Plasma Olink Proteomics Reveals Novel Biomarkers for Prediction and Diagnosis in Dilated Cardiomyopathy with Heart Failure
Bibliographic record
Abstract
In this study, we utilized the Olink Cardiovascular III panel to compare the expression levels of 92 cardiovascular-related proteins between patients with dilated cardiomyopathy combined with heart failure (DCM-HF) (n = 20) and healthy normal people (Normal) (n = 18). The top five most significant proteins, including SPP1, IGFBP7, F11R, CHI3L1, and Plaur, were selected by Olink proteomics. These proteins were further validated using ELISA in plasma samples collected from an additional cohort. ELISA validation confirmed significant increases in SPP1, IGFBP7, F11R, CHI3L1, and Plaur in DCM-HF patients compared to healthy controls. GO and KEGG analysis indicated that NT-pro BNP, SPP1, IGFBP7, F11R, CHI3L1, Plaur, BLM hydrolase, CSTB, Gal-4, CCL15, CDH5, SR-PSOX, and CCL2 were associated with DCM-HF. Correlation analysis revealed that these 13 differentially expressed proteins have strong correlations with clinical indicators such as LVEF and NT-pro BNP, etc. Additionally, in the GEO-DCM data sets, the combined diagnostic value of these five core proteins AUC values of 0.959, 0.773, and 0.803, respectively indicating the predictive value of the five core proteins for DCM-HF. Our findings suggest that these proteins may be useful biomarkers for the diagnosis and prediction of DCM-HF, and further research is prompted to explore their potential as therapeutic targets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".