Annals of the New York Academy of Sciences

Volume 1186, Issue 1 p. 223-239

ANNALS OF THE NEW YORK ACADEMY OF SCIENCES

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Socio-economic differentials in peripheral biology: Cumulative allostatic load

Teresa Seeman,

Teresa Seeman

Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

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Elissa Epel,

Elissa Epel

Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA.

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Tara Gruenewald,

Tara Gruenewald

Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

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Arun Karlamangla,

Arun Karlamangla

Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

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Bruce S. McEwen,

Bruce S. McEwen

Harold and Margaret Milliken Hatch Laboratory of Neuroendocrinology Laboratory, Rockefeller University, New York, NY, USA

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Teresa Seeman,

Teresa Seeman

Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

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Elissa Epel,

Elissa Epel

Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA.

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Tara Gruenewald,

Tara Gruenewald

Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

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Arun Karlamangla,

Arun Karlamangla

Division of Geriatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

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Bruce S. McEwen,

Bruce S. McEwen

Harold and Margaret Milliken Hatch Laboratory of Neuroendocrinology Laboratory, Rockefeller University, New York, NY, USA

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First published: 18 February 2010

https://doi.org/10.1111/j.1749-6632.2009.05341.x

Citations: 443

Address for correspondence: Teresa Seeman, Ph.D., Division of Geriatrics, Geffen School of Medicine, University of California, Los Angeles, 10945 Le Conte Ave, Suite 2339, Los Angeles, CA 90095-1687. [email protected]

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Abstract

This chapter focuses on evidence linking socio-economic status (SES) to “downstream” peripheral biology. Drawing on the concept of allostatic load, we examine evidence linking lower SES with greater cumulative physiological toll on multiple major biological regulatory systems over the life course. We begin by reviewing evidence linking lower SES to poorer trajectories of aging in multiple, individual physiological systems, followed by evidence of the resulting cumulative, overall burdens of physiological dysregulation seen among those of lower SES. The role of cumulative physiological dysregulation in mediating SES gradients in morbidity and mortality is then examined. We conclude with discussion of the question of interactions between SES (and other such environmental factors) and genetic endowment, and their potential consequences for patterns of physiological activity—an area of research that appears poised to contribute significantly to our understanding of how social conditions “get under the skin” to affect health and aging.

Introduction

Building on the previous chapter's focus on socio-economic status (SES) differences in central (brain) aging and function, this chapter examines evidence regarding SES impacts on “downstream” peripheral biology. We discuss the brain-mediated influences of SES on patterns of activity as well as aging of major peripheral physiological regulatory systems, including the autonomic nervous system, cardiovascular, metabolic, and inflammatory processes, presenting evidence for SES gradients in parameters of each of these major systems. Drawing on the concept of allostatic load (AL), we also examine the cumulative impact of SES across these multiple biological systems and parameters. Such a multisystems view of the impact of SES on rates of aging of peripheral biology provides a different and complementary perspective to that of SES effects on individual physiological systems. Although lower SES is nearly always associated with poorer trajectories of physiological aging when individual biological parameters are examined, the gradients are generally relatively modest while larger gradients are frequently evident when examining “cumulative” measures, representing multisystems indices of physiological aging, suggesting that lower SES is associated with more rapid aging of all major systems. We review the growing evidence for both the more modest SES gradients in individual systems/parameters as well as the recent literature focusing on cumulative indices of physiological dysregulations across multiple systems. We also examine the small, but growing, literature focused on testing the role of such cumulative physiological dysregulation in mediating observed SES gradients in risks for a range of diseases, disability, and mortality.

We also provide a brief overview of the latest work testing for interaction effects between SES (and other such environmental factors) and genetic endowment and their resulting impact on patterns of physiological activity—an area of research that appears poised to contribute significantly to a more substantive understanding of how social conditions “get under the skin” to affect health and aging over the life-course. Above and beyond providing clearer understanding of the physiological routes through which SES impacts health and aging, research on links between SES and peripheral biology can also contribute to the development of more evidence-based programs and policies to reduce current social inequalities. Information on peripheral biology could be used, for example, as an intermediate “outcome,” for tracking the more immediate, and hopefully beneficial impacts of such policies and programs.

Socioeconomic status gradients in patterns of activity in major regulatory systems

As the McEwen and Gianaros chapter outlines, growing evidence points to significant SES differences in brain development and function, beginning at the earliest stages of life and continuing across the life-course. Such differences in brain development and function impact, and indeed regulate, downstream peripheral biology through cortico-limbic pathways, including known interconnections between structures such as the prefrontal cortex, hippocampus and amygdala, and more downstream regulation of the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system (SNS), orchestrated in large part through the hypothalamus and adrenal gland.^1–5 In the following sections, we examine evidence linking SES to differences not only in such downstream HPA and SNS activity (e.g., circulating levels of cortisol, norepinephrine (NE), and/or epinephrine) but also in additional related regulatory systems reflecting cardiovascular, metabolic, and inflammatory parameters.

Table 1 provides an overview of major biological systems and their respective parameters that are our focus in light of their known associations with increased risks for major health outcomes.

Table 1. Major biological regulatory systems

System	Parameters commonly assessed
Cardiovascular	Diastolic/systolic blood pressure
Cardiovascular	Heart rate/pulse
Metabolism	Glucose, glycosylated hemoglobin
	Insulin
	Lipids (total, HDL, LDL cholesterol)
	Relative weight (BMI, WHR)
Hypothalamic-pituitary-adrenal (HPA) axis	Cortisol; dehydroepiandrosterone sulfate (DHEA-S)
Autonomic nervous system
Sympathetic nervous system (SNS)	Norepinephrine, epinephrine
SNS/Parasympathetic balance	Heart rate variability
Inflammation	C-reactive protein, IL-6

The negative health consequences of high blood pressure (particularly systolic) have been recognized for decades, including higher death rates, the onset of cardiovascular disease, and the loss of both physical and cognitive functioning.^6–9 Several studies have also found that high resting heart rate is associated with increased mortality risk (see Ref. 10 for review). Metabolic parameters, including higher total serum and LDL cholesterol, lower HDL cholesterol, and higher relative weight, are also recognized risk factors for poor health outcomes including mortality, cardiovascular disease, and functioning loss.^7,11–14 Elevated glucose levels—as indexed by fasting glucose or more integrated assessments of glucose metabolism such as glycosylated hemoglobin (HgA1c)—have also been related to heart disease risk;¹⁵ to higher mortality;¹⁶ and to poorer cognitive^17–20 and physical function.⁸ It is important to note that even modest elevations in these biomarkers are associated with adverse outcomes, even when they do not meet accepted thresholds for clinical intervention.^21,22

Dysregulations in HPA axis activity, particularly chronically elevated cortisol, have been related to increased risks for range of health problems, including obesity,²³ hypertension, diabetes mellitus type 2, lipid imbalance, atherosclerosis,^24–26 accelerated brain aging,²⁷ hippocampal atrophy, and cognitive impairment,^28–31 loss of bone mineral density,^32–34 sarcopenia,³⁵ and immune dysfunction.³⁶ Similarly, more chronic elevations in SNS activity can raise blood pressure and heart rate that promotes atherosclerosis, and appears to be independently associated with cancer mortality.³⁷

Exposure to chronically elevated inflammation processes has also been increasingly recognized as a significant health risk with links to increased risks for osteoporosis,^38,39 cardiovascular disease,^40–45 and cognitive decline.^46–48

As reviewed in McEwen and Gianaros chapter, these peripheral processes also have feedback loops to the brain such that downstream physiological activity frequently has significant impacts on “brain health” as well. For example, dysregulation of metabolism as in Type 2 diabetes has deleterious effects on the brain.⁴⁹ Elevated HPA and cardiovascular parameters have also been linked to poor cognitive functioning in many studies.^29,31^,^49–54 Accumulating evidence also indicates that inflammatory processes in the body are associated with reductions in volume of a key brain structure, the hippocampus⁵⁵ as well as poor sleep⁵⁶ (for additional details see chapter by McEwen and Gianaros). From our perspective, a significant and central feature of these biological processes is the degree to which they not only relate to major health risks but also exhibit gradients by SES. A growing body of evidence documents consistent SES gradients for nearly all of these biological risk parameters—with lower SES groups exhibiting higher levels of risk. Much of the earliest work presented evidence for these SES gradients in adults^12,57 but more recent work has shown that these same SES gradients are evident as early as the first 5 years of life,^58–60 as well as throughout childhood,⁵⁹^,^61–65 early adolescence,^66–68 and into early adulthood.^69–73 Figure 1A and B illustrate these gradients, showing levels of glycosylated hemoglobin (HgA1c)—an index of overall glucose metabolism over the past 6–8 weeks⁷⁴—by SES as indexed by the poverty income ratio (PIR), an index of household income relative to the federally defined poverty level.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

(A) High glycodylated hemoglobin* by Poverty Income Ration (PIR). SES gradients based on PIR in relation to glucose regulation among children (*i.e.*, ages 19 and under). (B) High glycosylated hemoglobin* by PIR. SES gradients based on PIR in relation to glucose regulation in adults (*i.e.*, ages 20 and above)—NHANES III (1988–1994).¹ PIR reflects the ratio of respondents’ reported household income to the federally defined poverty level for those living in the respondent's area of residence and with a household size similar to the respondent's.

As shown in Figure 1A, gradients in HgA1c levels are seen even as early as ages 1–5, with those from lower income (lower PIR) households showing higher prevalence of elevated HgA1c levels compared with children from higher income (higher PIR) families; Figure 1B documents that these gradients continue throughout adulthood as well. As shown in Figure 2, similar SES gradients are seen for a wide array of additional biological risk factors, including blood pressure, total and HDL cholesterol, and C-reactive protein (CRP) with lower SES associated with a higher prevalence of “high risk” values for all of these parameters—“high risk” being defined based clinically accepted criteria.^75–80 Indeed, a large body of evidence documents SES gradients for nearly all major peripheral biological parameters, including those reflecting HPA axis regulation,^{60,62,66,70,72,81–85} SNS activity,^{60,71,72,81,83,85} heart rate variability (an index of autonomic balance),^60,86–88 resting blood pressure,^{61,66,69,83,89–92} and reaction to challenge (see Ref. 93 for review), lipids/metabolic risks,^72,90,92^,^94–102 inflammation,^73,94^,^103–109 and relative weight and fat distribution patterns (i.e., body mass index, waist-hip ratio).^90,92,95,110 In addition to the foregoing evidence linking individual SES to major biological risk parameters, evidence also points to impacts from more macro-level, neighborhood-level SES (e.g., Refs. 111,112).

