRB1*09
11 (1.5%) 4 (1.2%) 1.29 0.4-4.1 0.65 0.82 DRB1*10
11 (1.5%) 4 (1.2%) 1.29 0.4-4.1 0.65 0.82 DRB1*11
89 (12.5%) 47 (14%) 0.87 0.6-1.3 0.48 0.82 DRB1*12
35 (4.9%) 15 (4.5%) 1.10 0.6-2.0 0.76 0.82 DRB1*13
142 (19.9%) 65 (19.4%) 1.02 0.7-1.4 0.84 0.84 DRB1*14
60 (7%) 17 (5.0%) 1.41 0.8-2.5 0.23 0.59 DRB1*15
134 (18.8%) 67 (20%) 0.92 0.7-2.3 0.64 0.82 DRB1*16
40 (5.6%) 10 (3.0%) 1.93 1.0-3.9 0.06 0.19
PsA - Psoriatic Arthritis, CI - Confidence Interval, OR - Odds Ratio
76
Table 5.13 - HLA-DQ allele distribution – PsA vs. Psoriasis
Allele PsA (N=712) Psoriasis (N=335) OR 95% CI P value FDR - p
DQB1*02
292 (41.1%) 142 (42.4%) 0.94 0.7-1.2 0.68 0.91 DQB1*0301
201 (28.3%) 107 (31.9%) 0.84 0.6-1.1 0.22 0.61
DQB1*0302
141 (19.8%) 64 (19.1%) 1.04 0.8-1.5 0.78 0.91
DQB1*0303 B*
100 (14.1%) 62 (18.5%) 0.72 0.5-1.0 0.06 0.42
DQB1*0304
3 (0.4%) 1 (0.3%) 1.41 0.1-13.6 0.76 DQB1*0305
6 (0.8%) 0 (0%) 0.09 DQB1*0401
2 (0.3%) 0 (0%) 0.33 DQB1*0402
29 (4.1%) 20 (6.0%) 0.67 0.4-1.2 0.17 0.59
DQB1*0501
183 (25.7%) 70 (20.9%) 1.31 0.9-1.8 0.09 0.42
DQB1*0502
42 (5.9%) 11 (3.3%) 1.85 0.9-3.6 0.07 0.42
DQB1*0503
47 (6.6%) 17 (5.1%) 1.32 0.7-2.3 0.33 0.66
DQB1*0504
0 (0%) 3 (0.4%) 0.08 DQB1*0601
15 (2.1%) 7 (2.1%) 1.0 0.4-2.5 0.98 0.99
DQB1*0602
121 (17%) 57 (17%) 1.0 0.7-1.4 0.99 0.99
DQB1*0603
85 (11.9%) 38 (11.3%) 1.06 0.7-1.6 0.77 0.91
DQB1*0604
40 (5.6%) 15 (4.5%) 1.27 0.7-2.3 0.44 0.77
DQB1*0605
2 (0.3%) 0 (0%) 0.33 DQB1*0609 DQB1*0613
9 1
(1.3%) (0.1%)
3 0
(0.9%) (0%)
1.42 0.4-5.2 0.59 0.32
0.91
PsA - Psoriatic Arthritis, CI - Confidence Interval, OR - Odds Ratio
77
PsA - Psoriatic Arthritis, CI - Confidence Interval, OR - Odds Ratio
Table 5.14 - Odds ratios comparing HLA allele frequencies in PsA (N=710) to psoriasis
(N=334) using logistic regression analysis
Univariate analysis Multivariate analysis (reduced model)
Allele OR 95% CI P value OR 95% CI P Value
HLA-B*08
HLA-B*27
HLA-B*38
HLA-B*57
1.53
4.99
1.49
0.58
1.08-2.15
2.89-8.66
0.97-2.29
0.42-0.82
0.015
<0.0001
0.067
0.001
1.61
5.17
1.65
1.12-2.30
2.95-9.04
1.06-2.57
0.009
<0.0001
0.026
HLA-C*01 2.54 1.44-1.51 0.001
HLA-C*02 2.41 1.44-2.02 0.0008
HLA-C*06
HLA-C*07
0.48
1.35
0.37-0.63
1.03-1.75
<0.0001
0.027
0.58 0.44-0.77 0.0002
HLA-C*12
HLA-DRB1*07
1.29
0.62
0.92-1.82
0.50-0.80
0.13
0.0004
78
5.4.5. Subgroup analysis based on age at onset of psoriasis
HLA-C*06 is associated with early onset psoriasis, family history of the disease and with
more severe psoriasis [178, 179]. A subgroup analysis was performed to determine whether
the association between HLA-C*06 and PsA is related to differences in the age at onset of
psoriasis between the two groups. The results are presented in Table 5.15.
The association between PsA and HLA-C*06 remained significant in the early onset
psoriasis group (OR 0.41, p<0.0001). These findings suggest that the lower frequency of
HLA-C*06 in the PsA group compared to psoriasis is not related to differences between the
two groups in the age at onset of psoriasis. In the late onset psoriasis group, the frequency of
HLA-C*06 was slightly lower in the PsA compared to the psoriasis group, however not
significantly, possibly due to the small number of individuals in each group.
Table 5.15 - Odds ratios comparing HLA-C*06 allele frequencies in PsA (N=687)* to
psoriasis (N=334) by age at onset of psoriasis
Age at onset of
Psoriasis PsA Psoriasis OR 95% CI P value
Early onset (<40)
162 (29.6%)
127 (50.8%)
0.41
0.30-0.55
<0.0001
Late onset (≥40)
26 (18.7%)
20 (23.8%)
0.74
0.38-1.42
0.36
All
188 (27.4%)
147 (47.1%)
0.47
0.36-0.63
<0.0001
PsA - Psoriatic Arthritis, CI - Confidence Interval, OR - Odds Ratio
*The reduced number of PsA patients is due to lack of information about age at onset of psoriasis for some of the patients.
79
5.4.6. The association of HLA alleles with Axial and Peripheral PsA
A subgroup analysis was employed to identify new HLA alleles associated with subtypes of
PsA (Tables 5.16-5.17). One additional HLA allele was identified as being associated with a
PsA subtype but not with the whole group of PsA patients. After multivariate analysis, the
frequency of HLA-B*39 was found to be increased in axial PsA compared to psoriasis (OR
2.51, 95% CI 1.25-5.01, p=0.009). This allele was not associated with peripheral PsA (OR
0.9, 95% CI 0.43-1.87, p=0.77). The remaining alleles associated with axial PsA were: HLA-
B*27, HLA-B*08 and HLA-B*38. Analysis of patients with peripheral PsA did not reveal
new associations as only HLA-C*06 and HLA-B*27 met significance criteria for association
with this subgroup compared to psoriasis.
80
Table 5.16 - Odds ratios comparing HLA allele frequencies in Axial PsA (N=255) vs.
psoriasis (N=334) using logistic regression analysis
Univariate analysis Multivariate analysis (reduced model)
Allele OR 95% CI P value OR 95% CI P Value
HLA-B*08 HLA-B*27
HLA-B*38
HLA-B*39 HLA-B*07 HLA-B*13 HLA-B*14 HLA-B*18 HLA-B*35 HLA-B*37 HLA-B*41 HLA-B*44 HLA-B*50 HLA-B*51 HLA-B*52 HLA-B*55 HLA-B*57 HLA-B*58 HLA-B*4001 HLA-B*4002 HLA-B*1501 HLA-B*1517 HLA-B*1509/10 HLA-C*01 HLA-C*02 HLA-C*03 HLA-C*04 HLA-C*05 HLA-C*06 HLA-C*07 HLA-C*08 HLA-C*12 HLA-C*15 HLA-C*17
1.75 7.34
1.80
1.86 0.86 0.57 0.59 0.94 0.75 0.51 0.43 0.72 0.91 0.82 1.97 0.64 0.71 1.30 0.89 1.97 0.58 1.08 0.14 2.94 3.26 0.69 0.83 0.85 0.54 1.25 0.63 1.56 0.83 0.48
1.17-2.64 4.09-13.23
1.09-2.97
0.96-3.62 0.56-1.35 0.32-0.97 0.32-1.07 0.45-1.94 0.47-1.21 0.19-1.33 0.11-1.59 0.48-1.07 0.34-2.42 0.41-1.63 0.55-7.06 0.22-1.91 0.46-1.07 0.32-5.27 0.41-1.49 0.55-7.06 0.29-1.14 0.33-3.59 0.02-1.12 1.55-5.58 1.85-5.77 0.43-1.11 0.54-1.28 0.52-1.37 0.38-0.76 0.90-1.74 0.35-1.13 1.04-2.34 0.32-2.17 0.13-1.84
0.007 <0.0001
0.02
0.07 0.54 0.04 0.08 0.85 0.24 0.17 0.20 0.10 0.84 0.56 0.29 0.42 0.10 0.71 0.76 0.29 0.11 0.89 0.06
0.0009 <0.0001
0.13 0.41 0.49
0.0005 0.18 0.12 0.03 0.70 0.28
2.46 9.08
2.55
2.51
1.59-3.79 4.95-16.65
1.50-4.34
1.25-5.01
<0.0001 <0.0001
0.0006
0.009
81
Table 5.17 - Odds ratios comparing HLA allele frequencies in peripheral PsA (N=323) vs.
psoriasis (N=334) using logistic regression analysis
Univariate analysis Multivariate analysis (reduced model)
Allele OR 95% CI P value OR 95% CI P Value
HLA-B*08 HLA-B*27 HLA-B*38 HLA-B*39 HLA-B*07 HLA-B*13 HLA-B*14 HLA-B*18 HLA-B*35 HLA-B*37 HLA-B*41 HLA-B*44 HLA-B*50 HLA-B*51 HLA-B*52 HLA-B*55 HLA-B*57 HLA-B*58 HLA-B*4001 HLA-B*4002 HLA-B*1501 HLA-B*1517 HLA-B*1509/10 HLA-C*01 HLA-C*02 HLA-C*03 HLA-C*04 HLA-C*05 HLA-C*06 HLA-C*07 HLA-C*08 HLA-C*12 HLA-C*15 HLA-C*17
1.51 3.80 1.07 0.90 1.11 0.60 0.69 1.47 0.92 0.40 0.57 0.97 0.93 0.79 1.82 1.03 0.55 1.03 1.66 1.83 0.92 1.03 0.68 2.25 1.64 1.18 0.94 1.22 0.46 1.47 0.66 1.04 1.13 0.64
1.04-2.29 2.09-6.93 0.64-1.81 0.43-1.87 0.74-1.65 0.36-0.99 0.41-1.18 0.78-2.75 0.61-1.41 0.15-1.05 0.19-1.71 0.68-1.39 0.37-2.31 0.41-1.51 0.53-6.30 0.42-2.52 0.36-0.83 0.25-4.17 0.87-3.18 0.53-6.30 0.52-1.61 0.33-3.24 0.24-1.94 1.19-4.27 0.89-2.98 0.79-1.77 0.63-1.40 0.80-1.87 0.33-0.64 1.07-1.99 0.38-1.14 0.69-1.56 0.49-2.61 0.21-1.97
0.03 <0.0001
0.78 0.77 0.61
0.048 0.18 0.22 0.72 0.06 0.31 0.90 0.87 0.47 0.33 0.93
0.005 0.96 0.12 0.33 0.76 0.95 0.47 0.01 0.11 0.41 0.76 0.34
<0.0001 0.015 0.13 0.84 0.76 0.43
3.66
0.47
1.99-6.72
0.34-0.66
<0.0001
<0.0001
82
5.4.7. Positive Predictive Value of the identified HLA risk alleles
To assess the utility of HLA risk alleles in identifying PsA patients among patients with
psoriasis, the PPV of each of the identified HLA risk allele was calculated as follows: HLA-
B*08 PPV=0.42, HLA-B*27 PPV=0.64, HLA-B*38 PPV=0.43, HLA-C*06 PPV=0.35.
5.4.8. Haplotype analysis - Psoriatic disease vs. Healthy controls The association between the extended haplotype of HLA-A-B-C-DR-DQ and PsD was
investigated. A total of 1560 possible extended haplotypes were imputed, however, only 9 of
them with a prevalence of >1% were included in the analysis. The results are presented in
Table 5.18. The following haplotypes were significantly increased in the PsD group
compared to healthy controls: HLA-A*01-B*57-C*06-DR*07-DQ*0303, HLA-A*02-B*13-
C*06-DR*07-DQ*02 and HLA-A*02-B*57-C*06-DR*07-DQ*0303. As expected, all of
these haplotypes included HLA-C*06, the strongest susceptibility allele for psoriasis. HLA-
DR*07 was also present in each of the risk haplotype given its LD with HLA-C*06 (D’=0.5).
83
PsD - Psoriatic Disease, CI - Confidence Interval, OR - Odds Ratio
Table 5.18 - Odds ratios comparing extended HLA haplotype frequencies in PsD to healthy
controls using logistic regression analysis
Haplotype Univariate analysis Multivariate analysis
(reduced model)
HLA-A-B-C-DR-DQ OR 95% CI P value OR 95% CI P value
*01-*08-*07-*03-*02 0.53 0.31-0.92 0.02
*01-*57-*06-*07-*0303 3.97 1.48-10.66 0.006 4.08 1.52-10.94 0.005
*02-*07-*07-*15-*0602 0.40 0.15-1.06 0.07
*02-*08-*07-*03-*02 2.54 0.69-9.27 0.15
*02-*13-*06-*07-*02 8.11 1.96-33.52 0.0039 8.82 2.12-36.61 0.003
*02-*44-*05-*04-*0301 0.66 0.25-1.74 0.39
*02-*57-*06-*07-*0303 10.71 2.11-54.29 0.004 10.44 2.08-52.47 0.004
*03-*35-*04-*01-*0501 0.44 0.12-1.67 0.22
*29-*44-*16-*07-*02 0.48 0.17-1.32 0.15
84
5.4.9. Linkage Disequilibrium of HLA-B and –C alleles
The MHC region contains many genes in strong LD. The magnitude of LD between HLA-C
and HLA-B alleles was extremely high with D’>0.90 for most combinations, which allowed
inferring the haplotypes for most individuals with high certainty. The LD values for the
haplotypes of interest are presented in Table 5.19.
The magnitude of LD for several of the HLA-C and –B alleles that were found to be
associated with PsA was high including: HLA-C*06/B*57 (D’=0.97), HLA-C*12/B*38
(D’=0.97), HLA-C*07/B*08 (D’=0.98), HLA-C*01/B*27 (D’=0.60) and HLA-C*02/B*27
(D’=0.61).
5.4.10. The association between HLA-B and –C haplotypes and PsA compared to Psoriasis
Based on the results of the single locus analysis that showed that most of the significant
associations are within the HLA-B and –C region, the haplotype analysis included only these
loci. A total of 107 possible haplotypes were estimated, however, due to low frequencies
only 23 common haplotypes were included in the analyses (Table 5.20). The following
haplotypes remained significantly associated with PsA compared to psoriasis after
multivariate analysis: HLA-B*18-C*07 (OR 10.1, p=0.004), HLA-B*27-C*01 (OR 41.1,
p<0.0001). HLA-B*27-C*02 (OR 19.9, p<0.0001), HLA-B*38-C*12 (OR 2.9, p=0.01),
HLA-B*08-C*07 (OR 2.6, p=0.004) and HLA-B*57-C*06 (OR 0.5, p=0.03). All of these
haplotypes include the risk alleles that were identified in the single locus analysis, the only
exception is HLA-B*18. For most of the haplotypes, the LD was high and ranged from 0.6 to
0.98, which illustrates the difficulty of investigating the independent effect of each of the
HLA alleles.
Analysis of the association of extended haplotypes across the MHC region with PsA revealed
only marginal associations with haplotypes that did not include the HLA risk alleles that
were identified before. Therefore, these associations may be explained by type I error.
85
Table 5.19 - Linkage Disequilibrium of selected HLA-B and –C alleles
HLA-B/HLA-C Frequency
Corr. Coeff.
Lowenstein D’
*18/*07 1.7% 0.06
0.19
*27/*01 2.4% 0.48 0.60
*27/*02 2.8% 0.53 0.61
*37/*06
1.6%
0.30 0.97
*38/*12
4.2% 0.68 0.97
*57/*06 6.5% 0.62 0.93
*08/*07 11% 0.56 0.98
Table 5.20 - Odds ratios comparing HLA-B/HLA-C haplotype frequencies in PsA (N=710) to psoriasis (N=334)
using logistic regression analysis
HLA-B/
HLA-C
Frequency LD
Univariate analysis Multivariate analysis
(Reduced model)
PsA
Psoriasis
D’ OR 95% CI P value OR 95% CI P
value
*18-*07 2.0% 0.6% 0.15 7.8 1.1-55.9 0.04 10.1 1.4-74.3 0.004 *27-*01 3.9% 0.7% 0.65 31.4 4.8-203.6 0.0003 41.1 6.4-266.1 <0.001
*27-*02 4.9% 1.3% 0.69 15.5 3.7-64.5 0.0002 19.9 4.7-84.1 <0.001
*38-*12 7.1% 4.8% 0.99 2.2 0.99-4.9 0.052 2.9 1.3-6.6 0.01
*08-*07 11.7% 8.5% 0.97 2.0 1.1-3.8 0.03 2.6 1.4-5.0 0.004
*57-*06 6.7% 11.5% 0.92 0.3 0.2-0.6 0.0004 0.5 0.3-0.9 0.03
*50-*06 1.4% 1.5% 1.00 0.9 0.2-4.1 0.87
*35-*04 6.4% 7.5% 0.87 1.0 0.5-2.0 0.91
*41-*17 0.9% 1.2% 0.91 0.6 0.1-3.4 0.54
*44-*16 2.6% 3.9% 0.91 0.4 0.2-1.2 0.11
*13-*06 4.1% 6.9% 1.00 0.3 0.2-0.8 0.009
*18-*12 1.0% 1.4% 0.23 0.4 0.1-2.4 0.32
*37-*06 1.3% 2.2% 1.00 0.4 0.1-1.4 0.14
*39-*07 1.3% 1.0% 0.37 2.0 0.3-12.6 0.47
*18-*05 1.1% 1.0% 0.24 2.3 0.3-16.2 0.42
*39-*12 1.7% 1.4% 0.50 1.3 0.3-6.2 0.71
*14-*08 3.7% 5.5% 0.98 0.5 0.2-1.1 0.08
*62-*03 3.1% 3.6% 0.85 0.7 0.3-2.0 0.55
*60-*03 3.2% 2.5% 1.00 1.6 0.5-4.7 0.43
*44-*05 6.3% 6.3% 0.78 1.0 0.5-2.2 0.95
*55-*03 1.2% 1.2% 0.80 1.0 0.2-5.5 0.99
*35-*04 6.4% 7.5% 0.87 0.7 0.4-1.5 0.37
87
5.4.11. Family based association study
To validate our results, the HLA alleles that were identified in the population based study
were further assessed in a family based association study. Altogether, 178 PsA probands, 30
psoriasis probands and 561 first degree family members belonging to 208 families were
analyzed. Of those, 4 families (2 PsA probands, 2 psoriasis probands and their 13 family
members) were excluded due to Mendelian errors. The study population included 48
complete trios (2 parents and a proband), and 156 families with affected-unaffected sib-pairs
with or without their parents. The detailed structure of the study population is presented in
Table 5.21. One hundred of 201 (49.7%) probands had a positive family history of psoriasis
and 42 out of 201 (20.8%) probands had a family history of PsA
There was over-transmission and a significant association between HLA-C*12 and PsA
patients (Table 5.22, p=0.005). However, no significant association with PsD (Table 5.23,
p=0.14) was found. HLA-B*38, which is in strong LD with HLA-C*12, was over-
transmitted to the PsA patients when compared to the sibs with psoriasis alone, however the
strength of the association was weaker (Table 5.22, p=0.04) and there was no significant
association with PsD (Table 5.23, p=0.14). HLA-B*39, that was associated with axial PsA
compared to psoriasis in the single locus analysis, was over-transmitted to patients with PsA
(Table 5.22, p=0.045) compared to psoriasis alone. There was over-transmission of HLA-
B*27 among PsA and PsD compared to psoriasis alone (Table 5.22, p=0.002) and healthy
controls (Table 5.23, p=0.04), respectively. There was no significant association between
HLA-B*08, C*01, C*02, and C*07 and either PsA and PsD. The direction of association for
HLA-C*02 and C*01 was in accordance with what was found in the population-based
association study, while for HLA-C*07 and B*08, the direction of association was the
opposite. Interestingly, HLA-C*06, the strongest risk allele for psoriasis, was not
significantly associated with either PsA or PsD. However, the test statistics (Z) indicates
over-transmission of HLA-C*06 to PsD probands compared to healthy siblings, while under-
transmission of the allele to PsA compared to siblings with psoriasis, corresponding to the
direction of association of HLA-C*06 in the population based study.
