Exploring the mechanism of comorbidity in patients with T1DM and COVID-19: Integrating bioinformatics and Mendelian randomization methods

During the coronavirus disease 2019 (COVID-19) pandemic, the incidence of type 1 diabetes mellitus (T1DM) has increased. Additionally, evidence suggests that individuals with diabetes mellitus may have increased susceptibility to severe acute respiratory syndrome coronavirus 2 infection. However, the specific causal relationships and interaction mechanisms between T1DM and COVID-19 remain unclear. This study aims to investigate the causal relationship between T1DM and COVID-19, utilizing differential gene expression and Mendelian randomization analyses. Differentially expressed gene sets from datasets GSE156035 and GSE171110 were intersected to identify shared genes, analyzed for functional enrichment. Mendelian randomization models were employed to assess causal effects, revealing no direct causal link between T1DM and COVID-19 in the European population (P > .05). Notably, DNA replication and sister chromatid cohesion 1 (DSCC1) showed negative causal associations with both diseases (T1DM: OR = 0.943, 95% CI: 0.898-0.991, P = .020; COVID-19: OR = 0.919, 95% CI: 0.882-0.958, P < .001), suggesting a protective effect against their comorbidity. This genetic evidence highlights DSCC1 as a potential target for monitoring and managing the co-occurrence of T1DM and COVID-19.

Authors

Institutions

Publication Details

Journal
Medicine
Published
2024-10-18
DOI
https://doi.org/10.1097/md.0000000000040128
Citations
1
Primary Topic
Diabetes and associated disorders
Type
article
Field-Weighted Citation Impact
0.55
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the mechanism of comorbidity in patients with T1DM and COVID-19: Integrating bioinformatics and Mendelian randomization methods

Xinghuan Liang, Zhenxing Huang, Tingliang Wang, Yun Zhang et al.
1 citations
Medicine
Diabetes and associated disorders
0.55
article

Exploring the mechanism of comorbidity in patients with T1DM and COVID-19: Integrating bioinformatics and Mendelian randomization methods

Xinghuan Liang, Zhenxing Huang, Tingliang Wang, Yun Zhang, Zuojie Luo, Chunjiao Wu
article en
1 citations

Abstract

During the coronavirus disease 2019 (COVID-19) pandemic, the incidence of type 1 diabetes mellitus (T1DM) has increased. Additionally, evidence suggests that individuals with diabetes mellitus may have increased susceptibility to severe acute respiratory syndrome coronavirus 2 infection. However, the specific causal relationships and interaction mechanisms between T1DM and COVID-19 remain unclear. This study aims to investigate the causal relationship between T1DM and COVID-19, utilizing differential gene expression and Mendelian randomization analyses. Differentially expressed gene sets from datasets GSE156035 and GSE171110 were intersected to identify shared genes, analyzed for functional enrichment. Mendelian randomization models were employed to assess causal effects, revealing no direct causal link between T1DM and COVID-19 in the European population (P > .05). Notably, DNA replication and sister chromatid cohesion 1 (DSCC1) showed negative causal associations with both diseases (T1DM: OR = 0.943, 95% CI: 0.898-0.991, P = .020; COVID-19: OR = 0.919, 95% CI: 0.882-0.958, P < .001), suggesting a protective effect against their comorbidity. This genetic evidence highlights DSCC1 as a potential target for monitoring and managing the co-occurrence of T1DM and COVID-19.

MedicineVol. 103(42)
Guangxi Medical University (CN), First Affiliated Hospital of GuangXi Medical University (CN)
Good health and well-being
Openalex Percentile: Top 25%
Diabetes and associated disorders
0.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.