Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus.

AIMS: Early detection of individuals with Type 2 diabetes mellitus or hypertension at risk for micro- or macroalbuminuria may facilitate prevention and treatment of renal disease. We aimed to discover plasma and urine metabolites that predict the development of micro- or macroalbuminuria. METHODS: Patients with Type 2 diabetes (n = 90) and hypertension (n = 150) were selected from the community-cohort 'Prevention of REnal and Vascular End-stage Disease' (PREVEND) and the Steno Diabetes Center for this case-control study. Cases transitioned in albuminuria stage (from normo- to microalbuminuria or micro- to macroalbuminuria). Controls, matched for age, gender, and baseline albuminuria stage, remained in normo- or microalbuminuria stage during follow-up. Median follow-up was 2.9 years. Metabolomics were performed on plasma and urine. The predictive performance of a metabolite for albuminuria transition was assessed by the integrated discrimination index. RESULTS: In patients with Type 2 diabetes with normoalbuminuria, no metabolites discriminated cases from controls. In patients with Type 2 diabetes with microalbuminuria, plasma histidine was lower (fold change = 0.87, P = 0.02) and butenoylcarnitine was higher (fold change = 1.17, P = 0.007) in cases vs. controls. In urine, hexose, glutamine and tyrosine were lower in cases vs. controls (fold change = 0.20, P < 0.001; 0.32, P < 0.001; 0.51, P = 0.006, respectively). Adding the metabolites to a model of baseline albuminuria and estimated glomerular filtration rate metabolites improved risk prediction for macroalbuminuria transition (plasma integrated discrimination index = 0.28, P < 0.001; urine integrated discrimination index = 0.43, P < 0.001). These metabolites did not differ between hypertensive cases and controls without Type 2 diabetes. CONCLUSIONS: Type 2 diabetes-specific plasma and urine metabolites were discovered that predict the development of macroalbuminuria beyond established renal risk markers. These results should be confirmed in a large, prospective cohort.

A urinary peptide biomarker set predicts worsening of albuminuria in type 2 diabetes mellitus.

AIMS/HYPOTHESIS: Microalbuminuria is considered the first clinical sign of kidney dysfunction and is associated with a poor renal and cardiovascular prognosis in type 2 diabetes. Detection of patients who are prone to develop micro- or macroalbuminuria may represent an effective strategy to start or optimise therapeutic intervention. Here we assessed the value of a urinary proteomic-based risk score (classifier) in predicting the development and progression of microalbuminuria. METHODS: We conducted a prospective case-control study. Cases (n = 44) and controls (n = 44) were selected from the PREVEND (Prevention of Renal and Vascular End-stage Disease) study and from the Steno Diabetes Center (Gentofte, Denmark). Cases were defined by transition from normo- to microalbuminuria or from micro- to macroalbuminuria over a follow-up of 3 years. Controls with no transitions in albuminuria were pair-matched for age, sex and albuminuria status. A model for the progression of albuminuria was built using a proteomic classifier based on 273 urinary peptides. RESULTS: The proteomic classifier was independently associated with transition to micro- or macroalbuminuria (OR 1.35 [95% CI 1.02, 1.79], p = 0.035). The classifier predicted the development and progression of albuminuria on top of albuminuria and estimated GFR (eGFR, area under the receiver operating characteristic [ROC] curve increase of 0.03, p = 0.002; integrated discrimination index [IDI]: 0.105, p = 0.002). Fragments of collagen and α-2-HS-glycoprotein showed significantly different expression between cases and controls. CONCLUSIONS/INTERPRETATION: Although limited by the relatively small sample size, these results suggest that analysis of a urinary biomarker set enables early renal risk assessment in patients with diabetes. Further work is required to confirm the role of urinary proteomics in the prevention of renal failure in diabetes.