Polygenic Score Approach to Predicting Risk of Metabolic Syndrome.

Area of Science:

• Genetics and Genomics
• Metabolic Diseases
• Clinical Diagnostics

Background:

• Metabolic syndrome (MetS) is a growing global health concern linked to obesity, diabetes, and hypertension.
• Increasing prevalence is driven by lifestyle and environmental factors, necessitating better management strategies.

Purpose of the Study:

• To investigate genetic factors contributing to MetS.
• To develop a prognostic tool for predicting MetS risk using polygenic scores.

Main Methods:

• Genotyped 40 genetic variants in 279 MetS patients and 397 controls.
• Utilized logistic regression to assess a polygenic score model for MetS risk prediction.

Main Results:

• Identified 18 significant genetic variants associated with MetS.
• The optimal polygenic score model achieved an AUC of 82.5%, significantly improving risk reclassification (NRI = 1.03%).
• Adding age and sex provided minimal predictive improvement; including all 40 variants did not enhance prediction.

Conclusions:

• Polygenic scores show potential for predicting MetS risk and patient outcomes.
• Development of population-specific diagnostic tools for MetS is recommended.
• Further research is needed to validate findings and explore underlying molecular mechanisms.