Abstract
Polycystic kidney disease (PKD) is a common hereditary kidney disorder with an insidious onset and complex mechanisms, making early diagnosis challenging. Current diagnostic methods, including imaging and genetic testing, have limitations. Imaging detects cysts only once formed, and genetic testing is costly, labor-intensive, and unable to guarantee complete diagnostic accuracy or predict onset and progression. Metabolomics may offer an approach to uncover PKD mechanisms by detecting metabolic alterations that precede clinical manifestations, enabling identification of disease-specific biomarkers, and providing earlier and more practical detection than imaging or genetic testing. This review synthesizes existing metabolomics studies of human PKD and renal cystic disease with significant metabolites extracted and pathway enrichment analyses conducted at the levels of overall cystic kidney disease and the autosomal dominant polycystic kidney disease (ADPKD)/Pkd1/non-Pkd1 subsets to identify significant pathways and their associated metabolic features. Overall, the top-ranked pathways were arginine biosynthesis; alanine, aspartate, and glutamate metabolism; glycine, serine, and threonine metabolism; and the citrate cycle, with glycine, serine, and threonine metabolism only associated with ADPKD/Pkd1 mouse models. These pathways indicate marked activation of amino acid metabolism accompanied by relative suppression of carbohydrate-related energy metabolism, suggesting a shift in metabolic substrate utilization rather than a complete loss of energy supply. This study provides a foundation for applying metabolomics to early diagnosis and mechanistic investigation of cystic kidney disease.
Keywords
metabolic pathway analysis, metabolomics, polycystic kidney disease, prediagnosis, renal cystic disease
Document Type
Journal Article
Date of Publication
9-1-2026
E-ISSN
15221466
ISSN
1931857X
Volume
331
Issue
3
PubMed ID
42490172
Publication Title
American Journal of Physiology - Renal Physiology
Publisher
American Physiological Society
School
School of Science
RAS ID
100617
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
First Page
F330
Last Page
F344
Recommended Citation
Chen, L., Broadhurst, D., Shafaei, A., Phillips, J. K., Tang, S., & Abbiss, H. (2026). Metabolomics in renal cystic disease: Links to mechanisms and identification of pathways for potential prediagnosis. American Journal of Physiology - Renal Physiology, 331(3), F330–F344. https://doi.org/10.1152/ajprenal.00334.2025