Airborne PM2.5 and Parkinson's disease: An updated meta-analysis
Notice bibliographique
Résumé
BackgroundAn association between atmospheric fine particulate matter (PM2.5) exposure and Parkinson's disease (PD) has been suggested by previous studies, but the results of current epidemiological studies are still inconclusive. ObjectiveTo systematically evaluate the relationship between exposure to ambient PM2.5 and the risk of PD, as well as to explore potential influencing factors, aiming to provide scientific evidence for formulating early prevention strategies for PD. MethodsCochrane Library, PubMed, Web of Science, Medline, Embase, China National Know-ledge Infrastructure (CNKI), Wanfang Database, and VIP Chinese Science and Technology Journal Database were queried. The search terms included Parkinson's disease, particulate matter 2.5, and PM2.5 in both Chinese and English. Cohort studies examining the association between atmospheric PM2.5 exposure and the risk of PD were collected and searched from the inception of each database to June 26, 2023. The identified literature was screened, and the basic information of the included studies and their research subjects, outcome indicators, quantitative results of each study, as well as the information required by bias risk assessment were extracted. The Newcastle-Ottawa Scale was employed to assess the risk of literature bias. Meta-analysis, subgroup analysis, sensitivity analysis, and publication bias analysis were conducted in Stata 15.0 software. ResultsTwelve cohort studies were identified. A total of 17443136 participants with follow-up periods ranging from 3.5 to 22 years were included in the analysis. The meta-analysis, utilizing a random-effects model, revealed that PD risk was elevated by 6% after exposure to PM2.5 [HR=1.06 (95%CI: 1.02, 1.11), P=0.006]. The subgroup analysis demonstrated that exposure to PM2.5 increased PD risk by 6% in North America [HR=1.06 (95%CI: 1.00, 1.12), P=0.033] and by 17% in East Asia [HR=1.17 (95%CI: 1.02, 1.33), P=0.020]. However, the effect was not statistically significant in Europe. PD risk exhibited a 7% rise [HR=1.07 (95%CI: 1.02, 1.14), P=0.011] in individuals aged 60 years and older, which was different from that in individuals younger than 60 years. Exposure to various concentrations of PM2.5 was observed to associate with an elevated risk of PD. The inclusion of adjustments for PD-related comorbidities did not alter the conclusion that ambient PM2.5 exposure might elevate the risk of PD. The studies with a follow-up duration exceeding 5 years and reporting more than 1000 PD cases suggested a significant increase in the risk of PD due to ambient PM2.5 exposure [HR=1.06 (95%CI: 1.01, 1.12), P=0.012; HR=1.06 (95%CI: 1.01, 1.11), P=0.027, respectively]. Conversely, no significant association was identified between ambient PM2.5 exposure and the risk of PD within the cohorts with a follow-up duration of less than 5 years and reporting fewer than 1000 PD cases [HR=1.09 (95%CI: 0.95, 1.26), P=0.214; HR=1.12 (95%CI: 0.98, 1.02), P=0.092, respectively]. The sensitivity analysis showed that the results were stable. The publication bias analysis and the combined trim-and-fill method showed that the results were robust. ConclusionThe risk of PD could be increased by ambient PM2.5 exposure and influenced by age and area. The research results might be affected by the duration of follow-up and the quantity of PD cases reported.
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 | 0,011 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,016 | 0,051 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».