(P11-015-24) Quantitative Analysis of a Panel of Advanced Glycation End Products Through the Integration of Enzymatic Hydrolysis and Targeted Metabolomics

Objectives: This study aimed to develop a sensitive and accurate method for measuring a panel of advanced glycation end products (AGEs) in biological samples. AGEs are proteins or lipids that become glycated as a result of exposure to reactive carbonyl species. Increased formation of AGEs has been implicated in the development of diabetic complications, chronic kidney diseases, and age-associated diseases.

Methods: Using the combination of enzymatic hydrolysis and a LC/MS-based targeted metabolomics, we developed a robust method to analyze a panel of 15 AGEs in biological samples. This method carefully addressed the challenges encountered in analyzing individual AGEs in biological samples, including the type of enzymes used for sample hydrolysis, matrix effects, and the selection of column. The developed method was applied to analyze AGEs in plasma and tissue samples collected from methylglyoxal (MGO) or the combination of MGO and high-fat diet treated mice. Five isotope-labeled AGEs were used as internal standards in this method.

Results: The developed method successfully addressed stability issues and peak splitting problems associated with certain AGEs. It also effectively reduced background AGEs from enzymes, eliminated the need for ion-pair reagents, and shortened the run time of each analysis. This method allows for accurate measurement of a panel of 15 major AGEs in biological samples. Furthermore, MGO and high-fat plus MGO treatments significantly increased the levels of MGO-related AGEs, such as MG-H1, CEL, and CEA, in mouse plasma and kidney.

Conclusions: The integrated approach of enzymatic hydrolysis and targeted metabolomics offers a robust platform for accurately and reliably determining a panel of important AGEs. This method can be used to further evaluate AGEs as potential biomarkers for monitoring the progression and developing treatment strategies for diabetic complications, kidney diseases, and age-associate chronic diseases.

Funding Sources: This work was supported by funding from NIH grant R01DK131753 to S. Sang.