The effects of an anti-inflammatory diet alone or in combination with acupuncture on mental health, anthropometric indices, and metabolic status in diabetic patients with depression: a randomized, controlled clinical trial
Bibliographic record
Abstract
BACKGROUND: The present clinical trial examined the efficacy of an anti-inflammatory diet combined with acupuncture compared to an anti-inflammatory diet alone and standard treatment in depressed patients with type 2 diabetes (T2DM). METHODS: In this 8-week randomized controlled clinical trial, 90 patients with T2DM who were experiencing mild to moderate depression were included. The participants were randomly assigned to one of three groups: (i) acupuncture combined with an anti-inflammatory diet, (ii) an anti-inflammatory diet alone, and (iii) standard treatment. The combination therapy group received acupuncture therapy twice a week. Mental health outcomes, biochemical parameters, dietary intake, and anthropometric indices were assessed at baseline and the end of the trial. RESULTS: Of the 90 diabetic patients, 83 completed the intervention. Acupuncture therapy combined with diet resulted in an ~20% reduction in depression and anxiety, 4.28 and 0.82% reduction in waist circumference (WC) and HbA1C levels, respectively at the end of the trial. This combination therapy also significantly decreased WC (p = 0.04) and HbA1c levels (p = 0.008), while increasing high-density lipoprotein cholesterol concentrations (p = 0.02) compared to diet alone. CONCLUSION: Our findings indicate that acupuncture, in conjunction with an anti-inflammatory diet, may be more effective in enhancing mental health, reducing HbA1C levels, and decreasing abdominal obesity compared to an anti-inflammatory diet alone in patients with T2DM experiencing mild-to-moderate depression after 8 weeks. However, further clinical trials with larger sample sizes and extended durations are recommended to confirm the efficacy of this adjunctive therapy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".