Genes, Journal Year: 2024, Volume and Issue: 16(1), P. 22 - 22

Published: Dec. 26, 2024

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

Language: Английский