Relationship between dietary magnesium intake and risk of type 2 diabetes: A meta-analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.065 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".