A Pilot Trial: Fish Oil and Metformin Effects on ApoB-Remnants and Triglycerides in Women With Polycystic Ovary Syndrome
Bibliographic record
Abstract
Abstract Context Women with polycystic ovary syndrome (PCOS) have increased incidence of atherogenic dyslipidemia and cardiovascular disease (CVD). Interventions targeting atherogenic dyslipidemia to reduce CVD risk are limited in women with PCOS. Objective This pilot study was conducted to determine the effect of 12 weeks of high dose fish oil (FO), metformin, and FO as an adjunct to metformin (FO-metformin) therapy on fasting and nonfasting plasma lipids and ApoB-remnants in young women with the metabolic syndrome (MetS) and PCOS. Methods In this open-label parallel pilot trial, women with MetS and PCOS (18-30 years of age) were randomized into 1 of 3 interventions: (1) FO; (2) metformin; and (3) FO-metformin. Plasma lipids and ApoB (48 and 100)-lipoproteins and triglycerides (TG) were measured in the fasted and postprandial state following a high-fat meal at baseline and postintervention. Results FO-metformin significantly lowered fasting plasma TG by >40% compared with FO and metformin treatments. Fasting plasma apoB48 was lowered 40% in FO-metformin and 15% in the FO groups from baseline to postintervention. ApoB48 area under the curve (ApoB48AUC), ApoB48 incremental AUC (ApoB48iAUC), ApoB100AUC, and ApoB100iAUC decreased in all groups from baseline to postintervention; however, these findings did not reach statistical significance. Conclusion The findings of this pilot trial show that high dose FO and FO-metformin combination therapy tend to lower fasting and postprandial plasma TG and ApoB-lipoprotein remnants compared with metformin; however, the study is limited by small sample size. These results may be clinically significant in individuals with PCOS for management of atherogenic dyslipidemia.
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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) | 0.002 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".