Chronic Kidney Disease Onset, Progression, and Cardiovascular Outcomes: Proteomics Informs Biology and Risk Stratification

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Chronic Kidney Disease Onset, Progression, and Cardiovascular Outcomes: Proteomics Informs Biology and Risk Stratification

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Co-author 1

Jijuan Zhang jane_sdu_edu@163.com Huazhong University of Science and Technology Department of Epidemiology and Biostatistics Wuhan China -

Co-author 2

Hancheng Yu sduyuhc@163.com Huazhong University of Science and Technology Department of Nutrition and Food Hygiene Wuhan China -

Co-author 3

Xianli Li lixianli0906@163.com Huazhong University of Science and Technology Department of Epidemiology and Biostatistics Wuhan China -

Co-author 4

Jinchi Xie jinchixie@126.com Huazhong University of Science and Technology Department of Epidemiology and Biostatistics Wuhan China -

Co-author 5

Yuxiang Wang wangyuxiang@163.com Huazhong University of Science and Technology Department of Epidemiology and Biostatistics Wuhan China -

Co-author 6

Yue Li liyue2425@163.com Huazhong University of Science and Technology Department of Epidemiology and Biostatistics Wuhan China -

Co-author 7

Kun Xu xukun_728@hust.edu.cn Huazhong University of Science and Technology Department of Nutrition and Food Hygiene Wuhan China -

Co-author 8

Gang Liu liugang026@hust.edu.cn Huazhong University of Science and Technology Department of Nutrition and Food Hygiene Wuhan China -

Co-author 9

Yunfei Liao yunfeiliao2012@163.com Wuhan Union Hospital Department of Endocrinology Wuhan China -

Co-author 10

Xiongzhong Ruan Xiongzruan@foxmail.com Chongqing Medical University Centre for Lipid Research Chongqing China -

Co-author 11

An Pan panan@hust.edu.cn Huazhong University of Science and Technology Department of Epidemiology and Biostatistics Wuhan China -

Co-author 12

Tingting Geng geng_tingting@hust.edu.cn Huazhong University of Science and Technology Department of Epidemiology and Biostatistics Wuhan China *

Co-author 13

Co-author 14

Co-author 15

Introduction

Large-scale proteomics provides a promising opportunity to understand staged kidney disorders and cardiovascular outcomes, yet research in this field is limited. This study utilized proteomics to enhance biological insights and risk stratification for these conditions.

Methods

This cohort study included 46,155 participants without prevalent chronic kidney disease (CKD), 13,471 participants with reduced renal function, and 3749 to 4358 participants with prevalent CKD from the UK Biobank. The Olink Explore 3072 platform quantified 2923 plasma proteins. Cox proportional hazards or logistic models were performed to explore associations of proteins with staged kidney disorders including reduced renal function, CKD, and end stage kidney disease (ESKD), and cardiovascular outcomes including coronary heart disease (CHD), stroke, and heart failure (HF). Pathway enrichment analyses were employed, and predictive models were developed for incident diseases.

Results

Median follow-up periods were 12.2-12.4 years. We found that 860 (29.5%) proteins were shared across at least two diseases, with 762 (26.1%) showing consistent association directions. Reduced renal function, CKD, and HF specifically shared the largest number of 173 (5.9%) proteins. Remarkably, polr2f, tnfrsf10b, and wfdc2 were positively associated with all six outcomes (hazards/odds ratios: 1.09-1.68), while apom, ctsv, nell1, pon1, umod, and serpina4 exhibited the largest number of three negative associations (hazards/odds ratios: 0.79-0.94). A total of 1190 (40.8%) proteins were unique to a single disease, with top disease-specific proteins being mybpc1 (reduced renal function), pon3 (CKD), rab44 (ESKD), atxn2 (CHD), and il1rl1 (HF). Additionally, 198 (6.8%) proteins exhibited interactions with age, 126 (4.3%) with sex, 9 (0.3%) with diabetes, 52 (1.8%) with hypertension, and 57 (2.0%) with dyslipidemia. Pathway analyses highlighted the extracellular region/space and cytokine-cytokine receptor interaction for disease-associated proteins. Incorporating predictive or shared proteins into clinical models significantly improved predictions of incident diseases,yielding Harrell's C indices of 0.706-0.923.

Conclusion

This study identified potential biomarkers or targets, demonstrated effect modifications by age, sex, and comorbidities on protein associations, advanced understanding of biology, and improved risk stratification for staged kidney disorders and cardiovascular outcomes.

Kewords