Abstract

Background

The incidence of kidney disease caused by thyroid cancer is rising worldwide. Observational studies cannot recognize whether thyroid cancer is independently associated with kidney disease. We performed the Mendelian randomization (MR) approach to genetically investigate the causality of thyroid cancer on immunoglobulin A nephropathy (IgAN).

Methods and results

We explored the causal effect of thyroid cancer on IgAN by MR analysis. Fifty-two genetic loci and single nucleotide polymorphisms were related to thyroid cancer. The primary approach in this MR analysis was the inverse variance weighted (IVW) method, and MR‒Egger was the secondary method. Weighted mode and penalized weighted median were used to analyze the sensitivity. In this study, the random-effect IVW models showed the causal impact of genetically predicted thyroid cancer across the IgAN risk (OR, 1.191; 95% CI, 1.131–1.253, P < 0.001). Similar results were also obtained in the weighted mode method (OR, 1.048; 95% CI, 0.980–1.120, P = 0.179) and penalized weighted median (OR, 1.185; 95% CI, 1.110–1.264, P < 0.001). However, the MR‒Egger method revealed that thyroid cancer decreased the risk of IgAN, but this difference was not significant (OR, 0.948; 95% CI, 0.855–1.051, P = 0.316). The leave-one-out sensitivity analysis did not reveal the driving influence of any individual SNP on the association between thyroid cancer and IgAN.

Conclusion

The IVW model indicated a significant causality of thyroid cancer with IgAN. However, MR‒Egger had a point estimation in the opposite direction. According to the MR principle, the evidence of this study did not support a stable significant causal association between thyroid cancer and IgAN. The results still need to be confirmed by future studies.

Details

Title
Causal associations between thyroid cancer and IgA nephropathy: a Mendelian randomization study
Author
Mei, Ziwei; Li, Fuhao; Chen, Ruizhen; Xiao, Zilong; Cai, Dongsheng; Lie, Jin; Xu, Qian; Wang, Yucheng; Chen, Jun
Pages
1-10
Section
Research
Publication year
2023
Publication date
2023
Publisher
BioMed Central
e-ISSN
14712164
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2865361559
Copyright
© 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.