Anti-Diabetic and Antiresorptive Pharmacotherapies for Prevention and Treatment of Type 2 Diabetes-Induced Bone Disease: Protocol for a Two-Part Systematic Review and Network Meta-Analysis
Notice bibliographique
Résumé
ABSTRACT Introduction Patients with type 2 diabetes mellitus (T2DM) are at risk for a variety of severe debilitating effects. One of the most serious complications experienced by T2DM patients are skeletal diseases caused by changes in the bone microenvironment. As a result, T2DM patients are at risk for higher prevalence of fragility fractures. There are a variety of treatments available for counteracting this effect. Some anti-diabetic medications, such as metformin, have been shown to have a positive effect on bone health without the addition of additional drugs into patients’ treatment plans. Chinese randomized controlled trial (RCT) studies have also proposed antiresorptive pharmacotherapies as a viable alternative treatment strategy. Previous network meta-analyses (NMAs) and meta-analyses regarding this topic did not include all available RCT trials, or only performed pairwise comparisons. We present a protocol for a two-part NMA that incorporates all available RCT data to provide the most comprehensive ranking of anti-diabetics (Part I) and antiresorptive (Part II) pharmacotherapies in terms of their ability to decrease fracture incidences, increase bone mineral density (BMD), improve indications of bone turnover markers (BTMs), and decrease pain in adult T2DM patients. Methods and Analysis We will search MEDLINE, EMBASE, PubMed, Web of Science, CINAHL, CENTRAL and Chinese literature sources (CNKI, CQVIP, Wanfang Data, Wanfang Med Online) for randomized controlled trials (RCTs) which fit our criteria. We will include adult T2DM patients who have taken anti-diabetics (Part I) or antiresorptive (Part II) therapies with relevant outcome measures in our study. We will perform title/abstract and full-text screening as well as data extraction in duplicate. Risk of bias (RoB) will be evaluated in duplicate for each study, and the quality of evidence will be examined using CINeMA in accordance to the GRADE framework. We will use R and gemtc to perform the NMA. We will report changes in BMD, BTM and pain scores in either weighted or standardized mean difference, and we will report fracture incidences as odds ratios. We will use the surface under the cumulative ranking curve (SUCRA) scores to provide numerical estimates of the rankings of interventions. Ethics and Dissemination The study will not require ethics approval. The findings of the two-part NMA will be disseminated in peer-reviewed journals and presented at conferences. We aim to produce the most comprehensive quantitative analysis regarding the management of T2DM bone disease. Our analysis should be able to provide physicians and patients with up-to-date recommendations for anti-diabetic medications and antiresorptive pharmacotherapies for maintaining bone health in T2DM patients. Systematic Review Registration International Prospective Register for Systematic Reviews (PROSPERO) — CRD42019139320 ARTICLE SUMMARY Strengths and limitations of this study Literature search in Chinese databases will yield new RCT evidence regarding the efficacy of anti-diabetics in treating T2DM bone disease Using network meta-analytical techniques to analyze the relative efficacy of antiresorptive therapies will allow us to include new treatment arms, such as zoledronic acid and risedronate. Only RCTs will be included and the quality of trials and networks will be evaluated using Risk of Bias, GRADE and comparison-adjusted funnel plots. Chinese clinicians may not use the same procedures and practices as Western clinicians, therefore the outcomes from Chinese RCTs may not apply to the Western healthcare systems. The study design does not allow the comparison of anti-diabetics with antiresorptive therapies or combinations of the two.
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 | 0,047 | 0,081 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,004 |
| Méta-épidémiologie (sens large) | 0,023 | 0,029 |
| Bibliométrie | 0,013 | 0,014 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,049 | 0,004 |
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 ».