The effects of magnesium supplementation on obesity measures in adults: a systematic review and dose-response meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Previous studies reported inconsistent findings regarding the effects of magnesium supplementation on obesity measures. This study was done to quantify the effect of magnesium supplementation on body weight, Body Mass Index (BMI), Waist Circumference (WC), Body Fat (BF) percentage and Waist to Hip Ratio (WHR). Four online databases (Scopus, PubMed, Google Scholar and Cochrane library) were searched until March 2020 using relevant keywords. Random-effects model was used to pool effect sizes; Cochran’s Q-test and I2 index assessed heterogeneity. Sensitivity analysis and Egger test were used to check the robustness of findings and the possibility of publication bias, respectively. Thirty-two RCTs including different dosage of magnesium (48–450 mg/d), and duration (6–24 weeks) were entered to this study. Magnesium supplementation resulted in a great reduction in BMI [Weighted Mean Difference (WMD): −0.21 kg/m2, 95% CI: −0.41, −0.001, P = 0.048, I2 = 89.5%, n = 22], which was mainly driven by the effect among those with magnesium deficiency, insulin resistance related disorders, and obesity at baseline. No significant change was observed in bodyweight, WC, BF percentage and WHR as compared to controls. However, the change in body weight, and WC was significant in subgroups of participants with insulin resistance related disorders, hypertension, obesity, magnesium deficiency at baseline, and females. We found a significant reduction in BMI following magnesium supplementation. The change in body weight and WC were evident in certain subgroups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".