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6
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 epidemiologists have amassed a large body of evidence
documenting social determinants of disease morbidity and mortality, including social inequality,
socioeconomic status, social isolation, and a lack of social support and social resources (see
Berkman, 2009; House, Landis, & Umberson, 1988; Seeman & Crimmins, 2001). A particular
strength of this research is evidence of prospective prediction of disease onset and mortality
following 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 assuage 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 observation 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, & Layton, 2010; House et al., 1988). Although these
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observations have yet to lead to significant attention to social 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 & Ostrove, 1999; Seeman et al., 2010; Taylor, Repetti, &
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 8-1. 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 biosocial connections
in epidemiologic investigations, researchers in the fields of social and health psychology have
been identifying the biological correlates 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 experimental manipulations that examine the biological sequelae of
varying social conditions (e.g., stressor experience in the presence or absence of 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 measuring 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 may influence health. A number of biological processes are implicated in theories
that posit that the “deterioration and decline” of aging is a byproduct 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 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
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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 & Ruvkun, 2001; Harman, 2003; Kirkwood & Austad,
2000; Martin, 2011; Parsons, 2003; Sohal, Mockett, & Orr, 2002; Sohal & Orr, 2012). The
relevant point here is that the occurrence and nature of expression of such biological 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 perspectives 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 activity in
response to threatening versus challenging social situations (e.g., Blascovich et al., 2003) and
specific neuroendocrine correlates of social status threats and accompanying cognitive and
affective states (e.g., Dickerson, Gruenewald, & Kemeny, 2004; Gruenewald, Kemeny, Aziz, &
Fahey, 2004; Henry, 1993), as well as more general organizing frameworks for understanding
biosocial connections, such as that found in the conceptualization of primary and secondary
physiological regulatory systems (e.g., McEwen & 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 systems, which enable the body to address the demands
of a social stimulus. 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 physiological functioning to meet demands
(McEwen, 1998; McEwen & Seeman, 1999; McEwen & Stellar, 1993; Sterling, 2004; Sterling &
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 demand 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 represents one model of
how social conditions that engage allostatic processes may increase risk of poor health.
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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 highlighted 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 socioeconomic 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
increased incidence of most diseases, faster disease progression, and greater disease-specific and
all-cause mortality risk, resulting in shorter length of life (see Adler & Ostrove, 1999;
Hemingway & Marmot, 1999; Kaplan & Keil, 1993; Marmot, 2006; Matthews & Gallo, 2011).
These links have been documented for both prestige (e.g., occupational status) and 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 biological pathways have been targets of increasing
focus (Miller, Chen, & Cole, 2009; Seeman et al., 2010). A very general conceptualization of the
routes through which SES might impact individual biological functioning is provided in Figure
6-1 and includes: (1) SES-patterned environmental 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.
Biomarker Correlates—Clarity or Confusion?
At first glance, the picture that emerges from the body of work examining 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 catecholamines; Cohen, Doyle, & 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 fastng glucose and insulin and
glycosylated hemoglobin, poorer lipid profiles; Danese et al., 2009; Loucks et al., 2007; Loucks,
Rehkopf, Thurston, & Kawachi, 2007; McLaren, 2007; Senese et al., 2009), and other indicators
of cardiovascular disease risk (e.g., high blood pressure, low heart rate variability, high
inflammation burden (Brunner et al., 1996; Colhoun, Hemingway, & 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.
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One interesting observation is the geographic and demographic variations 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 (BMI) or body weight
(McLaren, 2007; Sobal & 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 complex 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, psychosocial, 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 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 comprehensiveness 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 & Kuh, 2002; Pollitt, Rose, & Kaufman,
2005), with sensitive or critical period and accumulation of risk models receiving the most
attention. The sensitive or critical period model suggests 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 which occur outside the critical window. Sensitive 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 influence of more contemporary
SES characteristics. The negative impact of early life disadvantage has been documented for
cardiovascular and metabolic biomarkers (e.g., blood pressure, HDL cholesterol, insulin
resistance; Blane et al., 1996; Kivimaki, Smith, et al., 2006; 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 stronger predictor (Kivimaki, Smith, et al., 2006), 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 information from earlier life periods,
rendering it difficult to discern whether SES adversity experience actually alters biological
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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, Lawlor, et al., 2006; Kivimaki, Smith, et al., 2006).
