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8
Social Genomics and the Life Course:
Opportunities and Challenges for Multilevel Population
Research
Michael J. Shanahan
The past decade has brought remarkable advances in the integration of social and
biological models of health across the life course (see also Gruenewald, this volume). Research
is beginning to specify multilevel connections between diverse social experiences—reflecting
status, isolation, support, and stressors—and biological pathways, including neuroendocrine
processes and intracellular mechanisms involving the genome (Miller, Chen, and Cole, 2010).
Moreover, these multilevel complexities are now being studied with reference to life course
models (principally, sensitive period, accumulation, and pathway; Shanahan and Hofer, 2011).
This chapter focuses on one promising subfield of this larger literature, social genomic
studies of genetic transcription, and the opportunities and challenges that it presents for
demographers and social epidemiologists who study aging and health. Social genomics was
chosen as the focus for this chapter because this subfield attempts to interrelate social settings
with gene expression by way of chains of mediating factors, and thus illustrates the promise and
challenges of multilevel research in sharp relief.
The field of social genomics focuses on the mechanisms by which social experiences
regulate genetic activity (Cole, 2009). Transcription regulation refers to processes that govern the
rate at which DNA is transcribed into messenger RNA (mRNA), which in turn eventuates in
proteins. Thus, social genomic studies of transcription focus on how social experiences influence
mechanisms by which the information contained in the DNA is transcribed (or written out) into
mRNA. This focus on transcription is important because mRNAs serve as the molecular building
blocks for proteins, which are integral to virtually every biological process in the cell.
Social factors likely influence genetic activity by way of complex mediating chains
involving many levels of analysis, possibly extending, for example, from political economies to
people’s reactions to their immediate circumstance to intracellular mechanisms. By establishing
these meditational links between social experiences and transcriptional activity, scientists can
begin to understand how social experiences, like those associated with socioeconomic status
(SES), affect physical and mental health.
The possibility that social genomics can articulate such mechanisms is highly significant,
because most population research relies on nonexperimental survey data that makes causal
inference provisional. Human studies of transcription often suffer from the same limitations;
however, these human studies can then inform nonhuman animal studies that use experimental
designs, and even human experimental designs. When these diverse types of studies converge on
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a mechanistic model that links social experiences with expression profiles, causal inferences
about social context and health are indeed strengthened. Thus, a major payoff of social genomics
for demography and social epidemiology is the strengthening of causal claims by specifying the
mechanisms that link social experience with behavior and health.
Viewed broadly, social genomic research to date suggests a “two-stage” historiography:
(1) earlier research, conducted by social and behavioral scientists, identified putative social risk
factors and (2) investigations, conducted largely by health and biological psychologists, then
focused on how such risk factors “get under the skin,” eventuating in altered gene expression. As
a result of this two-stage exchange, increasingly sophisticated models are emerging that aspire to
mechanistically connect social risk factors, psychological and neuroendocrine mediators, and
molecular processes that, in turn, explain the emergence and progression of diseases. These
advances raise new questions for a “Stage Three,” the integration of population-based studies of
social risks with gene expression mechanisms in the study of health and aging.
Drawing on early research in this area, which examined several acute and chronic
stressors, the chapter begins by explaining the concept of transcription and, in general terms,
research strategies that are used to study social factors and transcription. The chapter then
reviews advances in social genomics with particular attention to studies of socioeconomic status,
which are of central interest to population researchers. (The chapter could also have focused on
social isolation as an illustrative example, and interested readers are directed to Cacioppo et al.,
2011.) Finally, the chapter identifies a series of opportunities and challenges for Stage Three,
population studies of health and aging that are informed by transcription studies. These
opportunities and challenges include (1) the collection of multilevel data including expression
data, relevant biomarkers associated with stress response, and neuroimaging data; (2) extensively
longitudinal designs that can adjudicate among several life course models; (3) the refinement of
measures of social context; (4) the use of large, diverse, population-based samples; and (5) the
strategic use of diverse designs to strengthen causal claims and to examine the effects of
socioeconomic status on gene expression in different policy settings, political economies, in
societies characterized by different demographic compositions, and among migrants. In turn,
such challenges and opportunities call for the innovative organization of interdisciplinary teams
of scientists.
The overarching point of this chapter, then, is that population-based research originally
inspired social genomic (transcription) studies, and the results of these investigations now
suggest future directions for population-based studies of social location, health, and aging.
Ideally, such studies will be designed to study multilevel, meditational processes as they extend
over many decades of life.
SOCIOECONOMIC STATUS, GENE TRANSCRIPTION, AND INFLAMMATORY
PROCESSES: STAGES ONE AND TWO
The Emergence of Transcription Studies of Social Experiences
Transcription depends on RNA polymerase (RNAP), an enzyme that attaches to the
promoter region near a gene and that then synthesizes mRNA from the DNA template strand. In
turn, the mRNA shuttles to the ribosomes, where encoded proteins are synthesized and then
perform two broadly defined functions. First, the synthesized proteins maintain and regulate the
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basic metabolic processes necessary for life. The ENCODE project—which draws on high-
throughput sequencing to examine the functional elements of the human genome—reveals that
transcription processes are quite complex, that the genome contains much more information than
is expressed at any given time, and that cells are highly selective in which genes are expressed
(The ENCODE Project Consortium, 2011; Weinstock, 2007, and accompanying articles). This
last point is evident when one considers that the diverse cells that make up the human body all
develop and are regulated with instructions from identical copies of the individual’s DNA.
