Biomarkers and Social Sciences

Elliot FriedmanBiomarkers, Genetics, and Social Sciences Workshop

July 7, 2010

Biomarkers and social sciences What is a “biomarker?”

Indicator of some biological process Biological processes are designed to serve

adaptive functions Can become damaging to health under certain

circumstances A potential mechanistic link between social factors

and health outcomes How social factors may “get under the skin”

Example – social status and health Health status is graded by social status

Living in poverty 12 fewer years of life 16 fewer years of disease/disability-free life

Social patterning of healthLife expectancy in England and Wales (1992-1996)

I II IIIN IIIM IV V0

102030405060708090

MenWomen

Social Class

Life

exp

ecta

ncy

Marmot, Perspect Biol Med, 2003

Source: Office of National Statistics Longitudinal Study

Class I – V differenceMen: 9.5 yrsWomen: 6.4 yrs

Example – social status and health Health status is graded by social status

Living in poverty 12 fewer years of life 16 fewer years of disease/disability-free life

What accounts for social gradients in health? Possible explanations

Differential access to health care But gradient seen in countries with universal health care

Poorer health behavior among the poor Explains only ~30% of the relationship

Psychosocial factors, especially stress

Psychological stress What makes stress stressful?

i.e. under what circumstances would we expect to see changes in biology?

Transaction between individual and environment Stressor

Controllability, predictability, severity Individual

Genetic background Life experiences Coping resources

Appraisal of threat to physical or psychological integrity Activation of stress response systems

Does the stress process offer insights into health disparities?

Stressor

Health

Illness

Stressor characteristi

cs

Individual attributes

High predictabilityHigh controlLow severity

Low predictabilityLow controlHigh severity

Positive appraisalAppropriate coping

Negative appraisalMismatched coping

Those with <HS education experience a greater number of severe stressors on a day-to-day basis1

1Almeida et al., 2005; 2Almeida et al., 2005; 3Lachman & Weaver, 1998

Perceived threat accounts for link between education and physical health symptoms2

Sense of control mediates link between income and health3

Cumulative experiences over the life-course

Example – social status and health Biomarkers

Should be associated with health outcomes of interest

Should exhibit patterning by social status Associated with relevant psychosocial factors

Candidate biomarkers: Neuroendocrine and autonomic hormones

Focus on cortisol Inflammatory proteins

Organizational model:psychosocial factors and physiological dysregulation

Social Status

Health Behaviors Psychosocial Processes

Neuroendocrine and autonomic regulatory

systems

Inflammatory proteins

Disease

Plan for the afternoon Part I Neuroendocrine and autonomic

hormones Part II Inflammatory proteins Part III Multi-system dysregulation

Part INeuroendocrine and autonomic hormones

Hormones Neuroendocrine

Hypothalamic-pituitary-adrenal (HPA) axis Hormone product: cortisol

Autonomic Sympathetic nervous system

Hormone products: epinephrine and norepinephrine Parasympathetic nervous system

Hormone product: acetylcholine Can also be measured indirectly

Control of heart rate

CRF – Corticotropin-releasing factor

ACTH – Adrenocorticotropic hormone

Cortisol

HPA Axis

Stress response External demand perceived as threatening

Need to marshal resources to adapt

Increase blood sugar Increase heart rate and blood pressure Inhibit non-essential functions

Digestion, protein synthesis, reproductive functions

HIPPOCAMPUSNegative Feedback

In the presence of sustained exposure to cortisol, feedback is lost

Cortisol and hippocampal volume

Lupien et al., Nat Neurosci., 1998

Measuring cortisol Source of cortisol

Urine Blood Saliva

Type of measurement Assessment of stable levels Assessment of dynamic change

Measuring cortisol Stable levels

Urinary cortisol Integrative measure Need to adjust for urine concentration

Typically by using creatinine levels Blood or saliva

Low lonely High lonely05

101520253035

Uri

nary

Cor

tiso

l (m

g/g

crea

tini

ne)

Sapolsky et al (1997), Archives of General Psychiatry, 54: 1137-1143

Kiecolt-Glaser et al. (1984), Psychosomatic Medicine, 46: 15-23

Measuring cortisol Dynamic change

Diurnal rhythm Cortisol awakening

response (CAR) Daily decline Area under the curve

(AUC)

Up to 50% increase in cortisol 30-45 min after awakening

Measuring cortisol Dynamic change

Response to challenge Amplitude of response

% of baseline Duration of response

Time to return to baseline

Evaluating cortisol What do the different assessments tell you?

