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Opportunities and Challenges in the Study of Biosocial Dynamics in Healthy Aging

Tara L. Gruenewald

INTRODUCTION

From the womb to the grave, the social world has a profound influence on individual health and well-being. As social animals, human bodies likely evolved to perceive and respond to social stimuli in ways that promote survival and adaptation. Thus, human physiology is attuned to characteristics of our social environment and interactions with others, and social behavior is likewise underpinned by complex biological processes. Social scientists have long been interested in identifying such biosocial dynamics and the roles these processes play in healthy or unhealthy patterns of aging across the life course. The objective of this chapter is to provide a brief overview of the current state of the study of biosocial processes in healthy aging. Research on the biological correlates of social status is reviewed to highlight promising methodological approaches for identifying biosocial connections and for probing biosocial theories of aging. Important methodological and analytical challenges that need to be addressed to significantly advance knowledge of biosocial processes involved in healthy aging are also discussed.

BIOSOCIAL INVESTIGATIONS: THE PROVINCE OF TWO TRADITIONS

The current state of study of biosocial processes involved in healthy human aging is primarily the confluence of accomplishments in two scientific fields: social epidemiology and social/health psychology. Social



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10 Opportunities and Challenges in the Study of Biosocial Dynamics in Healthy Aging Tara L. Gruenewald INTRODUCTION From the womb to the grave, the social world has a profound influence on individual health and well-being. As social animals, human bodies likely evolved to perceive and respond to social stimuli in ways that promote survival and adaptation. Thus, human physiology is attuned to character- istics of our social environment and interactions with others, and social behavior is likewise underpinned by complex biological processes. Social scientists have long been interested in identifying such biosocial dynamics and the roles these processes play in healthy or unhealthy patterns of ag- ing across the life course. The objective of this chapter is to provide a brief overview of the current state of the study of biosocial processes in healthy aging. Research on the biological correlates of social status is reviewed to highlight promising methodological approaches for identifying biosocial connections and for probing biosocial theories of aging. Important method- ological and analytical challenges that need to be addressed to significantly advance knowledge of biosocial processes involved in healthy aging are also discussed. BIOSOCIAL INVESTIGATIONS: THE PROVINCE OF TWO TRADITIONS The current state of study of biosocial processes involved in healthy human aging is primarily the confluence of accomplishments in two sci- entific fields: social epidemiology and social/health psychology. Social 217

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218 NEW DIRECTIONS IN THE SOCIOLOGY OF AGING epidemiologists have amassed a large body of evidence documenting social determinants of disease morbidity and mortality, including social inequal- ity, socioeconomic status, social isolation, and a lack of social support and social resources (see, Berkman, 2009; House, Landis, and Umberson, 1988; Seeman and Crimmins, 2001). A particular strength of this research is evidence of prospective prediction of disease onset and mortality fol- lowing measurement of social conditions, dampening concerns that such connections are primarily the result of selection processes (i.e., that poor health leads to unfavorable social conditions). The documentation of such links in large, nationally representative research cohorts also helps to as- suage concerns that associations occur only in subsets of individuals with distinct social characteristics (e.g., those of abject poverty, the extremely isolated). Perhaps the most notable strength of this research is the observa- tion that social risk factors rival or exceed traditional biomedical factors, such as smoking and cholesterol levels, in the power to predict poor health outcomes (Holt-Lunstad, Smith, and Layton, 2010; House et al., 1988). Although these observations have yet to lead to significant attention to so- cial risk factors in clinical health care, social factors are now more centrally positioned in the cross-hairs of those focused on policies and interventions designed to improve public health. As the evidence highlighting social conditions as key determinants of healthy aging has grown, so too has the motivation to understand how such conditions “get under the skin” to affect functioning and health (Adler and Ostrove, 1999; Seeman et al., 2010; Taylor, Repetti, and Seeman, 1997). In epidemiologic investigations, this motivation has fueled the addition of biological markers, or biomarkers, to study assessments in an effort to identify biological processes that might underlie links between social factors and health. This effort has been two-pronged: social studies of aging and health are increasingly incorporating biomarkers into study assessments, and biomedical studies are increasingly instituting more comprehensive measurement of social conditions. Examples of such studies are provided in Table 10-1. The result has been an exponentially increasing number of studies examining biosocial process that play a role in healthy aging. Occurring contemporaneously with the increasing examination of bio- social connections in epidemiologic investigations, researchers in the fields of social and health psychology have been identifying the biological corre- lates of social factors in smaller-scale investigations with more fine-grained measures of social and biological processes than is typically possible in large-scale epidemiologic studies. This has included investigations of the biological correlates of psychosocial stressors and other social factors (e.g., quality of social relationships, presence of supportive ties), as well as ex- perimental manipulations that examine the biological sequelae of varying social conditions (e.g., stressor experience in the presence or absence of