Because each person’s DNA contains a massive amount of information, it must be selectively
expressed to orchestrate coherent development.
Second, transcription is also an important (though not exclusive) mechanism of
adaptation, allowing the cell to respond to changing circumstances. The process of adaptation
depends on the regulation of RNAP and its attachment to the promoter region of a gene. The
promoter region includes response elements, short sequences of DNA that can bind molecular
flags known as transcription factors. Once a transcription factor attaches to a target response
element, it can promote or block the recruitment of RNAP. When the transcription factor
attaches to the response element and recruits RNAP, the gene is said to be up-regulated, meaning
that its rate of expression increases. Blocking (or repression) of RNAP leads to down-regulation.
Roughly 200 transcription factors operate in the mammalian genome, and each binds to a
specific stretch of DNA (i.e., a response element), which is typically 10–20 but possibly up to 40
nucleotides in length. Research has identified the nucleotide sequence for many major
transcription factors, and researchers can combine that information with data from the Human
Genome Project to make inferences about patterns of transcription factor activity.
Transcription factors themselves are the product of intracellular signaling cascades and
signals (or stimuli) originating from the environment. Heat shock factor was perhaps the first
transcription factor identified that is responsive to environmental stimuli, discovered in 1974 in
drosophila melongaster. This transcription factor is, not surprisingly, highly conserved across
species and indeed observed in humans. However, that transcription was downstream from social
experiences—that is, responsive to social circumstances by way of mediating chains—was only
recently documented, beginning with studies of psychosocial factors and HIV-1 progression
(Cole, 2008).
Several streams of evidence (involving humans and non-human primate experiments)
pointed to the activation of the sympathetic nervous system (SNS; typically associated with the
body’s “flight or fight” response to stressors) in increasing the HIV’s transcription and
replication and, hence, the progression of the disease. That is, psychosocial stressors and
reactivity to stressors eventuate in faster progression of HIV-1 because of increased viral
replication. Because the SNS is downstream from social stressors, however, this research
suggested a broader possibility: that, beyond the specific example of HIV-1, social experiences
could influence gene expression because of their effects on the nervous system.
Cole and his colleagues (2007) were among the first to examine this possibility, focusing
on social isolation and the transcriptional profiles of white blood cells (leukocytes). Consistent
with the two-stage historiography suggested earlier, this study began with social epidemiological
and sociological research identifying social isolation as a risk factor for diverse forms of distress
(e.g., Seeman, 1996), including physical diseases. In fact, Cole and his colleagues had earlier
shown (1994) that social isolation was associated with increased activity in threat-related
pathways of the SNS (for a recent review, see Irwin and Cole, 2011).
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The study’s focus on white blood cells was highly strategic. Gene expression is likely
tissue-specific, making the study of many of the body’s organs a highly invasive process.
Peripheral blood cells, in contrast, may be collected with minimal inconvenience to participants.
Also, white blood cells are integral to host resistance to infection and inflammatory processes,
which are implicated in cardiovascular disease, neurodegenerative disease, and likely depression.
Thus, this study of social isolation joined two very different research traditions: a considerable
body of population-based research on risk factors, and biological models of leukocytes, which
offer a non-invasive window into the immune system and inflammatory processes associated
with many common diseases of aging.
The basic issue, then, was whether genes were expressed differentially among socially
isolated versus socially integrated adults. In turn, the answer to this question was examined in
terms of three questions. First, are transcripts differentially expressed between socially isolated
and integrated people? Fourteen healthy adults from the Chicago Health, Aging, and Social
Relations Study were selected based on their relatively high or low scores on a standard measure
of subjective loneliness, the UCLA Loneliness Scale. Differential expression of mRNA was then
examined using global gene expression profiling and was observed in 209 transcripts (of roughly
22,000 examined transcripts) correcting for a false discovery rate. Thus, the social isolates
differed in their gene expression profiles from the socially integrated adults.
Second, what were the functions of these differentially expressed genes? The functions of
these transcripts were then identified using the Gene Ontology (GO) catalog, a directory that lists
the functions of gene products (http://www.geneontology.org/). The issue was whether the over-
and underexpressed transcripts had biological functions that would indicate how social isolation
could affect health. In fact, the GO categories indicated that lonely adults were, generally,
showing greater activation of innate immune cells (e.g., pro-inflammatory signaling). And third,
among the differentially expressed genes, could specific transcriptional pathways be identified
that would help explain immune activation? This question was examined using TELiS,
bioinformatics software that analyzes the prevalence of specific transcription factor binding
motifs (TFBM) in differentially expressed genes.
Several pathways were identified, but one—involving the glucocorticoid receptor (GR)—
is particularly significant because of its replication by other studies, its biological plausibility,
and its possible applicability beyond the specific case of social isolation. Glucocorticoids (Gs)
are a class of steroid hormones that are integral to the immune system; when bound to a
glucocorticoid receptor, the resulting complex migrates to the nucleus where it up-regulates
genes that code for anti-inflammatory proteins and slows the expression of pro-inflammatory
proteins by preventing other transcription factors from entering the nucleus. Thus, Gs and GRs
are central players in inflammatory responses.