Stable levels Urinary sample is most reliable Time scale: ½ to full day Q: How do social factors relate to stable levels of cortisol?

Diurnal patterns Time scale: hours; times of day Q: How do social factors relate to dynamics of daily cortisol

regulation? Response to challenge

Time scale: minutes Q: How do social factors relate to HPA system’s ability to

respond to and recover from an acute challenge?

Cortisol as candidate biomarker Cortisol is related to health outcomes

Cushing’s Disease associated with increased risk of cardiovascular disease

Daily cortisol rhythms linked to health

Cortisol and cancer survival

Sephton et al.(2000), Journal of the National Cancer Institute, 92: 994-1000

Cortisol as candidate biomarker Cortisol is related to health outcomes

Cushing’s Disease associated with increased risk of cardiovascular disease

Daily cortisol rhythms linked to health

Cortisol is patterned by social factors (including status)

Social factors and diurnal cortisol patterns

Hajat et al (2010), Psychoneuroendocrinology, 35: 932-943

Data from the Multi-Ethnic Study of Atherosclerosis (MESA; N = 935)

Social factors and diurnal cortisol patternsData from the study of Coronary Artery Risk Development in Adolescents (CARDIA; N = 781)

Cohen et al (2006), Psychosomatic Medicine, 68: 41-50

Cortisol as candidate biomarker Cortisol is related to health outcomes

Cushing’s Disease associated with increased risk of cardiovascular disease

Daily cortisol rhythms linked to health

Cortisol is patterned by social factors (including status)

Associated with relevant psychosocial factors Stressors increases cortisol levels

Social-evaluative threats are the most potent Mitigating factors

Perceptions of control or predictability Social support

Severe or chronic stress changes cortisol regulation Flatter diurnal slopes Amplified responses to acute stressors

Cortisol across the day

Steptoe et al., 2003

Low employment grade

High employment grade

Using cortisol data Tends to be positively skewed; often outliers

Trimming and log-transformations typically applied

Consider variables that affect cortisol and might represent confounds Serious illness Endocrine disorders 3rd trimester of pregnancy Steroid medications

Menstrual cycle timing Body weight Exercise levels Caffeine intake Smoking status Medication use

Common exclusion criteria

Typically statistically controlled

Using cortisol data Additional considerations

What time of day were data collected? Important for blood and saliva in particular

For daily samples, how was collection time verified? Critical for determining daily patterns, like CAR or

decline

Importance of sample timing

Kudielka et al (2003), Psychosomatic Medicine 65: 313-319

Age and the CAR:Rationale for multi-day cortisol sampling

Data from the Survey of Mid-Life in the United States (MIDUS; N = 1,143

Almeida et al (2009), Psychology and Aging, 24: 819-827

Summary Cortisol can be measured in many ways, each of

which provide different sorts of information

Social adversity and poorer health outcomes typically associated with Higher urinary levels Flatter diurnal slopes; higher evening values More pronounced reactivity to and delayed recovery from

challenge

Patterns are similar for urinary epinephrine and norepinephrine and for some indices of autonomic function (e.g. control of heart rate)

Inflammatory proteins

What is inflammation? Biological process

Important for host defense Maladaptive if not contained in space and time

Blood-borne proteins Pro-inflammatory cytokines

Interleukin-6 (IL-6) Acute phase proteins

C-reactive protein (CRP) Clotting factors

Fibrinogen Health implications

Linked to morbidity and mortality Increased risk of later cardiac events in healthy individuals Increased risk of cardiac mortality in patients with CVD Limited screening for CRP currently recommended

Sensitive to sociodemographic and psychosocial factors

Macrophage

“Large eater” Innate immune

system Many locations

around body Initiates

inflammatory response

Inflammatory response (local)

IL-6

CRP Fibrinogen

Inflammation Local

Contained, short-lived Proteins typically not detected in blood Critical for host defense