88
NA- Not applicable
Table 5.21 - Detailed family structure of the study population
Genotyping
data
available
No
siblings
1 or more healthy
sibling: no PsA or
psoriasis
1 or more
affected sibling:
PsA or psoriasis
Two or more
siblings: at least
1 healthy and at
least 1 affected
Total
PsA
proband
2 parents 40 35 14 5 94
1 parent NA 11 8 9 28
0 parents NA 32 15 7 54
Psoriasis
proband
2 parents 8 2 1 0 11
1 parent NA 4 0 0 4
0 parents NA 9 4 0 13
89
*Frequency of the allele in the study population **Number of informative families
Table 5.22 - Family based association test Affected (PsA probands) - Unaffected (psoriasis) sib-pairs and Trios (N families= 131, N Pedigrees= 165, N=462)
Allele Allele frequency* No. Families** Z P value
HLA-B*27 8.5% 33 3.0 0.002
HLA-B*08 13.6% 36 -1.4 0.15
HLA-B*38 4.7% 15 2.0 0.04
HLA-B*39 3.5% 13 2.0 0.045
HLA-C*06 12.7% 35 -0.6 0.57
HLA-C*12 8.5% 28 2.8 0.005
HLA-C*07 27.7% 69 -1.0 0.32
HLA-C*01 4.7% 19 1.5 0.14
HLA-C*02 5.7% 22 1.1 0.27
90
PsD – Psoriatic Disease (including PsA and psoriasis probands).
*Frequency of the allele in the study population **Number of informative families
Table 5.23 - Family based association test Affected (PsD)-Unaffected (Healthy controls) sib-pairs and Trios (N families= 204, N pedigrees=234, N=756)
Allele Allele frequency* No. Families** Z P value
HLA-B*27 6.9% 36 2.1 0.04
HLA-B*08 12.9% 49 -0.3 0.77
HLA-B*38 4.9% 24 1.4 0.14
HLA-B*39 2.4% 15 1.4 0.15
HLA-C*06 14.3% 55 1.3 0.18
HLA-C*12 8.9% 37 1.5 0.14
HLA-C*07 27.5% 87 -1.7 0.09
HLA-C*01 4.9% 26 1.3 0.21
HLA-C*02 4% 21 0.02 0.99
91
5.5. Discussion
The MHC region on chromosome 6 and in particular, the HLA class I region has been
consistently associated with both psoriasis and PsA [41, 188-192]. In this large case-control
study, I aimed to identify PsA specific genetic markers among HLA alleles. The previously
reported association between HLA-B*27 and PsA was confirmed. Furthermore, HLA-C*06
that was associated with PsD and PsA compared to healthy controls was significantly less
frequent in PsA patients compared to those with psoriasis alone. Two additional HLA alleles,
HLA-B*08 and HLA-B*38 were identified as potential genetic markers for PsA in patients
with psoriasis. HLA-B*39 may be a potential marker for axial PsA.
In both population and family based analysis we have shown that HLA-B*27 is a strong
genetic marker for PsA compared to psoriasis. The absence of a detectable association
between HLA-B*27 and the psoriasis group compared to healthy individuals suggests that
B*27 is not a marker for skin disease. HLA-B*27 was also part of two haplotypes, along
with HLA-C*02 and HLA-C*01, that showed a strong association with PsA versus psoriasis.
Previous studies in different ethnic groups have consistently shown that HLA-B*27 is an
independent risk allele for PsA that is unrelated to the skin disease [41, 285-287]. However,
the prevalence of HLA-B*27 among PsA patients in our study was 19.2% and ranged from
20 to 35% in previous studies, clearly much lower than its prevalence in AS (80-95% of the
patients) [288]. Therefore, this allele can only account for a small proportion of the total
genetic risk of PsA. Several studies have shown an association between HLA-B*27 and axial
involvement in PsA [285, 289, 290]. It has also been suggested that HLA-B*27 is associated
with earlier onset of joint manifestations in patients with psoriasis, compared to non-carriers
[290]. It may be argued that the HLA-B*27 allele is merely a risk allele for AS that co-exists
with psoriasis, given the relatively high prevalence of psoriasis in the general population.
However, since the frequency of HLA-B*27 is also increased among PsA patients with
peripheral joint involvement, it suggests that HLA-B*27 is an independent risk allele for
PsA. Our group have shown that there is an association between HLA-B*27 in the presence
of DRB1*07 and more severe peripheral involvement and accumulation of joint damage,
features that are different from those of AS [291]. Therefore, HLA-B*27 can be considered
92
as the strongest HLA risk allele for PsA among patients with psoriasis, and does differentiate
the two conditions.
Interpretation of the association between HLA-C*06 and PsA is more challenging since this
allele confers strong risk for psoriasis [180, 201, 202]. In the present study, the frequency of
HLA-C*06 was significantly higher in both psoriasis and PsA patients compared to controls.
However, the frequency of HLA-C*06 was lower in PsA patients compared to psoriasis. The
association remained significant in the subgroup of patients with early onset psoriasis,
however not among those with late onset disease. This appears to be inconsistent with the
report of Ho et al. who analyzed the association between HLA-C*06 and PsA. They
concluded that HLA-C*06 was associated only with early onset psoriasis and does not confer
any additional susceptibility to PsA. However, no direct comparison between PsA and
psoriasis was performed in that study. Analyzing their data shows that actually HLA-C*06 is
significantly decreased among PsA patients with early onset psoriasis compared to those with
psoriasis alone (169 (50.4%) vs. 354 (58.4%), p=0.02) [193]. Other studies have found a
lower prevalence of HLA-C*06 among PsA compared to psoriasis patients [287, 292]. In
addition, psoriatic nail involvement, a clinical marker for an increased risk of PsA, is more
common among HLA-C*06 negative psoriasis patients [178]. Although HLA-C*06 has not
been associated with a particular clinical subtype of PsA it has been found to increase the
psoriasis-arthritis latency period [293]. HLA-C*06 was also associated with a milder form of
arthritis among PsA patients. Ho et al. reported that PsA patients who carried HLA-C*06 had
fewer damaged joints and lower active joint counts [294]. It is challenging to explain the
strong association of HLA-C*06 with psoriasis while its lower frequency in PsA compared to
psoriasis. HLA-C*06 may be involved in different mechanisms that lead to the joint and the
skin disease. For example, it may be: antigen presentation and activation of the adaptive
immune system for the skin and inhibition of the innate immune system through interaction
with Killer cell Immunoglobulin like Receptors (KIRs) on NK cells in the joints. The
recently reported association between ERAP1 gene polymorphisms and psoriasis only among
patients carrying the HLA-C*0602 risk allele, suggests that antigen presentation may play a
role in the pathogenesis of psoriasis, as ERAP1 is involved in MHC class I peptide
processing [252]. Another explanation may be related to genetic heterogeneity of psoriasis. It
is possible that different HLA alleles are associated with a sub-type of psoriasis that is more
93
likely to develop PsA. Therefore, although the phenotype of psoriasis is similar, one HLA
allele is associated only with skin disease (HLA-C*06) while the other is associated with
both skin and joint involvement. A potential candidate is HLA-C*12, that is associated with
both PsA and psoriasis compared to healthy controls, and was reported in the past to be
associated with psoriasis among Caucasians [170].
Several of the alleles (HLA-C*02, HLA-C*01 and HLA-DRB1*07) that were found to be
associated with PsA in the univariate analysis were no longer significant after adjusting for
the effect of the other alleles. Several studies have suggested that HLA-DRB1*07 is
associated with PsA [192, 286]. In the present study, the frequency of HLA-DRB1*07 was
significantly lower in the PsA group compared to the psoriasis group. However, HLA-
DRB1*07 is in strong LD with HLA-C*06 among Caucasians and it is part of the ancestral
haplotypes AH.13 and AH.57 that contain this strong risk allele for psoriasis in addition to
HLA-B*13 or –B*57 [145]. In our study, these extended haplotypes were associated with
PsD compared to healthy controls. The association between HLA-DRB1*07 and PsA was no
longer significant after adjusting for the effect of HLA-C*06 suggesting that the risk allele is
HLA-C*06. Similarly, HLA-C*02 and HLA-C*01 showed an association with PsA
compared to psoriasis, that was lost after accounting for the effect of HLA-B*27 in the
multivariate analysis. There was a strong LD between HLA-C*02/C*01 and HLA-B*27
(HLA-B*27-C*01 D’=0.60, HLA-B*27-C*02 D’=0.61). These alleles are part of common
haplotypes found in 1-2.9% of the Caucasians [284] and in this study both haplotypes were
significantly associated with PsA compared to psoriasis. Therefore the association between
C*02/C*01 and PsA was not independent and was related to their LD with HLA-B*27.
These findings illustrate the difficulties in investigating the independent association of genes
in the MHC region that contains numerous potential risk loci in close proximity.
Two additional alleles were significantly associated with PsA compared to psoriasis in
multivariate analysis: HLA-B*08 and HLA-B*38. HLA-B*38 was strongly associated with
PsA compared to both psoriasis (multivariate model) and controls (univariate model). HLA-
B*38 has been reported to be more frequent among non-Ashkenazi Jews with PsA compared
to those with psoriasis alone and healthy controls in a small study from Israel [295]. Another
small study from Maryland also reported higher frequencies of HLA-B*38 in PsA compared
94
to psoriasis [292]. Additionally, HLA-B*38 has been associated with more peripheral joint
involvement [296]. However, in the subgroup analysis of our study, an association of HLA-
B*38 was detected in the group with axial PsA but not in the group with arthritis restricted to
the peripheral joints. HLA-B*38 is part of a common haplotype along with HLA-C*12. In
our study, this haplotype was associated with PsA compared to psoriasis. This haplotype has
been reported to be present in up to 8% of the Jewish population and in 1.3-3.4% of non-
Jewish Caucasians [284]. A recent GWAS suggested that HLA-C*12 is a risk allele for
psoriasis among Caucasians but not among Chinese [170]. The family based data in this
study supports the association between HLA-B*38 and C*12 and PsA, however in contrast to
the population based data, a larger test statistic was observed with HLA-C*12 compared to
HLA-B*38 (p=0.005 vs. p=0.04, respectively). Given the very high LD (D’=0.97) between
alleles in our sample, it is very difficult to dissect the independent effect of each one of them.
Larger samples sizes may be required for that purpose.
HLA-B*08 has not been previously reported to be associated with PsA. In our study, HLA-
B*08 frequencies were higher among PsA compared to psoriasis, but not different from
healthy controls. However, these results were not confirmed in our family based association
study. In the literature, the extended haplotypes across HLA class I and II genes, A*01-
C*07-B*08-DRB1*03-DQA1*05-DQB1*02-DPA1*01-DPB1*04, has been associated with
increased TNF production following rubella vaccination [297]. TNF is a major pro-
inflammatory cytokine in PsA and rubella vaccine was suggested to be a risk factor for
development of PsA among psoriasis patients [76]. HLA-B*08 is part of an ancestral
haplotype (8.1 AH) that also includes HLA-A*01-C*07-DRB1*03 and TNF-308A [284].
There have been conflicting results with regard to the association between PsA and the TNF-
308A polymorphism, which is part of the 8.1 AH [210-212]. Previous analysis of our cohort
combined with PsA patients from Newfoundland did not find an association with TNF-308A
polymorphism [223], however, these patients were compared to healthy controls and not to
psoriasis patients. In our study, HLA-B*08 was also included in the HLA-B*08-C*07
haplotype that was increased in patients with PsA compared to those with psoriasis alone.
HLA-C*07 was included in another haplotype that was associated with PsA (HLA-B*18-
C*07). In summary, HLA-B*08 may be an independent genetic marker for PsA among
95
psoriasis patients. However, its strong LD with other HLA alleles and relevant genes in the
MHC region precludes a more confident conclusion.
In summary, in this study the independent association between HLA-B*27 and PsA was
confirmed. HLA-C*06 was more frequent in patients with PsD compared to healthy controls,
however it was less frequent in patients with PsA compared to those with psoriasis. This
association was particularly strong among patients with early onset psoriasis. Whether HLA-
C*06 has a “dual” effect, acting as a risk factor for psoriasis and a protective factor for PsA
among patients with psoriasis, needs further investigation using a longitudinal study. Several
additional alleles, B*38 or C*12 and B*08, are potential genetic markers for PsA in patients
with psoriasis, and warrant further investigation.
96
Chapter 6. The effect of HLA risk alleles on the rate of development of PsA among psoriasis patients
97
6.1. Background
Approximately 85% of patients with psoriasis develop PsA concurrently or thereafter [31]. In
a previous chapter the incidence of PsA among psoriasis patients was found to be higher than
previously reported. In contrast to what has been thought in the past, the risk of developing
PsA was found to be constant over time. However this observation was based on a small
number of incident cases of PsA. There is limited information in the literature about factors
that affect the interval of time from the onset of psoriasis to PsA. The importance of this
issue is related to the challenge in investigating genetic risk factors for PsA among psoriasis
patients. When a case-control study design is used for the investigation of risk factors, one of
the assumptions is that the outcome of interest has a low frequency in the control group. This
assumption is usually true for investigation of a relatively rare disease in the general healthy
population. However, this assumption does not hold when psoriasis patients are used as the
control group for PsA. In this situation a significant proportion of the controls may become
cases. Therefore, significant associations may be related to differences in age at onset and
duration of psoriasis between the cases and the controls. It has been reported that early onset
psoriasis is associated with a prolonged interval between the onset of psoriasis to the
development of PsA [298]. Another study has found that HLA-C*06 also increases that
interval [293]. Therefore, there is an interest in investigating the association between genetic
risk factors and the rate of PsA among psoriasis patients.
The first aim of this chapter is to validate the results from Chapter 4 by using data pooled
from two large cohorts of psoriasis and PsA patients to further assess whether the risk of
developing PsA remains constant over time. The second aim is to investigate clinical and
genetic risk factors that affect the rate of development of PsA among psoriasis patients.
Concerns associated with combining the two cohorts were addressed by statistical modeling.
98
6.2. Methods
The present study is a retrospective cohort analysis of data pooled from 2 cohorts:
1. The University of Toronto PsA cohort.
2. The Toronto Psoriasis Cohort.
These cohorts were described in the Methods chapter (Groups 1 and 2).
Only PsA patients that developed their arthritis after the diagnosis of psoriasis were included.
6.2.1. Data collection and statistical analysis
The year of onset of psoriasis was ascertained from each patient at their initial visit to the
clinic. This was defined as the year the disease was diagnosed by a dermatologist. The onset
of PsA was defined as the year the disease was diagnosed by a rheumatologist, in most of the
patients this occurred shortly prior or at their first visit to the PsA clinic.
For each participant the cumulative person-years at risk were calculated. For the PsA cohort
person-years at risk were calculated from the diagnosis of psoriasis to the diagnosis of PsA,
while for the psoriasis patients it was the time interval from the diagnosis of psoriasis until
either the onset of PsA or the last assessment date, whichever came first. Patients with
psoriasis alone were censored at their last visit to the clinic. This duration of time was used to
assess the rate of PsA among psoriasis patients.
As discussed previously, some patients in the psoriasis cohort did not have any follow-up
visit after their initial screening visit and therefore did not contribute to follow-up time.
Analyses were performed twice, with and without these subjects. Two parametric models
were used to evaluate whether the risk of developing PsA, as modeled by the hazard
function, changes over time. A time homogeneous (exponential) model, that assumes a
constant hazard function, was used to estimate the rate of PsA among psoriasis patients. This
model was compared to a Weibull model that assumes a trend in hazard function. The lack of
99
a significant difference between the two models, as assessed by the LR test, was taken as lack
of evidence for time-dependent rate.
Performing survival analysis on data pooled from two different cohorts may lead to a
sampling bias due to truncation. Truncation of survival data occurs when only those
individuals whose event time lies within a certain observational window are included in a
sample. An individual whose event time is not in this window is not included in the sample
and hence no information on this subject is available [299]. Right truncation occurs when
only individuals that experienced the event of interest are observable. Such individuals are
the patients from the PsA cohort that have already developed the outcome of interest (PsA).
Left truncation occurs when patients that developed the outcome of interest before the
observational window are excluded. In the present study, they include the patients that
developed PsA before the onset of psoriasis.
The main impact on the analysis when data are truncated is that we must use the conditional
distribution of the event time given it fell in the truncation window when constructing the
likelihood. Parametric survival analysis for right truncated data (PsA) and for left truncated
& right censored data (psoriasis) was used to model the risk of developing PsA after the
onset of psoriasis. Parametric proportional hazard regression model was used to assess the
association between clinical and genetic variables to the interval of time from psoriasis to
PsA. The following HLA alleles that were identified as potential genetic markers for PsA
among psoriasis patients were included in the analysis: HLA-C*06, HLA-C*01, HLA-C*02,
HLA-C*12, HLA-C*07, HLA-B*27, HLA-B*38 and HLA-B*08. Two tailed p value < 0.05
was considered significant.
100
6.3. Results
447 patients with psoriasis and 772 patients with PsA were included in the analysis. Their
demographic and clinical characteristics are presented in Table 6.1. The mean age at onset of
psoriasis and PsA were 26.8±14.7 and 37.3±13.3 years. The sex ratio was 1.3:1 (male:
female). The proportion of early onset psoriasis was 82.8%. The mean time from the onset
of psoriasis to development of PsA was 12.2±10.7 years.
6.3.1. The rate of PsA among psoriasis patients over time
The distribution of the time to the development of PsA was fit with an exponential model,
which has a constant hazard rate. Tests for trend did not suggest a departure from the
constant hazard (Figure 6.1). These results concur with the findings among the prospective
psoriasis cohort (Chapter 4) that showed no trend in risk of PsA. We are able to confirm
these findings in a much larger cohort.
