@Article{info:doi/10.2196/54853,
author="Talanki, Ananya Sri
and Bajaj, Neha
and Trehan, Twinkle
and Thirunavukkarasu, Sathish",
title="Incidence, Risk, and Clinical Course of New-Onset Diabetes in Long COVID: Protocol for a Systematic Review and Meta-Analysis of Cohort Studies",
journal="JMIR Res Protoc",
year="2024",
month="Jun",
day="4",
volume="13",
pages="e54853",
keywords="COVID-19; SARS-CoV-2; type 1 diabetes; type 2 diabetes; new-onset diabetes; long COVID; incidence; cohort studies; postacute sequela; systematic review; meta-analysis",
abstract="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 ",
issn="1929-0748",
doi="10.2196/54853",
url="https://www.researchprotocols.org/2024/1/e54853",
url="https://doi.org/10.2196/54853",
url="http://www.ncbi.nlm.nih.gov/pubmed/38833277"
}