Cole and his colleagues found significantly less expression of GR-target genes in the
leukocytes of socially isolated adults when compared to socially integrated ones. This pattern is
consistent with the “glucocorticoid insensitivity hypothesis”: Chronically stressed individuals
become insensitive to the anti-inflammatory actions of Gs (such as cortisol), reflecting the down-
regulation of GR transcription factors. That is, chronic stress lessens the ability of G-GR
complex to work as a transcription factor that ultimately reduces inflammation (for experimental
support, see Cole, Mendoza, and Capitanio, 2009; for evidence suggesting that even low levels
of isolation could trigger G insensitivity, see Cole, 2008a).
Additional support for G resistance is found in Miller and his colleagues’ (2008) study of
links between chronic stress and transcription, a genome-wide expression study (using
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microarray technology) of family care-givers of brain cancer patients and matched controls.
They hypothesized that chronic stress elevates cortisol, which in turn leads to (1) an eventual,
compensatory down-regulation of GRs in monocytes (a type of white blood cell that is integral to
immune response) and (2) a consequent enhancement of pro-inflammatory signaling. Miller et al.
examined the transcription factor-binding motifs (TFBM) in the promoter regions of
differentially expressed genes. As expected, TFBMs occurred about 23 percent less frequently in
genes associated with glucocorticoid receptors and they occurred more frequently in genes
associated with pro-inflammation in care-givers, compared to controls. Overall, the results
suggested that social stress has a “transcriptional fingerprint” involving resistance to
glucocorticoids and mild systemic inflammation (for an additional example, focused on
interpersonal stress, see Miller et al., 2009).
Transcriptional Studies of SES
In addition to studies of acute and chronic stressors, research has also examined SES, of
central interest to demography and social epidemiology in the study of health disparities. Miller
and his colleagues (2009) examined why children’s SES affects health outcomes (such as
indicators of cardiovascular disease) in adulthood. Once again, and consistent with the two-stage
historiography, this study began with epidemiological observations suggesting that low SES in
childhood predicts coronary heart disease throughout adulthood even among people who attain
high levels of SES later in life (Kittelson et al., 2006). Miller et al. suggest that early adversity
increases the likelihood of a “defensive phenotype” characterized by exaggerated biological
response to stress, including inflammatory response. As stressors accumulate in the life course,
individuals with this defensive phenotype will be more prone to inflammatory diseases,
including some types of cardiovascular and respiratory diseases and cancers.
Consistent with this model, the authors observe that, controlling present SES, low SES
during childhood (as indicated by parents’ occupations during the child’s first five years of life)
was associated with several transcriptional patterns consistent with a defensive phenotype at
about age 34. These findings imply that early socioeconomic experiences result in durable
programming of the stress response system. Although the use of occupational prestige to indicate
“adversity” deserves further consideration, the empirical findings raise the possibility that early
experiences are capable of enduring biological programming (by way of transcription) in
response to social circumstances. The potential importance of early SES was likewise suggested
by research showing that SES at age two was associated with the expression of GR mRNA
during adolescence, a relationship that was not moderated by current SES (Miller and Chen,
2007). The authors conclude that “low SES operates most potently during this period [of early
childhood] of immune-system priming…in a way that favors the emergence of a pro-
inflammatory phenotype” (p. 408).
Two additional studies support the hypothesis that low SES early in life is associated with
genetic transcription patterns consistent with G insensitivity and add nuance to this basic pattern.
Chen et al. (2011) examined gene expression profiles among adults who grew up in low SES
households but experienced either high or low maternal warmth. The results suggested that low-
SES children with warm mothers showed reduced bioinformatic indications of pro-inflammatory
transcription factor activity (NF-κB) and immune activating transcription factor activity (AP-1)
compared to those who were low in SES early in life but experienced low maternal warmth. In
other words, high maternal warmth served as a protective factor for children from low SES
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households with respect to transcription patterns related to immune response and inflammation.
Consistent with Miller and Chen (2007), they also reported that current SES did not alter this
pattern of findings.
Second, Chen et al. (2011) examined genome-wide transcription profiles for T
lymphocytes of asthmatic children from low- or high-SES households. Low-SES children
showed overexpression of genes regulating inflammatory and catecholamine processes,
including some involved in chemokine activity, stress responses, and wound responses. Some of
the observed differences, however, appeared to be mediated by perceived threat. That is, children
from low-SES households were more likely to perceive threat in ambiguous situations, and this
tendency appeared to activate neuroendocrine processes that eventuated in changes in
inflammatory signaling pathways.
Summary
Social genomic studies of transcription are relatively new but have nevertheless led to a
cohesive body of research that point to biological mechanisms by which social experiences can
affect health. To date, evidence suggests that low SES in childhood and diverse
contemporaneous social stressors alter genetic expression by adolescence and also in mid-
adulthood. These alterations are consistent with a G resistance model such that prolonged
exposure to stressors is thought to lead to insensitivity to Gs, which would otherwise up-regulate
anti-inflammatory and down-regulate pro-inflammatory proteins. This resistance could, in turn,
be associated with diverse inflammatory illnesses, including asthma, cardiovascular disease, and
depression. Some evidence suggests that a sense of threat or heightened vigilance may serve as a
critical social psychological mediator that links social stressors with transcription profiles.
