Individuals across the full spectrum of glucose metabolism including normoglycaemic and diabetic patients
Care Setting
Cardiovascular risk assessment in outpatient or research settings
Key Highlights
High-throughput plasma proteomics identified four proteins (NT-proBNP, LTBP2, REN, GDF-15) independently associated with incident heart failure.
The protein panel–clinical factors (PPCF) model improved HF risk prediction (AUC 0.823) compared to clinical risk factors alone (AUC 0.773).
Limitations include low positive predictive value (16.8%), lack of HF subtype differentiation, and single baseline proteomic measurement over long follow-up.
Guideline-Based Recommendations
Diagnosis
Current HF risk prediction models rely on clinical factors and limited biomarkers; proteomic panels may enhance risk stratification but are not yet standard.
Distinguish between HFpEF and HFrEF in future proteomic studies due to differing pathophysiology.
Management
Proteomic risk models may guide early identification of high-risk individuals but require further validation before clinical implementation.
Consider cost, complexity, and incremental benefit when integrating multi-marker proteomic panels into practice.
Monitoring & Follow-up
Proteomic markers measured at a single time point may not capture dynamic risk; longitudinal assessment may improve predictive accuracy.
Risks
Potential for high false-positive rates due to low positive predictive value limits standalone screening utility.
Generalizability is limited by study populations predominantly of White ethnicity and healthier volunteers.
Patient & Prescribing Data
Individuals with varying glucose metabolism profiles without prior heart failure
Proteomic markers currently serve prognostic rather than causal roles; no direct therapeutic targets identified from proteomic data yet.
Clinical Best Practices
Incorporate proteomic data alongside clinical variables to improve HF risk stratification cautiously.
Recognize the heterogeneity of HF phenotypes and tailor risk prediction accordingly.
Validate proteomic models in diverse, real-world populations before widespread adoption.
Use proteomic findings to inform research on pathophysiological mechanisms rather than immediate clinical decision-making.