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OPPORTUNITIES AND CHALLENGES 219 TABLE 10-1  Examples of Longitudinal Studies of Aging and Health Incorporating Social and Biomarker Assessments Population-Based Surveys Community-Based or Cohort Surveys Health and Retirement Study (HRS) MacArthur Study of Successful Aging English Longitudinal Study of Ageing Whitehall Studies (ELSA) Normative Aging Study Social Environment and Biomarkers of Women’s Health and Aging Studies Aging Study (SEBAS) Multiethnic Study of Atherosclerosis (MESA) National Social Life, Health and Aging Coronary Artery Risk Development in Young Project (NSHAP) Adults (CARDIA) Study Study of Midlife in the United States Chicago, Health, Aging and Social Relations (MIDUS) Study (CHASRS) National Health and Nutrition Examination Surveys (NHANES) Cardiovascular Health Study (CHS) Cardiovascular Risk in Young Finns Study British Birth Cohort Studies (e.g., 1958, 1963) Costa Rican Longevity and Healthy Aging Study (CRELES) Twin Studies (SATSA, OCTO, SALT, GENDER, HARMONY) social support). Fueled by technological advances in the measurement of biomarkers in the field, a growing number of studies are also capturing the biological correlates of everyday social experiences as individuals go about their daily lives in their natural social environments. Biological Targets of Biosocial Investigations The social scientist seeking to understand how it is that a given social factor is linked to more or less healthy profiles of aging must first identify the biological pathways in the body that might play a role in differential health trajectories. Then, he or she must identify candidate biomarkers for assessing activity in target pathways and assess the feasibility of mea- suring such biomarkers. Various perspectives may shape the selection of targeted biological pathways. Disease-focused approaches typically target biomarkers (e.g., lipids, markers of inflammation) that play key roles in the pathophysiology of a given disease (e.g., atherosclerosis). Observation of an association between a social factor of interest and target biomarkers might then point to plausible pathways underlying connections between the social condition and the disease outcome. Biological theories of aging might also provide clues as to the pathways through which social factors

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220 NEW DIRECTIONS IN THE SOCIOLOGY OF AGING may influence health. A number of biological processes are implicated in theories that posit that the “deterioration and decline” of aging is a byprod- uct of damage versus repair forces occurring in the course of the physiology of life. These include oxidative damage and antioxidant defense or other repair mechanisms, as well as stochastic DNA damage in somatic cells and the efficacy of DNA repair mechanisms. Neuroendocrine, metabolic, and immune processes are also implicated in programmed aging processes (e.g., the neuroendocrine regulators of the Pacific salmon’s final upstream swim to mate, then die as a notable example), as well as in biological processes that may have been naturally selected for their reproductive and survival benefits in early life, but subsequently have negative consequences for well- being in later life. This concept is known as antagonistic pleiotropy; as an example, immune defense processes promoting survival, especially in the young, may upregulate inflammation burden and disease risk in the old. A discussion of the various biological processes hypothesized to underlie these mechanisms of “aging” are beyond the scope of this chapter, although readers should consult excellent available reviews (Finch and Ruvkun, 2001; Harman, 2003; Kirkwood and Austad, 2000; Martin, 2011; Parsons, 2003; Sohal and Orr, 2012; Sohal, Mockett, and Orr, 2002). The relevant point here is that the occurrence and nature of expression of such biologi- cal processes may well be sensitive to social environment input, and they represent mechanisms through which the social world may accelerate or decelerate healthy aging. Biological targets of study might also be guided by biopsychosocial per- spectives that postulate specific patterns of physiological activation through which social stimuli are transduced into electrochemical and biochemical signals in the body to orchestrate downstream physiology and behavior. This includes theoretical perspectives that posit specific biological signatures for specific psychosocial stimuli such as different patterns of autonomic ac- tivity in response to threatening versus challenging social situations (e.g., Blascovich et al., 2003) and specific neuroendocrine correlates of social sta- tus threats and accompanying cognitive and affective states (e.g., Dickerson, Gruenewald, and Kemeny, 2004; Gruenewald et al., 2004; Henry, 1993), as well as more general organizing frameworks for understanding biosocial connections, such as that found in the conceptualization of primary and sec- ondary physiological regulatory systems (e.g., McEwen and Seeman, 1999). Primary regulators, notably the neuroendocrine and nervous systems, are those that act as communication systems between the brain, where thoughts and emotions regarding the social world are processed, and the downstream physiological systems that carry out behavior and physiology essential for dealing with social demands. These systems initiate changes in secondary regulatory systems, such as the cardiovascular, metabolic, and immune sys- tems, which enable the body to address the demands of a social stimulus.