At the same time, many of the discussed studies rely on small, possibly unrepresentative
samples, and nonexperimental designs. Although efforts are made to statistically control
alternative explanations (e.g., by accounting for correlations among social stressors), such
studies likely can address these threats to causal inference in a limited fashion. However,
experimental designs that examine these links constitute Stage Two research (e.g., Cole,
Mendoza, and Capitanio, 2009) and, as these designs are increasingly used in conjunction with
nonexperimental designs, causal inferences will be strengthened. Thus, Stage-Three researchers
should pay close attention to the extent to which nonexperimental results have replicated in
experimental settings.
STAGE THREE: POPULATION-BASED MULTILEVEL STUDIES
This chapter has proposed a two-stage historiography according to which population-
based research identified putative social risk factors (Stage One) and, drawing on this research,
social genomic studies identified possible biological mechanisms by which these risk factors
could eventuate in diminished health (Stage Two). Yet what are the implications of these studies
for population research (Stage Three)? The Stage Two studies that have been reviewed raise a
number of distinct opportunities and challenges, including (1) the collection of multilevel data,
(2) examining diverse life course models, which require extensive longitudinal data, (3) the
refinements of measures based on diverse measurement strategies (including observation and
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tasks, for example), (4) the specification of cause and effect, and (5) the use of comparative
designs.
Collection of Multilevel Data
Mechanistic models of health and aging call for extensive information about the context,
people’s psychological states and behaviors, and biological processes. In the context of the
transcription studies discussed, for example, population-based studies should begin assessing
gene expression profiles for cells associated with immune processes in peripheral blood. The
initial body of research was based, quite understandably, on relatively small samples drawn non-
probabilistically from communities, which renders inference and the study of diversity difficult.
However, gene expression patterns may provide critical evidence of linking mechanisms that
connect social experiences with health. The collection of gene expression data in the context of
demographic and epidemiological research may be possible in the near future, although there are
presently practical barriers (e.g., peripheral blood draws require non-trivial processing in a
timely manner) that make large-scale collection from a geographically dispersed population
challenging and expensive. Until those barriers are addressed, however, several collection
strategies may be strategic.
First, the collection and processing of peripheral blood from subjects participating in
well-defined and characterized community samples is logistically plausible. Many community-
based samples have been studied for decades, resulting in rich descriptions of the participants’
social experiences. Particularly when such studies begin before or shortly after the birth of the
subjects and include valid, reliable measures of social, psychological, and medical assessments,
the collection of expression data could be highly informative. Second, smaller purposive samples
are also logistically feasible, which allow for the study of strategically defined groups. For
example, as noted, Cole and his colleagues (2008a) studied expression profiles among adults
who were pre-screened for social isolation, resulting in matched groups differing, apparently,
only on this characteristic. These strategies could involve embedding, the selection of a subset of
respondents from a larger, ongoing study, ideally with thorough matching on possible confounds.
Researchers interested in health disparities might examine, for example, groups that are
apparently resilient despite exposures to a risk factor (poverty, discrimination, etc.).
Whatever the research design and sampling, the resulting data will ideally include
multiple assessments to address a series of life course problems. Such a proposal is not new in
itself, with many studies attempting to collect multilevel data (e.g., famously, the National
Health and Nutrition Examination Surveys, or NHANES).
Life Course Models
That health and well-being reflect life course processes has long been appreciated,
although research is now accumulating that suggests the relevance of prenatal (and
intergenerational) experiences to health throughout adulthood, realities that call for data covering
at least the entirety of people’s lives, from conception to death.
At first glance, the results of gene transcription studies of SES appear largely consistent
with a sensitive or perhaps critical period model. However, extant evidence is not decisive and
indeed conceptual considerations suggest a hybrid model involving a sensitive or critical period
followed by a “chain of insults,” with perhaps accumulating disadvantage. Extensively
longitudinal data, ideally extending across generations, will be needed to resolve these issues.
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A sensitive period model posits that a specific biological system is highly plastic (i.e.,
subject to change, also referred to as programming) at specific points in development; that the
resulting biological change takes place in response to the environment; and that the biological
change is durable, potentially creating stable biological and/or behavioral tendencies. The critical
period model differs in that the period of plasticity is the only time during development in which
the biological system is open to change. That is, in contrast, the sensitive period model suggests
a time-span during which the system has heightened sensitivity to programming, but it may
change during other periods as well.
Adjudicating between these two models requires extensive longitudinal data that describe
the biological system and the social factors that are thought capable of changing it. Such data
would allow for the study of the purported sensitive or critical period, but also “before” and
“after” periods. Indeed, only data collection spanning “before-during-after” could inform
whether a period is sensitive or critical; whether observed changes endure and, if so, for how
long; and whether any enduring changes are in fact associated with biological and behavioral
tendencies in later life. Presently, research suggests that socioeconomic experiences before the
age of five are associated with gene transcription patterns perhaps as early as age 9 (Chen at al.,
2006; Miller and Chen, 2007) and as late as age 40 (Miller at al., 2009). Indeed, one study raises
the intriguing possibility that the one-to-two-year-old span is a sensitive period for GR, and the
two-to-three-year-old span is a sensitive period for toll-like receptor 4, which are proteins of the
innate immune system that recognize conserved features of potentially invasive microbes (Miller
and Chen, 2007). That is, different age periods may be sensitive with respect to different aspects
of the immune system.
