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8
Biomarkers in Social Science Research on Health and Aging: A Review of Theory and Practice

Douglas C. Ewbank


Five years ago, Cells and Surveys: Should Biological Measures Be Included in Social Science Research? (National Research Council, 2001a) identified biomarkers as an important tool for understanding the association of socioeconomic status with health and mortality. Since then, biomarkers have rapidly become a standard feature in large-scale social surveys, and there has been growth of a new, rich literature on this topic. This is an appropriate time to pause and review the theories behind the use of biomarkers and to assess how far we have progressed in applying them in social science research.

There are many reasons to collect biomarkers in population-based longitudinal studies. For example, Harris, Gruenewald, and Seeman (Chapter 5 in this volume) are primarily interested in understanding the role of various biological systems with a secondary interest in how they interact with social factors. Similarly, Straub, Cutolo, Zietz, and Scholmerich (2001) call for the inclusion of biomarkers in longitudinal epidemiological studies to document the progression of the “vicious cycle of immunosenescence, endocrinosenescence, and neurosenescence.” However, from the perspective of social scientists studying aging and health, the research priorities are generally more limited. Biomarkers can be useful for studying a variety of social behaviors and environments. They can operate as underlying risk factors (e.g., genetics or birth weight) or as intermediate variables. They can also provide an alternative to self-reports. For example, serum cotinine concentration has been used to evaluate the accuracy of self-reports of cigarette smoking (Perez-Stable, Marin, Marin,



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8 Biomarkers in Social Science Research on Health and Aging: A Review of Theory and Practice Douglas C. Ewbank F ive years ago, Cells and Surveys: Should Biological Measures Be Included in Social Science Research? (National Research Council, 2001a) iden- tified biomarkers as an important tool for understanding the asso- ciation of socioeconomic status with health and mortality. Since then, biomarkers have rapidly become a standard feature in large-scale social surveys, and there has been growth of a new, rich literature on this topic. This is an appropriate time to pause and review the theories behind the use of biomarkers and to assess how far we have progressed in applying them in social science research. There are many reasons to collect biomarkers in population-based lon- gitudinal studies. For example, Harris, Gruenewald, and Seeman (Chap- ter 5 in this volume) are primarily interested in understanding the role of various biological systems with a secondary interest in how they inter- act with social factors. Similarly, Straub, Cutolo, Zietz, and Scholmerich (2001) call for the inclusion of biomarkers in longitudinal epidemiological studies to document the progression of the “vicious cycle of immunose- nescence, endocrinosenescence, and neurosenescence.” However, from the perspective of social scientists studying aging and health, the research priorities are generally more limited. Biomarkers can be useful for study- ing a variety of social behaviors and environments. They can operate as underlying risk factors (e.g., genetics or birth weight) or as intermedi- ate variables. They can also provide an alternative to self-reports. For example, serum cotinine concentration has been used to evaluate the accuracy of self-reports of cigarette smoking (Perez-Stable, Marin, Marin, 

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 DOUGLAS C. EWBANK Brody, and Benowitz, 1990). However, much of the recent research on aging has focused on the effects of chronic social and psychological stress and measures associated with the biological concepts of “robustness” and “allostatic load.” Social scientists studying health and aging face a great challenge. The health outcomes we study involve interactions among a wide variety of physical and cognitive conditions. The social and behavioral environ- ments we want to relate to health problems are equally complex and difficult to measure. We therefore seek associations between very hetero- geneous causes and equally heterogeneous outcomes. Biomarkers offer more narrowly defined, more proximal intermediate outcomes or, as dis- cussed below, more specific characterization of social and behavioral environments. Before trying to evaluate recent progress in using biomarkers to study health and aging, it is useful to review a few of the social and biological concepts that are central to much of the research. This review therefore begins with discussions of chronic social–psychological stress and biologi- cal robustness. The next section considers strategies for identifying which biomarkers are potentially most valuable for social science research on stress and aging. This is followed by a discussion of life-cycle approaches to understanding the aging process from conception to senescence. The chapter concludes with a few words of caution for future research and an overview of the progress in using biomarkers in social science research on aging. SOCIAL AND BEHAvIORAL STRESS There are both short-term and long-term perspectives for studying the health impact of stress. In the short term, stress generally refers to events or circumstances that elicit an emotional (usually negative) response. Epi- sodes of stress can affect the immune system and therefore susceptibility to disease (Segerstrom and Miller, 2004). Recently, social scientists have begun to study the cumulative effects of repeated episodes of a wider range of social-psychological, physical, and environmental stressors. This literature emphasizes links between stress and such chronic diseases as cardiovascular disease and diabetes. We have a lot to learn about the sources of stress. For example, the Whitehall study quickly showed that the concept of “executive stress” was not consistent with the gradient they observed; the executives at the top of the hierarchy had the lowest risk of several major causes of death. This raises the question of whether we know real stress when we see it or whether we can identify sources of chronic stress. Ironically, biomarkers can help us recognize stressful circumstances.

