Machine learning prediction of hypertension integrating polygenic risk scores in inner Eurasian populations - Report - 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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Clinical Report: Predictive Modeling of Hypertension Using Machine Learning

Overview

This study evaluates the performance of polygenic risk scores (PRS) for hypertension in inner Eurasian populations and develops predictive models integrating PRS with questionnaire-based risk factors.

Background

Arterial hypertension is a significant contributor to cardiovascular morbidity and mortality globally. The increasing prevalence of hypertension and the low treatment rates highlight the importance of integrating genetic factors, such as polygenic risk scores, with traditional risk factors.

Data Highlights

PRS TypeOdds Ratio (95% CI)
Systolic Blood Pressure6.20 (5.22–7.36)
Diastolic Blood Pressure6.71 (5.58–8.06)
Pulse Pressure3.71 (3.16–4.35)

Key Findings

  • PRSs for systolic and diastolic blood pressure showed significant differences between the top and bottom deciles.
  • All evaluated PRSs were associated with hypertension across various ancestry groups in Russia.
  • A neural network model integrating PRSs and questionnaire-derived risk factors achieved a test ROC-AUC of 0.8245.
  • The systolic blood pressure PRS demonstrated the most robust transferability across diverse populations.
  • Hypertension is influenced by both genetic variants and environmental factors.

Clinical Implications

The integration of polygenic risk scores with traditional questionnaire-based risk factors can enhance the identification of individuals at high risk for hypertension.

Conclusion

The study provides insights into the role of polygenic risk scores in predicting hypertension risk across diverse populations.

Related Resources & Content

  1. Frontiers in Digital Health, 2026 -- Explainable and interpretable models for predicting early-onset hypertension in the Tlalpan 2020 cohort
  2. Frontiers in Cardiovascular Medicine, 2026 -- Polygenic Risk Score Translation Across Diverse Populations
  3. conexiant -- ML Model May Predict Preeclampsia Risk
  4. European Journal of Preventive Cardiology -- Genetic Risk Assessment for Coronary Heart Disease in a Cohort of 130,000 Adults from Mexico
  5. 2025 High Blood Pressure (BP) Guideline - Professional Heart Daily | American Heart Association
  6. A Randomized Trial of Intensive versus Standard Blood-Pressure Control | New England Journal of Medicine
  7. Blood pressure polygenic score predicts long-term blood pressure control and treatment-resistant hypertension - PMC
  8. 2025 High Blood Pressure (BP) Guideline - Professional Heart Daily | American Heart Association
  9. A Randomized Trial of Intensive versus Standard Blood-Pressure Control | New England Journal of Medicine
  10. Blood pressure polygenic score predicts long-term blood pressure control and treatment-resistant hypertension - PMC

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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