6.3.2. The effect of HLA risk alleles on rate of PsA among psoriasis
patients
Parametric proportional hazard regression model was used to assess the effect of HLA risk
alleles on the rate of development of PsA among psoriasis patients. The effect was assessed
from the onset of psoriasis. The results are presented in Table 6.2. Patients that had at least
one copy of HLA-C*06 had longer time intervals from the onset of psoriasis to PsA (mean of
13.8±10.4 vs. 11±10.9 years, respectively). This finding was confirmed by a regression
analysis that showed that psoriasis patients who are HLA-C*06 positive had a lower rate of
development of PsA over time compared to HLA-C*06 negative. This finding was
independent of the age at onset of psoriasis, sex and other HLA risk alleles with a Relative
Risk (RR) of 0.44 (95% CI 0.36- 0.54, p<0.001). None of the remaining HLA risk alleles had
any association with the rate of PsA. There were only 102 patients who were HLA-B*38
positive and only 73 of them had information about the age at onset of PsA. These numbers
101
were small and did not fit well with the truncation model and therefore they were excluded. It
is unlikely that the exclusion of HLA-B*38 from the full regression model has created a bias
that affected the association with HLA-C*06, since these HLA alleles do not usually appear
on the same haplotype.
Since HLA B*27 is the strongest genetic risk factor for PsA among psoriasis patients, an
additional analysis was performed to determine whether this allele has any effect on the time
to onset of PsA. The time to event was calculated from birth to the onset of PsA, and the
association between HLA risk alleles was assessed using a similar multivariate regression
model. The results (Table 6.3) provide no evidence that any of the alleles was associated with
the duration of time from birth to onset of PsA.
102
Table 6.1 - Characteristics of the study population
PsA cohort
(N=772)
Psoriasis cohort
(N=447)
Age at first clinic visit (Mean) 42.8±12.7
46.5±13.1
Age at onset of psoriasis (Mean) 24.9±13.5
30.1±16.3
Age at onset of PsA (Mean)* 37 ±13.1 54.8±12.1
Duration of time from onset of psoriasis to PsA (Mean)*
12.1±10.5
18.7±18.9
Sex: Female (%) 339 (43.9%) 192 (43%)
Early onset psoriasis (%) 671 (86.9%) 334 (74.7%)
HLA-C*06 (%) 167 (31.5%) 174 (41.6%)
HLA-B*27 (%)
87 (16.3%) 17 (4.1%)
HLA-B*38 (%) 74 (13.7%) 40 (9.5%)
HLA-B*08 (%) 106 (19.7%) 56 (13.3%)
HLA-C*01 (%) 64 (11.9%) 31 (7.4%)
HLA-C*02 (%) 56 (10.4%) 20 (4.8%)
HLA-C*07 (%) 232 (43.3%) 167 (39.8%)
HLA-C*12 (%) 124 (23.1%) 71 (16.9%)
*15 patients from the psoriasis cohort developed PsA during the follow-up.
Figure 6.1- The probability of developing PsA among psoriasis patients from the onset of psoriasis (Ps) assuming an exponential model.(prospective cohort) with and assuming they did not develop PsA. Similarly, the black lines denote the data pooled from the PsA and the psoriasis cohorts up visit.
The probability of developing PsA among psoriasis patients from the onset of psoriasis (Ps) assuming an exponential model. The red lines denote the psoriasis cohort alone
and without the patients that did not have any followassuming they did not develop PsA. Similarly, the black lines denote the data pooled from
horts with and without the patients that did not have any follow
103
The probability of developing PsA among psoriasis patients from the onset of The red lines denote the psoriasis cohort alone
the patients that did not have any follow-up visit assuming they did not develop PsA. Similarly, the black lines denote the data pooled from
the patients that did not have any follow-
104
PsA - Psoriatic Arthritis, CI - Confidence Interval, RR - Relative Risk
*Problem of monotone likelihood due to sparse data
Table 6.2 - The association between HLA alleles and the risk of PsA in analysis of time from
onset of psoriasis by parametric proportional hazard model (N=939)
Variable Univariate Full model Reduced model
RR 95% CI p RR 95% CI p RR 95% CI p
Sex: Male 1.32 0.42-4.15 0.64 1.25 0.44-3.62 0.68
Late onset Psoriasis
6.46 0.53-79.3 0.14 6.05 0.18-202.7 0.32
HLA-C*01 1.78 0.28-11.2 0.54 0.96 0.19-4.89 0.96
HLA-C*02 0.49 0.28-0.86 0.01 0.56 0.03-10.1 0.69
HLA-C*06 0.44 0.36-0.54 <0.001 0.57 0.11-2.86 0.49 0.44 0.36-0.54 <0.001
HLA-C*07 1.34 0.44-4.13 0.61 1.16 0.36-3.71 0.80
HLA-C*12 1.29 0.34-4.85 0.71 1.37 0.32-5.71 0.66
HLA-B*08 1.16 0.34-3.98 0.81 1.00 0.27-3.69 0.99
HLA-B*27 1.32 0.23-7.84 0.75 2.82 0.07-123.8 0.59
HLA-B*38* NA
105
PsA - Psoriatic Arthritis, CI - Confidence Interval, RR - Relative Risk
*Problem of monotone likelihood due to sparse data
Table 6.3 - The association between HLA alleles and risk of PsA in analysis of time from birth
by parametric proportional hazard model (N=939)
Variable Univariate Full model Reduced model
RR 95% CI p RR 95% CI p RR 95% CI p
Sex: Male 1.19 0.40-3.54 0.74 1.19 0.44-3.16 0.73
Late onset Psoriasis
2.72 0.45-16.45 0.27 2.63 0.35-19.81 0.34
HLA-C*01 1.41 0.27-7.22 0.68 1.49 0.41-5.41 0.54
HLA-C*02 0.54 0.25-1.16 0.11 0.91 0.08-10.0 0.93
HLA-C*06 0.61 0.35-1.09 0.09 0.68 0.17-2.76 0.60
HLA-C*07 1.15 0.42-3.18 0.78 0.93 0.30-2.91 0.91
HLA-C*12 0.91 0.33-4.44 0.85 0.75 0.21-2.64 0.66
HLA-B*08 1.09 0.33-3.59 0.88 1.07 0.29-3.93 0.92
HLA-B*27 0.73 0.27-1.97 0.53 0.61 0.24-1.58 0.31
HLA-B*38* NA
106
6.4. Discussion
This study combined data from two cohorts to assess the rate of PsA among psoriasis
patients. It confirmed that the rate of PsA remains constant following the onset of psoriasis
and in contrast to the previous notion; it does not decrease over time. In addition, the only
HLA allele that was associated with the time interval from psoriasis to PsA was HLA-C*06.
Carriers of HLA-C*06 had prolonged intervals from the onset to psoriasis to development of
PsA.
There are limited data about the incidence of PsA among psoriasis patients. The mean
duration of time from psoriasis to PsA is approximately 7 years [300]. Therefore it was
assumed by experts in the field that the risk of developing PsA is highest in the first years
following the onset of psoriasis. However, there are no actual data in the literature to support
that notion. In a recent retrospective study from Germany, the cumulative incidence of PsA
among psoriasis patients was 20.5% after 30 years and the rate of PsA was constant over
time [32]. In the present study, the distribution of the time to the development of PsA was fit
with an exponential model. The lack of a significant difference between the Weibull and the
exponential model suggests that the risk of developing PsA among psoriasis patients does not
change over time. These results counter the previous notion that the risk of developing PsA
decreases over time following the onset of psoriasis.
HLA-C*06 is the strongest genetic risk factor for psoriasis, however, in Chapter 5 we have
found that its frequency was significantly lower in PsA patients compared to those with
psoriasis alone. In the present study, HLA-C*06 allele was also associated with a prolonged
interval of time from psoriasis to PsA that supports its “protective” effect. HLA-C*06
carriers had more than double the interval of time from the onset of psoriasis to PsA. These
results are in accordance with a previous study of Queiro et al. that reported that HLA-C*06
positive PsA patients have a longer psoriasis-arthritis latency periods (9 vs. 5 years, p=0.03)
[293]. The explanation for this observation is unclear. HLA-C*06 is associated with more
severe psoriasis and early onset of the disease [178], while there is no difference in the age at
onset of PsA between HLA-C*06 positive or negative patients. It is possible that the
increased time interval among HLA-C*06 positive patients is merely related to the early
107
onset of psoriasis and that the expression of that allele does not affect the onset of PsA.
However, the observation that HLA-C*06 is less frequent among PsA patients and is also
related to a milder form of joint disease [294] suggests that this allele may have a
“protective” effect on PsA among patients with psoriasis that may be mediated through
different mechanisms than those of psoriasis.
None of the other HLA alleles was significantly associated with the duration of time from
psoriasis to PsA including the strongest risk allele for PsA, HLA-B*27. There are several
potential explanations for these findings. First, these alleles may only be associated with joint
disease and not with skin disease. However, the lack of association with interval from birth to
onset of PsA does not support this explanation. Secondly, although often the presence of risk
genes is associated with earlier onset of a disease, these genes can affect the susceptibility for
the disease without affecting its age at onset. Lastly, the power to detect an association is
dependent on the prevalence of these alleles. As suggested by the large confidence intervals
of the RR, for some of the HLA alleles the sample size was small and the study was
underpowered to detect an association.
A limitation of the present study is the reliance on self-reported age at onset of psoriasis,
although we do not assume that there is a difference in recall between patients with PsA and
those with psoriasis. The accuracy of reporting age at onset of psoriasis by patients with
psoriasis has been previously assessed by evaluating the age of onset distribution curve
[301]. It has been concluded that before the age of 20 years patients were accurate in
identifying the onset of psoriasis within two years, while later in life there was a tendency to
round off the age at onset to a multiple of 5 years. The duration of psoriasis prior to the
presentation to clinic also affected the accuracy of report. Despite this limitation,
dermatologists have noted that patients are able to detect the presence of psoriasis with fair
degree of accuracy [302].
In summary, in this study the rate of PsA among psoriasis patients was assessed. The study
confirmed previous observations that the rate of PsA among psoriasis patients remains
constant over time. Additionally, HLA-C*06 was found to be associated with a longer
interval of time from onset of psoriasis to development of PsA.
108
108
Chapter 7. The association of KIRs and their HLA ligands with PsA
109
7.1. Background
Natural Killer (NK) cells are thought to play an important role in the pathogenesis of
psoriasis and PsA [67]. Their activity is determined by a balance between activating and
inhibitory signals transmitted by a range of receptors including Killer Immunoglobulin-like
Receptors (KIRs) [63].
The KIR gene cluster spans approximately 150 kb in the leukocyte receptor complex on
chromosome 19q13. The KIR region is very diverse, and includes different combinations of
KIR genes as well as allelic polymorphisms. Each NK cell has a combination of inhibitory
and activating receptors that interact with certain HLA alleles to result in an immune
response. KIRs are classified into activating and inhibitory receptors based on the length of
their cytoplasmic tail. Activating receptors have a short (S) tail, while inhibitory receptors
have a long (L) tail. KIRs are also categorized into 2 groups on the basis of their external
immunoglobulin-like domain (2D or 3D) (Figure 7.1) [225]. The specificity of the different
KIRs for HLA class I molecules is determined by their extracellular structure. Studies have
shown that binding of inhibitory KIRs for specific HLA molecules correlates well with their
ability to inhibit NK cytolysis of target cells bearing those HLA allotypes [68, 226]. The
specificities of the different KIRs for HLA alleles are presented in Table 7.1. Overall, this
system provides a mechanism by which an individual’s HLA repertoire may directly
influence the type and extent of the immune response in the context of a particular KIR
haplotype.
Figure 7.1 - Illustration of the struct
Table 7.1
KIR2DL1 and 2DS1
HLA-C group 2
C*02 C*04 C*05 C*06 C*15 C*17 C*18
Illustration of the structure of the different type of KIRs[303]
Table 7.1 - KIR ligand specificities
KIR2DL2/3 and 2DS2 KIR3DL1 and 3DS1
HLA-C group 1
C*01 C*03 C*07 C*08 C*12 C*14 C*16
HLA-
B*2702B*3801
110
KIR3DL1 and 3DS1
-Bw4 – Ile80
B*77 B*63 B*62
B*2702 B*3801
B*49 B*51 B*52 B*53 B*57 B*58
111
Several studies have shown that polymorphisms of genes encoding activating KIRs are
associated with PsA. It was found that the frequencies of KIR2DS1 and KIR2DS2genes were
higher in PsA patients compared to healthy controls. HLA-C group homozygosity is also
associated with an increased risk of developing PsA [229, 230]. These studies, however,
compared PsA patients to healthy controls and since KIR genes have also been associated
with psoriasis, it is unclear whether these findings represent an association that is
independent of the skin disease.
KIR3DL1 binds the HLA-Bα1-helix around residues 76-80 with specificity for all HLA-Bw4
alleles containing isoleucine at heavy chain residues 80 (Bw4-80I) [232], whereas the ligand
for KIR3DS1 has not yet been determined. However, it was suggested that KIR3DS1 also
binds HLA-Bw4-80I since it shares 97% sequence homology in its extracellular domain with
KIR3DL1 [233]. Several studies reported higher frequencies of the activating KIR3DS1 gene
in Ankylosing Spondylitis (AS) patients compared to HLA-B*27 positive healthy controls,
suggesting that the interaction between the activating receptor and its putative ligand may
increase susceptibility to the disease [234]. However, other studies in AS were unable to
replicate these findings [235]. Ankylosing spondylitis belongs to the family of SpA as PsA
and in both diseases HLA-B*27 is an important susceptibility allele. To our knowledge, the
association between KIR3DS1 and PsA has not been tested previously.
In summary, several studies have shown an association between activating KIRs and both
psoriasis and PsA. However, it is unclear whether the KIRs; alone or in combination with
their HLA ligands confer an independent risk for PsA among psoriasis patients.
112
7.2. Methods
7.2.1. Study population
In this population based association study three groups of individuals from the same ethnic
group and geographic region were compared.
The PsA group included 710 adult Caucasian PsA patients who were recruited from the
University of Toronto PsA cohort. All patients satisfied the CASPAR criteria for
classification of PsA [22]. 333 psoriasis patients were recruited from the Toronto Psoriasis
cohort. These patients had the diagnosis of psoriasis confirmed by a dermatologist and were
assessed by a rheumatologist to rule out inflammatory arthritis. Therefore, at the time of
analysis, all psoriasis patients were free of arthritis. Control DNA was obtained from 707
healthy Caucasian volunteers, cadaveric organ donors from laboratories at the University
Health Network and from a commercial biobank.
The groups of PsA and psoriasis patients were combined to create a Psoriatic Disease group
(PsD) that was compared to the healthy control group.
7.2.2. KIR typing
Extracted genomic DNA was amplified by PCR using locus specific primers for each of the
following KIR genes: 2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 2DS1, 2DS2, 2DS3, 2DS4, 2DS5,
3DL1, 3DL2, 3DL3, 3DS1. The frequency of each KIR gene was expressed as the inferred
phenotype frequency in the sample population.
7.2.3. Sample size calculation
Power calculations indicate that a study comparing 710 PsA patients to 333 psoriasis patients
has >80% power to detect an odds ratio of 2.1, 1.8, 1.6 and 1.5, for the risk gene frequencies
of 0.05, 0.1, 0.2 and 0.4, respectively at 5% alpha (type I error level). Power calculations
were performed using PS Power and Sample Size Calculations version 3.0 [266].
113
7.2.4. Statistical analysis
The phenotypic frequencies of the KIR genes (the presence of at least one copy of the gene)
were compared between the PsD group vs. healthy controls and between PsA vs. psoriasis by
a LR test. Because of the known association of HLA-C*06 and B*27 with PsA, the study
focused on seven KIR genes: five genes that have HLA-C as their recognized ligand (i.e.,
KIR2DL1, -2DL2, -2DL3, -2DS1, and-2DS2) and two additional genes that have HLA-Bw4
group as their ligand (i.e. KIR3DS1 and KIR3DL1). The significance level was set at 0.05
for a 2-sided test.
Since the interaction between KIR genes and HLA class I molecules has a biologic basis, I
investigated the association between combinations of activating KIRs and their HLA ligands
with PsA. The following biological interactions were investigated:
1. KIR 3DS1 gene and HLA-Bw4-80I alleles.
2. KIR 2DS1 genes and HLA-C group 2 alleles.
3. KIR 2DS2 genes and HLA-C group 1 alleles.
These combinations were selected based on the inclusion of several susceptibility alleles for
PsA (HLA-B*27, HLA-B*38, HLA-C*12 and C*06) in these HLA groups. These alleles
have been found to be independently associated with PsA compared to psoriasis. HLA B*27
and HLA-B*38 are included in the HLA-Bw4 group, HLA-C*12 is included in the HLA-C
group 1 and HLA–C*06 is included in HLA-C group 2. Data from the literature also support
the association between these KIR genes and increased susceptibility to PsA [229, 230].
114
7.2.5. The combined effect of KIR 3DS1 and HLA-Bw4 alleles
HLA-B alleles were categorized according to the protein sequence alignment into HLA-Bw4
and HLA-Bw6. The HLA-Bw4 group was further classified into HLA-Bw4-80I and 80T
based on the presence of isoleucine or aspargine in position 80, respectively [232]. Due to the
high sequence homology between KIR3DS1 and 3DL1, it is assumed that they share HLA-
Bw4-80I group as a ligand [233]. Therefore, the interaction between KIR3DS1 and HLA-
Bw4-80I alleles was investigated. Assuming that the effect of KIR3DS1 is dependent on the
presence of HLA-Bw4-80I, a higher frequency of that combination in the PsA group
compared to the psoriasis group was anticipated.
The distribution of HLA-Bw4-80I and KIR3DS1 was compared in PsA vs. psoriasis and PsD
vs. healthy controls by a LR test. Due to apparent effects of multiple variables at the HLA
and KIR loci on the risk of developing PsA, a multivariate logistic regression analysis was
used to assess the association between various genotypes and PsA. The model included HLA
alleles that were previously found to be associated with PsA (HLA-B*27, HLA-C*06), the
activating KIR gene (KIR3DS1) and its inhibitory KIR homolog (KIR3DL1). The activating
KIR3DS1 gene in the presence of its HLA putative ligand, HLA-Bw4-80I was also used as a
covariate in the model. Stepwise logistic regression analysis with backward selection was
used to identify key factors that differentiate between PsA and psoriasis alone. Covariate
effects were considered statistically significant if the p-value from the 2-sided Wald-test was
less than 0.05.
7.2.6. The combined effect of KIR 2DS1/2DS2 and HLA-C1/C2 groups
HLA-C assignments were categorized, according to current protein sequence alignments, into
two C groups as follows: C group 1 - HLA-C*01, 03, 07, 08, 12, 14, and 16; C group 2 -
HLA-C*02, 04, 05, 06, 15, 17, and 18.
Based on data from the literature that suggested that the interaction between the activating
KIR2DS and the ligands for the inhibitory KIR homologs can lead to activation of NK cells
[304, 305], the following gene-gene interactions were investigated:
115
� KIR 2DS1 interacts with HLA-C*group 2
� KIR 2DS2 interacts with HLA-C*group 1
The combined effect of KIR2DS1 or KIR2DS2 and their corresponding HLA-C ligands was
assessed. The distribution of KIR2DS1 or KIR2DS2 and their corresponding HLA-C ligands
was compared in PsA vs. psoriasis and PsD vs. healthy controls by LR test. Assuming that
the effect of these activating KIRs is dependent on the presence of their corresponding HLA
ligands, higher frequencies of these combinations in the PsA group compared to the psoriasis
group were anticipated.
