Distinct Systemic Sclerosis Phenotypes Related to Race/Ethnicity: An Opportunity to Personalize Care?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».