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OPPORTUNITIES AND CHALLENGES 221 For example, it is nervous and neuroendocrine regulators, such as the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenal (HPA) axis, that orchestrate downstream cardiovascular (e.g., increased heart rate, blood pressure) and metabolic activity (e.g., production of the body’s primary fuel, glucose) to enable the body to address the demands of a social stressor (e.g., an argument with one’s spouse). The concept of allostasis has been proposed to explain how the activity of these primary and secondary regulators fluctuates to orchestrate physio- logical functioning to meet demands (McEwen, 1998; McEwen and Seeman, 1999; McEwen and Stellar, 1993; Sterling, 2004; Sterling and Eyer, 1988). As compared to the more tightly regulated setpoints of homeostatic processes (e.g., maintaining the body’s pH in a narrow range), allostatic processes are theorized to allow greater accommodation of physiological activity to varying demands (e.g., large increases in blood glucose to meet the energy demands of coping with a severe stressor). While often adaptive for addressing the de- mand at hand, allostatic activity may render individuals vulnerable to adverse functioning and health states under conditions when allostatic responses are engaged too often, are prolonged, initiate severe alterations in physiological activity, or are engaged under conditions with little adaptive benefit (e.g., activation of the HPA or SNS systems in response to worrying about a past social interaction). The wear and tear on body tissues and systems that can result from such allostatic states is referred to as “allostatic load” and rep- resents one model of how social conditions that engage allostatic processes may increase risk of poor health. What Is Learned from Biosocial Investigations?: An Example: Biological Correlates of Social Status The allostatic correlates of a wide array of social factors have been explored in both small- and large-scale observational and experimental studies. In this section, the biological correlates of social status are high- lighted to provide a flavor of the gains, the promises, and the challenges of biosocial investigations. One reason for this selection is that the connection between social status, most often conceptualized as some form of socio- economic status (SES), and health has long been a central focus of social epidemiology. A large and relatively consistent body of empirical evidence indicates that those of lower SES experience poorer health, including in- creased incidence of most diseases, faster disease progression, and greater disease-specific and all-cause mortality risk, resulting in shorter length of life (see, Adler and Ostrove, 1999; Hemingway and Marmot, 1999; Kaplan and Keil, 1993; Marmot, 2006; Matthews and Gallo, 2011). These links have been documented for both prestige (e.g., occupational status) and

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222 NEW DIRECTIONS IN THE SOCIOLOGY OF AGING resource-based (e.g., income, assets) measures of SES, as well as indicators that tap both dimensions (e.g., education). The consistently observed SES-health association has led to a hunt for the pathways that underlie the SES gradient in health, and biologi- cal pathways have been targets of increasing focus (Miller, Chen, and Cole, 2009; Seeman et al., 2010). A very general conceptualization of the routes through which SES might impact individual biological function- ing is provided in Figure 10-1 and includes (1) SES-patterned environ- mental exposures, including exposure to pollutants, carcinogens, toxins, and adverse neighborhood/community characteristics; (2) SES variations in psychosocial exposures and processes, including psychosocial stress, cognitive-perceptual, and emotional processes, and psychosocial resources (e.g., access to social support, control over environment); and (3) SES- patterned health behavior, including smoking, physical activity, diet, and drug/alcohol use. The overarching hypothesis is that SES profoundly shapes individual thought, feeling, and behavior, as well as exposure to the slings and arrows of life, in turn, affecting allostatic processes and subsequent health-relevant biological wear and tear. FIGURE 10-1  Conceptual model of potential pathways through which social status is linked to health. Fig6-1.eps bitmap

