Genetic modifiers of ambulation in the CINRG duchenne natural history study.
Bibliographic record
Abstract
We studied the effects of LTBP4 and SPP1 polymorphisms on age at loss of ambulation (LoA) in a multiethnic Duchenne muscular dystrophy cohort (DMD). We genotyped SPP1 rs28357094 and LTBP4 haplotype in 283/340 participants in the Cooperative International Neuromuscular Research Group Duchenne Natural History Study (CINRG-DNHS). Median ages at LoA were compared by Kaplan-Meier analysis and log-rank test. We controlled polymorphism analyses for concurrent effects of glucocorticoid corticosteroid (GC) treatment (time-varying Cox regression), and for population stratification (multidimensional scaling of genome-wide markers). Hispanic and South Asian participants (n = 18, 41) lost ambulation 2.7 and 2 years earlier than Caucasian (p = 0.003, <0.001). The TG/GG genotype at SPP1 rs28357094 was associated to 1.2-year earlier median LoA (p = 0.048). This difference was greater (1.9 years, p = 0.038) in GC-treated participants, whereas no difference was observed in untreated. Cox regression confirmed a significant effect of SPP1 genotype in GC-treated participants (HR 1.61, p = 0.016). LTBP4 genotype showed a direction of association with age at LoA as previously reported, but not statistically significant. After controlling for population stratification, we confirmed a strong effect of LTBP4 genotype in Caucasians (2.4 years, p = 0.024). Median age at LoA with the protective LTBP4 genotype in this cohort was 15.0 years, 16.0 if GC-treated. In conclusion, SPP1 rs28357094 acts as a pharmacodynamic biomarker of GC response, and LTBP4 haplotype modifies age at LoA in the CINRG-DNHS cohort. Adjustment for GC treatment and population stratification appears crucial in assessing genetic modifiers in DMD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".