diabetes mellitus at the time for diagnosis - diva...
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ACTAUNIVERSITATIS
UPSALIENSISUPPSALA
2017
Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Medicine 1361
Diabetes Mellitus at the Time forDiagnosis
Studies on Prognostic Factors
MATS MARTINELL
ISSN 1651-6206ISBN 978-91-513-0047-4urn:nbn:se:uu:diva-328382
Dissertation presented at Uppsala University to be publicly examined in Auditorium Minus,Gustavianum, Akademigatan 3, Uppsala, Friday, 13 October 2017 at 13:00 for the degreeof Doctor of Philosophy. The examination will be conducted in Swedish. Faculty examiner:Professor Claes-Göran Östenson (Karolinska Institutet, Institutionen för molekylär medicinoch kirurgi (MMK)).
AbstractMartinell, M. 2017. Diabetes Mellitus at the Time for Diagnosis. Studies on PrognosticFactors. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty ofMedicine 1361. 87 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-0047-4.
The aim for this thesis was to identify prognostic factors for chronic diabetes complications thatexist at the time of diabetes diagnosis.
Low level of education (<12 years) and low income (<60% of median) was found to increasethe risk to have high (>70 mmol/mol) HbA1c at the time of diagnosis with 34 % and 35 %,respectively.
Prevalence of diabetic retinopathy (DR) was 12% in a cohort of patients newly diagnosedwith diabetes. Diabetic macular edema was present in 11% of patients with type 2 diabetes(T2D) and 13% of those with Latent Autoimmune Diabetes in Adults (LADA). Low beta cellfunction and low level of education increased the risk for DR with 110% and 43%, respectively.For every unit of increase in body mass index, the risk for DR was reduced by 3%.
The cellular immunology of LADA patients was a mixture of that observed in both type 1(T1D) and T2D patients. Compared to patients with T1D, LADA patients had more B-regulatorylymphocytes and antigen presenting cells capable of producing interleukine-35. This indicatesa higher anti-inflammatory capacity in LADA patients compared to type T1D patients.
By imputing age, body mass index, HbA1c at diagnosis, beta cell function and insulinresistance in a cluster analysis, five distinct diabetes clusters were identified. The four clustersrepresenting T2D patients differed in incidence of DR, nephropathy and non-alcoholic fatty liverdisease. This was replicated with similar results in three geographically separate populations.
By studying socioeconomic background and factors present at the time of diagnosis we canbetter predict prognosis for chronic diabetes complications. These findings may facilitate better-targeted diabetes screening programs and more individually tailored treatment regimes.
Keywords: New-onset Diabetes Mellitus, socioeconomic position, epidemiology, diabetescomplications, diabetic retinopathy, cellular immunology, diabetes classification, type 2diabetes, latent autoimmune diabetes in adults (LADA), type 1 diabetes
Mats Martinell, Department of Public Health and Caring Sciences, Box 564, UppsalaUniversity, SE-75122 Uppsala, Sweden.
© Mats Martinell 2017
ISSN 1651-6206ISBN 978-91-513-0047-4urn:nbn:se:uu:diva-328382 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-328382)
List of Papers
This thesis is based on the following papers, which are referred to in the text by their Roman numerals.
I Martinell M, Hallqvist J, Pingel R, Dorkhan M, Groop L, Storm P, Rosengren AH, Stålhammar J. Education, immigration and income as risk factors for HbA1c >70 mmol/mol when diag-nosed with type 2 diabetes or latent autoimmune diabetes in the adult. A population based study cohort study. BMJ Open Diab Res Care 2017;5:e000346. doi:10.1136/bmjdrc-2016-000346
II Martinell M, Dorkhan M, Stålhammar J, Storm P, Groop L,
Gustavsson C. Prevalence and risk factors for diabetic retinopa-thy at diagnosis (DRAD) in patients recently diagnosed with type 2 diabetes (T2D) or latent autoimmune diabetes in the adult (LADA). Journal of Diabetes and Its Complications 2016;30:1456-1461
III Martinell M, Singh K, Luo Z, Espes D, Stålhammar J, Sandler
S, Carlsson P-O. Characterisation of Cellular Immunology in LADA Patients. In manus
IV Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M,
Carlsson A, Spegel P, Vikman P, Prasad RB, Mansour Aly D, Almgren P, Wessman Y, Shaat N, Mulder H, Lindholm E, Me-lander O, Hansson O, Malmqvist U, Lernmark Å, Lahti K, For-sén T, Toumi T, Rosengren AH, Groop L. Clustering of adult-onset diabetes into novel subgroups guides therapy and impro-ves prediction of outcome, Submitted
Reprints were made with permission from the respective publishers.
Contents
Introduction ................................................................................................... 11 Classifying Diabetes ................................................................................. 11 Diabetes and Socioeconomic Position ...................................................... 12
Resource-based measures ..................................................................... 12 Standard-based measures ...................................................................... 13
Chronic Diabetes Complications .............................................................. 14 Diabetic Retinopathy ............................................................................ 14
Diabetes and Cellular Immunology .......................................................... 16
Overall Objective and Specific Aims ............................................................ 17 Study design .............................................................................................. 18
Study I .................................................................................................. 18 Study II ................................................................................................. 18 Study III ................................................................................................ 18 Study IV ................................................................................................ 18
Settings .......................................................................................................... 19 Study populations ..................................................................................... 19
The All New Diabetics in Scania (ANDIS) ......................................... 19 ANDIS Eye Complication Study .......................................................... 19 The All New Diabetes in Uppsala (ANDiU) ........................................ 20 Eriksberg Primary Healthcare Centre (PHC) ....................................... 20 Diabetes Register in Vaasa (DIREVA by Finnish acronym) ............... 20 The Scania Diabetes Register (SDR) ................................................... 20 The Malmö Diet and Cancer (MDC) Study ......................................... 20
Inclusion process ....................................................................................... 21 Study I .................................................................................................. 21 Study II ................................................................................................. 22 Study III ................................................................................................ 22 Study IV ................................................................................................ 23
Variables ........................................................................................................ 24 Outcomes .................................................................................................. 24
Study I (HbA1c at diagnosis) ............................................................... 24 Study II (DRAD) .................................................................................. 24 Study III (Immune cells) ...................................................................... 25 Study IV (Cluster analysis) .................................................................. 29
Predictors .................................................................................................. 30
Diabetes classification .......................................................................... 30 The Homeostasis Model Assessment (HOMA) ................................... 31 Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA, by Swedish acronym) ...................................... 31 The Swedish National Diabetes Register (NDR) ................................. 32 Swedish National Patient Register ....................................................... 32 Swedish Prescribed Drug Register ....................................................... 32 Laboratory Medicine in Scania ............................................................ 32 The Clinical Laboratory in Uppsala ..................................................... 32 Genotyping ........................................................................................... 33 Definitions of Chronic Kidney Disease and Diabetic Kidney Disease 33 Definition of Non-Alcoholic Fatty Liver Disease ................................ 33
Confounders and Directed Acyclic Graphs (DAG) .................................. 34 DAG - Study I ...................................................................................... 34 DAG - Study II ..................................................................................... 35
Statistical methods ......................................................................................... 37 Regression analyses .................................................................................. 37
Logistic regression ................................................................................ 37 Generalized Estimating Equations ....................................................... 38
Results ........................................................................................................... 39 Study I ....................................................................................................... 39 Study II ..................................................................................................... 44 Study III .................................................................................................... 53
Innate immune cells .............................................................................. 55 Adaptive immune cells ......................................................................... 55 Regulatory immune cells ...................................................................... 55
Study IV .................................................................................................... 59 Cluster analysis ..................................................................................... 59 Genetic associations ............................................................................. 59 Replication of cluster analysis .............................................................. 62 Progression of disease and complications ............................................ 62
Discussion ..................................................................................................... 69 Study I ....................................................................................................... 69 Study II ..................................................................................................... 70 Study III .................................................................................................... 70 Study IV .................................................................................................... 70
Methodological considerations ..................................................................... 72 Study I ....................................................................................................... 72 Study II ..................................................................................................... 72 Study III .................................................................................................... 73 Study IV .................................................................................................... 73
Conclusions ................................................................................................... 74 Study I ....................................................................................................... 74 Study II ..................................................................................................... 74 Study III .................................................................................................... 74 Study IV .................................................................................................... 75
Clinical relevance and future directions ........................................................ 76 SEP effect on HbA1c at diagnosis: ........................................................... 76 Diabetic retinopathy: ................................................................................. 76 Cellular immunology ................................................................................ 76 Data-driven cluster classification of diabetes: .......................................... 77
Sammanfattning på svenska .......................................................................... 78
Acknowledgements ....................................................................................... 80
References ..................................................................................................... 82
Abbreviations
AHA ANDIS ANDiU ANOVA APC BMI Breg CD CKD CI CVD DCCT DIREVA DKD DME DR DRAD eGFR(crea) EQUALIS FPG GADA FoxP3 GEE HbA1c HC HLA HOMA HT IA-2 ICD
Antihypertensive agent All New Diabetics in Scania All New Diabetes in Uppsala Analysis of variance Antigen Presenting Cells Body Mass Index Regulatory B-lymphocytes Cluster of Differentiation Chronic Kidney Disease Confidence Interval Cardiovascular Disease The Diabetes Control and Complications Trial Diabetes Register in Vaasa (by Finnish acronym) Diabetic Kidney Disease Diabetic Macular Edema Diabetic Retinopathy Diabetic Retinopathy at Diagnosis estimated Glomerular Filtration Rate (creatinine) External Quality Assessment Service Fasting Plasma Glucose Glutamic Acid Decarboxylase Antibodies Forkhead box transcription factor P3 Generalized Estimating Equation Hemoglobin A1c Healthy Controls Human Leukocyte Antigen Homeostasis model assessment Hypertension Insulinoma-Associated protein 2 International Classification of Diseases and Related Health problems
IDF IFCC IHD IL LADA LISA MARD MDC MDRD MOD MODY NDR NK OAD OR PBMC PHC SAID SEP SD SDR SIDD SIRD T1D T2D TGF Treg WHO ZnT8
International Diabetes Federation International Federation of Clinical Chemistry Ischemic Heart Disease Interleukin Latent Autoimmune Diabetes in Adults Longitudinell Integrationsdatabas för Sjukförsäkrings- och Arbetsmarknadsstudier Mild Aged-Related Diabetes Malmö Diet and Cancer study Modification of Diet in Renal Disease Mild Obese Diabetes Maturity Onset Diabetes of the Young National Diabetes Register Natural Killer cells Oral Antidiabetic Drugs Odds Ratio Peripheral Blood Mononuclear Cells Primary Healthcare Centre Severe Autoimmune Diabetes Socioeconomic position Standard Deviation Scania Diabetes Registry Severe Insulin Deficient Diabetes Severe Insulin Resistant Diabetes Type 1 Diabetes Type 2 Diabetes Transforming Growth Factor Regulatory T-lymphocytes World Health Organization Zink transporter T8
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Introduction
Diabetes is a chronic condition of inability to sustain blood glucose homeo-stasis. This inability increases the risk for microvascular and macrovascular lesions, referred to as diabetes complications. From 1996 to 2003, diabetes prevalence in Sweden increased by 55%, but the incidence remained un-changed (1). This increase in survival with diabetes is primarily caused by a more effective treatment of macrovascular complications.
Even though acute diabetes complications are more dramatic and often life-threatening, the majority of morbidity and premature death is caused by chronic diabetes complications. By treating cardiovascular risk factors, chronic complications can be postponed and even avoided, but still many patients are severely affected. Accumulating evidence indicates that the treatment in the first years after diagnosis creates a metabolic memory that influences the risk for complications many years ahead (2-4).
Current classification of diabetes into type 1 diabetes (T1D) and type 2 diabetes (T2D) rely solely on the ability to secrete insulin and the presence of autoantibodies reactive against pancreatic beta-cells. Both T1D and T2D are very heterogeneous populations with regard to metabolic, biometric and prognostic measures, implying a more differentiated disease. This oversim-plification in diabetes classification makes it more difficult to identify small-er diabetes subgroups that would benefit from more individualized treat-ment.
To identify prognostic factors to complications at diagnosis and to tailor treatment regimens accordingly remains one of the major challenges in dia-betes care.
Classifying Diabetes Current diagnostic criteria are two Fasting Plasma Glucose (FPG) ≥7.0 mmol/L or one random non-FPG ≥11.1 mmol/L or plasma glucose ≥11.1 two hours after a standardised oral glucose tolerance test or one haemoglobin (Hb)A1c ≥48 mmol/mol) (5). However, the clinical presentation and pro-gression of diabetes differs widely. Thus to facilitate research and to develop effective treatment regimens, it is necessary to divide diabetes into more clinically homogeneous subgroups. Before 1980 diabetes was classified into juvenile- or mature-onset diabetes based on age at onset. The classification
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was changed in 1980 into insulin-dependent diabetes (IDD) and non-insulin-dependent diabetes (NIDD). In 1993, Tuomi et al. defined Latent Autoim-mune Diabetes in Adults (LADA) as being applicable to patients older than 35, with glutamate decarboxylase antibodies (GADA) and endogenous insu-lin secretion for at least 6 months after being diagnosed with Diabetes (6). LADA patients are phenotypically indistinguishable from NIDD at diagno-sis, but over time become more like IDD. Further, there are patients that at diagnosis have ketoacidosis and require insulin, but later recover metaboli-cally and need only Oral Antidiabetic Drugs (OAD). For this reason, diabe-tes classification was changed to the more etiology-based T1D and T2D. In clinical practice, adult patients without ketoacidosis at debut are considered to have T2D unless the clinical presentation is atypical, i.e., in need of exog-enous insulin within the first year after diagnosis. Other diabetes subgroups recognised by the World Health Organization (WHO) and the International Diabetes Federation (IDF) are gestational diabetes, secondary (to pancreatic disease) diabetes and Maturity Onset Diabetes of the Young (MODY) (7). Neonatal diabetes, Maternally Inherited Diabetes and Deafness (MIDD) and `mitochondrial myopathy, encephalopathy, lactic acidosis and stroke´ are rare types of diabetes with a strong genetic penetrance (8).
With current diabetes classification, 70-80% are classified as T2D (9, 10). However, the current treatment-guidelines do not rely on diabetes classifica-tion; rather they promote a more individual approach where treatments are adjusted according to biometric and metabolic parameters associated with acute and chronic diabetes complications (5, 11-13).
Diabetes and Socioeconomic Position Socioeconomic position (SEP) is an aggregate of resource-based and stand-ard-based measures used to describe an individual or group’s ability to influ-ence society (14). Resources refer to income, education and wealth, while standard refers to access to services, goods and knowledge. The link between SEP and health is complex and cannot be ascribed to a single factor and fac-tors that affect ability to influence society differ over time and between par-allel societies (14). Several studies link socioeconomic factors to risk acquir-ing diabetes and risk for chronic diabetes complications (15-26). Whether socioeconomic factors influence time with undiagnosed (untreated) diabetes is unclear.
Resource-based measures In mature markets, diabetes is more prevalent in populations with low in-come and low level of education (27). Whereas in emerging markets, the largest diabetes incidence is expected in populations with growing income
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and level of education, Figure 1 (28). This indicates that SEP associates dif-ferently depending on macroeconomic factors. Nevertheless, the populations at risk in both mature and emerging markets have a common denominator; a life-style with decreasing physical activity, more pre-processed food, more obesity and smoking (29).
Figure 1. Age-adjusted prevalence of diabetes in adults (20-79), 2015 and predicted for 2040. IDF Diabetes Atlas, 7th edn.
Standard-based measures In Sweden, it takes at least 10 years for the living standard of immigrants to equalise that of the native Swedish population (30). During this time, immi-grants have both fewer resources (lower income) and lower standards (lin-gual/cultural barriers and knowledge of healthcare system) (30). Several Swedish studies have shown that immigration increase risk for diabetes and diabetes complications (17, 20, 22, 31).
Diabetes prevalence differs largely over geographical regions, Figure 1. In Western Asian countries, diabetes prevalence is double or triple that of Sweden’s (28). This regional difference is partly due to genetic predisposi-tion for diabetes (32). For this reason, geographical origin is important when studying the effect of SEP on diabetes.
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Chronic Diabetes Complications Even though acute diabetes complications are life-threatening, the majority of morbidity and premature death attributed to diabetes is caused by chronic diabetes complications. Chronic diabetes complications can be categorised into micro- or macrovascular disease by the size of vessels affected (33). Microvascular complications (nephropathy, neuropathy and retinopathy) are caused by increased capillary leakage leading to organ failure. Macrovascu-lar complications (myocardial infarction, cerebral infarction and peripheral claudication) are ischemic manifestations caused by premature atherosclero-sis.
Diabetic Retinopathy Diabetic Retinopathy (DR) is a chronic microvascular complication and is the world’s leading cause of preventable blindness (34). It is staged by ob-serving specific lesions on retinal photographs, Table 1 and Figure 2. The early DR stages are usually asymptomatic, but if left untreated, DR will pro-gress to proliferative DR with macular oedema (DME). The incidence in-creases with diabetes duration and after 20 years with diabetes, the preva-lence of DR is 60-100% (35). Diabetic retinopathy at diagnosis (DRAD) is rare in T1D patients, but in T2D patients 12-20% have DRAD (36-38). For this reason, all patients are recommended base-line DR screening shortly after being diagnosed.
