Single Herbal Medicine for Insulin Resistance: Protocol for a Systematic Review and Meta-Analysis of Randomized Clinical Trials
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,085 | 0,112 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,006 |
| Méta-épidémiologie (sens large) | 0,025 | 0,031 |
| Bibliométrie | 0,010 | 0,012 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,006 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,070 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».