8 Anti-NT5c1A autoantibodies in systemic lupus erythematosus
Bibliographic record
Abstract
Background Autoantibodies to the 44 kDa cytosolic 5-nucleotidase 1A (NT5c1A/Mup44) are a biomarker for differentiating sporadic inclusion body myositis (sIBM) from other autoimmune myopathies. These antibodies have also been detected in 10%–20% of SLE patients but the clinical significance has not been reported. This study determined the frequency of anti-NT5c1A autoantibodies in a SLE cohort and then identify demographic, clinical, and serologic correlations. Methods Patients fulfilling the ACR or SLICC Classification Criteria for SLE were enrolled in a local cohort. Demographic, clinical information (disease activity SLEDAI-2K; damage SLICC/ACR Damage Index (SDI)), and sera were collected at time of enrollment. Antibodies to anti-NT5c1A were determined by an addressable laser bead immunoassay using a full-length human recombinant protein (Origene, Rockville, MD: Cat. #TP324617). The cutoff, established at 400 median fluorescence units (MFU), was two standard deviations above the mean of apparently healthy control sera. Univariable and multivariable analysis were performed to determine associations between the prevalence of high positive anti-NT5c1A and demographic (age, sex, race/ethnicity), clinical features (SLICC/ACR classification criteria, SLEDAI-2K and SDI total scores and subscales including myositis from SLEDAI-2K), medications, and other autoantibodies (anti-dsDNA, extractable nuclear antigens, and anti-phospholipid antibodies). Results 138 SLE patients were included; 89.1% were female with a mean age of 46.1 years (SD 18.1) and disease duration of 13.7 years (SD 11.6). The prevalence of positive anti-NT5c1A was 15.2% (21/138). Univariable analysis demonstrated that patients who had a positive anti-dsDNA (Odds Ratio (OR) 6.59 [95%CI: 2.21, 19.65]) or anti-nucleosome (OR 8.96 [95%CI: 2.43, 32.99]) were more likely to be positive for anti-NT5c1A. Patients with longer disease duration (OR 0.93 [95%:CI 0.88, 0.98]), proteinuria (24 hour urine protein greater than 500 mg on the SLICC criteria) (OR 0.20 [95%CI: 0.04, 0.88]), acute cutaneous SLE (OR 0.38 [95%CI: 0.15, 0.97] on the SLICC criteria), in particular malar rash (OR 0.25 [95%CI: 0.07, 0.89]) or photosensitivity (OR 0.27 [95%CI: 0.08, 0.84]) were less likely to be anti-NT5c1A positive. Multivariable analysis demonstrated that patients with proteinuria (OR 0.16 [95%CI: 0.03, 0.87]) were less likely to be anti-NT5c1A positive. Conclusions Anti-NT5c1A antibodies, a novel biomarker for sIBM, were found in 15.2% of SLE patients in keeping with previous reports. The patients were less likely to have a history of proteinuria and there was no association with myositis (on SLEDAI-2K). Further studies are needed to confirm these findings in larger SLE cohorts. Funding Source(s): The Arthritis Society Chair in Rheumatic Diseases at the Cumming School of Medicine, Calgary
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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 | 0.001 |
| 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 | 0.001 | 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".