The Diabetes Control and Complications Trial (DCCT) showed a 75% reduction in risk for DR by treating HbA1c to <48 mmol/mol (39). Apart from preventive action, there are a number of effective treatments to reduce loss of vision, but all of them are invasive and expensive.
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Table 1. International clinical diabetic retinopathy severity scale.
Disease Severity Level Findings Observable upon Dilated Ophthalmoscopy
No apparent retinopathy No abnormalities Mild NPDR Microaneurysms only
Moderate NPDR More than just microaneurysms but less than severe NPDR
Severe NPDR Any of the following and no signs of proliferative retinopathy:
I >20 intraretinal hemorrhages in each of four
quadrants
II Definite venous beading in two or more quad-
rants III Prominent IRMA in one or more quadrants PDR One or both of the following: Neovascularization Vitreous/preretinal haemorrhage NPDR = nonproliferative diabetic retinopathy; PDR = proliferative diabetic retinopathy; IRMA = intraret-inal microvascular abnormalities Figure 2. Picture of diabetic retinopathy with macular edema. 1. Microaneurysm (outpouching of capillary wall due to pericyte loss) 2. Hemorrhages (caused by ruptured microaneurysms) 3. Hard exudates (leakage of lipids and proteins from vessels due to breakdown of blood-retina barrier) 4. Macular edema (retinal thicken-ing and hard exudates within disc width of macula. Photograph by C. Gustavsson, ANDIS eye complication study, Lund University.
2
4
1 3
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Diabetes and Cellular Immunology T1D and LADA are both considered autoimmune diabetes by the presence of autoantibodies reactive against insulin-producing beta cells. The differ-ence lays in the insulin secretory capacity at diagnosis. LADA patients do (by definition) not require exogenous insulin the first six months after diag-nosis, whereas T1D patients are exogenous insulin dependent from the time of diagnosis (6). LADA also shares genetic features of both T1D and T2D (40). Why some people develop a LADA phenotype and others a T1D is unclear. At present, the focus on immunological characterisation of LADA has been on the presence of autoantibodies, with only a few studies on cellu-lar immunology (40, 41).
Cellular immunological response does not involve antibodies. Instead the cells involved use interleukines (IL) to enhance or reduce the immune response to antigenes. An important part of cellular immunology is the ability to learn which antigens not to react against, i.e., self-antigens. This ability is called immune tolerence.
Immune tolerance can be central (negative selection in bone-marrow or thymus of B- and T-lymphocytes reactive against self-antigens) or peripher-al (when bound to self-antigen B- and T-lymphocytes both undergo apopto-sis, become anergic or switch from pro-inflammatory ‘helper’ cells to anti-inflammatory ‘regulator’ cells). The immune system can also be divided into innate (cells with inherited immunological memory of tolerance) or adaptive (cells with aquired immunological memory of tolerance from exposure to, e.g., viruses).
Dysfunctional immune tolerance causes inappropriate inflammation, which if directed against self-antigens, result in autoimmune disease, includ-ing T1D (42-47). The immune cells regulate inflammation by secreting ei-ther pro- or anti-inflammatory cytokines. Immune cells that produce anti-inflammatory cytokines are regulatory T (Treg) cells, regulatory B (Breg) cells and tolerogenic Antigen-Presenting Cells (APC). Recent research indicates that these cells are important in the pathophysiology of T1D (48-50).
To separate and identify cell-types, specific cell surface markers are used. Treg cells are identified by cell surface markers called Cluster of Differentia-tion (CD): CD4, CD25, CD127 and the intracellular Forkhead box transcrip-tion factor P3 (FoxP3) (51). Breg cells are identified by CD19, CD38 and CD40 (14). Both Treg and Breg cells produce the anti-inflammatory cyto-kine IL10, IL-35 and Transforming Growth Factor (TGF)-β (7, 8, 15, 16). Natural Killer (NK) cells participate in the innate immune responses and can be either pro- or anti-inflammatory. In animal models of T1D, a knock-down of NK cells prevents the development of diabetes (52, 53). In LADA patients, but not in T2D patients, an altered frequency of total num-ber of NK cells has been reported (54, 55).
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Overall Objective and Specific Aims
Diabetes prevalence is increasing, and consequently more people are suffer-ing from chronic diabetes complications (1, 56). Clinical trials emphasises the importance of aggressive treatment the initial years after diagnosis to halt progression of chronic complications (3, 5, 13, 57-59). Early identification of patients at risk may facilitate treatment regimes better tailored to halt the progression to chronic complications. One way to make the sub-classes more homogeneous is to add variables to the classification criteria.
The first objective of this thesis is to facilitate early identification of indi-viduals at risk for chronic diabetes complications by studying how SEP in-fluences metabolic states at diagnosis, and the prevalence and risk factors for DRAD. The second objective is to describe characteristics in the cellular immunology of LADA and compare it to that of T1D and T2D. The third objective is to investigate whether adding more clinical measures to classify diabetes can better predict who is at risk to develop chronic diabetes compli-cations.
All four studies explore the clinical picture of diabetes at diagnosis from four different perspectives. The specific aims are:
• To investigate whether education, income or immigration influ-ence risk to have HbA1c >70 mmol/mol when diagnosed with T2D or LADA
• To investigate the prevalence of DRAD in patients with T2D and LADA. To investigate how socioeconomic, biometric and meta-bolic parameters influences the likelihood of having DRAD.
• To describe the cellular immunological profile of LADA and compare it to patients with T1D, T2D and healthy controls.
• To present a diabetes classification based on data-driven cluster analysis and to correlate these clusters to risk for chronic diabetes complications
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Study design Study I A population-based cohort study with a cross-sectional measurement of out-come.
Study II Register study on a population-based cohort of people recently diagnosed with diabetes (classified as T2D or LADA) with a cross-sectional measure-ment of outcome at their primary DR screening.
Study III Matched case-control study. T1D, T2D and HC were BMI-, age- and sex-matched to patients with LADA.
Study IV Cohort study with follow-up by register data.
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Settings
Study populations The All New Diabetics in Scania (ANDIS) ANDIS (All New Diabetics in Scania) is a study in the Scania region with the aims to estimate the prevalence and incidence of diabetes subgroups, to explore new ways to classify diabetes and to provide a platform for diabetes research (60). Inclusion is restricted to the Scania region in the southernmost part of Sweden. All health care providers in the region are invited to register patients.
Diabetes nurses in primary healthcare centres (PHC) or hospitals register information on geographical origin, family history of diabetes, previous ges-tational diabetes and history of pancreatitis and HbA1c at diagnosis. Upon registration FPG, fC-Peptide and GADA are analysed at the central laborato-ry in Malmö. Serum and DNA are stored and analysed at the central labora-tory in Malmö. ANDIS regularly updates biometric and metabolic parame-ters from the NDR.
For studies I and II, patients classified as LADA or T2D and registered before April 1st, 2012 and were used. Study IV all ANDIS patients registered before November 1st, 2016 were used.
The ANDIS project is approved by the Regional Ethical Board in Lund (Dnr 2006/584 with supplementary Dnr 2012/67).
ANDIS Eye Complication Study ANDIS eye complications study recruit patients from the ANDIS study upon appearance for their first DR screening. The aim is to estimate the preva-lence of DR in diabetes subgroups and to evaluate known and new risk fac-tors for DR. Retina status is assessed by a senior Ophthalmologist according to the International Clinical Diabetic Retinopathy Disease Severity and In-ternational Clinical Diabetic Macular Edema Disease Severity Scales (1, 60). Patients from the ANDIS eye complication were recruited for Study II.
The ANDIS eye complication study is approved by the Regional Ethical Board in Lund (Dnr 211/253).
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The All New Diabetes in Uppsala (ANDiU) The ANDiU study was launched in 2012 in the county of Uppsala, Sweden. The ANDiU design is similar to ANDIS, enabling cross-verification of re-sults. Patients from the ANDiU population were recruitedused for Studies III and IV.
ANDiU is approved by the Ethical board in Uppsala (Dnr: 2011/155).
Eriksberg Primary Healthcare Centre (PHC) The Eriksberg PHC is located in the Uppsala municipality and serves 10,000 registered individuals. Healthy controls (HC) were recruited at the Eriksberg PHC for Study III..
Diabetes Register in Vaasa (DIREVA by Finnish acronym) The Diabetes Register in Vaasa (DIREVA), Finland, was launched in 2013 as part of the Greater Bothnia Project (61). The objectives are the same as ANDIS and ANDiU and the genetic analysis are carried out at the same la-boratory in Malmö. DIREVA include all patients living in the Vaasa district, regardless of the duration of diabetes. DIREVA was used in Study IV to replicate results.
The Scania Diabetes Register (SDR) The Scania Diabetes Registry (SDR) started in 1996 and has included over 7,000 diabetes patients recruited at hospitals in Scania, Sweden (62). The majority of the patients come from the city of Malmö. , SDR was used in Study IV to replicate results.
The Malmö Diet and Cancer (MDC) Study The Malmö Diet and Cancer (MDC) Study is a population-based prospective cohort from Malmö, Sweden in which 30,000 individuals were recruited between 1991 and 1996. From this cohort, 6,103 patients were randomly selected and referred to as the MDC cardiovascular arm (MDC-CVA). Indi-viduals from MDC-CVA were used as genetic controls in Study IV.
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Inclusion process Study I Inclusion and exclusion of eligible participants is presented in Figure 3. Ex-cluded were patients with a diabetes diagnosis (International Classification of Diseases and Related Health problems (ICD)-10 code: E10-E14) in the National Patient Register or prescription of OAD (Anatomic Therapeutic Chemical (ATC)-code: A10) more than 400 days prior to ANDIS registra-tion, patients with missing HbA1c at the time of diagnosis and patients with no reported date for diagnosis. Also excluded were patients classified as T1D or as secondary diabetes. Figure 3. Flow-chart over the inclusion process of Study I.
EligibleN=5 200
Previous diabetesN=111
MissingHbA1c N=923
Uncertain diagnose date
N=20
T1D N=108
SecondarydiabetesN=40
Included3 794
(T2D: 3 525)(LADA: 269)
Declined participation
N=204
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Study II By 2013 the ANDIS eye complications study had screened 2,560 patients of the 5,200 patients eligible to Study I. After exclusion of the uncertain date for diabetes diagnosis (n=40), T1D (n=4) and secondary diabetes (n=65), 2,451 (2,288 T2D and 163 LADA) patients remained, Figure 4.
Figure 4. Flow-chart of the inclusion process of Study II.
Study III Figure 5 is a flow-chart of the inclusion process to Study III. The study pop-ulation was recruited from the ANDiU study (T2D and LADA patients) and at the Eriksberg PHC, Uppsala (T1D and HC). T1D, LADA and T2D were defined by the ANDIS/ANDiU criteria described below. The T1D, T2D and HC participants were age- (≤30 years at diagnosis, 10 year intervals up to >80 years), sex- and BMI- (≤15 Kg/m2, 5 unit intervals and >35 Kg/m2) matched to the LADA patients. Exclusion criteria were ongoing hormonal treatment (ATC codes H01-03), use of non-steroid anti-inflammatory drugs (ATC code M01), ongoing chemotherapy treatment (ATC codes L01-04) and other autoimmune disease (hypo/hyperthyroidism, rheumatic disease, vitiligo, psoriasis, celiac disease, Addison’s disease and inflammatory bowl disease).
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Figure 5. Flow-chart of the inclusion process of Study III. 1. Of the 17 registered LADA patients of the All New Diabetes in Uppsala (ANDiU) study, 13 were in-cluded. 2. By matching to the LADA patients by sex, age and body mass index (BMI), patients with type 1 diabetes (T1D, n=7), type 2 (T2D, n=13) diabetes and healthy controls (HC) were recruited from ANDiU. Another 11 participants with T1D were included from the department of Endocrinology, Akademiska hospital, Uppsala (UAS). Healthy controls (HC, n=13) were recruited upon arrival (with no infection or systemic inflammation) at the Eriksberg Primary Healthcare Centre (EVC).
Study IV Eligible for inclusion were people registered in ANDIS in the period January 1st, 2008 until November 1st, 2016, during which 177 clinics registered 14,625 patients (> 90% of eligible patients), aged 0-96 years within a median of 40 days (Inter Quartile Range (IQR) 12-99 days) after diagnosis. Patients <18 years old (N=932, 6.4%) were excluded. Patients with complete data for the clustering variables (N=8,980) were included in the further analyses. Patients with complete data for clustering analysis registered in SDR (N=1 466), DIREVA (N=5 107) and ANDiU (N=844) were used for replication analyses.
Follow-up data were extracted from the ANDIS eye complication study, the Swedish National Patient Register, the Swedish Prescribed Drugs Regis-ter, the National Diabetes Register (NDR) and Laboratory Medicine in Sca-nia.
Exclusion criteria
Included with T2D
(n=15)
Eligible LADA (n=17)
Included with T1D
(n=18)
Exclusion criteria
Included with LADA
(n=13)
Age, sex and BMI matched to included
LADA patients
ANDiU
1.
Included HC
(n=13)
UAS
T1D T2D T1D HC
EVC
2.
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Variables
Outcomes Study I (HbA1c at diagnosis) HbA1c at diagnosis was extracted from medical records. The Swedish Na-tional Board of Health and Welfare define poor metabolic control as HbA1c >70 mmol/mol (8.6%) (12). HbA1c was dichotomised to ≤70 mmol/mol or >70 mmol/mol.
HbA1c presented in Mono-S standard were converted into International Federation of Clinical Chemistry (IFCC) standard using the formula: HbA1c (IFCC) = HbA1c (Mono-S)*10.45-10.62.
Study II (DRAD) Two to three red-free 50° digital fundus photographs from both eyes, repre-senting central, nasal and temporal parts of the retina, were registered in the regional screening database IMAGEnet® i-base (TOPCON® Scandinavia, Mölndal, Sweden). In regular screening in Sweden, optical coherence to-mography (OCT) is not performed. All photographs were classified by a senior ophthalmologist, for the degree of DR and diabetic macular edema (DME) pursuant to the International Clinical Diabetic Retinopathy Disease Severity and International Clinical Diabetic Macular Edema Disease Severi-ty Scales (9), in which OCT is not necessary for grading DME. A subset of images (approximately 10%) was reclassified on a different occasion in or-der to estimate intra-individual variation. The repeatability was 97.5%. Dis-agreements in grading were related to the discernment of microaneurysms from small hemorrhages in mild versus early moderate DR and went in both directions, i.e., not interfering with the overall study results.
25
Study III (Immune cells) Flow cytometry is a technique to count cells and to detect biomarkers for differentiating cell types. With flow cytometry, it is possible to simultane-ously detect and separate thousands of cells per second.
For Study III we used a Histopaque-1077 (Sigma, St Louis, MO) to sepa-rate or sort immune cells from whole blood (51). First the cells were stained with specific biomarkers of the cells of interest, Table 2. Then the cells were fixed and permeabilised with a Fixation Permeabilization buffer (eBiosci-ence, San Diego, CA) for intracellular markers. The samples were run on LSR II Fortesa (BD; Franklin Lakes, NJ) using DivaDacker software (BD) and one million events were counted for analysis at Bovis, Uppsala Univer-sity, Uppsala, Sweden. The fluorescence minus one (FMO) isotype and sin-gle stained controls were used for gating strategies as described by Singh, K. et al (66).
Fluorescence-activated cell sorting (FACS) is a process where cell mark-ers are gated in a step-wise manner. Figures 6-9 shows how we identified-Breg, Treg, NK cells and APCs for Study III. The files were analysed on FlowLogic software (Inivai Technologies, Mentone, Australia).
Table 2. Antibodies for surface (Cluster of Diferentiation (CD), Epstein-Barr virus induced gene (Ebi), Helios, Human Leucocyte Antigen (HLA) and Lin3) and intra-cellular (Forkhead-box P3 (FoxP3)) antigens used in flow cytometry staining in Study III.
Marker Fluorochrome Clone Manufacturer CD3 APC-H7 SK7 BD CD4 FITC RPA-T4 eBioscience CD8 APC RPA-T8 BioLegend CD11b Brilliant Violet 421 ICRF44 BioLegend CD11c Brilliant Violet 421 B-ly6 BD CD15 PE-Cy7 W6D3 BioLegend CD16 PE 3G8 BioLegend CD19 APC-H7 SJ25C1 BD CD24 FITC ML5 BioLegend CD25 APC-H7 M-A251 BD CD38 Brilliant Violet 421 HIT2 BioLegend CD40 Brilliant Violet 605 5C3 BioLegend CD56 APC HCD56 BioLegend CD123 PE-Cy7 7G3 BD CD127 BV605 HIL-7R-M21 BD Ebi3 APC 607201 R&D Foxp3 PE-Cy7 PCH101 eBioscience Helios Pacific Blue 22F6 BioLegend HLA-DR APC-H7 L243 BD IL-12p35 PE 27537 R&D Lin 3 FITC BD
26
Figure 6. Fluorescence-activated cell sorting (FACS) gating procedure for separa-tion of IL35 producing B regulatory cells. First step was to isolate Lymphocytes by how the laser passes around (Forware Scatter (FSC)) and bounces off (Side Scatter (SSC)) the cells. Cluster of Differentiation (CD) 19 identified B-Lymphocytes. CD19, CD24 and CD40 identified B-regulatory cells. Ebi3 and IL-12p35 identified IL35 producing cells.