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 BIOSOCIAL SURVEYS They provide one type of evidence about which individuals in an orga- nization or society are most stressed, what kinds of social situations are most stressful, what kinds of social organizations relieve stress, and how individuals differ in their ability to handle stress. Much of the early research on this topic relied on measures associated with cardiovascular activation, which generally reflects reactions to recent events (up to 24 hours). Few of these studies were population-based. For example, cat- echolamines (epinephrine and norepinephrine) excretion rates (James and Brown, 1997) were used to identify which individuals in Otmoor villages in Oxfordshire were most stressed (Harrison, 1981) and which occupa- tions were most stressful (Jenner, Harrison, Day, Huizinga, and Salzano, 1982). However, as James and Brown have noted, these measures reflect “the complexity of the circumstance, including the subject’s perceptions and cognitive state, the nature of the social situation, potential food, stimulant and exercise effects, and the ambient environmental conditions, such as temperature or altitude” (James and Brown, 1997, p. 329). Recent research has emphasized biomarkers that reflect longer term damage to the body’s regulatory systems (see below). For example, chronic elevation of C-reactive protein (CRP) is a good marker for systemic infec- tion and is closely associated with coronary heart disease (Danesh et al., 2004). CRP levels have been used to examine the long-term effect of stress associated with socioeconomic status in Whitehall II (Owen, Poulton, Hay, Mohamed-Ali, and Steptoe, 2003), in Chicago (McDade, Hawkley, and Cacioppo, 2006), and in the United States as a whole (Alley et al., 2006). Psychologists studying the effects of stress have proposed five cat- egories of stress (Segerstrom and Miller, 2004). Most of their research has involved episodes of acute stress. Acute time�limited stressors involve artificial experiences produced in laboratory experiments, such as tasks involving mental arithmetic. Brief naturalistic stressors involve such events as students taking SAT exams. Although the effect of these brief stressors on immune function may cumulate to create serious health problems in the long run, they will be hard to document in large longitudinal surveys (Graham, Christian, and Kiecolt-Glaser, 2006). Social scientists are more apt to be interested in sources of stress that have longer effects. Stressful event sequences involve a single event that has a number of consequences for one’s life. Examples include natural disas- ters and the loss of a spouse or child. Although the effects may last for months or years, they can be expected to subside over time. Chronic stress� ors usually require restructuring social roles or personal identity. These include becoming a caregiver for someone with dementia or a traumatic injury that causes permanent disability (Graham et al., 2006). The final category is distant stressors, traumatic experiences, such as sexual assaults in the past, that can have long-term psychological effects.

