Comprehensive and quantitative urinary metabolomic profiling for improved characterization of diabetic nephropathy

Area of Science:

• Biochemistry
• Metabolomics
• Nephrology

Background:

• Diabetic nephropathy (DN) is a leading cause of chronic kidney disease and end-stage renal failure.
• Current diagnostic markers like albuminuria lack specificity and detect damage late.
• Urinary metabolic profiling offers a potential avenue for earlier and more specific detection.

Purpose of the Study:

• To characterize urinary metabolic alterations in patients with diabetic nephropathy (DN).
• To identify potential metabolite panels for the non-invasive diagnosis of DN.
• To explore the utility of metabolomics in understanding DN pathophysiology.

Main Methods:

• Targeted urinary metabolomics analysis quantifying 268 metabolites using the TMIC Urine MEGA Assay.
• Analysis of samples from 60 participants: 20 controls, 20 type 2 diabetes mellitus (DM-2), and 20 DN patients.
• Statistical analysis included Partial Least Squares Discriminant Analysis (PLS-DA) and penalized regression (LASSO, Elastic Net) with logistic regression.

Main Results:

• DN patients exhibited significant alterations in metabolites linked to oxidative stress, mitochondrial dysfunction, and inflammation.
• A panel of four metabolites (β-alanine, kynurenine, glucose, argininic acid) distinguished DN patients with an AUC of 0.905.
• Inclusion of estimated Glomerular Filtration Rate (GFR) and additional metabolites improved diagnostic performance (AUC=0.96).

Conclusions:

• Quantitative urinary metabolomics reveals key metabolic perturbations in DN.
• Candidate metabolite panels show potential for non-invasive DN characterization and diagnosis.
• Further validation in larger cohorts is needed to integrate metabolomics into precision diagnostics for diabetic kidney disease.