Due to apparent effects of multiple variables at the HLA and KIR loci on the risk of
developing PsA, a multivariate logistic regression analysis was used to assess the association
between various genotypes and PsA vs. psoriasis alone and PsD vs. healthy controls. The
model included HLA alleles that were previously found to be associated with PsA (HLA-
B*27), the activating KIR genes (KIR2DS1, KIR2DS2) and their inhibitory KIR homologues
(KIR2DL1, KIR2DL2, KIR2DL3). Additionally, the two activating KIR genes in the
presence of their HLA ligands (i.e., KIR2DS1 with group 2 HLA-C alleles, KIR2DS2 with
group 1 HLA-C alleles) were also used as covariates in the model. Stepwise logistic
regression analysis with backward selection was used to identify key factors that differentiate
between PsA and psoriasis alone and between PsD and healthy controls. Covariate effects
were considered statistically significant if the p-value from the 2-sided Wald-test was less
than 0.05.
116
7.3. Results
7.3.1. The association of KIR genes and PsA – single locus analysis
The phenotypic distributions of the different KIR genes in the PsD group vs. healthy controls
and in the patients with PsA vs. those with psoriasis are presented in Tables 7.2-7.3.
The framework genes KIR2DL4, KIR3DL2 and KIR3DL3 were present in almost all
subjects. Several individuals, however, did not carry these genes. These results were
confirmed by sequencing of the KIR region and by analyzing data from other family
members of these individuals. They represent a rare deletion of these framework genes. The
frequencies of inhibitory KIRs 2DL2 and 2DL3 were higher in the PsD group compared to
the healthy controls; however these gene frequencies were similar in PsA compared to
psoriasis alone. The frequency of KIR2DL1 was lower in the PsA group compared to
psoriasis alone (97% vs. 99.4%, p=0.02); however, its frequency was similar among the PsD
and the healthy control group. The frequency of KIR2DS2 was significantly higher in the
PsA group compared to healthy controls (54.7% vs. 48.9%, OR 1.3, p=0.03). The frequency
of KIR2DS2 also tended to be higher in the PsA group compared to psoriasis alone however,
it did not reach statistical significance (54.7% vs. 49.3%, p=0.09). The frequencies of the
remaining KIR genes were similar across the three groups.
7.3.2. The association between KIR genotypes and PsA
The most common genotype in all three groups was that corresponding to the homozygous
“A” haplotype (2DL1, 2DL3, 2DL4, 3DL1, 3DL2, 3DL3, and 2DS4), which was found in
approximately one third of the study population (Figure 7.2). More than 80% of the subjects
were represented by the 10 most frequent genotypes. There was no difference in the
distribution of the genotypes in the three groups.
117
7.3.3. KIR/HLA interaction
To investigate the biological interaction of KIRs and HLA genes, the frequencies of the
combined genotypes of KIRs and their corresponding HLA ligands were compared between
the groups based on a suggested model of effect that is elicited by the combination of
activating or inhibitory KIRs and their corresponding HLA ligands as described in the
Methods section.
7.3.4. The interaction of KIR 2DS1/2DS2 with HLA-C group 1/C group 2
There were significant differences in the frequencies of HLA-C group 1 and group 2 among
the PsA, psoriasis and controls (Table 7.4). The HLA-C group 2 alleles were more common
in patients with PsA compared to the healthy controls (p=0.01), but were less common
compared to psoriasis alone (p=0.03). While the frequency of HLA-C group 1 alleles was
higher among the PsA patients compared to those with psoriasis alone (p=0.02), it was
similar among the PsA group and the healthy controls.
The results of the multivariate analysis that investigated the association between PsA and the
activating KIRs, 2DS1 and 2DS2, and their corresponding HLA ligands compared to
psoriasis are presented in Table 7.5. In the presence of its corresponding ligand, HLA-C
group 1, KIR2DS2 was associated with PsA compared to psoriasis alone, suggesting that
their combination may increase the susceptibility to PsA among psoriasis patients. The
presence of KIR2DS2 in the absence of HLA-C group 1 alleles was also associated with PsA
compared to psoriasis. The level of significance and the OR for the KIR2DS2/HLA-C group
1 increased after inclusion of the corresponding inhibitory KIR2DL1 in the multivariate
regression model, suggesting that the combined effect of KIR2DS2/HLA-C group 1 is
stronger in the absence of the corresponding inhibitory KIR. In order to elucidate the
combined effect of KIR2DL2 and KIR2DS2, their frequencies were compared in PsA
patients vs. those with psoriasis alone. The results in Table 7.6 show that KIR2DL2 and
KIR2DS2 are highly concordant and appear separately only in 21 out of 1043 patients (2%).
However, the frequency of the activating KIR2DS2 without its inhibitory homologue,
KIR2DL2, was higher in the PsA group compared to psoriasis (13(1.8%) vs. 0), while the
118
frequency of the inhibitory KIR2DL2 without its activating homologue, KIR2DS2, was
higher in the psoriasis group compared to the PsA group (6 (1.8%) vs. 2 (0.3%)). These
figures explain the reason these two genes were not significantly associated with PsA in the
univariate, single locus analysis, however showed a strong association and an opposite
direction with PsA compared to psoriasis in the multivariate analysis. The small number of
patients in each discordant group (patients having a copy of either KIR2DS2 or KIR2DL2)
accounts for the broad confidence intervals that were observed in the multivariate analysis
for these co-variates.
With regards to KIR2DS1, in the presence of its ligand HLA-C group 2, KIR2DS1 had a
similar frequency in PsA as in psoriasis alone. However, the presence of KIR2DS1 with its
HLA ligand was associated with PsD compared to healthy controls, while KIR2DS2 and its
ligand were not associated with PsD (Table 7.7).
7.3.5. The interaction of KIR3DS1 with HLA-Bw4-80I group
The frequencies of KIR3DS1 were similar in the three groups (37.5% in the PsA group,
41.1% in the psoriasis group and 36.8% in the controls, p=0.16). The frequencies of carriers
of HLA-Bw4-80I were higher in the PsA and the psoriasis groups compared to the controls
(38.7% in the PsA, 39.4% in the psoriasis and 28% in the controls), but there was no
difference between the psoriasis and the PsA groups (p=0.24). The frequencies of KIR3DS1
were also similar among HLA-Bw4-80I positive individuals, suggesting that the interaction
between HLA-Bw4-80I alleles and KIR3DS1 is not associated with the susceptibility to PsA
(Table 7.8). These findings were confirmed in a regression analysis (Table 7.9). The
combination of HLA-Bw4-80I and KIR3DS1 was not significantly associated with PsA
compared to psoriasis in either the univariate or the multivariate regression model that
included HLA-B*27, HLA-C*06 and KIR3DL1. The presence of KIR3DS1 without its
corresponding HLA ligand showed an inverse association of borderline significance with
PsA.
119
Table 7.2 - KIR gene distribution – Psoriatic disease vs. Healthy controls
KIR PsD (N=1043) Controls (N=707) OR 95% CI P value
2DL1 1020
(97.8%) 684 (96.8%) 1.49 0.83-2.67 0.18 2DL2 548 (52.5%) 336 (47.5%) 1.22 1.00-1.48 0.04
2DL3
963 (92.3%) 634 (89.7%) 1.38 1.00-1.93 0.05
2DL4 1040 (99.8%) 706 (99.9%) 0.74 0.07-8.13 0.80
2DL5 524 (50.3%) 344 (48.7%) 1.06 0.88-1.29 0.49
2DS1 433 (41.5%) 271 (38.3%) 1.14 0.93-1.38 0.18
2DS2 553 (53.0%) 346 (48.9%) 1.17 0.97-1.42 0.09
2DS3 293 (28.1%) 183 (25.9%) 1.11 0.90-1.39 0.30
2DS4 987 (94.6%) 674 (95.3%) 0.86 0.56-1.34 0.51
2DS5 328 (31.3%) 213 (30.2%) 0.94 0.86-1.31 0.62
3DL1 988 (94.8%) 674 (95.3%) 0.89 0.57-1.39 0.62
3DL2 1042 (100%) 707 (100%) 3DL3 1040 (99.9%) 707 (100%) 1.00
3DS1 403 (38.6%) 260 (36.8%) 1.08 0.89-1.32 0.43
Table 7.3 - KIR gene distribution – PsA vs. Psoriasis
KIR
PsA (N=710) Psoriasis (N=333)
OR 95% CI P value
2DL1 689
(97%) 331 (99.4%) 0.19 0.04-0.85 0.02 2DL2 378 (53.2%) 170 (51.1%) 1.09 0.84-1.41 0.51
2DL3
649 (91.4%) 314 (94.3%) 0.64 0.38-1.09 0.10
2DL4 708 (99.9%) 332 (99.7%) 2.13 0.13-31.2 0.58
2DL5 353 (49.8%) 171 (51.5%) 0.93 0.72-1.21 0.60
2DS1 294 (41.1%) 139 (41.9%) 0.98 0.75-1.28 0.92
2DS2 389 (54.8%) 164 (49.3%) 1.25 0.96-1.62 0.09
2DS3 197 (27.8%) 96 (28.8%) 0.94 0.71-1.25 0.72
2DS4 671 (94.5%) 316 (94.9%) 0.92 0.51-1.66 0.79
2DS5 218 (30.7%) 109 (32.7%) 0.90 0.69-1.18 0.46
3DL1 671 (94.5%) 316 (95.5%) 0.81 0.44-1.79 0.51
3DL2 709 (100%) 333 (100%) 3DL3 709 (100%) 331 (99.7%) 0.31
3DS1 266 (37.5%) 137 (41.1%) 0.85 0.66-1.12 0.25
120
Figure 7.2 - The 10 most frequent KIR genotypes in PsA, psoriasis and healthy controls. A black box denotes the presence of at least one copy of the gene.
Table 7.4 - The frequency of HLA-C group 1/HLA-C group 2 alleles in PsA, psoriasis and
healthy controls
Group PsA (N=710)
Psoriasis (N=331)
P value PsA vs. Psoriasis
Controls (N=707)
P value PsA vs. Controls
HLA-C group 1
alleles
600 (84.5%)
261 (78.9%)
0.02 619
(87.6%) 0.11
HLA-C group 2
alleles
457 (64.4%)
236 (71.3%)
0.03 409
(57.9%) 0.01
2DL 2DS 3DL
1 2 3 4 5 1 2 3 4 5 1 2 3 3DS1
230 (32.5%) 203 (28.6%) 103 (30.9%)
104 (14.7%) 118 (16.6%) 48 (14.4%)
95 (13.4%) 70 (9.9%) 43 (12.9%)
52 (7.4%) 55 (7.6%) 25 (7.5%)
32 (4.5%) 34 (4.8%) 17 (5.1%)
26 (3.7%) 33 (4.7%) 22 (6.6%)
22 (3.1%) 30 (4.2%) 16 (4.8%)
17 (2.4%) 14 (2%) 4 (1.2%)
10 (1.4%) 11 (1.6%) 5 (1.5%)
9 (1.3%) 9 (1.3%) 6 (1.8%)
110 (15.5%) 134 (18.8%) 44 (13.2%)Remaining genotypes
Psoriasis
(n=707) (n=710) (n=333)
Control PsA
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Table 7.5 – The association between combinations of KIR2D/HLA based on activation
model and PsA (N=710) vs. Psoriasis (N=331)
KIR/HLA Univariate analysis Multivariate Reduced
Model
OR 95% CI P value OR 95% CI P value
KIR2DL1 0.2 0.04-0.9 0.03 0.2 0.05-0.9 0.03
KIR2DL2 0.5 0.8-1.4 0.51 0.08 0.02-0.4 0.001
KIR2DL3 0.6 0.4-1.1 0.11
KIR2DS1 with HLA-C group 2
KIR2DS1 without HLA-C group 2
KIR2DS2 without HLA-C group
0.9 0.7-1.3
0.75
1.1
1.0
0.8-1.6
0.6-1.6
0.58
0.95
11.5
2.4-55.0
0.002
KIR2DS2 with HLA-C group 1 1.4 1.1-1.8 0.02 16.3 3.6-74.1 0.0003
HLA-B*27 5.1 2.9-8.7 <0.0001 5.2 3.0-9.1 <0.0001
Table 7.6 - The frequencies of KIR2DS2 and KIR2DL2 in PsA and psoriasis
KIR2DS2 KIR2DL2 PsA
(N=710)
Psoriasis
(N=333)
+ + 376 (53%) 164 (49.2%)
+ - 13 (1.8%) 0 (0%)
- + 2 (0.3%) 6 (1.8%)
- - 319 (44.9%) 163 (49%)
+ The patient carries at least one copy of the gene. - The patient does not carry the gene
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Table 7.7 - The association between combinations of KIR2D/HLA based on activation model
and PsD (N=1041) vs. Controls (N=707)
KIR/HLA Univariate analysis Multivariate Reduced
Model OR 95% CI P value OR 95% CI P value
KIR2DL1 1.5 0.8-2.3 0.18
KIR2DL2 1.2 1.01-1.5 0.04 1.3 1.1-1.6 0.006
KIR2DL3 1.4 0.99-1.9 0.05 1.7 1.2-2.4 0.003 KIR2DS1 with HLA-C group2
KIR2DS1 without HLA-C group2
KIR2DS2 without HLA-C group 1
0.003
1.4
0.8
1.3
1.1-1.8
0.6-0.99
0.9-1.9
0.002
0.04
0.15
1.4
1.1-1.8
KIR2DS2 with HLA-C group1 1.1 0.9-1.3 0.38
HLA-B*27 2.2 1.6-3.0 <0.000 2.1 1.5-3.0 <0.0001
Table 7.8 - The frequency of KIR3DS1 in PsA and psoriasis
Group PsA
(N=709)
Psoriasis
(N=331)
Healthy Controls
(N=707)
P value
(trend)
Total 266 (37.5%) 137 (41.1%) 260 (36.8%) 0.17
HLA-Bw4-I80 (positive) 104 (38.1%) 54 (41.2%) 74 (37.6%) 0.78
HLA-Bw4-I80 (negative) 162 (37.1%) 83 (41.1%) 186 (36.5%) 0.5
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Table 7.9 - The association between combinations of KIR3D/HLA based on activation model
and PsA (N=709) vs. Psoriasis (N=331)
KIR/HLA Univariate analysis Multivariate Reduced Model
OR 95% CI P value OR 95% CI P value
KIR3DL1 0.8 0.4-1.5 0.51
KIR3DS1 without HLA-Bw4-80I
0.8 0.6-1.1 0.12 0.7 0.5-0.96 0.03
KIR3DS1 with HLA-Bw4-80I
HLA-C*06
HLA-B*27
1.1 0.7-1.6 0.73
0.5
5.1
0.4-0.6
2.9-8.8
<0.0001
<0.0001
0.5
4.7
0.4-0.7
2.7-8.2
<0.0001
<0.0001
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7.4. Discussion
In this study, the association between the KIR genes and PsA was investigated. In addition,
the gene-gene interaction between selected activating KIRs and their HLA ligands in the
susceptibility to PsA was assessed. Several “functional” models that represent the likely
outcomes of HLA /KIR interactions were tested.
HLA class I genes are strongly associated with susceptibility to PsA and psoriasis [153].
HLA molecules are expressed on target cells and play an important role in the activation of
NK cells through their interaction with KIR molecules. The activation is dependent on the
combination of variable KIRs and HLA-class I molecules [226]. It has been suggested that
certain combinations of receptor-ligand may result in altered NK cell mediated immunity
against pathogens which may in turn lead to autoimmunity [303, 306].
Our results show that the frequency of the inhibitory KIR2DL1 was lower in PsA patients
compared to those with psoriasis alone. However, this gene was observed in nearly all
individuals, therefore, this difference can only account for a small fraction of the risk.
KIR2DS2 was found to be associated with PsA compared to healthy controls. However,
although it was more frequent in the PsA group compared to the psoriasis patients, the
association was not significant. This may be due to the existence of a weak association since
the study was not powered to detect an association with an OR of less than 1.5. The direction
of the association was in accordance with our expectation: the activating KIR2DS2 was more
frequent in PsA while the inhibitory KIRs were less frequent in PsA compared to psoriasis.
In the literature, KIR2DS1 and KIR2DS2 have been associated with susceptibility to
psoriasis and PsA. It has been reported that KIR2DS1 is associated with psoriasis compared
to healthy controls among Japanese [229] and Caucasians from Brazil, Sweden and Poland
[230, 307, 308]. These results concur with our findings in the multivariate analysis that
showed that KIR2DS1 in the presence of its HLA-C group 2 ligand is associated with
psoriatic disease, suggesting that the strongest association of this gene is with the skin and
not the joint disease.
125
Two models of gene-gene interaction were investigated in this study. The combination of
HLA-Bw4-80I alleles and KIR3DS1 gene was found not to be associated with PsA or PsD.
This interaction has not been investigated in PsA previously. However, it was suggested to be
important in the susceptibility to AS. Two studies, one of Caucasians from Spain and the
other, Chinese people in China, have found a higher frequency of KIR3DS1 with a lower
frequency of the inhibitory KIR3DL1 among AS patients [234, 309, 310]. However, a large
study from the UK reported a lack of an association between KIR3DS1 and AS [235]. In our
study, KIR3DS1 by itself was not associated with PsA or with PsD. Possible explanations for
the lack of association with KIR3DS1 are that only a small proportion (less than 20%) of our
PsA patients are HLA-B*27 positive, as opposed to almost all of the AS patients in these
studies. Additional possible reasons include: an effect of different alleles encoded at other
KIR loci, expression of different combinations of KIRs encoded within the genome,
differences in populations and type I error.
Another interaction that was tested was between KIR2DS2 and 2DS1 and their
corresponding HLA-C ligands. It was hypothesized that the association with the activating
KIRs would be greatest in the presence of their HLA ligands. A significant association was
found between KIR2DS2 in the presence of its ligand HLA-C group 1, in which higher
frequencies were found in the PsA group, compared to psoriasis alone. However, that gene
combination was not associated with PsD, while KIR2DS1 in the presence of its ligand
HLA-C group 2 was associated with PsD. These associations may indicate that KIR2DS2 is a
susceptibility gene for PsA among psoriasis patients, while KIR2DS1 is associated only with
the skin disease. This model is supported by the “dual” associations that were found for
HLA-C*06 and PsA: a risk factor for psoriasis alone but an inverse association (“protective”
effect) for PsA compared to psoriasis. Since HLA-C*06 is included in HLA-C group 2 that is
the ligand for KIR2DS1, its interaction with KIR2DS1 may be important in the susceptibility
to psoriasis. However, since psoriasis patients who carry HLA-C*06 are less likely to have
HLA-C group 1, simply because one of their HLA-C alleles already belongs to HLA-C group
2, they are less likely to have a compatible ligand for the KIR2DS2 that may be a risk gene
for PsA among psoriasis patients.
126
In the previous literature, several models of interaction between KIR2DS and their HLA
ligands were assessed for susceptibility to PsA. Martin et al. suggested a different model
where these activating KIRs conferred risk for PsA when HLA ligands for the corresponding
inhibitory KIRs were missing [227]. This model was found to be associated with PsA
compared to healthy controls. They hypothesized that activating KIR molecules bind poorly
to HLA molecules compared with that observed for inhibitory KIR and that the ligands for
the KIR2DS molecules are not the same HLA molecules recognized by homologous
KIR2DL receptors. In our model we hypothesized that the activating KIRs bind the same
ligands as their corresponding inhibitory KIRs. Although the association between the
activating KIRs and the HLA class I molecules is weaker than that for the inhibitory KIRs, it
has been suggested that a high level of HLA class I expression, such as occurs under
conditions of IFN-γ production associated with infections, may allow binding of activating
KIRs. Furthermore, it has been shown that under these conditions, KIR2DS1 binds to the
HLA-C allele group of its corresponding inhibitory KIRs and leads to activation of NK cells
[304, 305].