Cumulative biological “wear and tear” (that is, allostatic load)

The concept of “allostatic load,” introduced in the early 1990s by McEwen and Stellar,¹¹³ proposes a multisystems view of the cumulative physiologic toll that may be exacted on the body over the course of a lifetime of efforts to adapt to life's demands.¹¹³ The concept of allostatic load is itself derived from Sterling and Eyer's¹¹⁴ concept of allostasis, meaning “stability through change”; allostasis refers to the physiological imperative that “an organism must vary parameters of its internal milieu and match them appropriately to environmental demands” (p. 638). This view of internal physiology emphasizes its constant dynamism, stressing that healthy functioning requires on-going adjustments of the internal physiologic milieu, with physiologic systems exhibiting fluctuating levels of activity as they respond and adapt to environmental demands. Moreover, each mediator involved in allostasis is part of a nonlinear network in which each mediator has biphasic effects on physiological outcomes and also has reciprocal regulatory influences on other mediators.¹¹⁵

Allostasis thus emphases the idea of optimal operating ranges of physiologic systems in contrast to the earlier homeostatic idea of optimal set-points. It also acknowledges that few homeostatic networks are solely under local regulation but rather are importantly regulated centrally by the brain (see also McEwen and Gianaros chapter). As such, our perceptions and cognitive-emotional responses to environmental stimuli can, and do, influence patterns of physiological activity and reactivity as, for example, when our blood pressure rises in anticipation of standing.¹¹⁴

The concept of allostatic load derives from a longer-term, cumulative view of the process of allostasis and the consequences of dysregulation in patterns of response to environmental demands (i.e., responses that no longer operate within optimal ranges). The premise is that while relatively short-term fluctuations in levels of physiologic activity are necessary for the body to respond successfully to stimuli (e.g., the fight or flight response in the face of various types of danger), excessive fluctuations either in terms of the extent, duration or frequency (e.g., responding to perceived “danger” everywhere) can result in wear and tear on the body's regulatory systems. This wear and tear is manifested in progressive dysregulations in the system's ability to maintain levels of system parameters within “normal operating ranges” in terms of resting levels (e.g., seated blood pressure or fasting glucose) and dynamic patterns of response (e.g., extent and duration of response). Allostatic load represents the cumulative physiological toll (i.e., the extent of such dysregulation) across multiple systems over time. It reflects both a multisystem and life-span orientation, visualizing disease risks as resulting from the individual's cumulative exposure over time to the “wear and tear” associated with elevations in physiologic activity across the body's multiple regulatory systems.

The idea that cumulative levels of “stress” may have deleterious effects on health and longevity has long intrigued investigators, dating back to early work on homeostasis^116,117 and continuing with the work of Selye^118–120 and others¹²¹ on the pathological consequences of excessive physiologic activation. Much of this work has tended to focus on the effects of stress and dysregulation in specific, individual biological parameters.^122–127 The concept of allostatic load proposes that biological risk can be more effectively conceptualized from a more cumulative, multisystems view and that such a concept can better contribute to our understanding of health outcomes—a view that is consistent with known multifactorial etiologies of most pathology. In part, this view stems from the realization that these individual mediators are coupled together in nonlinear networks and cannot be independent of each other.¹¹⁵

Selye himself wrote that “life is largely a process of adaptation to the circumstances in which we exist..(and)..the secret of health and happiness lies in successful adjustment to the ever-changing conditions on this globe; the penalties of failure in this great process of adaptation are disease and unhappiness” (pp. xv–xvi,¹²⁰). Allostatic load might then be viewed as an index of the relative degree of “failure” at a physiologic level—i.e., a marker of the cumulative, physiologic costs of previous efforts to cope with life's slings and arrows. It is the price that the body may ultimately pay for dysregulated patterns of physiological response to adaptational demands (e.g., patterns of under- or over-responding). As an “historical” index of prior physiologic toll, allostatic load also provides the background setting for current and future patterns of physiological response to stimuli and thereby contributes significantly to health risks.

Allostatic load differs from more traditional concepts of biological risk in two ways. The first is its focus on “the sum total of physiological dysregulation across systems”—a view closer to the reality of known system interconnections than approaches that focus on the role of one or another regulatory system. And, the second is its inclusion of relatively more modest forms of dysregulation in the accounting of biological risk. This view of biological risk proposes that relatively modest dysregulation (e.g., somewhat elevated BP and/or more frequent or prolonged elevations in response to stimuli) when cumulated across multiple systems may have significant impacts on health risks, even if none of the individual effects would be deemed either statistically or clinically “significant” in and of themselves. The sum of these effects is nonetheless hypothesized to exert a cumulative wear and tear on systems.

As a cumulative phenomenon, allostatic load is postulated to develop over the life course, with individuals accumulating AL at different rates. Both the initiation and progression of such dysregulation is postulated to be driven by individual differences in the frequency of exposure to real and perceived challenges and differences in their patterns of physiological responses to these challenges. Factors that may influence characteristic patterns of reactivity (i.e., maximum change from basal levels and duration of the response) include such things as (1) age (e.g., prolonged neuroendocrine responses at older ages have been found in some studies^124,128 though not all (see Epel et al., 2007¹²⁹ for review), as well as decreased vagal tone at rest,¹³⁰ decreased cardiovagal baroreflex sensitivity,¹³¹ and impaired functional immune responses to challenges, such as vaccination¹³²); (2) genetic influences (e.g., certain polymorphisms being associated with greater physiological dysregulation in the face of stressful conditions^133,134); (3) lifestyle/behavioral influences (e.g., hyper-reactivity of HPA, SNS, blood pressure, and glucose among the less physically fit and over-weight individuals,^135–141 and among those consuming greater sugar/glucose;^142–144 smoking has also been associated with increased acute HPA and SNS reactivity^145,146 but longer-term blunted HPA responses to acute stress¹⁴⁷ while alcohol consumption has been associated with increased HPA reactivity^148,149 along with decreased blood pressure and heart rate response to acute stress¹⁵⁰); (4) psychological influences (e.g., hyper-reactivity of cardiovascular and HPA systems among those with poorer self-esteem,¹⁵¹ poorer self-efficacy beliefs,^152,153 more passive coping¹⁵⁴, and greater hostility or Type A personality attributes^155–158); (5) inter-personal/social influences (e.g., greater HPA, SNS, and cardiovascular reactivity under conditions of social isolation and social conflict;^159–161); and (6) chronic stressor experience (e.g., greater SNS reactivity and lower functional immune activity to acute stressor experiences in those with a higher level of background stress;¹⁶² although lower levels of endocrine and immune reactivity have also been observed¹⁶³).

Though supported by less empirical evidence, additional factors likely to influence the frequency of reactivity include: (1) environmental exposures (e.g., neighborhoods where you live and work); (2) individual differences in what is perceived as “threatening.” Indeed, the latter is perhaps the most uniquely “human” feature of our stress responses. Specifically, the capacity of our brains to imagine scenarios (e.g., potential outcomes of situations) contributes a special feature to human stress responses, so that in the extreme case, one could experience prolonged stress responses solely due to perceptions/beliefs regardless of the actual, objective presence of danger or stressors. Conversely, some individuals may respond only minimally if at all to what most would view as major life challenges. Indeed, such individual differences in the interpretation of situations can result in dramatically different patterns of response at all levels (cognitive/emotional, behavioral, and physiological). Over time, as a result of individual differences in exposure to different situations and stimuli, and individual differences in cognitive/emotional reactions to these stimuli, population differences in allostatic load are postulated to develop. Older individuals will generally have more cumulative load; but within any given age group, there will be a range of allostatic loads that reflect differences in prior exposures and reactions to one's life-time experiences.

Empirical efforts to operationalize allostatic load

Though attracting considerable attention and enthusiasm from a theoretical/conceptual point of view, the multifaceted nature of allostatic load has proven to be difficult to operationalize in any straightforward or accepted, “gold standard” way. Despite continued work using various methodologies,^164–169 debate continues regarding how best to capture the multiple and inter-connected features of AL,^165,170 including questions regarding the range and scope of physiological measurements that should be included (e.g., which systems and which aspects of these systems) as well as methods for summarizing such information into one or more “cumulative indices.” As highlighted in the Evans and Kim chapter, these debates show remarkable parallels to earlier debates on development of cumulative indices of childhood psychosocial and environmental risk factors, including concerns about the use of indices that “cumulate” effects of different systems or domains into a single score (thereby obviating the ability to ascertain the impact of individual components) as well as concerns regarding the use of simple summative approaches that fail to account for the potentially differential impact of different components of risk (see Refs. 134,171 for overviews). As Rutter and others have shown, however, simple summative indices appear to capture the essential cumulative nature of such childhood risk factors and the cumulative number of such risks appears to predict outcomes more strongly than specific risk factors or combinations of such factors (see Refs. 134,172 for review). Other fields have also employed similar approaches to capture the combined impacts of multiple forms of child abuse^173,174 and environmental risks.¹⁷⁵

Initial efforts to operationalize AL followed a similar approach,¹⁷⁶ using a straightforward summative methodology. Such an approach also shares similarities with other indices of cumulative biological risk that combine information on multiple risk factors into a single score, including the metabolic syndrome^177,178 (which is calculated as a simple sum with equal weighting for all contributing factors) and Framingham Risk Score¹⁷⁹ (which is calculated with differential weight for component items). These latter cumulative scores—like their analogs in the childhood psychosocial risk literature—have been shown to predict outcomes better than their individual components.¹⁷⁹ Indeed, use of such cumulative assessment is now recommended for clinical decision-making related cardiovascular disease risk management.^80,180 Drawing on available data from the MacArthur Study of Successful Aging¹⁸¹ for an initial set of data on 10 biological parameters—resting systolic and diastolic BP; waist-hip ratio; total and HDL cholesterol; glycosylated hemoglobin; urinary free cortisol, NE, and epinephrine; dehydroepiandrosterone sulfate (DHEA-S), reflecting four major regulatory processes (HPA, SNS, cardiovascular, and metabolic)—we constructed an initial index of AL by simply summing the number of parameters for which the individual had a value that placed them in the top quartile (or bottom quartile for HDL cholesterol and DHEA-S) of that parameter's distribution within the MacArthur cohort.¹⁷⁶ It is important to note that this original set of 10 parameters was not meant to be comprehensive nor was it offered as a fixed/standard measure of AL, rather it was an initial attempt to operationalize AL using available data. Indeed, subsequent work from this study has been able to augment the panel of AL components with additional information on parameters of inflammation (CRP, interleukin-6 (IL-6))—the goal being to optimize our assessment of overall AL by incorporating information regarding the multiple regulatory systems involved in allostatic “adaptive” processes. This “inclusive” approach to operationalizing AL contrasts with that of many of the more traditional and targeted risk indices such as those developed to predict risks for cardiovascular disease where selection of a specific set of component physiological parameters is based on their known contribution to risks for the specific outcome in question. Operationalization of AL, by contrast, seeks to achieve an index that—as comprehensively as possible—reflects the overall total cumulative burden of physiological dysregulations in allostatic processes across as many regulatory systems as possible. Thus, the focus in terms of measuring AL is on capturing a multisystems array of information regarding the multiple major regulatory systems involved in adaptive “allostatic” processes rather than prediction of one or another specific outcome.

