Effectiveness of low-fat diet on the levels of insulin-like growth factor-1 and insulin-like growth factor binding proteins: a systematic review and meta-analysis of randomized controlled clinical trials
Bibliographic record
Abstract
Previous researches on the effect of low-fat diet (LF) on insulin-like growth factor-1 (IGF-1), and its binding proteins (IGFBPs) did not reach a consensus result, and there is no study summarizing these findings. Thus, this systematic review and meta-analysis of randomized control trials (RCTs) was performed to pool available evidence and answer the question whether dietary fat can affect IGF-1 and IGFBPs or not. PubMed, Scopus, ISI Web of Science, Google, Google scholar, ProQuest, and the Cochrane Library were searched without language restrictions until July 2, 2024 to retrieve related studies. Weighted mean difference and the corresponding variance were considered as the effect size. Standard tools were applied to assess the quality of the studies and evidence. Pooling data of the eligible studies showed no significant effect of LF diet on IGF-1 (six studies; participants = 1029.; pooled mean = 1.63 ng/ml, 95% CI= [-1.34, 4.59], P = 0.28, I 2 = 0.00%), and IGFBP-3 (five studies; participants = 969; pooled mean = 65.24 ng/ml, 95% CI= [-169.53, 300.00], P = 0.59, I 2 = 0.0%). The results of subgroup analysis for IGF-1 and IGFBP-3 also demonstrated no significant findings. For IGFBP-1, available evidence is insufficient since only two studies have been performed yet and their results are contradictory. This study indicated no significant effect of LF diet on IGF-1, and IGFBP-3 concentrations. Low certainty of evidence indicates that available evidence cannot support to draw a firm conclusion and future researches may change the estimates.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.043 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".