Distinct Systemic Sclerosis Phenotypes Related to Race/Ethnicity: An Opportunity to Personalize Care?
Bibliographic record
Abstract
Objectives To describe and compare systemic sclerosis (SSc) phenotypes according to race/ethnicity. Methods SSc patients enrolled in the Canadian Scleroderma Research Group cohort from 2004 to 2020 were included. Demographic, clinical and serological characteristics at baseline were collected using standardized questionnaires. Race/ethnicity was self-reported by participants, who were asked to identify with 1 (or more) of the following groups: White, Chinese, South Asian, Black, Filipino, Latin American, Southeast Asian, Arab, West Asian, Japanese, Korean, Indigenous (First Nations, Metis, Inuit) or none of the above. We compared clinical characteristics and serology, according to race/ethnicity. Results Of the 1727 CSRG participants, 80% indicated White race/ethnicity (n=1385), 5 % Indigenous (n=79), 3% Latin American (n=58), 1.6% Middle Eastern (n=27), 1.5% East/Southeast Asian (n=26), 1.2 % Black (n=21) and 0.8 % South Asian (n=12). Differences in demographic, clinical and serological characteristics according to race/ethnicity are highlighted in Table 1. White individuals were older at cohort entry and more frequently had limited SSc. Most SSc subjects were women, but men were affected in higher proportions among South Asians (39%) and East/Southeast Asians (23%). Although Raynaud’s phenomenon is almost universal in SSc, its prevalence was slightly lower among East/Southeast Asians (86%), who also had numerically lower frequency of digital ulcers (29%). Arthritis was relatively common among Latin Americans (55%), Blacks (47%) and possibly Indigenous individuals (39%) versus Whites (29%). Blacks also had higher frequency of diffuse SSc (67%), telangiectasias (79%) and myositis (40%), and the lowest mean pulmonary function test values. Indigenous individuals had higher prevalence of lower gastrointestinal involvement, including malabsorption (22%), bacterial overgrowth (15%) and need for hyperalimentation (9%). In regard to serological profiles, anti-centromere autoantibodies were positive in about one-third of SSc patients, but rare among Black individuals (6%). Anti-topoisomerase I autoantibodies were present in about one-third of Latin American, Black, East/Southeast Asian, Middle Eastern and South Asian patients, but in only 13% of White and Indigenous individuals. Finally, anti-RNA polymerase III autoantibodies were overrepresented among Indigenous individuals (30%). Table 1: Baseline demographic, clinical and serological characteristics of SSc individuals according to race/ethnicity Conclusion In this Canadian cohort, race/ethnicity was associated with distinct SSc phenotypes. Some of these findings may be due to genetic factors, but some findings may be related to referral patterns or migration trends. Additional investigations are underway to better understand our findings. If validated, the results could help personalize care in SSc.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".