Role of Advanced Glycated End Products (AGEs) in Predicting Diabetic Complications Using Machine Learning Tools: A Review from Biological Perspective

Abstract

AGEs are highly stable products formed by glycation of reducing sugars, proteins and lipids. Diabetes mellitus is the condition; where the cells cannot utilize the glucose present in body tend to glycate free proteins in surroundings and cause various complications. Once, the AGEs glycate tissue proteins, they accumulate over lifetime and therefore contributing to complications such as neurodegeneration, polyneuropathy, retinopathy, nephropathy and other macrovascular complications. AGEs can be formed in many ways and bind with RAGE cell surface receptor. Since the etiology of these diseases is now well understood and the biomarkers of the disease are available, it is important to consider them in all machine learning disease prediction tools. Machine learning and deep learning algorithms and tools are being used in predicting various diseases mostly on basis of medical history and symptoms. This review focuses about how different AGEs play role in pathogenesis of different diabetic complications and discusses the why these glycated proteins should be considered in prediction of diabetic complications using machine learning or by other means.