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 DOUGLAS C. EWBANK Studies of the long-term effect of stress on health, such as those related to occupational or race/ethnic relations, may involve frequent (perhaps daily) exposure to a variety of stressors, some of which don’t fit neatly into these categories. For example, we might want to include unreasonable expectations, distrust, or lack of respect, which may reflect the general social environment, rather than simply the effects of single incidents. There is no single biomarker that will identify all of the social and psychological circumstances that we consider stressful. In particular, it appears that different types of psychological stress can have very different effects on biomarkers. For example, within the category of stressful event sequences, the loss of a spouse is generally associated with a decline in natural inflammatory responses, which is not apparent following a breast biopsy (Segerstrom and Miller, 2004).1 The complex relationship between social environments and stress is apparent from animal models. Sapolsky (2005) has reviewed the literature on the physiological effects of social hierarchies in primates. He concludes that “no consensus exists as to whether dominant or subordinate animals are more physiologically ‘stressed.’ . . . Rank means different things in dif- ferent societies and populations.” He offers a number of generalizations. 1. In social groups with a relatively fixed hierarchy, subordinate individuals feel stress from subjugation. However, when struc- tures are more fluid, dominant individuals tend to be more stressed, since they must struggle to maintain their dominance. This suggests that results based on the Whitehall study that high- status jobs are associated with less stress may not be generalizable across a wide range of social organizations. In particular, a study of men in the Otmoor villages in Oxfordshire found higher levels of adrenaline associated with high social class (professional and manual versus nonmanual labor) (Jenner, Reynolds, and Harrison, 1980; Harrison, 1981). 2. Subordination is more stressful in species with greatest inequality in access to resources. 3. Coping strategies and social support including the presence of kin can ameliorate the effects of stressful circumstances. 4. Subordinates can develop social strategies for avoiding interac- tions with dominants. 1 Segerstrom and Miller provide an excellent meta-analysis of the effects of various types of stressors on a wide range of immune indicators, including 24 articles on the effects of chronic stress and 44 articles on the effects of important life events (Segerstrom and Miller, 2004).

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0 BIOSOCIAL SURVEYS These observations about primate societies are quite consistent with hypotheses that social scientists are beginning to study. For example, some discussions of the effects of social inequality have been framed in terms of stress (Geronimus, Bound, Waidmann, Colen, and Steffick, 2001), and researchers have used biomarkers to examine the importance of social support (Seeman et al., 2002) and social ties (Seeman et al., 2004). BIOLOgICAL ROBUSTNESS The challenge for social scientists is to identify a manageable number of biomarkers that capture the most important features of the aging pro- cess and the development of chronic diseases. Two related concepts—bio- logical robustness and allostatic load—provide a theoretical framework for selecting biomarkers for social science research. Systems analysis provides a useful way of thinking about how an organism manages the numerous changes in chemistry required to deal with regular and irregular challenges that result from the interactions with the environment. The challenges come from the physical environ- ment, stressful situations, persistent anxiety, and such behaviors as diet and smoking. This approach involves consideration of feedback loops, redundancy, and diversity of function. A central theme is the concept of robustness. Kitano states: “[r]obustness is a property that allows a sys- tem to maintain its functions despite external and internal perturbations. It is one of the fundamental and ubiquitously observed systems-level phenomena that cannot be understood by looking at the individual com- ponents” (2004, p. 826). Over time, the functioning of biological systems leads to the accu- mulation of damage that reduces robustness. Initially this can result in a delayed return to equilibrium following a challenge. Longer periods spent in disequilibrium cause additional damage. The rate of accumulation is determined, in part, by the frequency and severity of challenges as well as genetic and developmental factors that determine initial robustness. Eventually this process can lead to a state of chronic, severe disequilib- rium. The concept of robust response to challenge is central to many ways of thinking about the aging process. Proper functioning of biological systems does not rely on robust maintenance of a single equilibrium point (homeostasis). Instead, the appropriate balance point changes to minimize energy requirements (and expected requirements) in given circumstances. For example, when there is a perception of a need for defensiveness and vigilance, the mobilization of energy requirements is dominated by the sympathetic nervous system. At other times, the parasympathetic system imposes cognitive control