27
Figure 7. Fluorescence-activated cell sorting (FACS) gating procedure for separa-tion of IL35 producing T regulatory cells. First step was to isolate Lymphocytes by how the laser passes around (Forware Scatter (FSC)) and bounces off (Side Scatter (SSC)) the cells. Cluster of Differentiation (CD) 4 and CD25 identified T-Lymphocytes. CD127 identified regulatory T-lymphocytes (T-reg). The intracellular Fork-head box P3 antigen (FoxP3) further isolated the T-reg cells. Ebi3 and IL-12p35 identified IL35 producing cells.
28
Figure 8. Fluorescence-activated cell sorting (FACS) gating procedure for separa-tion of natural killer (NK) cells ending with isolation of IL35 NK cells. First step was to isolate Lymphocytes by how the laser passes around (Forware Scatter (FSC)) and bounces off (Side Scatter (SSC)) the cells. Cluster of Differentiation (CD) 3 and CD56 separated the cells into NK cells that are either CD3-CD56low or CD3-
CD56high. Ebi3 and IL-12p35 identified IL35 producing cells.
29
Figure 9. Fluorescence-activated cell sorting (FACS) gating procedure for separa-tion of antigen-presenting cells (APC) ending with isolation of IL35-producing APC. First Human Leucocyte Antigen (HLA) and LIN mix identified APC. With Cluster of Differentiation (CD) 123 and CD11c, CD11c+CD123- cells were isolated. Ebi3 and IL-12p35 identified IL35 producing cells.
Study IV (Cluster analysis) Cluster analysis groups (cluster) study objects in such a way that the objects within a cluster have more similarities in common than objects in separate clusters. It is an exploratory technique to find new patterns within popula-tions without applying assumptions. For cluster analysis, algorithms are ap-plied to specify which intra-cluster and inter-cluster distances characterise a cluster. In the cluster analysis of Study IV, patients with secondary diabetes were excluded, as were extreme outliers (>5 SD) of any cluster variable. GADA was included as a dichotomous variable.
First we applied TwoStep clustering, where the first step was to estimate the optimal number of clusters based upon silhouette width (together and
30
gender-wise) and the second step was to perform a hierarchical clustering. Then we verified the results to assess cluster-wise stability with the k-means clustering method. Because all GADA-positive patients were clustered to-gether in the TwoStep analysis, and k-means clustering is inappropriate for binary variables, we restricted the k-means clustering to GADA negative patients. Afterwards the dataset was resampled and cluster-analysed 100 times. Since distances between our clusters were non-euclidean (measured points were properties rather than location in a space) they were measured in Jaccard, which is a quota computed by comparing the most similar clusters of the resampled dataset with the clusters of the original dataset (1 minus ratio of sizes of intersection and union). If Jaccard is 1, there is no overlap-ping of adjacent clusters, and if Jaccard is 0 there is a total overlap of adja-cent clusters. A stable cluster should yield a Jaccard similarity >0.75, where-as a value between 0.6 and 0.75 is considered an indication of a cluster (67).
TwoStep clustering was performed in SPSS v.23 for 2 to 15 clusters using log-likelihood as a distance measure and Schwarz's Bayesian criterion for clustering. K-means clustering was performed with k=4 using the kmeansruns function (runs=100) in the fpc package in R.
Predictors Diabetes classification To assess diabetes classification at the time for diagnosis we used the fol-lowing criteria:
T1D: GADA >20 kE/L and fC-Peptide ≤0.3 nmol/L LADA: Age >35 years, GADA >20 kE/L and fC-Peptide >0.3 nmol/L T2D: GADA <10 kE/L and fC-Peptide >0.7 nmol/L Secondary diabetes: Any patient with a history of hospitalisation due to pan-creatitis
31
The Homeostasis Model Assessment (HOMA) The homeostasis model assessment (HOMA) was developed to estimate beta-cell function, insulin sensitivity and insulin resistance from fP-Insulin (or fC-Peptide) and FPG, by the Oxford Centre for Diabetes, Endocrinology and Metabolism (68). The model requires a steady state in glucose metabo-lism (FPG 3.5 – 25 mmol/L and fC-Peptide 0.2 – 3.5 nmol/L). The original formula correlates well to the hyperinsulinemic euglycemic glucose clamp (Rs=0.88, P< 0.0001) and the hyperglycemic clamp (Rs = 0.69, P< 0.01) (69).
The second HOMA version (HOMA2) accounts for variations in hepatic and peripheral resistance, increases in insulin secretion (when P-Glucose is >10 mmol/L) and circulating pro-insulin (70). HOMA2 is a non-linear equa-tion that is incorporated into computer platforms by downloading an applica-tion programme interface (68). The estimates of insulin sensitivity and beta cell function are given as percentages of a healthy population defined by the creators of HOMA2. Results from HOMA1 and HOMA2 differ primarily when P-Glucose is very high (70).
Estimation of insulin resistance and beta-cell function by steady-state sur-rogate methods can be calculated from a single blood test in primary healthcare setting, making it suitable for epidemiological studies (71). An-other advantage of HOMA is the possibility to use fC-Peptide as a surrogate to plasma insulin.
Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA, by Swedish acronym) Integrated database for labour market research (LISA by its Swedish acro-nym) is owned by Statistics Sweden and is a conglomerate of official regis-ters on income, social status, education, immigration and employment. LISA data are on an individual level and are classified as integrity-sensitive infor-mation. For this reason, all statistical analyses on LISA data (Study I and Study II) were performed on the Statistics Sweden micro data online plat-form (61).
Variables extracted from LISA were disposable income individualised from family income, highest accomplished level of education and year of immigration to Sweden. Data two years before the date for diabetes diagno-sis were used.
Disposable incomes individualised from family income were converted to percentages of median disposable income individualised from family income in the Scania region, of each given year. The variable was then categorised according to Eurostat recommendations into low (<60%), medium (60% - 150%) and high (>150%) income (72).
32
Statistics Sweden incorporates foreign accomplished education after con-verting it into corresponding Swedish level of education. Level of highest accomplished education was categorised into <10 years, 10-12 years and >12 years.
The Swedish National Diabetes Register (NDR) The Swedish National Diabetes Register (NDR) was launched in 1996 with the purpose to facilitate comparison of diabetes care between clinical units and to promote diabetes research (73). In 2012, 90% of patients with diabe-tes living in the Scania Region were registered in the NDR (74). Data from the NDR was used in studies I, II and IV. Variables extracted from the NDR were date for diagnosis, HbA1c at the tome of diagnosis (Study I), systolic and diastolic blood pressure (Study II).
Swedish National Patient Register The Swedish National Patient Register provided ICD-10 code and date for hospitalisation for diabetes (ICD-10 codes E10-E14), hypertension (I10) and ischemic heart disease (IHD, I20-I25).
Swedish Prescribed Drug Register The Swedish Prescribed Drug Register provided ATC codes, dose, strength, and date of expedition for cardiovascular and anti-hypertensive agents (AHA, ATC code C01-C10) and glucose-lowering agents (ATC code A10).
Laboratory Medicine in Scania (ANDIS) Laboratory Medicine in Scania measured FPG, C-Peptide, GADA, metabo-lites and DNA. FPG was analysed after an overnight fast using the HemoCue Glucose System (HemoCue AB, Ängelholm, Sweden). C-Peptide was meas-ured using an ElectroChemiLuminiscenceImmunoassay on Cobas (Roche). GADA was measured by RIA or ELISA. ZnT8A antibodies were measured using a Radio-Binding Assay.
The laboratory is, certified by the External quality assessment service (EQUALIS) (75).
The Clinical Laboratory in Uppsala (ANDiU) The central clinical chemistry laboratory at Uppsala University Hospital analysed blood hemoglobin (Hb), blood leucocyte count (Lkc), plasma c-reactive protein (CRP), transglutaminase (Tg) antibodies (ab), thyroxine (T4), triiodothyronine (T3), thyroid-stimulating hormone (TSH), TSH-
33
receptor (IgG) ab, thyroid peroxidase (TPO) ab, plasma IgA ab, serum corti-sol and 21-hydroxylase (IgG) ab, GADA 65 (IgG) ab, fC-peptide, FPG and HbA1c FPG was analysed after an overnight fast using the HemoCue Glu-cose System (HemoCue AB, Ängelholm, Sweden). C-Peptide was measured using an ElectroChemiLuminiscenceImmunoassay on Cobas (Roche). GADA was measured by RIA or ELISA.
The laboratory is, certified by the External quality assessment service (EQUALIS) (75).
Genotyping Genotyping of 172 SNPs previously associated with diabetes or diabetes-related traits were carried out using iPlex (Sequenom, San Diego, California, US) or TaqMan assays (Thermo Fisher Scientific). Patients with call rate < 90% were excluded.
Association between clusters and genotypes was calculated using the MLE method in SNPtest2 with adjustment for sex. Individuals without dia-betes from the MDC-CVA cohort (recruited in the same geographic region) were used as controls. Patients of known non-Swedish origin were excluded from the analysis comparing clusters and genotypes.
Definitions of Chronic Kidney Disease and Diabetic Kidney Disease The Modification of Diet in Renal Disease (MDRD) formula was used to calculate an estimated Glomerular Filtration Rate (eGFR) from plasma creat-inine concentration (76). Chronic Kidney Disease (CKD) was defined as eGFR<60 (CKD stage 3A) or <45 (CKD stage 3B) for more than 90 days. End-stage renal disease (ESRD) was defined as at least one eGFR below 15 mL/min/1.73m2.
Diabetic Kidney Disease (DKD) was defined as either End Stage Renal Disease (ESRD) or macroalbuminuria defined as at least two out of three consecutive visits with Albumin Excretion Rate (AER) ≥200 µg/min or AER ≥300 mg/24h or Albumin-Creatinine Ratio (ACR) ≥25/35 mg/mmol for men/women.
Definition of Non-Alcoholic Fatty Liver Disease Non-alcoholic fatty liver disease (NAFLD) was defined as BMI >28 Kg/m2 and at least two Alanine transferase (ALAT) measurements >1.1-1.2 µkat/L for men and >0.7-0.85 µkat/L for women.
34
Confounders and Directed Acyclic Graphs (DAG) Correlation does not imply causality. Apart from the exposure variable to be studied, many other variables may affect the overall measurement. To strengthen the argumentation that a correlation has a causative mechanism, background knowledge on how different variables affect the outcome meas-urement is needed. Some variables have an independent influence on out-come while others mediate or interfere (collide) with the association studied. Variables that both influence the exposure and the outcome are regarded as confounders and are in the regression models adjusted for (77).
There are several techniques to decide which factors are potential con-founders. For Study I and Study II, we used Directed Acyclic Graph (DAG) (78). In a DAG, all factors are given a node (circle). Then arrows are di-rected toward the node it influences (causal pathway). In summary, the nodes are available data and the direction of the arrows represents the back-ground knowledge of the researcher. When all arrows are placed, cofound-ing, mediating and colliding pathways are decided upon.
DAG - Study I According to the DAG (Figure 10), age, sex and country of origin precede the exposures and are potentially associated with both the exposures and the outcome and should therefore always be adjusted for when studying an asso-ciation between any of the exposures and the outcome. Further, depending on which exposure is of interest, the DAG reveals that that an exposure could either confound or mediate the association between another exposure and the outcome.
35
Figure 10. Directed acyclic graphs (DAG) visualising causal pathways from socio-economic (education, immigration and income) exposures in green and ischemic heart disease (IHD), body mass index (BMI), insulin sensitivity (IS) and beta cell function (BC), to outcome (HbA1c >70 mmol/mol at diagnosis). Age, sex and origin are in red because they are regarded as confounders to all three socioeconomic expo-sures.
DAG - Study II According to the DAG (Figure 11), age and sex precede the exposures and are potentially associated with both the exposures and the outcome and should therefore always be adjusted for when studying an association be-tween any of the exposures and the outcome. Exposures that were more closely related to each other were grouped into three clusters, Figure 11. The three clusters were: (1) SEP (education, immigration and income) cluster, (2) cluster of cardiovascular disease (CVD) risk factors (BMI, AHA and IHD) and (3) glycemic state at diagnosis cluster (HbA1c, beta cell function and insulin sensitivity).
HbA1c >70 mmol/mol
Age
Income
Origin
Sex Immi- gration
BMI IS
BC Edu- cation
IHD
36
Figure 11. Directed Acyclic Graph (DAG) used in Study II. Outcome (in bold) is diabetes retinopathy at diagnosis (DRAD). Socioeconomic exposures (education (Edu), immigration and income) in blue, cardiovascular risk factors (ischemic heart disease (IHD), anti-hypertensive agents (AHA) and body mass index (BMI)) in green and glycemic state at diagnosis (insulin sensitivity (IS), beta cell function (BC) and HbA1c at diagnosis) in orange.
DRAD
BC
IS BMI
Age AHA
Sex
Edu- cation
Income
IHD
Immi- gration
Time
HbA1c
37
Statistical methods
Regression analyses Regression analyses are statistical methods to estimate relationships between variables to predict how the dependent (outcome) variable varies if the inde-pendent (exposure) variable changes. Confounders are assumed to correlate with both the dependent and independent variable; hence they should also be included in the regression model.
Logistic regression Logistic regression is a regression model used when the dependent variable is binary. The results are given as odds ratios (OR). OR represents the odds for an outcome to occur given a particular exposure, divided by the odds of the outcome occurring in the absence of the exposure. If OR is greater than one, the odds for the outcome increase when the independent variable changes. If OR is less than 1 the odds for the outcome to occur decrease when the independent variable changes. Independent variables that result in OR other than one are considered to be associated with the outcome. The three logistic regression models in Study I that were based on the DAG in Figure 10 were:
Model 1: Level of education was adjusted for age, sex and country of origin. Model 2: Immigration was adjusted for age, sex, country of origin and level
of education. Model 3: Level of income was adjusted for age, sex, country of origin, level
of education and immigration.
Family history of diabetes, beta cell function, insulin sensitivity and BMI were not considered to influence our exposures and were not included in the regression models, Figure 10.
38
Generalized Estimating Equations The Generalized Estimating Equation (GEE) is a technique that focuses on estimating the average response of the population rather than on the individ-ual-specific effect (as in ordinary regression analysis). With GEE, it is possi-ble to account for intra-individual correlations. In Study II, GEE was used to set each eye as a dependent variable, hence doubling the number of observa-tions. According to the DAG for Study II (Figure 11) the following GEE models were made:
SEP Model 1: Level of education was adjusted for age and sex. Model 2: Immigration was adjusted for age, sex and level of education. Model 3: Income was adjusted for age, sex, level of education and immigra-
tion.
CVD risk factors Model 4: BMI was adjusted for age, sex, level of education, immigration and
income. Model 5: AHA was adjusted for age, sex, level of education, immigration
and income and BMI. Model 6: IHD was adjusted for age, sex, level of education, immigration and
income BMI and AHA.
Glycemic state at diagnosis Model 7: Beta cell function was adjusted for age, sex, BMI, AHA and IHD. Model 8: Insulin sensitivity was adjusted for age, sex, BMI, AHA and IHD Model 9: HbA1c at diagnosis was further adjusted for beta cell function and
insulin sensitivity. Study I revealed that a low level of education was correlated with high HbA1c at diagnosis (Table 4). Therefore, HbA1c at diagnosis was further adjusted for level of education. However, this did not change estimates at all.
39
Results
Study I Patient characteristics are shown in Tables 3 and 4. T2D patients were more often male, with ≤9 years of highest accomplished education as well as high-er BMI and insulin sensitivity. While LADA patients had lower beta cell function and more often HbA1c >70 mmol/mol at diagnosis.
T2D patients had higher odds to have HbA1c >70 mmol/mol at diagnosis if the level of education (Model 1) or income (Model 3) were low, Table 5. There was a trend of increased risk for patients who immigrated ≤10 years before diagnosis (Model 2). In Table 6, ORs for HbA1c >70 mmol/mol at diagnosis in patients with LADA are shown. There was a trend of increased risk in patients who immigrated ≤10 years before diagnosis.
40
Table 3. Number (N) and percentage (%) over outcome (HbA1c >70 mmol/mol), exposures (level of education, immigration and income), confounders (sex and coun-try of origin) and ischemic heart disease (IHD), stratified over patients with either type 2 diabetes (T2D) or Latent Autoimmune Diabetes in Adults (LADA).
T2D LADA
N % N %
HbA1c at diagnosis*** >70 1043 30 118 44
(mmol/mol) ≤70 2482 70 151 56
Level of education (years)* <10 1124 32 67 25
10-12 1600 46 126 48
>12 749 22 71 27
Immigration (years) ≤10 178 5 11 4
>10 1 3347 95 258 96
Income (% of median) <60% 391 11 27 10
60%-150% 1985 56 151 56
>150% 1139 32 90 34
Sex* Male 2092 59 140 52
Female 1433 41 129 48
Country of origin EU15 2897 82 234 87
Western Asia 448 13 25 9
Other 180 5 10 4
Ischemic heart disease Yes 354 10 22 8
No 3171 90 247 92
Family history of diabetes Yes 1973 56 142 53
No 1552 44 127 47
Chi2 test * P<0.05, *** P<0.001,
41
Tabl
e 4.
Num
ber (
N),
mea
n, st
anda
rd d
evia
tion
(SD
), m
edia
n an
d in
ter-
quar
tile
rang
e (I
QR
) ove
r age
HbA
1c a
t dia
gnos
is, b
eta
cell
func
tion,
in
sulin
sens
itivi
ty a
nd b
ody
mas
s ind
ex (B
MI)
, stra
tifie
d ov
er p
atie
nts w
ith e
ither
type
2 d
iabe
tes (
T2D
) or L
aten
t Aut
oim
mun
e D
iabe
tes i
n A
dults
(LA
DA
).
