Impacts of Curcumin Supplementation on Cardiometabolic Risk Factors in Patients With Polycystic Ovary Syndrome: A Systematic Review and Dose−Response Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background and Aim Patients with polycystic ovary syndrome (PCOS) commonly have cardiometabolic risk factors. Oxidative stress (OS) significantly contributes to the development of cardiometabolic diseases. Curcumin (CUR) exhibits antioxidant properties that aid in OS regulation. This systematic review and dose–response meta‐analysis of randomized clinical trials (RCTs) evaluated the effects of CUR supplementation on cardiometabolic risk factors in women with PCOS. Methods A systematic search across various databases was implemented to identify eligible RCTs published until January 2024. A meta‐analysis was conducted employing a random‐effects model. Results Eight RCTs were included in the meta‐analysis. It was indicated that CUR supplementation substantially reduced fasting blood sugar (FBS) (standardized mean difference [SMD]: −0.40 mg/dL, 95% confidence interval [CI]: −0.59, −0.21; p < 0.001), insulin (SMD: −0.32 µU/mL, 95% CI: −0.49, −0.14; p < 0.001), homeostasis model assessment of insulin resistance (HOMA‐IR) (SMD: −0.36, 95% CI: −0.54, −0.19; p < 0.001), and total cholesterol (TC) (SMD: −0.34 mg/dL, 95% CI: −0.61, −0.08; p = 0.01). In addition, it substantially increased the quantitative insulin sensitivity check index (QUICKI) (SMD: 0.37, 95% CI: 0.13, 0.61; p < 0.001) in the CUR‐treated group compared with the control group. However, CUR did not have significant impacts on body mass index (BMI), body weight, serum levels of follicle‐stimulating hormone (FSH), triglycerides (TG), dehydroepiandrosterone (DHEA), high‐density lipoprotein (HDL), testosterone, low‐density lipoprotein (LDL), and luteinizing hormone (LH). Conclusion This study revealed that CUR may have the potential to enhance cardiometabolic health by reducing hyperglycemia, insulin resistance, and serum TC levels in women with PCOS.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".