Urinary Lipid Metabolites and Progression of Kidney Disease in Individuals with Type 2 Diabetes

Background: Some individuals with diabetes experience a fast decline (FD) in kidney function; however, standard clinical measurements often fail to identify these high-risk fast decliners, complicating individualized medical approaches. Lipids are closely associated with diabetic kidney disease (DKD). This study aimed to investigate whether urinary lipid metabolite profiles can predict the rapid progression of kidney dysfunction in patients with type 2 diabetes (T2D). Methods: We conducted targeted lipid profiling analyses to select differential lipid metabolites between participants with DKD and those with uncomplicated diabetes using univariate statistical analysis. Random Forest (RF) and Boruta analyses were performed to identify candidate biomarkers based on these differential metabolites. FD in kidney function was defined according to the quartiles of the annual rate of eGFR decline during the follow-up period. Receiver operating characteristic (ROC) curves were used to assess whether these lipid metabolites improve the prediction of rapid eGFR decline. Results: The abundance of the 21 differential metabolites between the two groups were markedly higher in the DKD group compared to matched uncomplicated diabetes group. Nine and eight candidate biomarkers upon the 21 lipid metabolites were selected by Boruta and RF, respectively. The baseline concentrations of the 21 individual lipid metabolites were significantly higher in the RD than the non-RD group, where these urinary lipid metabolites were found to be predictors for future kidney function decline in patients with T2D, outperforming albuminuria. Conclusion: These findings highlight the association of lipid metabolites with DKD and demonstrate the potential of using urinary lipids to assess the risk of rapid progression of kidney dysfunction in individuals with T2D. Funding: Government Support – Non-U.S.