Post-translational modifications of collagen type I in osteogenesis imperfecta: Systematic review and meta-analysis
Bibliographic record
Abstract
Osteogenesis imperfecta (OI) is a rare genetic disorder most often caused by mutations in genes that encode collagen type I. OI collagen-I differs from healthy collagen-I due to the underlying mutation and altered post-translational modifications (PTMs). The objective of this study was to use knowledge synthesis to quantify the levels of selected PTMs, hydroxylysine (HYL), hydroxyproline (HYP) and glycosylation (GLY) in OI collagen-I. A systematic search in Medline, Ovid and Web of Science, identified 701 studies reporting on PTM outcomes for OI patients with collagen-I mutation. We excluded animal studies, and reports for OI patients with mutations other than in collagen-I. After screening, we included 36 qualitative studies and 25 quantitative studies for meta-analysis. All qualitative studies reported that OI collagen-I was overmodified. Meta-analysis of studies with quantitative data was performed using normalized mean difference as a study-level effect size and a random-effects model with the Hunter and Smith with sample size correction. The hydroxylysine dataset included 150 patients across 20 studies and had an effect size of 0.33 (confidence interval (CI) 0.21, 0.45). HYL levels were higher in bone-derived collagen-I than in fibroblast- or dentine-derived. The hydroxyproline dataset included 141 patients across 17 studies and had an effect size of 0.00 (CI: -0.02, 0.02). The glycosylation dataset included 17 patients across 5 studies and had an effect size of 0.55 (CI: 0.38, 0.71). Patients with the most severe form of OI (type 2) had the highest levels of collagen-I HYL and GLY. Our study provides new insights into collagen-I pathophysiology in OI, generating new hypotheses regarding the role of PTM in mediating disease presentation in different tissues and overall severity.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".