Interactive effects of outdoor fine particulate matter and metabolic, behavioural, and psychosocial risk factors on cardiovascular disease: analysis of the PURE-China cohort study
Notice bibliographique
Résumé
Abstract Background Long-term PM2.5 exposure has been associated with an elevated risk of cardiovascular disease (CVD) and its associated risk factors.[1–4] There is a paucity of studies investigating the interactive effects of PM2.5 exposure and a comprehensive list of risk factors including metabolic, behavioural, and psychosocial risk factors on CVD risk.[5] Purpose This study aims to examine the interactive effects of PM2.5 exposure and metabolic, behavioural, and psychosocial risk factors on incident CVD. Methods The Prospective Urban Rural Epidemiology (PURE)-China cohort study is a population-based cohort study recruiting participants aged 35–70 years from 115 communities in 12 provinces of China between 2005 and 2009, and followed up until August 31, 2021.[6] Participants’ metabolic, behavioural, and psychosocial risk factor information was recorded. PM2.5 concentrations were extracted from a 1x1 km2 global model.[7,8] For this analysis, we included participants aged 35–70 years at baseline without a history of CVD, with at least one follow-up visit. The primary outcome was a composite of major cardiovascular events (CVD deaths, myocardial infarction, stroke, and heart failure). Additive interaction was measured by relative excess risk due to interaction (RERI), the proportion of disease attributable to interaction (AP), and the synergy index (SI).[9,10] To comply with the additive interaction method, we classified concentrations of PM2.5 into low and high levels by the median of 3-year average PM2.5 concentration (45.7 μg/m3, IQR: 33.7-74). Cox proportional hazard models were used to assess the associations of metabolic, behavioural, and psychosocial risk factors on incident CVD by PM2.5 concentrations. Results The PURE-China study recruited 47 931 participants, of which 39 329 are eligible for this analysis. During the median follow-up period of 11.9 years (IQR: 9.6-12.6), 1462 and 2155 major CVD events had occurred in the low and high PM2.5 group respectively. There was a synergistic interaction between PM2.5 and the systolic blood pressure (SBP) for CVD: RERI: 0.363, 95% CI: 0.196-0.531; AP: 0.098, 95% CI: 0.081-0.115; SI: 1.155, 95% CI: 1.132-1.177; high LDL cholesterol: RERI: 0.219, 0.003-0.436; AP: 0.135, 0.008-0.263; SI: 1.549, 0.947-2.533. A significant synergistic interaction was also identified between PM2.5 and household air pollution (RERI: 0.457, 0.292-0.622; AP: 0.277, 0.184-0.37; SI: 3.362, 1.409-8.019). Interactions between PM2.5 and low grip strength on CVD were significantly synergistic, but antagonistic between PM2.5 and low physical activity (RERI: -0.248, -0.485--0.012; AP: -0.174, -0.349-0.001; SI: 0.633, 0.412-0.971). Conclusion Addictive interaction was between PM2.5 and SBP, high LDL cholesterol, household air pollution, low grip strength and low physical activity on incident CVD, indicating the benefit of coordinated control strategies for metabolic, physical, and environmental risk factors for the prevention of CVD.
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 | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| 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 ».