Association of Biomarker Clusters With Cardiac Phenotypes and Mortality in Patients With HIV Infection

Background: Although individual cardiac biomarkers are associated with heart failure risk and all-cause mortality in HIV-infected individuals, their combined use for prediction has not been well studied.

Methods and results: Unsupervised k-means cluster analysis was performed blinded to the study outcomes in 332 HIV-infected participants on 8 biomarkers: ST2, NT-proBNP (N-terminal pro-B-type natriuretic peptide), hsCRP (high-sensitivity C-reactive protein), GDF-15 (growth differentiation factor 15), cystatin C, IL-6 (interleukin-6), D-dimer, and troponin. We evaluated cross-sectional associations of each cluster with diastolic dysfunction, pulmonary hypertension (defined as echocardiographic pulmonary artery systolic pressure ≥35 mm Hg), and longitudinal associations with all-cause mortality. The biomarker-derived clusters partitioned subjects into 3 groups. Cluster 3 (n=103) had higher levels of CRP, IL-6, and D-dimer (inflammatory phenotype). Cluster 2 (n=86) displayed elevated levels of ST2, NT-proBNP, and GDF-15 (cardiac phenotype). Cluster 1 (n=143) had lower levels of both phenotype-associated biomarkers. After multivariable adjustment for traditional and HIV-related risk factors, cluster 3 was associated with a 51% increased risk of diastolic dysfunction (95% confidence interval, 1.12-2.02), and cluster 2 was associated with a 67% increased risk of pulmonary hypertension (95% confidence interval, 1.04-2.68), relative to cluster 1. Over a median 6.9-year follow-up, 48 deaths occurred. Cluster 3 was independently associated with a 3.3-fold higher risk of mortality relative to cluster 1 (95% confidence interval, 1.3-8.1), and cluster 2 had a 3.1-fold increased risk (95% confidence interval, 1.1-8.4), even after controlling for diastolic dysfunction, pulmonary hypertension, left ventricular mass, and ejection fraction.

Conclusions: Serum biomarkers can be used to classify HIV-infected individuals into separate clusters for differentiating cardiopulmonary structural and functional abnormalities and can predict mortality independent of these structural and functional measures.