Exploring the link between autoimmune disorders and the risk of developing multiple sclerosis
Notice bibliographique
Résumé
Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system which leads to demyelination and neurodegeneration. While the cause of MS remains unknown, current research points to key genetic, environmental, and infectious factors which play a role in the onset of disease. The aim of the research undertaken in this thesis was to investigate the possible role of autoimmune disorders (AiDs) in the etiology of MS and to determine whether specific AiDs confer an increased risk for MS. The AiDs examined in this thesis are rheumatoid arthritis (RA), type-1 diabetes (T1D), psoriasis, Crohn’s disease (CD), ulcerative colitis (UC), systemic lupus erythematosus (SLE), celiac disease, hypothyroidism, and hyperthyroidism. Published studies yielded conflicting results; some studies found that T1D, psoriasis, CD, SLE, and hypothyroidism were associated with an increased risk of MS, while others found no evidence of an association with MS.The association between AiDs and the risk of MS was studied using data from the Canadian, Italian, and Norwegian components of the Environmental Risk Factors in Multiple Sclerosis (EnvIMS) study, a multi-national case-control study. Cases (N = 2,242) were frequency matched to controls (N = 3,992) on sex and age in each country. Three exposure windows were defined to assess the association between the AiDs and MS; exposure window one (EW1) was the diagnosis of the AiD any time prior to MS, exposure window two (EW2) required a minimum 5-year time lag between the diagnosis of the AiD and MS, and exposure window 3 (EW3) only included AiDs diagnosed at age 18 years or younger. The association between the AiDs and MS in each exposure window was explored in two ways: 1) the association between having any AiD and the risk of MS, and 2) the association between each of the AiDs and the risk of MS (for EW1 and EW2 only), where numbers were sufficient to permit such analyses. The statistical approach was logistic regression, adjusted for age and sex, followed by models adjusted for additional confounders.Our results, presented as adjusted odds ratios (95% CI), suggest evidence of an association between the diagnosis of any AiD and the risk of MS in Canada using EW1 and EW2 (1.47 (1.07-2.03) and 1.61 (1.13-2.29), respectively) and in Italy (1.36 (1.02-1.82) and 1.41 (1.03-1.93), respectively) adjusted for age and sex. This association was not evident when the exposure period was defined as EW3 in Canada (0.95 (0.53-1.73)) or in Italy (1.26 (0.76-2.07)). An increased risk of MS related to the presence of any AiD was not observed in Norway using EW1 (1.00 (0.77-1.30), EW2 (1.09 (0.83-1.45)), or EW3 (0.75 (0.48-1.18)). When AiDs were examined individually, hypothyroidism was found to be associated with an increased risk of MS. Specifically in Canada when the exposure period was defined as EW1 or EW2 (1.92 (1.14-3.23) and 2.24 (1.25-4.01), respectively) and in Italy using exposure period EW1 (1.93 (1.12-3.32)) when adjusting for age, sex, and past body size. This increased risk of MS was not observed in Norway using EW1 or EW2 (1.13 (0.68-1.88) and 1.19 (0.66-2.15), respectively). Psoriasis also showed an increased risk of MS in Canada (1.86 (1.03-3.37)), but not in Italy (1.38 (0.77-2.47)) or Norway (1.31 (0.89-1.93)), when using EW1 after adjusting for age, smoking, smoking history, and past body size. Our findings suggest that having any AiD may increase the risk of MS when the exposure window is not restricted to the childhood or adolescent period. We also found that hypothyroidism showed the strongest association with an increased risk MS when the exposure window is defined as any time prior to MS in both Canada and Italy and with a 5-year time lag prior to MS in Canada. These findings could indicate there is a common genetic or environmental risk factor linking hypothyroidism and MS
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 | 0,001 |
| 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 | 0,001 |
| 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 ».