Urinary metabolic profiling in Autosomal Dominant Polycystic Kidney Disease (ADPKD).


Oleg Mayboroda, Leiden University Medical Center, Leiden, The Netherlands (o.a.mayboroda@lumc.nl)
Shosha Dekker, Leiden University Medical Center, Leiden, The Netherlands
Aswin Verhoeven, Leiden University Medical Center, Leiden, The Netherlands
Darius Soonawala, Leiden University Medical Center, Leiden, The Netherlands
Dorien Peters, Leiden University Medical Center, Leiden, The Netherlands
Johan Fijter, Leiden University Medical Center, Leiden, The Netherlands

Background: The disease course of ADPKD is highly variable and the advent of renoprotective treatment requires early risk stratification. The markers to select the patients at high-risk of rapid progression in the early stages of their disease are highly needed. Here, we applied targeted, quantitative metabolic profiling to evaluate whether changes in the urinary metabolome are associated with estimated GFR (eGFR) and with disease progression (eGFR decline) in ADPKD.


Methods: Targeted, quantitative metabolic profiling (¹H NMR-spectroscopy) was performed on spot urine samples using the KIMBLE workflow[1]. A discovery ADPKD cohort (n=338) was used for model building and tuning and an independent cohort (n=163) was used for validation. Multivariate modelling and linear regression were used to dissect and validate the associations between metabolic composition of urine and the annual change in eGFR.

Results: Twenty-nine known urinary metabolites were quantified from the spectra using a semi-automatic quantification routine (KIMBL). A correlation analysis of the quantified metabolites revealed a strong association with eGFR in the discovery cohort. We applied a model optimization routine resulting in selection of four metabolites. In combination, they served as a good predictor for actual eGFR. The annual change in eGFR was best described a single metabolic ratio. The model built on this predictor outperformed the models built only on the clinical risk markers (eGFR and total kidney volume) and remained significant after adjustment for potential confounders. The associations between urinary metabolites and eGFR, and with eGFR decline over time were validated in an independent cohort.

Conclusion: Quantitative NMR profiling enabled identification of two non overlapping sets of the urinary metabolic markers: one that describes a baseline eGFR and another describing annual change in eGFR or disease progression (eGFR decline) in ADPKD. The finding showed strong additional value beyond that of clinical risk markers for the management of ADPKD.



[1] Verhoeven et al., KIMBLE: A versatile visual NMR metabolomics workbench in KNIME, Analytica Chimica Acta, 2018



Abstract Reference & Short Personal Biography of Presenting Author

PhD, associate professor, graduated from the University of St. Petersburg (St. Petersburg, Russian Federation) and obtained his PhD from the Institute of Evolutional Physiology and Biochemistry of the Russian Academy of Science (St. Petersburg, Russian Federation) in 1992. After completing his post-doctoral trainings at the Technische Universität Carolo-Wilhelmina of Braunschweig and the University of Magdeburg he moved to The Netherlands where he joined the Department of Parasitology, currently the Center of Proteomics and Metabolomics, of the Leiden University Medical Center. His current interests are the application of metabolomics in epidemiological studies as well as data analysis.


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