Effects of green coffee supplementation on paraoxonase-1 activity and malondialdehyde levels in Iranian women with polycystic ovary syndrome: a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Polycystic ovary syndrome (PCOS) is a common, heterogeneous clinical syndrome affecting women. Investigating oxidative stress in women is crucial, as it is linked to insulin resistance and endothelial dysfunction. Chlorogenic acid, a bioactive component found in green coffee, has numerous documented health benefits. This study aimed to assess the beneficial effects of green coffee consumption on paraoxonase-1 (PON-1) activity and malondialdehyde (MDA) levels in women with PCOS. METHODS: This study was a double-blind randomized clinical trial that included 44 patients with PCOS. Participants were randomly assigned to either the intervention or control group. For 6 weeks, the intervention group (n=22) received 400 mg of green coffee supplements, while the control group (n=22) received 400 mg of a starch-based placebo. Anthropometric indices, dietary assessments, and physical activity levels were evaluated before and after the 6-week intervention period. Additionally, blood samples were collected for laboratory analysis. RESULTS: Supplementation with green coffee increased PON-1 levels by 3.5 units, a significant finding (p=0.038). Additionally, the intake of green coffee supplements significantly reduced blood cholesterol levels by 18.8 units (p=0.013) and triglyceride levels by 6.1 units (p=0.053). However, no significant differences were observed in the levels of MDA, high-density lipoprotein, low-density lipoprotein, fasting blood sugar, insulin, or homeostatic model assessment of insulin resistance as a result of the intervention. CONCLUSION: Supplementation with green coffee alters PON-1 activity and cholesterol levels in women with PCOS. However, it has no significant impact on MDA levels or glycemic status.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".