Metabolomics in Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a leading cause of chronic kidney failure in people with diabetes, driven by complex metabolic disturbances that precede overt loss of renal function. Metabolomics, the comprehensive study of small-molecule metabolites in biological systems, has emerged as a powerful approach to unravel these disturbances, identify early-stage biomarkers and elucidate pathogenic pathways. Using techniques such as nuclear magnetic resonance spectroscopy and mass spectrometry, both targeted and untargeted metabolomic studies have profiled serum, urine and tissue specimens to reveal perturbations in amino acid metabolism, lipid species, energy intermediates and gut microbiota-derived compounds. Key findings include altered levels of branched-chain amino acids, polyols such as myo-inositol, aromatic metabolites and specific phospholipids that correlate with glomerular filtration rate, albuminuria and progression risk. Integration with machine-learning algorithms has further refined risk stratification and predictive modelling. Collectively, these efforts provide mechanistic insights into DKD onset and progression, inform patient selection for therapeutic interventions and point towards novel targets for disease modification.

Research from Nature Portfolio

One untargeted study identified phenyl sulfate, a metabolite produced by gut microbiota, as a driver of albuminuria and podocyte injury. Experimental administration of phenyl sulfate induced proteinuria in diabetic models, while inhibition of its microbial precursor enzyme ameliorated renal damage, suggesting a therapeutic axis. Another lipidomic investigation of individuals with type 1 diabetes revealed that specific sphingomyelin and alkyl-acyl phosphatidylcholine species are inversely associated with risk of renal endpoint events and mortality. These lipids exhibited cross-sectional correlations with estimated glomerular filtration rate (eGFR) and longitudinal predictions of renal decline. A third work employing high-throughput NMR spectroscopy profiled dozens of circulating metabolites in diabetic and non-diabetic cohorts, demonstrating that amino acids (glycine, phenylalanine) and energy-related metabolites (citrate, glycerol) consistently correlate with eGFR, while lipoprotein lipid subclasses show distinct associations in diabetic versus non-diabetic kidney disease.

Metabolomics in Diabetic Kidney Disease publication trend

The graph below shows the total number of articles in metabolomics in diabetic kidney disease across all publications each year (not limited to Nature Index journals).

Technical terms

Metabolomics: The systematic analysis of small molecules (metabolites) in cells, tissues or biofluids to characterise physiological and pathological states.

Untargeted metabolomics: A global profiling strategy that detects and quantifies as many metabolites as possible without a predefined list.

eGFR (estimated glomerular filtration rate): A calculated measure of kidney function based on serum creatinine, age, sex and other variables.

Albuminuria: The presence of albumin in urine, indicating glomerular damage and a risk factor for DKD progression.

Multi-omics: The integration of two or more high-throughput biological datasets (e.g. genomics, proteomics, metabolomics) to gain comprehensive molecular insights.

References

1. Integration of metabolomics and peptidomics reveals distinct molecular landscape of human diabetic kidney disease. Theranostics (2023).
2. Endogenous adenine mediates kidney injury in diabetic models and predicts diabetic kidney disease in patients. Journal of Clinical Investigation (2023).
3. Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites. Journal of Medical Internet Research (2024).
4. Gut microbiome-derived phenyl sulfate contributes to albuminuria in diabetic kidney disease. Nature Communications (2019).
5. Lipidomic analysis reveals sphingomyelin and phosphatidylcholine species associated with renal impairment and all-cause mortality in type 1 diabetes. Scientific Reports (2019).
6. Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations. Scientific Reports (2018).
7. Metabolomic Assessment Reveals Alteration in Polyols and Branched Chain Amino Acids Associated With Present and Future Renal Impairment in a Discovery Cohort of 637 Persons With Type 1 Diabetes. Frontiers in Endocrinology (2019).