Evidence map›Paper›PMID 37311601›Full record

ArticleBMJ open diabetes research & care2023

Causal associations between type 1 diabetes and COVID-19 infection and prognosis: a two-sample Mendelian randomization study.

Xin-Ling Ma, Qi-Yun Shi, Qi-Gang Zhao, Qian Xu, Shan-Shan Yan, Bai-Xue Han, Chen Fang, Lei Zhang, Yu-Fang Pei

Open access · goldAbstract read
In one paragraph

Article in BMJ open diabetes research & care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.3field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Xin-Ling Ma *Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China.
Qi-Yun Shi *Department of Endocrinology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Qi-Gang ZhaoDepartment of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China.
Qian XuDepartment of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China.
Shan-Shan YanDepartment of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China.
Bai-Xue HanDepartment of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China.
Chen FangDepartment of Endocrinology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.ORCID 0000-0003-4329-9471
Lei ZhangJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China ypei@suda.edu.cn lzhang6@suda.edu.cn.
Yu-Fang PeiDepartment of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, China ypei@suda.edu.cn lzhang6@suda.edu.cn.ORCID 0000-0002-9157-0759
Soochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIt has been suggested that type 1 diabetes was associated with increased COVID-19 morbidity and mortality. However, their causal relationship is still unclear. Herein, we performed a two-sample Mendelian randomization (MR) to investigate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. RESEARCH DESIGN AND

methodsThe summary statistics of type 1 diabetes were obtained from two published genome-wide association studies of European population, one as a discovery sample including 15 573 cases and 158 408 controls, and the other data as a replication sample consisting of 5913 cases and 8828 controls. We first performed a two-sample MR analysis to evaluate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. Then, reverse MR analysis was conducted to determine whether reverse causality exists.

resultsMR analysis results showed that the genetically predicted type 1 diabetes was associated with higher risk of severe COVID-19 (OR=1.073, 95% CI: 1.034 to 1.114, p

conclusionsType 1 diabetes had a causal effect on severe COVID-19 and death after COVID-19 infection. Further mechanistic studies are needed to explore the relationship between type 1 diabetes and COVID-19 infection and prognosis.

Indexed as

COVID-19Diabetes Mellitus, Type 1Genome-Wide Association StudyHumansMendelian Randomization AnalysisCOVID-19Diabetes Mellitus, Type 1InfectionsPrognosis

Identifiers

PMID37311601
PMCPMC10276960
OpenAlexW4380537740

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.