Objective and subjective sleep measures are associated with HbA1c and insulin sensitivity in the general population: Findings from the ORISCAV-LUX-2 study.

AIM: To analyze the association of objective and subjective sleep measures with HbA1c and insulin sensitivity in the general population. METHODS: Using a cross-sectional design, data from 1028 participants in the ORISCAV-LUX-2 study from the general population in Luxembourg were analyzed. Objective sleep measures were assessed using accelerometers whereas subjective measures were assessed using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Sleep measures were defined as predictors, while HbA1c and quantitative insulin sensitivity check index (QUICKI) scores were considered outcomes. Linear and spline regression models were fitted by progressively adjusting for demographic and lifestyle variables in the total sample population as well as by stratified analyses using gender, obesity status, depressive symptoms and diabetes status. RESULTS: In fully adjusted models, total and deep sleep durations were associated with lower HbA1c (mmol/mol) levels, whereas sleep coefficients of variation (%) and poor sleep efficiency, as measured by PSQI scores (units), were associated with higher HbA1c levels. In stratified models, such associations were observed mainly in men, and in subjects who had depressive symptoms, were overweight and no diabetes. In addition, total sleep, deep sleep, coefficients of variation and poor sleep efficiency as measured by PSQI revealed non-linear associations. Similarly, greater insulin sensitivity was associated with longer total sleep time and with PSQI-6 (use of sleep medication). CONCLUSION: Associations were more frequently observed between sleep characteristics and glycaemic control with the use of objective sleep measures. Also, such associations varied within subgroups of the population. Our results highlight the relevance of measuring sleep patterns as key factors in the prevention of diabetes.

Metabolically healthy and unhealthy weight statuses, health issues and related costs: Findings from the 2013-2015 European Health Examination Survey in Luxembourg.

AIM: To investigate the relationship between metabolically healthy and unhealthy weight statuses and a wide range of related health issues, and healthcare and loss-of-productivity costs. METHODS: A total of 693 men and 729 women, aged 25-64 years, took part in the European Health Examination Survey conducted in Luxembourg between 2013 and 2015. Metabolically unhealthy normal-weight profiles were defined as having two or more cardiometabolic abnormalities (high blood pressure, high fasting glucose or triglycerides, low HDL cholesterol and/or previously diagnosed hypertension or diabetes) in people with normal weight. Metabolically healthy overweight/obesity was defined as having fewer than two of the above-mentioned abnormalities in people with overweight or obesity. For the present report, the participants' anthropometric, clinical, biological, sociodemographic, lifestyle and health-related data were analyzed. RESULTS: Of the participants with normal weight, 20% had a metabolically unhealthy profile, whereas 60% with overweight and 30% with obesity had a metabolically healthy profile. Comparisons between metabolically healthy and unhealthy normal weight, overweight and/or obesity status revealed that participants presented with a metabolically unhealthy profile independently of weight status (P<0.0001). People with a metabolically healthy profile were more likely to perceive their health as good (66%; P<0.0001), and to report no physical pain (64%; P=0.03), no limitations in daily activities (66%; P=0.0008), no difficulties getting in or out of a bed or chair (63%; P=0.02) or dressing and undressing (63%; P=0.003), going shopping (63%; P=0.053) or doing occasional heavy housework (64%; P=0.007); they also displayed fewer gastrointestinal (63%; P=0.02), arthrosis (64%; P=0.001) and sleep apnoea issues (63%; P=0.002) compared with those with a metabolically unhealthy profile. Healthcare- and loss-of-productivity-related costs were higher with a metabolically unhealthy profile, with differences of up to € 3000 (P=0.02). CONCLUSION: The present work has highlighted that, independently of weight status, people may develop a metabolically unhealthy profile associated with several health issues as well as higher healthcare and loss-of-productivity costs.