The activating KIR2DS are attractive candidates for exerting a direct effect on pathogenesis,
as they interact with several HLA-C alleles that are strongly associated with psoriasis and
PsA. However, to date, the activating KIRs have not been identified as susceptibility genes
for psoriasis by genome-wide scans. This might be attributable to the complex relation
between HLA and other KIRs, as very large sample sizes are required to determine a
statistical interaction. Additional studies, including functional studies, will be required to
determine the relation between KIR2DS2/2DS1 and PsA.
Activation of NK cells that is mediated through binding of activating KIRs can explain
several diseases that are associated with KIRs and HLA genes. It has been shown that the
combination of HLA-Bw4 alleles containing Ile-80 and KIR3DS1 are associated with a
slower progression of AIDS [233, 236] and type I diabetes is associated with KIR2DS2 in the
presence of HLA-C group 1 [311]. Selective tissue expression of HLA-class I genes may
explain the association of the different diseases with certain gene combinations. Another
explanation is that the peptide preferences of KIR–HLA interactions may define which
combinations are important in particular tissues. In addition, the mechanism of activation of
127
NK cells is complex and is not mediated only through the interaction of KIR/HLA. NK cells
carry additional receptors including Leukocyte Immunoglobulin Like Receptors (LILR) and
CD94/NKG2 that mediate signal transmission that lead to activation of NK cells [312].
Furthermore, the complexity of KIR is compounded by allelic diversity and each KIR gene
has between four to nineteen alleles. These polymorphisms were not assessed in our study
but can affect NK cell activation.
The KIR gene diversity is related to genetic mechanisms that have led over many generations
to different combinations of KIR genes as well as allelic polymorphism. This diversity in
different human populations results in various activation and inhibition signaling potentials
that when combined with HLA diversity may be related to neutral selection in response to
exogenous stimuli [306]. Since the activating KIRs are present in approximately half of the
population, they may have an evolutionary advantage that may be related to a better ability of
the immune system to fight infections. This advantage was demonstrated in the protection of
KIR genes against AIDS progression. On the other hand, since KIRs may increase the
susceptibility to immune-mediated diseases, such as PsA, that are not associated with
mortality at young age, they continue to exist in the population.
Chapter 8. The association between environmental factors and onset of PsA in patients with psoriasis
129
8.1. Background
In the majority of the patients with PsA, inflammatory arthritis develops on average 7 years
after the onset of the skin disease [35]. PsA is a complex disease with genetic and
environmental risk factors playing a major role in disease susceptibility [73]. Genetic factors
alone cannot account for all cases of PsA. One of the suggested pathogenic models for PsA is
that psoriasis patients who carry susceptibility genes for arthritis develop PsA after exposure
to triggering environmental factors [74]. Suggested factors include physical and emotional
trauma, infections and hormonal changes[75]. A Few studies have systematically assessed
environmental risk factors for PsA among psoriasis patients and these showed conflicting
results [76, 77]. The objective of this case-control study is to investigate the association
between several putative environmental risk factors and the occurrence of PsA among
psoriasis patients.
130
8.2. Methods
8.2.1. Study population
Cases – This group included adults with recent onset PsA that satisfied the CASPAR
classification criteria [22] selected from the University of Toronto PsA cohort. For the
purpose of this study, I identified all patients with PsA of less than 7 years duration since
diagnosis. I attempted to contact all patients in the database that satisfied the study criteria,
either during their follow-up visit in the clinic, or by telephone or mail.
Controls: This group included psoriasis patients without arthritis that were recruited from the
Toronto Psoriasis Cohort, which enrolls psoriasis patients uncomplicated by arthritis and
aims to study risk factors for PsA. These patients are assessed by a rheumatologist initially to
rule out the presence of inflammatory arthritis, and then annually for symptoms or signs of
inflammatory arthritis. If inflammatory arthritis is diagnosed, the subject is considered to
have developed the outcome of interest and is censored. This process ensures that all of the
psoriasis cohort subjects are free of arthritis. For this study, the controls were recruited
consecutively upon their visit to the clinic for their annual follow-up visit.
8.2.2. Data collection
Demographic and clinical data were available from the cohorts’ computerized databases.
Severity of psoriasis was assessed by the highest PASI in the first 3 years of follow-up.
Severe psoriasis was defined as PASI≥10.
Based on literature that has suggested that different environmental exposures are associated
with PsA, a broad range of potential triggering factors was investigated. A questionnaire for
the assessment of these exposures was designed. The study population was asked to report
whether they have had any of the environmental exposure events under investigation in the
previous 10 years. Patients were asked to specify for each event, the year of occurrence,
whether they required medical consultation or if they were admitted to hospital (See
Appendix 1). The following exposures were recorded:
131
1. Physical trauma that included injuries, fractures and road traffic accidents.
2. Infections that required antibiotic treatment and infectious diarrhea.
3. Vaccinations to hepatitis A and B, influenza, pneumococcus, rubella and tetanus.
4. Emotional stress, including death of a close family member, divorce or separation,
moving house, change of job, becoming unemployed and treatment for anxiety or
depression.
5. Female hormonal exposures – ages of first and last menstrual periods, use of
hormonal contraception and hormone replacement therapy, fertility treatment and
pregnancies.
6. Occupational exposures – I hypothesized that recurrent micro-trauma that are related
to certain occupations may be associated with PsA. The literature was searched for
studies that assessed the association between several occupational tasks and
musculoskeletal problems. The cut-off levels that were found to increase the risk of
these problems were chosen [313]. Only subjects that have been employed in the last
10 years were asked to report exposures to the following: prolonged standing of more
than 30 minutes/hour, squatting for more than 5 minutes/hour, lifting cumulative
heavy loads of more than 100 pounds/ hour, pushing cumulative loads of more than
200 pounds/hour, using vibrating tools for more than 4 hours/day, frequent repetitive
hand movements of more than 45 minutes/hour, forceful gripping of more than 4
hours/day and bending of the wrist for more than 4 hours/day .
7. Smoking status – If a person smoked at least one cigarette a day for at least one year
before the diagnosis of PsA or before the date of assessment for the psoriasis only
group, they were defined as “ever smoked”.
8. Alcohol – Alcohol consumption was categorized as social, daily, or non-drinker
before the diagnosis of PsA or before the date of assessment for patients in the
psoriasis group.
132
Recall bias is a potential problem in this type of study. I expected patients with arthritis to
recall more environmental events, since patients may link these events to the onset of
arthritis. In order to minimize this bias, the environmental events were not related explicitlyto
the onset of the arthritis, but the participants were simply asked to report about events that
occurred within the 10 years prior to the assessment date and thus a linkage between the
onset of arthritis and exposure was avoided. Only exposures that occurred before the onset of
arthritis were considered. In order to ensure that cases and controls had equal duration of
time for potential exposures, a reference date was assigned for each control that was derived
from the date of onset of arthritis for the corresponding matched case having a similar
duration of psoriasis. Thus the matching ensured only a similar time period at risk of
exposure and therefore potential confounding variables were controlled for through
regression analysis.
8.2.3. Statistical analysis
Baseline descriptive statistics were computed with continuous variables summarized by their
means and standard deviations and categorical variables summarized by proportions. The
proportions of patients that were exposed to the different types of environmental factors
before the reference date were calculated. For several of the exposures, mostly in the
vaccination section, a significant proportion of the responses were “don’t know” or missing.
Missing variables were assigned a distinct code. This process allowed us to avoid a selection
bias by excluding those individuals from the full regression model. Since there were too
many environmental factors for inclusion as co-variates in the full regression model I
performed a “screening” step. This step aimed to detect the most significant exposures (p
value <0.1) after adjusting for age, sex, level of education, severity and duration of psoriasis,
and included only those in the final logistic regression model. I then constructed a logistic
regression model that included all of the exposures that were found to be significant in the
“screening” step adjusting for the same confounders. Logistic regression analysis was used to
identify key factors predictive of PsA. Covariate effects were considered statistically
significant if the p-value from the 2-sided Wald-test was less than 0.05. As this study was
considered to be explorative, no adjustment for multiple testing was applied.
133
8.3. Results
8.3.1. Study population characteristics
One hundred and ninety PsA patients that satisfied the study criteria were identified through
a search in the computerized PsA database. I attempted to contact all of them, however, I was
unable to contact 29 (6 deceased) and 2 were excluded due to poor English language skills.
Overall, 159 patients from each group were included in the study (83.6% for the PsA group).
I then aimed to recruit a similar number of psoriasis patients. A total of 196 psoriasis patients
was approached for enrollment of whom 159 (81.1%) completed the questionnaire.
The demographic and clinical characteristics of the study population are presented in Table
8.1. The cases and controls had a similar mean duration of psoriasis and male to female ratio.
Level of education, often associated with socioeconomic status, was also similar in the two
groups.
The psoriasis group was slightly older than the PsA group (by a mean of 3.5 years). The
mean duration of PsA at the time of assessment was relatively short, 3.1 ± 2.2 years, which
indicates the recent onset of the disease and minimizes problems due to recall. A mean time
window of 6.9±2.2 years prior to the onset of arthritis and a comparable time period for the
controls were captured (range 2 to 10 years).
Surprisingly there was not a significant difference in psoriasis severity between the PsA and
psoriasis patients. This may be explained by the fact that many of the psoriasis patients were
reviewed after several phototherapy treatments which may have improved their skin scores,
while many of the PsA patients were reviewed prior to starting therapy at a time when their
PASI scores were higher.
8.3.2. Proportion of exposure to the different factors
The proportions of patients that were exposed to different environmental factors are
presented in Table 8.2.
134
8.3.3. Smoking and alcohol consumption
The proportion of smokers (current and past) was lower in the PsA group compared to the
psoriasis group (40.2% vs. 56%, respectively), leading to a significant inverse association
between smoking and PsA (OR 0.6, 95% CI 0.36-0.88). The ORs for past smokers (OR 0.52,
95% CI 0.31-0.88, p=0.015) and currents smokers (OR 0.54, 95% CI 0.31-0.96, p=0.038)
compared to those who never smoked were similar.
The proportions of social, daily and non-drinkers were found to be similar in the PsA and
psoriasis groups.
8.3.4. Occupational exposures
Only the 148 PsA and 146 psoriasis patients who were employed during the 10 years prior to
the assessment date were evaluated. Lifting cumulative heavy loads of at least 100
pound/hour was more common in the PsA group (30.1% vs. 13%, OR 2.8, 95% CI 1.48-
4.98). The following occupational tasks were more common in the PsA group: squatting for
at least 5 minutes/hour (30.8% vs. 21.9%, 95% CI 0.92-2.66, p=0.1) and pushing cumulative
heavy weights of more than 200 pounds/hour (19.8% vs. 12.3%, 95% CI 0.83-3.06, p=0.16).
However, as noted by the p values, none of these differences reached statistical significance
at the 5% level. No particular pattern of joint involvement was associated with carrying
heavy loads.
8.3.5. Infections
A history of infectious diarrhea in the exposure time window under study was more common
in the PsA group however the difference did not reach statistical significance (12.7% vs. 7%,
p=0.19). Other infections that required antibiotic treatment were significantly associated with
PsA (OR 1.7, 95% CI 1.01-2.78, p=0.19). In addition, the proportion of severe infections that
required hospitalization was significantly higher in the PsA group during the exposure time
window under study (7.2% vs. none in the psoriasis group, p<0.001). The types of infections
135
reported in the PsA group were lower respiratory tract infection (11%), upper respiratory
tract infection (22.2%), urinary tract infection (8.3%), skin and soft tissue infections (25%),
sinus infection (8.3%), sepsis (2.8%), hepatitis C or B (8.3%) and other type of infections
(13.9%). There was no significant difference in the types of infections between the psoriasis
and the PsA group.
8.3.6. Injuries
There was no difference between the cases and control groups in the proportions of subjects
that had road traffic accidents and fractures. However, more PsA patients reported having
other types of injuries (21.4% vs. 10.7%, OR 2.1, 95% CI 1.13-4.01, p=0.02). The types of
injuries in the PsA group were very variable and included cuts, falls, burns, blunt and
penetrating trauma, and affected all body areas, often several body parts at once. Therefore, it
was difficult to analyze the correlation between the site of injury and the joint of onset of the
arthritis. Polyarticular pattern was more common among patients with a history of infection
(74%) compared to those without (58.6%). There was a trend for an association between a
history of infection and a polyarticular pattern at presentation (OR 2, 95%CI 0.95-4.25,
p=0.06). Therefore, it is unlikely that there was a significant misclassification with reactive
arthritis, as the typical pattern of this type of arthritis is oligoarticular.
8.3.7. Vaccination
Since vaccinations were reported to be associated with PsA in a previous study, I included
this exposure in the analysis although up to 30% of the information was missing. The
proportion of patients with missing information was similar in the cases and the controls. I
analyzed the data twice, first by excluding the patients with the missing information from the
analysis and again by considering the missing data as “no exposure” to the particular
vaccination. There were no significant changes in the OR of the two analyses. The following
vaccinations were assessed: hepatitis A and B, pneumococcus, rubella and tetanus. There
were no statistically significant differences in the proportions of exposures to the different
136
types of vaccinations in the two groups during the exposure time window under study after
adjusting for potential confounders.
8.3.8. Emotional stress
Exposures to the following potential emotional stressful events were assessed: death of a
close family member, divorce or separation, moving house, changing job, becoming
unemployed and treatment for depression or anxiety. None of these factors was found to be
associated with PsA in the univariate or multivariate analyses.
8.3.9. Female hormonal exposures
Overall 73 psoriasis and 68 PsA female patients completed the female hormonal
questionnaire. The following events were investigated: age at first menstrual period,
becoming menopausal, use of oral contraceptive pills, use of hormone replacement therapy
and pregnancy. The results are presented in Table 8.3. None of these exposures were
significantly associated with PsA in the univariate or multivariate analyses.
8.3.10. Multivariable logistic regression analysis
The exposures that were found to be associated with PsA after adjustment for age, sex, level
of education, severity and duration of psoriasis were included in a full logistic regression
model to test their independent effect. The results of the analysis are presented in Table
8.4.The following variables remained significantly associated with PsA: smoking (OR 0.47,
95% CI 0.29-0.77), occupations that required lifting heavy weights (OR 2.92, 95% CI 1.56-
5.46) and infections that required antibiotic treatment (OR 1.72, 95% CI 1.01-2.95). Having a
history of an injury was of borderline significance after inclusion in the full logistic
regression model (OR 1.98, p=0.054).
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Table 8.1 - Clinical characteristics of the study population
Variable PsA (N=159) Psoriasis (N=159) P value
Age* (Years ± SD) Sex: Male (%) Ethnicity - Caucasian Family history of psoriasis (%) Family history of PsA (%) Education status (%)
- College/University Duration of psoriasis (Years ± SD) Duration of PsA (Years ± SD) Pattern of arthritis at diagnosis (%)
- Polyarthritis - Oligoarthritis - Spine+polyarthritis - Spine+oligoarthritis - Distal (DIP,PIP) only
Maximal PASI (in the first 3 years of follow-up) Severe Psoriasis (%)
44.9±13.1 89 (55.9%)
124 (81.6%) 70 (44.6%) 13 (8.2%)
122 (77.7%)
17.2±13 3.1±2.2
57 (42.7%) 40 (29.8%) 22 (16.4%) 12 (8.9%) 3 (2.2%) 7.3±9.6
33 (20.9%)
48.4±13.3 86 (54.1%)
137 (86.2%) 71 (44.9%)
5 (3.1%)
127 (79.9%) 18.6±14.5
NA
NA
7.1±7.2
34 (21.4%)
0.02
0.08 0.91 0.09
0.58 0.36
0.83
0.89
* At first visit to the clinic
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*Adjusted for age, sex, duration and severity of psoriasis, level of education. OR- Odds ratio, CI – Confidence Interval,
highlighted rows indicate covariates achieving statistical significance.
Table 8.2 – The association between environmental exposures and PsA (N=318)
Exposure Frequency
PsA
Frequency
Psoriasis
OR
95% CI
p
value
Adjusted
p value*
Car accidents that required medical treatment 6 (3.8%) 5 (3.2%)
1.1 0.32-3.74 0.79 0.89
Fractures 16 (10.1%)
14 (8.9%) 1.2 0.54-2.51 0.72 0.69
Injuries that required medical attention
30 (18.9%) 17 (10.7%) 1.8 0.95-3.49 0.04 0.07
Any other injury 34 (21.4%) 17 (10.7%) 2.1 1.13-4.01 0.01 0.02
Infectious diarrhea 20 (12.7%) 11 (7%) 1.7 0.76-3.79 0.09 0.19
Infections that required Antibiotic
treatment*
53 (34.4%) 37 (23.7%) 1.8 1.01-2.78 0.04 0.05
Infections that required hospitalization 11 (7.2%) 0 (0%) <0.001
Vaccination –Hepatitis A 3 (2.5%) 4 (3.6%) 0.8 0.14-3.07 0.63 0.59
Vaccination – Hepatitis B
29 (22.6%) 19 (16.2%) 1.4 0.75-2.79 0.21 0.27
Vaccination – Pneumococcus 30 (23.6%) 20 (17.1%) 1.4 0.74-2.71 0.51 0.29
Vaccination - Flu 59 (41.5%) 60 (44.7%) 1.0 0.60-1.61 0.58 0.94
Vaccination - Rubella 4 (2.9%) 5 (3.9%) 0.9 0.22-3.46 0.68 0.84
Vaccination - Tetanus
6 (4.8%) 4 (3.7%) 1.0 0.26-3.84 0.66 0.99
Death in the family 41 (26.8%) 44 (28.9%) 1.1 0.63-1.79 0.67 0.82
Divorce/Separation 17 (10.9%) 12 (7.7%) 1.5 0.69-3.35 0.33 0.29
Move house 74 (50%) 64 (43%) 1.1 0.68-1.82 0.24 0.68
Changed job 70 (46.7%) 56 (40%) 1.2 0.73-2.06 0.25 0.44
Becoming unemployed 34 (22.7%) 35 (22.8%) 0.9 0.54-1.66 0.96 0.85
Treated for anxiety/depression 21 (13.9%) 26 (16.8%) 0.8 0.39-1.46 0.47 0.41
Occupational tasks-
- Prolonged standing (>30 min/Hr)
95 (65.7%) 85 (58.2%) 1.3 0.77-2.05 0.22 0.34
- Squatting (>5 min/Hr) 45 (30.8%) 32 (21.9%) 1.7 0.92-2.66 0.09 0.10
- Lifting heavy weights (>100 pound/Hr) 44 (30.1%) 19 (13%) 2.8 1.48-4.98 <0.001 0.001
- Pushing heavy weights (>200 pound/Hr) 29 (19.8%) 18 (12.3%) 1.7 0.83-3.06 0.08 0.16
- Using vibrating tools (>4 Hr/Day) 7 (4.8%) 8 (5.5%) 0.8 0.27-2.32 0.79 0.67
- Repetitive hand movement (>45 min/Hr) 88 (6%) 85 (5.8%) 1.1 0.65-1.69 0.72 0.83
- Forceful gripping (>4 Hr/Day) 28 (19.2%) 21 (14.4%) 1.0 0.64-2.27 0.27 0.56
- Bending the wrist (>4 Hr/Day) 65 (44.5%) 65 (44.5%) 0.9 0.59-1.53 1.00 0.84
Smoking –
- Ever vs. Never Alcohol consumption
- Social vs. None - Daily vs. None
64
88 13
(40.2%)
(28.2%) (4.2%)
89
83 11
(56.0%)
(26.6%) (3.5%)
0.6
0.8 0.8
0.36-0.88
0.49-1.31 0.34-2.03
0.005
0.45 0.52
0.01
0.39 0.68
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Table 8.3 – The Association between female hormonal exposures and PsA
Exposure
PsA (N=68)
Psoriasis (N=73)
OR
95% CI
p value
Adjusted p value*
Age at first period 12.5±1.6
12.7±1.6
0.85
Becoming post-menopausal 13/19 (68.4%)
12/20 (60%)
1.8 0.31-11.49 0.58
0.50
Using oral OCT 24 (35.3%)
26 (35.6%)
1.6
0.71-4.24
0.96
0.23
Using HRT 9 (13.2%)
10 (13.7%)
0.7 0.26-2.33
0.93 0.66
Pregnancy Fertility treatment
13 (19.1%)
5 (7.5%)
13 (17.8%)
7 (9.6%)
1.2
0.9
0.47-2.90
0.26-2.98
0.84
0.63
0.73
0.83
OR – Odds ratio, HRT – Hormone Replacement Therapy OCT - Oral Contraceptive Therapy, CI – Confidence Interval *Adjusted for age, duration and severity of psoriasis and education level.
