Cardiovascular Biomarkers and Diastolic Dysfunction in Patients With Chronic Chagas Cardiomyopathy
Bibliographic record
Abstract
Background: Chronic Chagas Cardiomyopathy is a unique form of cardiomyopathy, with a significantly higher mortality risk than other heart failure etiologies. Diastolic dysfunction (DD) plays an important role in the prognosis of CCM; however, the value of serum biomarkers in identifying and stratifying DD has been poorly studied in this context. We aimed to analyze the correlation of six biochemical markers with diastolic function echocardiographic markers and DD diagnosis in patients with CCM. Methods: Cross-sectional study of 100 adults with different stages of CCM. Serum concentrations of amino-terminal pro-B type natriuretic peptide (NT-proBNP), galectin-3 (Gal-3), neutrophil gelatinase-associated lipocalin (NGAL), high-sensitivity troponin T (hs-cTnT), soluble (sST2), and cystatin-C (Cys-c) were measured. Tissue Doppler imaging was used to measure echocardiographic parameters indicating DD. Multivariate logistic regression models adjusted by age, sex, BMI, and NYHA classification were used to evaluate the association between the biomarkers and DD. Results: From the total patients included (55% male with a median age of 62 years), 38% had a preserved LVEF, but only 14% had a normal global longitudinal strain. Moreover, 64% had a diagnosis of diastolic dysfunction, with most of the patients showing a restrictive pattern (n = 28). The median levels of all biomarkers (except for sST2) were significantly higher in the group of patients with DD. Higher levels of natural log-transformed NTproBNP (per 1-unit increase, OR = 3.41, p < 0.001), Hs-cTnT (per 1-unit increase, OR = 3.24, p = 0.001), NGAL (per 1-unit increase, OR = 5.24, p =0.003), and Cys-C (per 1-unit increase, OR = 22.26, p = 0.008) were associated with increased odds of having diastolic dysfunction in the multivariate analyses. Finally, NT-proBNP had the highest AUC value (88.54) for discriminating DD presence. Conclusion: Cardiovascular biomarkers represent valuable tools for diastolic dysfunction assessment in the context of CCM. Additional studies focusing mainly on patients with HFpEF are required to validate the performance of these cardiovascular biomarkers in CCM, allowing for an optimal assessment of this unique population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".