Acupuncture for cognitive impairment in type 2 diabetes mellitus: a systematic review and Bayesian network meta-analysis protocol
Bibliographic record
Abstract
Background: The global prevalence of diabetes mellitus (DM) has exceeded 589 million, with the proportion of type 2 diabetes mellitus (T2DM) patients with comorbid cognitive impairment (CI) as high as 49.1%, which has a serious impact on disease management and quality of life. While acupuncture has been shown to improve cerebral glucose metabolism and synaptic plasticity, existing clinical trials are plagued by critical limitations: small sample sizes (often <100 participants) fail to detect subtle treatment effects, methodological heterogeneity in acupuncture protocols (e.g., varying acupoint selections, stimulation intensities, and treatment durations) hinders evidence synthesis, and insufficient blinding (e.g., sham acupuncture controls with low plausibility) introduces performance bias. These limitations have constrained the generalizability of findings and created a significant evidence gap in defining acupuncture's role in diabetic cognitive impairment (DCI). The aim of this study is to assess the efficacy and safety of acupuncture for DCI through a systematic review with Bayesian network meta-analysis (NMA), addressing these methodological flaws via rigorous evidence synthesis. Methods and analysis: Following PRISMA-NMA and STRICTA-2010 guidelines, we will systematically search 10 databases (PubMed, Cochrane Library, CNKI, etc.) from inception to 30 June 2025 and gray literature (January 1, 1995, to June 30, 2025) for randomized controlled trials (RCTs) comparing acupuncture with pharmacotherapy, sham acupuncture, or standard care. Inclusion criteria: adults (≥18 years) with T2DM and CI diagnosed via validated scales such as Montreal Cognitive Assessment (MoCA), and Mini-Mental State Examination (MMSE). Primary outcomes include cognitive function scores; secondary outcomes assess adverse events (CTCAE v5.0). The risk of bias will be evaluated using Cochrane RoB 2.0 and GRADE. Bayesian NMA will synthesize direct and indirect evidence to simultaneously assess the relative efficacy and safety of acupuncture. Expected findings and conclusion: The study will clarify the relative efficacy and safety of acupuncture in improving DCI and provide evidence-based treatment options for clinical use. The results of Bayesian analyses may support acupuncture as a low-cost, low-risk adjunctive therapy, especially for patients intolerant of conventional medications. The results of the study will fill the current evidence gap and promote the standardized use of acupuncture in the management of DCI. Systematic review registration: CRD42021275161.
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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 | 0.055 | 0.083 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.022 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.004 |
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".