Clinical Correlates of CENP-A and CENP-B Antibodies in a Large Cohort of Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To study the clinical phenotypes of centromeric proteins (CENP)-A- and CENP-B-positive patients with systemic sclerosis (SSc) and to compare them to anticentromere antibody (ACA)-positive and negative SSc patients. METHODS: Sera samples were collected from 802 patients with SSc enrolled in a multicenter cohort study. Antibodies to CENP-A and B were detected by ELISA, and ACA by indirect immunofluorescence. Associations with clinical and other serological manifestations of SSc were investigated. RESULTS: CENP-A antibodies were detected in 276 (34%), CENP-B in 286 (36%), and ACA in 279 (35%) patients. Patients having ACA, CENP-A, and/or CENP-B resembled each other and differed from the remainder of the cohort in the following respects: older chronologically and at disease onset; more commonly women; more likely to have limited disease and lower skin scores; less likely to have finger ulcers, digital tuft resorption, or finger contractures; more likely to have pulmonary hypertension; less likely to have interstitial lung disease, scleroderma renal crisis, inflammatory arthritis, and inflammatory myositis; and having lower overall disease severity. CENP-A and/or B status was predictive of the extent of skin involvement over time. Patients with limited disease who were CENP-A-negative at baseline were more likely to progress to diffuse disease compared to CENP-A-positive patients (OR 2.55, 95% CI 1.37, 4.85, p = 0.004). CONCLUSION: Clinical immunology laboratories are increasingly using high-throughput ELISA tests for CENP antibodies, with or without ACA detected by indirect immunofluorescence. The phenotype of CENP-A and/or B-positive patients is generally similar to that associated with ACA.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".