AGEomics Biomarkers and Machine Learning—Realizing the Potential of Protein Glycation in Clinical Diagnostics

Abstract

Protein damage by glycation, oxidation and nitration is a continuous process in the physiological system caused by reactive metabolites associated with dicarbonyl stress, oxidative stress and nitrative stress, respectively. The term AGEomics is defined as multiplexed quantitation of spontaneous modification of proteins damage and other usually low-level modifications associated with a change of structure and function—for example, citrullination and transglutamination. The method of quantitation is stable isotopic dilution analysis liquid chromatography—tandem mass spectrometry (LC-MS/MS). This provides robust quantitation of normal and damaged or modified amino acids concurrently. AGEomics biomarkers have been used in diagnostic algorithms using machine learning methods. In this review, I describe the utility of AGEomics biomarkers and provide evidence why these are close to the phenotype of a condition or disease compared to other metabolites and metabolomic approaches and how to train and test algorithms for clinical diagnostic and screening applications with high accuracy, sensitivity and specificity using machine learning approaches.

An Introduction to the Special Issue “Protein Glycation in Food, Nutrition, Health and Disease”

“…The remarkable emerging role of RAGE in early life nurturing as well as the mother–infant bond and the role of glycation in organelle stress as well as metabolic derangement in kidney disease are also described [ 48 , 49 ]. The clinical diagnostics of glycation is now advancing from the role of A1C and glycated albumin in the assessment of glycemic control in diabetes [ 4 ] to the application of machine learning for the development of diagnostic algorithms with combinations of plasma and urinary AGEs as features for the risk prediction of diabetic kidney disease and other clinical conditions [ 50 ]. The health benefits of decreasing the clinical exposure of dietary AGEs to the microbiome were tested robustly in the “deAGEing Trial”—a 4-week diet low or high in AGEs in obese subjects.…”