Anti–<scp>Valosin‐Containing</scp> Protein (<scp>VCP</scp>/p97) Autoantibodies in Inclusion Body Myositis and Other Inflammatory Myopathies
Bibliographic record
Abstract
OBJECTIVE: The rationale for this study was based on reports that valosin-containing protein (VCP) mutations are found in hereditary inclusion body myositis (IBM) and VCP was detected in rimmed vacuoles of sporadic IBM (sIBM) muscle biopsies. Autoantibodies to VCP have not been reported in sIBM or other inflammatory myopathies (IIMs). The aim of this study was to determine the frequency and clinical significance of anti-VCP antibodies in sIBM and other IIMs. METHODS: Sera were collected from 73 patients with sIBM and 383 comparators or controls, including patients with IIM (n = 69), those with juvenile dermatomyositis (JDM) (n = 67), those with juvenile idiopathic arthritis (JIA) (n = 47), those with primary biliary cholangitis (PBC) (n = 105), controls that were age matched to patients with sIBM (similarly aged controls [SACs]) (n = 63), and healthy controls (HCs) (n = 32). Immunoglobulin G antibodies to VCP were detected by addressable laser bead immunoassay using a full-length recombinant human protein. RESULTS: Among patients with sIBM, 26.0% (19/73) were positive for anti-VCP. The frequency in disease controls was 15.0% (48/320). Among SACs, the frequency was 1.6% (1/63), and in HCs 0% (0/32). Frequencies were 17.5% (11/63) for IIM, 25.7% (27/105) for PBC, 3.0% (2/67) for JDM, and 17.0% (8/47) for JIA. The sensitivity, specificity, positive predictive value, and negative predictive value of anti-VCP for sIBM were 26.0%, 87.2%, 28.4%, and 85.9%, respectively. Of patients with sIBM, 15.1% (11/73) were positive for both anti-VCP and anti-cytosolic 5'-nucleotidase 1A (NT5c1A). Eleven percent of patients (8/73) were positive for anti-VCP, but negative for anti-NT5c1A. CONCLUSION: Anti-VCP has low sensitivity and moderate specificity for sIBM but may help fill the seronegative gap in sIBM. Further studies are needed to determine whether anti-VCP is a biomarker for a clinical phenotype that may have clinical value.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".