At the same time, several opportunities are suggested by the complexities of the sensitive
period model and the extant data. First, although socioeconomic status before the age of five is
thought to be decisive, less is known about the “before” and “after” periods. The reviewed
studies rely largely on retrospective measures of SES in childhood, and control present SES.
However, the role of SES patterns before birth and after age five have not been studied
prospectively. With respect to the “before” period, a large body of evidence points to the
possibly powerful roles of maternal experiences on fetal development (for an overview, see
Godfrey, Gluckman, and Hanson, 2010) and of intergenerational transmission of gene expression
patterns in response to social experiences of grandparents (for a review, see Morgan and
Whitelaw, 2008). To the extent that these prenatal experiences are correlated with SES during
infancy and toddlerhood, it is conceivable that they could play causal roles in shaping
transcription patterns.
With respect to trajectories of SES after age five, there are likely a limited number of life
course trajectories of SES (Hallqvist et al., 2004; Rosvall et al., 2006), raising the possibility
that, for example, few people with chronically low SES before age five experience high SES
over the next five years. Thus, statistical control of present SES may not be entirely effective in
simulating group comparisons between high and low SES toddlers, controlling subsequent SES
trajectories. In any event, it may take very large samples to adequately study people with diverse
longitudinal patterns of SES.
Second, although socioeconomic status in early childhood is hypothesized to be the
causal contextual agent, it is unclear when altered transcription patterns emerge. The lag between
the environmental exposure and these changes, or the induction period, is unknown. One
possibility is that transcription patterns change very soon after, or during, the sensitive period, a
possibility for which there is presently no evidence. An additional possibility is that the sensitive
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period model is characterized by a longer induction period, meaning that there is an appreciable
interval of time between the environmental exposure and altered transcription. Extant data are
presently consistent with an induction period extending somewhere between exposure at ages 1
to 3 and altered transcription perhaps as early as age 9. However, more data are needed.
Third, the “chains of risk model” posits that risks (such as low SES) increase the
likelihood of subsequent disadvantages, creating a chain reaction of challenges, but very little
extant data sheds light on this possibility. Miller and his colleagues (2011) propose such a model,
the “defensive phenotype model,” arguing that early chronic stressors such as low SES are
associated with pro-inflammatory tendencies (as discussed above), but also vigilance and
mistrust of others, diminished self-regulation, and a proclivity for risky behaviors. That is, GR
insensitivity is integral to the defensive phenotype, but the latter is broader and includes
psychosocial processes. According to this perspective, early chronic stressors are also associated
with heightened biological responses to other stressors, which accentuates the pro-inflammatory
tendencies.
Thus, children growing up in low-SES households (i.e., subjected to chronic stressors)
are characterized by a constellation of biological, psychological, and social challenges that, in
turn, create yet more stressors, diminish their capacity to cope with stressors, and make them
more responsive to the negative effects of stressors. The resulting chronic inflammation is then
thought to lead, over many years, to inflammatory disease states, although pre-disease
indications may be observable by late childhood (Koenig, Walker, Romeo, and Lupien, 2011;
Lupien, McEwen, Gunnar, and Heim, 2009). However, the types of social, psychological, and
biological experiences that would connect early SES and later inflammatory gene expression
patterns are not well studied. Indeed, very little is known about how children in low SES settings
may, through their behaviors, create stressors and impede effective coping and social supports.
These considerations suggest a critical or sensitive period that, in turn, is accompanied by a chain
of social, psychological, and biological risks with considerable positive feedback among the
types of risk and over time.
Fourth, while life course epidemiology and demography recognize the chain of risk
model (e.g., Hayward and Gorman, 2004; Kuh et al., 2003), life course sociology has proposed
an additional, complementary form of risk accumulation. O’Rand (2006) proposes a cumulative
disadvantage model, according to which early disadvantages (like those associated with low
SES) initiate strongly path-dependent exposure to risks, a “chain of insults” that extends across
the phases of life. In contrast, people with advantageous early circumstances encounter a
strongly path-dependent sequence of enriched environments marked by high levels of social
capital, interpersonal relationships that facilitate the attainment of goals and positive
development. In keeping with a large empirical literature, O’Rand emphasizes the importance of
the SES of the family-of-origin, which is highly influential with respect to lifelong patterns of
social capital and social risks. The distinguishing feature of this model, however, is that
differences attributable to initial disadvantage are magnified over time (analogous to compound
interest) (DiPrete and Eirich, 2006). Some evidence suggests that this insight may be important
in understanding health disparities in late adulthood (e.g., Dupre, 2007; Willson, Shuey and
Elder, 2007). That is, the “chains of risk” model refers to the accumulation of a risk factor or
factors, and O’Rand’s model refers to how the effect of early risk is accentuated over time.
