Machine learning prediction of hypertension integrating polygenic risk scores in inner Eurasian populations - Takeaways - MDSpire

Machine learning prediction of hypertension integrating polygenic risk scores in inner Eurasian populations

  • By

  • Vera Tsvetkova

  • Aleksandra Denisova

  • Saleem Mansour

  • Layal Shaheen

  • Iskandar Hweijeh

  • Leushin Artem

  • Travin Grigorii

  • Dilya Turkmenova

  • Liya Valieva

  • Anna Kim

  • Dmitrii Kharitonov

  • Anna Ilinskaya

  • Maria Poptsova

  • Valery Ilinsky

  • Alexander Rakitko

  • July 8, 2026

  • 0 min

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  • 1

    The study evaluated polygenic risk scores (PRS) for hypertension in a cohort of 175,704 individuals from inner Eurasian populations.

  • 2

    PRSs for systolic and diastolic blood pressure showed significant differences between the top and bottom deciles, with odds ratios of 6.20 and 6.71, respectively.

  • 3

    The neural network model integrating PRSs and questionnaire-based risk factors achieved a test ROC-AUC of 0.8245, indicating strong predictive performance for hypertension.

  • 4

    All evaluated PRSs were consistently associated with hypertension across various ancestry groups in Russia and neighboring regions.

  • 5

    The systolic blood pressure PRS demonstrated the most robust transferability, remaining informative in genetically diverse cohorts.

Original Source(s)

Machine learning prediction of hypertension integrating polygenic risk scores in inner Eurasian populations

  • by Vera Tsvetkova, Aleksandra Denisova, Saleem Mansour, Layal Shaheen, Iskandar Hweijeh, Leushin Artem, Travin Grigorii, Dilya Turkmenova, Liya Valieva, Anna Kim, Dmitrii Kharitonov, Anna Ilinskaya, Maria Poptsova, Valery Ilinsky, Alexander Rakitko

  • July 8, 2026

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