The choice of “top risk quartile” as our criterion for counting a given parameter was based on two considerations. First, this allowed for a common definition of “higher risk” for all of the components of our summary score; use of “clinically defined higher risk” guidelines was not feasible as such guidelines did not (and still do not) exist for many of the constituent parameters (e.g., 12-h overnight urinary free cortisol, NE, and epinephrine (EPI)). Second, given the original design of the MacArthur study (i.e., enrollment of a cohort of relatively high functioning 70–79 years old), our chosen criteria provided an assessment of which individuals within this cohort exhibited relatively “higher risk” profiles compared with their counterparts in the cohort. We also elected not to define gender-specific definitions as we would then be unable to compare men and women in the cohort based on a common definition.

Using this simple count index, analyses of the MacArthur Study data demonstrated that baseline AL was associated with significantly increased risks for a range of major outcomes, including risks for mortality, incident and recurrent cardiovascular disease, cognitive and physical decline. These associations were found both with respect to an initial 2.5 year follow-up period¹⁷⁶ as well as a longer 7-year period,¹⁶⁴ and the effects were found to result from the contributions of multiple regulatory systems represented in the index,¹⁶⁴ providing support for the cumulative nature of the risks reflected in such a summary index of AL. Subsequent work has shown that these associations are replicated when a variety of alternative scoring methods that incorporate more complex and detailed scoring algorithms are used to create indices of AL, including canonical correlation analyses,¹⁶⁹ grade-of-membership^166,167,182 and recursive partitioning.¹⁶⁸ The rationale for moving to these more complex scoring systems is their ability to incorporate more detailed information on specific values for the various component physiological parameters (rather than simply “counting” whether or not one's value is above or below a defined threshold value). And, in some cases (e.g., recursive partitioning), their ability to account for potential nonlinearities in risk (i.e., interactions between component physiological parameters). To date, however, evidence on their relative performance in predicting outcomes as compared to simpler count indices remains sparse. Available comparative data suggest that more complex scoring systems that incorporate greater information are, as expected, more strongly related to outcomes.¹⁶⁶ (Seeman and Karlamangla unpublished data) However, the “gains” in predictive ability appear to be relatively modest. For example, data from the MacArthur Aging Study suggests that the original simple count index captures a majority of the relevant health risk information—e.g., correlations with decline in cognitive and physical function of 0.12 and 0.30, respectively, for the count index as compared with correlations of 0.17 and 0.39 based on the canonical AL index (Karlamangla and Seeman, unpublished data). Though suggestive, such comparative data remains sparse and the question of how best to approach cumulative scoring of AL remains a topic of significant interest and discussion. More comparative work is clearly needed to better evaluate the relative “gains” from more complex scoring systems such as canonical correlations, grade-of-membership, and recursive partitioning, before any “best practice” guidelines can be offered with respect to scoring of AL.

SES gradients in cumulative biological “wear and tear” (that is, allostatic load)

Empirical research has also provided evidence of the cumulative nature of AL across the life-course¹⁸³ as well as evidence supporting the hypothesis that the added stresses and resource constraints associated with lower SES are associated with faster accumulation of AL (i.e., greater age-specific burdens of AL). Data from the MacArthur Study again provided the initial evidence to support this hypothesis, documenting not only the hypothesized SES differences but also the contribution of these SES-associated differences in AL to explaining observed SES gradients in mortality.⁸⁵ Building on this initial evidence, Singer and Ryff¹⁸⁴ demonstrated that both adult and childhood SES contributed to observed differences in AL for adults in their early 1960s.

Consistent with the hypothesis that lower SES is associated with faster accumulation of AL (i.e., greater “wear-and-tear” on physiological regulatory systems), SES related gradients emerge as early as the first 5 years of life and persist throughout childhood, adulthood, and older age.^{58,83,85,185–187} Analyses of nationally representative U.S. data for adults aged 20 and over from the National Health & Nutrition Survey (NHANES) have also documented these same SES gradients in AL^188–190 and the faster age-specific accumulation associated with poverty.¹⁹¹ Figure 3 illustrates both of these trends, showing SES gradients in each of three adult age groupings as well as the greater cumulative AL among those of lower SES within each of these age groups. Although SES gradients in accumulated AL narrow in the oldest age group, consistent with the hypothesis that all are susceptible to age-associated increases in AL, it is important to note that higher SES older adults exhibit levels of AL that are similar to levels experienced in lower SES adults during middle-age. Also, consistent with evidence linking cumulative exposure to lower SES conditions to increased health risks (see Cohen et al chapter), one of the few studies to examine SES histories in relation to AL found that greater exposure to lower SES (e.g., reporting low SES as a high school student and as an older adult) was associated with the highest levels of AL while consistently high SES was associated with the lowest levels of AL.¹⁸⁴ Among those reporting changes in SES, increases in SES from childhood to adulthood were associated with levels of AL closer to the “consistently high SES” group while downward mobility was associated with levels closer to the “consistently low SES” group.¹⁸⁴

A much smaller AL literature speaks to the question of whether and how much such cumulative profiles of biological risk actually mediate SES differentials in health outcomes. Several published reports provide evidence supporting the role of multiple biological systems in mediating SES differentials in mortality.^12,57,85,174 Consistent with the basic concept of AL (focusing on the known co-occurrence of biological risks), examination of specific biological parameters or regulatory systems/processes provides more modest evidence of mediation, with the most extensive/complete mediation seen when the broadest array of biological systems are taken into account. Analyses from the MacArthur Study of Successful Aging, for example, document that the total AL index mediates some 35% of the education gradient in mortality while none of its component items or subscales accounts for anything close to this, with most accounting for under 15%.⁸⁵ Preliminary evidence also suggests that links between SES and AL are at least partially explained by SES-related gradients in factors such as health behaviors⁸⁵ but additional work is needed to elucidate more fully the ways in which SES contributes to the accumulation of AL.

Future directions

To date, research on AL has largely focused on testing of main effects of SES. However, a small but growing body of evidence suggests that future research should consider potential moderating effects of SES with respect to the impacts of other psychosocial and/or environmental “stressors/stimuli” on physiological parameters and accumulation of AL. Current evidence, for example, suggests that lower SES may synergize with other stressors, potentiating the negative impact of “stressors” on accumulation of AL.^{67,83,192,193} Availability of psychosocial resources may also convey greater relative benefits in the context of lower SES. For example, sense of control is more strongly associated with better health among those of lower SES¹⁹⁴ while presence of supportive social ties was strongly related to more positive reports of psychological well-being in lower SES groups.¹⁹⁵ Though as yet not linked to biological parameters, one might hypothesize that in addition to these better self-reports of health, those of lower SES who enjoy such psychosocial resources will be found to exhibit lower cumulative AL than might otherwise be expected. Investigation of these potentially important modifiers of overall SES effects on biology will be an important next steps in understanding the multiple and complex pathways through which SES affects peripheral biology and ultimately morbidity and mortality.

The question of gene-by-environment interactions is another rapidly developing area of research where important new findings are likely in coming years. To date, a small but growing body of evidence points to significant interactions between genetic endowment and environmental conditions, with different genetic profiles rendering individuals more or less responsive to environmental conditions.¹³⁴ Significantly, these interactions appear to be a persistent feature across the life-course. Adverse early childhood conditions, for example, have been found to increase the genetic contribution to patterns of morning cortisol rise¹⁹⁶ as well as cortisol reactivity to an unfamiliar situation¹⁹⁷ with MZ twins showing increased concordance as compared with DZ twins. Adverse early childhood conditions have also been shown to exacerbate the genetic liability of a polymorphism in the 5-HTT serotonin transporter gene, resulting in heightened risks for depression.^133,198 Poorer childhood conditions have also been associated with differences in neural responses to negative emotional stimuli¹⁹⁹ as well as differences in physiological reactivity to challenge in college-aged students.⁸⁴ At older ages, reported loneliness, a marker of impoverished social conditions, has been shown to synergize with transcriptional control pathways resulting in over-expression of gene bearing pro-inflammatory Nf-κB/Rel transcription factors and under-expression of genes bearing antiinflammatory glucocorticoid response elements.²⁰⁰

A third direction for future work is to more fully evaluate the role of biological processes in mediating SES links to health. Here, it will be important to balance our interest in understanding specific biological pathways with the reality that these pathways are multiple and interacting. Consequentially, a focus on one or another pathway, while illuminating with respect to that pathway's individual role, will not provide the needed level of understanding as to how SES “gets under the skin” to impact major health outcomes. As the data reviewed above clearly highlight, SES gets under the skin in multiple inter-related ways as a function of its multiple influences on our brain's cognitive-emotional processes and their related peripheral biological consequences. It is the cumulative total of these biological effects that likely accounts for the broad and consistent gradients seen for SES with multiple health outcomes. Only through a more comprehensively multisystems approach to modeling biological mediation of SES effects on health can we hope to reach a true picture of these processes. Work on one such multisystems concept—allostatic load—has already highlighted the added value of a multi-systems view of the biological pathways mediating SES influences on health, showing that a more comprehensive assessment of biological risks associated with SES (i.e., our index of AL) does the best job of explaining observed SES gradients in mortality risk.⁸⁵ Though a gold standard approach to developing such cumulative biological indices has yet to be determined, evidence to date clearly points to the value of finding ways to evaluate the multiple, cumulative impacts of SES on our physiology if we are to gain a true understanding of how the consistent and persistent SES gradients in nearly all health outcomes come to be—and have any hope of altering these SES-related health disparities.

