A Mendelian randomization study: physical activities and chronic kidney disease

Abstract

Increasing evidence has shown that physical activity is related to a lower risk of chronic kidney disease (CKD), thus indicating a potential target for prevention. However, the causality is not clear; specifically, physical activity may protect against CKD, and CKD may lead to a reduction in physical activity. Our study examined the potential bidirectional relationship between physical activity and CKD by using a genetically informed method. Genome-wide association studies from the UK Biobank baseline data were used for physical activity phenotypes and included 460,376 participants. For kidney function (estimated Glomerular Filtration Rate (eGFR) and CKD, with eGFR < 60 mL/min/1.73 m²), CKDGen Consortium data were used, which included 480,698 CKD participants of European ancestry. Mendelian randomization (MR) analysis was used to determine the causal relationship between physical activities and kidney function. Two-sample MR genetically predicted that heavy DIY (do it yourself) (e.g., weeding, lawn mowing, carpentry, and digging) decreased the risk of CKD (odds ratio [OR] = 0.287, 95% CI = 0.117–0.705, p = 0.0065) and improved the level of eGFR (β = 0.036, 95% CI = 0.005–0.067, p = 0.021). The bidirectional MR showed no reverse causality. It is worth noting that other physical activities, such as walking for pleasure, strenuous sports, light DIY (e.g., pruning and watering the lawn), and other exercises (e.g., swimming, cycling, keeping fit, and bowling), were not significantly correlated with CKD and eGFR. This study used genetic data to provide reliable and robust causal evidence that heavy physical activity (e.g., weeding, lawn mowing, carpentry, and digging) can protect kidney function and further lower the risk of CKD.

Keywords:

• Physical activities
• chronic kidney disease
• estimated glomerular filtration rate
• causal estimates
• Mendelian randomization

Acknowledgments

We would like to thank Zhenming Liu for providing for R language programming guidance.

Authors’ contributions

Rui Xiao: Conceptualization, Methodology, Software, Writing- Original draft preparation; Li Dong: Visualization, Investigation; Bo Xie and Beizhong Liu: Supervision, Writing- Reviewing and Editing.

Disclosure statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Additional information

Funding

This study was supported by the Natural Science Foundation of Yongchuan District Science and Technology Bureau (Grant ID: 2022yc-jckx20012, project leader: Li Dong) and the Postgraduate Innovation Fund of the Fifth Clinical College of Chongqing Medical University (Grant ID: YJSCX202301, project leader: Rui Xiao).