The Effect of Low‐Fat Diets Versus High‐Fat Diet on Sex Hormones: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
In the light of the precursor of sex hormones, dietary fat may affect their levels. In the present meta-analysis, we sought to compare the effects of low-fat versus high-fat diets on sex hormones and their metabolites in adults. Databases were searched up to June 2024 to identify randomized controlled trials comparing low-fat diets (fat intake ≤30% of total energy) with high-fat diets in relation to circulating sex hormone levels. A random-effects meta-analysis was performed, incorporating data from 11 trials with 888 participants. Studies were selected based on their comparison of low-fat versus high-fat diets and the measurement of sex hormone concentrations. The meta-analysis found no significant differences in serum levels of various sex hormones between low-fat and high-fat diet groups. The hormones assessed included estradiol (95% CI, -7.71, 1.10), estrone (95% CI, -12.39, 24.76), sex hormone-binding globulin (95% CI, -3.22, 0.70), dehydroepiandrosterone (95% CI, -0.38, 0.46), testosterone (95% CI, -14.48, 13.50), progesterone (95% CI, -2.82, 1.75), and androstenedione (95% CI, -0.54, 0.03). We found no significant effect of low-fat diets on sex hormones compared with high-fat diets. Our findings, however, were tempered by the small number of studies and low certainty of evidence.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".