Policy

Evidence linking lower SES conditions to greater/faster cumulative dysregulation in nearly all major biological systems provides clear evidence of the health toll such socio-economic conditions exact. Such evidence underscores the need for broader, societal level interventions to reduce health disparities associated with socio-economic disparities. Importantly, testing the effectiveness of any such interventions must include relatively shorter-term outcomes that can provide early evidence that the policy or program in question is having desired impacts. Peripheral biological parameters represent a potentially valuable category of such “intermediate” or short-term outcomes. Because such parameters are known risk factors for major disease outcomes but have shorter time horizons than do more downstream disease outcomes in terms of their responsiveness to changing conditions, such parameters might be used as a more immediate outcome to track and evaluate the effects of policies or programs designed to reduce health disparities. Importantly, efforts to use biological parameters as “intermediate” outcomes should avoid a narrow focus on one or another biological parameter or system and incorporate assessments of the multiple biological systems likely to be positively impacted by these interventions. Evidence of positive trends in peripheral biological risk profiles (reflecting positive trends in multiple parameters/systems) would hopefully support the maintenance of such a policy/program so that longer-term disease outcomes would ultimately be seen to be affected as well. For example, shift work²⁰¹ and short-turn around time for air crews on trans-meridianal flights²⁰² have health and cognitive effects that could be ameliorated by work policies. Indeed, one could argue that current efforts to evaluate many programs targeted at reducing health disparities by relying only on evidence for changes in disease outcomes (and frequently failing to see such changes in what are generally relatively short time horizons), may have failed to identify what are actually effective programs by using outcomes with time horizons that are not appropriately matched to evaluation periods. With their likely shorter time horizon, peripheral biological profiles would appear to be good candidates for such shorter-term evaluations—providing early evidence of the impact of a program or policy on risk profiles that are known to predict longer-term disease risks. Evidence that a policy/program appears to be generating positive trends in peripheral biological parameters would hopefully allow for the continuation of such a policy/program for sufficient time to see impacts on longer-term disease outcomes.

Conflicts of interest

The authors declare no conflicts of interest.

