A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Value of Multiomics Over Clinical Risk Factors in Hypertension Prediction
Tekijät: Vuori, Matti; Ruuskanen, Matti O.; Jousilahti, Pekka; Salomaa, Veikko; Yeo, Li-Fang; Kauko, Anni; Vaura, Felix; Havulinna, Aki; Liu, Yang; Méric, Guillaume; Inouye, Michael; Knight, Rob; Lahti, Leo; Niiranen, Teemu
Kustantaja: Lippincott Williams & Wilkins
Julkaisuvuosi: 2025
Lehti: Hypertension
ISSN: 0194-911X
eISSN: 1524-4563
DOI: https://doi.org/10.1161/HYPERTENSIONAHA.125.25358
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Osittain avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1161/hypertensionaha.125.25358
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/505988431
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, a 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.
Keywords: genome; hypertension; metabolome; microbiota; risk factors.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
T. Niiranen was supported by the Research Council of Finland (grants 321351 and 354447), the Sigrid Jusélius Foundation, and the Finnish Foundation for Cardiovascular Research. M.O. Ruuskanen was supported by the Research Council of Finland (338818) and the Finnish Cultural Foundation. L-F. Yeo was supported by the European Union´s Horizon Europe Framework program for research and innovation 2021 to 2027 under the Marie Skłodowska-Curie grant agreement No 101126611. L. Lahti and M.O. Ruuskanen were supported by the European Union’s Horizon 2020 research and innovation program (grant 952914). This work was supported by core funding from the British Heart Foundation (RG/18/13/33946, RG/F/23/110103), NIHR Cambridge Biomedical Research center (BRC-1215–20014, NIHR203312), Cambridge BHF center of Research Excellence (RE/18/1/34212, RE/18/1/34212), as well as by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome.
