Does exercise beneficially affect sex hormones when added to hypo-caloric diets in adults with overweight or obesity? A systematic review and meta-analysis of controlled clinical trials
Bibliographic record
Abstract
OBJECTIVE: There is no consensus of opinion if exercise beneficially affects sex hormones if added to weight-loss diets. The purpose of this study was to perform a systematic review and meta-analysis of controlled clinical trials to evaluate the effect of adding exercise to a hypo-caloric diet during a weight-loss program, on serum testosterone, estradiol, and sex hormone-binding globulin (SHBG) in adults with overweight/obesity. DESIGN: Systematic review and meta-analysis of the literature. METHODS: Online databases including PubMed/MEDLINE, EMBASE, Scopus, ISI Web of Science, and Google Scholar were searched up to April 2021. A random-effects model was applied to compare mean changes in sex hormones and SHBG between participants undergoing a hypo-caloric diet with or without exercise. RESULTS: In total, 9 eligible clinical trials with 462 participants were included. Out of these, seven, three, and four studies illustrated changes in testosterone, estradiol, and SHBG, respectively. The meta-analysis revealed that exercise had no significant effect on circulating testosterone (WMD = -0.03 nmol/L, 95% CI: -0.11, 0.06, P = 0.51), estradiol (WMD = -0.46 pg/mL, 95% CI: -1.57, 0.65, P = 0.42), and SHBG (WMD = 0.54 nmol/L, 95% CI: -2.63, 3.71, P = 0.74) when added to low-calorie diets. CONCLUSION: The addition of exercise to a hypo-caloric diet provided no additional improvement in sex hormone profiles. Further, well-designed randomized controlled trials with longer follow-up periods in both sexes are recommended to confirm and expand the current results.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".