Large‐Scale Plasma Proteomics Identifies Early Molecular Deviations and Improves Risk Prediction for Heart Failure Among Individuals With Obesity
Authors' conclusion
Large-scale plasma proteomics substantially improves HF risk prediction in individuals with obesity and reveals long-standing molecular alterations preceding clinical onset. A simplified four-protein panel maintains robust predictive accuracy and provides a practical approach for the early detection and targeted prevention of obesity-related HF.
Study context
- Population
- Participants with obesity (BMI ≥ 30 kg/m²) from the UK Biobank with baseline measurements of 2911 circulating proteins, up to 16 years of follow-up727 developed heart failure (HF)
- Participants
- 9,831 participants
- Outcome
- Incident HF
- Follow-up
- Up to 16 years
Methodological detailsproteomics-based risk score development and validation, held-out test set, LASSO regression, LightGBM forward feature selection
- Proteomics-based risk score development and validation
- Held-out test set
- LASSO regression
- LightGBM forward feature selection
Main finding
As reported in the abstract
Proteins significantly associated with HF (multivariable Cox)
578
Proteins selected by LASSO for protein risk score (PRS)
81
PRS and HF risk, training cohort: HR
3.57
95% CI 3.19–4.00
PRS and HF risk, test cohort: HR
2.45
95% CI 2.20–2.74
Observational data: associations, not effects.
Further findings
Incremental prediction of PRS
- ΔC beyond age and sex
- 0.091
- ΔC beyond PCP-HF model
- 0.052
- Consistent gains in NRI and IDI
Protein trajectories before HF onset
- Proteomic deviations detectable before diagnosis
- up to 16 years
Discrimination of four-protein panel (GDF15, NT-proBNP, TNFRSF10B, CTHRC1)
- Four-protein panel AUC
- 0.789
- NT-proBNP alone AUC
- 0.695
- Four-protein panel + PCP-HF combined AUC
- 0.803
Original publication
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