rs10530053 - NFAT5

Magnitude 2.2 · 2 studies on file

Reported associations

• Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease. - Nature medicine (2019) · Karlsson T, Rask-Andersen M, Pan G, Höglund J, Wadelius C, Ek WE, Johansson Å · PubMed 31501611

  Visceral adipose tissue (VAT)-fat stored around the internal organs-has been suggested as an independent risk factor for cardiovascular and metabolic disease , as well as all-cause, cardiovascular-specific and cancer-specific mortality . Yet, the contribution of genetics to VAT, as well as its disease-related effects, are largely unexplored due to the requirement for advanced imaging technologies to accurately measure VAT. Here, we develop sex-stratified, nonlinear prediction models (coefficient of determination = 0.76; typical 95% confidence interval (CI) = 0.74-0.78) for VAT mass using the UK Biobank cohort. We performed a genome-wide association study for predicted VAT mass and identified 102 novel visceral adiposity loci. Predicted VAT mass was associated with increased risk

• New role of fat-free mass in cancer risk linked with genetic predisposition - Unknown journal (n.d.) · Unknown authors · PubMed 38538606

  ABSTRACT: Cancer risk is associated with the widely debated measure body mass index (BMI). Fat mass and fat-free mass measurements from bioelectrical impedance may further clarify this association. The UK Biobank is a rare resource in which bioelectrical impedance and BMI data was collected on ~ 500,000 individuals. Using this dataset, a comprehensive analysis using regression, principal component and genome-wide genetic association, provided multiple levels of evidence that increasing whole body fat (WBFM) and fat-free mass (WBFFM) are both associated with increased post-menopausal breast cancer risk, and colorectal cancer risk in men. WBFM was inversely associated with prostate cancer. We also identified rs615029[T] and rs1485995[G] as associated in independent analyses with both PMB

Auto-generated from study metadata. AI-synthesised commentary is added when this entry is regenerated through content-service's LLM mode.