biomarkers and social sciences
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Biomarkers and Social Sciences. Elliot Friedman Biomarkers, 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 - PowerPoint PPT PresentationTRANSCRIPT
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Biomarkers and Social Sciences
Elliot FriedmanBiomarkers, Genetics, and Social Sciences Workshop
July 7, 2010
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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”
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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
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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
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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
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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?
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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
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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
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Organizational model:psychosocial factors and physiological dysregulation
Social Status
Health Behaviors Psychosocial Processes
Neuroendocrine and autonomic regulatory
systems
Inflammatory proteins
Disease
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Plan for the afternoon Part I Neuroendocrine and autonomic
hormones Part II Inflammatory proteins Part III Multi-system dysregulation
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Part INeuroendocrine and autonomic hormones
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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
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CRF – Corticotropin-releasing factor
ACTH – Adrenocorticotropic hormone
Cortisol
HPA Axis
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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
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HIPPOCAMPUSNegative Feedback
In the presence of sustained exposure to cortisol, feedback is lost
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Cortisol and hippocampal volume
Lupien et al., Nat Neurosci., 1998
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Measuring cortisol Source of cortisol
Urine Blood Saliva
Type of measurement Assessment of stable levels Assessment of dynamic change
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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
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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
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Measuring cortisol Dynamic change
Response to challenge Amplitude of response
% of baseline Duration of response
Time to return to baseline
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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?
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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
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Cortisol and cancer survival
Sephton et al.(2000), Journal of the National Cancer Institute, 92: 994-1000
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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)
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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)
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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
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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
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Cortisol across the day
Steptoe et al., 2003
Low employment grade
High employment grade
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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
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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
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Importance of sample timing
Kudielka et al (2003), Psychosomatic Medicine 65: 313-319
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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
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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)
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Inflammatory proteins
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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
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Macrophage
“Large eater” Innate immune
system Many locations
around body Initiates
inflammatory response
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Inflammatory response (local)
IL-6
CRP Fibrinogen
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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
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Inflammation and cardiovascular disease
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Social status and health Inflammatory proteins as candidate
biomarkers Should be associated with health outcomes of
interest
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Inflammation as nexus
Inflammation
Cardiovascular Disease
Diabetes
Cancer
Alzheimer’s Disease
Rheumatoid arthritis
E.g. Interleukin-6 (IL-6)
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Social status and health Inflammatory proteins as candidate
biomarkers Should be associated with health outcomes of
interest Should exhibit patterning by social status
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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
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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
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Poverty and very high CRP (>10 mg/L) in NHANES IV
Alley et al., Brain Behav. Immun., 2006
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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
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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
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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
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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
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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)
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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
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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
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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
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Neuroendocrine and autonomic regulation
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Macrophage
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Cortisol regulation of immune function
Miller et al., 2002
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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)
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EpigeneticsYoung MZ twins Old MZ twins
Fraga et al. (2005), PNAS:10604-10609
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Regulation of inflammation
NF-kB
Cortisol
GCR
Inflammatory proteins
NF-kB is pro-inflammatory; glucocorticoids are anti-inflammatory
Norepinephrine
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Chronic stress and gene expression
Miller et al., 2008
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Early-life SES and NF-kB and GR signaling
Miller et al., 2009
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Chronic stress and regulation of inflammation
Miller et al., 2008
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Multi-system dysregulation
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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
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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
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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
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SES and allostatic load
Seeman et al., 2004
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Allostatic load and mortality risk
Seeman et al., 2004
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Recursive partitioning (regression trees)
Gruenewald et al (2006), PNAS, 103: 14158-14163
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Recursive partitioning (regression trees)
Gruenewald et al (2006), PNAS, 103: 14158-14163
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Recursive partitioning (regression trees)
Gruenewald et al (2006), PNAS, 103: 14158-14163
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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
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Organizational model:psychosocial factors and physiological dysregulation
Social Status
Health Behaviors Psychosocial Processes
Neuroendocrine and autonomic regulatory
systems
Inflammatory proteins
Disease
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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
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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