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 DOUGLAS C. EWBANK that can better balance resources. Therefore, the appropriate balance point depends, in part, on perceptions about the environment. These perceptions are managed by the prefrontal cortex of the brain, which provides the likely link between social and psychological stress and health. Thayer and Friedman note that “in modern society . . . inhibition [of the sympathetic system], delayed response, and cognitive flexibility are vital for successful adjustment and self-regulation.” However, such emotional states as worry and anxiety, reinforced by feedback loops, can lead to “prolonged prefrontal inactivity [that] can lead to hypervigilance, defensiveness, and perseveration” (Thayer and Friedman, 2004, p. 577). They postulate that the resulting imbalance between the sympathetic and parasympathetic systems may be the link between persistent feelings of stress and the development of chronic diseases. Social science research on the relationship between stress and health need not involve complex modeling of the biological linkages between cognition and other biological systems. Instead, we can use biomark- ers that reflect important aspects of these systems as intermediate vari- ables. Most research using biomarkers has examined associations with single biomarkers. However, McEwen and colleagues have described a more holistic approach that links social and psychological events to long- term health outcomes (McEwen and Stellar, 1993; Seeman, Singer, Rowe, Horwitz, and McEwen, 1997; McEwen, 1998). They use the term “allosta- sis” much like robustness to refer to a system’s maintenance of stability. 2 The term “allostatic load” (AL) then refers to the cumulated damage resulting from repeated cycles of allostasis. McEwen and Stellar defined it as “the strain on the body produced by repeated ups and downs of physi- ologic response, as well as by the elevated activity of physiologic systems under challenge, and changes in metabolism . . . that can predispose the organism to disease” (McEwen et al., 1993, p. 2094). Insulin resistance and type II diabetes offer a simple example. Insu- lin resistance involves reduced sensitivity of cells to insulin-stimulated glucose absorption. This leads to an increased secretion of insulin and a slower response to increases in serum glucose levels (glucose intoler- ance).3 As the condition worsens over a period of years, the body loses its 2 McEwen points out that the terms “homeostasis” and “allostasis” are sometimes used in the narrow sense of stable levels of those functions immediately necessary to sustain life (e.g., temperature, pH, oxygen tension) and at other times they are expanded to include systems that are more variable (e.g., hormone levels, heart rate, blood pressure). He prefers to limit homeostasis to mean stability in those systems that are most essential to life. He uses the term allostasis in the broader sense to include those systems that maintain homeostasis by responding to changing environment (McEwen, 2000). 3An alternative view is that this progression begins with inadequate production of insulin that only becomes a problem when there is decreased sensitivity to insulin. In either case, the

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 BIOSOCIAL SURVEYS ability to produce enough insulin to stabilize serum glucose levels, which results in diabetes, a chronic disequilibrium in glucose levels. Excess serum glucose leads to the glycosylation of proteins that causes damage to numerous other biological systems (especially the circulatory system). One result is a reduction in the ability of the pancreas to produce insulin. 4 This simple model of insulin response to changes in serum glucose levels illustrates the progression from poor short-term responsiveness to chronic instability that develops in many systems in response to challenges. This “glucose-centric” view of insulin resistance is grossly oversim- plified. For example, the insulin resistance (or metabolic) syndrome is generally defined as including elevated insulin levels, glucose intoler- ance, obesity (especially a concentration of fat around the waist), elevated blood pressure, elevated serum triglycerides, and decreased high-density lipoproteins (HDL).5 The last four are also commonly recognized risk factors for another major health problem: heart disease. The symptoms of the metabolic syndrome are related through complex feedback loops involving adiposity, triglycerides, insulin, leptin, interleukin-6 (IL-6), and tumor-necrosis factor α (TNFα) (Kitano et al., 2004). IL-6 and TNFα involve stimulation of the immune system, which plays an important role in the feedback loops that lead to chronic insulin resistance and which may contribute to the development of vascular disease. Their release is attenuated by the parasympathetic nervous system, thus providing an important link to emotional stress (Thayer and Friedman, 2004). This might explain, in part, the association of insulin resistance with affective disorders (including depression) and Alzheimer’s disease in addition to diabetes and heart disease (Rasgon and Jarvik, 2004). SELECTINg THE MOST USEFUL BIOMARkERS The list of biomarkers that could be included in large social science surveys is expanding rapidly (McDade et al., 2006). Singer, Ryff, and Seeman state that the operationalization of allostatic load is still in its infancy, and they provide guidelines for selecting biomarkers that provide insights into the concept. They propose that “any operationalization of the concept of AL should signal pending onset of diverse kinds of dis- initial result is a decreased robustness in the body’s ability to deal with a sudden increase in serum glucose. 4 For a complete description of the metabolic syndrome from the perspective of system analysis and robustness, see Kitano (2004). 5 There is extensive evidence of statistical associations among the components of this syndrome, but the biological and clinical significance of these associations and the exact list of elements of the syndrome are still hotly debated (Kahn, Buse, Ferrannini, and Stern, 2005; Kahn, 2006).

