Large-Scale Plasma Proteomics Identifies Early Molecular Deviations and Improves Risk Prediction for Heart Failure...

Area of Science:

• Cardiovascular Disease Research
• Proteomics and Biomarker Discovery
• Obesity Medicine

Background:

• Heart failure (HF) poses a significant global health burden, with obesity as a major risk factor.
• Existing HF risk prediction models lack specificity for individuals with obesity.
• There is a critical need for advanced biomarkers for personalized risk stratification and prevention in obese populations.

Purpose of the Study:

• To develop and validate a plasma proteomics-based protein risk score (PRS) for predicting incident HF in individuals with obesity.
• To identify specific protein biomarkers that can enhance HF risk prediction.
• To explore the potential for early detection of HF through proteomic analysis.

Main Methods:

• Analysis of 9831 UK Biobank participants with obesity (BMI ≥ 30 kg/m²).
• Measurement of 2911 circulating proteins and up to 16 years of follow-up.
• Development of a PRS using LASSO regression and validation in a test set, with identification of a minimal protein panel.

Main Results:

• A PRS built from 81 proteins demonstrated strong association with HF risk (HR 3.57 in training, 2.45 in test).
• The PRS significantly improved HF prediction beyond established models (e.g., PCP-HF), with consistent gains in discrimination.
• Proteomic deviations were detectable up to 16 years before HF diagnosis, and a four-protein panel showed robust predictive accuracy.

Conclusions:

• Large-scale plasma proteomics substantially enhances HF risk prediction in obese individuals.
• The study identified long-standing molecular alterations preceding clinical HF onset.
• A simplified four-protein panel offers a practical approach for early detection and targeted prevention of obesity-related HF.