All of these considerations suggest a highly nuanced life course model: a sensitive or
critical period, with a possibly short induction period followed by chains of social,
psychological, and biological risks with extensive positive feedback among them; the child’s
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behaviors reflecting social disadvantages but also creating stressors and undermining coping and
social supports; predisease symptoms observable by late childhood; and disease states emerging
in adulthood, perhaps according to a power function. Clearly, such possibilities call for
multilevel data, extensively longitudinal data.
Refining Measures of Social Risk Factors
As noted, Stage Two can inform Stage Three by suggesting refinements in measures.
There is impressive evidence linking SES with “flexible resources” (e.g., knowledge about
health) by which people avoid risky behaviors and other threats to health, engage in health-
promoting behaviors, and attempt to address symptoms and disease states (e.g., Phelan, Link,
and Tehranifar, 2010). The multilevel research on gene expression, however, suggests two
additional mechanisms by which SES could influence health and these mechanisms suggest new
avenues for the measurement of social context.
First, as noted, early pronounced, chronic stressors may lead people to view ambiguous
situations as threatening, which in turn activates neuroendocrine processes that eventuate in
changes in inflammatory signaling pathways (Irwin and Cole, 2011). The evidence for this link,
between stressors and sense of threat and vigilance, is complex but hinges on changes in the
corticolimbic circuitry, which is associated with memory and emotion (Miller, Chen, and Parker,
2011). In any event, a possible link between SES and the activation of the corticolimbic circuitry
suggests the refinement of measures of the social environment to more directly assess contextual
features that would foster a sense of threat, vigilance, and mistrust. Irwin and Cole’s (2011)
review of connections between the SNS and threat suggests the importance of violence, hostility,
aggression, interpersonal loss, trauma, and physical exhaustion. Thus, research could directly
assess how specific aspects of SES and features that context that are strongly associated with
SES are associated induce threat, vigilance, and mistrust; SNS mechanisms; and gene
expression.
Presently, some evidence supports this focus. Harsh, insensitive, and cold parenting
likely fosters such reactions in children and indeed mediates links between SES and, for
example, internalizing and externalizing symptoms (e.g., Conger and Donnellan, 2007). Many
indicators of neighborhood disorganization and the built environment—crime, violence, safety,
racism, sense of community, abandoned buildings, and dilapidated and disrepaired structures—
likely breed vigilance and mistrust (Sampson, Morenoff, and Gannon-Rowley, 2002). However,
extant studies apparently do not assess sense of threat, vigilance, and mistrust. Indeed, the
assessment of threat in population-based studies may be difficult. One extant measure, CAUSE,
developed and used by Chen and her colleagues, uses videos of ambiguous situations (e.g., a
clerk watching a customer in a store from a distance) to elicit interpretative remarks from the
subject (e.g., “the clerk thinks the customer is going to steal something” or “the clerk wonders if
the customer needs help”). Such a measure might be administered to large groups of people with
the use of computers or personal digital assistants. In any event, it is unclear whether sense of
threat or vigilance could be assessed by survey instruments, suggesting the need for behaviorally
based assessments.
Less well-studied are aspects of daycares, preschools, schools, and racial discrimination
that might aggravate these feelings in children. Also, social capital typically refers to ties
between people that are characterized by trust and reciprocity. However, associations between
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networks (structural features and dynamics) and mistrust and vigilance have not been studied.
Thus, a key unresolved issue is how SES is associated with specific features of families,
neighborhoods, schools, and social networks that breed a sense of threat, mistrust, and vigilance.
And, in turn, little is known about how sense of threat then creates more stressors for the person,
contributing to the chain of risk model.
Second, early chronic stressors are thought to influence the cortiostriatal circuitry, which
is central to the processing of reward-related information and self-regulation (including, for
example, impulse control and goal-directed behaviors) (e.g., Miller et al., 2011). Very little is
known about the specific features of social context that can account for this association, and
whether such features reflect aspects of SES. Gianaros and his colleagues (2011) suggest that
some of this relationship, once again, reflects turbulent family relationships that are traceable to
socioeconomic need. However, they also propose and report evidence consistent with a cultural
argument, according to which SES is associated with a “cultural-intellectual orientation” that
stimulates social and intellectual skills. According to this perspective, such family-based
activities as engaging in intellectual discussions and attending cultural events positively
influences brain development, which in turn facilitates cognitive abilities and capacities for self-
regulation. There are presently no standard measures of the features of communities,
neighborhoods, families, schools, and social networks that would provide these experiences,
however. And once again, it will be difficult to link such measures to behaviors associated with
the corticostriatal activity (prime examples being impulsive behavior or discounting of future
rewards), which typically are based on behavioral tasks (e.g., Eisenberg et al., 2007). Thus, a
major pathway by which SES may affect inflammatory processes involves diminished reward-
related information processing and self-regulation, but the specific features of social context that
would link SES with these behaviors are not known.
Both of the discussed mechanisms suggest the development of new measures that focus
on features of social context that are graded by SES and that heighten a sense of vigilance,
mistrust, and threat and that diminish self-regulatory capacities. Further, it may be that
behavioral measures will be needed to assess these reactions to settings. In addition to measures
of context and these behaviors, research could also incorporate imaging technology to directly
assess corticolimbic and striatal activity (e.g., Gianaros and Manucj, 2010). Given logistical
considerations, such an effort would likely be embedded in a larger study, but would provide
crucial evidence bearing on whether connections between social context and, for example, sense
of threat did indeed reflect differences in the corticolimbic system.
