Lifestyle and chronic kidney disease: A machine learning modeling study

Wenjin Luo; Lilin Gong; Xiangjun Chen; Rufei Gao; Bin Peng; Yue Wang; Ting Luo; Yi Yang; Bing Kang; Chuan Peng; Linqiang Ma; Mei Mei; Zhiping Liu; Qifu Li; Shumin Yang; Zhihong Wang; Jinbo Hu

doi:10.3389/fnut.2022.918576

Lifestyle and chronic kidney disease: A machine learning modeling study

Front Nutr. 2022 Jul 22:9:918576. doi: 10.3389/fnut.2022.918576. eCollection 2022.

Authors

Wenjin Luo¹, Lilin Gong¹, Xiangjun Chen¹, Rufei Gao², Bin Peng³, Yue Wang¹, Ting Luo¹, Yi Yang¹, Bing Kang⁴, Chuan Peng⁵, Linqiang Ma¹, Mei Mei¹, Zhiping Liu¹, Qifu Li¹, Shumin Yang¹, Zhihong Wang¹, Jinbo Hu¹

Affiliations

¹ Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
² Laboratory of Reproductive Biology, School of Public Health and Management, Chongqing Medical University, Chongqing, China.
³ School of Public Health and Management, Chongqing Medical University, Chongqing, China.
⁴ Department of Clinical Nutrition, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
⁵ The Chongqing Key Laboratory of Translational Medicine in Major Metabolic Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Abstract

Background: Individual lifestyle varies in the real world, and the comparative efficacy of lifestyles to preserve renal function remains indeterminate. We aimed to systematically compare the effects of lifestyles on chronic kidney disease (CKD) incidence, and establish a lifestyle scoring system for CKD risk identification.

Methods: Using the data of the UK Biobank cohort, we included 470,778 participants who were free of CKD at the baseline. We harnessed the light gradient boosting machine algorithm to rank the importance of 37 lifestyle factors (such as dietary patterns, physical activity (PA), sleep, psychological health, smoking, and alcohol) on the risk of CKD. The lifestyle score was calculated by a combination of machine learning and the Cox proportional-hazards model. A CKD event was defined as an estimated glomerular filtration rate <60 ml/min/1.73 m², mortality and hospitalization due to chronic renal failure, and self-reported chronic renal failure, initiated renal replacement therapy.

Results: During a median of the 11-year follow-up, 13,555 participants developed the CKD event. Bread, walking time, moderate activity, and vigorous activity ranked as the top four risk factors of CKD. A healthy lifestyle mainly consisted of whole grain bread, walking, moderate physical activity, oat cereal, and muesli, which have scored 12, 12, 10, 7, and 7, respectively. An unhealthy lifestyle mainly included white bread, tea >4 cups/day, biscuit cereal, low drink temperature, and processed meat, which have scored -12, -9, -7, -4, and -3, respectively. In restricted cubic spline regression analysis, a higher lifestyle score was associated with a lower risk of CKD event (p for linear relation < 0.001). Compared to participants with the lifestyle score < 0, participants scoring 0-20, 20-40, 40-60, and >60 exhibited 25, 42, 55, and 70% lower risk of CKD event, respectively. The C-statistic of the age-adjusted lifestyle score for predicting CKD events was 0.710 (0.703-0.718).

Conclusion: A lifestyle scoring system for CKD prevention was established. Based on the system, individuals could flexibly choose healthy lifestyles and avoid unhealthy lifestyles to prevent CKD.

Keywords: chronic kidney disease; cohort study; lifestyle; machine learning; scoring system.

Abstract

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