140
OR – Odds ratio, CI- Confidence Interval, highlighted rows indicate covariates achieving statistical significance
Table 8.4 - Full regression model adjusted for age, sex, education level, duration and severity
of psoriasis
Exposure
OR
95% CI
P value
Smoking status – Ever vs. Never 0.47 0.29-0.77 0.003
Any injury 1.98 0.99-3.96 0.054
Occupational task- Lifting heavy weight 2.92 1.56-5.46 0.0008
Infection that required antibiotic treatment 1.72 1.01-2.95 0.046
Duration of psoriasis (1 year increase) 0.99 0.98-1.01 0.63
Age (1 year increase) 0.99 0.97-1.01 0.29
Sex (Female) 0.86 0.53-1.42 0.57
Severe Psoriasis (PASI>10) 0.89 0.49-1.61 0.70
Level of education (College/University vs. else) 1.18 0.66-2.13 0.56
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8.4. Discussion
In this study the association between a number of a priori chosen environmental factors and
PsA among patients with psoriasis was investigated. The results suggest that there is an
association between PsA and infections, physical trauma including physically demanding
occupational tasks, and smoking.
Only a limited number of studies have investigated environmental risk factors for PsA.
Pattison et al. compared the prevalence of environmental exposures among 98 British PsA
and 163 psoriasis patients over a window of exposure that ranged from 5 to 10 years prior to
the onset of arthritis. Information about environmental exposures was collected through
questionnaires. In their study, physical trauma, rubella vaccination, oral ulcers and moving
house were found to be associated with PsA [76]. Another study by Thumboo et al. used an
administrative database from Rochester, Minnesota and retrospectively evaluated exposure to
several environmental risk factors among 60 PsA and 120 psoriasis patients [77]. They did
not define a specific window of exposure. In that study, pregnancy was found to be
protective of PsA, while steroid use was associated with higher risk of the disease. These
studies had several limitations; first, their small sample size precluded identifying important
associations due to reduced power. The use of an administrative database as a source of
information may have led to underestimation of events that are often not coded or are coded
incorrectly. Pattison’s study included controls from a dermatology clinic while the PsA
patients were recruited from their rheumatologists or by advertisement. The controls were not
evaluated to exclude the presence of PsA. It has been shown that PsA is underestimated
among psoriasis patients, as approximately 18% of the psoriasis patients that were found to
have inflammatory arthritis were unaware of their condition and had never been seen by a
rheumatologist [119]. The problem of misclassification of patients and controls may
significantly affect the results in case-control studies where the outcome (PsA in this case) is
not rare among the control group. Misclassification can decrease the power of the study to
detect significant associations. In the present study, I aimed to minimize the misclassification
of cases and controls by having a rheumatologist evaluate all of the controls to rule out
clinical inflammatory arthritis. The cases and controls were recruited from the same program,
and were evaluated according to the same protocol.
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Several case reports and case series have highlighted the potential role of local trauma in the
pathogenesis of PsA [80, 314, 315]. However, only a few studies have systematically
assessed its role in PsA. Scarpa et al.[118] reported more acute medical events in the PsA
group, including injuries that occurred less than 10 days before the onset of the arthritis,
compared to the group with rheumatoid arthritis. Pattison et al.[76] found that the
occurrence of an injury was more common in patients with PsA, compared to psoriasis
patients. However, Thumboo et al. did not find an association between trauma and PsA[77].
In our study, patients with PsA had double the risk of experiencing injuries in the time
window under study compared to psoriasis patients. However, in the full regression model,
that association became borderline and there was a small reduction in effect size after the
inclusion of injuries in the full regression model, suggesting that injuries were related to one
of the other risk factors in the model. The most likely candidate is physically demanding
occupations that involved lifting heavy weights, because when that covariate was excluded
from the model in order to include those individuals that did not work, injuries were found to
be associated with PsA (data not shown).
Work related physical activity can lead to musculoskeletal disorders and specific
occupational tasks can lead to osteoarthritis [316, 317]. The role of occupational related
physical activities in the pathogenesis of PsA has not been thoroughly investigated. In
ankylosing spondylitis, occupations that require recurrent bending, twisting and stretching
are associated with more functional limitation and higher radiographic spinal scores [318]. In
the present study occupations that were associated with lifting heavy weights were more
common in the PsA group, and recurrent squatting and pushing heavy weights also tended to
be more common in the PsA group. Recurrent microtrauma that are associated with these
physically demanding occupations may lead to greater susceptibility to PsA.
It has been suggested that infections play a role in the pathogenesis of PsA. The role of
infection as a triggering event for arthritis is well established in reactive arthritis that has
some common features with PsA [87]. Infections of the gastrointestinal and urogenital tract
are most commonly associated with reactive arthritis. While the frequency of urogenital tract
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infection was similar in the two groups, infectious diarrhea tended to be more common in the
PsA group. HIV infection that has been linked to severe psoriasis and PsA [95] was found
only in the psoriasis group.
An interesting finding was the decreased prevalence of smoking among the PsA patients.
While smoking is a well-established risk factor for psoriasis [109, 319],in the present study it
was found to be significantly less common among the PsA cases. A similar trend was
reported by Pattison et al. who found a trend to fewer smokers among the PsA patients [76].
In their study the OR (0.68) was similar to the OR in our study (0.5) but was not significant
possibly due to a smaller sample size. Rakkhit et al. reported that the time to development of
PsA decreases with smoking prior to psoriasis onset and increases with smoking after
psoriasis onset [114]. Smoking was also found to have a protective effect in the pathogenesis
of ulcerative colitis, a disorder that bears some clinical and genetic similarities to PsA [320-
322]. The mechanisms underlying these observations are unknown, but suggested
explanations include decreased expression of IL-1β, IL-8 and altered response of the toll-like
receptor pathway to infectious agents among smokers [323, 324]. Another mechanism that
may explain the protective effect of smoking is through the activation of the nicotinic
receptor. Nicotine can activate the alpha7 nicotinic acetylcholine receptor that inhibits
intracellular pro-inflammatory pathways that are associated with development of arthritis
[325, 326]. This pathway is a target for novel therapeutic agents for treatment of arthritis.
There are several limitations to this study. Its retrospective nature may have led to a recall
bias that stems from the tendency of the cases to recall more events that preceded the onset of
arthritis than the controls. I have tried to minimize that problem in several ways. By
approaching patients with a recent onset of arthritis, the study population had a short interval
from the onset of PsA of approximately 3 years. I also avoided linking environmental
exposures and arthritis in the questionnaire, and requested information about events that
occurred in the past 10 years. I have also assessed recall bias by comparing the reported
information about exposure to infections and injuries with available data from a
computerized database that stores medical records from 6 medical centers and outpatient
clinics in Toronto. I aimed to assess whether there has been under-reporting of events by
psoriasis patients. Overall, three out of 3 psoriasis patients reported a previous injury and 0
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out of 2 reported an infection. In the PsA group, only 1 out of 3 reported an injury and 1 out
of 6 reported an infection. Therefore, although these figures are small, it seems that recall
bias is not a threat to the validity of the study as the rates of reporting were not lower in the
psoriasis group.
In summary, in this study the association between several environmental exposures and PsA
was investigated. Infections that required antibiotic treatment and occupations that involved
lifting heavy weights were associated with PsA, while there was an inverse association with
the disease. These results need to be confirmed by a prospective study among psoriasis
patients.
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Chapter 9. Smoking is inversely associated with development of psoriatic arthritis among psoriasis patients
146
9.1. Background
Several models have been suggested to describe the genetic and clinical association between
psoriasis and PsA. Since the majority of the patients with PsA develop inflammatory arthritis
following the onset of the psoriasis, PsA may be considered as a “disease within a disease”
with psoriasis as the parent disease. PsA may be considered a more severe phenotype of the
psoriatic disease that occurs due to a greater number of susceptibility genes or exposure to
environmental factors. Additional susceptibility genes or environmental risk factors on the
background of psoriasis are postulated to lead to development of PsA [73].
Genetic studies have shown a strong association of the MHC region on chromosome 6p with
psoriasis and PsA. In psoriasis, the strongest and most consistently reported association is
with HLA-C*06 [327]. While great effort has been expended in the investigation of genetic
risk factors of these diseases, the role of environmental factors has been less defined,
particularly in PsA. In psoriasis, smoking, alcohol consumption, obesity, stressful life events
and infections have been considered to be potential risk factors [75, 328], while physical
trauma, vaccinations and pregnancies have been suggested as risk factors for PsA [75].
Smoking was associated with psoriasis in several case-control studies [105, 106, 329].
Recently, a prospective cohort study has established the association between smoking and
psoriasis [109]. It has been suggested that the effect of smoking may be modified in the
presence of HLA-C*06.Their co-existence was associated with a significantly higher risk of
developing psoriasis compared to the presence of each factor individually and led to a
significantly higher risk of developing psoriasis [330]. In the Chapter 8 the prevalence of
smoking was found to be decreased among PsA patients compared to psoriasis patients
without arthritis. The aim of this study was to validate these results by analyzing the
information available for two large cohorts of PsA and psoriasis patients and to further
investigate the association between smoking and PsA and its interaction with HLA-C*06
allele.
147
9.2. Methods
9.2.1. Study population In this case-control study, two groups of patients were compared:
Cases – This group included 791 Adult prevalent PsA patients that were all part of the
University of Toronto Psoriatic Arthritis cohort. These patients satisfied the CASPAR criteria
for classification of PsA [22].
Controls – This group included 404 psoriasis patients without arthritis that were recruited
from the University of Toronto Psoriasis Cohort. The diagnosis of psoriasis was confirmed
by a dermatologist. All patients were evaluated by a rheumatologist to exclude inflammatory
arthritis. For the purpose of this study psoriasis patients that developed PsA during the follow
up were removed.
The cohorts were described in details in the methods section (Chapter 4).
Reference population -For comparison, smoking rates among the general population of
Ontario were obtained from the National Population Health Survey (NPHS), a longitudinal
survey conducted by Statistics Canada since 1994. The survey was designed to measure the
health status and determinants of health among Canadians and provides periodic cross-
sectional information. For our study we used the 2005 NPHS survey. The survey collected
information at the provincial level and included 17,276 respondents. The sample was
weighted and the results extrapolated to the population of Ontario. Available summary tables
that were stratified by age groups and gender were used for analysis [331].
9.2.2. Patients selection
PsA patients included in the study were registered in the PsA clinic between 1978 and 2010.
Psoriasis patients included in the study were registered in the Psoriasis cohort between 2006
and 2010. PsA and psoriasis patients within the database with available information about
their smoking status at the time of the diagnosis of PsA and at their first visit, respectively,
were included in the study.
148
9.2.3. Determination of smoking status
Smoking status was categorized as current smoker, past smoker and life-time non-smoker. A
smoker was defined as a person who smoked ≥ 1 cigarette per day for at least 1 year.
At enrolment, all study participants were asked to report their current smoking status and
what was their smoking status at the time of the diagnosis of PsA (for PsA patients) and at
the time of the diagnosis of psoriasis (for psoriasis patients). Patients were also asked to
report the number of cigarettes and smoking years at the above defined time point.
The reference dates for determination of smoking status were: the time of diagnosis of PsA
(for PsA patients) and the first visit (for psoriasis patients). These dates were chosen because
information about potential confounders was available only from the first assessment date.
Information about potential confounders was retrieved from the database including: age, sex,
level of education, duration of psoriasis, severity of psoriasis and alcohol consumption.
Similar to smoking status, the reference dates used to determine alcohol consumption were:
prior to the diagnosis of PsA (for PsA patients) and at first visit (for psoriasis patients).
Alcohol consumption was categorized as daily (≥ 1 alcoholic beverage a day), social (≥ 1
alcoholic beverage a week) and non-drinker. Since smoking is known to be associated with
the severity of psoriasis; patients were categorized as those with severe psoriasis (Psoriasis
Area Severity Index (PASI) ≥ 10 or mild to moderate psoriasis (PASI< 10) based on the
maximal PASI score in the first 3 years of the follow-up.
9.2.4. HLA-C*06 Allele typing
HLA-C*06 is the strongest known risk factor for psoriasis [327]. Recently, it was suggested
that the interaction between HLA-C*06 and smoking may play a role in the susceptibility to
psoriasis [330]. We therefore investigated whether the effect of smoking is modified by this
allele. For HLA typing, extracted genomic DNA was amplified by PCR using locus specific
primers for the HLA-C locus. PCR amplicons were identified by Sequence Specific
149
Oligonucleotide (SSO) probes. Ambiguous results were resolved using Sequence Specific
Primers (PCR-SSP).
9.2.5. Statistical analysis
Baseline descriptive statistics were computed with continuous variables summarized by their
means and standard deviations and categorical variables summarized by proportions. The
proportions with exposure to smoking were compared between the PsA and psoriasis patients
through calculation of the Odds Ratio (OR) and the corresponding 95% Confidence Interval
(CI).
In addition, the prevalence of smokers in the PsA and psoriasis groups was assessed within
the same gender and age groups as in the NPHS survey. The prevalence of smoking status in
each group was then compared with data on smoking rates from the NPHS through
Standardized Prevalence Ratios (SPRs). In this analysis only PsA patients that were
diagnosed between 1999 and 2010 were included to avoid bias due to changing trends in
cigarette smoking over the years.
In order to avoid bias due to ecological trends in smoking, an additional analysis was
performed in a subset of the study population that included 404 PsA and psoriasis patients
matched by age (within 5 years) and gender. Each psoriasis patient was assigned a reference
year that was derived from the year of onset of PsA for the corresponding case. From the
available information we were able to infer the years of exposure to smoking for 381 of the
matched psoriasis patients. I have then performed a conditional logistic regression for
matched pairs data to determine the association between smoking status and PsA compared
to psoriasis.
In order to adjust for potential confounders, a logistic regression model that included all
observations was performed. The model included as a key predictor smoking status and the
following potential confounders as covariates: age, sex, duration of psoriasis, level of
education, alcohol consumption and severity of psoriasis. Multivariate logistic regression
analysis was used to assess the independent association between smoking and PsA. A
sensitivity analysis was performed to assess whether the exclusion of patients with missing
150
information of psoriasis severity and alcohol consumption from the multivariate analysis
affected the results by the removal of these variables from the regression model.
Only Caucasians were included in the analysis of the interaction between HLA-C*06 and
smoking. The proportions with smoking exposure were compared between PsA and psoriasis
after stratification by HLA-C*06 status using a LR test. A logistic regression analysis was
performed to assess the statistical interaction between HLA-C*06 and smoking.
The significance level was set at 0.05 for a 2-sided test. Statistical analysis was performed
using SAS 9.2© statistical software.
151
9.3. Results
Overall 791 PsA and 404 psoriasis patients with complete information were included in the
study. Most of the PsA patients enrolled the cohort over the last decade (20.6% from 1978 to
1989, 27% from 1990 to 1999 and 52.4% from 2000 to 2010). Overall, 257 PsA patients
were not included in the analysis due to missing information about their smoking status. The
majority of these patients were evaluated at the establishment of the PsA cohort, at that time
such information was not routinely collected. Comparison of the excluded PsA patients to
those included in the study revealed no significant differences in demographic and clinical
characteristics apart from gender. The proportion of females was slightly higher in the PsA
patients with missing information (49.8% vs. 43.8%). The median time interval from the
onset of PsA until the first assessment in the clinic was 3 years.
The PsA patients were on average 10 years younger and had a shorter duration of psoriasis
(Table 9.1). These differences are related to the use of different reference dates for
determination of smoking status: year at onset of PsA (ranged from 1951 to 2010) for the
PsA group vs. first assessment date for the psoriasis group (ranged from 2006 to 2010). PsA
patients had more severe psoriasis as reflected by maximal PASI scores. There were no
differences in the male to female ratio and age at onset of psoriasis. Among the psoriasis
patients, 7 (1.7%) were using biologic agents and 23 (5.7%) were using immunosuppressive
agents.
9.3.1. Comparison of smoking status among PsA and psoriasis to the general population
The smoking rates among psoriasis and PsA patients were compared to the general
population in Ontario through SPRs. The SPRs for smokers among males with psoriasis and
PsA were: 1.12 (95% CI 1.00-1.26) and 1.27 (95% CI 1.09-1.48), respectively. The SPRs for
smokers among females with psoriasis and PsA were: 1.29 (95% CI 1.12-1.47) and PsA 1.28
(95% CI 1.14-1.43), respectively.
152
9.3.2. Comparison of smoking status between PsA and psoriasis
The distribution of smoking status among patients with PsA and psoriasis alone is presented
in Table 9.2. The proportions of current smokers and past smokers were higher in the
psoriasis patients than the PsA patients (current smokers 26.7% vs. 21.9% and past smokers
30.2% vs. 23.4%, p=0.0006, respectively). The psoriasis group also smoked, on average, for
more years than the PsA group; however, these results may be attributed to the mean older
age of psoriasis patients. There was no difference in the mean number of cigarettes smoked
per day between the PsA and the psoriasis group; however, this information was available
only for a much smaller sample, particularly in the PsA group (N=226).
9.3.3. Analysis for matched pairs data
An analysis for matched pairs was performed to determine whether the association between
smoking and PsA was due to ecological trends in smoking. Of the 404 matched pairs of PsA
and psoriasis patients I was able to infer the smoking status at the reference year for 381
psoriasis patients. The results confirmed the inverse association between current smoking and
PsA compared to psoriasis patients (Table 9.3). In the conditional regression analysis
“current smoking” was less common in PsA compared to psoriasis patients (OR 0.57, 95%
CI 0.39-0.82, p=0.002). In contrast, the prevalence of past smokers was higher in patients
with PsA compared to those with psoriasis (OR 1.92, 95% CI 1.2-3.07, p=0.007). This
change in the direction of the association for the “past smoking” group may be related to the
different reference dates considered for the determination of the status of smoking in the
analysis presented in Table 9.2.