Robust response indicative of optimal health Systemic

Indicative of chronic disease or disease risk Measurable blood levels of inflammatory

proteins Predictive of adverse health outcomes

Inflammation and cardiovascular disease

Social status and health Inflammatory proteins as candidate

biomarkers Should be associated with health outcomes of

interest

Inflammation as nexus

Inflammation

Cardiovascular Disease

Diabetes

Cancer

Alzheimer’s Disease

Rheumatoid arthritis

E.g. Interleukin-6 (IL-6)

Social status and health Inflammatory proteins as candidate

biomarkers Should be associated with health outcomes of

interest Should exhibit patterning by social status

SES and inflammation Income and education

NHANES1; MESA2; Framingham offspring3; Heart and Soul Study4; Health, Aging, and Body Composition Study5; CARDIA6

Occupational status7

Community SES8

Composite of income and education Subjective social status9

Hypothesized by Adler et al, 2000

1Alley et al., 2005; 2Ranjit et al., 2007; 3Loucks et al., 2006; 4Lubbock et al., 2005; 5Koster et al., 2006; 6Gruenewald et al., 2009; 7Owen et al., 2003; 8Petersen et al., 2008; 9Demakakos et al., Soc Sci Med, 2008

Education and inflammation in Framingham offspring

00.5

11.5

22.5

33.5

44.5

5

CRP (mg/L)IL-6 (pg/mL)

Education

Loucks et al., Am J. Epidemiol., 2006

Poverty and very high CRP (>10 mg/L) in NHANES IV

Alley et al., Brain Behav. Immun., 2006

Subjective social status and inflammation in ELSA

Lowest 2 3 4 5 6 7 8 9 Highest0

0.51

1.52

2.53

3.54

CRP (mg/L)Fibrinogen (g/L)

Subjective social status

Demakakos et al., Soc Sci Med, 2008

Income and IL-6

Q1 Q2 Q3 Q4 Q50

0.1

0.2

0.3

0.4

0.5

Pre-Tax Household Income (quintiles)

Log

Seru

m IL

-6

Lowest Highest

Friedman & Herd (2010), Psychosomatic Medicine, 73: 290-300

Income and CRP

Q1 Q2 Q3 Q4 Q50

0.1

0.2

0.3

Pre-Tax Household Income (quintiles)

Log

Seru

m C

RP

Lowest Highest

Friedman & Herd (2010), Psychosomatic Medicine, 73: 290-300

Social status and health Inflammatory proteins as candidate

biomarkers Should be associated with health outcomes of

interest Should exhibit patterning by social status Associated with relevant psychosocial factors

Inflammation as nexus

Inflammation

Socioeconomic status

Psychological functioning

Social and familial

relationships

Cardiovascular Disease

Diabetes

Cancer

Alzheimer’s Disease

Rheumatoid arthritis

Age

Neurobiological functioning

E.g. Interleukin-6 (IL-6)

Measuring inflammatory proteinsCollection method Advantages Disadvantages

Blood sample Many assays calibrated for blood samplesCan analyze multiple proteins in single sample (now hundreds of proteins)

InvasiveCollection requires trained phlebotomistsSamples must be processed and frozen quickly after collection

Blood spot Minimally invasiveCan be easily used in the fieldVery stable at room temperature for long periods

Few assays available (CRP, but not IL-6)Quality of spot important; may require trained staff

Saliva Non-invasiveCan be easily used in the field

Few assays available (IL-6, but not CRP)Sample must be frozen soon after collection

Using data on inflammatory proteins Like cortisol, can be positively skewed

Especially IL-6 and CRP; not fibrinogen Typically log-transformed

Important covariates Acute illness

Discard cases with CRP levels >10 mg/L Obesity

BMI and Waist-hip ration are strongest predictors of most inflammatory proteins

Medications E.g. antihypertensives; antidepressants

Sex Women typically have higher CRP levels than men

Important to control for use of HRT

Integrative model:regulation of inflammation How does inflammation machinery “listen” to

social factors? Regulation by neuroendocrine and autonomic

systems Influences

Cortisol is anti-inflammatory Norepinephrine and epinephrine have pro-

inflammatory actions

Neuroendocrine and autonomic regulation

Macrophage

Cortisol regulation of immune function

Miller et al., 2002

Integrative model:role for epigenetics Epigenetics

Genome codes for proteins “epigenome” determines whether and when genes

are activated and proteins are actually made Gene X environment interaction in “real time”

