Effects of oral contraceptives on metabolic parameters in adult premenopausal women: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To estimate the effect of oral contraceptives (OC) containing different progestins on parameters of lipid and carbohydrate metabolism through a systematic review and meta-analysis. PATIENTS AND METHODS: Premenopausal women aged 18 or older, who received oral contraceptives containing chlormadinone, cyproterone, drospirenone, levonorgestrel, desogestrel, dienogest, gestodene or norgestimate, for at least 3 months. Outcome variables were changes in plasma lipids, BMI, insulin resistance and plasma glucose. We searched MEDLINE and EMBASE for randomized trials and estimated the pooled within-group change in each outcome variable using a random-effects model. We performed subgroup analyses by study duration (<12 months vs ≥12 months) and polycystic ovary syndrome (PCOS) status. RESULTS: Eighty-two clinical trials fulfilled the inclusion criteria. All progestins (except dienogest) increased plasma TG, ranging from 12.1 mg/dL for levonorgestrel (P < 0.001) to 35.1 mg/dL for chlormadinone (P < 0.001). Most progestins also increased HDLc, with the largest effect observed for chlormadinone (+9.6 mg/dL, P < 0.001) and drospirenone (+7.4 mg/dL, P < 0.001). Meanwhile, levonorgestrel decreased HDLc by 4.4 mg/dL (P < 0.001). Levonorgestrel (+6.8 mg/dL, P < 0.001) and norgestimate (+11.5 mg/dL, P = 0.003) increased LDLc, while dienogest decreased it (-7.7 mg/dL, P = 0.04). Cyproterone slightly reduced plasma glucose. None of the progestins affected BMI or HOMA-IR. Similar results were observed in subgroups defined by PCOS or study duration. CONCLUSION: Most progestins increase both TG and HDLc, their effect on LDLc varies widely. OC have minor or no effects on BMI, HOMA-IR and glycemia. The antiandrogen progestins dienogest and cyproterone displayed the most favorable metabolic profile, while levonorgestrel displayed the least favorable.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.041 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".