The Effects of Folate Supplementation on Diabetes Biomarkers Among Patients with Metabolic Diseases: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Abstract Although several studies have evaluated the effect of folate supplementation on diabetes biomarkers among patients with metabolic diseases, findings are inconsistent. This review of randomized controlled trials (RCTs) was performed to summarize the evidence on the effects of folate supplementation on diabetes biomarkers among patients with metabolic diseases. Randomized-controlled trials (RCTs) published in PubMed, EMBASE, Web of Science and Cochrane Library databases up to 1 September 2017 were searched. Two review authors independently assessed study eligibility, extracted data, and evaluated risk of bias of included studies. Heterogeneity was measured with a Q-test and with I2 statistics. Data were pooled by using the fix or random-effect model based on the heterogeneity test results and expressed as standardized mean difference (SMD) with 95% confidence interval (CI). A total of sixteen randomized controlled trials involving 763 participants were included in the final analysis. The current meta-analysis showed folate supplementation among patients with metabolic diseases significantly decreased insulin (SMD –1.28; 95% CI, –1.99, –0.56) and homeostasis model assessment of insulin resistance (HOMA-IR) (SMD –1.28; 95% CI, –1.99, –0.56). However, folate supplementation did not affect fasting plasma glucose (FPG) (SMD –0.30; 95% CI, –0.63, 0.02) and hemoglobin A1C (HbA1c) (SMD –0.29; 95% CI, –0.61, 0.03). The results of this meta-analysis study demonstrated that folate supplementation may result in significant decreases in insulin levels and HOMA-IR score, but does not affect FPG and HbA1c levels among patients with metabolic diseases.
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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.033 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".