9.3.4. Multivariate regression analysis
A multivariable logistic regression analysis was performed to adjust for potential
confounders; the results are presented in Table 9.4. After adjustment, current smoker
compared to life-time non-smoker remained inversely associated with PsA (OR 0.54, 95% CI
0.38-0.77), while past smoker vs. life-time non-smoker was no longer significant (OR 0.87,
153
95% CI 0.62-1.21). The increase in OR in the “past-smoker” group suggests a confounding
effect of one or several of the co-variates included in the model. These results were not
appreciably changed after removal of alcohol consumption and psoriasis severity from the
regression model to allow the inclusion of patients with missing data in the analysis.
9.3.5. The interaction between HLA-C*06 and smoking
In the previous sections HLA-C*06 was found to be decreased in patients with PsA
compared to those with psoriasis alone. I have assessed whether the effect of smoking is
modified by HLA-C*06 status. Overall 572 PsA and 376 psoriasis Caucasians were included
in the analysis. In a subgroup analysis (Figure 9.1), the inverse association between smoking
and PsA compared to psoriasis was found only among those who were HLA-C*06 negative
(current smoking vs. non-smoker: OR 0.46, 95% CI 0.3-0.7, p=0.0004 and past smoker vs.
non-smoker: OR 0.46, 95% CI 0.3-0.7, p=0.0003). Among those who were HLA-C*06
carriers, there was no significant difference in smoking status between the two groups
(current smoker vs. non-smoker OR 0.97, 95% CI 0.6-1.7, p=0.91 and past smoker vs. non-
smoker OR 0.96, 95% CI 0.6-1.6, p=0.87). The interaction between HLA-C*06 and smoking
status was statistically significant in a logistic regression analysis (HLA-C*06-current
smoking OR 2.11, 95% CI 1.04-4.32, p=0.039, HLA-C*06-past smoking OR 2.07, 95% CI
1.05-4.11, p= 0.035) suggesting a departure from a multiplicative model.
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*Determined at the time of the diagnosis of PsA (for PsA patients) and at first assessment (for psoriasis patients).
Table 9.1 – Demographic and clinical characteristics of the study population
Variable Psoriasis (N=404) PsA (N=791) P value
Age (Years ± SD)* 46.3 ±13.1 36.7 ±13.0 <0.001 Sex: Female (%) 177 (43.8%) 335 (41.6%) 0.66 Age at Onset Ps 30.2(16.0) 28.6 (14.6) 0.08 Age at Onset PsA --- 36.7 (13.0) Duration of Psoriasis* 16.1 ±14.1 8.5 ±10.2 <0.001 Level of Education (%)
- College/University
311(77.2%)
499 (66.3%)
<0.001
PASI (Max. in 3 years)* Severe Psoriasis
6.4 ±6.1 64 (16.1%)
8.7 ±10.1 220 (28.8%)
<0.001 <0.001
Psoriatic nail lesions
Alcohol consumption - Daily
- Social
288 (72.2%)
38 (9.4%) 252 (62.5%)
511 (92.6%)
85 (11.6%) 428 (58.3%)
<0.001
0.53 0.64
Skin lesions prior to arthritis --- 560 (72.3%)
Arthritis pattern at diagnosis - Distal joints only - Oligoarthritis
- Polyarthritis
- Axial only
- Axial+Distal joints
- Axial+Oligoarthritis
- Axial+Polyarthritis
--- 58 (8.1%)
253 (35.2%) 256 (35.6%)
28 (3.9%) 6 (0.8%)
41 (5.7%) 77 (10.7%)
Total Damaged Joints --- 2.9 ±7.5
155
* Psoriasis (N=209) and PsA (N= 226)
Table 9.3 – Smoking status at the time of the diagnosis by matched pairs (N=381 pairs)
PsA group
Never smoked Current smoker Past smoker
Psoriasis
group
Never smoked 138 (32.6%) 37 (9.7%) 38 (10%)
Current smoker 72 (18.9%) 27 (7.1%) 26 (6.8%)
Past smoker 16 (4.2%) 11 (2.9%) 16 (4.2%)
Grey box – concordant pairs. White box – discordant pairs.
Table 9.2 – Smoking characteristics among psoriasis and PsA patients
Psoriasis (N=404) PsA (N=791) P value
Smoking status
- Never smoked
- Current
- Past
174 (43%)
108 (26.7%)
122 (30.2%)
433 (54.7%)
173 (21.9%)
185 (23.4%)
0.0006
No. of Cigarettes Smoked (per day)*
- 1-9 cigarettes
- 10-19 cigarettes
- More than 20 cigarettes
59 (33.3%)
53 (29.9%)
65 (36.7%)
73 (31.1%)
54 (23.2%)
106 (45.5%)
0.15
No. of Years Smoked* 19.4 (12.6) 16.3 (11.4) 0.0064
156
*Determined at the time of the diagnosis for PsA and at first visit for psoriasis.
Table 9.4 – The association between smoking status and PsA (N=701) compared to psoriasis alone (N=400) by logistic regression analysis
Univariate Model Multivariate Model
Covariate OR 95% CI P-value OR 95% CI P-value
Smoking Status:
- Current vs. Life-time Non- Smokers
0.65
0.48-0.89
0.007
0.54
0.38-0.77
0.0007
- Past vs. Life-time Non- smokers 0.66 0.49- 0.88 0.006 0.87
0.62-1.21
0.41
Age -1 yr increase* 0.95 0.94 - 0.96 <0.0001 0.95 0.94-0.96 <0.0001
Sex: Male vs. Female 1.06 0.82 - 1.36 0.67 1.04 0.78-1.39 0.78
Duration of Psoriasis -1 yr increase* 0.95 0.94- 0.96 <0.0001 0.96 0.95-0.97 <0.0001
Alcohol Consumption:
- Social vs. None
0.94
0.69-1.21
0.53
0.98
0.71-1.35
0.91
- Daily vs. None 1.11 0.71-1.75 0.64 1.57 0.94-2.63 0.08
Severe psoriasis: Yes vs. No
2.06 1.51-2.82
<0.0001
1.83 1.29-2.60
0.0007
Level of education - College/University
0.58
0.44-0.77
0.0001
0.58
0.42-0.80
0.0009
157
158
9.4. Discussion
In this study the association between smoking and PsA was analyzed. I have shown that
smoking is inversely associated with PsA compared to psoriasis patients without arthritis.
The prevalence of smoking among patients with PsA was higher than in the general
population. I have also found an interaction between smoking and HLA-C*06, as the inverse
association between smoking and PsA was present only among patients who were HLA-
C*06 negative.
The role of smoking as a risk factor for psoriasis is well established. The first studies drew
attention to the linkage between smoking and palmo-plantar pustular psoriasis [104]. Since
then several case-control studies have established the association between smoking and
psoriasis vulgaris [105-108], the most common type of psoriasis. The most conclusive
epidemiological evidence for the causative role of smoking in the pathogenesis of psoriasis
was provided by the Nurses’ Health Study II that showed, for the first time, a strong
association between smoking and incident psoriasis in a large population-based prospective
cohort study [109]. In this study the relative risk of psoriasis was 1.8 for current smokers and
1.4 for past smokers. The dose-effect relation between smoking intensity and psoriasis risk
also supports its etiological role. Smoking has also been associated with more severe
psoriasis and poor response to treatment [110, 111]. The independent association between
smoking and psoriasis remained significant even after adjustment for another potential
confounder, excessive alcohol consumption, that is also increased among psoriasis patients
[112, 113]. In the present study the SPRs of smoking were higher in men and women with
psoriasis and PsA compared to the general population.
One of the challenges in investigating risk factors for PsA is selecting the appropriate control
group. When comparing PsA to healthy controls, it is uncertain whether any significant
association is related to the skin or the joint disease. Therefore, psoriasis patients without
arthritis comprise a more informative control group. There are few studies that have
investigated environmental risk factors for PsA among psoriasis patients. In a study by
Pattison et al., smoking was less common among patients with PsA compared to those with
159
psoriasis alone [76]; however it did not reach statistical significance, possibly due to the
small sample size. Rakkhit et al. found a temporal association between psoriasis, PsA and
smoking [114]. They reported that the duration of time from the onset of psoriasis to
development of PsA decreases with a history of smoking prior to psoriasis onset and
increases with smoking after psoriasis onset. On the other hand, a recent study from
Singapore has found similar proportions of smokers among patients with psoriasis and PsA
patients [38]. In the present study, current smoking was associated with PsA even after
adjustment for potential confounders. Previous studies were also limited by a potential
misclassification of cases and controls since the exclusion of PsA among psoriasis patients
was based upon self-report. Misclassification may decrease the power to detect significant
association. One of the strengths of this study is the careful phenotyping of both cases and
controls by experienced rheumatologists.
The interaction between genetic and environmental risk factors has received only minimal
attention in genetic studies of psoriasis. It has been suggested that HLA-C*06 may modify
the effect of smoking and lead to an increased risk for psoriasis of about 11-fold over that for
non-smokers without HLA-C*06 [330], much higher than the combined effect of both risk
factors. Statistical interaction in epidemiology refers to a model dependent concept and is
considered to be present on a multiplicative scale when the joint effect of risk factors differs
from the product of the effect of the individual factors [332]. In other words, the effect of one
risk factor depends on the presence of the other risk factor. Biological interaction, on the
other hand, measures the joint effect of two risk factors that act together in the same causal
mechanism of a disease. In this study I analyzed the statistical interaction between HLA-
C*06 and smoking. The inverse association between smoking and PsA was present only
among patients who were HLA-C*06 negative. These findings were explained by identifying
a statistically significant interaction term between HLA*06 and smoking status in a logistic
regression analysis. The interaction between HLA-C*06 and smoking may explain the lack
of association between smoking and PsA in studies that recruited the entire study population
from dermatology clinics, as opposed to our study that enrolled cases and controls from two
different sources though within the same centre [38]. By sampling only patients with severe
psoriasis, it is likely that the proportion of HLA-C*06 carriers was high, and the studies were
thus underpowered to detect an association between smoking and PsA.
160
The inverse association between smoking and PsA is intriguing as smoking is a known risk
factor for numerous diseases. Smoking is associated with increased risk of psoriasis as well
as rheumatoid arthritis [333]. On the other hand, smoking has a protective effect against
ulcerative colitis that bears some clinical and genetic similarities to PsA [320]. Current
smokers have a 40% lower risk of developing ulcerative colitis [321]. Smoking also reduces
the rate of flare-ups, hospitalizations and use of steroids in ulcerative colitis [322]. The
mechanisms underlying these observations are unknown, but suggested explanations include
decreased expression of IL-1β, IL-8 and altered response of the toll-like receptor pathway to
infectious agents among smokers [323, 324]. Another mechanism that may explain the
protective effect of smoking is through the activation of the nicotinic receptor. Nicotine can
activate the alpha7 nicotinic acetylcholine receptor that inhibits intracellular pro-
inflammatory pathways that are associated with development of arthritis [325, 326]. This
pathway is a target for novel therapeutic agents for the treatment of arthritis.
There are several limitations to the study that are inherent to a case-control study. First, recall
bias is one of the limitations of a retrospective study. In this study, PsA patients had to report
their smoking status at the time of the diagnosis, while the psoriasis patients had to report
their current smoking status, therefore it is possible that the information about smoking status
was less accurate for the PsA patients. However, the information has been collected
systematically in all patients upon their first visit to the clinic which was usually close to the
time of the diagnosis of PsA and in that way, minimized recall bias. The reliability of a
retrospective report of smoking status at a specific time-point has been extensively
investigated. It has been shown that people can reliably recall aspects of first tobacco use
occurring on average 30 years before the time of assessment [334]. Other types of biases in
report of smoking habits are more difficult to control. They may depend on factors such as:
socio-cultural aspects associated with the acceptability of smoking, prior knowledge about
the association between smoking and the disease and on the amount of emphasis that is laid
by the investigator on the smoking question. These sources of potential bias are associated
with any case-control study and are acknowledged as one of the limitation of this type of
study.
Another potential threat to the validity of the study is related to ecological trends in smoking
habits over time, since different reference dates were used for determination of smoking
161
status. However, given the trend of decline in smoking over the past decades [335], the true
difference in smokers between the groups may be underestimated. Furthermore, I have
confirmed the inverse association between current smoking at the time of the diagnosis and
PsA compared to psoriasis in a subset of patients in a matched-pair analysis.
One of the difficulties in designing a case-control study is the selection of an appropriate
control group that would reflect the source population from which the cases have originated.
In this study, all of the controls had psoriasis and came from the same geographic region as
the cases. Since most of the patients came from dermatology clinics they reflect a moderate
to severe spectrum of the disease. It has been reported that patients with severe psoriasis are
at higher risk of developing PsA [24], therefore this group of psoriasis patients serves as an
appropriate control group, although the generalizability of the results may be limited. The
study population was well phenotyped and I was able to adjust for differences between the
groups in the statistical analysis. However, I am aware that there may be other confounders
that were not considered and may explain the differences in smoking prevalence between the
two groups.
Finally, I was unable to analyze the exact temporal relation between smoking and the onset
of psoriasis and PsA. There were conflicting results with regards to the association between
past smoking and PsA compared to psoriasis patients: in the univariate analysis past smoking
was less frequent among patients with PsA, however in the multivariate analysis the
association between past smoking and PsA was not statistically significant while in the
matched-pair analysis past smoking was increased among PsA patients compared to psoriasis
patients. These differences in the direction of association may be explained by the lag
between reference dates used to determine smoking status. I cannot determine the effect of
smoking cession on the risk of developing PsA in patients with psoriasis. This question
would require a prospective cohort study design with an appropriate statistical analysis.
In summary, current smoking was less frequent in patients with PsA compared to those with
psoriasis alone. Furthermore, the findings support a possible interaction between HLA-C*06
and smoking. Among patients who are HLA-C*06 positive, the association between PsA and
smoking was smaller than in HLA-C*06 negative and not statistically significant. Additional
162
studies that investigate environmental risk factors for PsA and gene-environment interaction
are required to confirm these results.
163
Chapter 10. General Discussion
The nature of the relationship between PsA and psoriasis is not completely clear. PsA can be
viewed as a “disease within a disease” and psoriasis patients can be considered as a
population at risk for developing PsA. There has been limited information in the literature
about the clinical, environmental and genetic risk factors for PsA among patients with
psoriasis. The present study aimed to fill this gap by directly comparing two well
characterized cohorts of PsA and psoriasis patients from the same geographic area.
The incidence of PsA among patients with psoriasis was investigated, for the first time, in a
prospective cohort of psoriasis patients. The estimated annual incidence of PsA of 1.87 cases
per 100 psoriasis patients was higher than previously reported. Although several
epidemiologic studies have assessed the incidence of PsA in the general population, there is
limited information about the incidence of PsA among psoriasis patients. In a population
based retrospective study that used medical records to confirm the diagnosis of PsA, Wilson
et al., from Rochester, estimated that the cumulative incidence of PsA was 3.1% after 10
years from the onset of psoriasis and 5.1% after 20 years [33]. Several reasons, other than
geographic and ethnic differences, may explain the differences in the cumulative incidence
between our study and those of the group from Rochester. First, Wilson et al. identified cases
based on a retrospective review of computerized medical records, while in our study each
subject was examined prospectively by a rheumatologist. Underestimation and
misclassification of cases are major threats in epidemiological studies that rely on
computerized data registries, particularly since widely accepted classification criteria for PsA
were unavailable until recently. Furthermore, underestimation of PsA among psoriasis
patients may be even more pronounced. In a German study, approximately 18% of the
psoriasis patients that were found to have inflammatory arthritis were unaware of their
condition and had never seen by a rheumatologist [119]. A referral bias may also account
for the difference, since by relying on medical records some of the mild cases of arthritis may
not come to medical attention. The prospective design of this study and careful assessment
of each subject allowed us to diagnose early and milder cases that potentially could have
escaped medical attention. Finally, the differences in the source populations of psoriasis
164
patients may explain some of the differences. The present study included mainly patients
with moderate to severe psoriasis from dermatology clinics, while the Rochester study
assessed patients with a wider range of psoriasis severities. Several studies in the past
suggested that the severity of psoriasis is associated with higher risk of developing PsA [24,
262]. A recently published study from Europe that assessed the presence of PsA among
psoriasis patients attending dermatology clinics reported a cumulative incidence of PsA that
was closer to that found in our study (20.5% of patients developed PsA after 30 years from
the diagnosis of psoriasis) [32]. In summary, our results of 4 years of follow-up of a newly
established cohort of psoriasis patients without arthritis suggest that the incidence rate of PsA
may be higher than previously reported, particularly among patients with moderate to severe
psoriasis.
In contrast to the prevailing notion that the greatest risk of developing PsA is during the first
years following the onset of psoriasis, the retrospective analysis of data pooled from both the
PsA and psoriasis cohorts demonstrated that the risk of developing PsA among patients with
psoriasis does not change over time [336]. There are limited data about the incidence of PsA
among psoriasis patients. The mean duration of time from psoriasis to PsA is approximately
7 years [300]. Therefore it was assumed by experts in the field that the risk of developing
PsA is highest in the first years following the onset of psoriasis. However, there are no actual
data in the literature to support that notion. A recent retrospective study from Germany
supports our findings. The authors have found that the rate of PsA was constant over time
[32].
A primary aim of this thesis was to identify PsA specific genetic markers among HLA
alleles. The previously reported association between HLA-B*27 and PsA was confirmed.
Furthermore, HLA-C*06 that was associated with PsD and PsA compared to healthy controls
was significantly less frequent in PsA patients compared to those with psoriasis alone. Two
additional HLA alleles, HLA-B*08 and HLA-B*38 were identified as potential genetic
markers for PsA in patients with psoriasis. HLA-B*39 may be a potential marker for axial
PsA.
165
Our group has previously assessed the association between HLA antigens and PsA compared
to patients with psoriasis alone using serological methods [32]. In that study, HLA-B27 and
B7 antigens conferred an increased risk for PsA, while HLA-B17 (split to B*57 and B*58),
Cw6 and DR7 occurred with a lower frequency among PsA than in those with psoriasis
alone. HLA-B38 and B39 were associated with polyarthritis while HLA-B27, Cw2 and
DRw52 were associated with axial involvement. In the present study I was able to replicate
several of these associations using an independent sample, this time using molecular
techniques to detect HLA alleles.
In both population and family based analysis I have found that HLA-B*27 is a strong genetic
marker for PsA among psoriasis patients. The absence of a detectable association between
HLA-B*27 and the psoriasis group compared to healthy individuals suggests that B*27 is not
a marker for skin disease. Previous studies in different ethnic groups have consistently shown
that HLA-B*27 is an independent risk allele for PsA that is unrelated to the skin disease [41,
285-287]. However, the prevalence of HLA-B*27 among PsA patients in our study was
19.2% and ranged from 20 to 35% in previous studies, clearly much lower than its
prevalence in AS (80-95% of the patients) [288]. Therefore, this allele can only account for a
small proportion of the total genetic risk of PsA.
HLA-C*06 allele confers a strong risk for psoriasis [180, 201, 202]. In the present study, the
frequency of HLA-C*06 was significantly higher in both psoriasis and PsA patients
compared to controls. However, the frequency of HLA-C*06 was lower in PsA patients
compared to psoriasis. Other studies have found a lower prevalence of HLA-C*06 among
PsA compared to psoriasis patients [287, 292]. In addition, psoriatic nail involvement, a
clinical marker for an increased risk of PsA, is more common among HLA-C*06 negative
psoriasis patients [178]. Furthermore, HLA-C*06 has been found to increase the psoriasis-
arthritis latency period [293]. In this thesis I have also found that HLA-C*06 allele was
associated with a prolonged interval of time from psoriasis to PsA that supports its
“protective” effect. HLA-C*06 carriers had more than double the interval of time from the
onset of psoriasis to PsA. It is challenging to explain the strong association of HLA-C*06
with psoriasis while its lower frequency in PsA compared to psoriasis. HLA-C*06 may be
involved in different immunological mechanisms that lead to the joint and the skin disease.