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OPPORTUNITIES AND CHALLENGES 223 Biomarker Correlates—Clarity or Confusion? At first glance, the picture that emerges from the body of work exam- ining biological correlates of SES is consistent with that examining health correlates: Those of lower SES have poorer biological profiles for most major biological regulatory systems, including nervous, neuroendocrine, cardiovascular, metabolic, and immune systems. For example, those of lower SES have been found to have higher levels of HPA and SNS hormones hypothesized to be elevated under conditions of stress (e.g., cortisol and cat- echolamines; Cohen, Doyle, and Baum, 2006; Janicki-Deverts et al., 2007; Steptoe et al., 2003), higher biomarker levels indicative of poor metabolic functioning (e.g., greater body mass index, higher fasting glucose and insu- lin and glycosylated hemoglobin, poorer lipid profiles; Danese et al., 2009; Loucks et al., 2007a,b; McLaren, 2007; Senese et al., 2009), and other in- dicators of cardiovascular disease risk (e.g., high blood pressure, low heart rate variability, high inflammation burden) (Brunner et al., 1996; Colhoun, Hemingway, and Poulter, 1998; Gruenewald et al., 2009; Hemingway et al., 2003; Koster et al., 2006; Sloan et al., 2005). Upon closer inspection, however, there are nuances in the consistency of findings in this literature that suggest that further attention to methods, measurement, and theory might be fruitful. One interesting observation is the geographic and demographic varia- tions in associations between SES and some biomarker indicators. For example, a review of 57 studies published over a 30-year period (1966- 1996) indicated that a majority of investigations in the United States and Canada observed higher levels of blood pressure in those of lower SES (Colhoun et al., 1998), but associations were less consistent in the United Kingdom, Australasia, Asia, South Africa, and some European countries. Well-documented geographic and demographic (sex) variations have also been observed for associations between SES and body mass index or body weight (McLaren, 2007; Sobal and Stunkard, 1989). These variations are notable because cross-study consistency in the validity and reliability of measurement of blood pressure and weight is likely considerably higher than is the case for other biomarkers that have to be assessed via saliva, blood, or urine samples and measured with com- plex assays (e.g., hormone or immune biomarkers). Thus, assuming fairly similar measurement of these biomarkers across samples, focus is shifted to other potential explanations for geographic and demographic variations in SES-biomarker associations, including characteristics of SES measurement, cultural variations in the meaning of SES, and its environmental, psychoso- cial, and behavioral correlates, and the role of social institutions and more macro social factors (e.g., universal access to health care) in moderating links between individual-level SES and biological functioning. A review of

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224 NEW DIRECTIONS IN THE SOCIOLOGY OF AGING research that sheds light on each of these possibilities is beyond the scope of this review, but these observations point to the need for increasing compre- hensiveness and sophistication in the measurement and theorization of the social, psychological, and behavioral alongside the biological in biosocial investigations. Biosocial Investigations—An Opportunity for Life Course Explorations? Another aspect of research on SES gradients in biological functioning that is ripe for further attention is a better understanding of when, and how, SES becomes embodied across the life course. A number of life course frameworks have been proposed to explain the role of biosocial processes in healthy aging (Ben-Shlomo and Kuh, 2002; Pollitt, Rose, and Kaufman, 2005), with sensitive or critical period and accumulation of risk models receiving the most attention. The sensitive or critical period model sug- gests that connections between SES and biological functioning may vary depending on life course phase. Strict critical period models posit that events that occur within specific and narrow windows of development may permanently “tune” bodily systems and subsequent disease risk, which will be unaffected by risk exposures that occur outside the critical window. Sen- sitive period models posit that risk exposures at certain life course phases (e.g., early childhood) may simply have a stronger negative impact than those that occur in other life phases (e.g., late adulthood). Growing evidence indicates that SES disadvantage in early life predicts poorer physiological functioning in later life, above and beyond the influ- ence of more contemporary SES characteristics. The negative impact of early life disadvantage has been documented for cardiovascular and meta- bolic biomarkers (e.g., blood pressure, high-density lipoprotein cholesterol, insulin resistance; Blane et al., 1996; Kivimaki et al., 2006b; Poulton et al., 2002) and the HPA hormone cortisol (Li et al., 2007). When SES-adversity indicators from different life course phases are pitted against each other as predictors of later-life biological risk, sometimes childhood SES is a stron- ger predictor (Kivimaki et al., 2006b), while in other cases, more recent SES conditions exhibit greater predictive power (e.g., Blane et al., 1996). A significant limitation of these investigations is the lack of biological in- formation from earlier life periods, rendering it difficult to discern whether SES-adversity experience actually alters biological functioning in earlier life course phases. An analysis where such information was available in the Cardiovascular Risk in Young Finns Study indicated that SES disparities in blood pressure emerged early in childhood and persisted across the life course, with early life alterations accounting for much of the association between childhood SES and adult blood pressure (Kivimaki et al., 2006a,b).