Can be long-lasting (intergenerational)

EpigeneticsYoung MZ twins Old MZ twins

Fraga et al. (2005), PNAS:10604-10609

Regulation of inflammation

NF-kB

Cortisol

GCR

Inflammatory proteins

NF-kB is pro-inflammatory; glucocorticoids are anti-inflammatory

Norepinephrine

Chronic stress and gene expression

Miller et al., 2008

Early-life SES and NF-kB and GR signaling

Miller et al., 2009

Chronic stress and regulation of inflammation

Miller et al., 2008

Multi-system dysregulation

Multi-system dysregulation Rationale

Changes in multiple biological systems associated with both social factors and different disease states

Biological risk of disease may be best captured by multi-system perspective

Allostatic load Homeostasis - “steady state” Allostasis - “stability through change” Maintaining vital functions “costs” an organism

Over time, organism may not be able to return to optimal levels of function (“wear and tear”)

Costs are higher when demands are greater or more persistent e.g. low social status Cost benefit assessment: survival in the short-term in exchange for disease

in the long-term

Allostatic loadWhat to measure?

Earlier formulation (10 items) Recent formulation (19 items) Systolic blood pressure Diastolic blood pressure Waist-hip ratio Lipids (total and HDL

cholesterol) Glycosylated hemoglobin Urinary cortisol, DHEA-S Urinary norepinephrine Urinary epinephrine

Cardiovascular Systolic blood pressure Diastolic blood pressure Heart rate/pulse

Metabolism Glucose Glycosylated hemoglobin Insulin Lipids (total, HDL, LDL cholesterol) Body mass index/waist-hip ratio

HPA Urinary cortisol, DHEA-S

Autonomic Urinary norepinephrine/epinephrine Heart rate variability

Inflammation IL-6, CRP

Allostatic loadHow to measure? Summary score

Number of items in top (i.e. “highest risk”) quartile of distribution Bottom quartile for HDL cholesterol and DHEA-S

Other approaches that have been used Canonical correlation

Karlamangla et al (2002), Journal of Clinical Epidemiology, 55: 696-710 Recursive partitioning

Gruenewald et al (2006), Proceedings of the National Academy of Sciences, 103: 14158-14163.

Grade-of-membership Seplacki et al (2006), Demography, 43: 165-183.

Current Conclusion No clear “gold standard” for either items to include or construction of

risk index

SES and allostatic load

Seeman et al., 2004

Allostatic load and mortality risk

Seeman et al., 2004

Recursive partitioning (regression trees)

Gruenewald et al (2006), PNAS, 103: 14158-14163

Recursive partitioning (regression trees)

Gruenewald et al (2006), PNAS, 103: 14158-14163

Recursive partitioning (regression trees)

Gruenewald et al (2006), PNAS, 103: 14158-14163

Allostatic load Advantages

Captures possible dysregulation across biological systems that are associated with stress and health outcomes

Better prediction of health than individual markers alone

Critiques Loss of information at opposite extremes Examinations of mediation have not been

supportive

Organizational model:psychosocial factors and physiological dysregulation

Social Status

Health Behaviors Psychosocial Processes

Neuroendocrine and autonomic regulatory

systems

Inflammatory proteins

Disease

General Conclusions Biological systems are designed to respond to

environmental demands Static and dynamic biomarker measures provide

different kinds of information Biological systems are designed to interact

Allostatic load assesses multi-system risk, but not multi-system interactions

Allostasis perspective Physiological dysregulation later in life may be the

price of survival earlier in life

Perspectives on biomarkers Why study them?

‘Canaries in the coal mine’ Biological systems are highly tuned to the

environment Even at the genetic level Lens on social processes

Biological systems inter-related Measurement of individual factors may be less informative

than measurement of their associations/combinations Biological systems are dynamic

Designed to “do” something Health may be more related to biological function than

levels of biomarkers