References

1 McEwen, B.S. 2008. Central effects of stress hormones in health and disease: Understanding the protective and damaging effects of stress and stress mediators. Eur. J. Pharmacol. 583: 174–185.
10.1016/j.ejphar.2007.11.071
CASPubMedWeb of Science®Google Scholar
2 McEwen, B.S. 2003. Early life influences on life-long patterns of behavior and health. Ment. Retard. Dev. Disabil. Res. Rev. 9: 149–154.
10.1002/mrdd.10074
PubMedWeb of Science®Google Scholar
3 Gunnar, M. & K. Quevedo. 2007. The neurobiology of stress and development. Annu. Rev. Psychol. 58: 145–173.
10.1146/annurev.psych.58.110405.085605
CASPubMedWeb of Science®Google Scholar
4 Gunnar, M. & D. Vazquez. 2006. Stress neurobiology and developmental psychopathology. In Developmental Psychopathology: Developmental Neuroscience. D. Cicchetti & D. Cohen, Eds.: 533–577. Wiley. New York .
Web of Science®Google Scholar
5 Palkovits, M. 1987. Organization of the stress response at the anatomical level. In Progress in Brain Research. E.R. de Kloet, V.M. Wiegant & D. de Wied, Eds.: 47–55. Elsevier Sci. Amsterdam .
Web of Science®Google Scholar
6 Zelinski, E.M. et al . 1998. Do medical conditions affect cognition in older adults? Health Psychol. 17: 504–512.
10.1037/0278-6133.17.6.504
CASPubMedWeb of Science®Google Scholar
7 Kaplan, G.A. & J.E. Keil. 1993. Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation 88: 1973–1998.
10.1161/01.CIR.88.4.1973
CASPubMedWeb of Science®Google Scholar
8 Seeman, T.E. et al . 1994. Predicting changes in physical performance in a high-functioning elderly cohort: MacArthur studies of successful aging. J. Gerontol. 49: M97–M108.
10.1093/geronj/49.3.M97
CASPubMedWeb of Science®Google Scholar
9 Tzourio, C. et al . 1999. Cognitive decline in individuals with high blood pressure: a longitudinal study in the elderly. EVA Study Group. Epidemiol. Vascular Aging. Neurology. 53: 1948–1952.
CASPubMedWeb of Science®Google Scholar
10 Zhang, G.Q. & W. Zhang. 2009. Heart rate, lifespan, and mortality risk. Ageing Res Rev. 8: 52–60.
10.1016/j.arr.2008.10.001
PubMedWeb of Science®Google Scholar
11 Benfante, R., D. Reed & J. Brody. 1985. Biological and social predictors of health in an aging cohort. J Chronic Dis. 38: 385–395.
10.1016/0021-9681(85)90134-1
CASPubMedWeb of Science®Google Scholar
12 Lynch, J.W. et al . 1996. Do cardiovascular risk factors explain the relation between socioeconomic status, risk of all-cause mortality, cardiovascular mortality, and acute myocardial infarction? Am. J. Epidemiol. 144: 934–942.
10.1093/oxfordjournals.aje.a008863
CASPubMedWeb of Science®Google Scholar
13 Marmot, M. et al . 1997. Social inequalities in health: next questions and converging evidence. Soc. Sci. Med. 44: 901–910.
10.1016/S0277-9536(96)00194-3
CASPubMedWeb of Science®Google Scholar
14 Khaw, K.T. et al . 2001. Glycated haemoglobin, diabetes, and mortality in men in Norfolk cohort of european prospective investigation of cancer and nutrition (EPIC-Norfolk). BMJ 322: 15–18.
10.1136/bmj.322.7277.15
CASPubMedGoogle Scholar
15 Reaven, G.M. 1988. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 37: 1595–1607.
10.2337/diab.37.12.1595
CASPubMedWeb of Science®Google Scholar
16 Fried, L.P. et al . 1998. Risk factors for 5-year mortality in older adults: the Cardiovascular Health Study. JAMA 279: 585–592.
10.1001/jama.279.8.585
CASPubMedWeb of Science®Google Scholar
17 Craft, S. et al . 1993. Effects of hyperglycemia on memory and hormone levels in dementia of the Alzheimer type: a longitudinal study. Behav. Neurosci. 107: 926–940.
10.1037/0735-7044.107.6.926
CASPubMedWeb of Science®Google Scholar
18 Gradman, T.J. et al . 1993. Verbal learning and/or memory improves with glycemic control in older subjects with non-insulin-dependent diabetes mellitus. J. Am. Geriatr Soc. 41: 1305–1312.
10.1111/j.1532-5415.1993.tb06480.x
CASPubMedWeb of Science®Google Scholar
19 Manning, C., J. Hall & P. Gold. 1990. Glucose effects on memory and other neuropsychological tests in elderly humans. Psychol. Sci. 5: 307–311.
10.1111/j.1467-9280.1990.tb00223.x
Web of Science®Google Scholar
20 Reaven, G.M. et al . 1990. Relationship between hyperglycemia and cognitive function in older NIDDM patients. Diabetes Care 13: 16–21.
10.2337/diacare.13.1.16
CASPubMedWeb of Science®Google Scholar
21 Park, S. et al . 1996. GHb is a better predictor of cardiovascular disease than fasting or postchallenge plasma glucose in women without diabetes. Rancho Bernardo Study. Diabetes Care 19: 450–456.
10.2337/diacare.19.5.450
CASPubMedWeb of Science®Google Scholar
22 van Oijen, M. et al . 2008. Fasting insulin levels and cognitive decline in older women without diabetes. Neuroepidemiology 30: 174–179.
10.1159/000126909
PubMedWeb of Science®Google Scholar
23 Epel, E. et al . 2001. Stress may add bite to appetite in women: a laboratory study of stress-induced cortisol and eating behavior. Psychoneuroendocrinology 26: 37–49.
10.1016/S0306-4530(00)00035-4
CASPubMedWeb of Science®Google Scholar
24 Brindley, D.N. & Y. Rolland. 1989. Possible connections between stress, diabetes, obesity, hypertension and altered lipoprotein metabolism that may result in atherosclerosis. Clin. Sci. (Lond.) 77: 453–461.
10.1042/cs0770453
CASPubMedWeb of Science®Google Scholar
25 Bjorntorp, P. 1990. “Portal” adipose tissue as a generator of risk factors for cardiovascular disease and diabetes. Arteriosclerosis 10: 493–496.
10.1161/01.ATV.10.4.493
CASPubMedWeb of Science®Google Scholar
26 Sapolsky, R.M., L.C. Krey & B.S. McEwen. 1986. The neuroendocrinology of stress and aging: the glucocorticoid cascade hypothesis. Endocr. Rev. 7: 284–301.
10.1210/edrv-7-3-284
CASPubMedWeb of Science®Google Scholar
27 Landfield, P.W. 1987. Modulation of brain aging correlates by long-term alterations of adrenal steroids and neurally-active peptides. Prog. Brain Res. 72: 279–300.
10.1016/S0079-6123(08)60215-0
CASPubMedWeb of Science®Google Scholar
28 McEwen, B.S. et al . 1999. Corticosteroids, the Aging Brain and Cognition. Trends Endocrinol. Metab. 10: 92–96.
10.1016/S1043-2760(98)00122-2
CASPubMedWeb of Science®Google Scholar
29 Lupien, S. et al . 1994. Basal cortisol levels and cognitive deficits in human aging. J. Neurosci. 14: 2893–2903.
10.1523/JNEUROSCI.14-05-02893.1994
CASPubMedWeb of Science®Google Scholar
30 Lupien, S.J. et al . 1998. Cortisol levels during human aging predict hippocampal atrophy and memory deficits. Nat. Neurosci. 1: 69–73.
10.1038/271
CASPubMedWeb of Science®Google Scholar
31 Karlamangla, A.S. et al . 2005. Urinary cortisol excretion as a predictor of incident cognitive impairment. Neurobiol. Aging 26(Suppl. 1): 80–84.
10.1016/j.neurobiolaging.2005.09.037
CASPubMedWeb of Science®Google Scholar
32 Michelson, D. et al . 1996. Bone mineral density in women with depression. N. Engl. J. Med. 335: 1176–1181.
10.1056/NEJM199610173351602
CASPubMedWeb of Science®Google Scholar
33 Dennison, E. et al . 1999. Profiles of endogenous circulating cortisol and bone mineral density in healthy elderly men. J. Clin. Endocrinol. Metab. 84: 3058–3063.
10.1210/jc.84.9.3058
CASPubMedWeb of Science®Google Scholar
34 Reynolds, R.M. et al . 2005. Cortisol secretion and rate of bone loss in a population-based cohort of elderly men and women. Calcif. Tissue Int. 77: 134–138.
10.1007/s00223-004-0270-2
CASPubMedWeb of Science®Google Scholar
35 Lamberts, S.W., A.W. van den Beld & A.J. van der Lely. 1997. The endocrinology of aging. Science. 278: 419–424.
10.1126/science.278.5337.419
CASPubMedWeb of Science®Google Scholar
36 Dhabhar, F.S. & B.S. McEwen. 1997. Acute stress enhances while chronic stress suppresses cell-mediated immunity in vivo: a potential role for leukocyte trafficking. Brain Behav Immunol. 11: 286–306.
10.1006/brbi.1997.0508
CASPubMedWeb of Science®Google Scholar
37 Thomas, F. et al . 2001. Pulse pressure and heart rate: independent risk factors for cancer? J. Clin. Epidemiol. 54: 735–740.
10.1016/S0895-4356(00)00353-X
CASPubMedWeb of Science®Google Scholar
38 Yun, A.J. & P.Y. Lee. 2004. Maldaptation of the link between inflammation and bone turnover may be a key determinant of osteoporosis. Med. Hypotheses 63: 532–537.
10.1016/S0306-9877(03)00326-8
PubMedWeb of Science®Google Scholar
39 Ginaldi, L., M.C. Di Benedetto & M. De Martinis. 2005. Osteoporosis, inflammation and ageing. Immun. Ageing 2: 14.
10.1186/1742-4933-2-14
PubMedGoogle Scholar
40 Tracy, R.P. et al . 1997. Relationship of C-reactive protein to risk of cardiovascular disease in the elderly. Results from the Cardiovascular Health Study and the Rural Health Promotion Project. Arterioscler. Thromb. Vasc. Biol. 17: 1121–1127.
10.1161/01.ATV.17.6.1121
CASPubMedWeb of Science®Google Scholar
41 Harris, T.B. et al . 1999. Associations of elevated interleukin-6 and C-reactive protein levels with mortality in the elderly. Am. J. Med. 106: 506–512.
10.1016/S0002-9343(99)00066-2
CASPubMedWeb of Science®Google Scholar
42 Tice, J.A. et al . 2003. The relation of C-reactive protein levels to total and cardiovascular mortality in older U.S. women. Am. J. Med. 114: 199–205.
10.1016/S0002-9343(02)01497-3
CASPubMedWeb of Science®Google Scholar
43 Wang, T.J. et al . 2002. C-reactive protein is associated with subclinical epicardial coronary calcification in men and women: the Framingham Heart Study. Circulation 106: 1189–1191.
10.1161/01.CIR.0000032135.98011.C4
CASPubMedWeb of Science®Google Scholar
44 Gussekloo, J. et al . 2000. C-reactive protein is a strong but nonspecific risk factor of fatal stroke in elderly persons. Arterioscler Thromb. Vasc. Biol. 20: 1047–1051.
10.1161/01.ATV.20.4.1047
CASPubMedWeb of Science®Google Scholar
45 Danesh, J. et al . 2004. C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease. N. Engl. J. Med. 350: 1387–1397.
10.1056/NEJMoa032804
CASPubMedWeb of Science®Google Scholar
46 Alley, D.E. et al . 2008. Inflammation and rate of cognitive change in high-functioning older adults. J. Gerontol. A Biol. Sci. Med. Sci. 63: 50–55.
10.1093/gerona/63.1.50
PubMedWeb of Science®Google Scholar
47 Weaver, J.D. et al . 2002. Interleukin-6 and risk of cognitive decline: MacArthur studies of successful aging. Neurology 59: 371–378.
10.1212/WNL.59.3.371
CASPubMedWeb of Science®Google Scholar
48 Schmidt, R. et al . 2002. Early inflammation and dementia: a 25-year follow-up of the Honolulu-Asia Aging Study. Ann. Neurol. 52: 168–174.
10.1002/ana.10265
CASPubMedWeb of Science®Google Scholar
49 Gold, S.M. et al . 2007. Hippocampal damage and memory impairments as possible early brain complications of type 2 diabetes. Diabetologia 50: 711–719.
10.1007/s00125-007-0602-7
CASPubMedWeb of Science®Google Scholar
50 Stampfer, M.J. 2006. Cardiovascular disease and Alzheimer's disease: common links. J. Intern. Med. 260: 211–223.
10.1111/j.1365-2796.2006.01687.x
CASPubMedWeb of Science®Google Scholar
51 Yaffe, K. 2007. Metabolic syndrome and cognitive disorders: is the sum greater than its parts? Alzheimer Dis. Assoc. Disord. 21: 167–171.
10.1097/WAD.0b013e318065bfd6
PubMedWeb of Science®Google Scholar
52 Seeman, T.E. et al . 1997. Increase in urinary cortisol excretion and memory declines: MacArthur studies of successful aging. J. Clin. Endocrinol. Metab. 82: 2458–2465.
10.1210/jc.82.8.2458
CASPubMedWeb of Science®Google Scholar
