Is DPP-4 Use Associated With a Decreased Mortality for COVID-19 Diabetic Patients? A Meta-Analysis
Notice bibliographique
Résumé
Abstract Introduction: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Dipeptidyl peptidase-4 (DPP-4) is a multi-expressed glycoprotein that is speculated to be a functional SARS-CoV-2 receptor. Previous studies remain controversial regarding whether DPP-4 use is associated with reduced risk for COVID-19 diabetic patients. Thus, this meta-analysis is performed. Method: A comprehensive literature search on PubMed was conducted to identify all relevant studies published prior to October 2020. This meta-analysis was reported in conformity to the Preferred Reporting Project declared by the Systematic Review and Meta-Analysis (PRISMA). The quality assessment was performed by the Newcastle-Ottawa Scale (NOS). The pooled odds ratio (OR) and 95% confidence intervals (CI) were calculated. Random-effect model or fixed-effect model was used based on heterogeneity. Subgroup analyses were performed based on types of diabetes, geographic locations, study designs, and different sample sizes. Sensitivity analysis and publication bias detection were also performed. All statistical analyses were performed using RevMan and STATA 12.0 statistical software, and all P values were two-tailed, the test level was 0.05. Result: 69 articles were obtained. 5 articles involving 49,989 participants were included. All included studies were considered moderate to high quality. No decreased mortality of COVID-19 diabetic patients was found among DPP-4 users (OR 0.86, 95%CI: 0.22,3.41, P=0.083, I2=81%). In the subgroup analysis, studies in Asia (OR 3.11, 95%CI: 0.78, 12.34, P=0.001, I2=70%) did not found reduced mortality, whereas studies in Europe (OR 0.36, 95%CI: 0.23, 0.56, P<0.00001, I2=0%) were associated with reduced mortality. Based on study designs, the four case-control studies (OR 1.27, 95%CI: 0.27, 5.93, P=0.76, I2=89%) did not find reduced mortality, but one cohort study (OR 0.13, 95%CI: 0.02, 0.84, P=0.03) showed a reduced mortality. The four studies investigating Type 2 Diabetes Mellitus (T2DM) did found reduced mortality (OR 0.74, 95%CI: 0.13, 4.24, P=0.73, I2=90%). For sample size >200, reduced risk of mortality (OR 0.28, 95%CI: 0.07, 1.15, P=0.08, I2=32%) was found, however, for sample ≤200, no statistically significant association (OR 1.44, 95%CI: 0.23, 8.89, P=0.70, I2=93%) was found. Sensitivity analysis by changing models and omitting each study at a time confirm the stability of the result. Begg’s test (z=-0.24, P=1.000) and Egger’s test (t=0.56, P=0.618) did not detect a significant risk of publication bias. Conclusion: The current meta-analysis did not find reduced mortality for COVID-19 diabetic patients who take DPP-4. However, subgroup-analyses found reduced mortality in Europe. More high-quality original studies are needed to further explore the association between DPP-4 use and the mortality risk of COVID-19.
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 | 0,010 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,015 | 0,053 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».