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Chronic kidney disease onset, progression, and cardiovascular outcomes: proteomics informs biology and risk
Jijuan Zhang1, Hancheng Yu2, Xingyue Song3
1Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
Insights
This large-scale proteomics study reveals shared and unique plasma proteins associated with chronic kidney disease (CKD) and cardiovascular diseases (CVD), improving risk prediction for these conditions.
Area of Science:
- Proteomics
- Cardiovascular disease research
- Nephrology
Background:
- Large-scale proteomics offers insights into chronic kidney disease (CKD) and cardiovascular disease (CVD) but remains under-researched.
- This study leverages proteomics to enhance understanding of disease biology and risk stratification for CKD and CVD.
Purpose of the Study:
- To identify shared and unique plasma proteins associated with CKD and CVD.
- To explore the biological pathways involved in these diseases.
- To develop improved predictive models for incident CKD and CVD.
Main Methods:
- A cohort study of 44,779 participants without CKD and 3,749-4,272 with CKD from the UK Biobank.
- Quantification of 2,923 plasma proteins using the Olink Explore 3072 platform.
- Analysis of protein associations with CKD, end-stage kidney disease, coronary heart disease (CHD), stroke, and heart failure (HF) using Cox models, Mendelian randomization, and pathway analyses.
Main Results:
- Identified 598 proteins shared across at least two diseases, with 471 unique to a single disease. CKD and heart failure shared the most proteins (279).
- Specific proteins (e.g., POLR2F, TNFRSF10B, IGFBP2) were associated with multiple diseases, with genetic support for some.
- Pathway analyses implicated cell adhesion, signal transduction, and cytokine-cytokine receptor interactions. Incorporating predictive proteins improved risk prediction models for CKD, CHD, stroke, and HF.
Conclusions:
- This research deepens the understanding of the biological mechanisms underlying CKD and CVD.
- The identified proteins provide a basis for earlier disease detection and integrated risk stratification strategies.
Background:
Large-scale proteomics provides an opportunity to understand chronic kidney disease (CKD) and cardiovascular disease, yet research in this field is limited. This study utilized proteomics to inform biology and risk stratification for these diseases.
Methods:
This cohort study included 44,779 participants free of prevalent CKD, and 3,749-4,272 participants with prevalent CKD from the UK Biobank. The Olink Explore 3072 platform quantified 2,923 plasma proteins. Cox proportional hazards models were used to assess associations of proteins with kidney diseases including CKD and end stage kidney disease, and cardiovascular diseases including coronary heart disease (CHD), stroke, and heart failure (HF). Mendelian randomization examined genetic associations, pathway analyses identified biological pathways, and predictive models were developed for incident diseases.
Results:
Median follow-up periods were 12.2-12.6 years. We identified 598 (20.5%) proteins shared across ≥ 2 diseases, with 595 (20.4%) showing consistent directions of associations, and 471 (16.1%) unique to a single disease. CKD and HF specifically shared the largest number of 279 (9.6%) proteins. POLR2F, TNFRSF10B, and IGFBP2 were positively associated with all five diseases, with Mendelian randomization supporting genetic associations of POLR2F with CHD and IGFBP2 with hypertensive renal disease. Pathway analyses highlighted cell adhesion, signal transduction, and cytokine-cytokine receptor interaction for disease-associated proteins. Incorporating predictive proteins into clinical models improved risk prediction for CKD, CHD, stroke, and HF, yielding Harrell's C indices of 0.750-0.818 (corresponding increases of 0.027-0.090).
Conclusions:
This study deepens insights into disease biology and provides a foundation for early detection and integrated risk stratification in CKD and cardiovascular disease.
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