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OPPORTUNITIES AND CHALLENGES 225 Exciting opportunities are on the horizon for identifying how SES experiences at different phases of the life course get under the skin to affect a wide array of biological processes and how SES disparities in biological functioning might track across the life time. Evidence of SES disparities in childhood of levels of primary neuroendocrine regulators (HPA and SNS hormones), as well as in downstream secondary regulatory systems (e.g., cardiovascular and metabolic biomarkers), is accumulating from cross-sectional and short-term longitudinal investigations (e.g., Evans and English, 2002; Evans and Kim, 2007; Goodman et al., 2005). These biological imprints of social adversity in childhood may provide clues as to the trajectories of healthy or unhealthy aging that lie ahead. Longitudinal, life course investigations that concurrently measure social and biological factors from childhood to adulthood will be particularly fruitful for un- derstanding when social adversity is embodied for different systems, the permanency of biological imprints, and the genetic, psychosocial, and be- havioral modifiers of these links. Of course, one needs to be mindful of the considerable burden involved in tracking cohorts for long periods of time in advocating for such designs. Another life course model receiving growing attention in biosocial investi- gations is the accumulation of risk model. As applied to biosocial investigations, this model posits that greater overall exposure to adverse social conditions (e.g., low SES) across the life course accumulates to have a greater toll on biological functioning in later adulthood. The expansive battery of biomark- ers recently added to the second wave of the Study of Midlife in the United States (MIDUS), a longitudinal study of health and aging, allowed for an exploration of the range of biological systems that might be sensitive to life course SES adversity experience. Analyzing data from MIDUS, Gruenewald and colleagues (2012b) documented greater dysregulation across seven dif- ferent biological indices (SNS, parasympathetic nervous system, HPA system, cardiovascular system, glucose metabolism, lipid metabolism, inflammation) in adults with greater experience of SES adversity (assessed with multiple indicators of education, income, and financial strain) across childhood and adulthood. Figure 10-2 demonstrates that greater life course SES adversity left its mark in adulthood on all of the physiological indices examined. Ag- gregation of individual system risk indices into a multisystem measure of allostatic load (AL) also revealed a steep SES gradient in biological dysregula- tion. The difference in AL between those in the highest and lowest quintiles of lifetime SES adversity was equivalent to a 17-year age difference in AL, suggesting accelerated “biological aging” in those with greater cumulative adversity experience. Support for the hypothesis that greater social adversity experience might accelerate the “aging” or “weathering” (Geronimus, 1992) of bio- logical systems is also found in analyses of population-based surveys, such

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226 NEW DIRECTIONS IN THE SOCIOLOGY OF AGING 0.40 0.35 0.30 Mean System Risk Score 0.25 0.20 0.15 0.10 0.05 0.00 SNS PNS HPA INF CV MET-G MET-L SES disadvantage quintile: Lowest 2nd 3rd 4th Highest FIGURE 10-2  Mean levels of biological system dysregulation by quintiles of cu- mulative SES adversity (across childhood and adulthood) in the Study of Midlife in the United States (MIDUS). Fig6-2.eps NOTE: CV = cardiovascular system, HPA = hypothalamic-pituitary-adrenal axis, INF = inflammation, MET-G = glucose metabolism, MET-L = lipid metabolism, PNS = parasympathetic nervous system, SES = socioeconomic status, SNS = sympathetic nervous system. SOURCE: Gruenewald et al. (2012b). Reprinted with permission. as the National Health and Nutrition Examination Surveys (NHANES). Geronimus and colleagues (2006) found that poverty, ethnic minority sta- tus (“non-Hispanic Black”), and being female were each associated with a greater likelihood of experiencing high AL in NHANES III participants aged 18-64. Moreover, these factors interacted to predict the occurrence of high AL at earlier stages of the life course and disparities widened with age. In an analysis of both NHANES III and IV data, Crimmins, Kim, and Seeman (2009) replicated the finding of widening SES disparities in AL with age up until the 60s. However, they further documented the dra- matic consequence of earlier weathering in the socially disadvantaged with analyses suggesting that SES disparities in biological risk disappear at older ages because the poor with high AL die before reaching older adulthood. These investigations are compelling examples of the value of biomarker data in understanding how life course characteristics of SES adversity may

