Disentangling Genetic Risks for Metabolic Syndrome.

Area of Science:

• Genetics
• Metabolic Disorders
• Cardiovascular Health

Background:

• Metabolic syndrome (MetS) affects a quarter of the global population, increasing risks for heart disease, type 2 diabetes, and premature death.
• Understanding the genetic underpinnings of MetS is crucial for developing effective interventions and reducing healthcare burdens.

Purpose of the Study:

• To investigate if the genetics of metabolic syndrome components align with their observed phenotypic clustering.
• To identify novel genetic loci associated with a common genetic factor influencing MetS.

Main Methods:

• Utilized a multivariate approach analyzing genetic correlations among key MetS components: fasting glucose, HDL cholesterol, systolic blood pressure, triglycerides, and waist circumference.
• Performed a genome-wide association study (GWAS) on a derived genetic factor capturing these correlations, employing a one-factor model.
• Developed a polygenic risk score based on the MetS factor GWAS findings.

Main Results:

• A single genetic factor effectively models the genetic correlations among MetS components.
• The common genetic factor GWAS identified 235 associated loci, significantly expanding upon previous research.
• A substantial proportion of these loci (22.5%) are linked to multiple MetS components, highlighting MetS heterogeneity.
• Genes at associated loci show enriched expression in brain regions, particularly GABAergic and dopaminergic neurons.
• The polygenic risk score explained 5.9% of the variance in MetS.

Conclusions:

• Metabolic syndrome is a genetically complex and heterogeneous disorder.
• The identified genetic factor and loci provide mechanistic insights into MetS development.
• Findings suggest potential therapeutic strategies, including drugs like fenofibrate, targeting multiple MetS components.