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Blood biomarkers improve the prediction of prevalent and incident severe chronic kidney disease

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Abstract

Background

The prevalence of chronic kidney disease (CKD) is high. Identification of cases with CKD or at high risk of developing it is important to tailor early interventions. The objective of this study was to identify blood metabolites associated with prevalent and incident severe CKD, and to quantify the corresponding improvement in CKD detection and prediction.

Methods

Data from four cohorts were analyzed: Singapore Epidemiology of Eye Diseases (SEED) (n = 8802), Copenhagen Chronic Kidney Disease (CPH) (n = 916), Singapore Diabetic Nephropathy (n = 714), and UK Biobank (UKBB) (n = 103,051). Prevalent CKD (stages 3–5) was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2; incident severe CKD as CKD-related mortality or kidney failure occurring within 10 years. We used multivariable regressions to identify, among 146 blood metabolites, those associated with CKD, and quantify the corresponding increase in performance.

Results

Chronic kidney disease prevalence (stages 3–5) and severe incidence were 11.4% and 2.2% in SEED, and 2.3% and 0.2% in UKBB. Firstly, phenylalanine (Odds Ratio [OR] 1-SD increase = 1.83 [1.73, 1.93]), tyrosine (OR = 0.75 [0.71, 0.79]), docosahexaenoic acid (OR = 0.90 [0.85, 0.95]), citrate (OR = 1.41 [1.34, 1.47]) and triglycerides in medium high density lipoprotein (OR = 1.07 [1.02, 1.13]) were associated with prevalent stages 3–5 CKD. Mendelian randomization analyses suggested causal relationships. Adding these metabolites beyond traditional risk factors increased the area under the curve (AUC) by 3% and the sensitivity by 7%. Secondly, lactate (HR = 1.33 [1.08, 1.64]) and tyrosine (HR = 0.74 [0.58, 0.95]) were associated with incident severe CKD among individuals with eGFR < 90 mL/min/1.73 m2 at baseline. These metabolites increased the c-index by 2% and sensitivity by 5% when added to traditional risk factors.

Conclusion

The performance improvements of CKD detection and prediction achieved by adding metabolites to traditional risk factors are modest and further research is necessary to fully understand the clinical implications of these findings.

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Data availability

Regarding Singapore Epidemiology of Eye Diseases data, in order to adhere to local IRB guidelines, we regret to inform that we are unable to share data relevant to this study openly. Nevertheless, we respectfully urge interested parties to contact corresponding author for further enquiries if necessary.

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Funding

This study was funded by: Singapore Epidemiology of Eye Diseases: National Medical Research Council (NMRC): NMRC/CIRG/1371/2013, NMRC/CIRG/1488/2018, NMRC/STaR/016/2013 and NMRC/OFLCG/001/2017. Singapore Diabetic Nephropathy cohort: Singapore Alexandra Health Fund Research Program and STAR grant 20201, National Medical Research Council NMRC/MOH-000066, NMRC/MOH-0000714 and OFLCG/001/2017. The Augustinus Foundation, the Danish Kidney Foundation, the Danish Society of Nephrology Foundation, the Helen and Ejnar Bjørnow Foundation, the Research Fund at Rigshospitalet, the Advokat Bent Thorbergs Foundation, and the Danish Diabetes Academy.

Author information

Authors and Affiliations

  1. Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, The Academia, Level 6, Singapore, 169856, Singapore

    Simon Nusinovici, Hengtong Li, Crystal Chong, Marco Yu, Tien-Yin Wong, Gavin S. W. Tan, Ching-Yu Cheng & Charumathi Sabanayagam

  2. Eye-ACP, Duke-NUS Medical School, Singapore, Singapore

    Simon Nusinovici, Marco Yu, Tien-Yin Wong & Ching-Yu Cheng

  3. Department of Nephrology, Rigshospitalet University Hospital, Copenhagen, Denmark

    Ida Maria Hjelm Sørensen & Susanne Bro

  4. Department of Clinical Biochemistry, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark

    Line Stattau Bisgaard & Christina Christoffersen

  5. Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark

    Line Stattau Bisgaard & Christina Christoffersen

  6. Clinical Research Unit, Diabetes Centre, Department of Medicine, Khoo Teck Puat Hospital, Singapore, Singapore

    Sylvia Liu, Jian-Jun Liu & Lim Su Chi

  7. Saw Swee Hock School of Public Heath, National University of Singapore, Singapore, Singapore

    Lim Su Chi

  8. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Lim Su Chi

  9. Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Tien-Yin Wong, Gavin S. W. Tan, Ching-Yu Cheng & Charumathi Sabanayagam

Authors
  1. Simon Nusinovici
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  2. Hengtong Li
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Contributions

Substantial contributions to the conception or design of the work (SN, TYW, GSWT, CS); or the acquisition (MY, IMHS, LSB, CC, SB, JJL, LSC, CYC), analysis (SN, HL, CC, MY, SL), or interpretation of data for the work (SN, HL, IMHS, LSB, CC, SB, JJL, LSC, TYW, GSWT, CYC, CS). Drafting the work (SN, HL, IMHS, LSB, JJL, CS) or revising it critically for important intellectual content (CC, MY, CC, SB, SL, LSC, TYW, GSWT, CYC). Final approval of the version to be published (all the authors). Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved (all the authors).

Corresponding author

Correspondence to Simon Nusinovici.

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Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Ethical approval

This study was performed in line with the principles of the 1964 Declaration of Helsinki. Approvals were granted by the Institutional Review Board of SingHealth (Singapore Epidemiology of Eye Disease study), the Regional Scientific Ethical Committee and the Danish Data Protection Agency (Copenhagen Chronic Kidney Disease study), the Singapore National Healthcare Group ethics committee (Singapore Diabetic Nephropathy study), and the North West Multi-centre Research Ethics Committee (MREC), the Research Tissue Bank (RTB) as well as a Human Tissue Authority (HTA) licence (UK Biobank study).

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Informed consent was obtained from all individual participants included in the study.

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Nusinovici, S., Li, H., Chong, C. et al. Blood biomarkers improve the prediction of prevalent and incident severe chronic kidney disease. J Nephrol (2024). https://doi.org/10.1007/s40620-023-01872-w

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