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 & English, 2002; Evans & 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 understanding when social adversity is embodied for different systems,
the permanency of biological imprints, and the genetic, psychosocial and behavioral 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 investigations 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
biomarkers recently added to the second wave of the Study of Midlife in the U.S. (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 the
MIDUS Study, Gruenewald and colleagues (2012) documented greater dysregulation across
seven different biological indices (sympathetic nervous system, 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 6-2 demonstrates that
greater life course SES adversity left its mark in adulthood on all of the physiological indices
examined. Aggregation of individual system risk indices into a multi-system measure of
allostatic load (AL) also revealed a steep SES gradient in biological dysregulation. 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 biological systems is also found in analyses of
population-based surveys, such as the National Health and Nutrition Examination Surveys
(NHANES). Geronimus and colleagues (2006) found that poverty, ethnic minority status (“non-
Hispanic Black”), and being female were each associated with a greater likelihood of
experiencing high AL in NHANES III participants age 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,
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they further documented the dramatic 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 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 associations 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 last 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, discrimination, social-evaluative threat;
(Dickerson & Kemeny, 2004; Kiecolt-Glaser, McGuire, Robles, & Glaser, 2002; Seeman &
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, & 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 still fairly rare, such efforts are taking the form of the
addition of small substudies within larger-scale survey investigations in which more detailed
assessments of biosocial processes are collected on subsets of participants.
Exploring Biosocial Connections in the Wild
One notable form of this substudy approach is the effort to collect data on biosocial
processes in individuals’ natural social environments, as they navigate the challenges and social
interactions of daily life. An example of such an approach comes from the Whitehall II Study of
British civil servants, which has been a fruitful source of knowledge regarding the biosocial
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processes that may underlie social status gradients in health. A substudy of 202 Whitehall II
participants examined cardiovascular and neuroendocrine parameters across a workday, with
cardiovascular measurements occurring every 20 minutes and 10 saliva samples collected for the
assay of the HPA hormone cortisol, at various points from waking to bedtime (Steptoe, Kunz-
Ebrecht, et al., 2003). This investigation revealed that those of lower occupational status have
higher blood pressure and heart rate levels, but only in the morning, while cortisol levels are
higher primarily during working hours. This latter finding was reversed for females, with higher
status women having higher levels of cortisol across the day. Taken alone, these findings merely
add to those of other smaller-scale studies of more select samples which document SES
variations in cardiovascular activity across the workday (e.g., Gallo, Bogart, Vranceanu, & Walt,
2004). However, one advantage of collecting such data within a larger longitudinal study of
health and aging is the ability to integrate substudy findings within the larger web of observed
biosocial and social-health associations in the parent cohort. Although yet to be done to a great
extent, the possibilities of such analyses are numerous.
Despite the methodological challenges, ambulatory substudies of biosocial processes are
growing in number. In addition to the Whitehall Study, numerous large-scale studies of health
and aging, including the MIDUS Study, the English Longitudinal Study of Ageing (ELSA), the
Coronary Artery Risk Development in Young Adults (CARDIA) Study, and the Multi-Ethnic
Study of Atherosclerosis (MESA), have incorporated diurnal saliva sampling in participants’
natural environments. The focus has primarily been the measurement of the HPA hormone,
cortisol, but the range of biomarkers that can be measured in saliva continues to expand. Our
recent explorations of diurnal salivary cortisol variations in the MIDUS cohort indicate that
greater SES adversity is linked to lower cortisol output in the morning but a flatter slope of
decline across the day leading to higher evening levels (Gruenewald et al., 2012). Similar SES
variations in diurnal cortisol activity have been observed in other large cohort studies (Cohen et
al., 2006; Hajat et al., 2010; Kumari et al., 2010). The changing nature of SES variations in
cortisol activity across the day (lower at some points but higher at others in the more
disadvantaged) may render it difficult to discern SES differences with either single snapshot
assessments or measures which aggregate information over longer time periods (and thus
obscure within period variation). This may explain the less stark SES gradients for the HPA
index in Figure 6-2, which included a 12-hour aggregate urinary measure of cortisol activity. The
increasing inclusion of ambulatory and clinic-based assessments of biological activity (cortisol,
cardiovascular) in large cohort studies will allow for a comparison of physiological measurement
methods best able to capture the effect of social conditions on our physiology, as well as enhance
our ability to understand the mediators and moderators of such links.