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OPPORTUNITIES AND CHALLENGES 227 shape disease risk and longevity. These analyses also highlight the complex interactions that may be occurring between SES and other demographic factors (gender, race), as well as other individual and social conditions as yet unexplored. Promising Approaches Time for a Confluence of Traditions? An advantage of adding biomarker measures to large-scale, population- based studies of aging and health is the ability to examine biosocial as- sociations with sufficient power in samples representative of the general population or specific subgroups. Another advantage is the opportunity to track the role of biosocial processes in the development of health conditions over time in longitudinal investigations. A disadvantage is that the design constraints of such investigations tend to allow only a limited “snapshot” measurement of psychosocial, behavioral and biological states at any given assessment. Many psychosocial, behavioral, and biological factors fluctuate considerably over time and context, within and between individuals, and important nuances of such fluctuations are not well captured in large-scale survey investigations. These nuances have been investigated in smaller-scale observational and experimental studies over the past few decades, primarily carried out in the fields of social and health psychology. The impressive knowledge gained from these biosocial investigations could, and has, filled many volumes, so will not be reviewed in detail here. However, highlights include findings that biological functioning is intricately tied to characteristics of social interactions and social stressor experience (e.g., social conflict, discrimina- tion, social-evaluative threat; Dickerson and Kemeny, 2004; Kiecolt-Glaser et al., 2002; Seeman and McEwen, 1996) and social characteristics that individuals bring with them to social interactions (e.g., background level of social integration and support, social conflict history, cultural norms; Uchino, Cacioppo, and Kiecolt-Glaser, 1996). What is emerging from this body of work is a picture of complex interconnections between our social, psychological, and biological worlds. Despite the incredible advantage of such investigations in giving us a more detailed understanding of these complex interactions, the design constraints of these smaller-scale investigations typically limit the use of population-based samples, as well as samples of sufficient size to track and predict incident disease. Given that the relative strengths of the smaller- scale social and health psychology investigations are the weaknesses of the large-scale survey study, and vice-versa, a particularly exciting trend is the effort to combine these two designs within single studies. Although

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232 NEW DIRECTIONS IN THE SOCIOLOGY OF AGING processes confer reproductive or survival benefits in young adulthood but increase risk of ill-being in later adulthood? Or do these social experiences and biopsychosocial responses merely accelerate the forces of damage, and lessen the forces of repair, that shape trajectories of healthy aging across the life course? A few aspects of these findings bear additional mention. The first is that low SES individuals with the “defensive” gene expression phenotype described above exhibited indicators of greater proinflammatory and HPA activity (Chen et al., 2011; Miller et al., 2009), suggesting that expression profiles bear a connection with more commonly measured “downstream” biomarkers. Second, there is increasing identification of the psychological correlates of expression profiles. For example, Chen and colleagues (2009) found that the threat perception style they had previously identified as more common in low SES children also appeared to underlie much of the association between SES and the defensive phenotype identified in gene expression studies. A third important point is that other social factors may moderate SES variations in gene expression profiles—those of low SES who experienced high levels of maternal warmth in childhood were less likely to show the more “risky” proinflammatory gene expression characteristic of the disadvantaged (Chen et al., 2011). Attending to the Positive One final suggestion for understanding SES variations in health comes in the form of turning attention toward understanding the social distribu- tion of the positive goods in life. There has long been recognition that health is more than just the absence of ill-being and that it also encompasses various forms of social and psychological well-being (see Ryff, 1989; Ryff and Keyes, 1995). These include forms of social well-being (e.g., a sense of social connectedness, collective efficacy), hedonic well-being (e.g., feeling happy and satisfied), and eudaimonic well-being (having a sense of pur- pose in life, feeling engaged, valued, useful, in control), what some have characterized as indicators of flourishing or thriving (Keyes, 1998, 2002). A growing body of evidence also suggests social patterning of such forms of well-being with those with greater social disadvantage often reporting less of these goods (e.g., Diener et al., 2002; Keyes, Shmotkin, and Ryff, 2002). Beyond the injustice of social variations in these desired ends, social disparities in psychosocial well-being may also explain differential pat- terns of healthy aging. Lower hedonic and eudaimonic well-being predicts less favorable trajectories of cognitive and physical functioning, greater morbidity and mortality risk, and shorter length of life (Boyle et al., 2009, 2010; Cohen and Pressman, 2006; Gruenewald et al., 2007; Gruenewald, Liao, and Seeman, 2012; Okamoto and Tanaka, 2004; Pitkala et al., 2004;