T2
D
LAD
A
N
m
ean
SD
med
ian
IQR
N
m
ean
SD
med
ian
IQR
HbA
1c (m
mol
/mol
)***
3,
525
63.9
25
.3
53.1
31
.4
269
73.2
30
.4
62.5
47
.9
Age
3,
525
59.4
11
.6
66.0
16
.0
269
57.8
13
.1
60.0
19
.0
Bet
a ce
lls fu
nctio
n (%
)***
2,
989
83.2
44
.2
78.2
52
.7
219
59.5
39
.1
54.3
51
.2
Insu
lin se
nsiti
vity
(%)*
**
2,98
9 43
.0
28.0
37
.4
24.9
21
9 64
.0
38.3
55
.2
45.5
BM
I (kg
/m2)
***
3,50
7 30
.9
5.8
30.2
7.
1 26
3 28
.6
5.8
28.1
7.
1
***
P<0.
001
42
Tabl
e 5.
Odd
s rat
ios (
95%
Wal
d co
nfid
ence
inte
rval
s) fo
r HbA
1c >
70
mm
ol/m
ol (8
,6 %
) at t
he ti
me
of d
iagn
osis
with
T2D
whe
n ex
pose
d to
lo
w e
duca
tion,
rece
nt im
mig
ratio
n or
low
inco
me,
adj
uste
d fo
r con
foun
ding
acc
ordi
ng to
thre
e di
ffer
ent m
odel
s.
Cru
de
Mod
el 1
M
odel
2
Mod
el 3
Educ
atio
n (y
ears
) <1
0 1.
15 (0
.94
- 1.4
2)
1.34
(1.0
8 - 1
.66)
**
1.35
(1.0
9 –
1.67
)**
1.30
(1.0
5 - 1
.61)
*
10-1
2 1.
26 (1
.04
- 1.5
2)*
1.26
(1.0
3 - 1
.54)
* 1.
27 (1
.04
– 1.
54)*
1.
24 (1
.02
- 1.5
2)*
>12
1.00
(ref
.) 1.
00 (r
ef.)
1.00
(ref
.) 1.
00 (r
ef.)
Imm
igra
tion
≤10
1.62
(1.1
9 –
2.21
)**
1.26
(0.8
8 - 1
.82)
1.
17 (0
.80
- 1.7
0)
(yea
rs)
>10
1 1.
00 (r
ef.)
1.00
(ref
.) 1.
00 (r
ef.)
Inco
me2
<60%
1.
41 (1
.11
- 1.8
0)**
1.35
(1.0
2 - 1
.79)
*
60%
- 15
0%
1.02
(0.8
7 - 1
.20)
1.14
(0.9
6 - 1
.35)
>150
%
1.00
(ref
.)
1.00
(ref
.)
Obs
erva
tions
3
473
3 47
3 3
473
3 47
3 *
p <
0.05
, **
p <
0.01
1 In
clud
es p
atie
nts b
orn
in S
wed
en
2 Per
cent
age
of m
edia
n di
spos
able
inco
me
indi
vidu
alis
ed fr
om fa
mily
inco
me
in th
e Sc
ania
regi
on o
ne fi
scal
yea
rs p
rior t
o di
abet
es d
iagn
osis
. Mod
el 1
was
adj
uste
d fo
r age
, sex
an
d co
untry
of o
rigin
. Mod
el 2
was
adj
uste
d fo
r Mod
el 1
and
edu
catio
n. M
odel
3 w
as a
djus
ted
for M
odel
2 a
nd im
mig
ratio
n.
43
Tabl
e 6.
Odd
s rat
ios (
95%
Wal
d co
nfid
ence
inte
rval
s) fo
r HbA
1c >
70
mm
ol/m
ol (8
.6%
) at t
he ti
me
of d
iagn
osis
with
LA
DA
whe
n ex
pose
d to
lo
w e
duca
tion,
rece
nt im
mig
ratio
n or
low
inco
me,
adj
uste
d fo
r con
foun
ding
acc
ordi
ng to
thre
e di
ffer
ent m
odel
s.
Cru
de
Mod
el 1
M
odel
2
Mod
el 3
Educ
atio
n (y
ears
) <1
0 0.
54 (0
.30
– 0.
96)*
0.
79 (0
.42
- 1.4
7)
0.78
(0.4
2 - 1
.46)
0.
78 (0
.42
- 1.4
6)
≥1
0 1.
00 (r
ef.)
1.00
(ref
.) 1.
00 (r
ef.)
1.00
(ref
.)
Imm
igra
tion
(yea
rs) ≤1
0 2.
32 (0
.66
– 8.
11)
2.90
(0.4
8 –
17.7
) 2.
72 (0
.44
– 16
.9)
>10
1 1.
00 (r
ef.)
1.00
(ref
.) 1.
00 (r
ef.)
Inco
me
2 <6
0%
1.42
(0.6
4 –
3.19
)
1.34
(0.5
1 –
3.53
)
≥
60%
1.
00 (r
ef.)
1.
00 (r
ef.)
Obs
erva
tions
26
4 26
4 26
4 26
4 *
p <
0.05
1 In
clud
es p
atie
nts b
orn
in S
wed
en
2 Per
cent
age
of m
edia
n di
spos
able
inco
me
indi
vidu
alis
ed f
rom
fam
ily in
com
e in
the
Scan
ia r
egio
n tw
o fis
cal y
ears
prio
r to
dia
bete
s di
agno
sis.
Mod
el 1
was
ad
just
ed f
or a
ge,
sex
and
coun
try o
f or
igin
. M
odel
2 w
as a
djus
ted
for
Mod
el 1
and
edu
catio
n. M
odel
3 w
as a
djus
ted
for
Mod
el 2
and
im
mig
ratio
n.
44
Study II The prevalence of DRAD was 12% in both T2D and LADA patients, Table 7. Prevalence of DME was 11% and 13% in T2D and LADA patients, re-spectively. All DME were within vascular arch except for in one patient with T2D who had edema ≤500 um from fovea (classified as severe DME), Table 8 (79).
Table 7. Number (N) and proportion (%) over classification of diabetes retinopathy (DR) at primary screening. Stratified over patients recently diagnosed with type 2 diabetes (T2D) or Latent Autoimmune Diabetes in Adults (LADA).
T2D LADA
Left eye Right eye Left eye Right eye
DR N (%) N (%) N (%) N (%)
Ungradeable 14 (0.6%) 1 (0.6%)
No 2,012 (89%) 2,011 (88%) 142 (87%) 147 (90%)
Mild 148 (6%) 168 (7%) 15 (9%) 11 (7%)
Moderate 107 (4%) 102 (5%) 5 (3%) 5 (3%)
Severe non-proliferative 3 (0%) 6 (0%)
Proliferative 4 (0%) 1 (0%)
N (cumulative %) 2,288 (100%) 2,288 (100%) 163 (100%) 163 (100%)
Ungradeable eyes include 9 eyes with missing images, 2 eyes with cataracts, 2 with status post branch-vein thrombosis, 1 with status post-central vein thrombosis and 1 due to previous trauma.
45
Table 8. Number (N) and proportion (%) over classification of diabetes macula edema (DME) at primary screening. Stratified over patients recently diagnosed with type 2 diabetes (T2D) or Latent Autoimmune Diabetes in Adults (LADA).
T2D LADA
Left eye Right eye Left eye Right eye
DME N (%) N (%) N (%) N (%) Ungradeable 14 (0.6%) 1 (0.6%) No DME 2,037 (89%) 2,051 (90%) 141 (87%) 142 (87%) Within vascular arc 236 (10%) 234 (10%) 20 (12%) 21 (13%) >500 mm from fovea 0 1 (0%) 0 0 ≤500 mm from fovea 1 (0%) 1 (0%) 0 0 N (cumulative %) 2,288 (100%) 2,288 (100%) 163 (100%) 163 (100%) Ungradeable include missing images (n=6), cataracts (n=6) and status peripheral vein thrombosis (2), status post central vein thrombosis (n=1) and age-related macular degeneration (n=1).
Tables 9 (T2D) and 10 (LADA) show characteristics of patients with or without DRAD. Blood pressure measurements were missing for 45% of the patients. For this reason, AHA was used as a surrogate measure in the GEE models. In T2D patients with DRAD, it was more common with beta cell function <50% (31% vs. 20%, p <0.001) but HbA1c at diagnosis did not differ, Table 9. There were no statistically significant differences in charac-teristics between LADA patients with or without DRAD, Table 10.
In Table 11, OR for DRAD in patients with T2D are shown. Low (<50%) beta cell function and low level of education (<10 years) were associated with increased risk for DRAD, OR 2.10; 95% CI 1.61-2.75 and 1.43; 95% CI 1.04-1.96, respectively. Every unit of BMI decrease reduced the risk of DRAD by 3% (OR 0.97; 95% CI 0.95–0.99). In Table 12, OR for DRAD in patients with LADA are shown. The low number of patients in each cell resulted in estimates with very broad confidence intervals.
46
Table 9. Number (N), means and standard deviation (SD) of characteristics in pa-tients diagnosed with type 2 diabetes. Stratified over diabetes retinopathy at diagno-sis (DRAD) or no DRAD. EU15 (the 15 member states of the European Union 1995-2004), W.A. (Western Asian countries: Afghanistan, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Syria, Turkey).
Type 2 diabetes DRAD No DRAD
N Mean/% sd N Mean/% sd
Time from diagnosis (days) 361 137.4 326.6 1,671 133.4 271.4 Age (years) 402 59.4 11 1,886 59 11.4 Sex Male 253 63% 1,132 60%
Female 149 37% 754 40% Origin EU15 358 89% 1,679 89% W.A. 28 7% 113 6% Other 16 4% 94 5% Smoker Yes 33 15% 159 15%
No 189 85% 899 85% Level of education <10 141 36% 560 30% (years) 10-12 176 45% 859 46%
>12 14 19% 448 24% Percentage of <60 48 12% 188 10% median income 60-150 217 54% 1,093 58%
>150 137 34% 603 32% Immigration ≤10 24 6% 94 5% (years) >101 378 94% 1,792 95% BMI (Kg/m2) 400 30.4 5.2 1,881 31 5.8 BMI (>30 Kg/m2) Yes 208 52% 978 52%
No 192 48% 902 48% HbA1c (mmol/mol) 402 66.8 25.8 1,886 63 25.3 Beta cell function (%) <50%*** 129 31% 377 20%
50%-75% 101 25% 453 24% >75%*** 177 44% 1,037 55%
Insulin sensitivity (%) <25% 80 20% 377 20% 25%-50% 201 50% 1,018 54%
>50% 121 30% 585 26% Systolic blood pressure (mmHg) 222 136,1 13,3 1,028 135.2 13.9 Diastolic blood pressure (mmHg) 222 79.2 8,8 1,028 78.9 8.6 Anti hypertensive Yes 125 31% 585 31% agents No 277 69% 1,301 69% Ischemic heart disease Yes 52 13% 208 11% No 350 87% 1,678 89% Observations 402 1,886 1 And patients born in Sweden Tests: continuous variables (ttest), categorical variables (Chi2-test) Adjusted for mass sensitivity (Bonferroni) * p < 0.05, ** p < 0.01, *** p < 0.001
47
Table 10. Number (N), mean and standard deviation (SD) of characteristics in pa-tients diagnosed with Latent Autoimmune Diabetes in Adults (LADA). Stratified over diabetes retinopathy at diagnosis (DRAD) or no DRAD. EU15 (the 15 member states of the European Union 1995-2004), W.A. (Western Asia countries). LADA
DRAD No DRAD N mean sd N mean sd
Time from diagnosis (days) 24 143.4 193.2 118 113.4 188
Age (years) 28 58.8 11.4 135 56.8 13.4
Sex Male 20 71% 63 47%
Female 8 29% 72 53%
Origin EU15 27 96% 123 91%
W.A. 1 4% 7 5%
Other 0 - 5 4%
Smoker Yes 2 12% 6 9%
No 15 88% 60 91%
Level of education <10 10 33% 26 19%
(years) 10-12 12 44% 76 56%
>12 6 22% 34 25%
Percent of <60 0 0% 18 13%
median income 60-150 15 54% 74 55%
>150 13 46% 45 33%
Immigration ≤10 1 4% 5 4%
(years) >101 27 96% 130 96%
BMI (Kg/m2) 27 29,9 4.8 133 28.5 5.8
BMI (>30 Kg/m2) Yes 14 52% 43 32%
No 13 48% 90 68%
HbA1c (mmol/mol) 28 64,5 25,8 135 74,0 27,5 Beta cell function (%) <50% 11 39% 62 46%
50%-75% 10 36% 34 25%
>75% 7 25% 42 29%
Insulin sensitivity (%) <25% 1 4% 16 12%
25%-50% 13 46% 42 31%
>50% 14 50% 77 57% Systolic blood pressure (mmHg) 16 139.0 10,7 66 130.1 13.5 Diastolic blood pressure (mmHg) 16 80.4 6,0 66 76.6 8.4
Anti-hypertensive agents Yes 11 39% 30 22% No 17 61% 105 78%
Ischemic heart disease Yes 3 11% 7 5% No 25 89% 128 95%
Observations 28 135 1 And patients born in Sweden Tests: continuous variables (ttest), categorical variables (Chi2-test)
Adjusted for mass sensitivity (Bonferroni)
48
Tabl
e 11
. Odd
s rat
ios (
95%
Wal
d co
nfid
ence
inte
rval
s) fo
r dia
bete
s ret
inop
athy
afte
r rec
ent t
ype
2 di
abet
es d
iagn
osis
whe
n ex
pose
d to
leve
l of
educ
atio
n, le
vel o
f inc
ome,
imm
igra
tion,
bet
a ce
ll fu
nctio
n, in
sulin
sens
itivi
ty, H
bA1c
at d
iagn
osis
, bod
y m
ass i
ndex
(BM
I), t
reat
men
t with
an
ti-hy
perte
nsiv
e ag
ents
(AH
A) o
r isc
hem
ic h
eart
dise
ase
(IH
D).
Mod
el 1
M
odel
2
Mod
el 3
M
odel
4
Mod
el 5
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
Age
at d
iagn
osis
(yea
rs)
1.00
[0
.99-
1.01
] 1.
00
[0.9
9-1.
01]
1.00
[0
.99-
1.01
] 1.
00
[0.9
9-1.
01]
1.00
[0
.99-
1.01
] Se
x M
ale
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Fem
ale
0.85
[0
.68-
1.07
] 0.
85
[0.6
8-1.
07]
0.86
[0
.68-
1.09
] 0.
90
[0.7
1-1.
14]
0.89
[0
.70-
1.13
] Le
vel o
f edu
catio
n (y
ears
) <1
0 1.
43
[1.0
4-1.
96]
1.41
[1
.04-
1.93
] 1.
43
[1.0
4-1.
96]
1.50
[1
.09-
2.05
] 1.
51
[1.1
0-2.
07]
10
-12
1.15
[0
.85-
1.55
] 1.
14
[0.8
5-1.
53]
1.15
[0
.85-
1.55
] 1.
20
[0.8
9-1.
62]
1.20
[0
.89-
1.63
]
>12
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Imm
igra
tion
(yea
rs)
≤10
1.15
[0
.67-
1.98
] 1.
15
[0.6
7-1.
98]
1.16
[0
.67-
2.01
] 1.
16
[0.6
7-2.
01]
>10
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Leve
l of i
ncom
e (%
) <6
0 1.
03
[0.6
8-1.
57]
1.02
[0
.67-
1.55
] 1.
03
[0.6
8-1.
56]
60
-150
0.
92
[0.7
2-1.
18]
0.92
[0
.71-
1.18
] 0.
92
[0.7
2-1.
18]
>1
50
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Isch
emic
hea
rt di
seas
e N
o 1.
00
[ref
. cat
.]
Yes
1.
22
[0.8
7-1.
72]
AH
A tr
eatm
ent
No
1.00
[r
ef. c
at.]
Y
es
0.94
[0
.73-
1.20
] B
MI (
Kg/
m2 )
0.97
* [0
.95-
0.99
] 0.
97*
[0.9
5-0.
99]
Obs
erva
tions
4,
516
4,
516
4,
516
4,
516
4,
516
M
odel
1: E
duca
tion
is a
djus
ted
for a
ge a
nd s
ex. M
odel
2: I
mm
igra
tion
is a
djus
ted
for a
ge, s
ex a
nd e
duca
tion.
Mod
el 3
: Inc
ome
is a
djus
ted
for a
ge, s
ex, e
duca
tion
and
imm
igra
-
tion.
Mod
el 4
: IH
D is
adj
uste
d fo
r age
, sex
, edu
catio
n, im
mig
ratio
n, in
com
e, A
HA
and
BM
I. M
odel
5: B
MI i
s adj
uste
d fo
r age
, sex
, edu
catio
n, im
mig
ratio
n an
d in
com
e. A
djus
ted
for m
ass s
ensi
tivity
(Bon
ferr
oni)
* p
< 0.
05, *
* p
< 0.
01, *
** p
< 0
.001
49
Tabl
e 11
con
tinue
d.
