Diabetes is the leading cause of end stage kidney disease in the United States. Currently there is no biomarker to identify patients at high risk of progression of diabetic kidney disease (DKD) when GFR is preserved (>90 mL/min) and urine albumin excretion is within normal limit. In this study we aim to test the predictive power of C16-C24 free fatty acids (FFA)s and C40-C46 triacylglycerols (TAG)s in plasma to predict progression of DKD at early stage when estimated GFR (eGFR) is greater than 90 mL/min and urine albumin-creatinine ratio (ACR) is less than 30 mg/g. This study will be a case control observation in which the case group is defined as progression of DKD in longitudinal follow up visits. Patient population is the patients with type-1 diabetes. Study samples are selected from 4 established cohorts of patients with type-1 diabetes including the Steno Diabetes Center Copenhagen, the Finnish Diabetic Nephropathy (FinnDiane), the Colorado Coronary Artery Calcification in Type 1 diabetes (CACT1), and the Pittsburgh Epidemiology of Diabetic Complications (EDC). Sampling is based on the application of the inclusion and exclusion criteria. The inclusion criteria are age of 18 years or older at the time of sample selection, eGFR ?90 ml/min, ?3 longitudinal measure of eGFR, and follow up of more than 4 years. Exclusion criterion is age<18 years. Case group is defined as patients with type-1 diabetes who had >3 ml/min/year loss in eGFR during follow up. Control group is defined as patients with type-1 diabetes who had no or less than 1 mL/min/year loss in eGFR during follow up, frequency matched by age, sex, race, and eGFR at baseline with the case group. Overall, 350 patients including non-progressors and progressors with a 2:1 ratio are selected. After selection, patients will be randomly split to the training (57 progressors and 117 non- progressors) and validation cohorts. Outcome is progression of DKD defined as >3 mL/min/year loss in eGFR during follow up visits. Clinical data and plasma samples at baseline visit (corresponding date of matching cases and controls) are available. Targeted lipidomic studies (based on our preliminary data) will be applied to quantify the proposed lipids in multiple reaction monitoring (MRM) mode using an AB Sciex Triple Quadrupole/QTRAP 6500+ mass spectrometer. For analysis, we will apply t-test with false discovery rate correction for multiple comparisons using a compound by compound comparison for ability to predict DKD progression. Additionally, we will use principal component for data reduction, and will incorporate the significant lipids as well as the principal components separately in adjusted logistic regression models to test the independent prediction of proposed markers on DKD progression. We will calculate c-statistics and compare it to that of eGFR and ACR to assess the improvement of classification power. We will replicate the analysis in the validation subset consisting of 175 patients including non-progressors and progressors with 2:1 ratio. Collectively, we anticipate identifying a quantitative prognostic lipid panel that accurately predicts early DKD progression.
Diabetes is the leading cause of end stage kidney disease in the United States. To preserve kidney function and to halt diabetic kidney disease from progression, the individuals at high risk should be identified very early in the course when their kidney function is preserved and their urinary albumin excretion is within normal limit. Currently, there is no biomarker to identify individuals at higher risk of progression of diabetic kidney disease at such early stage. In this study, we are proposing testing the predictive power of a panel of lipid biomarkers to predict progression of diabetic kidney disease at such early stage in patients with type-1 diabetes when their kidney function is preserved and their urine albumin excretion is normal.