53 Lee, B.K. et al . 2007. Associations of salivary cortisol with cognitive function in the Baltimore memory study. Arch. Gen. Psychiatry 64: 810–818.
10.1001/archpsyc.64.7.810
CASPubMedWeb of Science®Google Scholar
54 Li, G. et al . 2006. Salivary cortisol and memory function in human aging. Neurobiol. Aging 27: 1705–1714.
10.1016/j.neurobiolaging.2005.09.031
CASPubMedWeb of Science®Google Scholar
55 Marsland, A.L. et al . 2008. Interleukin-6 covaries inversely with hippocampal grey matter volume in middle-aged adults. Biol. Psychiatry 64: 484–490.
10.1016/j.biopsych.2008.04.016
CASPubMedWeb of Science®Google Scholar
56 Friedman, E.M. et al . 2005. Social relationships, sleep quality, and interleukin-6 in aging women. Proc. Natl. Acad. Sci. USA 102: 18757–18762.
10.1073/pnas.0509281102
CASPubMedWeb of Science®Google Scholar
57 Marmot, M.G. et al . 1997. Contribution of job control and other risk factors to social variations in coronary heart disease incidence. Lancet 350: 235–239.
10.1016/S0140-6736(97)04244-X
CASPubMedWeb of Science®Google Scholar
58 Seeman, T. 2007. Social determinants of health inequalities: drivers of cumulative risks and biological consequences. Invited address, W. H. O. C. o. t. S. D. o. H., Ed . New Orleans , LA .
Google Scholar
59 Eldeirawi, K. & R.B. Lipton. 2003. Predictors of hemoglobin A1c in a national sample of nondiabetic children: the Third National Health and Nutrition Examination Survey, 1988–1994. Am. J. Epidemiol. 157: 624–632.
10.1093/aje/kwg023
PubMedWeb of Science®Google Scholar
60 Boyce, W.T. 2006. Social stratification, health, and violence in the very young. Ann. N.Y. Acad. Sci. 1036: 47–68.
10.1196/annals.1330.003
PubMedWeb of Science®Google Scholar
61 Chen, E., K.A. Matthews & W.T. Boyce. 2002. Socioeconomic differences in children's health: how and why do these relationships change with age? Psychol. Bull. 128: 295–329.
10.1037/0033-2909.128.2.295
PubMedWeb of Science®Google Scholar
62 Lupien, S.J. et al . 2000. Child's stress hormone levels correlate with mother's socioeconomic status and depressive state. Biol. Psychiatry 48: 976–980.
10.1016/S0006-3223(00)00965-3
CASPubMedWeb of Science®Google Scholar
63 Boyce, W.T. & D.P. Keating. 2004. Should we intervene to improve childhood circumstances? In A life course approach to chronic disease epidemiology. D. Kuh & Y. Ben-Shlomo, Eds.: 415–455. Oxford University Press. Oxford , UK .
Google Scholar
64 Hertzman, C. 1999. Population health and human development. In Developmental health and the wealth of nations: social, biological, and educational dynamics. D.P. Keating & C. Hertzman, Eds.: 21–40. Guilford Press. New York .
Google Scholar
65 Freedman, D.S. et al . 2007. Childhood overweight and family income. MedGenMed. 9: 26.
PubMedGoogle Scholar
66 Evans, G.W. & P. Kim. 2007. Childhood poverty and health: cumulative risk exposure and stress dysregulation. Psychol. Sci. 18: 953–957.
10.1111/j.1467-9280.2007.02008.x
PubMedWeb of Science®Google Scholar
67 Evans, G.W. & L.A. Marcynyszyn. 2004. Environmental justice, cumulative environmental risk, and health among low- and middle-income children in upstate New York. Am. J. Public Health 94: 1942–1944.
10.2105/AJPH.94.11.1942
PubMedWeb of Science®Google Scholar
68 Matthews, K.A. et al . 1997. Does background stress heighten or dampen children's cardiovascular responses to acute stress? Psychosom. Med. 59: 488–496.
10.1097/00006842-199709000-00005
CASPubMedWeb of Science®Google Scholar
69 Hardy, R. et al . 2003. Birthweight, childhood social class, and change in adult blood pressure in the 1946 British birth cohort. Lancet 362: 1178–1183.
10.1016/S0140-6736(03)14539-4
PubMedWeb of Science®Google Scholar
70 Cohen, S. et al . 2006. Socioeconomic status, race, and diurnal cortisol decline in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Psychosom. Med. 68: 41–50.
10.1097/01.psy.0000195967.51768.ea
PubMedWeb of Science®Google Scholar
71 Janicki-Deverts, D. et al . 2007. Socioeconomic status is related to urinary catecholamines in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Psychosom. Med. 69: 514–520.
10.1097/PSY.0b013e3180f60645
PubMedWeb of Science®Google Scholar
72 Seeman, T.E. et al . In preparation. Social environment characteristics and biological risk profiles: coronary artery risk development in young adults (CARDIA).
Google Scholar
73 Gruenewald, T.L. et al . Submitted. Association of socioeconomic status with inflamation markers in black and white men and women in the coronary artery risk development in young adults (CARDIA) study. Soc. Sci. Med.
PubMedGoogle Scholar
74 Bode, B.W. et al . 2007. Advances in hemoglobin A1c point of care technology. J. Diabetes Sci. Technol. 1: 319–323.
10.1177/193229680700100314
Google Scholar
75 Ridker, P.M. 2003. Cardiology Patient Page. C-reactive protein: a simple test to help predict risk of heart attack and stroke. Circulation 108: e81–e85.
10.1161/01.CIR.0000093381.57779.67
CASPubMedWeb of Science®Google Scholar
76 Seccareccia, F. et al . 2001. Heart rate as a predictor of mortality: the MATISS project. Am. J. Public Health 91: 1258–1263.
10.2105/AJPH.91.8.1258
CASPubMedWeb of Science®Google Scholar
77 Osei, K. et al . 2003. Is glycosylated hemoglobin A1c a surrogate for metabolic syndrome in nondiabetic, first-degree relatives of African-American patients with type 2 diabetes? J. Clin. Endocrinol. Metab. 88: 4596–4601.
10.1210/jc.2003-030686
CASPubMedWeb of Science®Google Scholar
78 Golden, S. et al . 2003. Use of glycated hemoglobin and microalbuminuria in the monitoring of diabetes mellitus. Evid. Rep. Technol. Assess (Summ.) 1–6.
CASPubMedWeb of Science®Google Scholar
79 Chobanian, A.V. et al . 2003. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 42: 1206–1252.
10.1161/01.HYP.0000107251.49515.c2
CASPubMedWeb of Science®Google Scholar
80 Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. 2001. Executive summary of the third report of The National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). JAMA 285: 2486–2497.
10.1001/jama.285.19.2486
CASPubMedWeb of Science®Google Scholar
81 Cohen, S., W.J. Doyle & A. Baum. 2006. Socioeconomic status is associated with stress hormones. Psychosom. Med. 68: 414–420.
10.1097/01.psy.0000221236.37158.b9
PubMedWeb of Science®Google Scholar
82 Li, L. et al . 2007. Life-time socio-economic position and cortisol patterns in mid-life. Psychoneuroendocrinology 32: 824–833.
10.1016/j.psyneuen.2007.05.014
CASPubMedWeb of Science®Google Scholar
83 Evans, G.W. & K. English. 2002. The environment of poverty: multiple stressor exposure, psychophysiological stress, and socioemotional adjustment. Child. Dev. 73: 1238–1248.
10.1111/1467-8624.00469
PubMedWeb of Science®Google Scholar
84 Taylor, S.E. et al . 2004. Early environment, emotions, responses to stress, and health. J. Pers. 72: 1365–1393.
10.1111/j.1467-6494.2004.00300.x
PubMedWeb of Science®Google Scholar
85 Seeman, T.E. et al . 2004. Cumulative biological risk and socio-economic differences in mortality: MacArthur studies of successful aging. Soc. Sci. Med. 58: 1985–1997.
10.1016/S0277-9536(03)00402-7
PubMedWeb of Science®Google Scholar
86 Hemingway, H. et al . 2005. Does autonomic function link social position to coronary risk? The Whitehall II study. Circulation 111: 3071–3077.
10.1161/CIRCULATIONAHA.104.497347
PubMedWeb of Science®Google Scholar
87 Thayer, J.F. & R.D. Lane. 2009. Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neurosci. Biobehav. Rev. 33: 81–88.
10.1016/j.neubiorev.2008.08.004
PubMedWeb of Science®Google Scholar
88 Thayer, J.F. & E. Sternberg. 2006. Beyond heart rate variability: vagal regulation of allostatic systems. Ann. N.Y. Acad. Sci. 1088: 361–372.
10.1196/annals.1366.014
CASPubMedWeb of Science®Google Scholar
89 Colhoun, H.M., H. Hemingway & N.R. Poulter. 1998. Socio-economic status and blood pressure: an overview analysis. J. Hum. Hypertens 12: 91–110.
10.1038/sj.jhh.1000558
CASPubMedWeb of Science®Google Scholar
90 Winkleby, M.A., S.P. Fortmann & D.C. Barrett. 1990. Social class disparities in risk factors for disease: eight-year prevalence patterns by level of education. Prev. Med. 19: 1–12.
10.1016/0091-7435(90)90001-Z
CASPubMedWeb of Science®Google Scholar
91 Lehman, B.J. et al . 2009. Relationship of early life stress and psychological functioning to blood pressure in the CARDIA study. Health Psychol. 28: 338–346.
10.1037/a0013785
PubMedWeb of Science®Google Scholar
92 Matthews, K.A. et al . 1989. Educational attainment and behavioral and biologic risk factors for coronary heart disease in middle-aged women. Am. J. Epidemiol. 129: 1132–1144.
10.1093/oxfordjournals.aje.a115235
CASPubMedWeb of Science®Google Scholar
93 Steptoe, A. & M. Marmot. 2002. The role of psychobiological pathways in socio-economic inequalities in cardiovascular disease risk. Eur. Heart J. 23: 13–25.
10.1053/euhj.2001.2611
CASPubMedWeb of Science®Google Scholar
94 Brunner, E. et al . 1996. Childhood social circumstances and psychosocial and behavioural factors as determinants of plasma fibrinogen. Lancet 347: 1008–1013.
10.1016/S0140-6736(96)90147-6
CASPubMedWeb of Science®Google Scholar
95 Brunner, E.J. et al . 1997. Social inequality in coronary risk: central obesity and the metabolic syndrome. Evidence from the Whitehall II study. Diabetologia 40: 1341–1349.
10.1007/s001250050830
CASPubMedWeb of Science®Google Scholar
96 Wamala, S.P. et al . 1999. Education and the metabolic syndrome in women. Diabetes Care 22: 1999–2003.
10.2337/diacare.22.12.1999
CASPubMedWeb of Science®Google Scholar
97 Karlamangla, A.S. et al . 2005. Impact of socioeconomic status on longitudinal accumulation of cardiovascular risk in young adults: the CARDIA Study (USA). Soc. Sci. Med. 60: 999–1015.
10.1016/j.socscimed.2004.06.056
PubMedWeb of Science®Google Scholar
98 Lehman, B.J. et al . 2005. Relation of childhood socioeconomic status and family environment to adult metabolic functioning in the CARDIA study. Psychosom. Med. 67: 846–854.
10.1097/01.psy.0000188443.48405.eb
PubMedWeb of Science®Google Scholar
99 Langenberg, C. et al . 2006. Social circumstances and education: life course origins of social inequalities in metabolic risk in a prospective national birth cohort. Am. J. Public Health 96: 2216–2221.
10.2105/AJPH.2004.049429
PubMedWeb of Science®Google Scholar
100 Loucks, E.B. et al . 2007. Socioeconomic position and the metabolic syndrome in early, middle, and late life: evidence from NHANES 1999–2002. Ann. Epidemiol. 17: 782–790.
10.1016/j.annepidem.2007.05.003
PubMedWeb of Science®Google Scholar
101 Broughton, M.A. et al . 2006. Predictors and outcomes of household food insecurity among inner city families with preschool children in Vancouver. Can. J. Public Health 97: 214–216.
10.1007/BF03405588
PubMedWeb of Science®Google Scholar
102 Maty, S.C. et al . 2008. Childhood socioeconomic position, gender, adult body mass index, and incidence of type 2 diabetes mellitus over 34 years in the Alameda County Study. Am. J. Public Health 98: 1486–1494.
10.2105/AJPH.2007.123653
PubMedWeb of Science®Google Scholar
103 McDade, T.W., L.C. Hawkley & J.T. Cacioppo. 2006. Psychosocial and behavioral predictors of inflammation in middle-aged and older adults: the Chicago health, aging, and social relations study. Psychosom. Med. 68: 376–381.
10.1097/01.psy.0000221371.43607.64
PubMedWeb of Science®Google Scholar
