New role of fat-free mass in cancer risk linked with genetic predisposition

In this study we assessed the accuracy of dual X-ray absorptiometry (DXA) compared to magnetic resonance imaging (MRI) for evaluating body composition. Using data from 32,961 participants in the UK Biobank, including 1,928 re-scanned participants after about two and a half years, we examined cross-sectional and longitudinal agreements in DXA and MRI measurements within android and gynoid regions. Our results showed that DXA reliably captured fat measurements but overestimated lean mass compared to MRI, particularly in android regions for men (4.10 vs. 1.74 kg) and women (2.92 vs. 1.10 kg). Longitudinal MRI data revealed a 4-5% muscle and lean mass decrease, undetected by DXA, which showed lean mass increases in women at the follow-up visit. Although DXA is practical for population-level fat assessments, MRI remains the preferred method for detailed and precise longitudinal body composition analysis.

show abstract

Polygenic Score Approach to Predicting Risk of Metabolic Syndrome

Timasheva,

Kochetova,

Balkhiyarova

et al. 2024

Genes

View full text Add to dashboard Cite

Background/Objectives: Metabolic syndrome (MetS) is a complex condition linking obesity, diabetes, and hypertension, representing a major challenge in clinical care. Its rising global prevalence, driven by urbanization, sedentary lifestyles, and dietary changes, underscores the need for effective management. This study aims to explore the genetic mechanisms behind MetS, including adiposity, inflammation, neurotransmitters, and β-cell function, to develop a prognostic tool for MetS risk. Methods: We genotyped 40 genetic variants across these pathways in 279 MetS patients and 397 healthy individuals. Using logistic regression, we evaluated the prognostic capability of a polygenic score model for MetS risk, both independently and with other factors like sex and age. Results: Logistic regression analysis identified 18 genetic variants significantly associated with MetS. The optimal predictive model used polygenic scores calculated with weights assigned to the 18 loci (AUC 82.5%, 95% CI 79.4–85.6%), with age and sex providing a minimal, non-significant improvement (AUC 83.3%, 95% CI 80.2–86.3%). The addition of the polygenic score significantly improved net reclassification (NRI = 1.03%, p = 3.42 × 10−50). Including all 40 variants did not enhance prediction (NRI = −0.11, p = 0.507). Conclusions: Polygenic scores could aid in predicting MetS risk and health outcomes, emphasizing the need for diagnostic tools tailored to specific populations. Additional research is warranted to corroborate these conclusions and explore the molecular mechanisms of MetS.

show abstract

New role of fat-free mass in cancer risk linked with genetic predisposition

Cited by 3 publications

References 65 publications

HDAC5 controls a hypothalamic STAT5b-TH axis, the sympathetic activation of ATP-consuming futile cycles and adult-onset obesity in male mice

HDAC5 controls a hypothalamic STAT5b-TH axis, the sympathetic activation of ATP-consuming futile cycles and adult-onset obesity in male mice

Comparing DXA and MRI body composition measurements in cross-sectional and longitudinal cohorts

Polygenic Score Approach to Predicting Risk of Metabolic Syndrome

Contact Info

Product

Resources

About