Urinary proteomic signature of mineralocorticoid receptor antagonism by spironolactone: evidence from the HOMAGE trial
Bibliographic record
Abstract
Abstract Background Heart failure (HF) is characterised by collagen deposition. Urinary proteomic profiling (UPP) detects many sequenced peptides, for >70% derived from collagens. Purpose UPP and serum fibrosis markers were analysed together in patients at risk of HF with as objective to generate insights into the antifibrotic action of spironolactone. Methods In the open-label HOMAGE trial, patients were randomised to usual therapy combined or not with spironolactone 25-50 mg/d and followed for 9 months (n=290; 23.8% women; median age: 73 years). UPP was done by capillary electrophoresis coupled with mass spectrometry; 1498 urinary peptides with detectable signal in ≥30% of patients were analysed. Serum markers of COL1A1 synthesis (PICP and PICP/CITP) were measured. After rank normalisation of the biomarker distributions, between-group differences in their changes were assessed by multivariable-adjusted models. Correlations between the changes in urinary peptides and in serum PICP and PICP/CITP were compared between groups using Fisher Z transform. Results Among all the urinary peptides, only the changes in collagen fragments remained significantly different (p<0.05) between randomisation groups after accounting for baseline levels, covariables and multiple testing. Compared to the control group, spironolactone reduced 16 of 27 collagen-derived urinary peptides. From baseline to 9 months, serum PICP and the PICP/CITP ratio decreased from 79.0 to 75.4 µg/L and from 21.3 to 18.3, respectively (p≤0.0129), reflecting decreased COL1A1 synthesis. Spironolactone did not affect the correlations between changes in urinary COL1A1 fragments and the serum fibrosis markers. Conclusions Spironolactone downregulated urinary collagen-derived peptides, probably by shrinking the body-wide pool of collagens. Spironolactone did not affect the interaction between urinary and serum fibrosis markers, suggesting that collagen scaffolding is maintained, thereby explaining why some urinary collagen fragments increased. Combining urinary and serum fibrosis biomarkers opens new avenues for discovery of antifibrotic drugs and refines insight in the action of antifibrotic drugs.Figure 1Figure 2
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".