| Title: | The Time-Varying Impact of COVID-19 on the Acute Kidney Disorders: A Historical Matched Cohort Study and Mendelian Randomization Analysis |
| Journal: | Health Data Science |
| Published: | 1 Jan 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39011273/ |
| DOI: | https://doi.org/10.34133/hds.0159 |
Publication 12867
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Abstract
Background: This study aimed to explore the time-varying impact of COVID-19 on acute kidney disorders, including acute kidney injury and other acute kidney diseases. Methods: From the UK Biobank, 10,121 participants with COVID-19 were matched with up to 3 historically unexposed controls by age, sex, Townsend deprivation index, and the status of hospitalization or receiving critical care. We investigated the association between COVID-19 and incidence of acute kidney disorders, within the first 4 weeks after infection, using conditional and time-varying Cox proportional hazard regression. In addition, one-sample Mendelian randomization, utilizing the polygenic risk score for COVID-19 as an instrumental variable, was conducted to explore the potential causality of the association. Results: In the matched cohort study, we observed a significant association between COVID-19 and acute kidney disorders predominantly within the first 3 weeks. The impact of COVID-19 was time dependent, peaking in the second week (hazard ratio, 12.77; 95% confidence interval, 5.93 to 27.70) and decreasing by the fourth week (hazard ratio, 2.28; 95% confidence interval, 0.75 to 6.93). In subgroup analyses, only moderate to severe COVID-19 cases were associated with acute worsening of renal function in a time-dependent pattern. One-sample Mendelian randomization analyses further showed that COVID-19 might exert a "short-term" causal effect on the risk of acute kidney disorders, primarily confined to the first week after infection. Conclusions: The risk of acute kidney disorders following COVID-19 demonstrates a time-varying pattern. Hazard effects were observed only in patients with moderate or severe but not mild COVID-19.</p>
9 Authors
- Chunyang Li
- Chao Zhang
- Jie Chen
- Yilong Chen
- Zhiye Ying
- Yao Hu
- Huan Song
- Ping Fu
- Xiaoxi Zeng
1 Application
| Application ID | Title |
|---|---|
| 54803 | Deciphering complex traits - phenotypic and genetic associations between traits in the UK Biobank Cohort |
Enabling scientific discoveries that improve human health