Pilot study identifying myosin heavy chain 7, desmin, insulin‐like growth factor 7, and annexin <scp>A</scp> 2 as circulating biomarkers of human heart failure
Bibliographic record
Abstract
In-depth proteomic analyses offer a systematic way to investigate protein alterations in disease and, as such, can be a powerful tool for the identification of novel biomarkers. Here, we analyzed proteomic data from a transgenic mouse model with cardiac-specific overexpression of activated calcineurin (CnA), which results in severe cardiac hypertrophy. We applied statistically filtering and false discovery rate correction methods to identify 52 proteins that were significantly different in the CnA hearts compared to controls. Subsequent informatic analysis consisted of comparison of these 52 CnA proteins to another proteomic dataset of heart failure, three available independent microarray datasets, and correlation of their expression with the human plasma and urine proteome. Following this filtering strategy, four proteins passed these selection criteria, including myosin heavy chain 7, insulin-like growth factor-binding protein 7, annexin A2, and desmin. We assessed expression levels of these proteins in mouse plasma by immunoblotting, and observed significantly different levels of expression between healthy and failing mice for all four proteins. We verified antibody cross-reactivity by examining human cardiac explant tissue by immunoblotting. Finally, we assessed protein levels in plasma samples obtained from four unaffected and four heart failure patients and demonstrated that all four proteins increased between twofold and 150-fold in heart failure. We conclude that MYH7, IGFBP7, ANXA2, and DESM are all excellent candidate plasma biomarkers of heart failure in mouse and human.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".