Relationship between high dietary fat intake and Parkinson’s disease risk: a meta-analysis
Notice bibliographique
Résumé
OBJECTIVE: To assess whether dietary fat intake influences Parkinson's disease risk. DATA SOURCES: We systematically surveyed the Embase and PubMed databases, reviewing manuscripts published prior to October 2018. The following terms were used: ("Paralysis agitans" OR "Parkinson disease" OR "Parkinson" OR "Parkinson's" OR "Parkinson's disease") AND ("fat" OR "dietary fat" OR "dietary fat intake"). DATA SELECTION: Included studies were those with both dietary fat intake and Parkinson's disease risk as exposure factors. The Newcastle-Ottawa Scale was adapted to investigate the quality of included studies. Stata V12.0 software was used for statistical analysis. OUTCOME MEASURES: The primary outcomes included the relationship between high total energy intake, high total fat intake, and Parkinson's disease risk. The secondary outcomes included the relationship between different kinds of fatty acids and Parkinson's disease risk. RESULTS: Nine articles met the inclusion criteria and were incorporated into this meta-analysis. Four studies scored 7 and the other five studies scored 9 on the Newcastle-Ottawa Scale, meaning that all studies were of high quality. Meta-analysis results showed that high total energy intake was associated with an increased risk of Parkinson's disease (P = 0.000, odds ratio (OR) = 1.49, 95% confidence interval (CI): 1.26-1.75); in contrast, high total fat intake was not associated with Parkinson's disease risk (P = 0.123, OR = 1.07, 95% CI: 0.91-1.25). Subgroup analysis revealed that polyunsaturated fatty acid intake (P = 0.010, OR = 1.03, 95% CI: 0.88-1.20) reduced the risk of Parkinson's disease, while arachidonic acid (P = 0.026, OR = 1.15, 95% CI: 0.97-1.37) and cholesterol (P = 0.002, OR = 1.09, 95% CI: 0.92-1.29) both increased the risk of Parkinson's disease. Subgroup analysis also demonstrated that, although the results were not significant, consumption of n-3 polyunsaturated fatty acids (P = 0.071, OR = 0.88, 95% CI: 0.73-1.05), α-linolenic acid (P = 0.06, OR = 0.86, 95% CI: 0.72-1.02), and the n-3 to n-6 ratio (P = 0.458, OR = 0.89, 95% CI: 0.75-1.06) were all linked with a trend toward reduced Parkinson's disease risk. Monounsaturated fatty acid (P = 0.450, OR = 1.06, 95% CI: 0.91-1.23), n-6 polyunsaturated fatty acids (P = 0.100, OR = 1.15, 95% CI: 0.96-1.36) and linoleic acid (P = 0.053, OR = 1.11, 95% CI: 0.94-1.32) intakes were associated with a non-significant trend toward higher PD risk. Saturated fatty acid (P = 0.619, OR = 1.01, 95% CI: 0.87-1.18) intake was not associated with Parkinson's disease. CONCLUSION: Dietary fat intake affects Parkinson's disease risk, although this depends on the fatty acid subtype. Higher intake of polyunsaturated fatty acids may reduce the risk of Parkinson's disease, while higher cholesterol and arachidonic acid intakes may elevate Parkinson's disease risk. However, further studies and evidence are needed to validate any link between dietary fat intake and Parkinson's disease.
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 | 0,018 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,018 | 0,056 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».