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.
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.
Authors: Pierre-Jean Saulnier; Manjula Darshi; Kevin M Wheelock; Helen C Looker; Gudeta D Fufaa; William C Knowler; E Jennifer Weil; Stephanie K Tanamas; Kevin V Lemley; Rintaro Saito; Loki Natarajan; Robert G Nelson; Kumar Sharma Journal: Metabolomics Date: 2018-06-08 Impact factor: 4.290
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