104 Tabassum, F. et al . 2008. Effects of socioeconomic position on inflammatory and hemostatic markers: a life-course analysis in the 1958 British birth cohort. Am. J. Epidemiol. 167: 1332–1341.
10.1093/aje/kwn055
PubMedWeb of Science®Google Scholar
105 Loucks, E.B. et al . 2006. Association of educational level with inflammatory markers in the Framingham Offspring Study. Am. J. Epidemiol. 163: 622–628.
10.1093/aje/kwj076
PubMedWeb of Science®Google Scholar
106 Janicki-Deverts, D. et al . 2008. History of unemployment predicts future elevations in C-reactive protein among male participants in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Ann. Behav. Med. 36: 176–185.
10.1007/s12160-008-9056-5
PubMedWeb of Science®Google Scholar
107 Nazmi, A. & C.G. Victora. 2007. Socioeconomic and racial/ethnic differentials of C-reactive protein levels: a systematic review of population-based studies. BMC Public Health 7: 212.
10.1186/1471-2458-7-212
PubMedWeb of Science®Google Scholar
108 Taylor, S.E. et al . 2006. Relationship of early life stress and psychological functioning to adult C-reactive protein in the coronary artery risk development in young adults study. Biol. Psychiatry 60: 819–824.
10.1016/j.biopsych.2006.03.016
CASPubMedWeb of Science®Google Scholar
109 Alley, D.E. et al . 2006. Socioeconomic status and C-reactive protein levels in the US population: NHANES IV. Brain Behav. Immunol. 20: 498–504.
10.1016/j.bbi.2005.10.003
CASPubMedWeb of Science®Google Scholar
110 Power, C. et al . 2005. The contribution of childhood and adult socioeconomic position to adult obesity and smoking behaviour: an international comparison. Int. J. Epidemiol. 34: 335–344.
10.1093/ije/dyh394
PubMedWeb of Science®Google Scholar
111 Diez-Roux, A.V., B.G. Link & M.E. Northridge. 2000. A multilevel analysis of income inequality and cardiovascular disease risk factors. Soc. Sci. Med. 50: 673–687.
10.1016/S0277-9536(99)00320-2
CASPubMedWeb of Science®Google Scholar
112 Merkin, S.S. et al . 2009. Neighborhoods and cumulative biological risk profiles by race/ethnicity in a national sample of U.S. adults: NHANES III. Ann. Epidemiol. 19: 194–201.
10.1016/j.annepidem.2008.12.006
PubMedWeb of Science®Google Scholar
113 McEwen, B.S. & E. Stellar. 1993. Stress and the individual. Mechanisms leading to disease. Arch. Intern. Med. 153: 2093–2101.
10.1001/archinte.1993.00410180039004
CASPubMedWeb of Science®Google Scholar
114 Sterling, P. & J. Eyer. 1988. Allostasis: a new paradigm to explain arousal pathology. In Handbook of life stress, cognition and health. S. Fisher & J. Reason, Eds.: 631–651. Wiley & Sons. New York , NY .
Google Scholar
115 McEwen, B.S. 2006. Protective and damaging effects of stress mediators: central role of the brain. Dialogues Clin Neurosci. 8: 367–381.
10.31887/DCNS.2006.8.4/bmcewen
PubMedGoogle Scholar
116 Bernard, C. 1878. Les Phenomenes de la vie. Librarie J-B Baillier et Fils. Paris .
Google Scholar
117 Cannon, W. 1929. The wisdom of the body. Physiol. Rev. 9: 399–431.
10.1152/physrev.1929.9.3.399
Web of Science®Google Scholar
118 Selye, H. 1950. The physiology and pathology of exposure to stress. Acta Montreal.
Google Scholar
119 Selye, H. 1974. Stress in health and disease. Butterworth Press. Boston .
Google Scholar
120 Selye, H. 1978. The stress of life. McGraw Hill. New York .
Google Scholar
121 Weiner, H. 1991. Social and psychobiological factors in autoimmune disease. In Psychoneuroimmunology. R. Ader, O.L. Felten & N. Cohen, Eds.: 995–1011. Academic Press. New York .
Google Scholar
122 Matthews, K.A. et al . 1986. Handbook of stress, reactivity, and cardiovascular disease. John Wiley & Sons. New York .
Google Scholar
123 Munck, A. & P.M. Guyre. 1991. Glucocorticoids and immune function. In Psychoneuroimmunology. R. Ader, O.L. Felten & N. Cohen, Eds.: 447–474. Academic Press. New York .
10.1016/B978-0-12-043780-1.50020-8
Google Scholar
124 Seeman, T.E. & R.J. Robbins. 1994. Aging and hypothalamic-pituitary-adrenal response to challenge in humans. Endocr. Rev. 15: 233–260.
CASPubMedWeb of Science®Google Scholar
125 Despres, J.P. et al . 1990. Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis 10: 497–511.
10.1161/01.ATV.10.4.497
CASPubMedWeb of Science®Google Scholar
126 Abboud, F.M. 1982. The sympathetic system in hypertension. State-of-the-art review. Hypertension 4: 208–225.
10.1161/01.HYP.4.3_Pt_2.208
CASPubMedWeb of Science®Google Scholar
127 Bjorntorp, P. 1988. The associations between obesity, adipose tissue distribution and disease. Acta Med. Scand. Suppl. 723: 121–134.
PubMedGoogle Scholar
128 Rowe, J.W. & B.R. Troen. 1980. Sympathetic nervous system and aging in man. Endocr. Rev. 1: 167–179.
10.1210/edrv-1-2-167
CASPubMedGoogle Scholar
129 Epel, E., H. Burke & O. Wolkowitz. 2007. Psychoneuroendocrinology of aging: focus on anabolic and catabolic hormones. In Handbook of health psychology of aging. C. Aldwin, A. Spiro & C. Park, Eds.: 119–141. Guildford Press.
Google Scholar
130 Lee, P.Y., A.J. Yun & K.A. Bazar. 2004. Conditions of aging as manifestations of sympathetic bias unmasked by loss of parasympathetic function. Med. Hypotheses 62: 868–870.
10.1016/j.mehy.2003.11.024
PubMedWeb of Science®Google Scholar
131 Monahan, K.D. 2007. Effect of aging on baroreflex function in humans. Am. J. Physiol. Regul. Integr. Comp. Physiol. 293: R3–R12.
10.1152/ajpregu.00031.2007
CASPubMedWeb of Science®Google Scholar
132 Gouin, J.P., L. Hantsoo & J.K. Kiecolt-Glaser. 2008. Immune dysregulation and chronic stress among older adults: a review. Neuroimmunomodulation 15: 251–259.
10.1159/000156468
CASPubMedWeb of Science®Google Scholar
133 Caspi, A. et al . 2003. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science 301: 386–389.
10.1126/science.1083968
CASPubMedWeb of Science®Google Scholar
134 Rutter, M. 1981. Protective factors in children's responses to stress and disadvantage. In Primary prevention of psychopathology. M. Kent & J. Rolf, Eds.: 49–74. University Press of New England. Hanover , NH .
Web of Science®Google Scholar
135 Dvorak, R.V. et al . 2000. Respiratory fitness, free living physical activity, and cardiovascular disease risk in older individuals: a doubly labeled water study. J. Clin. Endocrinol. Metab. 85: 957–963.
10.1210/jc.85.3.957
CASPubMedWeb of Science®Google Scholar
136 DiPietro, L. et al . 2006. Exercise and improved insulin sensitivity in older women: evidence of the enduring benefits of higher intensity training. J. Appl. Physiol. 100: 142–149.
10.1152/japplphysiol.00474.2005
CASPubMedWeb of Science®Google Scholar
137 Yeckel, C.W., J. Dziura & L. DiPietro. 2008. Abdominal obesity in older women: potential role for disrupted fatty acid reesterification in insulin resistance. J. Clin. Endocrinol. Metab. 93: 1285–1291.
10.1210/jc.2007-1875
CASPubMedWeb of Science®Google Scholar
138 Hamdy, O., S. Porramatikul & E. Al-Ozairi. 2006. Metabolic obesity: the paradox between visceral and subcutaneous fat. Curr. Diabetes Rev. 2: 367–373.
10.2174/1573399810602040367
PubMedGoogle Scholar
139 Landsberg, L. 2008. Body fat distribution and cardiovascular risk: a tale of 2 sites. Arch. Intern. Med. 168: 1607–1608.
10.1001/archinte.168.15.1607
PubMedWeb of Science®Google Scholar
140 Landsberg, L. 2001. Insulin-mediated sympathetic stimulation: role in the pathogenesis of obesity-related hypertension (or, how insulin affects blood pressure, and why). J. Hypertens 19: 523–528.
10.1097/00004872-200103001-00001
CASPubMedWeb of Science®Google Scholar
141 Landsberg, L. 2008. The sympatho-adrenal system in the metabolic syndrome. In The metabolic syndrome: Epidemiology, clinical treatment, and underlying mechanisms. B.C. Hansen & G.A. Bray, Eds.: 85–104. Humana Press. Totowa , NJ .
10.1007/978-1-60327-116-5_6
Web of Science®Google Scholar
142 Rohleder, N. & C. Kirschbaum. 2007. Effects of nutrition on neuro-endocrine stress responses. Curr. Opin. Clin. Nutr. Metab. Care 10: 504–510.
10.1097/MCO.0b013e3281e38808
CASPubMedWeb of Science®Google Scholar
143 Kern, S. et al . 2008. Glucose metabolic changes in the prefrontal cortex are associated with HPA axis response to a psychosocial stressor. Psychoneuroendocrinology 33: 517–529.
10.1016/j.psyneuen.2008.01.010
CASPubMedWeb of Science®Google Scholar
144 Kirschbaum, C. et al . 1997. Effects of fasting and glucose load on free cortisol responses to stress and nicotine. J. Clin. Endocrinol. Metab. 82: 1101–1105.
10.1210/jc.82.4.1101
CASPubMedWeb of Science®Google Scholar
145 Frederick, S.L. et al . 1998. Cortisol and response to dexamethasone as predictors of withdrawal distress and abstinence success in smokers. Biol. Psychiatry 43: 525–530.
10.1016/S0006-3223(97)00423-X
CASPubMedWeb of Science®Google Scholar
146 Yun, A.J. et al . 2005. The smoking gun: many conditions associated with tobacco exposure may be attributable to paradoxical compensatory autonomic responses to nicotine. Med. Hypotheses 64: 1073–1079.
10.1016/j.mehy.2004.11.040
CASPubMedWeb of Science®Google Scholar
147 Lovallo, W.R. 2006. Cortisol secretion patterns in addiction and addiction risk. Int. J. Psychophysiol. 59: 195–202.
10.1016/j.ijpsycho.2005.10.007
PubMedWeb of Science®Google Scholar
148 Dai, X., J. Thavundayil & C. Gianoulakis. 2002. Response of the hypothalamic-pituitary-adrenal axis to stress in the absence and presence of ethanol in subjects at high and low risk of alcoholism. Neuropsychopharmacology 27: 442–452.
10.1016/S0893-133X(02)00308-1
CASPubMedWeb of Science®Google Scholar
149 Thayer, J.F. et al . 2006. Alcohol use, urinary cortisol, and heart rate variability in apparently healthy men: Evidence for impaired inhibitory control of the HPA axis in heavy drinkers. Int. J. Psychophysiol. 59: 244–250.
10.1016/j.ijpsycho.2005.10.013
PubMedWeb of Science®Google Scholar
150 Yoda, T. et al . 2008. Effects of alcohol on autonomic responses and thermal sensation during cold exposure in humans. Alcohol 42: 207–212.
10.1016/j.alcohol.2008.01.006
CASPubMedWeb of Science®Google Scholar
151 Seeman, T.E. et al . 1995. Self-esteem and neuroendocrine response to challenge: MacArthur studies of successful aging. J. Psychosom. Res. 39: 69–84.
10.1016/0022-3999(94)00076-H
CASPubMedWeb of Science®Google Scholar
152 Bandura, A. et al . 1985. Catecholamine secretion as a function of perceived coping self-efficacy. J. Consult. Clin. Psychol. 53: 406–414.
10.1037/0022-006X.53.3.406
CASPubMedWeb of Science®Google Scholar
153 Wiedenfeld, S.A. et al . 1990. Impact of perceived self-efficacy in coping with stressors on components of the immune system. J. Pers. Soc. Psychol. 59: 1082–1094.
10.1037/0022-3514.59.5.1082
PubMedWeb of Science®Google Scholar
154 Rosenberger, P.H. et al . 2004. Physical recovery in arthroscopic knee surgery: unique contributions of coping behaviors to clinical outcomes and stress reactivity. Psychol. Health 19: 307–320.
10.1080/0887044042000193460
Web of Science®Google Scholar
155 Allen, M.T. et al . 1987. Type A behavior pattern, parental history of hypertension, and cardiovascular reactivity in college males. Health Psychol. 6: 113–130.
CASPubMedWeb of Science®Google Scholar
156 Ward, M.M. et al . 1986. Cardiovascular responses in type A and type B men to a series of stressors. J. Behav. Med. 9: 43–49.
10.1007/BF00844643
CASPubMedWeb of Science®Google Scholar
157 Anderson, N.B. et al . 1986. Type A behavior, family history of hypertension, and cardiovascular responsivity among black women. Health Psychol. 5: 393–406.