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 DOUGLAS C. EWBANK ability and disease . . . [and] should represent the biological signature of cumulative antecedent challenges” (Singer, Ryff, and Seeman, 2005, pp. 113-114). Therefore, we want both leading indicators as well as measures of cumulative damage. To limit the number of indicators, we need mea- sures associated with multiple health conditions. We can see aspects of these criteria in the 10 measures included in the original operationalization of allostatic load (Seeman et al., 1997). The list included six items that are generally part of the metabolic syndrome: systolic and diastolic blood pressure, HDL, the ratio of total cholesterol to HDL, glycosylated hemoglobin, and waist-hip ratio (a measure of abdom- inal obesity). Three are chemical messengers that are primary mediators of allostatic load: cortisol (a major stress hormone) and its antagonist, DHEAS, as well as two measures of sympathetic nervous system activity, epinephrine, and norepinephrine. Different measures are used to measure different stages of the accu- mulation of allostatic load. We have already seen that the metabolic syn- drome is associated with multiple important diseases, including diabetes and heart disease. Among the elements of the metabolic syndrome, the waist-hip ratio is a risk factor or precursor of disease, whereas glyco- sylated hemoglobin measures the cumulative effect of serum glucose levels over the past few months. The Whitehall II study included a fast- ing glucose tolerance test that directly measures the body’s response to challenge. Singer, Ryff, and Seeman note that the complex dynamics of the immune system “suggest[s] that a large battery of the immune measures be incorporated in an allostatic load scoring scheme. On the other hand, because our larger objective is to use biological indicators that reflect possible malfunction across multiple systems . . . it is important to have a limited set of biomarkers in place for each system, but use those that are sensitive to broadly based dysregulation” (Singer et al., 2005, pp. 130-131, emphasis in original). In this context, they discuss the value of adding IL-6 to the list of bio- markers. Trauma, infection, fever, and stressful experiences are all associ- ated with elevated levels of IL-6 (Singer et al., 2005). Elevated levels are also associated with a wide range of health conditions and with several forms of disability and poor health, including depression.6 The immune system is tightly integrated with the nervous system and the endocrine system (Cacioppo, Berntson, Sheridan, and McClintock, 2000; Glaser and Kiecolt-Glaser, 2005), which means that immune markers are associated with broader dysregulation. Measurements of Il-6 were included in the Social Environment and Biomarkers of Aging Study in Taiwan (Goldman, 6 Note that these characteristics diminish its usefulness in a clinical context.

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 BIOSOCIAL SURVEYS Glei, Seplaki, Liu, and Weinstein, 2005), a nested case-control study in Whitehall II (Brunner et al., 2002) and the Health ABC Study (Koster et al., 2006). Similar criteria can be used to select genes for social science research on health.7 For example, a relatively common allele of the ApoE gene has been shown to be associated with increased risk of ischemic heart disease and Alzheimer disease (Farrer et al., 1997; Eichner et al., 2002). In addi- tion, both ApoE genotype and serum concentration of ApoE are involved in regulating the acquired immune response (Colton, Brown, and Vitek, 2005). Similarly, the central role of IL-6 in the immune system has led to a number of studies of mortality associated with a common allele of the gene for IL-6 (Christensen, Johnson, and Vaupel, 2006). Several studies have examined the association of various causes of morbidity or mortality with individual biomarkers or subindexes contain- ing some of the elements of allostatic load (Seeman, McEwen, Rowe, and Singer, 2001; Goldman et al., 2005; Chapter 3 in this volume). This type of analysis is important for testing the value of including specific biomark- ers, and it may help to understand the specific mechanisms at work in a population. However, the potential value of an index of allostatic load is that it doesn’t simply measure a number of important biological sys- tems. Rather it should recognize that these systems are not independent. Although the early signs of chronic dysregulation may first appear in one system or another, examining biomarkers of separate systems risks missing the forest for the trees. Although early reports are encouraging, the value of indexes of biomarkers based on the theory of allostatic load is still uncertain. BIOMARkERS IN A LIFE-CyCLE CONTEXT Aging is a process. Much recent research is based on the hypoth- esis that many common chronic health problems, such as cardiovascular disease, diabetes, and dementia, can result from the cumulated effects of repeated or sustained social and physical challenges (Garruto, Little, James, and Brown, 1999). Yet socioeconomic characteristics can change over time and environmental or social change (e.g., moving to a new location, becoming unemployed or widowed) is itself associated with increased psychological stress. There is evidence that the negative effects 7 Genetic material collected in social science surveys can be used to perform a large number of genetic tests. They can therefore contribute important information on genotype frequen- cies for a large number of genes and may be useful for studying genetic risk factors for disease. However, only a few genotypes might be important enough to include in analyses of the associations between social factors and health.