Integrating Laboratory-Based and Epidemiological Approaches
The addition of laboratory-based challenge substudies in large-scale, longitudinal studies
of aging (e.g., Whitehall, MIDUS, CARDIA, MESA) is another example of the integration of
epidemiological and social/health psychology approaches. Challenge paradigms expose
participants to a standardized set of demanding and challenging activities (e.g., difficult
cognitive tasks, public speaking) to examine the psychological and physiological consequences
of “stressor” exposure. The advantage of such methods is that investigators can compare the
psychobiological responses of individuals that vary on social characteristics to the same
stimulus, negating concerns about variations in social characteristics “selecting” participants into
certain stressors (e.g., conflictual interactions). Investigations of physiological reactivity within
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the Whitehall cohort have revealed that those of lower SES have delayed recovery of
cardiovascular parameters (Steptoe et al., 2002; Steptoe, Willemsen, Kunz-Ebrecht, & Owen,
2003) and longer-lasting stress-associated increases in the inflammatory biomarker, interleukin-6
(Brydon, Edwards, Mohamed-Ali, & Steptoe, 2004). However, no reactivity and recovery
differences emerged for other inflammatory (e.g., fibrinogen, tumor necrosis factor alpha) and
immune (e.g., natural killer cell number; Owen et al., 2003) biomarkers. Thus, SES variations in
stress reactivity are more complex than the simple message of those of lower status exhibiting
greater reactivity and less recovery on all biomarker indicators. Lack of stress response
differences for many biomarkers does not negate the possibility that stress is a mechanism
through which SES variations in health occur, as those of lower SES may simply experience a
greater absolute level of stress and its associated physiological correlates.
Another advantage of the laboratory challenge paradigm is the opportunity to more
carefully pinpoint the psychological processes that might underlie SES variations in
physiological reactivity. Chen and colleagues have documented that low SES children are more
likely to interpret ambiguous social situations as threatening than high SES children (Chen,
Langer, Raphaelson, & Matthews, 2004; Chen & Matthews, 1999), which may reflect cognitive
interpretation tendencies shaped by an accumulation of less favorable social experiences. This
tendency has also been shown to increase with aging (Chen & Matthews, 1999). Furthermore,
such threat perception tendencies, and increases in these tendencies over time, partially explain
the greater cardiovascular reactivity of low SES adolescents in laboratory challenge paradigms
(Chen et al., 2004), as well as greater ambulatory cardiovascular activity during social
interactions (Chen, Matthews, & Zhou, 2007) in individuals’ normal social environments.
Increases in threat perception biases also played a more significant role in predicting future
physiological reactivity in lower SES African American adolescents (Chen & Matthews, 2001),
suggesting that social stressors (SES, minority status) may interact over time.
Moving Upstream
Another promising area of research is the effort to identify the neural processes that may
mediate SES variations in the processing of social stimuli. There has been an explosive growth in
the field of social neuroscience in the last decade, which seeks to understand the neural processes
that mediate social behavior and social information processing. This has led to an increased
understanding of the brain structures involved in these processes, including the prefontal cortex,
the hippocampus and the amygdala (Gianaros & Manuck, 2010; McEwen & Gianaros, 2010).
One of the important roles these social brain structures play is in the modulation of activity of
primary regulatory systems (nervous and neuroendocrine systems) in response to the processing
of social and emotional stimuli. Thus, a better understanding of SES variations in neural activity
may provide clues as to SES variations in the activity of downstream physiological systems.
Although preliminary, data is accumulating indicating SES correlates of neural activity.
Gianaros and colleagues (2008) have found that young adults who perceive they came from
lower status families exhibit greater amygdala activation in response to viewing angry faces in a
functional magnetic resonance imaging (fMRI) investigation, consistent with the threat
perceptions biases found in the work of Chen and colleagues. Gianaros and colleagues (2011)
also recently reported that prefrontal cortex activity varies in midlife adults as a function of
childhood SES, with those from lower SES backgrounds showing a lower cortical response to
reward stimuli in fMRI assessments. These findings are intriguing in that they suggest
differential patterns of activity in brain structures that modulate downstream stress regulatory
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systems. However, the links between patterns of functional neural activity in response to various
stimuli (e.g., a threatening “social” stimulus in a scanner) and downstream physiology are only
beginning to be mapped out. Another concern is that almost all of this research has been
conducted on small and select samples. Neuroscience assessments, including fMRI and EEG
measurements, were recently assessed for a small subset of MIDUS Study participants, allowing
linkage to the wide array of biomarkers and psychosocial information collected in the larger
MIDUS II cohort. These data are ripe for analyses of how SES and other social factors may
influence patterns of neural activity and subsequent downstream disease-relevant physiology.
Another approach through which to assess more “upstream” modulation of physiological
functioning is through the study of social regulation of gene expression. Genetic potential is only
realized when specific genes are turned on, or “expressed,” via transcription and translation. One
of the first studies to show that a social factor could regulate gene expression was conducted by
Cole and colleagues (2007) and examined a small subset of participants selected for high and
low social loneliness from the Chicago Health Aging and Social Relations Study (CHASRS).
