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Objectives: Increasing obesity rates and accelerating climate change represent two global health challenges shaped by lifestyle change and human environmental modifications. Yet, few studies have considered how these issues may interact to exacerbate disease risk., Methods: In this theory article, we explore evidence that obesity-related disease and climatic changes share socio-ecological drivers and may interact to increase human morbidity and mortality risks. Additionally, we consider how obesity-climate change interactions may disproportionately affect vulnerable populations and how anthropological research can be applied to address this concern., Results: Interactions between heat stress and cardiometabolic disease represent an important pathway through which climate change and obesity-related morbidities may jointly impair health. For example, individuals with higher body fatness and obesity-related metabolic conditions (eg, type 2 diabetes) exhibit a reduced ability to dissipate heat. The risk of poor health resulting from these interactions is expected to be heterogeneous, with low- and middle-income countries, individuals of lower socioeconomic status, and minority populations facing a greater disease burden due to relative lack of resource access (eg, air conditioning). Moreover, older adults are at higher risk due to aging-associated changes in body composition and loss of thermoregulation capabilities., Conclusions: Few policy makers appear to be considering how interventions can be designed to simultaneously address the medical burden posed by increasing obesity rates and climate change. Anthropological research is well situated to address this need in a nuanced and culturally-sensitive way; producing research that can be used to support community resilience, promote holistic well-being, and improve health outcomes., (© 2020 Wiley Periodicals LLC.)

Objectives: Changes in sleep patterns often occur in older adults. Previous studies have documented associations between sleep duration, sleep quality, and obesity risk in older individuals, yet few studies have examined these trends in lower-income countries. The present cross-sectional study uses nationally representative datasets from six countries to examine these relationships., Methods: Two hypotheses related to obesity risk and sleep patterns were tested using data from the first wave of the World Health Organization's Study on global AGEing and adult health (SAGE). This longitudinal study draws on samples of older adults (>50 years old) in six middle-income countries (China, Ghana, India, Mexico, Russian Federation, and South Africa). Self-report data were used to measure sleep duration, sleep quality, lifestyle and sociodemographic information, while anthropometric measurements were collected to assess body mass index (BMI) and waist circumference (WC). Multiple linear regressions were used to examine the relationship between sleep patterns and obesity risk while controlling for lifestyle factors., Results: Shorter sleep durations in both men and women were significantly associated with higher BMI and WC measures (P < 0.05). Low sleep quality did not significantly contribute to increased obesity risk. Surprisingly, high sleep quality was significantly associated with increased male BMI and WC in China and India (P < 0.01)., Conclusions: This study documented an association between short sleep duration and increased obesity risk, which is important given the global increase of obesity-related diseases., (© 2014 Wiley Periodicals, Inc.)

ObjectivesInformation concerning physical growth among small-scale populations remains limited, yet such data are critical to local health efforts and to foster basic understandings of human life history and variation in childhood development. Using a large dataset and robust modeling methods, this study aims to describe growth from birth to adulthood among the indigenous Shuar of Amazonian Ecuador.MethodsMixed-longitudinal measures of height, weight, and body mass index (BMI) were collected from Shuar participants (n = 2,463; age: 0-29 years). Centile growth curves and tables were created for each anthropometric variable of interest using Generalized Additive Models for Location, Scale, and Shape (GAMLSS). Pseudo-velocity and Lambda-Mu-Sigma curves were generated to further investigate Shuar patterns of growth and to facilitate comparison with United States Center for Disease Control and Prevention and multinational World Health Organization growth references.ResultsThe Shuar are small throughout life and exhibit complex patterns of growth that differ substantially from those of international references. Similar to other Amazonians, Shuar growth in weight compares more favorably to references than growth in height, resulting in BMI curves that approximate international medians. Several additional characteristics of Shuar development are noteworthy, including large observed variation in body size early in life, significant infant growth faltering, extended male growth into adulthood, and a markedly early female pubertal growth spurt in height. Phenotypic plasticity and genetic selection in response to local environmental factors may explain many of these patterns.ConclusionsProviding a detailed reference of growth for the Shuar and other Amazonian populations, this study possesses direct clinical application and affords valuable insight into childhood health and the ecology of human growth.

Background: Self-reported (SR) body mass index (BMI) values are often used to determine obesity prevalence. However, individuals frequently overestimate their height and underestimate their weight, resulting in artificially lower obesity prevalence rates. These patterns are especially apparent among older adults and overweight individuals. The present cross-sectional study uses nationally representative datasets from five countries to assess the accuracy of SR BMI values in diverse settings. Methods: Samples of older adults (=50 years old) and comparative samples of younger adults (18-49 years old) were drawn from five middle-income countries (China, India, Mexico, Russian Federation, and South Africa) in the World Health Organization's Study on global AGEing and adult health (SAGE). Participant-reported and researcher-obtained height and weight measures were used to calculate SR and measured BMI, respectively. Paired t-tests assessed differences between SR and measured BMI values by country. Linear regressions examined the contribution of measured weight and age to differences between SR and measured BMI. Results: Significant differences between SR and measured BMI values were observed (p < 0.05), but the direction of these discrepancies varied by country, age, and sex. Measured weight significantly contributed to differences between SR and measured BMI in all countries (p < 0.01). Age did not contribute significantly to variation in BMI discrepancy, except in China (p < 0.001). Conclusions: These results suggest that SR BMI may not accurately reflect measured BMI in middle-income countries, but the direction of this discrepancy varies by country. This has considerable implications for obesity-related disease estimates reliant on SR data. [ABSTRACT FROM AUTHOR]

Metabolic syndrome (MetS) is a cluster of harmful conditions which occur together, such as insulin resistance, abdominal obesity, and hypertension. The global prevalence of MetS is growing rapidly, with some estimates suggesting over one billion people worldwide experience increased morality and disease rates linked with this syndrome. One possible factor contributing to MetS risk is changes in microbiome composition. Approximately 100 trillion bacteria and other microbes reside in the human intestinal tract, collectively termed the gut microbiome. Humans and microbes share a long evolutionary history, with many of these microbes influencing human health outcomes. However, environmental conditions have changed dramatically with human technological innovations; many of these changes (e.g. diets high in processed foods and sedentary lifestyles) appear to impact human-microbe relationships. In general, recent changes in diet and activity patterns have been linked to decreased microbiome diversity, elevating inflammation and metabolic disease risk and likely promoting the development of MetS. Targeting patient diet or exercise patterns may therefore help doctors better treat patients suffering from MetS. Still, additional work is needed to determine how the microbiome responds to changes in patient activity and diet patterns across culturally and biologically diverse human populations. [ABSTRACT FROM AUTHOR]