The effects of ethnicity, socioeconomic status and autoantibodies on clinical outcome in patients with systemic lupus erythematosus
Notice bibliographique
Résumé
Non-biological factors are impoftant determinants of health ancl known to impact on the olltcome of many chronic conclitions.Specifically, socioeconornic status (SES), which is affectecl by multiple factols including education level, income, and type of occt4ration can affect health related behaviors, attitudes to health care, and potentially affect access to or compliance with health care interveutions.Poor SES has aclverse effects on chronic conclitions such as diabetes ancl there is eviclence to suggest it may also aclversely affect SLE outcome.Thus, several factors are potentially imporlant in determining disease severity ancl outcome as reflectecl by measules of disease activity, encl organ damage and mortality.This thesis u,ill review the published literature addressing the roles of ethnicity, socioeconomic status and autoantibody profìle, in particular antibodies to extractable nuclear antigen, in detelrnining morbiclity and mortality in SLE.In addition, a systematic review of the literature studying ENA associations with clinical features will be presented as well as a f-omral analysis of the roles of ethnicity, socioeconomic statLrs and antiboclies to extractable nuclear antigens on clinical outcomes in the Manitoba Lnpns population. CHAPTEII2The role of genetics in SLE, Ethnic dilïelences in the plevalence and sevelity of SLE have been leportecl.African Americans, Hispanics, Afro-Caribbeans, Asian Orientals and Native North Americar.lIndians (First Nations) have all been shown to have a higher incidence and severity of SLE compared to Caucasians of the same areas.In contrast, SLE is rare in West ancl Central Africa.(ll 12) (13;1a).This variability may relate to differences in genetic backgror"urd or envirorunental and cultural influences.In the case of lupus in patients of Afi'ican ancestry, the increasing prevalence gradient of lupus in populations fi'om Africa to Er.rrope or North America suggests that a potential interaction between genetic background(s) or the admixtul'e of genetic backgrounds and elrvirorllental int'iuences may contribute to the development of SLE (15;16).SimiÌarly, Hispanic populations from the USA, Latin America, ancl Mexico have also shown differences in SLE sevelity, autoantibody procluction and genetic background(17-22).Many of these findings have been demonstrated thlough a multicenter collabolative study: the Lupr:s in Minority Populations Natule velslrs Nuture (LUMINA) ancl many of the LUMINA findings have been supported by a recent large rnulticenter coholt from Latin Amelica: the Grr-rpo Latinoanericano de Estudio clel Lupus (GLADEL) study ( 23).Comparisons of Hispanics from continental USA (Texas) and the island of Puerto Rico analyzecl by the LUMINA study have shown higher disease activity, nlore orgall involvement, higher frequency of anti-clsDNA autoantibodies, ancl more damage accrual in patients from Texas(21).Althougli these diffelellces wele mecliated by several factors including genetics, environmental factors and social factors, genetics appeared to be the rlost imporlant.Hispanics have mixtures of Western European Qnainly Spanish), African and Amerinclian ancestry although the influences of each ancestry vary between Flispariic subpopulations.Hispanics from Texas are believed to have a higher proportion of Amerindian ancestry, primarily Aztecs and Mayas, rvhile Hispanics from Puerto Rico may have Tainos background.The authors of this work suggest that the greater severity of lupus in Texan l{ispanics may be relateci in part, to Amerindian genes.Native Americans (First Nations) share genetic ancestry with Asian-Orientals.Similar to Asian Orientals, several Native American grollps have been shou'n to have an increased incidence and prevalence of lnpus compared to Caucasians.Disease severity varies in groups with high disease prevalence with some Native Amelican (Filst Nations) gror,4rs having relatively rnilcl disease and others quite severe disease witli high frequencies of serious end organ involvemetf (reviewed in (13)).Specific genetic associations in lupus have been studied by determining the associations o1' individual gene alleles with disease and by genetic linkage studies that associate chrornosolnal regions witli disease.Like other autoimrnurìe conditions.rnultiple genes are likely requirecl to develop SLE.Potential candiclate genes would likely contribute to clisease susceptibility ancl the incluction of autoimnunity, immune specifìcity, or the inclividual host response.Several lupus-associated genes have been identihed that relate to histocompatibility FILA ìraplotypes, complement components ancl cytokines, and iurmunoglobulin receptol's.In addition, specifrc fèatures of SLE rnay have genetic preclispositions.Interpretation of genetic associations in lnpns is clifficult in many cases due to concerlls of linkage disequilibrium in which thele is close association of the marl
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».