Analyses indicated that of humans’ 20,000+ genes, 209 showed significant expression
differences in older adults high and low in loneliness. An analysis of the functional activity of
differentially expressed genes revealed three primary functional typologies—an upregulation of
genes involved in inflammatory activity and a downregulation of genes involved in antibody
production and in immune response to viral infection. These gene expression differences are
notable because they may affect variations in downstream physiological functioning which
explain the significantly greater risk for poor health outcomes in lonely versus non-lonely
individuals (see Cacioppo, Hawkley, Norman, & Berntson, 2011; Hawkley & Cacioppo, 2010)
and may represent a pattern of earlier “biological aging” in the lonely.
Gene expression profiles have also been found to vary as a function of SES. Collectively
these studies suggest that SES adversity in childhood is associated with gene expression profiles
in adolescence and adulthood characterized: (1) as proinflammatory, (2) by diminished
glucocorticoid receptor expression and signaling, which may result in less effective control of
HPA output and remove the glucocorticoid-mediated brake on inflammation, and (3) by
upregulation of catecholamine associated transcription promoter pathways that suggest that the
sympathetic nervous system may be playing a role in delivering proinflammatory signals to our
genes (Chen et al., 2009; Miller & Chen, 2007; Miller et al., 2009). Miller and colleagues (2007)
have labeled this a “defensive” phenotype and suggest that SES adversity experiences early in
life “program” these biological systems during sensitive or critical periods of early childhood,
leading to heightened susceptibility in adulthood for the development of the many conditions
associated with greater inflammatory and HPA activity. Are such processes a social form of
antagonistic pleiotropy, whereby these biopsychosocial 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, Miller, Kobor, & Cole, 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
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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 towards understanding the social distribution 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 &
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 purpose 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, Ng, Harter, & Arora; Keyes,
Shmotkin, & Ryff, 2002). Beyond the injustice of social variations in these desired ends, social
disparities in psychosocial well-being may also explain differential patterns 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,
Barnes, Buchman, & Bennett, 2009; Boyle, Buchman, Barnes, & Bennett, 2010; Cohen &
Pressman, 2006; Gruenewald et al., 2007; Gruenewald, Liao, & Seeman, 2012; Okamoto &
Tanaka, 2004; Pitkala, Laakkonen, Strandberg, & Tilvis, 2004; Steptoe & 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 neuroendocrine,
cardiovascular, and inflammatory routes (Pressman & Cohen, 2005; Ryff et al., 2006; Ryff,
Singer, & Dienberg Love, 2004; Steptoe, Dockray, & 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 biosocial investigations
are tempered by a number of challenges. One is the domain of measurement challenges. On the
biological side, numerous challenges 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 advances have also led to a significant increase in the biomarkers that can be
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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 navigation 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 characteristics 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 temporal
influences on smoking behavior.
The importance of establishing the prognostic significance of biomarkers 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 research have solidly established such connections. The value of biosocial research will be in
the identification of the biological pathways which underlie these links, and in the potential use
of biomarkers as surrogate endpoints or indicators which can be used to better understand the
impact of adverse social conditions on biological well-being, to track the efficacy of health-
promotion policies and interventions, and to intervene at appropriate points in the life course to
place individuals on more healthy trajectories of aging.
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TABLE 6-1 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 (ELSA) Whitehall Studies
Social Environment and Biomarkers of Aging
Normative Aging Study
Study (SEBAS)
National Social Life, Health and Aging
Women’s Health and Aging Studies
Project (NSHAP)
Study of Midlife in the U.S. (MIDUS) Multiethnic Study of Atherosclerosis (MESA)
National Health and Nutrition Examination Coronary Artery Risk Development in Young
Surveys (NHANES) Adults (CARDIA) Study
Chicago, Health, Aging and Social Relations
Cardiovascular Health Study (CHS)
Study (CHASRS)
Cardiovascular Risk in Young Finns Study
British Birth Cohort Studies (e.g., 1958, 1963)
Costa Rican Longevity and
Healthy Aging Study (CRELES)
Twin Studies (SATASA, OCTO, SALT,
GENDER, HARMONY)
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FIGURE 6-1 Conceptual model of potential pathways through which social status is linked to
health.
SOURCE: Gruenewald et al. (2012). Reprinted with permission.
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FIGURE 6-2 Mean levels of biological system dysregulation by quintiles of cumulative SES
adversity (across childhood and adulthood) in the Study of Midlife in the U.S. (MIDUS).
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
Lowest 2nd 3rd 4th Highest
SES disadvantage quintile:
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