Effect of Low-Fat Diet on Depression Score in Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Clinical Trials
Bibliographic record
Abstract
CONTEXT: Current evidence on the effect of a low-fat (LF) diet on depression scores has been inconsistent. OBJECTIVE: To explore the effect of an LF diet on depression scores of adults by systematic review and meta-analysis of randomized controlled trials (RCTs). DATA SOURCES: The PubMed, ISI Web of Science, Scopus, and CENTRAL databases were searched from inception to June 7, 2023, to identify trials investigating the effect of an LF diet (fat intake ≤30% of energy intake) on the depression score. DATA EXTRACTION: Random-effects meta-analyses were used to estimate pooled summary effects of an LF diet on the depression score (as Hedges g). DATA ANALYSIS: Finding from 10 trials with 50 846 participants indicated no significant change in depression score following LF diets in comparison with usual diet (Hedges g = -0.11; 95% CI, -0.25 to 0.03; P = 0.12; I2 = 70.7% [for I2, 95% CI, 44%, 85%]). However, a significant improvement was observed in both usual diet and LF diets when the content of protein was 15-20% of calorie intake (LF, normal protein diet: n = 5, Hedges g = -0.21, 95% CI, -0.24 to -0.01, P = 0.04, I2 = 0%; usual, normal protein diet: n = 3, Hedges g = -0.28, 95% CI, -0.51 to -0.05, P = 0.01, I2 = 0%). Sensitivity analysis also found the depression score improved following LF diet intervention in participants without baseline depression. CONCLUSION: This study revealed that LF diet may have small beneficial effect on depression score in the studies enrolled mentally healthy participants. Moreover, achieving to adequate dietary protein is likely to be a better intervention than manipulating dietary fat to improve depression scores. However, it is not clear whether this effect will last in the long term. Conducting more studies may change the results due to the low-certainty of evidence. SYSTEMATIC REVIEW REGISTRATION: CRD42023420978 (https://www.crd.york.ac.uk/PROSPERO).
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".