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 DOUGLAS C. EWBANK of stress begin in utero (Graham et al., 2006). Documenting a cohort’s history of early life conditions, social and psychological stress, and the appearance of dysfunction of several systems would require a longitu- dinal study of extraordinary duration. For this reason, it will always be necessary to piece together results from different studies to create syn- thetic cohorts. A good example of this approach is work by Seeman and colleagues that compares results from the Wisconsin Longitudinal Study (ages 58 and 59) and the MacArthur Study of Successful Aging (initially 70-79) (Seeman et al., 2002). Figure 8-1 suggests how a life-cycle approach could piece together results from a number of longitudinal surveys. The list of studies could be expanded to include research at younger ages on the short-term effects of stress at different parts of the life cycle (Lundberg, 2005). Among adoles- cents and young adults, research should focus on the prevalence of risk factors and on measures of short-term responses to stress. For example, some studies on this topic suggest that responsiveness to stress at younger ages may interact with genetic predisposition to cause problems of hyper- tension at later ages (Light et al., 1999). The Whitehall II study covers FIgURE 8-1 A life-cycle depiction of the ages included in six studies that em- ployed biomarkers. The black rectangles show the ages included in the baseline and the grey indicate ages included in the follow-up surveys. "fixed image"-- not easily altered

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 BIOSOCIAL SURVEYS a middle-aged group and provides evidence about both the response to acute stress and the effects of chronic stress. It included nested case- control studies to examine differences in heart rate, blood pressure, and cortisol levels through the work day that can be compared with simpler case-control studies (Matthews et al., 2000; Steptoe et al., 2003). They have also examined heart rate variability (Hemingway et al., 2005), which Thayer and Friedman emphasize as an important marker for parasym- pathetic nervous system (Thayer and Friedman, 2004). At the older ages, research can focus on chronic stress and chronic dysregulation. The life-cycle approaches used in the social sciences parallel the more biological “life history” approach to studying development and aging. This approach is based on the fact that available resources must be allo- cated among three processes: growth, maintenance, and reproduction. It has been used to examine the development of the immune system at early ages that can have serious implications for mortality at the older ages (McDade, 2005). It is also the basis for the “disposable soma” theory of the evolution of aging (Kirkwood and Austad, 2000). Life-cycle models have been used extensively to study a wide range of topics in sociology and demography. However, the simplicity in diagrams of such models is deceiving. Issues of causal ordering and feedback loops raise some of the most serious modeling and statistical issues facing social scientists (National Research Council, 2001b). In addition, there are risks in combining results from different types of samples in different popula- tions. At this point, we may not know enough about what is important to be able to tell when results from studies in one population can reliably be extrapolated to other populations. A FEW WORDS OF CAUTION The extensive literature on the relationship between the ApoE gen- otype and mortality suggests two important lessons that apply to all research on biomarkers. First, type 2 errors may be more frequent than type 1 errors in some types of research and are potentially more serious. The search for genes associated with longevity has led to many (appar- ently) false positives. However, there have also been false negatives in longitudinal studies of single genes (Bader, Zuliani, Kostner, and Fellin, 1998; Juva et al., 2000). Social scientists tend to obsess about avoiding false positives (p-values) and we are rarely concerned with the risk of false negatives (power). This attitude seems to be heightened in biomedical research. One reason is that clinicians have to be cautious in applying new research findings since the cost of type 1 errors can have serious effects. A second factor is that in genetic epidemiology scans of the genome gener- ally involve a large number of multiple comparisons, which increases the

