Epigenetic Signatures for Continuous Metabolic Health Monitoring in at Risk Populations

Abstract

Among the frequently occurring metabolic disorders that persist to cause significant morbidity among the at-risk groups are obesity, diabetes, and cardiovascular diseases. The standard biomarkers of such disorders cannot typically be monitored continuously and in real time. In the given research paper, it is discussed whether the epigenetic signatures and, specifically, DNA methylation can serve as such an indicator of the present-day metabolic health. The state-of-the-art epigenomic tools, including bisulfite sequencing and DNA methylation arrays, are being used to measure the extent of epigenetic changes in individuals in high-risk groups, including obese patients, prediabetic patients, and hypertensive patients. The study discovered that some patterns of methylation are very much associated with the indicators of metabolic health, such as insulin resistance, lipid profiles, and body mass index. Besides, this study proposes a machine learning model that will utilize these epigenetic markers to predict the metabolic changes with high accuracy, sensitivity, and specificity. It has been demonstrated that the application of epigenetic signatures is not only applicable in the context of sustained metabolic health testing but could also be applicable in the sphere of personalized medicine, and is rather an inexpensive and less invasive methodology of early disease diagnosis. This paper suggests that it can be used in future applications in real-time monitoring systems and that longitudinal studies are necessary to further validate such observations. The achievement of the goal of developing the application of epigenetic biomarkers into a routine practice with the at-risk population is preconditioned by this article to reach the desired state of improved metabolic health and prevent diseases