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OPPORTUNITIES AND CHALLENGES 233 Steptoe and Wardle, 2011). The biological pathways through which these forms of well-being may promote healthier trajectories of aging are just beginning to be elucidated, but accumulating evidence suggests neuroendo- crine, cardiovascular, and inflammatory routes (Pressman and Cohen, 2005; Ryff et al., 2006; Ryff, Singer, and Dienberg Love, 2004; Steptoe, Dockray, and Wardle, 2009). The promising methodological approaches highlighted above, including identification of upstream physiological modulators and assessing biological correlates of social conditions in the lab and in the wild, may well help us better understand social disparities in flourishing and thriving and the implications for healthy aging. SOME CHALLENGES OF BIOSOCIAL INVESTIGATIONS Measurement The potential promises of various methodological approaches in bio- social investigations are tempered by a number of challenges. One is the domain of measurement challenges. On the biological side, numerous chal- lenges abound in collecting biomarker samples, including the relatively invasive nature of measurements (i.e., obtaining samples of blood, urine, or saliva, via physical measurement of the body), the detailed instruction needed for both research staff and participant, and for many biomarkers, sensitivity to the temporal and contextual characteristics of biomarker collection (e.g., time of day, whether to obtain “resting” or “challenge” measures). Biomarkers tend to be relatively expensive to measure (both in collection and processing costs), but costs tend to decrease somewhat with greater adoption in scientific and clinical realms. Technological ad- vances have also led to a significant increase in the biomarkers that can be measured and the mediums (e.g., hair, finger-prick blood spots) and condi- tions (e.g., return of samples via unrefrigerated, postal mail) for capturing physiological samples. However, much work remains in establishing the reliability and validity of these newly developed methods. Of course, most of the challenges of biomarker data collection are also the challenges of social factor data collection. Comprehensive measure- ment of social factors is often invasive (e.g., life experience interview, daily measurements) and requires detailed training and instruction to collect accurately. Social measures are also very sensitive to the temporal and con- textual characteristics of data collection. Technological advances have also increased the mediums through which social measures are obtained (e.g., smartphones and other personal communication electronic devices, global positioning system or GPS tracking), and such data collection innovations are also associated with substantial monetary requirements.

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234 NEW DIRECTIONS IN THE SOCIOLOGY OF AGING Perhaps the biggest challenge is concurrent high-quality measurement of both social and biological characteristics within studies. Early large-scale biosocial investigations tended to add fairly crude measures of one or the other domain depending on the original study framework with the result that failure to observe significant or strong biosocial associations led to as- persions on the whole enterprise (i.e., not seeing the “value” of biomarker or social condition measurement). Fortunately, more focused investigations have continued to document rich biosocial connections, and efforts have been made to better “capture” both social and biological characteristics in larger-scale investigations. However, the considerable participant and in- vestigator burden associated with such efforts continues to plague the field. Capturing and Analyzing the Multilevel and Temporal Complexity Another challenge for understanding biosocial processes is the method- ological and analytical challenge of capturing and understanding multilevel and temporally complex processes. This overview has presented an incredi- bly simplistic directional and temporal model of biosocial connections, with the focus primarily at the individual level. Even at the individual level, this overview has neglected a discussion of the complex patterns of development or “aging” within biological, psychological, and social realms, to say noth- ing of intra-individual cross-level interactions. But most biosocial processes, even those that may be most tightly coupled during narrow windows of development, operate in a bidirectional, iterative process over time, nested within multiple levels of influences from the most micro biological to the most macro social. Biosocial studies of health and aging have particularly neglected measurement of bidirectional flows of influence between indi- viduals and upward to larger units of social organization. Methodological advances, including linked ambulatory monitoring of experiences, activity, and interaction of multiple social actors over time within defined geo- graphic boundaries, including measurement of group-level characteristics, may aid in understanding of such bidirectional flows of influence. However, careful measurement of multilevel characteristics of biosocial processes requires stepping outside one’s disciplinary comfort zone and working in multidisciplinary teams, which academic science has been slow to reward. The temporal and directional challenges also require time-, labor-, and monetary-intensive longitudinal investigations coupled with employment of sophisticated analytic techniques that can appropriately parse units of influence among a complicated web of associations. Nonetheless, the im- portance of such endeavors is being increasingly acknowledged by public health support systems, such as the National Institutes of Health, which has spurred scientific research initiatives for the study of social network analy- ses and multilevel systems processes in understanding health and aging.

