Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independent of obesity.

We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterize their association with intermediate phenotypes, and to investigate their role in type 2 diabetes (T2D) risk among normal-weight, overweight, and obese individuals. We investigated the association of genetic scores with euglycemic-hyperinsulinemic clamp- and oral glucose tolerance test-based measures of insulin resistance and secretion and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight, and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs per allele [95% CI], -0.03 [-0.04, -0.01]; P = 0.004). This score was associated with lower BMI (-0.01 [-0.01, -0.0]; P = 0.02) and gluteofemoral fat mass (-0.03 [-0.05, -0.02; P = 1.4 × 10(-6)) and with higher alanine transaminase (0.02 [0.01, 0.03]; P = 0.002) and γ-glutamyl transferase (0.02 [0.01, 0.03]; P = 0.001). While the secretion score had a stronger association with T2D in leaner individuals (Pinteraction = 0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI or waist strata (Pinteraction > 0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.The MRC-Ely Study was funded by the Medical Research Council (MC_U106179471) and Diabetes UK. We are grateful to all the volunteers, and to the staff of St. Mary’s Street Surgery, Ely and the study team. The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust. We are grateful to all the volunteers for their time and help, and to the General Practitioners and practice staff for assistance with recruitment. We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. DBS and RKS are funded by the Wellcome Trust, the U.K. NIHR Cambridge Biomedical Research Centre and the MRC Centre for Obesity and Related Metabolic Disease. Genotyping in ULSAM was performed by the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se), which is supported by Uppsala University, Uppsala University Hospital, Science for Life Laboratory - Uppsala and the Swedish Research Council (Contracts 80576801 and 70374401). The RISC Study was supported by European Union grant QLG1-CT-2001-01252 and AstraZeneca. The RISC Study Project Management Board: B Balkau, F Bonnet, SW Coppack, JM Dekker, E Ferrannini, A Golay, A Mari, A Natali, J Petrie, M Walker. We thank all EPIC participants and staff for their contribution to the study. We thank the lab team at the MRC Epidemiology Unit for sample management and Nicola Kerrison of the MRC Epidemiology Unit for data management. Funding for the EPIC-InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197).In addition, EPIC-InterAct investigators acknowledge funding from the following agencies: PWF: Swedish Research Council, Novo Nordisk, Swedish Diabetes Association, Swedish Heart-Lung Foundation; LCG: Swedish Research Council; NS: Health Research Fund (FIS) of the Spanish Ministry of Health; Murcia Regional Government (Nº 6236); LA: We thank the participants of the Spanish EPIC cohort for their contribution to the study as well as to the team of trained nurses who participated in the recruitment; RK: German Cancer Aid, German Ministry of Research (BMBF); TJK: Cancer Research UK; PMN: Swedish Research Council; KO: Danish Cancer Society; SP: Compagnia di San Paolo; JRQ: Asturias Regional Government; OR: The Västerboten County Council; AMWS and DLvdA: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands; RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government; IS: Verification of diabetes cases was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht; IB: Wellcome Trust grant 098051 and United Kingdom NIHR Cambridge Biomedical Research Centre; MIM: InterAct, Wellcome Trust (083270/Z/07/Z), MRC (G0601261); ER: Imperial College Biomedical Research.This is the author accepted manuscript. The final version is available from the American Diabetes Association via http://dx.doi.org/10.2337/db14-031

The Double Burden of Malnutrition: A Systematic Review of Operational Definitions

