High whole-body fat percentage is independently associated with increased mortality. We aimed to identify a simple anthropometric linear equation that is more accurate than the body mass index (BMI) to estimate whole-body fat percentage among adult individuals. National Health and Nutrition Examination Survey (NHANES) 1999–2004 data (n = 12,581) were used for model development and NHANES 2005–2006 data (n = 3,456) were used for model validation. From the 365 anthropometric indices generated, the final selected equation was as follows: 64 − (20 × height/waist circumference) + (12 × sex), named as the relative fat mass (RFM); sex = 0 for men and 1 for women. In the validation dataset, compared with BMI, RFM better predicted whole-body fat percentage, measured by dual energy X-ray absorptiometry (DXA), among women and men. RFM showed better accuracy than the BMI and had fewer false negative cases of body fat-defined obesity among women and men. RFM reduced total obesity misclassification among all women and all men and, overall, among Mexican-Americans, European-Americans and African-Americans. In the population studied, the suggested RFM was more accurate than BMI to estimate whole-body fat percentage among women and men and improved body fat-defined obesity misclassification among American adult individuals of Mexican, European or African ethnicity.

ObjectiveTo determine whether geographical elevation is inversely associated with diabetes, while adjusting for multiple risk factors.Design and MethodsThis is a cross-sectional analysis of publicly available online data from the Behavioral Risk Factor Surveillance System, 2009. Final dataset included 285,196 US adult subjects. Odds ratios were obtained from multilevel mixed-effects logistic regression analysis.ResultsAmong US adults (≥20 years old), the odds ratio for diabetes were 1.00 between 0−499 m of altitude (reference), 0.95 (95% confidence interval, 0.90 to 1.01) between 500−1,499 m, and 0.88 (0.81 to 0.96) between 1,500−3,500 m, adjusting for age, sex, body mass index, ethnicity, self-reported fruit and vegetable consumption, self-reported physical activity, current smoking status, level of education, income, health status, employment status, and county-level information on migration rate, urbanization, and latitude. The inverse association between altitude and diabetes in the US was found among men [0.84 (0.76 to 0.94)], but not women [1.09 (0.97 to 1.22)].ConclusionsAmong US adults, living at high altitude (1,500−3,500 m) is associated with lower odds of having diabetes than living between 0−499 m, while adjusting for multiple risk factors. Our findings suggest that geographical elevation may be an important factor linked to diabetes.

Most of the literature related to high altitude medicine is devoted to the short-term effects of high-altitude exposure on human physiology. However, long-term effects of living at high altitudes may be more important in relation to human disease because more than 400 million people worldwide reside above 1500 m. Interestingly, individuals living at higher altitudes have a lower fasting glycemia and better glucose tolerance compared with those who live near sea level. There is also emerging evidence of the lower prevalence of both obesity and diabetes at higher altitudes. The mechanisms underlying improved glucose control at higher altitudes remain unclear. In this review, we present the most current evidence about glucose homeostasis in residents living above 1500 m and discuss possible mechanisms that could explain the lower fasting glycemia and lower prevalence of obesity and diabetes in this population. Understanding the mechanisms that regulate and maintain the lower fasting glycemia in individuals who live at higher altitudes could lead to new therapeutics for impaired glucose homeostasis.

Objective To determine the association between altitude and obesity in a nationally representative sample of the Peruvian adult population. Design and Methods This is a cross-sectional analysis of publicly available data from the Food and Nutrition National Center (CENAN, Peru), period 2009-2010. Prevalence ratio of obesity and abdominal obesity was determined as a measure of association. Obesity and abdominal obesity were diagnosed based on direct anthropometric measurements. Results The final dataset consisted of 31,549 individuals ≥20 years old. The prevalence ratio of obesity was as follows: 1.00 between 0–499 m (reference category), 1.00 (95% confidence interval 0.87-1.16) between 500–1,499 m, 0.74 (0.63-0.86) between 1,500–2,999, and 0.54 (0.45-0.64) at ≥3,000 m, adjusting for age, sex, self-reported physical activity, out-migration rate, urbanization, poverty, education, and geographical latitude and longitude. In the same order, the adjusted prevalence ratio of abdominal obesity was 1.00, 1.01 (0.94-1.07), 0.93 (0.87-0.99), and 0.89 (0.82-0.95), respectively. We found an interaction between altitude and sex and between altitude and age (P<0.001, for both interactions) on the association with obesity and abdominal obesity. Conclusions Among Peruvian adult individuals, we found an inverse association between altitude and obesity, adjusting for multiple covariates. This adjusted association varied by sex and age.

OBJECTIVE Insulin resistance is a powerful risk factor for Type 2 diabetes and a constellation of chronic diseases, and is most commonly associated with obesity. We examined if factors other than obesity are more substantial predictors of insulin sensitivity under baseline, non-stimulated conditions. DESIGN AND METHODS Metabolic assessment was performed in healthy dogs (n=90). Whole-body sensitivity from euglycemic clamps (SICLAMP) was the primary outcome variable, and was measured independently by IVGTT (n=36). Adiposity was measured by MRI (n=90), and glucose-stimulated insulin response was measured from hyperglycemic clamp or IVGTT (n=86 and 36, respectively). RESULTS SICLAMP was highly variable (5.9 to 75.9 dl/min per kg per μU/ml). Despite narrow range of body weight (mean, 28.7±0.3 kg), adiposity varied ∼8-fold and was inversely correlated with SICLAMP (p<0.025). SICLAMP was negatively associated with fasting insulin, but most strongly associated with insulin clearance. Clearance was the dominant factor associated with sensitivity (r=0.53, p<0.00001), whether calculated from clamp or IVGTT. CONCLUSIONS These data suggest that insulin clearance contributes substantially to insulin sensitivity, and may be pivotal in understanding the pathogenesis of insulin resistance. We propose that hyperinsulinemia due to reduction in insulin clearance is responsible for insulin resistance secondary to changes in body weight.