Mod
el 6
M
odel
7
Mod
el 8
M
odel
9
Mut
ually
adj
uste
d
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
Tim
e fr
om d
iagn
osis
(day
s)
1.00
[1
.00-
1.00
] A
ge a
t dia
gnos
is (y
ears
) 1.
00
[0.9
9-1.
01]
1.00
[0
.99-
1.01
] 1.
00
[0.9
9-1.
01]
1.00
[0
.99-
1.01
] 1.
00
[0.9
9-1.
01]
Sex
Mal
e 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] Fe
mal
e 0.
89
[0.7
0-1.
13]
0.92
[0
.73-
1.15
] 0.
88
[0.7
0-1.
10]
0.93
[0
.74-
1.16
] 1.
05
[0.8
1-1.
34]
Leve
l of e
duca
tion
(yea
rs)
<10
1.50
[1
.10-
2.06
] 1.
46
[1.0
5-2.
04]
10
-12
1.20
[0
.89-
1.62
] 1.
12
[0.8
1-1.
53]
>1
2 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] Im
mig
ratio
n (y
ears
) ≤1
0 1.
16
[0.6
7-2.
01]
1.02
[0
.55-
1.89
] >1
0 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] Le
vel o
f inc
ome
(%)
<60
1.03
[0
.68-
1.56
] 0.
94
[0.6
0-1.
48]
60
-150
0.
92
[0.7
2-1.
18]
0.94
[0
.72-
1.23
]
>150
1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] Is
chem
ic h
eart
dise
ase
No
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Y
es
1.39
[0
.99-
1.94
] 1.
29
[0.9
2-1.
80]
1.41
[1
.01-
1.97
] 1.
36
[0.9
5-1.
94]
AH
A tr
eatm
ent
No
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Y
es
0.94
[0
.73-
1.20
] 0.
97
[0.7
6-1.
24]
0.93
[0
.73-
1.18
] 0.
98
[0.7
7-1.
26]
0.88
[0
.65-
1.17
] B
MI (
Kg/
m2 )
0.97
[0
.95-
0.99
] 0.
99
[0.9
7-1.
01]
0.98
[0
.96-
1.00
] 0.
99
[0.9
7-1.
01]
0.98
[0
.96-
1.01
] B
eta
cell
func
tion
(%)
<50
2.10
***
[1.6
1-2.
75]
1.77
***
[1.2
8-2.
44]
2.01
***
[1.4
3-2.
85]
50
-75
1.27
[0
.96-
1.67
] 1.
18
[0.8
8-1.
58]
1.28
[0
.93-
1.74
]
>75
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Insu
lin se
nsiti
vity
(%)
<25
1.08
[0
.77-
1.52
] 0.
89
[0.6
4-1.
25]
1.03
[0
.70-
1.51
]
25-5
0 0.
85
[0.6
3-1.
13]
0.80
[0
.62-
1.05
] 0.
88
[0.6
5-1.
17]
>5
0 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] H
bA1c
at d
iagn
osis
(mm
ol/m
ol)
1.00
[1
.00-
1.01
] 1.
00
[1.0
0-1.
01]
Obs
erva
tions
45
16
45
02
45
02
45
02
40
00
M
odel
6: A
HA
is a
djus
ted
for a
ge, s
ex, e
duca
tion,
imm
igra
tion,
inco
me
and
BM
I. M
odel
7 (b
eta
cell
func
tion)
and
Mod
el 8
(ins
ulin
sen
sitiv
ity) a
re a
djus
ted
for a
ge, s
ex, I
HD
, A
HA
and
BM
I. M
odel
9: H
bA1c
at d
iagn
osis
is a
djus
ted
age,
sex
IHD
, AH
A, B
MI,
beta
cel
l fun
ctio
n an
d in
sulin
sens
itivi
ty. A
djus
ted
for m
ass s
ensi
tivity
(Bon
ferr
oni)
* p
< 0.
05, *
* p
< 0.
01, *
** p
< 0
.001
50
Tabl
e 12
. Odd
s rat
ios (
95%
Wal
d co
nfid
ence
inte
rval
s) fo
r dia
bete
s ret
inop
athy
afte
r rec
ent l
aten
t aut
oim
mun
e di
abet
es in
adu
lt (L
AD
A) d
i-ag
nosi
s whe
n ex
pose
d to
leve
l of e
duca
tion,
leve
l of i
ncom
e, im
mig
ratio
n, b
eta
cell
func
tion,
insu
lin se
nsiti
vity
, HbA
1c a
t dia
gnos
is, b
ody
mas
s ind
ex (B
MI)
, tre
atm
ent w
ith a
nti-h
yper
tens
ive
agen
ts (A
HA
) or i
sche
mic
hea
rt di
seas
e (I
HD
).
Mod
el 1
M
odel
2
Mod
el 3
M
odel
4
Mod
el 5
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
Age
at d
iagn
osis
(yea
rs)
1.01
[0
.97-
1.04
] 1.
00
[0.9
7-1.
04]
1.00
[0
.97-
1.04
] 1.
00
[0.9
6-1.
04]
1.01
[0
.97-
1.04
] Se
x M
ale
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Fem
ale
0.28
[0
.11-
0.74
] 0.
30
[0.1
2-0.
78]
0.32
[0
.12-
0.86
] 0.
35
[0.1
3-0.
97]
0.35
[0
.13-
0.95
] Le
vel o
f edu
catio
n (y
ears
) <1
0 2.
48
[0.7
2-8.
60]
2.48
[0
.71-
8.62
] 2.
45
[0.7
0-8.
52]
2.07
[0
.56-
7.68
] 2.
22
[0.6
1-8.
10]
10
-12
1.02
[0
.32-
3.29
] 0.
97
[0.3
0-3.
10]
1.02
[0
.32-
3.29
] 0.
97
[0.2
9-3.
22]
0.94
[0
.29-
3.09
]
>12
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Imm
igra
tion
(yea
rs)
≤10
em
pty
empt
y em
pty
empt
y >1
0 Le
vel o
f inc
ome
(%)
<60
empt
y em
pty
empt
y
60-1
50
1.07
[0
.45-
2.58
] 1.
15
[0.4
7-2.
85]
1.16
[0
.47-
2.84
]
>150
1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] Is
chem
ic h
eart
dise
ase
No
1.00
[r
ef. c
at.]
Y
es
1.52
[0
.35-
6.62
] A
HA
trea
tmen
t N
o 1.
00
[ref
. cat
.]
Yes
2.
12
[0.8
2-5.
49]
BM
I (K
g/m
2 ) 1.
02
[0.9
5-1.
11]
0.99
[0
.96-
1.01
]
Obs
erva
tions
28
6
286
28
6
280
28
0
Mod
el 1
: Edu
catio
n is
adj
uste
d fo
r age
and
sex
. Mod
el 2
: Im
mig
ratio
n is
adj
uste
d fo
r age
, sex
and
edu
catio
n. M
odel
3: I
ncom
e is
adj
uste
d fo
r age
, sex
, edu
catio
n an
d im
mig
ra-
tion.
Mod
el 4
: IH
D is
adj
uste
d fo
r age
, sex
, edu
catio
n, im
mig
ratio
n, in
com
e, A
HA
and
BM
I. M
odel
5: B
MI i
s adj
uste
d fo
r age
, sex
, edu
catio
n, im
mig
ratio
n an
d in
com
e.
51
Tabl
e 12
con
tinue
d.
Mod
el 6
M
odel
7
Mod
el 8
M
odel
9
Mut
ually
adj
uste
d
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
OR
[9
5% C
.I.]
Tim
e fr
om d
iagn
osis
(day
s)
1.00
[1
.00-
1.00
] A
ge a
t dia
gnos
is (y
ears
) 1.
00
[0.9
6-1.
04]
1.01
[0
.97-
1.04
] 1.
01
[0.9
8-1.
05]
1.01
[0
.97-
1.05
] 0.
97
[0.9
3-1.
02]
Sex
Mal
e 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] Fe
mal
e 0.
35
[0.1
3-0.
95]
0.34
[0
.13-
0.92
] 0.
30
[0.1
2-0.
77]
0.30
[0
.11-
0.84
] 0.
32
[0.1
0-1.
05]
Leve
l of e
duca
tion
(yea
rs)
<10
2.17
[0
.59-
7.95
] 5.
17
[1.0
2-26
.06]
10-1
2 0.
99
[0.3
0-3.
27]
0.90
[0
.23-
3.58
]
>12
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Imm
igra
tion
(yea
rs)
≤10
em
pty
empt
y >1
0 Le
vel o
f inc
ome
(%)
<60
empt
y em
pty
60
-150
1.
14
[0.4
6-2.
79]
1.27
[0
.43-
3.72
]
>150
1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.] Is
chem
ic h
eart
dise
ase
No
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Y
es
1.74
[0
.44-
6.91
] 1.
72
[0.4
5-6.
60]
1.86
[0
.46-
7.59
] 1.
16
[0.2
2-6.
04]
AH
A tr
eatm
ent
No
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[1
.00-
1.00
] 1.
00
[ref
. cat
.] 1.
00
[ref
. cat
.]
Yes
2.
12
[0.8
2-5.
49]
2.20
[0
.87-
5.60
] 2.
46
[0.9
6-6.
30]
2.64
[0
.98-
7.10
] 2.
23
[0.6
2-7.
97]
BM
I (K
g/m
2 ) 1.
02
[0.9
4-1.
11]
1.05
[0
.97-
1.14
] 1.
07
[0.9
7-1.
17]
1.07
[0
.97-
1.19
] 1.
03
[0.9
1-1.
16]
Bet
a ce
ll fu
nctio
n (%
) <5
0 1.
52
[0.3
5-6.
62]
2.03
[0
.44-
9.42
] 5.
09
[0.7
2-36
.03]
50-7
5 1.
31
[0.3
4-5.
06]
1.34
[0
.36-
5.02
] 3.
18
[0.6
1-16
.57]
>75
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
Insu
lin se
nsiti
vity
(%)
<25
0.38
[0
.03-
4.8
0]
6.49
[0
.43-
99.1
8]
2.47
[0
.15-
40.3
9]
25
-50
1.99
[0
.74-
5.3
5]
9.71
[0
.78-
120.
2]
4.86
[0
.34-
69.6
3]
>5
0 1.
00
[1.0
0-1.
00]
1.00
[r
ef. c
at.]
1.00
[r
ef. c
at.]
HbA
1c a
t dia
gnos
is (m
mol
/mol
)
0.
99
[0.9
7-1.
01]
0.98
[0
.96-
1.01
] O
bser
vatio
ns
280
286
286
286
252
Mod
el 6
: AH
A is
adj
uste
d fo
r age
, sex
, edu
catio
n, im
mig
ratio
n, in
com
e an
d B
MI.
Mod
el 7
(bet
a ce
ll fu
nctio
n) a
nd M
odel
8 (i
nsul
in s
ensi
tivity
) are
adj
uste
d fo
r age
, sex
, IH
D,
AH
A a
nd B
MI.
Mod
el 9
: HbA
1c a
t dia
gnos
is is
adj
uste
d fo
r age
, sex
IHD
, AH
A, B
MI,
beta
cel
l fun
ctio
n an
d in
sulin
sens
itivi
ty.
52
As mentioned, the risk for DRAD in patients with T2D decreases with in-creasing BMI, Table 11, Model 5. This is in line with publications on risk for DRAD from Scotland, but is the opposite of the Gutenberg Health Study (37, 38, 80). We remade the regression for Model 5 with splined BMI (knots at BMI 25, 30 and 35 Kg/m2) and found that the association was more pro-nounced in patients with BMI >35 Kg/m2, Table 13. The estimates for BMI did not increase when adjusted for HbA1c at diagnosis, which would be expected if patients with BMI >35 Kg/m2 were diagnosed at an earlier stage due to opportunistic screening (Model 2).
Table 13. Odds ratios (OR) for diabetes retinopathy at diagnosis estimated with regression knots at body mass index (BMI) 30 and 35 Kg/m2. Model 1 (BMI) Model 2 (HbA1c) OR [95% C.I.] OR [95% C.I.] BMI (Kg/m2) <30 0.95 [0.90-1.00] 0.95 [0.91-1.00]
30-35 1.03 [0.96-1.12] 1.03 [0.95-1.11] >35 0.93 [0.87-0.99] 0.93 [0.87-1.00]
Age (years) Age 1.00 [0.99-1.01] 1.00 [0.99-1.01] Sex Male 1.00 [ref. cat.] 1.00 [ref. cat.]
Female 0.88 [0.69-1.12] 0.92 [0.72-1.16] Education <10 1.52 [1.11-2.08] 1.48 [1.08-2.03] (years) 10-12 1.21 [0.90-1.64] 1.19 [0.88-1.61]
>12 1.00 [ref. cat.] 1.00 [ref. cat.] Immigrated ≤10 years 1.17 [0.67-2.02] 1.18 [0.68-2.04]
>10 years1 1.00 [ref. cat.] 1.00 [ref. cat.] Income2 <60% 1.02 [0.67-1.55] 0.97 [0.64-1.47]
60%-150% 0.92 [0.72-1.18] 0.90 [0.70-1.15] >150% 1.00 [ref. cat.] 1.00 [ref. cat.]
HbA1c (mmol/mol) 1.01 [1.00-1.01] Observations 4,502 4,502 1. Patients born in Sweden included. 2. Percentage of median disposable income individu-alized from family income in the Scania region for each given year.
53
Study III Proportions with heredity for diabetes or other autoimmune diseases, as well as antidiabetic treatment, statin treatment and antihypertensive treatment are shown in Table 14. Diabetes in family and autoimmune disease in family were most common in patients with T2D. Medication with statin was most common in LADA patients. Apart from this, the study groups did not differ in characteristics. Mean, SD, median and IQR of characteristics are present-ed in Table 15. Patients with T1D had lower C-Peptide concentration com-pared to LADA patients. Table 14. Proportions (%) of diabetes (DM) or autoimmune disease (AD) in family (defined as parents, siblings or children).
Controls (n=13) LADA (n=14) T1D (n=16) T2D (n=16)
DM in family 0% 31% 39% 40%
AD in family 15% 15% 22% 27% Oral DM treat-ment 0% 69% 11% 80%
Insulin treatment 0% 8% 100% 7%
Statin 23% 62% 44% 33% Anti hypertensive treatment 31% 31% 50% 50%
54
Tabl
e 15
. Mea
ns, s
tand
ard
devi
atio
n (S
.D),
med
ian
and
inte
r-qu
artil
e ra
nge
(IQ
R) o
f age
, bod
y m
ass i
ndex
(BM
I), p
lasm
a (p
) c-r
eact
ive
pro-
tein
(CR
P), b
lood
(B) h
aem
oglo
bin
(Hb)
, leu
cocy
te c
ount
(Lkc
), es
timat
ed g
lom
erul
ar fi
ltrat
ion
rate
(eG
FR),
fast
ing
(f) C
-Pep
tide,
glu
tam
ic
acid
dec
arbo
lyca
se (G
AD
) ant
ibod
ies (
ab),
isle
t ant
igen
(IA
)-2
ab in
hea
lthy
cont
rols
and
pat
ient
s with
late
nt a
utoi
mm
une
diab
etes
in a
dult
(LA
DA
), ty
pe 1
dia
bete
s (T1
D) o
r typ
e 2
diab
etes
(T2D
). T-
test
s com
parin
g m
eans
to h
ealth
y co
ntro
ls, e
xcep
t for
P-G
luco
se, H
bA1c
, fC
-Pe
ptid
e, G
AD
ab
and
IA-2
ab
whe
re m
eans
wer
e co
mpa
red
to L
AD
A.
C
ontro
ls (n
=13)
LA
DA
(n=1
4)
T1D
(n=1
6)
T2D
(n=1
6)
Mea
n (S
.D)
Me-
dian
IQ
R
Mea
n (S
.D)
Me-
dian
IQ
R
Mea
n (S
.D)
Me-
dian
IQ
R
Mea
n (S
.D)
Me-
dian
IQ
R
Age
(yea
rs)
62
11
61
59
66
66
9 68
61
71
66
6
67
63
68
64
8 65
62
69
BM
I (K
g/m
2 ) 29
4
28
26
31
28
2 28
26
29
27
2.
9 28
24
28
29
4
29
27
33
P-C
RP
(mg/
L)
1.2
1.3
0.7
0.4
2.1
1.3
0.6
1.0
0.9
2.0
1.6
1.2
1.3
0.9
2.6
2.4
3.0
1.4
1.1
2.0
B-H
b (g
/L)
145
9 14
3 13
8 15
1 14
5 11
14
7 13
5 15
5 14
2 11
14
3 13
7 14
7 14
1 16
14
5 12
4 15
0
B-L
kc (1
0(9)
/L)
6.0
1.4
6.0
5.7
6.8
6.6
2.6
6.0
5.0
9.7
5.8
1.7
5.5
4.1
7.1
5.4
0.9
5.5
5.0
5.8
eGFR
(m
L/m
i/1.7
3)
79
13
82
78
87
79
12
81
64
90
83
7 83
81
89
83
9
86
76
90
P-G
luco
se
(mm
ol/L
) 5.
8 0.
7 5.
7 5.
2 6.
2 8.
1 2.
1 7.
1 6.
5 11
.0
10.6
3.
6 9.
5 8.
0 13
.1
10.1
2.
4 9.
8 8.
