Genotype–phenotype correlations in valosin-containing protein disease: a retrospective muticentre study
Bibliographic record
Abstract
Background Valosin-containing protein (VCP) disease, caused by mutations in the VCP gene, results in myopathy, Paget’s disease of bone (PBD) and frontotemporal dementia (FTD). Natural history and genotype–phenotype correlation data are limited. This study characterises patients with mutations in VCP gene and investigates genotype–phenotype correlations. Methods Descriptive retrospective international study collecting clinical and genetic data of patients with mutations in the VCP gene. Results Two hundred and fifty-five patients (70.0% males) were included in the study. Mean age was 56.8±9.6 years and mean age of onset 45.6±9.3 years. Mean diagnostic delay was 7.7±6 years. Symmetric lower limb weakness was reported in 50% at onset progressing to generalised muscle weakness. Other common symptoms were ventilatory insufficiency 40.3%, PDB 28.2%, dysautonomia 21.4% and FTD 14.3%. Fifty-seven genetic variants were identified, 18 of these no previously reported. c.464G>A (p.Arg155His) was the most frequent variant, identified in the 28%. Full time wheelchair users accounted for 19.1% with a median time from disease onset to been wheelchair user of 8.5 years. Variant c.463C>T (p.Arg155Cys) showed an earlier onset (37.8±7.6 year) and a higher frequency of axial and upper limb weakness, scapular winging and cognitive impairment. Forced vital capacity (FVC) below 50% was as risk factor for being full-time wheelchair user, while FVC <70% and being a full-time wheelchair user were associated with death. Conclusion This study expands the knowledge on the phenotypic presentation, natural history, genotype–phenotype correlations and risk factors for disease progression of VCP disease and is useful to improve the care provided to patient with this complex disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".