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Enregistrement W2608403753

The interplay between environmental risk factors for multiple sclerosis

2017· dissertation· en· W2608403753 sur OpenAlexaboutno aff
Kjetil Bjørnevik

Notice bibliographique

RevueBergen Open Research Archive (BORA) (University of Bergen) · 2017
Typedissertation
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCarcinogens and Genotoxicity Assessment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMultiple sclerosisEnvironmental healthMedicineImmunology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background: Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system whose etiology is unknown. While several genetic factors and environmental exposures, including low vitamin D, smoking, infectious mononucleosis (IM) and obesity, have been consistently associated with increased MS risk, they are unlikely to fully explain the individual disease risk. Further, little is known about the underlying mechanisms by which they may affect disease risk. Objective: The main objectives of this study were to examine how exposure to selected environmental factors in specific age periods was associated with MS risk and to disclose whether the associations varied between different populations using the same methodology. In detail, we sought 1) to examine how frequency of outdoor activity, as a proxy for sun exposure and vitamin D levels, in specific age periods from birth to disease onset was associated with MS risk, 2) to examine to which degree prior exposure to known environmental risk factors could explain the association between level of education and MS risk and 3) to examine how the interplay between smoking and IM affected MS risk in our study populations. Methods: We used data from the large multi-national population-based case-control study Environmental Risk Factors in MS (EnvIMS), which included participants from Norway, Italy, Serbia, Sweden and Canada. For the two first articles, data from Norway and Italy was available, while for the third article data from Sweden was also available. In total, this included 1904 patients and 3694 controls. In the countries included in our analyses, patients were recruited from regional or national MS registries, while four times as many age and sex frequency-matched controls were randomly selected from population registries. All patients had been diagnosed according to the McDonald or the Poser criteria, and had clinical onset within 10 years prior to data collection. All participants were older than 18 years at time of selection. Cases and controls in each country reported on prior exposure to selected environmental factors in specific age periods of life using an identical selfadministered questionnaire (EnvIMS-Q), which had been developed specifically for our study. For the current analyses, information on outdoor activity, sunscreen use, hair color, smoking, IM, body size, cod liver oil supplementation, fatty fish intake and level of education was used. The controls were randomly assigned an index age based on the distribution of age of onset among the cases and exposure after disease onset or index age was not considered exposure. The association between disease and exposure was estimated as odds ratios (OR) with 95% confidence intervals (95% CI) using logistic regression. All analyses were adjusted for age and sex. Results: In the first article, we found a significant inverse association between frequency of outdoor activity and MS risk in Norway and Italy. The magnitude of the association was strongest between age 16 and 18 in Norway (OR 1.83, 95% CI: 1.30- 2.59), and between birth and age 5 years in Italy (OR 1.56, 95% CI: 1.16-2.10). We observed seasonal differences in the association in Norway, whereas we observed a significant association for outdoor activity during summer, but not in the winter. For Italy, the association was similar for summer and winter. In addition, we found a significant association between sunscreen use and MS risk during childhood in Norway after accounting for outdoor activity (OR 1.67, 95% CI: 1.06-2.63). In the second article, we found an inverse association between level of education and MS risk in Norway (OR highest vs lowest level: 0.53, 95% CI: 0.41-0.68). The association remained significant after adjusting for smoking, IM, outdoor activity, cod liver oil, fatty fish consumption and body size. Further, the association remained similar after we excluded patients with early onset of disease, defined as onset before age 28. In the third article, we found a statistical significant negative multiplicative interaction between smoking and IM in the risk of MS. Among those who reported IM, we observed no increased disease risk associated with smoking. Similarly, the effect estimates for the association between IM and MS risk were considerably lower among ever-smokers compared to never smokers. The interaction was similar in Norway, Italy, and Sweden. Lastly, we observed similar results on when estimating the interaction on the additive scale, although they did not reach statistical significance. Conclusion: The findings of this study add to the evidence that vitamin D has a protective effect on MS risk, and indicate that adolescence is a sensitive period for exposure. Still, exposure earlier in life might also be of importance. Further, established risk factors cannot fully explain the association between level of education and MS risk in Norway, suggesting that currently unknown environmental exposures associated with lower level of education may be important for disease risk. Lastly, our findings indicate a competing antagonism between smoking and IM in the risk of MS, which suggests that the two risk factors operate on shared biological pathways.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,609
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0030,000
Communication savante0,0000,000
Science ouverte0,0030,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,057
Tête enseignante GPT0,336
Écart entre enseignants0,278 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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