Time spent in sedentary posture is associated with waist circumference and cardiovascular risk

Background The relationship between metabolic risk and time spent sitting, standing and stepping has not been well established. The present study aimed to determine associations of objectively measured time spent siting, standing and stepping, with coronary heart disease (CHD) risk. Methods A cross-sectional study of healthy non-smoking Glasgow postal workers, n=111 (55 office-workers, 5 women, and 56 walking/delivery-workers, 10 women), who wore activPAL physical activity monitors for seven days. Cardiovascular risks were assessed by metabolic syndrome categorisation and 10-y PROCAM risk. Results Mean(SD) age was 40(8) years, BMI 26.9(3.9)kg/m-2 and waist circumference 95.4(11.9)cm. Mean(SD) HDL-cholesterol 1.33(0.31), LDL-cholesterol 3.11(0.87), triglycerides 1.23(0.64)mmol/l and 10-y PROCAM risk 1.8(1.7)%. Participants spent mean(SD) 9.1(1.8)h/d sedentary, 7.6(1.2)h/d sleeping, 3.9(1.1)h/d standing and 3.3(0.9)h/d stepping, accumulating 14,708(4,984)steps/d in 61(25) sit-to-stand transitions per day. In univariate regressions - adjusting for age, sex, family history of CHD, shift worked, job type and socio-economic status - waist circumference (p=0.005), fasting triglycerides (p=0.002), HDL-cholesterol (p=0.001) and PROCAM-risk (p=0.047) were detrimentally associated with sedentary time. These associations remained significant after further adjustment for sleep, standing and stepping in stepwise regression models. However, after further adjustment for waist circumference, the associations were not significant. Compared to those without the metabolic syndrome, participants with the metabolic syndrome were significantly less active – fewer steps, shorter stepping duration and longer time sitting. Those with no metabolic syndrome features walked >15,000 steps/day, or spent >7h/day upright. Conclusion Longer time spent in sedentary posture is significantly associated with higher CHD risk and larger waist circumference

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Supplementary Material for: Muscle Strength and Fitness in Pediatric Obesity: a Systematic Review from the European Childhood Obesity Group

The increasing prevalence of paediatric obesity and related metabolic complications has been mainly associated with lower aerobic fitness while less is known regarding potential musculoskeletal impairments. The purpose of the present systematic review was to report the evidence regarding muscular fitness in children and adolescents with obesity. A systematic article search was conducted between November 2014 and June 2015 using MEDLINE, EMBASE, CINAHL psycINFO, SPORTDiscus and SocINDEX. Articles published in English and reporting results on muscle strength and muscular fitness in children and adolescents aged 6 to 18 years were eligible. Of 548 identified titles, 36 studies were included for analyses. While laboratory-based studies described higher absolute muscular fitness in youth with obesity compared with their lean peers, these differences are negated when corrected for body weight and lean mass, then supporting field-based investigations. All interventional studies reviewed led to improved muscular fitness in youth with obesity. Children and adolescents with obesity display impaired muscular fitness compared to healthy-weight peers, which seems mainly due to factors such as excessive body weight and increased inertia of the body. Our analysis also points out the lack of information regarding the role of age, maturation or sex in the current literature and reveals that routinely used field tests analysing overall daily muscular fitness in children with obesity provide satisfactory results when compared to laboratory-based data

Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests

The problem of the automatic determination of the first and second ventilatory thresholds (VT1 and VT2) from cardiopulmonary exercise test (CPET) still leads to controversy. The reliability of the gold standard methodology (i.e. expert visual inspection) feeds into the debate and several authors call for more objective automatic methods to be used in the clinical practice. In this study, we present a framework based on a collaborative approach, where a web-application was used to crowd-source a large number (1245) of CPET data of individuals with different aerobic fitness. The resulting database was used to train and test an artificial intelligence (i.e. a convolutional neural network) algorithm. This automatic classifier is currently implemented in another web-application and was used to detect the ventilatory thresholds in the available CPET. A total of 206 CPET were used to evaluate the accuracy of the estimations against the expert opinions. The neural network was able to detect the ventilatory thresholds with an average mean absolute error of 178 (198) mlO2/min (11.1%, r = 0.97) and 144 (149) mlO2/min (6.1%, r = 0.99), for VT1 and VT2 respectively. The performance of the neural network in detecting VT1 deteriorated in case of individuals with poor aerobic fitness. Our results suggest the potential for a collective intelligence system to outperform isolated experts in ventilatory thresholds detection. However, the inclusion of a larger number of VT1 examples certified by a community of experts will be likely needed before the abilities of this collective intelligence can be translated into the clinical use of CPET

Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests

The problem of the automatic determination of the first and second ventilatory thresholds (VT1 and VT2) from cardiopulmonary exercise test (CPET) still leads to controversy. The reliability of the gold standard methodology (i.e. expert visual inspection) feeds into the debate and several authors call for more objective automatic methods to be used in the clinical practice. In this study, we present a framework based on a collaborative approach, where a web-application was used to crowd-source a large number (1245) of CPET data of individuals with different aerobic fitness. The resulting database was used to train and test an artificial intelligence (i.e. a convolutional neural network) algorithm. This automatic classifier is currently implemented in another web-application and was used to detect the ventilatory thresholds in the available CPET. A total of 206 CPET were used to evaluate the accuracy of the estimations against the expert opinions. The neural network was able to detect the ventilatory thresholds with an average mean absolute error of 178 (198) mlO2/min (11.1%, r = 0.97) and 144 (149) mlO2/min (6.1%, r = 0.99), for VT1 and VT2 respectively. The performance of the neural network in detecting VT1 deteriorated in case of individuals with poor aerobic fitness. Our results suggest the potential for a collective intelligence system to outperform isolated experts in ventilatory thresholds detection. However, the inclusion of a larger number of VT1 examples certified by a community of experts will be likely needed before the abilities of this collective intelligence can be translated into the clinical use of CPET