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. 2025 Jun 26;21(4):90.
doi: 10.1007/s11306-025-02291-7.

Metabolomic profiling of renal cyst fluid in advanced ADPKD: insights from dialysis and transplantation cohorts

Simon Heckscher #  1 , Nicolas A Ihlo #  2 , Jan Schueler  3 , Fabian Kellermeier  1 , Jens M Werner  4 , Barbara Nuebel  5 , Verena Gross  6 , Matthias May  6 , Bernd Wullich  5 , Martin Kammerl  7 , Carsten Gnewuch  8 , Ralph Burkhardt  8 , Björn Buchholz  9 , Eric Pion  3 , Thiha Aung  3   7 , Miriam Banas  10 , Hans J Schlitt  4 , Peter J Oefner  1 , Katja Dettmer  1 , Wolfram Gronwald  1 , Merle Behr  2 , Silke Haerteis  11 , Katharina M Schmidt  12
Affiliations

Metabolomic profiling of renal cyst fluid in advanced ADPKD: insights from dialysis and transplantation cohorts

Simon Heckscher et al. Metabolomics. .

Abstract

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder characterized by progressive renal cyst formation, often leading to end-stage kidney disease (ESKD). In contrast to the urinary metabolome in ADPKD, the composition of renal cyst fluid remains largely unexplored.

Methods: We conducted a comprehensive metabolomic analysis of renal cyst fluid from 26 ADPKD patients (20 on dialysis, six with kidney transplants) using ¹H-NMR spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Cysts were clustered based on metabolite profiles, and differences were analyzed across groups defined by renal function status (dialysis vs. transplant), cyst volume, and cyst fluid sodium concentrations.

Results: Dialysis patients and transplant recipients differed significantly in their renal cyst fluid metabolomes. The former exhibited higher concentrations of myoinositol, creatinine, sucrose, τ-methylhistidine, trigonelline, and sarcosine, while the latter showed increased levels of leucine, isoleucine, valine and alanine. Remarkably, metabolites of the immunosuppressive prodrug mycophenolate mofetil were detected in renal cyst fluids after kidney transplantation. Despite intra- and interindividual variability, cyst fluid from the same patient displayed greater homogeneity. Interestingly, metabolomic profiles were not altered by cyst size.

Conclusion: This first systematic metabolomic analysis of renal cyst fluid in advanced ADPKD reveals distinct metabolic signatures linked to renal function status. The data provides novel insights into the pathophysiology of ADPKD and highlight the potentials of renal cyst fluid metabolomics for identifying biomarkers and therapeutic targets.

Keywords: ADPKD; Cyst fluid; Mass spectrometry; Metabolomics; NMR spectroscopy; Patient clustering.

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Conflict of interest statement

Declarations. Conflict of interest: The authors declare no competing interests. Ethics approval and consent to participate: Informed consent was obtained from all subjects and/or their legal guardians and experiments were approved by the ethics committee of the University of Regensburg (no. 20-1886-101) and of the Friedrich-Alexander-University Erlangen-Nuremberg (no. 23-404-Bn). All patient data gets encrypted, and every patient gets an internal patient ID. All data protection guidelines from the University of Regensburg are met. Consent for publication: All authors agree with the publication of this manuscript.

Figures

Fig. 1
Fig. 1
Clustered heatmaps of cyst fluid measurements. A Absolute quantified 1H-NMR and B MS fingerprinting data. First column indicates attribution of cysts to patients with white entries for patients with only one cyst sampled. Second column shows the renal function status: dialysis (green) or transplant (orange). Third column depicts the cyst sodium concentrations with values above (green) or below (orange) 100 mmol/L, non-reliable sodium measurements are left white. Larger and smaller values are depicted in red and blue, respectively, while missing values are shown in black. In Figure A, the highest and lowest measured concentrations (mM) for each molecule are given in the respective cells. Groups of patients and molecules based on the clustering are also marked. The rows are marked by the cyst ID with * indicating fluid which may be a mixture from multiple cysts
Fig. 1
Fig. 1
Clustered heatmaps of cyst fluid measurements. A Absolute quantified 1H-NMR and B MS fingerprinting data. First column indicates attribution of cysts to patients with white entries for patients with only one cyst sampled. Second column shows the renal function status: dialysis (green) or transplant (orange). Third column depicts the cyst sodium concentrations with values above (green) or below (orange) 100 mmol/L, non-reliable sodium measurements are left white. Larger and smaller values are depicted in red and blue, respectively, while missing values are shown in black. In Figure A, the highest and lowest measured concentrations (mM) for each molecule are given in the respective cells. Groups of patients and molecules based on the clustering are also marked. The rows are marked by the cyst ID with * indicating fluid which may be a mixture from multiple cysts
Fig. 2
Fig. 2
UMAPs of 1H-NMR (left) and LC-MS (right) data. Measurements of different cyst fluid samples from the same patient are depicted by the same color. Patients that had only a single cyst sampled and analyzed are shown in black. Renal function status is marked by dots (dialysis) and crosses (transplant).
Fig. 3
Fig. 3
Plots of sodium concentration for cysts separated by the renal function status of the patients. The patients are marked with the same color as in Fig. 2
Fig. 4
Fig. 4
Plots of pyroglutamate, pyruvate, and trimethylamine N-oxide measured by NMR for cysts with different sodium concentrations. Cysts with sodium concentration ≥ 100 mmol/L are shown in green, and those with concentration < 100 mmol/L in orange. The patients are sorted by the respective intercept in the Mixed Linear Effects Model.
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