ArticlePLoS neglected tropical diseases2026
Plasma proteomics improves risk prediction in heart failure and reveals unique biology in chronic chagas cardiomyopathy.
Article in PLoS neglected tropical diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundChronic Chagas cardiomyopathy (CCC) remains a major cause of heart failure (HF)-related mortality in Latin America and is increasingly recognized as a global health concern. Prognostic models developed in non-Chagas populations often perform poorly in CCC, highlighting the need for etiology-specific risk stratification. METHODOLOGY/PRINCIPAL
findingsWe applied high-throughput plasma proteomics to evaluate 2-year mortality risk in CCC compared with other HF etiologies. Baseline plasma from 1,212 adults with heart failure with reduced ejection fraction (HFrEF; LVEF <50%) was analyzed to quantify 734 circulating proteins. CCC was confirmed in 191 participants (16%) by dual Trypanosoma cruzi serology. Two-year mortality was higher in CCC than in the overall HF cohort (26% vs. 16%, p < 0.01). Feature-selection methods identified a nine-protein panel (P9: C1QA, CCL4, REN, EGLN1, COL9A1, GP1BA, ITM2A, CNPY2, NT-proBNP) that improved risk classification compared with NT-proBNP alone, increasing F1-macro by 20% (0.674 vs. 0.560) and integrated time-dependent discrimination for 2-year mortality (iAUC) by 6%. Performance gains varied by HF etiology. Improvements were greatest in hypertensive (+40%) and ischemic (+21%) HF, whereas in CCC the P9 panel underperformed NT-proBNP alone (-16%), suggesting distinct underlying disease biology. External validation in the UK Biobank confirmed generalizability: compared with NT-proBNP, P9 improved F1-macro by 18% and iAUC by 7.4%, reaching an F1-macro of 0.612 in the highest-risk tertile. Pathway enrichment identified 14 CCC-specific pathways, mainly related to fibrosis, integrin signaling, immune dysregulation, and impaired protein trafficking. Exploratory analyses also highlighted potential pathway-linked therapeutic targets consistent with distinct CCC mechanisms. CONCLUSIONS/SIGNIFICANCE: The P9 proteomic panel improved mortality risk prediction beyond NT-proBNP and the MAGGIC clinical score across most HF etiologies and showed consistent performance in an independent population-based cohort. In contrast, in CCC P9 underperformed NT-proBNP alone, highlighting the distinct biological features of this disease. These findings underscore the limitations of universal biomarker models in CCC and support the need for etiology-specific risk stratification strategies.
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