Key result
A 101-protein risk score was independently associated with incident heart failure and improved the prediction C-index from 0.727 to 0.787 when added to the clinical PCP-HF model.
Why the study?
Current clinical models have limited ability to identify hypertensive individuals at high risk for heart failure before overt disease, and plasma proteomics may improve risk prediction.
Does plasma proteomic profiling improve the prediction of incident heart failure in hypertensive individuals beyond conventional clinical risk models?
Cohort (n=16,128)
Yes
Does plasma proteomic profiling improve the prediction of incident heart failure in hypertensive individuals beyond conventional clinical risk models?
Plasma proteomic profiling significantly enhances the prediction of incident heart failure in hypertensive patients beyond traditional clinical risk scores like the PCP-HF model.
Answers are grounded in this paper and cite their sources.
101-protein score was associated with better HF prediction beyond PCP-HF in hypertension; extends biomarker panels but remains hypothesis-generating pending validation.
Background: Hypertension is a major risk factor for heart failure (HF), but current clinical models have limited ability to identify high-risk individuals before overt disease. Plasma proteomics may capture early molecular changes and improve risk prediction. Methods: We analysed 16,128 hypertensive UK Biobank Pharma Proteomics Project participants without baseline HF. Plasma levels of 2,911 proteins were measured using the Olink Explore platform. Participants were randomly split 1:1 into training and validation sets. HF-associated proteins were identified using Cox models in the training set, followed by LASSO regression to construct a protein risk score (PRS). Model performance was assessed in the validation set using Harrell's C-index, time-dependent ROC analysis, Net Reclassification Improvement (NRI), Integrated Discrimination Improvement (IDI), calibration, and bootstrap validation. A parsimonious panel was derived using LightGBM and sequential forward feature selection. Results: During a median follow-up of 15.1 years, 1,074 participants developed HF. In the training set, 534 proteins were associated with HF after adjustment for demographic and clinical risk factors and were enriched in cytokine signalling, extracellular matrix organisation, and immune-cell migration pathways. The 101-protein PRS was independently associated with incident HF. In the validation set, adding the PRS increased the C-index from 0.670 to 0.786 beyond age and sex, and from 0.727 to 0.787 beyond the Pooled Cohort Equations to Prevent Heart Failure (PCP-HF) model, with improved reclassification and discrimination. PRS levels were elevated years before HF diagnosis. A six-protein panel comprising NT-proBNP, GDF15, BCAN, WFDC2, CTHRC1, and CNTN5 achieved an AUC of 0.775, outperforming NT-proBNP alone. Conclusions: Plasma proteomic profiling improves HF risk prediction among hypertensive individuals beyond conventional clinical factors and may support earlier risk stratification and precision prevention.
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Li et al. (2026) conducted a cohort in Heart failure in hypertension (n=16,128). 101-protein risk score (PRS) vs. PCP-HF clinical risk model was evaluated on Incident heart failure (HF) (HR 2.99, 95% CI 2.75-3.25, p=1.92 × 10−146). A 101-protein risk score was independently associated with incident heart failure and improved the prediction C-index from 0.727 to 0.787 when added to the clinical PCP-HF model.
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