Genetic insights into metabolic syndrome: polygenic scores as predictive tools for risk assessment

Article

Timasheva, Y., Kochetova, O., Balkhiyarova, Z., Korytina, G., Prokopenko, I. and Nouwen, A. 2025. Genetic insights into metabolic syndrome: polygenic scores as predictive tools for risk assessment. Journal of Hypertension. 43 (Suppl 1), pp. e30-e31. https://doi.org/10.1097/01.hjh.0001115500.42551.51
TypeArticle
TitleGenetic insights into metabolic syndrome: polygenic scores as predictive tools for risk assessment
AuthorsTimasheva, Y., Kochetova, O., Balkhiyarova, Z., Korytina, G., Prokopenko, I. and Nouwen, A.
Abstract

Objective:
Metabolic syndrome (MetS) is a condition characterized by central obesity, insulin resistance, dyslipidemia, and arterial hypertension, which increase the risk of cardiovascular disease and diabetes. The rising prevalence of MetS worldwide necessitates a deeper understanding of its genetic and molecular mechanisms. This study aimed to identify genetic pathways related to adiposity, inflammation, neurotransmitters, β-cell function, and blood pressure regulation to develop a prognostic tool for MetS risk.

Design and method:
Single nucleotide variants (SNVs) in 40 genes were analyzed in 279 MetS patients and 397 controls. Logistic regression evaluated the predictive utility of polygenic scores, independently and with age and sex. Public datasets provided insights into gene-cluster associations with phenotypic and metabolic traits.

Results:
Eighteen SNVs were significantly associated with MetS, including CDKAL1 rs9295474C (OR = 2.63, PFDR = 1.79 × 10-9), NPY2R rs1047214C (OR = 2.12, PFDR = 1.50 × 10-7), ADRA2A rs1800544G (OR = 2.18, PFDR = 3.98 × 10-6), and CHRM1 rs2067477A (OR = 3.08, PFDR = 3.01 × 10-5). ADCY3 rs17799872 showed associations with BMI (β = 2.16, PFDR = 4.38 × 10-13), waist circumference (β = 6.19, PFDR = 1.52 × 10-6), height (β = -3.86, PFDR = 8.65 × 10-6), fasting insulin (β = 4.13, PFDR = 2.14 × 10-4), HOMA-IR (β = 1.48, PFDR = 2.69 × 10-4), CRP (β = 1.06, PFDR = 1.10 × 10-4), and TNF-α (β = 7.50, PFDR = 0.001).

Weighted polygenic scores for these 18 loci predicted MetS risk with an AUC of 82.5% (95% CI 79.4–85.6%). Adding polygenic scores to age and sex significantly improved classification accuracy (NRI = 1.03%, p = 3.42 × 10-50). Using all 40 loci did not enhance predictions, but combining polygenic scores with age and sex improved the AUC to 83.3% (95% CI 80.2–86.3%). Gene-cluster analysis linked specific loci to distinct metabolic traits, offering insights into MetS mechanisms.

Conclusions:
Weighted polygenic scores for MetS-associated loci predict MetS risk and outcomes, supporting their use in tailored diagnostics. Further validation is needed to confirm findings and elucidate molecular pathways.

Sustainable Development Goals3 Good health and well-being
Middlesex University ThemeHealth & Wellbeing
PublisherLippincott, Williams & Wilkins
JournalJournal of Hypertension
ISSN0263-6352
Electronic1473-5598
Publication dates
PrintMay 2025
Publication process dates
Accepted2025
Deposited12 Sep 2025
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1097/01.hjh.0001115500.42551.51
Event34th European Meeting on Hypertension and Cardiovascular Protection
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