Low fat-diet and circulating adipokines concentrations: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Low-fat diets have gained considerable attention in the management of obesity. The present meta-analysis evaluated randomized controlled trials (RCTs) to determine whether adults adhering to low-fat diets (≤ 30% of total energy intake) experience more significant changes in serum adipokine levels compared to those following high-fat diets. MAIN TEXT: A comprehensive search was conducted in PubMed, Scopus, Web of Science, and CENTRAL for eligible RCTs up to February 4, 2025. Weighted mean differences (WMD) were calculated and pooled using a random-effects model. Forty-eight trials were included in this study. The meta-analysis found no significant effects of low-fat diets on serum leptin (WMD = 0.06 ng/ml; 95% CI: -0.33, 0.45; P = 0.76; I² = 64.57%), resistin (WMD = -0.67 ng/ml; 95% CI: -1.52, 0.17; P = 0.12; I² = 86.53%), or adiponectin (WMD = 0.07 ng/ml; 95% CI: -0.29, 0.43; P = 0.76; I² = 90.29%). Subgroup analysis showed a significant decrease in adiponectin levels among females (n = 4; WMD = -0.47 ng/ml; P = 0.02; I² = 0%). However, low-fat diets with higher protein content increased adiponectin levels (n = 3; WMD = 1.78 ng/ml; P < 0.001; I² = 0%). Sensitivity analysis revealed that excluding the study by Heggen et al. (2012) resulted in a significant reduction in serum resistin levels (WMD = -0.93 ng/ml; P = 0.04; I² = 86.9%). CONCLUSIONS: Low-fat diets may have beneficial effects on resistin levels. Additionally, low-fat diets with higher protein content may increase adiponectin levels. However, due to the uncertainty of the available evidence, firm conclusions cannot be drawn. Further high-quality research is needed to confirm these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.155 | 0.015 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".