Value of Multiomics Over Clinical Risk Factors in Hypertension Prediction.

Journal: Hypertension (Dallas, Tex. : 1979)
Published Date: Dec 11, 2025

Abstract

BACKGROUND: Several omics methods have been successfully used in hypertension prediction. However, the predictive ability of various multiomics data has not been compared in the same study sample, and it is unknown whether they provide additional predictive value over a good clinical risk factor score. METHODS: Clinical data augmented with modern multiomics methods (systolic blood pressure polygenic risk score, nuclear magnetic resonance metabolite profiling, and gut microbiota) were assessed in 2573 nonhypertensive participants of the FINRISK 2002 cohort. All combinations of these different methods were incorporated into cross-validated machine learning models to predict incident hypertension. Model performance of all combinations of these was assessed using the area under the curve (AUC). Information on incident hypertension was collected using nationwide healthcare register data. RESULTS: Over a mean follow-up of 18.0 years, 393 participants developed hypertension. Models that included the clinical and genetic data resulted in the highest mean AUC (0.735) compared with clinical risk factors alone (AUC=0.725). In the whole study sample, an SD increase in the polygenic risk score was associated with 29% (95% CI, 14%-46%) greater odds of incident hypertension after adjusting for clinical risk factors. Combining metabolome (AUC=0.709) or microbiota (AUC=0.720) data with clinical risk factors did not result in improved risk prediction. CONCLUSIONS: The best prediction combination model for incident hypertension was the clinical model augmented with a polygenic risk score. These data suggest that polygenic risk scores provide limited incremental value over clinical risk factors when assessing risk of incident hypertension.

Authors

  • Matti Vuori
    Department of Internal Medicine, University of Turku and Turku University Hospital, Finland (M.V., L.-F.Y., A.K., T.N.).
  • Matti O Ruuskanen
    Department of Public Health, The Finnish Institute for Health and Welfare (THL), Helsinki (M.O.R., P.J., V.S., A.H., T.N.).
  • Pekka Jousilahti
    Department of Public Health, The Finnish Institute for Health and Welfare (THL), Helsinki (M.O.R., P.J., V.S., A.H., T.N.).
  • Veikko Salomaa
    National Institute for Health and Welfare, Helsinki, Finland.
  • Li-Fang Yeo
    Department of Internal Medicine, University of Turku and Turku University Hospital, Finland (M.V., L.-F.Y., A.K., T.N.).
  • Anni Kauko
    Department of Internal Medicine, University of Turku and Turku University Hospital, Finland (M.V., L.-F.Y., A.K., T.N.).
  • Felix Vaura
    Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki (F.V., A.H.).
  • Aki Havulinna
    Department of Public Health, The Finnish Institute for Health and Welfare (THL), Helsinki (M.O.R., P.J., V.S., A.H., T.N.).
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Guillaume Méric
    Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia (Y.L., G.M., M.I.).
  • Michael Inouye
    Cambridge Baker Systems Genomics Initiative, Baker Heart Research Institute - BHRI, Melbourne, Victoria, Australia [email protected].
  • Rob Knight
    Department of Pediatrics, University of California, San Diego School of Medicine, La Jolla, CA 92093, USA; Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA 92093, USA.
  • Leo Lahti
    Department of Computing, University of Turku, Turku, Finland.
  • Teemu Niiranen
    Department of Internal Medicine, University of Turku and Turku University Hospital, Finland (M.V., L.-F.Y., A.K., T.N.).

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