The Effects of Vitamin D Supplementation on Glucose Metabolism and Lipid Profiles in Patients with Gestational Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
This systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to summarize the effect of vitamin D supplementation on glucose homeostasis parameters and lipid profiles in gestational diabetes (GDM) patients. We conducted an electronic systematic search of MEDLINE, and 4 other research databases from inception to August 2016, in addition to performing hand searches and consulting with experts in the field. The index of heterogeneity between studies was determined using Cochran (Q) and I-squared tests. Given the existing heterogeneity between studies, a fix or random effect model was performed to estimate the standardized mean difference (SMD) for each variable by using inverse variance method and Cohen statistics. Six randomized clinical trials (187 subjects and 184 controls) were included. The results showed that vitamin D supplementation significantly reduced the homeostasis model assessment of insulin resistance (HOMA-IR) [SMD -0.66; 95% confidence interval (CI), -1.14 to -0.18], homeostatic model assessment-B cell function (HOMA-B) (SMD -0.52; 95% CI, -0.79 to -0.25), LDL-cholesterol levels (SMD -0.33; 95% CI, -0.58 to -0.07), and significantly increased quantitative insulin sensitivity check index (QUICKI) (SMD 0.73; 95% CI, 0.26 to 1.20). We found no beneficial effect of vitamin D supplementation on fasting plasma glucose (FPG), insulin, HbA1c, total-, HDL-cholesterol, and triglycerides concentrations. In conclusion, this meta-analysis demonstrated that vitamin D supplementation may lead to an improvement in HOMA-IR, QUICKI, and LDL-cholesterol levels, but did not affect FPG, insulin, HbA1c, triglycerides, total- and HDL-cholesterol levels; however, vitamin D supplementation increased HOMA-B.
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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.010 | 0.009 |
| 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) | 0.002 | 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".