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OPPORTUNITIES AND CHALLENGES 235 Prognostic Significance of Social Variations in Biomarkers The impetus for many biosocial investigations is to identify links be- tween social factors and biological processes that may explain social gradi- ents in health outcomes. As reviewed, social variations in biomarker levels and activity have been observed in many investigations. These variations in biological functioning can emerge early in life and persist across the lifetime. However, what is needed in this literature is commensurate evi- dence demonstrating that social disparities in biomarkers underlie social disparities in actual health outcomes. To date, investigations that have at- tempted to study each link in the chain simultaneously have examined the explanatory power of individual or clusters of biomarkers in explaining SES gradients in cardiovascular health outcomes or mortality. Individual biomarkers of inflammation (e.g., interleukin-6, C-reactive protein, fibrino- gen) have been found to account for small to moderate proportions of SES gradients in incident cardiovascular disease/events and mortality, while more traditional cardiovascular risk factors (e.g., blood pressure, metabolic biomarkers) have been shown to play smaller, or no mediating roles (e.g., Loucks et al., 2009; Marmot et al., 2008; Ramsay et al., 2009; Rosvall et al., 2008). Greater explanatory power is observed when examining compos- ites of cardiovascular or cardiovascular/metabolic biomarkers (Marmot et al., 2008), as well as for multisystem indices, such as captured in allostatic load measures (Seeman et al., 2004). The increasing incorporation of biomarkers into longitudinal studies of aging will allow for these needed mediational analyses in the near future. However, the collection of valid incident disease information is difficult in large-scale survey studies. Mortality occurrence is somewhat easier to assess via links with mortality registries, but investigations must have in place appropriate human subject consent, as well as identifying information (e.g., Social Security number), to allow for identification of deaths through such systems. Nonetheless, the value of establishing the predictive validity of commonly assessed biomarkers is worth tackling these methodological challenges. Important benefits would be greater faith in using biomarkers as intermediate health endpoints, to better evaluate the success of health promotion interventions and policies, and to track the effect of social condi- tions on physical well-being. LOOKING FORWARD As this overview makes clear, the marriage of social and biological measurements in both large-scale, social epidemiological and smaller-scale, laboratory and observational investigations has been successfully achieved. Much has been learned from this union about the intricate ties between

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236 NEW DIRECTIONS IN THE SOCIOLOGY OF AGING humans’ social, psychological, and biological worlds. However, as in all marriages, there is still much to learn. One important focus for the future is the exploration of life course models of biosocial connections, including a greater understanding of the range of social conditions linked to bio- logical processes, the characteristics of biosocial interactions at different phases of the life course, and how such processes operate across time to influence healthy aging. A second important focus for the future is a better understanding of the interaction of social and demographic factors, at both micro and macro levels, in shaping associations with biological factors. The currently limited understanding of geographic and demographic variations in biosocial associations suggests that additional theoretical and method- ological development in this area is needed. As discussed, there are a number of promising approaches that are poised to considerably advance knowledge of biosocial connections. Ef- forts to combine the methods and paradigms of social epidemiology and social and healthy psychology may be particularly fruitful. The field seems to be on the eve of incredible advancements in knowledge of the social regulation of “upstream” biological processes, including neural and genetic activity. Efforts in these areas, if appropriately coupled with continuing focus on “downstream” biological processes, will significantly propel our understanding of the biological pathways that underlie social disparities in healthy aging forward. It is necessary to engage these tools to understand not only the biology that underlies ill-being but also that which promotes flourishing and thriving. As noted, there are also some challenges that may impede easy naviga- tion of this journey. As with many areas of scientific inquiry, measurement challenges are considerable. The methodological and analytical challenges of adequately capturing the multilevel, bidirectional, and temporal charac- teristics of biosocial processes are also daunting. However, such challenges are not unique to study of biosocial processes and are characteristic of the study of many risk factors of unhealthy aging. For example, such challenges also plague the study of behavioral risk factors like smoking. It is doubtful that any in the health promotion realm would advocate avoiding scientific investigation of smoking because of the multilevel, multivariable, or tem- poral influences on smoking behavior. The importance of establishing the prognostic significance of biomark- ers and their role in explaining social disparities in health outcomes was also discussed. One should not confuse this with a need to establish the prognostic significance of social factors for healthy aging—decades of re- search have solidly established such connections. The value of biosocial research will be in the identification of the biological pathways that underlie these links, and in the potential use of biomarkers as surrogate endpoints or indicators that can be used to better understand the impact of adverse social

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