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Updated: Apr 24, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Precision biomarker discovery in hypertension through explainable AI and proteomics

Karthik Sekaran1, Hatem Zayed2

  • 1Bioinformatics Core, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg, Luxembourg.

Journal of Human Hypertension
|April 22, 2026
PubMed

Insights

Researchers identified key blood proteins linked to early hypertension using advanced AI. These novel biomarkers, including Renin and TFPI, could improve early disease detection and management.

Area of Science:

  • Cardiovascular Disease Research
  • Proteomics and Bioinformatics
  • Artificial Intelligence in Medicine

Background:

  • Hypertension is a significant global health issue, driving cardiovascular disease.
  • Limited availability of reliable blood-based biomarkers for early hypertension detection.
  • Need for innovative diagnostic tools to identify hypertension at its initial stages.

Purpose of the Study:

  • To identify circulating proteins associated with stage 1 hypertension using plasma proteomics.
  • To leverage explainable machine learning for biomarker discovery in hypertension.
  • To validate potential protein biomarkers for early hypertension detection.

Main Methods:

  • Analysis of plasma proteomic profiles from 778 participants (224 hypertension cases, 554 controls) from the Qatar Biobank.
  • Differential protein expression analysis (1305 proteins) adjusted for age and sex.
  • Application of CatBoost classifier and SHapley Additive exPlanations (SHAP) for model interpretation.

Main Results:

  • Identification of 36 proteins significantly associated with stage 1 hypertension.
  • Observed characteristic patterns: lower Renin, sRAGE, ghrelin, IL-1RAcP; higher TFPI, QORL1, HSP70, C5a.
  • CatBoost model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.7985.
  • Pathway analysis indicated involvement of oxidative stress and vascular function.

Conclusions:

  • Renin, TFPI, sRAGE, QORL1, ghrelin, HSP70, IL-1RAcP, and C5a are proposed as candidate circulating biomarkers for hypertension.
  • Explainable AI effectively translates proteomic data into interpretable biomarker candidates.
  • Further validation in diverse cohorts is essential for clinical application.

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