Circulating protein biomarkers identified in two independent clinical trial cohorts of glucocorticoid-naive Duchenne muscular dystrophy patients.
Bibliographic record
Abstract
Blood-accessible biomarkers offer promising insights into the pathogenesis of Duchenne muscular dystrophy (DMD) and other muscle diseases. Here, we quantified the relative abundance of 7,289 serum proteins using SomaScan proteomics in pre-treatment samples from 51 boys with DMD (aged 4 to <7) and 13 healthy controls from the VISION DMD (VBP15-004) trial. An independent validation cohort of untreated DMD boys (aged 4 to <8) from the FOR-DMD trial was also analyzed. Of the proteins screened, 26% and 15% were significantly elevated and decreased, respectively, in the serum of young DMD boys compared to controls (adjusted p-value < 0.05). A high correlation (Spearman r = 0.85) in fold changes was observed between the two datasets. Many proteins with altered levels overlapped with known markers of muscle injury, inflammation, regeneration, and extracellular matrix remodeling. Selected biomarkers were queried in two published muscle mRNA and a muscle snRNAseq dataset in DMD biopsies. Novel factors involved in muscle regeneration and ECM remodeling were identified. This larger-scale, multi-clinical trial-based cohort study in untreated DMD boys substantially expands the catalog of circulating biomarkers, highlighting early-stage pathological processes. These findings can help identify new therapeutic targets and develop clinically actionable biomarkers to assess disease progression and response to therapies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".