Ethnic Differences in Autoantibody Diversity and Hierarchy: More Clues from a US Cohort of Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To determine the autoantibody repertoire and clinical associations in a multiethnic cohort of American patients with systemic sclerosis (SSc). METHODS: There were 1000 patients with SSc (196 Hispanic, 228 African American, 555 white, and 21 other) who were screened for antinuclear antibodies (ANA), including anticentromere antibodies (ACA) by indirect immunofluorescence assay, antitopoisomerase-1 (topo-1/Scl-70) by immunodiffusion, and anti-RNA polymerase III (RNAP III) by ELISA. Sera from 160 patients with mainly nucleolar and/or speckled ANA pattern, but negative for ACA, Scl-70, and RNAP III, were further characterized by immunoprecipitation for SSc-specific antibodies. RESULTS: The prevalence of antibodies against RNAP III, Th/To, and PM/Scl did not differ significantly among the ethnic groups. The frequency of anti-Scl-70 was lowest in whites (18.0%) compared with 24.0% and 26.8% in Hispanics and African Americans (p = 0.01), respectively. Compared with African American patients, Hispanic and white subjects had a higher frequency of ACA (p < 0.0001) and lower frequency of U3-RNP (p < 0.0001). U3-RNP antibodies were uniquely higher in African American patients, independent of clinical subset, while Th/To autoantibodies were associated with limited cutaneous SSc in white subjects. Overall, Hispanic and African American patients had an earlier age of onset and a predominance of diffuse cutaneous SSc compared with their white counterparts. CONCLUSION: SSc-specific antibodies may predict disease subset; however, the hierarchy of their prevalence differs across ethnic groups. This study provides the most extensive analysis to date on the relevance of autoantibodies in the diagnosis and clinical manifestations of SSc in Hispanic American patients.
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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 | 0.001 | 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".