%0 Journal Article
%@ 1929-0748
%I JMIR Publications
%V 13
%N 
%P e54853
%T Incidence, Risk, and Clinical Course of New-Onset Diabetes in Long COVID: Protocol for a Systematic Review and Meta-Analysis of Cohort Studies
%A Talanki,Ananya Sri
%A Bajaj,Neha
%A Trehan,Twinkle
%A Thirunavukkarasu,Sathish
%+ Department of Family and Preventive Medicine, Emory University School of Medicine, 1518 Clifton Rd NE, Atlanta, GA, 30322, United States, 1 4703578308, sathish.thirunavukkarasu@emory.edu
%K COVID-19
%K SARS-CoV-2
%K type 1 diabetes
%K type 2 diabetes
%K new-onset diabetes
%K long COVID
%K incidence
%K cohort studies
%K postacute sequela
%K systematic review
%K meta-analysis
%D 2024
%7 4.6.2024
%9 Protocol
%J JMIR Res Protoc
%G English
%X Background: COVID-19, an infectious disease pandemic, affected millions of people globally, resulting in high morbidity and mortality. Causing further concern, significant proportions of COVID-19 survivors endure the lingering health effects of SARS-CoV-2, the pathogen that causes COVID-19. One of the diseases manifesting as a postacute sequela of COVID-19 (also known as “long COVID”) is new-onset diabetes. Objective: The aim of this study is to examine the incidence of new-onset diabetes in patients with long COVID and assess the excess risk compared with individuals who tested negative for COVID-19. The study also aims to estimate the population-attributable fraction for COVID-19 as a risk factor for new-onset diabetes in long COVID and investigate the clinical course of new-onset diabetes cases. Methods: This is a protocol for a systematic review and meta-analysis. PubMed, MEDLINE, Embase, Scopus, and Web of Science databases will be systematically searched to identify articles published between December 2019 and July 2024. A comprehensive search strategy for each database will be developed using a combination of Medical Subject Headings terms, subject headings, and text words to identify eligible studies. Cohort studies and randomized controlled trials (only control arms) involving patients with COVID-19 of any age, with follow-up data on new-onset diabetes in long COVID, will be considered for inclusion. Controls will comprise individuals who tested negative for COVID-19, with or without other respiratory tract infections. Three independent reviewers (AST, NB, and TT) will perform article selection, data extraction, and quality assessment of the studies. A fourth reviewer (ST) will review the identified studies for final inclusion in the analysis. The random-effects DerSimonian-Laird models will be used to estimate the pooled incidence proportion (%), incidence rate of diabetes (per 1000 person-years), and risk ratio (with 95% CIs) for diabetes incidence. Results: A total of 1972 articles were identified through the initial search conducted in August 2023. After excluding duplicates, conducting title and abstract screening, and completing full-text reviews, 41 articles were found to be eligible for inclusion. The search will be updated in July 2024. Currently, data extraction is underway, and the meta-analysis is expected to be completed in August 2024. Publication of the study findings is anticipated by the end of 2024. Conclusions: The study findings should provide valuable insights to inform both clinical practice and public health policies regarding the effective management of new-onset diabetes in patients with long COVID. International Registered Report Identifier (IRRID): DERR1-10.2196/54853 
%M 38833277
%R 10.2196/54853
%U https://www.researchprotocols.org/2024/1/e54853
%U https://doi.org/10.2196/54853
%U http://www.ncbi.nlm.nih.gov/pubmed/38833277