0 11
.3
HbA
1c
(mm
ol/m
ol)
37
2 37
36
38
45
8
44
38
50
62
12
61
52
70
58
19
50
46
65
fC-P
eptid
e (n
mol
/L)
1.1
0.4
1.1
0.8
1.4
0.8
0.3
0.9
0.6
1.0
0.2
0.4
0.0
0.0
0.1
1.2
0.4
1.1
0.9
1.6
GA
D a
b Ig
G
(IE/
mL)
0
0 0
0 0
60
98
4 0
141
334
696
32
1 99
0.
8 0.
7 1
0 1
IA-2
ab
IgG
(I
E/m
L)
0 0
0 0
0 0
0 0
0 0
29
82
0 0
4 0
0 0
0 0
55
Innate immune cells
The proportion of innate APC immune cells was lower in LADA patients compared to T1D patients and HC (Table 16, Figures 12 A and B). There was a gradual increase (T1D – LADA – T2D) in proportion of CD11c+CD123+ cells among HLA-DR+Lin- between the three diabetes sub-groups (Figure 12C).
Adaptive immune cells We observed a higher proportion of Breg cells in LADA patients than in T1D and T2D patients as well as in healthy controls (Table 16). The propor-tion of Treg cells was similar between the groups (Table 16, Figure 13A).
Regulatory immune cells The proportions of IL-35+ cell among Treg cells and tTreg cells were lower in LADA than in HC (Table 16, Figure 13B and C). The proportion of IL-35+ cells among Breg cells was higher in LADA patients than in T1D pa-tients (Table 16, Figure 13E). The proportion of IL-35+ cells among APCs was higher in LADA patients than in T1D patients (Table 16, Figure 13F).
56
Table 16. Proportions (higher (+), lower (-), no difference (±)) of innate immune cells, adaptive immune cells and proportion regulatory interleukin (IL) -35 produc-ing cells in age, sexed and bmi matched patients with type 1 diabetes (T1D, type 2 diabetes (T2D) and healthy controls, compared to patients with Latent Autoimmune Diabetes in Adults (LADA). Antigen presenting cells (APC), natural killer (NK) cells, regulatory T-lymphocytes (Treg), regulatory B-lyphocytes (Breg). One-way ANOVA with Dunnett’s post-hoc test was used for comparisons (n = 13-16/group). P-value <0.05 was considered as significant difference in proportions.
Proportion compared to LADA Identifiers T1D T2D Control
Innate immune cells
APC CD11c+CD123- + ± +
Neutrophil CD15low ± ± ±
NK CD3-CD56highCD16+ ± − −
Adaptive immune cells
Treg CD4+CD25+CD127-
Foxp3+ ± ± ±
tTreg CD4+CD25+CD127-
Foxp3+Helios+ ± ± ±
Breg CD19+CD40+CD38+ − − −
Proportion IL-35 producing regulatory cells tTreg ± ± +
Treg ± ± +
Breg − ± ±
APC − ± ±
57
Figure 12. (A) The proportion of (A) CD11c+CD123-HLA-DR+Lin- APCs. (B) The proportion of CD11c+CD123- among HLA-DR+Lin- cells. (C) The proportion of CD11c+CD123+ APCs among HLA-DR+Lin- cells. One-way ANOVA with Dun-nett’s post-hoc test was used for comparisons, n = 13-16/group. * and ** denote p<0.05 and p<0.01, respectively.
A
HC T1D LADA T2D0
2
4
6
% C
D11
c+ CD
123- H
LA-D
R+ L
in-
****
B
HC T1D LADA T2D0
20
40
60
80
100
% o
f CD
11c+ C
D12
3- cel
lsam
ong
HLA
-DR
+ Lin
-
**
C
HC T1D LADA T2D0
20
40
60
80
100
% o
f CD
123+ C
D11
c+ ce
llsam
ong
HLA
-DR
+ Lin
- ****
58
Figure 13. The proportion of (A) Treg cells. (B) The proportion of IL-35+ cells among Treg cells. (C) The proportion of IL-35+ cells among Tregs. (D) The propor-tion of Breg cells. (E) The proportion of IL-35+ cells among Breg cells. (F) The proportion of IL-35+ cells among APCs. HC denotes healthy controls. One-way ANOVA with Dunnett’s post-hoc test was used for comparisons, n = 13-16/group. * and ** denote p<0.05 and p<0.01, respectively.
A
HC T1D LADA T2D0.0
0.5
1.0
1.5
2.0
2.5
% T
reg
cells
B
HC T1D LADA T2D0
20
40
60
80
100
% o
f IL-
35+
cells
am
ong
Tre
g ce
lls
**
C
HC T1D LADA T2D0
20
40
60
80
100
% o
f IL-
35+
amon
g tT
reg
cells
*
D
HC T1D LADA T2D0
1
2
3
% B
reg
cells
*** ******
HC T1D LADA T2D0
50
100
150
% o
f IL-
35+
cells
am
ong
Bre
g ce
lls
*
E
HC T1D LADA T2D0
50
100
150
% o
f IL-
35+
cells
amon
g An
tigen
pre
sent
ing
cells
***
F
59
Study IV
Cluster analysis The cluster analysis resulted in five clusters. Cluster stability was >0.8 Jac-card means for all clusters regardless of sex, indicating cluster stability. Based on phenotype characteristics, the clusters were named SAID (severe autoimmune diabetes), SIDD (severe insulin deficient diabetes), SIRD (se-vere insulin-resistant diabetes), MOD (mild obese diabetes) and MARD (mild age-related diabetes). Patient characteristics of each cluster are found in Table 17. In short, SAID (6.4%) is characterised by low age at onset, high HbA1c at diagnosis and low fC-Peptide. Insulin therapy was initiated for 42% at registration. SIDD (17.5%) is characterised by high HbA1c at diag-nosis and low fC-Peptide. For 29% insulin therapy was initiated at the time of ANDIS registration. SIRD (15.3%) is characterised by high HbA1c and severe insulin resistance. MOD (21.6%) is characterised by low age at diag-nosis, high BMI and highest proportion diabetes in family, history of gesta-tional diabetes and non-Scandinavian origin. MARD is the largest cluster (39.1%) and characterised by near-diagnostic HbA1c and high age at diag-nosis.
Genetic associations Genetic loci previously shown to be associated with T2D and related traits were analysed to see if there was a genetic support for observed differences between clusters, Table 18 (81). Each cluster was compared to a cohort of people without diabetes from the same geographical region (MDC, de-scribed previously) (82). No genetic variant was associated (p<0.01) with all clusters. The T2D-associated variant in the TCF7L2 (rs7903146) gene (83) was associated with SIDD, MOD and MARD, but not with SIRD. A variant in IGF2BP2 (rs4402960) was associated with SIDD and MARD, but not with SIRD or MOD. The variant rs10401969 in the TM6SF2 gene previous-ly associated with NAFLD was associated with SIRD, but not with MOD suggesting that SIRD is characterised by unhealthy obesity and MOD by more healthy obesity (84).
60
Table 17. Patient characteristics in ANDIS using k-means clustering. Mean and standard deviation (SD) of HbA1c at diagnosis, body mass index (BMI), age at diagnosis, insulin secretion (HOMA2B), insulin resistance (HOMA2IR), insulin treatment, metformin treatment, family history of diabetes and origin. Stratified over severe autoimmune diabetes (SAID), severe insulin deficient diabetes (SIDD), se-vere insulin-resistant diabetes (SIRD), mild obese diabetes (MOD) and mild age-related diabetes (MARD). Homeostasis model assessment (HOMA).
SAID SIDD SIRD MOD MARD
N 577 1,575 1,373 1,942 3,513
Frequency (%) 6.4 17.5 15.3 21.6 39.1
Men (%) 55 65 59 52 62
HbA1C at diagnosis (mmol/mol) 80(31) 102(20) 54(16) 58(16) 50(10)
BMI (Kg/m2) 28(6) 29(5) 32(5) 36(5) 28(3)
Age at diagnosis, (years) 51(18) 57(11) 65(9) 49(10) 67(9)
HOMA2B (%) 57(45) 48(29) 151(47) 95(33) 87(26)
HOMA2IR 2.2(1.6) 3.2(1.7) 5.5(2.7) 3.4(1.2) 2.6(0.8)
Family history of diabetes (%) 59 64 56 70 58
History of gestational diabetes (% of women) 10 8 5 22 5
Non-Scandinavian origin (%) 15 27 15 32 17
Treatment at registration
Insulin (%) 42 29 4 3 2
Metformin (%) 45 79 49 59 44
61
Tabl
e 18
. Stro
nges
t gen
etic
ass
ocia
tions
with
seve
re a
utoi
mm
une
diab
etes
(SA
ID),
seve
re in
sulin
def
icie
nt d
iabe
tes (
SID
D),
seve
re in
sulin
re
sist
ant d
iabe
tes (
SIR
D),
mild
obe
se d
iabe
tes (
MO
D) a
nd m
ild a
ge-r
elat
ed d
iabe
tes (
MA
RD
) cal
cula
ted
by m
axim
um li
kelih
ood
estim
atio
ns
usin
g ge
ogra
phic
ally
mat
ched
con
trols
(CTR
L) w
ithou
t dia
bete
s. Si
ngle
nuc
leot
ide
poly
mor
phis
m (S
NP)
. SN
P G
ene
CTR
L SA
ID
SID
D
SIR
D
MO
D
MA
RD
N=2
754
N=3
13
N=6
76
N=6
03
N=7
27
N=1
341
MA
F O
R (9
5% C
onf.I
nt.)
OR
(95%
Con
f.Int
.) O
R (9
5% C
onf.I
nt.)
OR
(95%
Con
f.Int
.) O
R (9
5% C
onf.I
nt.)
rs28
5427
5 H
LA_D
QB
1 0.
13
2.05
(1.6
9-2.
56)*
***
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Replication of cluster analysis By applying the same cluster coordinates used in ANDIS, similar cluster proportions was found in the SDR, DIREVA and ANDiU cohorts, Figures 14 A-E. In DIREVA the clustering were similar in patients with newly (<2 years) diagnosed diabetes (Figure 14C) as in patients with longer diabetes duration (mean 10.15±10.34 years) (Figure 14D). The frequencies of the diabetes clusters in DIREVA largely corresponded with those seen in AN-DIS. However, SIDD was less frequent (14.6% vs. 17.5%). This difference can possibly be ascribed to a higher proportion of non-Scandinavians in ANDIS (25%) compared to DIREVA (1%).
Progression of disease and complications At registration, insulin had been prescribed to 41.9% of patients in SAID and to 29.1% in SIDD (Table 17). Time to insulin was shortest in SAID compared to MARD, followed by SIDD, Figure 15A. The proportion of patients on metformin was the highest in SIDD and the lowest in SAID (Ta-ble 17). Time to oral diabetes treatment other than metformin was also shortest in SIDD, followed by SIRD and MOD, Figure 15C. Time to treat-ment goal (HbA1c <52 mmol/mol) was the longest for SIDD even though SIDD patients were prescribed insulin, metformin and any other oral antidi-abetic drug the shortest time after diagnosis (Figures 15D).
In ANDIS, DRAD risk was significantly higher in SIDD than in the reference cluster MARD (OR 1.6; 95% C.I. 1.3-1.9). In ANDIU, DRAD risk was elevated in both SIDD (OR 4.6; 95% C.I. 3.0-7.0) and MOD compared to MARD (OR 2.2; 95% C.I 1.4-3.3), Figures 16 A and B.
NAFLD was most prevalent in SIRD and MOD, Figure 16C. SIRD had increased risk of both CKD (OR 1.8; 95% C.I. 1.3-2.7) and DKD (OR 2.2; 95% C.I. 1.4-3.3) after adjustment for age, sex and duration of diabetes. Risk of DKD was also higher in SIDD (OR 2.0; 95% C.I. 1.2-3.1), Figure 16 D.
63
HOMA2B HOMA2IR
HBA1C BMI AGE
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30
40
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Figure 14. Distributions of HbA1c (mmol/mol) at diagnosis, BMI (kg/m2), age (years), HOMA2-B (%) and HOMA2-IR at registration in for each cluster in three geographically separated cohorts. (A) ANDIS, (B) SDR, (C) DIREVA (diabetes duration <2 years), (D) DIREVA (diabetes duration ≥2 years, mean 10.15±10.34 years) and (E) ANDiU.
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64
HOMA2B HOMA2IR
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65
HOMA2−B HOMA2−IR
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80
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100
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E. ANDiU
66
Figure 15. Cox regressions of time to treatment with insulin (A), metformin (B), other oral antidiabetic drug (C) and to reach treatment goal (HbA1c <52mmol/mol) (D). Severe Autoimmune Diabetes (SAID) has the shortest time to insulin. Severe Insulin Deficient Diabetes (SIDD) has a shorter time to insulin, metformin and any other oral medication than Severe Insulin-Resistant Diabetes (SIRD), Mild Obese Diabetes (MOD) and Mild Age-Related Diabetes (MARD). Despite this, SIDD reached the treatment goal significantly later than other clusters (D).
A. Time to insulin
B. Time to metformin
Diabetes duration (years)
Diabetes duration (years)
67
C. Time to other antidiabetic drug
D. Time to HbA1c <52 mmol/mol
Diabetes duration (years)
Diabetes duration (years)
68
Figure 16. Prevalence of different stages of diabetic retinopathy at primary screen-ing in (A) ANDIS and (B) ANDiU. (C) Non-alcoholic fatty liver disease and (D) Chronic Kidney Disease (DKD in DIREVA by clusters assigned based on ANDIS cluster coordinates. Severe Autoimmune Diabetes (SAID), Severe Insulin-Deficient Diabetes (SIDD), Severe Insulin-Resistant Diabetes (SIRD), Mild Obese Diabetes (MOD) and Mild Age-Related Diabetes (MARD).
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69
Discussion
All four studies in this thesis explore factors accessible at the time of diag-nosis that influences the prognosis of diabetes.
Study I The results indicate that there is a socioeconomic gradient related to glyce-mic state at the time of diagnosis. An advantage is that the study is based on newly diagnosed cases of T2D and LADA in a defined population. Our out-come, HbA1c at diagnosis, has no bias due to duration-dependent selection of outcomes. The exposures were identified and registered before and inde-pendent of the outcome, ruling out reversed causation and dependent mis-classification of the exposures. Glycemic state at diagnosis can be viewed as a function of duration from onset to diagnosis and the aggressiveness of the disease, i.e., how fast blood glucose rises. Given the design of our study, it was impossible to determine time between onset and diagnosis. The most important factor to influence aggressiveness of diabetes is the ability to se-crete insulin. By using the HOMA2 calculator, beta-cell function was esti-mated from fC-peptide and FPG, but this only gives a measure of beta-cell function at time of ANDIS registration (that can be up to a year after diagno-sis). Because GADA positivity is linked to the deterioration of beta-cells (85), and consequently a more aggressive disease, we grouped the study population into T2D and LADA. The cohort was identified and recruited at the time of the outcome with no information on when the pathophysiological process leading to diabetes began. This impedes the interpretation of how low SEP influences HbA1c at diagnosis, since it could be either due to dif-ferences in duration from onset to diagnosis (i.e., delayed diagnosis) or ag-gressiveness of the disease.
Associations between SEP and diabetes incidence, prevalence and risk for diabetes complications are described in many papers (15-18, 23-27, 86-90), but the association between SEP and glycemic state at diagnosis is only sparsely studied (24, 91). Given the accumulating evidence of a link between metabolic memory and diabetes complications, factors that influence glyce-mic state at diagnosis are relevant in the endeavour to avoid future complica-tions.
70
Study II The prevalence of DRAD was 12% in T2D patients. This is considerably lower than publications from Scotland (19.3%) and the UK (19.0%), but equal to the Gutenberg Health Study (13%) from 2016 (37, 38, 80). In this population, DRAD prevalence was 13% in LADA patients. To our knowledge, prevalence of DRAD in LADA patients has not previously been described. Low beta-cell function and low level of education increased risk for DRAD and every unit increase in BMI decreased the risk. A possible explanation of this reciprocal relationship between BMI and DRAD may be that obese people are more often exposed to opportunistic diabetes screening and have a greater chance to be diagnosed sooner after debut. An alternative explanation is that obese patients have a less aggressive form of diabetes and are therefore less prone to have DRAD, as discussed in Study IV.
Study III In Study III, we observe differences in proportions of immune cells in LA-DA patients compared to other types of diabetes and healthy controls. We found that the frequency of Breg cells was higher in LADA patients than in healthy controls, T1D and T2D patients. This is in agreement with Deng et al. that found the lowest frequency of IL-10 producing B-cells in T1D pa-tients when compared to LADA and T2D patients (47). Lower proportion of IL-35+ cells among Breg cells in T1D patients than LADA patients further suggest that the response of Breg cells in LADA patients is similar to T2D patients.
Tolerogenic APCs protect beta-cells in animal models of T1D (92), and tolerogenic APCs produce IL-35 in human peripheral blood (93). In a previ-ous study, Singh et al. showed that destruction of beta-cells was prevented by giving systemic IL-35 to two different animal models of T1D (49). We found that IL-35+ APCs were more frequent in LADA than in T1D patients, indicating a higher anti-inflammatory capacity in LADA patients. This may contribute to the slower loss of insulin secretory capacity characteristic of LADA.
