Relationship between dietary magnesium intake and risk of type 2 diabetes: A meta-analysis
Notice bibliographique
Résumé
BackgroundDiabetes is a major contributor to global burden of disease. The role of magnesium in the prevention of diabetes has aroused concern. However, the research results on the impact of dietary magnesium on the risk of diabetes are hitherto inconsistent. ObjectiveTo evaluate the association between dietary magnesium intake and the risk of diabetes through a systematic review. MethodsPubMed, Web of Science, China National Knowledge Infrastructure, Wanfang databases were searched for prospective studies that contained risk estimates for magnesium intake-associated diabetes and were published from January 1, 2000 to December 31, 2021. Two researchers independently screened the literature according to a set of pre-prepared inclusion and exclusion criteria, extracted the data according to an unified data extraction table, and evaluated the quality of included articles with Newcastle-Ottawa Scale (NOS). R 4.0.3 software and Stata SE16.0 software were used for meta-analysis and subgroup meta-analysis, and Higgins I2 statistics were used to test the heterogeneity of the included studies. The sources of heterogeneity were analyzed by univariate meta regression. ResultsA total of 14 articles involving 17 prospective cohort studies (1065267 participants and 40506 patients with diabetes) were included in the study. The NOS scores ranged from 8 to 9, with an average of 8.6, indicating that the included studies were classified as being high quality. The highest quintile of magnesium intake group reduced the risk of diabetes by 22% (RR=0.78, 95%CI: 0.73-0.82) compared with the lowest quintile group. This association was not substantially modified by geographic region, sex, or follow-up length. The highest quintile of dietary magnesium intake in the Americas and Asia were associated with 22% and 26% reductions in the risk of type 2 diabetes respectively compared with the lowest quintile group (the Americas, RR=0.78, 95%CI: 0.73-0.84; Asia, RR=0.74, 95%CI: 0.63-0.88); The highest quintile of dietary magnesium intake in female, male and without gender stratified were associated with 22%, 19% and 46% reductions in the risk of type 2 diabetes respectively compared with the lowest quintile group (Female RR=0.78, 95%CI: 0.73-0.84; Male RR=0.81, 95%CI: 0.74-0.89; Both RR=0.54, 95%CI: 0.42-0.68); Compared with the lowest quintile groups, the groups with the highest quintile of dietary magnesium intake with a follow-up time of less than 10 years and more than 10 years reduced the risk of type 2 diabetes by 26% and 20% respectively (≤10 years, RR=0.74, 95%CI: 0.65-0.83; ＞10 years, RR=0.80, 95%CI: 0.75-0.85). After adjusting for hypertension, the highest quintile of dietary magnesium intake group reduced the risk of type 2 diabetes by 20% compared with the lowest quintile group (RR=0.80, 95%CI: 0.74-0.85). The year of publication (P<0.05) or the sex of the subjects (P<0.05) may be the source of heterogeneity by meta regression test. The results of Egger’s test for funnel plot asymmetry suggested publication bias. ConclusionThe combined data supports a role for high magnesium intake in reducing the risk of type 2 diabetes. Because it is difficult to separate the effect of magnesium intake on diabetes risk from other factors, large-scale and clinical randomized controlled trials are needed to directly assess the impact of magnesium on the incidence rate of diabetes.
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 | 0,013 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,018 | 0,065 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,003 | 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 ».