Specificity of Causes and Effects
As more detailed data from different levels of analyses are collected, issues of causal
specificity will be more readily addressable. The extant evidence presently suggests that chronic,
pronounced stressors in early childhood are associated with GR resistance and other pro-
inflammatory mechanisms. Indeed, the replication of the GR resistance model across multiple
studies that examine different types of stressors is impressive and noteworthy in genetic research
as an apparently robust pattern of replication. At the same time, these findings raise the issue of
specificity in two respects. First, does a given inflammatory condition reflect one specific social
risk factor, or even one specific “signature set” of risk factors (i.e., specificity of contextual
cause; L. Shanahan, Copeland, Costello, and Angold, 2008)? It may be that that a wide range of
early, chronic stressors—stressors associated with SES—are functionally equivalent, meaning
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that they are substitutable contextual experiences that trigger the same biological pathways.
Presently, the mediating role of the cortico-striatal and limbic systems appear to be crucial,
suggesting that any social experiences that could affect these systems could initiate causal chains
that lead to pro-inflammatory patterns.
Viewed from the perspective of necessity and sufficiency, several possibilities cannot be
ruled out. Because several different stressors appear to trigger GR resistance, it is unlikely that
any one stressor is necessary and sufficient. Social isolation, parental stress due to a child’s
severe illness, low SES, and child maltreatment have all been associated with the up-regulation
of pro-inflammatory and down-regulation of anti-inflammatory transcriptional pathways. Miller
and his colleagues (2011) note that both low SES and child maltreatment share many common
social, psychological, and biological consequences, perhaps because both are associated with the
corticolimbic and corticostriatal processes discussed above.
At the same time, SES is not a stressor but rather many stressors are SES-graded. Indeed,
childhood maltreatment is substantially correlated with a wide range of SES-graded stressors,
including poverty, family conflict, neighborhood disorganization, parental substance abuse,
sibling hostility, geographic mobility, income instability, and parental psychopathology. This
network of correlations among stressors is of particular concern, raising the issue of whether any
one stressor is necessary and sufficient, unnecessary but sufficient, necessary but insufficient, or
unnecessary and insufficient. The issue can only be resolved with large, diverse samples that
assess a wide range of stressors. Moreover, the study of these possibilities may be facilitated by
diverse statistical models, including methods well suited to the study of conjunctive and
disjunctive patterns among possible predictors (e.g., Eliason and Stryker, 2009; Hastie, 2009).
Once environmental specificity with respect to transcriptional patterns associated with
inflammatory pathways is addressed, a second type of specificity remains to be considered:
whether a social risk factor or signature set of such factors predict only one or multiple outcomes
(i.e., specificity of outcome). Research suggests that GR resistance and other pro-inflammatory
pathways are associated with a range of diseases, possibly including cardiovascular disease,
depression, asthma symptoms, and, in principle, other conditions (e.g., arthritis, allergies, and
several cancers). Do risk factors that trigger GR resistance explain all of these disease states, or
are there specific patterns of risk factors associated with specific inflammatory diseases?
Answering this question will depend on data collection efforts that include a wide range of
inflammatory symptoms and disease states. It may be that stressor-inflammatory symptom
associations are characterized by multifinality (the same causal agents leading to different
outcomes), equifinality (diverse causal agents leading to the same outcome), or both—types of
complexity that are often not considered in empirical research.
Diverse, Mutually Informing Research Designs
The use of diverse, mutually informing research designs has been discussed as a way to
study the full complexity of a phenomena while allowing for strong causal inference whenever
possible. From a demographic perspective, comparative designs also broaden the scope of
inquiry by focusing attention to distinct social settings. In the case of social genomics, for
example, how do distinct macro-social contexts trigger pro-inflammatory transcription patterns?
A central issue is the extent to which low-SES children are exposed to settings that heighten a
sense of threat and vigilance and these comparative strategies may shed light on this problem.
First, what policies protect low-SES children from these experiences and thus prevent or retard
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the emergence of vigilance, difference in gene expression, and inflammatory conditions?
Comparisons of low-SES households in different regimes of transfer payments, political
economies, and societies could address this question. With respect to support for families and
children, salient policy differences might include transfer payments to low-SES households, the
provision of daycare and adequate health insurance, investments in schools and training
opportunities, and the provision of safe, affordable housing. Indeed, gene expression profiles and
related biological substrates (particularly biomarkers of the immune system) could be assessed in
evaluation studies of specific policies, which could be especially informative when the policies
have been applied to randomized groups.
With respect to political economies, for example, Esping-Andersen (1990) influentially
distinguished among liberal (e.g., the United States), corporatist-statist (e.g., Germany), and
social democratic (e.g., Sweden) regimes. Such distinctions produce variation in life course
patterns of school, work, and family, and perhaps they also bear on the stress load created by low
SES. The corporatist and social democratic regimes provide substantially more support to low-
income households when compared to liberal regimes, but the social democratic society
socializes costs associated with family life, and includes thorough welfare provisions for workers
and the unemployed. With respect to other societal differences, not necessarily reflecting
political economic policies, it may be that the distribution of SES conditions its effects on
families. A very large body of research suggests that health is less favorable in societies where
income differences are greater (Wilkinson and Pickett, 2006). To the extent that minority status
is associated with discrimination (a potentially potent chronic stressor), societal distributions of
race and ethnic groups may also bear on how much stress is suffered by low-SES children and
their families.
