Effect of Chromium on Glucose and Lipid Profiles in Patients with Type 2 Diabetes; A Meta-analysis Review of Randomized Trials
Bibliographic record
Abstract
PURPOSE: Chromium (Cr) as an essential trace element in metabolism of carbohydrate, lipid and protein is currently prescribed to control diabetes mellitus (DM). The objective of this meta-analysis was to compare the effect of Cr versus placebo (Pl) on glucose and lipid profiles in patients with type 2 DM. METHODS: Literature searches in PubMed, Scopus, Scirus, Google Scholar and IranMedex was made by use of related terms during the period of 2000-2012. Eligible studies were randomized clinical trials (RCTs) with intake of Cr higher than 250 µg at least for three months in type 2 DM. Glycated hemoglobin (HbA1c), fasting blood sugar (FBS), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), triglyceride (TG), and body mass index (BMI) were the main outcomes. RESULTS: Seven out of 13 relevant studies met the criteria and were included in the meta-analysis. HbA1c change in diabetic patients in Cr supplement therapy comparing to Pl was -0.33 with 95%CI= -0.72 to 0.06 (P= 0.1). Change of FBG in Cr therapy vs. Pl was -0.95 with 95%CI= -1.42 to -0.49 (P< 0.0001). TC change in Cr therapy vs. Pl was 0.07 with 95%CI= -0.16 to 0.31 (P= 0.54). TG change in diabetic patients in Cr supplement therapy comparing to Pl was -0.15 with 95%CI= -0.36 to 0.07 (P= 0.18). CONCLUSIONS: Cr lowers FBS but does not affect HbA1c, lipids and BMI.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".