7 research outputs found
Recommended from our members
Wearables, smartphones, and artificial intelligence for digital phenotyping and health
Ubiquitous progress in wearable sensing and mobile computing technologies, alongside growing diversity in sensor modalities, has created new pathways for the collection of health and well-being data outside of laboratory settings, in a longitudinal fashion. Wearable and mobile devices have the potential to provide low-cost, objective measures of physical activity, clinically relevant data for patient assessment, and scalable behavior monitoring in large populations. These data can be used in both interventional and observational studies to derive insights regarding the links between behavior, health. and disease, as well as to advance the personalization and effectiveness of commercial wellness applications. Today, over 400,000 participants have had their behavior tracked prospectively using accelerometers for epidemiological studies across the globe. Traditionally, epidemiologists and clinicians have relied upon self-report measures of physical activity and sleep which, while valuable in the absence of alternatives, are subject to bias and often provide partial, incomplete information Physical behavior data extracted from wearable devices are being used to derive sensor-assessed, objective measures of physical behaviors, overcoming the limitations of self-report with the aim of relating these to clinical endpoints and eventually applying the findings to preventive and predictive medicine. Moreover, the application of artificial intelligence (AI), sensor fusion, and signal processing to wearable sensor data has led to improved human activity recognition and behavioral phenotyping. Here, we review the state of the art in wearable and mobile sensing technology in epidemiology and clinical medicine and discuss how AI is changing the field
Associations between body mass index-related genetic variants and adult body composition: the Fenland cohort study
Background/Objective: Body mass index (BMI) is a surrogate measure of adiposity but does not distinguish fat from lean or bone mass. The genetic determinants of BMI are thought to predominantly influence adiposity but this has not been confirmed. Here we characterise the association between BMI-related genetic variants and body composition in adults.
Subjects/Methods: Among 9667 adults aged 29-64 years from the Fenland study, a genetic risk score for BMI (BMI-GRS) was calculated for each individual as the weighted sum of BMI-increasing alleles across 96 reported BMI-related variants. Associations between the BMI-GRS and body composition, estimated by DXA scans, were examined using age-adjusted linear regression models, separately by sex.
Results: The BMI-GRS was positively associated with all fat, lean and bone variables. Across body regions, associations of the greatest magnitude were observed for adiposity variables e.g. for each standard deviation (SD) increase in BMI-GRS predicted BMI, we observed a 0.90 SD (95% CI: 0.71, 1.09) increase in total fat mass for men (P=3.75×10‾²¹) and a 0.96 SD (95% CI: 0.77, 1.16) increase for women (P=6.12×10‾²²). Associations of intermediate magnitude were observed with lean variables e.g. total lean mass: men: 0.68 SD (95% CI: 0.49, 0.86) (P=1.91×10‾¹²); women: 0.85 SD (95% CI: 0.65, 1.04) (P=2.66×10‾¹⁷) and of a lower magnitude with bone variables e.g. total bone mass: men: 0.39 SD (95% CI: 0.20, 0.58) (P=5.69×10‾⁵); women: 0.45 SD (95% CI: 0.26, 0.65) (P=3.96×10‾⁶). Nominally significant associations with BMI were observed for 28 SNPs. All 28 were positively associated with fat mass and 13 showed adipose-specific effects.
Conclusion: In adults, genetic susceptibility to elevated BMI influences adiposity more than lean or bone mass. This mirrors the association between BMI and body composition. The BMI-GRS can be used to model the effects of measured BMI and adiposity on health and other outcomes.The Fenland Study is supported by the Medical Research Council (MC_U106179471). This work was supported by the Medical Research Council [Unit Programme numbers MC_UU_12015/2 and MC_UU_12015/1]. Genotyping was supported by the Medical Research Council (MC_PC_13046
Recommended from our members
Risk-taking summary statistics UK Biobank
Here, we provide the summary statistics for our GWAS of self-reported risk-taking propensity amongst 436,236 white European participants of the UK Biobank (UKB) study, measured by the question: “Would you describe yourself as someone who takes risks?”. GWAS testing for associations between SNPs and self-reported risk-taking was performed using a linear mixed model (LMM) implemented in BOLT-LMM. Sex, age and genotyping array were included as covariates. SNPs were filtered based on info >0.5 and minor allele frequency >1%