Differences in policy settings, political economics, and demographic features of societies
can also be studied by comparing migrants to a new setting and people who remained at the
origin. Such a strategy has been used to study dietary changes and health, for example, and has
the advantage of controlling, in the aggregate, for genetic factors that might otherwise
distinguish, for example, low-SES children from two different countries. This strategy could be
used to compare and contrast low-SES origin and destination groups and how their social
location is associated with threat and self-regulation, and transcription patterns. Ideally, such a
design would involve non-voluntary migrants to control for selection to migration.
In any event, these comparative strategies could be used to study how the stress load
created by low SES differs by social systems.
CONCLUSIONS
A large and complex body of research suggests that social experiences of early childhood
may have lifelong implications for the immune system and the emergence of inflammatory
diseases. This body of research began with studies of social risk factors and health (Stage One)
and then progressed to the study of how such risk factors could conceivably “get under the skin”
(Stage Two). Broadly, early chronic stressors are associated with pro-inflammatory tendencies
(e.g., due to GR resistance), but also vigilance and mistrust of others, diminished self-regulation,
and a proclivity for risky behaviors. Children growing up in low-SES households (i.e., subjected
to chronic stressors) are thus characterized by a constellation of biological, psychological, and
social challenges that, in turn, create yet more stressors, diminish their capacity to cope with
stressors, and make them more responsive to the negative effects of stressors. In this way, society
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may “get under the skin,” but behavior then creates a feedback to one’s social circumstances,
creating yet more stressors (i.e., bidirectionality). The resulting chronic inflammation is then
thought to lead, over many years, to inflammatory disease states, although pre-disease
indications may be observable by late childhood.
This emerging model, in turn, suggests a symbiotic relationship between Stage Two and
population-based studies of aging and health (Stage Three). On the one hand, Stage Two
research provides evidence of linking mechanisms that may connect social experiences and
health, mechanisms that are necessary for any convincing causal account of social risk factors
and health. On the other hand, Stage Three studies can validate and extend Stage Two research.
To date, logistic considerations have prohibited the application of genome-wide expression
studies to large, representative samples. However, just such samples are needed to validate Stage
Two studies and to study diverse patterns of social experiences and trajectories of symptoms and
disease states.
Moreover, the results from Stage Two studies suggest unique challenges and
opportunities for Stage Three research. Given that social experiences may be biologically
embedded before age five, given the central role that chains of risk and processes of
accumulation play in creating stress, and given the emergence of diseases over many decades,
extensively longitudinal data are imperative. And, further, given that none of these relationships
is determinative, extensively longitudinal research is also needed to study life course patterns
that are associated with resilience and varying patterns of vulnerability. As noted, the basic
model emphasizes the importance of very early experiences. At the same time, several studies
reported GR resistance profiles among adults (e.g., Cole, 2008a; Miller et al., 2008), based on
their contemporaneous experiences. The connections between these two sets of findings are
unclear, but perhaps there are windows of vulnerability throughout life.
The Stage Two research also suggests new themes with respect to measurement and
modeling. Given mechanisms suggested by Stage Two research, future studies could profitably
focus on specific aspects of the social context that heighten a sense of threat, vigilance, and
mistrust, and that undermine self-regulatory capacities. Such refinements may be challenging
because these behaviors are likely best assessed with behavioral measures and, ideally, would be
accompanied by neuro-imaging studies. The reviewed studies also suggest very high levels of
contingency among social experiences and the psychological and biological cascades that they
initiate. That is, it may be that many different stressors are essentially substitutable, equally
capable of instilling threat and increasing the likelihood of pro-inflammatory transcription
patterns (i.e., equifinality). This challenge of contingency is compounded by the possibility that
the same social experiences could produce different inflammatory symptoms and disease states
(i.e., multifinality). Thus, methods that are sensitive to high levels of contingency—e.g., fuzzy
set analysis, machine-learning techniques—will be appropriate, although their application may
depend on further methodological developments that strengthen their inferential basis.
Most directly appealing to the traditional domain of demography, gene expression
profiles can be studied in diverse comparative frameworks to understand how much stress a
given social system generates. Such comparisons could involve different policies as they bear on
the lives of low-SES households and different political economies, comparisons that may be
especially informative when involving origin and destination groups of nonvoluntary migration.
Finally, considerable attention has been devoted to risk behaviors (smoking, drinking, inactivity,
poor diet, etc.) as crucial explanations for socioeconomic differences in health. Stage Two
research appears to complement this focus, suggesting that stressors associated with low SES led
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to diminished self-regulation, which in turn may well be associated with a wide range of risk
behaviors.
Population-based studies of health have traditionally had an admirably interdisciplinary
quality. As the models that describe connections between social, psychological, and biological
levels of analysis become increasingly complex, greater attention should be paid to how such
teams are organized and encouraged. The payoff for such efforts will be increasingly thorough
explanations of SES gradients in health, and thus the scientific basis for effective prevention and
intervention.
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