166
For example, it may be: antigen presentation and activation of the adaptive immune system
for the skin and inhibition of the innate immune system through interaction with Killer cell
Immunoglobulin like Receptors (KIRs) on NK cells in the joints. The recently reported
association between ERAP1 gene polymorphisms and psoriasis only among patients carrying
the HLA-C*0602 risk allele, suggests that antigen presentation may play a role in the
pathogenesis of psoriasis, as ERAP1 is involved in MHC class I peptide processing [252].
Another explanation may be related to genetic heterogeneity of psoriasis. It is possible that
different HLA alleles are associated with a sub-type of psoriasis that is more likely to
develop PsA. Therefore, although the phenotype of psoriasis is similar, one HLA allele is
associated only with skin disease (HLA-C*06) while the other is associated with both skin
and joint involvement. A potential candidate is HLA-C*12, that is associated with both PsA
and psoriasis compared to healthy controls, and was reported in the past to be associated with
psoriasis among Caucasians [170].
Two additional alleles were significantly associated with PsA compared to psoriasis in
multivariate analysis: HLA-B*08 and HLA-B*38. HLA-B*38 was strongly associated with
PsA compared to both psoriasis and controls. HLA-B*38 has been reported to be more
frequent among PsA patients compared to those with psoriasis alone in several small studies
[292, 295]. Additionally, HLA-B*38 has been associated with more peripheral joint
involvement [296]. However, in the subgroup analysis of our study, an association of HLA-
B*38 was detected in the group with axial PsA but not in the group with arthritis restricted to
the peripheral joints. HLA-B*38 is part of a common haplotype along with HLA-C*12. In
our study, this haplotype was associated with PsA compared to psoriasis. A recent GWAS
suggested that HLA-C*12 is a risk allele for psoriasis among Caucasians [170]. The family
based data in our study supports the association between HLA-B*38 and C*12 and PsA,
however in contrast to the population based data, a stronger association was observed with
HLA-C*12 compared to HLA-B*38. Given the very high LD between alleles in our sample,
it is very difficult to dissect the independent effect of each one of them. Larger samples sizes
may be required for that purpose.
HLA-B*08 has not been previously reported to be associated with PsA. In our study, HLA-
B*08 frequencies were higher among PsA compared to psoriasis, but not different from
167
healthy controls. However, these results were not confirmed in our family based association
study. In the literature, the extended haplotypes across HLA class I and II genes, A*01-
C*07-B*08-DRB1*03-DQA1*05-DQB1*02-DPA1*01-DPB1*04, has been associated with
increased TNF production following rubella vaccination [297]. TNF is a major pro-
inflammatory cytokine in PsA and rubella vaccine was suggested to be a risk factor for
development of PsA among psoriasis patients [76]. HLA-B*08 is part of an ancestral
haplotype (8.1 AH) that also includes HLA-A*01-C*07-DRB1*03 and TNF-308A [284].
There have been conflicting results with regard to the association between PsA and the TNF-
308A polymorphism, which is part of the 8.1 AH [210-212]. Previous analysis of our cohort
combined with PsA patients from Newfoundland did not find an association with TNF-308A
polymorphism [223], however, these patients were compared to healthy controls and not to
psoriasis patients. In summary, HLA-B*08 may be an independent genetic marker for PsA
among psoriasis patients. However, its strong LD with other HLA alleles and relevant genes
in the MHC region precludes a more confident conclusion.
HLA molecules are expressed on target cells and play an important role in the activation of
NK cells through their interaction with KIR molecules. Since I have found that the HLA
class I genes are strongly associated with susceptibility to PsA and psoriasis [153], the
biological interaction between selected activating KIR genes and their HLA ligands in the
susceptibility to PsA was assessed. Several “functional” models that represent the likely
outcomes of HLA /KIR interactions were tested.
Overall, KIR genes showed only weak associations with both PsA and psoriasis. The
frequency of the inhibitory KIR2DL1 gene was lower in PsA patients compared to those with
psoriasis alone. However, this gene was observed in nearly all individuals, therefore, this
difference can only account for a small fraction of the risk. The activating KIR2DS2 gene
was found to be associated with PsA compared to healthy controls. However, although it was
more frequent in the PsA group compared to the psoriasis patients, the association was not
significant. This may be due to the existence of a weak association since the study was not
powered to detect an association with an OR of less than 1.5. The direction of the association
was in accordance with our expectation: the activating KIR2DS2 gene was more frequent in
PsA while the inhibitory KIRs were less frequent in PsA compared to psoriasis. In the
168
literature, KIR2DS1 and KIR2DS2 have been associated with susceptibility to psoriasis and
PsA. It has been reported that KIR2DS1 is associated with psoriasis compared to healthy
controls among Japanese [229] and Caucasians from Brazil, Sweden and Poland [230, 307,
308]. These results concur with our findings in the multivariate analysis that showed that
KIR2DS1 in the presence of its HLA-C group 2 ligand is associated with psoriatic disease,
suggesting that the strongest association of this gene is with the skin and not the joint disease.
Two models of KIR-HLA interaction were investigated in this study. The combination of
HLA-Bw4-80I alleles and KIR3DS1 gene was found not to be associated with PsA or PsD.
This interaction has not been investigated in PsA previously. However, it was suggested to be
important in the susceptibility to AS. Two studies have found a higher frequency of
KIR3DS1 with a lower frequency of the inhibitory KIR3DL1 among AS patients [234, 309,
310]. However, a large study from the UK reported a lack of an association between
KIR3DS1 and AS [235]. The second interaction that was tested was between the activating
genes KIR2DS2 and KIR2DS1 and their corresponding HLA-C ligands. It was hypothesized
that the association with the activating KIRs would be greatest in the presence of their HLA
ligands. A significant association was found between KIR2DS2 in the presence of its ligand
HLA-C group 1, in which higher frequencies were found in the PsA group, compared to
psoriasis alone. However, that gene combination was not associated with PsD, while
KIR2DS1 in the presence of its ligand HLA-C group 2 was associated with PsD. These
associations may indicate that KIR2DS2 is a susceptibility gene for PsA among psoriasis
patients, while KIR2DS1 is associated only with the skin disease. This model is supported by
the “dual” associations that were found for HLA-C*06 and PsA: a risk factor for psoriasis
alone but an inverse association (“protective” effect) for PsA compared to psoriasis. Since
HLA-C*06 is included in HLA-C group 2 that is the ligand for KIR2DS1, its interaction with
KIR2DS1 may be important in the susceptibility to psoriasis. However, since psoriasis
patients who carry HLA-C*06 are less likely to have HLA-C group 1, simply because one of
their HLA-C alleles already belongs to HLA-C group 2, they are less likely to have a
compatible ligand for the KIR2DS2 that may be a risk gene for PsA among psoriasis patients.
Genetic factors alone cannot account for all cases of PsA. I hypothesized that psoriasis
patients who carry susceptibility genes for arthritis develop PsA after exposure to triggering
169
environmental factors [74]. In this thesis the association between several putative
environmental risk factors and the occurrence of PsA among psoriasis patients was
investigated. The results suggest that there is an association between PsA and infections,
physical trauma including physically demanding occupational tasks, and smoking [337].
Only a limited number of studies have investigated environmental risk factors for PsA.
Pattison et al. have found that physical trauma, rubella vaccination, oral ulcers and moving
house were associated with PsA compared to psoriasis alone [76]. Another study by
Thumboo et al. [77] have found that pregnancy was protective of PsA, while steroid use was
associated with higher risk of the disease. These studies had several limitations including:
their small sample size, the use of an administrative database as a source of information that
may have led to underestimation of events and potential misclassification of cases and
controls as psoriasis patients were not evaluated to exclude the presence of PsA. In the
present study, I aimed to minimize the misclassification of cases and controls by having a
rheumatologist evaluate all of the controls to rule out clinical inflammatory arthritis. The
cases and controls were recruited from the same program, and were evaluated according to
the same protocol.
Several case reports and case series have highlighted the potential role of local trauma in the
pathogenesis of PsA [80, 314, 315]. However, only a few studies have systematically
assessed its role in PsA. Scarpa et al. [118] reported more acute medical events in the PsA
group, including injuries that occurred less than 10 days before the onset of the arthritis,
compared to the group with rheumatoid arthritis. Pattison et al. [76] found that the
occurrence of an injury was more common in patients with PsA, compared to psoriasis
patients. However, Thumboo et al. did not find an association between trauma and PsA [77].
In our study, patients with PsA had double the risk of experiencing injuries in the time
window under study compared to psoriasis patients. The role of occupational related physical
activities in the pathogenesis of PsA has not been thoroughly investigated. In ankylosing
spondylitis, occupations that require recurrent bending, twisting and stretching are associated
with more functional limitation and higher radiographic spinal scores [318]. In the present
study occupations that were associated with lifting heavy weights were more common in the
PsA group, and recurrent squatting and pushing heavy weights also tended to be more
170
common in the PsA group. Recurrent microtrauma that are associated with these physically
demanding occupations may lead to greater susceptibility to PsA.
It has been suggested that infections play a role in the pathogenesis of PsA. The role of
infection as a triggering event for arthritis is well established in reactive arthritis that has
some common features with PsA [87]. Infections of the gastrointestinal and urogenital tract
are most commonly associated with reactive arthritis. While the frequency of urogenital tract
infection was similar in the two groups, infectious diarrhea tended to be more common in the
PsA group.
I have found that smoking is inversely associated with PsA compared to psoriasis patients
without arthritis. The prevalence of smoking among patients with PsA was higher than in the
general population. I have also found an interaction between smoking and HLA-C*06, as the
inverse association between smoking and PsA was present only among patients who were
HLA-C*06 negative. Pattison et al. have found that smoking was less common among
patients with PsA compared to those with psoriasis alone [76]; however it did not reach
statistical significance, possibly due to the small sample size. Rakkhit et al. founda temporal
association between psoriasis, PsA and smoking [114]. They reported that the duration of
time from the onset of psoriasis to development of PsA decreases with a history of smoking
prior to psoriasis onset and increases with smoking after psoriasis onset. On the other hand, a
recent study from Singapore has found similar proportions of smokers among patients with
psoriasis and PsA patients [38]. In the present study, current smoking was associated with
PsA even after adjustment for potential confounders.
The interaction between genetic and environmental risk factors has received only minimal
attention in genetic studies of psoriasis. It has been suggested that HLA-C*06 may modify
the effect of smoking and lead to an increased risk for psoriasis of about 11-fold over that for
non-smokers without HLA-C*06 [330], much higher than the combined effect of both risk
factors. In this study I analyzed the statistical interaction between HLA-C*06 and smoking.
The inverse association between smoking and PsA was present only among patients who
were HLA-C*06 negative. The interaction between HLA-C*06 and smoking may explain the
lack of association between smoking and PsA in studies that recruited the entire study
171
population from dermatology clinics, as opposed to our study that enrolled cases and controls
from two different sources [38]. By sampling only patients with severe psoriasis, it is likely
that the proportion of HLA-C*06 carriers was high, and the studies were thus underpowered
to detect an association between smoking and PsA.
The inverse association between smoking and PsA is intriguing as smoking is a known risk
factor for numerous diseases. Smoking is associated with increased risk of psoriasis as well
as rheumatoid arthritis [333]. On the other hand, smoking has a protective effect against
ulcerative colitis that bears some clinical and genetic similarities to PsA [320]. Current
smokers have a 40% lower risk of developing ulcerative colitis [321]. Smoking also reduces
the rate of flare-ups, hospitalizations and use of steroids in ulcerative colitis [322]. The
mechanisms underlying these observations are unknown, but suggested explanations include
decreased expression of IL-1β, IL-8 and altered response of the toll-like receptor pathway to
infectious agents among smokers [323, 324]. Another mechanism that may explain the
protective effect of smoking is through the activation of the nicotinic receptor. Nicotine can
activate the alpha7 nicotinic acetylcholine receptor that inhibits intracellular pro-
inflammatory pathways that are associated with development of arthritis [325, 326]. This
pathway is a target for novel therapeutic agents for the treatment of arthritis.
10.1. Conclusions
This work provides valuable information about the epidemiology of PsA. I was able to
estimate for the first time the incidence of PsA in a prospective cohort study of patients with
psoriasis. The results contradict the commonly held notion that the risk of developing PsA
decreases over time following the onset of psoriasis. Furthermore, the incidence of PsA
among psoriasis patients as estimated in this thesis is significantly higher than previously
reported in the literature suggesting that PsA is underestimated in patients with psoriasis.
The results of the thesis help to better understand the pathogenesis of PsA. I have shown that
genetic and environmental factors are associated with the development of PsA among
patients with psoriasis, and that there may be interactions between these factors. In
172
accordance with the previous literature, the role of trauma is suggested as a triggering event
for PsA and additional novel potential environmental risk factors were identified:
occupations that involve heavy lifting and infections. Furthermore, I have shown for the first
time that active smoking is inversely associated with PsA among psoriasis patients and that
its effect is modified by the HLA-C*06 allele. This association, if replicated in future studies,
may contribute to the understanding of the pathogenesis of the disease and may have
implications related to treatment, as medications that target signaling pathway involving the
nicotinic receptor may be effective in PsA.
The association between HLA alleles and PsA was investigated. I was able to replicate the
previously reported associations of PsA with HLA-B*27 and B*38 and to identify novel
associations with HLA-B*08, HLA-C*06 and HLA-B*39. The KIR genes, 2DS2 and 2DL1
were also associated with PsA among psoriasis patients. These genetic markers differentiate
patients with PsA from those with psoriasis alone and may serve as markers for higher risk of
PsA among psoriasis patients. Early treatment in these patients may prevent joint damage and
disability that result from a delayed diagnosis and therapy of PsA.
10.2. Future directions
Future studies related to HLA alleles and their association with PsA can focus on grouping
these alleles based on antigen binding motifs. The high levels of polymorphism in the HLA
molecules are required for presentation of a diverse repertoire of peptides to immune cells.
Most of the variation in protein sequences is located in the HLA antigen binding pockets.
HLA associations can be analyzed by incorporating structural and functional information
about HLA molecules. This process may identify associations that may be related to the
pathogenic mechanism of the disease based on variation in functional sequences.
We also aim to further investigate the “protective” effect of smoking in the susceptibility to
PsA that has been identified in the present study. One of the potential mechanisms that may
explain the “protective” effect of smoking is through the activation of alpha7 nicotinic
acetylcholine receptor that inhibits intracellular pro-inflammatory pathways. We aim to
173
investigate the association of polymorphisms within the alpha7 nicotinic acetylcholine
receptor gene and their interaction with smoking to PsA.
Finally, this study has used the information collected from a recently established psoriasis
cohort. This cohort was established to investigate genetic and clinical risk factors for PsA. A
prospective cohort is considered the preferred design to evaluate risk factors in epidemiologic
studies. Since at the present time there are not sufficient incident cases of PsA to evaluate
possible risk factors, we had to use case-control and retrospective study designs which are
subjected to more bias. In the future we will be able to validate these results by analyzing the
data that has been collected prospectively, by continuing to follow the Toronto Psoriasis
Cohort.
174
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Appendices
Appendix 1 - Questionnaire for the assessment of exposure to environmental factors
1 Employment NO YES
1a. In the last 10 years, have you ever had paid employment?
� �
go to 2a
� �
complete sections below
current
or
past
c p
part-time
or
full-time
p f
# of Years Worked
i. Job #1 (specify): �� ��
ii. Job #2 (specify): �� ��
iii. Job #3 (specify): �� ��
iv. Job #4 (specify): �� ��
v. Job #5 (specify): �� ��
vi. Job #6 (specify): �� ��
In the last 10 years, did you ever have a job that involved:
NO YES
1b. …standing on your feet for more than 30 minutes per hour?
� �
1c. …squatting for more than 5 minutes per hour? � �
1d. …lifting cumulative loads of more than 100 pounds per hour?
� �
1e. …pushing cumulative loads of more than 200 pounds per hour?
� �
1f. …using a vibrating tool for more than 4 hours a day?
� �
1g. …frequent repetitive hand movements for more than 45 minutes per hour?
� �
1h. …forceful gripping for more than 4 hours per day?
� �
1i. …bending your wrist for more than 4 hours per day?
� �
2 Life Events
In the last 10 years, have you experienced any of the following:
NO YES Year of Event #1
Year of Event #2
2a. Death of a close family member (e.g. parent,
sibling, child or partner)?
� ��
197
2b. Divorce or separation � ��
2c. Moved house � ��
2d. Changed job � ��
2e. Become unemployed � ��
2f. Treated for anxiety or depression � ��
3 Infections and Immunizations
3a. In the last 10 years have you had any of the following immunizations?
Don’t Know
NO YES Year specify year(s) immunized
i. BCG (for tuberculosis) � � ��
ii. Hepatitis A � � ��
iii. Hepatitis B � � ��
iv. Influenza (the flu shot) � � ��
v. Pneumovax (for pneumonia) � � ��
vi. Rubella � � ��
vii. Tetanus � � ��
viii. Other (specify) � � ��
ix. Other (specify) � � ��
3b. In the last 10 years, have you had any serious infections?
(i.e. requiring antibiotic treatment or admission to hospital)
NO YES
Year
Were you treated with antibiotics?
Were you admitted to hospital?
� �
go to 3c
� �
complete sections below
Don’t Know
NO YES NO YES
i. Type of infection #1 (specify): ____ � � � � �
ii. Type of infection #2 (specify): ____ � � � � �
iii. Type of infection #3 (specify): ____ � � � � �
iv. Type of infection #4 (specify): ____ � � � � �
v. Type of infection #5 (specify): ____ � � � � �
3c. In the last 10 years: have you had infective diarrhea?
(e.g. food poisoning or ‘traveler’s diarrhea’)
Don’t Know
NO YES
Year
Were you treated with antibiotics?
� �
go to 4
� �
go to 4
� �
complete sections below
Don’t Know
NO YES
i. Incident #1 (specify diagnosis): ____ � � �
ii. Incident #2 (specify diagnosis): ____ � � �
iii. Incident #3 (specify diagnosis): ____ � � �
4 Physical Trauma
4a. In the last 10 years, have you been involved in a road traffic accident?
NO YES
Year
Were you seen in
emergency?
Were you admitted to hospital?
� �
go to 4b
� �
complete sections below NO YES NO YES
i. Accident #1 (describe): ____ � � � �
ii. Accident #2 (describe): ____ � � � �
198
4b. In the last 10 years, Have you had any fractures (broken bones)?
NO YES
Year
Were you seen in
emergency?
Were you admitted to hospital?
� �
go to 4c
� �
complete sections below NO YES NO YES
i. Fracture #1 (which bone?): ____ � � � �
ii. Fracture #2 (which bone?): ____ � � � �
iii. Fracture #3 (which bone?): ____ � � � �
4c. In the last 10 years, have you had any other injury (e.g. fall, cut, accident etc.) for which you consulted your family doctor, walk-in clinic or emergency room?
NO YES
Year
Were you seen in
emergency?
Did you consult a family doctor?
�
� �
complete sections below NO YES NO YES
i. Other injury #1 (describe): ____ � � � �
ii. Other injury #2 (describe): ____ � � � �
iii. Other injury #3 (describe): ____ � � � �