Large-scale plasma proteomic profiling enhances prediction of heart failure among hypertensive individuals: prospective insights from the UK Biobank.
5 affiliations
- 1Department of Vascular Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
- 2Molecular Biology Research Center, School of Life Sciences, Hunan Province Key Laboratory of Basic and Applied Hematology, Central South University, Changsha, China.
- 3Institute of Vascular Diseases, Central South University, Changsha, China.
- 4Center of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- 5Department of Cardiology, The Second Xiangya Hospital of Central South University, Changsha, China.
Abstract
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.
Related
- Self-supervised plasma proteomic representations for prospective disease prediction across varying protein availability
- Sex-Equitable Machine Learning Prediction of Ankle Reinjury in Individuals with Chronic Ankle Instability: A 2-Year Prospective Cohort Study with Fairness Audit
- Profiling Amino Acid Substitutions in Proteomic Datasets of Alzheimer’s Disease Reveals Biological Significance
- Comprehensive Molecular Profiling for Precision Diagnostics: Integrating Genomic, Proteomic, and Epigenetic Landscapes in Human Pathology
- Extracellular flux analysis and proteomic profiling indicate ibogaine promotes synaptic plasticity and neural repair by a metabolic preconditioning mechanism
BibTeX
@article{li2026largescale,
title = {{Large-scale plasma proteomic profiling enhances prediction of heart failure among hypertensive individuals: prospective insights from the UK Biobank.}},
author = {Maohua Li and Xiao He and Shasha Xiao and Jixiang Wang and Lunchang Wang and Xin Li and Chang Shu and Zhaowei Zhu and Jiehua Li},
journal = {Journal of global health},
year = {2026},
month = oct,
doi = {10.7189/jogh.16.04313},
url = {https://europepmc.org/article/MED/42895254},
note = {CC BY},
}
RIS
TY - JOUR
TI - Large-scale plasma proteomic profiling enhances prediction of heart failure among hypertensive individuals: prospective insights from the UK Biobank.
AU - Maohua Li
AU - Xiao He
AU - Shasha Xiao
AU - Jixiang Wang
AU - Lunchang Wang
AU - Xin Li
AU - Chang Shu
AU - Zhaowei Zhu
AU - Jiehua Li
PY - 2026
DA - 2026/10/19
JO - Journal of global health
DO - 10.7189/jogh.16.04313
UR - https://europepmc.org/article/MED/42895254
AB - 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.
SN - 2047-2978
SN - 2047-2986
ER -