Study IV Classification of a disease serves two purposes; first it makes it easier to obtain more homogeneous groups for research (in a heterogeneous disease), secondly it facilitates more precise treatment regimes. The current diabetes classification does not fulfil these objectives. The T2D domination of 75%-80% is too indiscriminate and the clinical heterogeneity is too large to be
71
used for revising diabetes management. Under-dimensioned healthcare re-sources in the countries with the highest rise in diabetes inci-dence/prevalence will not manage to treat and monitor all patients equally. Therefore, it will be increasingly important to early identify patients at risk for diabetes. In light of this, it is urgent to find criteria that can be used to facilitate a more individualised diabetes care.
In Study IV we present a novel diabetes classification based on clustering of age, BMI, HbA1c at diagnosis, beta-cell function and insulin sensitivity in patients recently diagnosed with diabetes. Five clusters were identified. SAID included all T1D and LADA patients while the T2D patients were divided upon the SIDD, SIRD, MOD and MARD clusters. The highest prev-alence of DRAD was found in the SIDD cluster, which was characterised by low beta-cell function at diagnosis. This is in line with the findings of Study II, where low beta-cell function was a strong predictor for DRAD. The high-est prevalence of CKD and NAFLD was found in SIRD. We replicated the analyses with similar results in three geographically separated populations.
This cluster analysis only represents a first step to identify diabetes sub-groups and it is possible that more clustering variables can provide a more precise classification. An advantage of the cluster classification is the use of continuous variables, instead of more commonly used cut-off thresholds. However, it should be possible to include dichotomous variables (e.g., geno-types) or genetic risk scores in future enhancements.
Since the current diabetes classification was launched, immense knowledge on how diabetes is affected by genetics, immunology, biochemis-try and SEP has surfaced. In Study IV, we decided to classify diabetes by clinical presentation at diagnosis. With a clinical approach, we avoided a classification of etiology that requires complex analysis techniques. Instead we studied how well this ‘clinical’ classification corresponds to genetics and progression.
We cannot at this stage claim that the new clusters represent different eti-ologies of diabetes, nor that this represents the optimal classification of dia-betes subtypes. The fact that clustering gave similar results in newly diag-nosed patients and patients with longer diabetes duration, and that the key variable C-Peptide remained relatively stable over time, suggests that the clusters are stable and at least partially mechanistically distinct rather than representing different stages of the same disease. Notably, hepatic insulin resistance seems to be a feature of NAFLD, as the NAFLD-associated SNP in the TM6SF2 gene was associated with SIRD, but not with MOD.
72
Methodological considerations
Study I The underlying objective for this study was to investigate whether SEP in-fluences a delay in diabetes diagnosis. Optimally, we would follow a cohort from the time they did not have diabetes, to when they receive the diagnosis. For obvious reasons, this is not possible. Therefore, we have postulated that by comparing two conditions with supposedly different speed in deteriora-tion of the condition, the one with a slower pacing is more sensitive to varia-tions in SEP, than a condition that aggravates faster. We have considered T2D to be a condition that in general deteriorates slower than LADA, and is thus more susceptible to differences in SEP.
We selected education, income and immigration, as measures of SEP. There are other factors that influence SEP that we did not obtain. Occupa-tional status and other chronic diseases or disabilities influences income and the risk of acquiring diabetes, and it would be interesting to study its asso-ciation with HbA1c at diagnosis. Diet, smoking and physical activity influ-ence risk to acquire diabetes, but we consider them not to influence our out-come in this setting.
An asset is that information on our exposures was obtained from official registers. These registers have very little missing data, but they rely on law-abiding citizens. If you avoid taxes, the income data will be false.
Certain psychiatric diseases may influence risk for delayed diagnosis. Such chronic conditions also influence SEP.
PHCs with shortage on doctors or nurses influence access to care, which may affect the risk for delayed diagnosis.
Study II The limitations in Study I also apply to this study.
The ANDIS eye complication study includes patients on appearance to their first DR screening. This is a way to avoid bias by selection, but the bias may be at an earlier stage. Which people do not turn up for screening? Are there PHCs that fail to refer patients? Are there other eye clinics that they go to (and who choose those clinics)? These questions expose limitations of the
73
study. However, gender proportions, age span, income, education, immigra-tion and ethnical mixture were the same as in Study I.
There was no information on ever-smoking, smoking-cessation or on smoking quantities. Blood-pressure measurements were missing in 45% of cases. Instead we used AHA as a surrogate metric. AHA does not measure the physiological stress hypertension pose on the vasculature. There are sev-eral classes of AHA that have different pharmacodynamic effects. Pharma-ceuticals that inhibit angiotensin-converting enzymes may have a protective effect on retina since retina synthesises angiotensin, which in animal studies triggers the DR pathophysiology (33). The information on IHD was collect-ed from the Swedish Patient Register. This register does not include diagno-ses from the primary care. Stable IHD is mostly diagnosed and managed by the primary care and patients only appear in the Swedish Patient Register when hospitalised. It is also possible that patients suffer an IHD event abroad and are never hospitalised under an IHD diagnosis in Sweden.
Study III The laboratory techniques applied in Study III are very complex and strenu-ous to carry out, hence the small number of participants. Consequently, we cannot draw any conclusions that can be applied to larger diabetes popula-tions. The results of this study are primarily to be considered as descriptive of the cellular immunology in diabetes rather than as a comparison between diabetes sub groups.
Study IV Our cluster classification has several limitations. It uses clinical parameters measured at diagnosis, without knowledge of delayed diagnosis. Study I and Study II indicated that SEP might affect time to diagnosis, resulting in worse base-line status that may affect cluster affiliation. The mean follow-up time was 5.4 years, which is short for evaluating chronic diabetic complications. That CVD is more prevalent in MARD is not surprising, given that high age is one characteristic of the cluster and it is well known that high age is a risk factor for CVD. Longer follow-up time, follow-up blood sampling and repli-cation in the ANDiU study would further reassure the robustness of the clas-sification.
74
Conclusions
Study I People with a low level of education or low income are more often diag-nosed with T2D when HbA1c is >70 mmol/mol. A wider understanding of how SEP factors influence the clinical presentation at diagnosis may facili-tate information campaigns or screening programmes designed to target populations at risk for delayed diagnosis, and thereby improving progno-sis/delaying complications.
Study II The prevalence of DRAD in patients diagnosed with T2D or LADA is 12%. Beta cell function <50% doubles the risk for DRAD. There is a negative association between BMI and risk for DRAD. This association is more pro-nounced in patients with BMI >35 Kg/m2 regardless of HbA1c at diagnosis, beta-cell function or insulin sensitivity. Estimation of beta cell function from (f)C-Peptide and (f)P-Glucose may be a valuable tool to identify patients at risk for DRAD.
Study III The composition of immunological cells seen in patients with LADA is a mixture of immunological features observed in both T1D and T2D. The changes in APCs and Breg cells in LADA are more similar to those ob-served in T2D patients, whereas the changes in neutrophils, NK and Treg cells are more similar to that of T1D patients. The LADA patients had a higher capacity to secrete anti-inflammatory IL-35+ than T1D patients. This may contribute to the slower loss of insulin secretory capacity characteristic of LADA. To assess whether there are differences in cellular immunology between diabetes types, studies on larger populations would be needed.
75
Study IV By clustering clinical measures at diagnosis, five diabetes subgroups can be defined. These subgroups are more homogeneous than the current diabetes subgroups. Increasing clinical measures to classify diabetes may facilitate more individualised diabetes care and research.
76
Clinical relevance and future directions
SEP effect on HbA1c at diagnosis: Prolonged untreated hyperglycemia increases risk for future cardiovascular complications. Study I indicates that people with low SEP are at increased risk to be in a worse metabolic state when diagnosed. This insight may be used to complement the work to overcome the socioeconomic inequalities associated with diabetes information campaigns and more opportunistic screening in populations with low SEP.
One difficulty we had interpreting the results was discriminating between the effects of pathophysiological progression of the disease and that of SEP. By replicating the study with the more refined diabetes classification used in Study IV, a more robust result may be attained.
Diabetic retinopathy: We found HOMA2 estimated beta-cell function to be a strong predictor for DRAD, regardless of other known risk factors. By measuring FPG and fC-Peptide at diagnosis, this high-risk group can easily be identified. Whether this association is caused by insulin deficiency, C-Peptide deficiency or if it reflects a subclass of T2D (SIDD described in Study IV) needs to be an-swered in future studies.
The ANDIS eye complication study continues with the aims to investi-gate whether the rate in HbA1c lowering affects the incidence and progres-sion of retinopathy and how initial glucose-lowering treatment influences DR incidence and progression.
Cellular immunology There is a strong correlation between the humeral immune response (i.e., antibodies reactive against pancreatic beta-cells) and deterioration of insulin-secreting capacity. However, the antibody titres do not explain differentia-tion between T1D and LADA. In Study III, we identify differences in the cellular immune system that indicate higher anti-inflammatory capability in LADA patients that may protect the beta-cells from destruction. In T1D
77
animal studies IL35 administration has halted and even reversed the progres-sion of beta-cell deterioration. Before we can test this in humans, these find-ings need to be replicated in larger study populations.
Data-driven cluster classification of diabetes: A refined diabetes classification can be valuable for healthcare providers to identify patients at risk for complications and more effectively allocate re-sources.
The clusters presented in Study IV are a suggestion for diabetes classifi-cation and should not, at this stage, be seen as a presentation of different etiologies of diabetes. A follow-up study with new genotyping is planned to study epigenetic changes and metabolomics. It is important to study whether patients (especially from the periphery of clusters) can move between clus-ters and the exact overlap of weaker association signals will need to be in-vestigated in larger cohorts. It might also be possible to refine the classifica-tion further by including additional cluster variables, e.g., biomarkers, geno-types or genetic risk scores.
78
Sammanfattning på svenska
Diabetes är en folksjukdom som orsakar stort lidande, förtida död och höga samhällskostnader. Sjukdomen ger en ökad risk för följdsjukdomar i små blodkärl (retinopati, nefropati och neuropati) och i stora kärl (arterioskleros, hjärtinfarkt och stroke). Trots att vi vet vilka faktorer som ökar risken för följdsjukdomar lyckas vi inte fullt ut förhindra dessa.
Forskning visar att processer som leder fram till kroniska följdsjukdomar initieras av obehandlad hyperglykemi många år tidigare. Därför är bästa möjliga behandling de första åren efter diagnos extra viktig. Men variation av sjukdomsförlopp och utveckling av följdsjukdomar är stor inom alla tre diabetesundergrupperna; typ 1 diabetes (T1D, 10-15 %), typ 2 diabetes (T2D, 70-80 %) och latent autoimmune diabetes in adults (LADA, 10-15 %). En revision av rådande diabetesklassificering kan förbättra klinisk väg-ledning för optimal behandling.
Denna avhandling baseras på fyra delarbeten.
Delarbete 1 Undersöker om socioekonomisk position (definierad genom inkomst, utbild-ning och antal år boende i Sverige) påverkar risk för högt långtidsblodsocker (HbA1c) vid diagnostillfället. Studien visar att personer med lägre utbild-ning (mindre än 12 år) eller låg inkomst (<60% av medianinkomst) har större risk att ha ett högt HbA1c (>70 mmol/mol) när de får sin diabetesdia-gnos.
Vår slutsats är att låg utbildning och låg inkomst är två faktorer som ökar risken för att patienten har ett sämre utgångsläge redan när den diagnostise-ras. Denna kunskap kan bidra till arbetet att minska den ofördelaktiga socio-ekonomiska gradient som existerar för att utveckla kroniska diabeteskompli-kationer.
Delarbete 2 I det andra delarbetet undersöks förekomst av och riskfaktorer för ögonkom-plikationer (retinopati) vid första ögonbottenfotograferingen. Ögonläkare undersökte näthinnefotografier av 2451 patienter som tagits inom ett halvår efter att de fått sin diabetesdiagnos. Retinopati kunde konstateras på 12 % av patienter med typ 2 diabetes och hos 11 % med LADA. Låg utbildning ökade risken med 43 % och nedsatt förmåga att frisätta insulin ökade risken
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med 110 %. Ett överraskande resultat var att högt BMI minskade risken för retinopati.
Studien visar att retinopati vid första undersökningen är lika vanligt före-kommande hos patienter med T2D som hos de med LADA. Låg utbildning och låg förmåga att frisätta insulin ökar risken för att patienter med T2D har retinopati redan när de diagnostiseras med diabetes.
Delarbete 3 I det tredje arbetet beskrivs sammansättning och skillnader i cellulär immu-nologi hos patienter med LADA, T1D, T2D och hos personer utan diabetes. Flödescytometri användes för att räkna och karakterisera immunologiska celler i venöst blod hos ålder, kön och BMI matchade försökspersoner (14st med LADA, 16st med T1D, 16st med T2D och 13st utan diabetes).
Våra resultat visar att den cellulära immunologin hos patienter med LADA är en blandning av den som ses i patienter med T1D och T2D. LADA patienternas sammansättning av B-lymfocyter och antigen presenting cells (APC) var mer lik T2D medan uppsättningen av natural killer (NK) cells var mer lik den vid T1D. Studien indikerar en större förmåga att frisätta antiinflammatorsk interleukin 35 hos patienter med LADA jämfört med de med T1D, vilket kan vara en del av förklaringen till att vissa personer ut-vecklar T1D medan andra utvecklar LADA.
Delarbete 4 I det fjärde arbetet presenteras ett förslag till ny diabetesklassifikation base-rad på klusteranalys av ålder, BMI, HbA1c, insulinresistens och betacells-funktion. Fem kluster kunde identifieras; severe autoimmun diabetes (SAID) som inkluderade alla med typ 1 diabetes eller LADA, severe insulin defici-ent diabetes (SIDD), severe insulin resistant diabetes (SIRD), mild obese diabetes (MOD) och mild age-related diabetes (MARD). Klusteranalysen replikerades i ytterligare tre geografiskt åtskilda populationer med liknande resultat. SIDD var associerad till ökad förekomst av retinopati och SIRD med leverförfettning och nedsatt njurfunktion. Denna diabetesklassifikation identifierar fyra undergrupper till T2D med olika risker för följdsjukdomar. Uppföljande studier krävs för att se om patienter kan växla kluster över tid eller om indelningen är stabil.
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Acknowledgements
This thesis would not have been possible without the help and support and I have received from many people over the years. In particular, I acknowledge the following people:
Jan Stålhammar, primary supervisor, professional mentor and friend who both encourage and criticise me when I need it the most! Thank you for find-ing a new path when I ended up in a scientific cul-de-sac! Without you I would not have become a general practitioner and probably would not have been introduced to diabetes research.
Johan Hallqvist, co-supervisor. Thank you for opening my eyes to the complexity of epidemiology and for never losing faith in that I eventually would understand what you wanted me to understand….
Mozhgan Dorkhan, co-supervisor. Without your support and welcoming to the ANDIS team, this thesis would not exist.
Per-Ola Carlsson, for welcoming me to his research team and having faith in me as an ANDiU project leader. You are undoubtedly the best ballboard that a novel diabetes researcher can have!
Ronnie Pingel, Statistician Uppsala University for invaluable help in find-ing means to test our hypothesis and guiding me round all the pitfalls Carin Gustavsson, without you Study II would never exist. Thank you for inviting med to the ANDIS eye complication study! Kailash Singh, fellow PhD student and co-author for introducing me to the complex and extremely interesting world of immunology
Leif Groop and the Lund University Diabetes Centre (LUDC), for granting access to the ANDIS cohort, and for inviting me to work with the fantastic team behind Study IV.
Anders Rosengren, Petter Storm, Emma Ahlqvist, Johan Hultman and the ANDIS team for support, advise and positive energy!
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Violeta Armijo del Valle, Rebecca Hilmius and Karin Kjellström for doing a great work in ANDiU and collecting blood tests for Study III. Per Kristiansson, Margaretha Eriksson and fellow PhD students at AMPM, Uppsala University, for encouragement constructive feedback. My father Sven Jakobson, for challenging me to be more thorough in both thinking and writing. You make me progress! My mother Margareta for unconditioned love and always being there for me! My father in law, Hans Deuschl (and Ninni in memoriam) for your love, support and for always being there for us. Kia, my beautiful wife and partner in life. You are my everything, and more! And finally, our fantastic children Lina, Lovisa and Jakob for enduring eight years with an absent-minded (and at times stressed-out) father!
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References
1. Ringborg A, Lindgren P, Martinell M, Yin DD, Schon S, Stalhammar J. Prevalence and incidence of Type 2 diabetes and its complications 1996-2003--estimates from a Swedish population-based study. Diabet Med. 2008;25(10):1178-86.
2. Aronson D, Rayfield EJ. How hyperglycemia promotes atherosclerosis: molecular mechanisms. Cardiovasc Diabetol. 2002;1:1.
3. Aschner PJ, Ruiz AJ. Metabolic memory for vascular disease in diabetes. Diabetes technology & therapeutics. 2012;14 Suppl 1:S68-74.
4. Reddy MA, Zhang E, Natarajan R. Epigenetic mechanisms in diabetic complications and metabolic memory. Diabetologia. 2015;58(3):443-55.
5. Ryden L, Grant PJ, Anker SD, Berne C, Cosentino F, Danchin N, et al. ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: The Task Force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD). Eur Heart J. 2013.
6. Tuomi T, Groop LC, Zimmet PZ, Rowley MJ, Knowles W, Mackay IR. Antibodies to glutamic acid decarboxylase reveal latent autoimmune diabetes mellitus in adults with a non-insulin-dependent onset of disease. Diabetes. 1993;42(2):359-62.
