The Effects of Curcumin on Glycemic Control and Lipid Profiles Among Patients with Metabolic Syndrome and Related Disorders: A Systematic Review and Metaanalysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: This systematic review and meta-analysis of randomized controlled trials (RCTs), were performed to determine the effects of curcumin intake on glycemic control and lipid profiles among patients with metabolic syndrome (MetS) and related disorders. METHODS: We searched the following databases up until January 2018: MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials. The relevant data were extracted and evaluated for quality of the studies in accordance with the Cochrane risk of bias tool. Data were pooled using the inverse variance method and expressed as standardized mean difference (MDs) with 95% confidence intervals (95% CI). RESULTS: Twenty-six trials with 1890 participants were included in the current meta-analysis. The findings demonstrated the significant association between curcumin intake and reduced fasting glucose levels (SMD -0.78; 95% CI, -1.20, -0.37; P<0.001), homeostasis model of assessment-estimated insulin resistance (SMD -0.91; 95% CI, -1.52, -0.31; P=0.003) and HbA1c (SMD -0.92; 95% CI, -1.37, -0.47; P<0.001). In addition, curcumin supplementation was significantly associated with triglyceride (SMD -1.21; 95 % CI, -1.78, -0.65; P<0.001) and total cholesterol reduction (SMD -0.73; 95 % CI, -1.32, -0.13; P= 0.01). However, curcumin intake significantly increased insulin levels (SMD 0.92; 95% CI, 0.06, 1.78; P=0.036). We found no significant effect of curcumin supplementation on LDL- (SMD -0.52; 95% CI, -1.14, 0.11; P=0.10) and HDL-cholesterol levels (SMD 0.28; 95% CI, -0.22, 0.77; P=0.27). CONCLUSION: Overall, curcumin consumption was associated with a significant reduction in fasting glucose, HOMA-IR, HbA1c, triglycerides and total cholesterol levels among patients with MetS and related disorders, but did not affect LDL- and HDL-cholesterol levels.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.037 |
| Bibliometrics | 0.010 | 0.008 |
| 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".