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Context: Insulin resistance is associated with multiple complex diseases; however, precise measures of insulin resistance are invasive, expensive, and time-consuming. Objective: Develop estimation models for measures of insulin resistance, including insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR) from metabolomics data. Design: Insulin Resistance Atherosclerosis Family Study (IRASFS). Setting: Community based. Participants: Mexican Americans (MA) and African Americans (AA). Main outcome: Estimation models for measures of insulin resistance, i.e. SI and HOMA-IR. Results: Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net regression were used to build insulin resistance estimation models from 1274 metabolites combined with clinical data, e.g. age, sex, body mass index (BMI). Metabolite data were transformed using three approaches, i.e. inverse normal transformation, standardization, and Box Cox transformation. The analysis was performed in one MA recruitment site (San Luis Valley, Colorado (SLV); N = 450) and tested in another MA recruitment site (San Antonio, Texas (SA); N = 473). In addition, the two MA recruitment sites were combined and estimation models tested in the AA recruitment sample (Los Angeles, California; N = 495). Estimated and empiric SI were correlated in the SA (r2 = 0.77) and AA (r2 = 0.74) testing datasets. Further, estimated and empiric SI were consistently associated with BMI, low-density lipoprotein cholesterol (LDL), and triglycerides. We applied similar approaches to estimate HOMA-IR with similar results. Conclusions: We have developed a method for estimating insulin resistance with metabolomics data that has the potential for application to a wide range of biomedical studies and conditions. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Insufficient sleep is increasingly recognised as a major risk factor for the development of obesity and diabetes, and short-term sleep loss in clinical studies leads to a reduction in insulin sensitivity. Sleep loss-induced metabolic impairments are clinically relevant, since reductions in insulin sensitivity after sleep loss are comparable to insulin sensitivity differences between healthy individuals and those with impaired glucose tolerance. However, the relative effects of sleep loss vs high-fat feeding in the same individual have not been assessed. In addition, to our knowledge no diurnal (active during the daytime) non-human mammalian model of sleep loss-induced metabolic impairment exists, which limits our ability to study links between sleep and metabolism. Methods: This study examined the effects of one night of total sleep deprivation on insulin sensitivity and beta cell function, as assessed by an IVGTT, before and after 9 months of high-fat feeding in a canine model. Results: One night of total sleep deprivation in lean dogs impaired insulin sensitivity to a similar degree as a chronic high-fat diet (HFD)(normal sleep: 4.95 ± 0.45 mU−1 l−1 min−1; sleep deprivation: 3.14 ± 0.21 mU−1 l−1 min−1; HFD: 3.74 ± 0.48 mU−1 l−1 min−1; mean ± SEM). Hyperinsulinaemic compensation was induced by the chronic HFD, suggesting adequate beta cell response to high-fat feeding. In contrast, there was no beta cell compensation after one night of sleep deprivation, suggesting that there was metabolic dysregulation with acute sleep loss that, if sustained during chronic sleep loss, could contribute to the risk of type 2 diabetes. After chronic high-fat feeding, acute total sleep deprivation did not cause further impairments in insulin sensitivity (sleep deprivation + chronic HFD: 3.28 mU−1 l−1 min−1). Conclusions/interpretation: Our findings provide further evidence that sleep is important for metabolic health and establish a diurnal animal model of metabolic disruption during insufficient sleep. [ABSTRACT FROM AUTHOR]

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

Mitochondria play an integral role in ATP production in cells and are involved in glucose metabolism and insulin secretion, suggesting that variants in the mitochondrial genome may contribute to diabetes susceptibility. In a study of Finnish families ascertained for type 2 diabetes mellitus (T2DM), we genotyped single nucleotide polymorphisms (SNPs) based on phylogenetic networks. These SNPs defined eight major haplogroups and subdivided groups H and U, which are common in Finns. We evaluated association with both diabetes disease status and up to 14 diabetes-related traits for 762 cases, 402 non-diabetic controls, and 465 offspring of genotyped females. Haplogroup J showed a trend toward association with T2DM affected status (OR 1.69, P=0.056) that became slightly more significant after excluding cases with affected fathers (OR 1.77, P=0.045). We also genotyped non-haplogroup-tagging SNPs previously reported to show evidence for association with diabetes or related traits. Our data support previous evidence for association of T16189C with reduced ponderal index at birth and also show evidence for association with reduced birthweight but not with diabetes status. Given the multiple tests performed and the significance levels obtained, this study suggests that mitochondrial genome variants may play at most a modest role in glucose metabolism in the Finnish population. Furthermore, our data do not support a reported maternal inheritance pattern of T2DM but instead show a strong effect of recall bias. [ABSTRACT FROM AUTHOR]

We evaluated the ability of the Relative Fat Mass (RFM) to estimate whole-body fat percentage among children and adolescents who participated in the National Health and Nutrition Examination Survey from 1999 through 2006 (n = 10,390). The RFM equation for adults (64 − (20 × height/waist circumference) + (12 × sex)) may be used for adolescents 15 to 19 years of age. For children and adolescents 8 to 14 years of age, we suggest a modified RFM equation, named as the RFMp (RFM pediatric): 74 − (22 × height/waist circumference) + (5 × sex). In both equations, sex equals 0 for boys and 1 for girls. RFMp was more accurate than BMI to estimate whole-body fat percentage (measured by dual energy X-ray absorptiometry, DXA) among girls (percentage of estimates that were <20% of measured body fat percentage, 88.2% vs. 85.7%; P = 0.027) and boys 8 to 14 years of age (83.4% vs. 71.0%; P < 0.001). RFM was more accurate than BMI among boys 15 to 19 years of age (82.3% vs. 73.9%; P < 0.001) but slightly less accurate among girls (89.0% vs. 92.6%; P = 0.002). Compared with BMI-for-age percentiles, RFMp had lower misclassification error of overweight or obesity (defined as a DXA-measured body fat percentage at the 85th percentile or higher) among boys 8 to 14 years of age (6.5% vs. 7.9%; P = 0.018) but not girls (RFMp: 8.2%; BMI-for-age: 7.9%; P = 0.681). Misclassification error of overweight or obesity was similar for RFM and BMI-for-age percentiles among girls (RFM: 8.0%; BMI-for-age: 6.6%; P = 0.076) and boys 15 to 19 years of age (RFM: 6.9%; BMI-for-age: 7.8%; P = 0.11). RFMp for children and adolescents 8 to 14 years of age and RFM for adolescents 15 to 19 years of age were useful to estimate whole-body fat percentage and diagnose body fat-defined overweight or obesity. [ABSTRACT FROM AUTHOR]