7. Federation WHOaID. Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia. 2006.
8. Groop L, Pociot F. Genetics of diabetes – Are we missing the genes or the disease? Mol Cell Endocrinol. 2014;382(1):726-39.
9. The Swedish National Diabetes Register (NDR) [Available from: https://www.ndr.nu/ - /english.
10. Jansson SP, Andersson DK, Svardsudd K. Prevalence and incidence rate of diabetes mellitus in a Swedish community during 30 years of follow-up. Diabetologia. 2007;50(4):703-10.
11. International Diabetes Federation Guideline Development G. Global guideline for type 2 diabetes. Diabetes Res Clin Pract. 2014;104(1):1-52.
12. National Guidelines for Diabetes Care. Support for governance and management. In: (Socialstyrelsen) TNBoHaW, editor. 2015.
13. Chamberlain JJ, Rhinehart AS, Shaefer CF, Jr., Neuman A. Diagnosis and Management of Diabetes: Synopsis of the 2016 American Diabetes Association Standards of Medical Care in Diabetes. Ann Intern Med. 2016;164(8):542-52.
14. Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997;18:341-78.
83
15. Chaturvedi N, Jarrett J, Shipley MJ, Fuller JH. Socioeconomic gradient in morbidity and mortality in people with diabetes: cohort study findings from the Whitehall Study and the WHO Multinational Study of Vascular Disease in Diabetes. Bmj. 1998;316(7125):100-5.
16. Robinson N, Lloyd CE, Stevens LK. Social deprivation and mortality in adults with diabetes mellitus. Diabet Med. 1998;15(3):205-12.
17. Sundquist K, Chaikiat A, Leon VR, Johansson SE, Sundquist J. Country of birth, socioeconomic factors, and risk factor control in patients with type 2 diabetes: a Swedish study from 25 primary health-care centres. Diabetes Metab Res Rev. 2011;27(3):244-54.
18. Demakakos P, Marmot M, Steptoe A. Socioeconomic position and the incidence of type 2 diabetes: the ELSA study. Eur J Epidemiol. 2012;27(5):367-78.
19. Sacerdote C, Ricceri F, Rolandsson O, Baldi I, Chirlaque MD, Feskens E, et al. Lower educational level is a predictor of incident type 2 diabetes in European countries: the EPIC-InterAct study. Int J Epidemiol. 2012;41(4):1162-73.
20. Li X, Sundquist J, Zoller B, Bennet L, Sundquist K. Risk of hospitalization for type 2 diabetes in first- and second-generation immigrants in Sweden: a nationwide follow-up study. J Diabetes Complications. 2013;27(1):49-53.
21. Christine PJ, Auchincloss AH, Bertoni AG, Carnethon MR, Sanchez BN, Moore K, et al. Longitudinal Associations Between Neighborhood Physical and Social Environments and Incident Type 2 Diabetes Mellitus: The Multi-Ethnic Study of Atherosclerosis (MESA). JAMA Intern Med. 2015;175(8):1311-20.
22. Rawshani A, Svensson AM, Rosengren A, Zethelius B, Eliasson B, Gudbjornsdottir S. Impact of ethnicity on progress of glycaemic control in 131,935 newly diagnosed patients with type 2 diabetes: a nationwide observational study from the Swedish National Diabetes Register. BMJ Open. 2015;5(6):e007599.
23. Walker JJ, Livingstone SJ, Colhoun HM, Lindsay RS, McKnight JA, Morris AD, et al. Effect of socioeconomic status on mortality among people with type 2 diabetes: a study from the Scottish Diabetes Research Network Epidemiology Group. Diabetes Care. 2011;34(5):1127-32.
24. Fosse-Edorh S, Fagot-Campagna A, Detournay B, Bihan H, Eschwege E, Gautier A, et al. Impact of socio-economic position on health and quality of care in adults with Type 2 diabetes in France: the Entred 2007 study. Diabetic Med. 2015;32(11):1438-44.
25. Rawshani A, Svensson AM, Zethelius B, Eliasson B, Rosengren A, Gudbjornsdottir S. Association Between Socioeconomic Status and Mortality, Cardiovascular Disease, and Cancer in Patients With Type 2 Diabetes. JAMA Intern Med. 2016;176(8):1146-54.
26. Walker J, Halbesma N, Lone N, McAllister D, Weir CJ, Wild SH, et al. Socioeconomic status, comorbidity and mortality in patients with type 2 diabetes mellitus in Scotland 2004-2011: a cohort study. J Epidemiol Community Health. 2016;70(6):596-601.
27. Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. Int J Epidemiol. 2011;40(3):804-18.
28. IDF Diabetes Atlas - 7th edition, (2015). 29. Basu S, Stuckler D, McKee M, Galea G. Nutritional determinants of
worldwide diabetes: an econometric study of food markets and diabetes prevalence in 173 countries. Public Health Nutr. 2013;16(1):179-86.
84
30. Hjern A. Migration and public health: Health in Sweden: The National Public Health Report 2012. Chapter 13. Scand J Public Health. 2012;40(9 Suppl):255-67.
31. Bennet L, Groop L, Lindblad U, Agardh CD, Franks PW. Ethnicity is an independent risk indicator when estimating diabetes risk with FINDRISC scores: a cross sectional study comparing immigrants from the Middle East and native Swedes. Primary care diabetes. 2014;8(3):231-8.
32. Glans F, Elgzyri T, Shaat N, Lindholm E, Apelqvist J, Groop L. Immigrants from the Middle-East have a different form of Type 2 diabetes compared with Swedish patients. Diabet Med. 2008;25(3):303-7.
33. Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiol Rev. 2013;93(1):137-88.
34. Mariotti SP. Global data on visual impairments 2010. World health Orgnization; 2012.
35. Klein R, Klein BE, Moss SE. The Wisconsin Epidemiologic Study of Diabetic Retinopathy. XVI. The relationship of C-peptide to the incidence and progression of diabetic retinopathy. Diabetes. 1995;44(7):796-801.
36. Davidson MB. Diabetic retinopathy is not present in newly diagnosed diabetic patients. Diabetologia. 2016;59(12):2727-.
37. Looker HC, Nyangoma SO, Cromie D, Olson JA, Leese GP, Black M, et al. Diabetic retinopathy at diagnosis of type 2 diabetes in Scotland. Diabetologia. 2012;55(9):2335-42.
38. Kostev K, Rathmann W. Diabetic retinopathy at diagnosis of type 2 diabetes in the UK: a database analysis. Diabetologia. 2013;56(1):109-11.
39. The Diabetes Control Complications Trial/Epidemiology of Diabetes Interventions Complications Research Group. Retinopathy and Nephropathy in Patients with Type 1 Diabetes Four Years after a Trial of Intensive Therapy. New Engl J Med. 2000;342(6):381-9.
40. Laugesen E, Ostergaard JA, Leslie RD, Danish Diabetes Academy W, Workshop S. Latent autoimmune diabetes of the adult: current knowledge and uncertainty. Diabet Med. 2015;32(7):843-52.
41. Radenkovic M, Silver C, Arvastsson J, Lynch K, Lernmark A, Harris RA, et al. Altered regulatory T cell phenotype in latent autoimmune diabetes of the adults (LADA). Clin Exp Immunol. 2016;186(1):46-56.
42. Choi J, Leung PS, Bowlus C, Gershwin ME. IL-35 and Autoimmunity: a Comprehensive Perspective. Clin Rev Allergy Immunol. 2015.
43. Fonseca-Camarillo G, Furuzawa-Carballeda J, Yamamoto-Furusho JK. Interleukin 35 (IL-35) and IL-37: Intestinal and peripheral expression by T and B regulatory cells in patients with Inflammatory Bowel Disease. Cytokine. 2015;75(2):389-402.
44. Liu H, Zhang W, Tian FF, Kun A, Zhou WB, Xiao B, et al. IL-35 Is Involved in the Pathogenesis of Guillain-Barre Syndrome Through Its Influence on the Function of CD4+ T Cells. Immunol Invest. 2015;44(6):566-77.
45. Nakano S, Morimoto S, Suzuki S, Tsushima H, Yamanaka K, Sekigawa I, et al. Immunoregulatory role of IL-35 in T cells of patients with rheumatoid arthritis. Rheumatology (Oxford). 2015;54(8):1498-506.
46. Wang W, Li P, Chen YF, Yang J. A potential immunopathogenic role for reduced IL-35 expression in allergic asthma. J Asthma. 2015;52(8):763-71.
47. Deng C, Xiang Y, Tan T, Ren Z, Cao C, Huang G, et al. Altered Peripheral B-Lymphocyte Subsets in Type 1 Diabetes and Latent Autoimmune Diabetes in Adults. Diabetes Care. 2016;39(3):434-40.
85
48. Pino SC, Kruger AJ, Bortell R. The role of innate immune pathways in type 1 diabetes pathogenesis. Curr Opin Endocrinol Diabetes Obes. 2010;17(2):126-30.
49. Singh K, Kadesjo E, Lindroos J, Hjort M, Lundberg M, Espes D, et al. Interleukin-35 administration counteracts established murine type 1 diabetes - possible involvement of regulatory T cells. Scientific reports. 2015;5:12633.
50. Huang J, Xiao Y, Xu A, Zhou Z. Neutrophils in type 1 diabetes. Journal of Diabetes Investigation. 2016;7(5):652-63.
51. Espes D, Singh K, Sandler S, Carlsson PO. Increased Interleukin-35 Levels in Patients With Type 1 Diabetes With Remaining C-Peptide. Diabetes Care. 2017;40(8):1090-5.
52. Rodacki M, Milech A, de Oliveira JE. NK cells and type 1 diabetes. Clin Dev Immunol. 2006;13(2-4):101-7.
53. Maruyama T, Watanabe K, Yanagawa T, Kasatani T, Kasuga A, Shimada A, et al. The suppressive effect of anti-asialo GM1 antibody on low-dose streptozotocin-induced diabetes in CD-1 mice. Diabetes Res. 1991;16(4):171-5.
54. Piatkiewicz P, Milek T, Bernat-Karpinska M, Ohams M, Czech A, Ciostek P. The dysfunction of NK cells in patients with type 2 diabetes and colon cancer. Arch Immunol Ther Exp (Warsz). 2013;61(3):245-53.
55. Akesson C, Uvebrant K, Oderup C, Lynch K, Harris RA, Lernmark A, et al. Altered natural killer (NK) cell frequency and phenotype in latent autoimmune diabetes in adults (LADA) prior to insulin deficiency. Clin Exp Immunol. 2010;161(1):48-56.
56. Ringborg A, Martinell M, Stalhammar J, Yin DD, Lindgren P. Resource use and costs of type 2 diabetes in Sweden - estimates from population-based register data. Int J Clin Pract. 2008;62(5):708-16.
57. Kohner EM, Aldington SJ, Stratton IM, Manley SE, Holman RR, Matthews DR, et al. United Kingdom Prospective Diabetes Study, 30: diabetic retinopathy at diagnosis of non-insulin-dependent diabetes mellitus and associated risk factors. Arch Ophthalmol. 1998;116(3):297-303.
58. Action to Control Cardiovascular Risk in Diabetes Study G, Gerstein HC, Miller ME, Byington RP, Goff DC, Jr., Bigger JT, et al. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;358(24):2545-59.
59. Home PD. Impact of the UKPDS--an overview. Diabet Med. 2008;25 Suppl 2:2-8.
60. The ANDIS Study [All New Diabetics in Scania]. Available from: http://andis.ludc.med.lu.se/all-new-diabetics-in-scania-andis/.
61. The diabetes registry in Vaasa-DIREVA [Available from: https://www.vaasankeskussairaala.fi/globalassets/hallinnon-tiedostot/primarvardsenheten/direva---diabetesrekisteri/englanti/patient-briefing-from-the-diabetes-register-direva.pdf.
62. The Scania Diabetes Registry (SDR) The Swedish National Data Service (SND) [Available from: https://dev.snd.gu.se/en/catalogue/study/EXT0074?tab=description
63. Region Skåne, demography [Available from: http://www.skane.se/sv/Webbplatser/Strukturbild-for-skane/
66. Singh K, Hjort M, Thorvaldson L, Sandler S. Concomitant analysis of Helios and Neuropilin-1 as a marker to detect thymic derived regulatory T cells in naive mice. Scientific reports. 2015;5:7767.
67. Hennig C. Cluster-wise assessment of cluster stability. Comput Stat Data An. 2007;52(1):258-71.
86
68. Oxford Uo. The Homeostasis Model Assessment (HOMA) Calculator [Available from: https://www.dtu.ox.ac.uk/homacalculator/.
69. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412-9.
70. Levy JC, Matthews DR, Hermans MP. Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care. 1998;21(12):2191-2.
71. Muniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab. 2008;294(1):E15-26.
72. EUROSTAT-Statistics explained. Quality of life indicators - economic and physical safety [Available from: http://ec.europa.eu/eurostat/statistics-explained/index.php/Quality_of_life_indicators_-_economic_and_physical_safety.
73. The Swedish National Diabetes Register (NDR) [Available from: https://www.ndr.nu/ - /english.
74. Nationella diabetesregistret. Årsrapport av 2012 års resultat (National Diabetes Register. Annual report of results of 2012) [cited 2017 January 17]. Available from: https://www.ndr.nu/pdfs/Arsrapport_NDR_2012.pdf.
75. EQUALIS - External quality assessment [Available from: http://www.equalis.se/en/start/.
76. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999;130(6):461-70.
77. Wunsch G. Confounding and control. Demographic Research. 2007;16:97-120. 78. Best N, Green P. Structure and uncertainty: Graphical models for
understanding complex data. Significance. 2005;2(4):177-81. 79. Wilkinson CP, Ferris FL, 3rd, Klein RE, Lee PP, Agardh CD, Davis M, et al.
Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 2003;110(9):1677-82.
80. Ponto KA, Koenig J, Peto T, Lamparter J, Raum P, Wild PS, et al. Prevalence of diabetic retinopathy in screening-detected diabetes mellitus: results from the Gutenberg Health Study (GHS). Diabetologia. 2016;59(9):1913-9.
81. Prasad RB, Groop L. Genetics of type 2 diabetes-pitfalls and possibilities. Genes (Basel). 2015;6(1):87-123.
82. Manjer J, Carlsson S, Elmstahl S, Gullberg B, Janzon L, Lindstrom M, et al. The Malmo Diet and Cancer Study: representativity, cancer incidence and mortality in participants and non-participants. Eur J Cancer Prev. 2001;10(6):489-99.
83. Lyssenko V, Lupi R, Marchetti P, Del Guerra S, Orho-Melander M, Almgren P, et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest. 2007;117(8):2155-63.
84. Liu YL, Reeves HL, Burt AD, Tiniakos D, McPherson S, Leathart JB, et al. TM6SF2 rs58542926 influences hepatic fibrosis progression in patients with non-alcoholic fatty liver disease. Nature communications. 2014;5:4309.
85. Lundgren VM, Isomaa B, Lyssenko V, Laurila E, Korhonen P, Groop LC, et al. GAD antibody positivity predicts type 2 diabetes in an adult population. Diabetes. 2010;59(2):416-22.
87
86. Bennet L, Johansson SE, Agardh CD, Groop L, Sundquist J, Rastam L, et al. High prevalence of type 2 diabetes in Iraqi and Swedish residents in a deprived Swedish neighbourhood--a population based study. Bmc Public Health. 2011;11:303.
87. Chaikiat A, Li X, Bennet L, Sundquist K. Neighborhood deprivation and inequities in coronary heart disease among patients with diabetes mellitus: a multilevel study of 334,000 patients. Health & place. 2012;18(4):877-82.
88. Novak M, Bjorck L, Giang KW, Heden-Stahl C, Wilhelmsen L, Rosengren A. Perceived stress and incidence of Type 2 diabetes: a 35-year follow-up study of middle-aged Swedish men. Diabet Med. 2013;30(1):e8-16.
89. Bachmann MO, Eachus J, Hopper CD, Davey Smith G, Propper C, Pearson NJ, et al. Socio-economic inequalities in diabetes complications, control, attitudes and health service use: a cross-sectional study. Diabet Med. 2003;20(11):921-9.
90. Brown AF, Ettner SL, Piette J, Weinberger M, Gregg E, Shapiro MF, et al. Socioeconomic position and health among persons with diabetes mellitus: a conceptual framework and review of the literature. Epidemiol Rev. 2004;26:63-77.
91. Stoddard PH, G. Vijayaraghavan, M. Schillinger, D. Disparities in undiagnosed diabetes among United States-Mexico border populations. Revista panamericana de salud pública. 2010.
92. Creusot RJ, Giannoukakis N, Trucco M, Clare-Salzler MJ, Fathman CG. It's time to bring dendritic cell therapy to type 1 diabetes. Diabetes. 2014;63(1):20-30.
93. Dixon KO, van der Kooij SW, Vignali DA, van Kooten C. Human tolerogenic dendritic cells produce IL-35 in the absence of other IL-12 family members. Eur J Immunol. 2015;45(6):1736-47.
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A doctoral dissertation from the Faculty of Medicine, UppsalaUniversity, is usually a summary of a number of papers. A fewcopies of the complete dissertation are kept at major Swedishresearch libraries, while the summary alone is distributedinternationally through the series Digital ComprehensiveSummaries of Uppsala Dissertations from the Faculty ofMedicine. (Prior to January, 2005, the series was publishedunder the title “Comprehensive Summaries of UppsalaDissertations from the Faculty of Medicine”.)
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