The Effects of Melatonin Supplementation on Glycemic Control: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Abstract This systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to clarify the effect of melatonin supplementation on glycemic control. Databases including PubMed, MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials were searched until July 30th, 2018. Two reviewers independently assessed study eligibility, extracted data, and evaluated the risk of bias for included trials. Heterogeneity among included studies was assessed using Cochran’s Q test and I-square (I2) statistic. Data were pooled using random-effect models and standardized mean difference (SMD) was considered as the overall effect size. Twelve trials out of 292 selected reports were identified eligible to be included in current meta-analysis. The pooled findings indicated that melatonin supplementation significantly reduced fasting glucose (SMD=–6.34; 95% CI, –12.28, –0.40; p=0.04; I2: 65.0) and increased the quantitative insulin sensitivity check index (QUICKI) (SMD=0.01; 95% CI, 0.00, 0.02; p=0.01; I2: 0.0). However, melatonin administration did not significantly influence insulin levels (SMD=–1.03; 95% CI, –3.82, 1.77; p=0.47; I2: 0.53), homeostasis model assessment of insulin resistance (HOMA-IR) (SMD=–0.34; 95% CI, –1.25, 0.58; p=0.37; I2: 0.37) or HbA1c levels (SMD=–0.22; 95% CI, –0.47, 0.03; p=0.08; I2: 0.0). In summary, the current meta-analysis showed a promising effect of melatonin supplementation on glycemic control through reducing fasting glucose and increasing QUICKI, yet additional prospective studies are recommended, using higher supplementation doses and longer intervention period, to confirm the impact of melatonin on insulin levels, HOMA-IR and HbA1c.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".