Across the full range, higher muscle mass (relative to body size) is associated with better insulin sensitivity and lower risk of PDM. Further research is needed to examine the effect of appropriate exercise interventions designed to increase muscle mass on incidence of diabetes.

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.

BackgroundSarcopenia often co-exists with obesity, and may have additive effects on insulin resistance. Sarcopenic obese individuals could be at increased risk for type 2 diabetes. We performed a study to determine whether sarcopenia is associated with impairment in insulin sensitivity and glucose homeostasis in obese and non-obese individuals.MethodologyWe performed a cross-sectional analysis of National Health and Nutrition Examination Survey III data utilizing subjects of 20 years or older, non-pregnant (N = 14,528). Sarcopenia was identified from bioelectrical impedance measurement of muscle mass. Obesity was identified from body mass index. Outcomes were homeostasis model assessment of insulin resistance (HOMA IR), glycosylated hemoglobin level (HbA1C), and prevalence of pre-diabetes (6.0≤ HbA1C<6.5 and not on medication) and type 2 diabetes. Covariates in multiple regression were age, educational level, ethnicity and sex.Principal FindingsSarcopenia was associated with insulin resistance in non-obese (HOMA IR ratio 1.39, 95% confidence interval (CI) 1.26 to 1.52) and obese individuals (HOMA-IR ratio 1.16, 95% CI 1.12 to 1.18). Sarcopenia was associated with dysglycemia in obese individuals (HbA1C ratio 1.021, 95% CI 1.011 to 1.043) but not in non-obese individuals. Associations were stronger in those under 60 years of age. We acknowledge that the cross-sectional study design limits our ability to draw causal inferences.ConclusionsSarcopenia, independent of obesity, is associated with adverse glucose metabolism, and the association is strongest in individuals under 60 years of age, which suggests that low muscle mass may be an early predictor of diabetes susceptibility. Given the increasing prevalence of obesity, further research is urgently needed to develop interventions to prevent sarcopenic obesity and its metabolic consequences.

There is a growing interest in understanding how the experience of socioeconomic status (SES) adversity across the life course may accumulate to negatively affect the functioning of biological regulatory systems important to functioning and health in later adulthood. The goal of the present analyses was to examine whether greater life course SES adversity experience would be associated with higher scores on a multi-system allostatic load (AL) index of physiological function in adulthood. Data for these analyses are from 1,008 participants (92.2% White) from the Biomarker Substudy of the Study of Midlife in the US (MIDUS). Multiple indicators of SES adversity in childhood (parent educational attainment, welfare status, financial situation) and two points in adulthood (educational attainment, household income, difficulty paying bills, availability of money to meet basic needs, current financial situation) were used to construct SES adversity measures for each life course phase. An AL score was constructed using information on 24 biomarkers from 7 different physiological systems (sympathetic and parasympathetic nervous systems, hypothalamic-pituitary-adrenal axis, cardiovascular, lipid metabolism, glucose metabolism, inflammatory immune activity). Analyses indicate higher AL as a function of greater SES adversity at each phase of, and cumulatively across, the life course. Associations were only moderately attenuated when accounting for a wide array of health status, behavioral and psychosocial factors. Findings suggest that SES adversity experience may cumulate across the life course to have a negative impact on multiple biological systems in adulthood. An important aim of future research is the replication of current findings in this predominantly White sample in more ethnically diverse populations.

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Compared with alcohol consumption among earlier cohorts, that among recent cohorts declined more slowly with increasing age, suggesting that negative health effects of alcohol could increase in the future.

Data from the nationally representative US National Health and Nutrition Examination Survey (NHANES) III cohort were used to examine the hypothesis that socio-economic status is consistently and negatively associated with levels of biological risk, as measured by nine biological parameters known to predict health risks (diastolic and systolic blood pressure, pulse, HDL and total cholesterol, glycosylated hemoglobin, c-reactive protein, albumin and waist-hip ratio), resulting in greater cumulative burdens of biological risk among those of lower education and/or income. As hypothesized, consistent education and income gradients were seen for biological parameters reflecting cardiovascular, metabolic and inflammatory risk: those with lower education and income exhibiting greater prevalence of high-risk values for each of nine individual biological risk factors. Significant education and income gradients were also seen for summary indices reflecting cumulative burdens of cardiovascular, metabolic and inflammatory risks as well as overall total biological risks. Multivariable cumulative logistic regression models revealed that the education and income effects were each independently and negatively associated with cumulative biological risks, and that these effects remained significant independent of age, gender, ethnicity and lifestyle factors such as smoking and physical activity. There were no significant ethnic differences in the patterns of association between socio-economic status and biological risks, but older age was associated with significantly weaker education and income gradients.

Our results have significant and sobering implications: older Americans face increased disability, and society faces increased costs to meet the health care needs of these disabled Americans.