BACKGROUND AND AIMS: Albuminuria is not an effective marker for early diagnosis of diabetic renal complication with several subjects progressing to chronic kidney disease without any albuminuria. A biomarker that can predict early changes of the diabetic kidney will be useful in effective management of type 2 diabetes. Mass spectrometry based metabolomics approach offers tremendous promise for the identification of novel metabolite biomarkers. METHODS: A case-control approach was carried out to identify renal biomarkers among Asian Indian subjects in a hospital setting. A total of 29 subjects were included in the study that included groups of diabetic controls, diabetic subjects with eGFR >90 ml/min/1.72 m2 and diabetic subjects with eGFR between 60 and 89 ml/min/1.72 m2 and eGFR between 15 and 30 ml/min/1.72 m2. We employed an un-targeted mass spectrometry method for the identification of plasma metabolites. RESULTS: A total of 1414 and 975 metabolites were identified in the positive and negative ion mode respectively. 19 metabolites were up regulated and 18 metabolites were down regulated in CKD2 and CKD4 groups when compared to control. Correlation analysis of the differential metabolites revealed Pregnenolone sulfate, creatinine and ganglioside GA1 to be negatively correlated and hexyl glucoside, all-trans-carophyll yellow and PG to be positively correlated with eGFR. CONCLUSION: We have identified Pregnenolone sulfate, GA1, PG and all-trans-Carophyll yellow as biomarkers for early identification of diabetic nephropathy. These markers could aid in better management of diabetic nephropathy that may result delaying the progression of the disease.
BACKGROUND AND AIMS: Albuminuria is not an effective marker for early diagnosis of diabetic renal complication with several subjects progressing to chronic kidney disease without any albuminuria. A biomarker that can predict early changes of the diabetic kidney will be useful in effective management of type 2 diabetes. Mass spectrometry based metabolomics approach offers tremendous promise for the identification of novel metabolite biomarkers. METHODS: A case-control approach was carried out to identify renal biomarkers among Asian Indian subjects in a hospital setting. A total of 29 subjects were included in the study that included groups of diabetic controls, diabetic subjects with eGFR >90 ml/min/1.72 m2 and diabetic subjects with eGFR between 60 and 89 ml/min/1.72 m2 and eGFR between 15 and 30 ml/min/1.72 m2. We employed an un-targeted mass spectrometry method for the identification of plasma metabolites. RESULTS: A total of 1414 and 975 metabolites were identified in the positive and negative ion mode respectively. 19 metabolites were up regulated and 18 metabolites were down regulated in CKD2 and CKD4 groups when compared to control. Correlation analysis of the differential metabolites revealed Pregnenolone sulfate, creatinine and gangliosideGA1 to be negatively correlated and hexyl glucoside, all-trans-carophyll yellow and PG to be positively correlated with eGFR. CONCLUSION: We have identified Pregnenolone sulfate, GA1, PG and all-trans-Carophyll yellow as biomarkers for early identification of diabetic nephropathy. These markers could aid in better management of diabetic nephropathy that may result delaying the progression of the disease.