10.1037/0278-6133.5.4.393
CASPubMedWeb of Science®Google Scholar
158 Weidner, G. et al . 1989. Hostility and cardiovascular reactivity to stress in women and men. Psychosom. Med. 51: 36–45.
10.1097/00006842-198901000-00004
CASPubMedWeb of Science®Google Scholar
159 Seeman, T.E. & B.S. McEwen. 1996. Impact of social environment characteristics on neuroendocrine regulation. Psychosom. Med. 58: 459–471.
10.1097/00006842-199609000-00008
CASPubMedWeb of Science®Google Scholar
160 Cacioppo, J.T. et al . 2002. Loneliness and health: potential mechanisms. Psychosom. Med. 64: 407–417.
10.1097/00006842-200205000-00005
PubMedWeb of Science®Google Scholar
161 Steptoe, A. et al . 2004. Loneliness and neuroendocrine, cardiovascular, and inflammatory stress responses in middle-aged men and women. Psychoneuroendocrinology 29: 593–611.
10.1016/S0306-4530(03)00086-6
CASPubMedWeb of Science®Google Scholar
162 Pike, J.L. et al . 1997. Chronic life stress alters sympathetic, neuroendocrine, and immune responsivity to an acute psychological stressor in humans. Psychosom. Med. 59: 447–457.
10.1097/00006842-199707000-00015
CASPubMedWeb of Science®Google Scholar
163 Matthews, K.A., B.B. Gump & J.F. Owens. 2001. Chronic stress influences cardiovascular and neuroendocrine responses during acute stress and recovery, especially in men. Health Psychol. 20: 403–410.
10.1037/0278-6133.20.6.403
CASPubMedWeb of Science®Google Scholar
164 Seeman, T.E. et al . 2001. Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. Proc. Natl. Acad. Sci. USA 98: 4770–4775.
10.1073/pnas.081072698
CASPubMedWeb of Science®Google Scholar
165 Singer, B., C. Ryff & T.E. Seeman. 2004. Operationalizing allostatic load. In Allostasis, homeostasis, and the cost of physiological adaptation. J. Schulkin, Ed.: 113–149.
10.1017/CBO9781316257081.007
Web of Science®Google Scholar
166 Seplaki, C.L. et al . 2005. A comparative analysis of measurement approaches for physiological dysregulation in an older population. Exp. Gerontol. 40: 438–449.
10.1016/j.exger.2005.03.002
PubMedWeb of Science®Google Scholar
167 Seplaki, C.L. et al . 2006. Measurement of cumulative physiological dysregulation in an older population. Demography 43: 165–183.
10.1353/dem.2006.0009
PubMedWeb of Science®Google Scholar
168 Gruenewald, T.L. et al . 2006. Combinations of biomarkers predictive of later life mortality. Proc. Natl. Acad. Sci. USA 103: 14158–14163.
10.1073/pnas.0606215103
CASPubMedWeb of Science®Google Scholar
169 Karlamangla, A.S. et al . 2002. Allostatic load as a predictor of functional decline. MacArthur studies of successful aging. J. Clin. Epidemiol. 55: 696–710.
10.1016/S0895-4356(02)00399-2
PubMedWeb of Science®Google Scholar
170 Nielsen, L., T. Seeman & A. Hahn.
Google Scholar
171 Rutter, M. 1983. Statistical and personal interactions: facets and perspectives. In Human development: an interactional perspective. D. Magnusson & V. Allen, Eds.: 295–319. Academic Press. New York .
Web of Science®Google Scholar
172 Barocas, R., R. Seifer & A.J. Sameroff. 1985. Defining environmental risk: multiple dimensions of psychological vulnerability. Am. J. Community Psychol. 13: 433–447.
10.1007/BF00911218
CASPubMedWeb of Science®Google Scholar
173 Dong, M. et al . 2004. The interrelatedness of multiple forms of childhood abuse, neglect, and household dysfunction. Child. Abuse Negl. 28: 771–784.
10.1016/j.chiabu.2004.01.008
PubMedWeb of Science®Google Scholar
174 Dong, M. et al . 2004. Insights into causal pathways for ischemic heart disease: adverse childhood experiences study. Circulation 110: 1761–1766.
10.1161/01.CIR.0000143074.54995.7F
PubMedWeb of Science®Google Scholar
175 DeFur, P.L. et al . 2007. Vulnerability as a function of individual and group resources in cumulative risk assessment. Environ. Health Perspect. 115: 817–824.
10.1289/ehp.9332
CASPubMedWeb of Science®Google Scholar
176 Seeman, T.E. et al . 1997. Price of adaptation–allostatic load and its health consequences. MacArthur studies of successful aging. Arch. Intern. Med. 157: 2259–2268.
10.1001/archinte.1997.00440400111013
CASPubMedWeb of Science®Google Scholar
177 Reaven, G.M. 2007. The individual components of the metabolic syndrome: is there a raison d’etre? J. Am. Coll. Nutr. 26: 191–195.
10.1080/07315724.2007.10719601
CASPubMedWeb of Science®Google Scholar
178 Houston, M.C. et al . 2005. Addressing the global cardiovascular risk of hypertension, dyslipidemia, and insulin resistance in the southeastern United States. Am. J. Med. Sci. 329: 276–291.
10.1097/00000441-200506000-00008
PubMedWeb of Science®Google Scholar
179 Wilson, P.W. et al . 1998. Prediction of coronary heart disease using risk factor categories. Circulation 97: 1837–1847.
10.1161/01.CIR.97.18.1837
CASPubMedWeb of Science®Google Scholar
180 Poulter, N. 2003. Global risk of cardiovascular disease. Heart 89(Suppl. 2): ii2–5; discussion ii35–37.
PubMedWeb of Science®Google Scholar
181 Berkman, L.F. et al . 1993. High, usual and impaired functioning in community-dwelling older men and women: findings from the MacArthur Foundation Research Network on Successful Aging. J. Clin. Epidemiol. 46: 1129–1140.
10.1016/0895-4356(93)90112-E
PubMedWeb of Science®Google Scholar
182 Seplaki, C.L. et al . 2004. How are biomarkers related to physical and mental well-being? J. Gerontol. A Biol. Sci. Med. Sci. 59: 201–217.
10.1093/gerona/59.3.B201
PubMedWeb of Science®Google Scholar
183 Crimmins, E.M. et al . 2003. Age differences in allostatic load: an index of physiological dysregulation. Exp. Gerontol. 38: 731–734.
10.1016/S0531-5565(03)00099-8
PubMedWeb of Science®Google Scholar
184 Singer, B. & C.D. Ryff. 1999. Hierarchies of life histories and associated health risks. Ann. N.Y. Acad. Sci. 896: 96–115.
10.1111/j.1749-6632.1999.tb08108.x
CASPubMedWeb of Science®Google Scholar
185 Evans, G.W. 2003. A multimethodological analysis of cumulative risk and allostatic load among rural children. Dev. Psychol. 39: 924–933.
10.1037/0012-1649.39.5.924
PubMedWeb of Science®Google Scholar
186 Seeman, T. et al . In press. Modeling multi-system biological risk in young adults: coronary artery risk development in young adults study (CARDIA). Am. J. Hum. Biol.
Google Scholar
187 Hu, P. et al . 2007. The associations between socioeconomic status, allostatic load and measures of health in older Taiwanese persons: Taiwan social environment and biomarkers of aging study. J. Biosoc. Sci. 39: 545–556.
10.1017/S0021932006001556
PubMedWeb of Science®Google Scholar
188 Geronimus, A.T. et al . 2006. “Weathering” and age patterns of allostatic load scores among blacks and whites in the United States. Am. J. Public Health 96: 826–833.
10.2105/AJPH.2004.060749
PubMedWeb of Science®Google Scholar
189 Seeman, T. et al . 2008. Education, income and ethnic differences in cumulative biological risk profiles in a national sample of US adults: NHANES III (1988–1994). Soc. Sci. Med. 66: 72–87.
10.1016/j.socscimed.2007.08.027
PubMedWeb of Science®Google Scholar
190 Sabbah, W. et al . 2008. Effects of allostatic load on the social gradient in ischaemic heart disease and periodontal disease: evidence from the Third National Health and Nutrition Examination Survey. J. Epidemiol. Community Health 62: 415–420.
10.1136/jech.2007.064188
CASPubMedWeb of Science®Google Scholar
191 Crimmins, E.M., J.K. Kim & T.E. Seeman. 2009. Poverty and biological risk: the earlier “aging” of the poor. J. Gerontol. A Biol. Sci. Med. Sci. 64: 286–292.
10.1093/gerona/gln010
PubMedWeb of Science®Google Scholar
192 Lynch, J.W. et al . 1998. Does low socioeconomic status potentiate the effects of heightened cardiovascular responses to stress on the progression of carotid atherosclerosis? Am. J. Public Health 88: 389–394.
CASPubMedWeb of Science®Google Scholar
193 Fiocco, A.J., R. Joober & S.J. Lupien. 2007. Education modulates cortisol reactivity to the Trier Social Stress Test in middle-aged adults. Psychoneuroendocrinology 32: 1158–1163.
10.1016/j.psyneuen.2007.08.008
CASPubMedWeb of Science®Google Scholar
194 Lachman, M.E. & S.L. Weaver. 1998. The sense of control as a moderator of social class differences in health and well-being. J. Pers. Soc. Psychol. 74: 763–773.
10.1037/0022-3514.74.3.763
CASPubMedWeb of Science®Google Scholar
195 Ryff, C.D., B.H. Singer & K.A. Palmersheim. 2004. Social inequalities in health and well-being: the role of relational and religious protective factors. In How healthy are we?: a national study of well-being at midlife. O.G. Brim, C.D. Ryff & R.C. Kessler, Eds.: 90–123. University of Chicago Press. Chicago , IL .
Google Scholar
196 Ouellet-Morin, I. et al . 2008. Daytime cortisol secretion in 6-month-old twins: genetic and environmental contributions as a function of early familial adversity. Biol. Psychiatry.
PubMedWeb of Science®Google Scholar
197 Ouellet-Morin, I. et al . 2008. Variations in heritability of cortisol reactivity to stress as a function of early familial adversity among 19-month-old twins. Arch. Gen. Psychiatry 65: 211–218.
10.1001/archgenpsychiatry.2007.27
CASPubMedWeb of Science®Google Scholar
198 Taylor, S.E. et al . 2006. Early family environment, current adversity, the serotonin transporter promoter polymorphism, and depressive symptomatology. Biol. Psychiatry 60: 671–676.
10.1016/j.biopsych.2006.04.019
CASPubMedWeb of Science®Google Scholar
199 Taylor, S.E. et al . 2006. Neural responses to emotional stimuli are associated with childhood family stress. Biol. Psychiatry 60: 296–301.
10.1016/j.biopsych.2005.09.027
PubMedWeb of Science®Google Scholar
200 Cole, S.W. et al . 2007. Social regulation of gene expression in human leukocytes. Genome Biol. 8: R189.1–R189.13.
10.1186/gb-2007-8-9-r189
CASWeb of Science®Google Scholar
201 Knutsson, A. 2003. Health disorders of shift workers. Occup. Med. (Lond.) 53: 103–108.
10.1093/occmed/kqg048
PubMedWeb of Science®Google Scholar
202 Cho, K. 2001. Chronic ‘jet lag’ produces temporal lobe atrophy and spatial cognitive deficits. Nat. Neurosci. 4: 567–568.
10.1038/88384
CASPubMedWeb of Science®Google Scholar

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Volume1186, Issue1

The Biology of Disadvantage: Socioeconomic Status and Health

February 2010

Pages 223-239

Socio-economic differentials in peripheral biology: Cumulative allostatic load

Abstract

Introduction

Socioeconomic status gradients in patterns of activity in major regulatory systems

Cumulative biological “wear and tear” (that is, allostatic load)

Empirical efforts to operationalize allostatic load

SES gradients in cumulative biological “wear and tear” (that is, allostatic load)

Future directions

Policy

Conflicts of interest

References

Citing Literature

Figures

References

Information

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Socio-economic differentials in peripheral biology: Cumulative allostatic load

Abstract

Introduction

Socioeconomic status gradients in patterns of activity in major regulatory systems

Cumulative biological “wear and tear” (that is, allostatic load)

Empirical efforts to operationalize allostatic load

SES gradients in cumulative biological “wear and tear” (that is, allostatic load)

Future directions

Policy

Conflicts of interest

References

Citing Literature

Figures

References

Related

Information