Large-scale plasma proteomic profiling enhances prediction of heart failure among hypertensive individuals: prospective insights from the UK Biobank.

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.