Single Herbal Medicine for Insulin Resistance: Protocol for a Systematic Review and Meta-Analysis of Randomized Clinical Trials
Bibliographic record
Abstract
Background Insulin resistance (IR) is a central factor in the pathogenesis and progression of metabolic disorders, such as type 2 diabetes mellitus and obesity. Chinese herbal medicine (CHM) has been investigated as a potential therapy to enhance insulin sensitivity. Compared to multiherb formula therapy, single-herb therapy provides a clearer understanding of its pharmacological effects and mechanisms of action. A systematic review of the available evidence is needed to elucidate the potential effectiveness and harm of single CHMs for IR. Objective This study aims to conduct a systematic review and meta-analysis to evaluate the potential effectiveness and harm of single herbs for the treatment of IR, thereby providing a clearer understanding of the efficacy and safety profiles of single CHMs. Methods A systematic review and meta-analysis, in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020, will be conducted to evaluate the efficacy and safety of single herbs for IR. Various databases, such as Cochrane Central Register of Controlled Trials, MEDLINE, Embase, Allied and Complementary Medicine Database, China National Knowledge Infrastructure, Chinese BioMedical Literature Database, and the Wanfang Database, will be searched. Randomized controlled trials comparing single herbs or extracts originated from single herbs with a placebo or no treatment for adults diagnosed with IR-related diseases (eg, type 2 diabetes mellitus and obesity) will be included. A total of 2 researchers will independently perform study selection, data extraction, and quality assessment. The risk of bias (RoB) tool will be used to assess the quality of included studies. The overall certainty of evidence will be assessed using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). The primary outcome will include measurements that assess IR, such as the hyperinsulinemic-euglycemic clamp, homeostatic model assessment, insulin sensitivity index, and oral glucose tolerance test. Secondary outcomes will include adverse events. Meta-analysis will be performed with RevMan (version 5.4; The Cochrane Collaboration). The heterogeneity of the synthetic data will be assessed using the chi-square test and the I2 statistic. Results Based on the data on IR-associated outcomes (eg, hyperinsulinemic-euglycemic clamp, homeostatic model assessment, insulin sensitivity index, and oral glucose tolerance test) and adverse event rates, this study will provide an evidence-based review and high-quality synthesis regarding the efficacy and safety of single CHMs for IR. Conclusions This systematic review and meta-analysis will rigorously synthesize existing evidence to clarify the efficacy and safety of single CHMs in ameliorating IR, offering critical insights for their integration into evidence-based therapies for metabolic disorders. By focusing on single-herb therapy, the findings may promote the application of CHM for IR, bridge the gap between traditional applications and evidence-based practice, and ultimately optimize the role of CHM in integrative metabolic health management. Trial Registration PROSPERO CRD42024589362; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024589362 International Registered Report Identifier (IRRID) PRR1-10.2196/68915
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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 | 0.085 | 0.112 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.025 | 0.031 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.070 | 0.007 |
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".