Proteomic Approaches for Heart Failure Prediction in Dysglycaemic Patients

Overview

High-throughput plasma proteomics identified four proteins associated with heart failure (HF) risk in individuals across glucose metabolism profiles, improving risk prediction beyond clinical factors. However, limitations in population diversity, outcome specificity, and predictive value temper immediate clinical application.

Background

Heart failure remains a major cause of morbidity and mortality among patients with abnormal glucose metabolism, yet current risk prediction models offer limited early identification. Traditional models rely on clinical factors and a few biomarkers but lack comprehensive systems-level insight. Proteomics offers potential to enhance risk stratification by capturing complex molecular alterations linked to HF development in dysglycaemia.

Data Highlights

Positive predictive value (PPV) of PPCF model: 16.8%

Key Findings

• Four proteins—NT-proBNP, LTBP2, REN, and GDF-15—were independently associated with incident heart failure.
• The PPCF model combining these proteins with clinical variables improved discrimination (AUC 0.823) over clinical factors alone (AUC 0.773).
• Model performance was consistent across glucose metabolism states, with better results in overt diabetes.
• Study cohorts were predominantly White UK Biobank participants, limiting generalizability.
• Outcome definition did not differentiate HFpEF from HFrEF, which have distinct pathophysiologies and proteomic profiles.
• Single baseline protein measurements with long follow-up raise questions about capturing short-term versus long-term risk.

Clinical Implications

While proteomic risk models show promise in enhancing HF prediction in dysglycaemic patients, their modest incremental benefit over simpler models and low positive predictive value limit immediate clinical utility. Further validation in diverse populations and differentiation of HF subtypes are needed before routine implementation. Proteomic profiling may ultimately aid early intervention strategies if integrated with mechanistic insights.

Conclusion

Proteomic approaches represent an important advance in heart failure risk stratification among patients with abnormal glucose metabolism, but current evidence highlights challenges in clinical translation. Future research should focus on validation, mechanistic understanding, and refinement of predictive models to realize their full potential.

References

1. Zhang et al. 2023 -- Evaluation of large-scale plasma proteomics for prediction of heart failure in individuals with a full range of glucose metabolism profiles