Background Despite increasing research on the double burden of malnutrition (DBM; i.e., coexisting over- and undernutrition), there is no global consensus on DBM definitions. Objectives To identify published operational DBM definitions, measure their frequency of use, and discuss implications for future assessment. Methods Following a structured search of peer-reviewed articles with terms describing “overnutrition” [e.g., overweight/obesity (OW/OB)] and “undernutrition” (e.g., stunting, micronutrient deficiency), we screened 1920 abstracts, reviewed 500 full texts, and extracted 623 operational definitions from 239 eligible articles. Results We organized three identified DBM dimensions (level of assessment, target population, and forms of malnutrition) into a framework for building operational DBM definitions. Frequently occurring definitions included coexisting: 1) OW/OB and thinness, wasting, or underweight (n = 289 occurrences); 2) OW/OB and stunting (n = 161); 3) OW/OB and anemia (n = 74); and 4) OW/OB and micronutrient deficiency (n = 73). Conclusions Existing DBM definitions vary widely. Putting structure to possible definitions may facilitate selection of fit-for-purpose indicators to meet public health priorities

Understanding Growth and Malnutrition in Baka Pygmy Children

We determined stunting, wasting, and obesity frequencies in a total 1092 2-to-12 year old Baka Pygmy children from anthropometric and health data gathered in 34 villages in the Djoum-Mintom region in southeastern Cameroon in four health campaigns in 2010 and 2017–9. We compare these to the WHO Child Growth Standards, Amazonian Tsiname growth references for inter-population comparisons and the study population itself. Population-specific growth charts were constructed using GAMLSS modelling. Our results show that Baka children have one of the highest global rates of stunting relative to the WHO child growth standard with 57.8% for 2-to-12 year olds and 64% and 73% for 2-to-4 year old girls and boys, respectively. Frequencies of wasting, overweight, and low BMI were low at 3.4%, 4.6% and 4.3%, respectively, for 2-to-12 year olds. Underweight was at 25.5%, in the upper range for sub-Saharan Africa. Edemas indicated rare severe malnutrition (0.3%). Uncertainties in age estimation had dramatic effects on the reliability of estimated individual z-scores but distributions of z-scores were robust at a population level. In the context of the recent evidence for genetic adaptation of the Pygmies’ small stature to the tropical forest environment we argue that WHO child standards for weight and BMI are applicable. However, standards for height are clearly not adequate for Pygmy people. To achieve UN Sustainable Development Goals, we recommend that Pygmy specific growth standards are developed for the various, genetically differing Pygmy tribes

Associations between sleep duration and sleep debt with insulin sensitivity and insulin secretion in the EGIR-RISC Study

Aim: Extremes in sleep duration play an important role in the development of type 2 diabetes. We examined the associations between sleep duration and sleep debt with estimates of insulin sensitivity and insulin secretion. Methods: Data were derived from the European multi-centre EGIR-RISC study. Sleep duration and sleep debt were derived from a sleep questionnaire asking about sleeping time during the week and during the weekend. Insulin sensitivity and insulin secretion were estimated from a 2-hour Oral Glucose Tolerance Test, with samples every 30 minutes. Associations between sleep duration and sleep debt with insulin sensitivity and insulin secretion, were analysed by multiple linear regression models corrected for possible confounders. Results: Sleep data were available in 1002 participants, 46% men, mean age 48 ± 8 years, who had an average sleep duration of 7 ± 1 hours [range 3–14] and an average sleep debt (absolute difference hours sleep weekend days minus weekdays) of 1 ± 1 hour [range 0–8]. With regard to insulin sensitivity, we observed an inverted U-shaped association between sleep duration and the Stumvoll MCR in (mL/kg/min), with a corrected β (95% CI) of 2.05 (0.8; 3.3) and for the quadratic term −0.2 (−0.3; −0.1). Similarly, a U-shaped association between sleep duration and log HOMA-IR in (µU/mL), with a corrected βs of −0.83 (−1.4; −0.24) and 0.06 (0.02; 0.10) for the quadratic term. Confounders showed an attenuating effect on the associations, while BMI mediated 60 to 91% of the association between sleep duration and insulin sensitivity. No significant associations were observed between sleep duration with insulin secretion or between sleep debt with either insulin sensitivity or insulin secretion. Conclusions: Short and long sleep duration are associated with a lower insulin sensitivity, suggesting that sleep plays an important role in insulin resistance and may provide the link with development of type 2 diabetes