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 DOUGLAS C. EWBANK risk of type 2 errors. However, in certain circumstances, false negatives may outnumber false positives. For example, standard power calculations set the risk of a type 1 error at 0.05 and the risk of a type 2 error at 0.20. False negatives may also be more damaging than false positives because negative results tend to discourage further research. This emphasizes the need for replication and multiple tests of good hypotheses even when they don’t pan out immediately.8 Second, at this stage of research using biomarkers, there is a serious risk of unwarranted generalization. The use of biomarkers in social sci- ence research is still quite new. We are eager to draw conclusions and to base the design of new studies on previous results. However, a true negative in one population might be a true positive in another popula- tion. This can happen for both biological and sociocultural reasons. For example, the ApoE e34 genotype is not associated with excess risk of death in African Americans but it is associated with excess risk in Euro- peans (Ewbank, 2007). If research on the topic had started with studies of African Americans, this line of research might have ended after only one or two studies. Similar problems can arise with social variables. As noted above, similar social positions (e.g., manager) or events (e.g., widowhood) may be associated with very different levels of stress in different social settings. As scientists, we are trained to detect patterns and draw conclu- sions. However, we have to be cautious when results are not confirmed in subsequent studies—especially when the studies are of populations that are biologically and socially as different as Whitehall and Taiwan. CONCLUDINg COMMENTS The use of biomarkers in social science research holds great promise. Biomarkers are quantifiable intermediate variables that can act between the complex and heterogeneous social factors and the equally diverse and complex health outcomes. Chang and colleagues note that “it is too early for us to answer the really big question: Does the inclusion of biomarkers in household surveys help us to understand SES differences in health, par- ticularly with regard to the role of stressful experience?” (Chapter 3 in this volume). It is still early, but I am convinced that biomarkers will enable us to greatly expand our understanding of the effects of social factors on health. We are still testing new, promising biomarkers, and we are just beginning to get the data needed to sort out the effects of various kinds 8 Many biomarkers measure normal levels of hormones that fluctuate over time. In many cases, it may be that the easiest way to increase the power of a test is to carry out multiple measurements on each individual (Jenner et al., 1980; Segerstrom, Lubach, and Coe, 2006).

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 BIOSOCIAL SURVEYS of stress at different ages. Work is still needed to better operationalize and test the concept of allostatic load. We have learned a lot in the past 10 years. First, a number of research- ers have gained experience collecting samples and working with medi- cal labs that can analyze them. This is a fundamental step and one that required a significant amount of work. Second, there are now data on several biomarkers in very different populations. We are just beginning to see comparable analyses of similar data in different settings. We have expanded the list of biomarkers that have been tested, and we have moved beyond the list of 10 initially used to measure allostatic load. In addition to these lessons, the new data from Taiwan should teach us to be cautious about drawing conclusions from cross-sectional data (see Chap- ter 3). Similarly, the Whitehall II study has demonstrated the advantages of working in a defined, structured population in which it is possible to collect a wider range of biomarkers and the social structure is easier to define. It is not necessary for all studies to be nationally representative. The experience thus far also offers lessons to guide future research. First, research at the older ages needs to be combined with studies of the early signs of reduced robustness in younger adults. The research on younger ages will require using biological challenges, like two-hour glucose tolerance tests, that are difficult to carry out in large population studies. Second, we need to be aware that some biomarkers might be associated with stress in different ways at different ages. There may also be different associations with different types of stress. This will complicate comparisons among studies until we have a sufficient amount of data to begin disentangling these relationships. Finally, I think the most encouraging development in the past 10 years is the emergence of a cohort of young researchers who feel that biomark- ers are central to what we do and how we do it. The pioneering research makes it easy to imagine what this approach has to offer and provides valuable models for future research. ACkNOWLEDgMENTS This research was supported by grant number R01-AG-016683 from the National Institute on Aging. REFERENCES Alley, D.E., Seeman, T.E., Kim, J.K., Karlamangla, A., Hu, P., and Crimmins, E.M. (2006). Socioeconomic status and C-reactive protein levels in the U.S. population: NHANES IV. Brain, Behavior, and Immunity, 0(5), 498-504.

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