Predictive biomarkers for type 2 of diabetes mellitus: Bridging the gap between systems research and personalized medicine

Abstract

The global incidence of metabolic disorders like type 2 diabetes mellitus (DM2) has assumed epidemic proportions, leading to adverse health and socio-economic impacts. It is therefore of critical importance the early diagnosis of DM2 patients and the detection of those at increased risk of disease. In this respect, Precision Medicine (PM) is an emerging approach that includes practices, tests, decisions and treatments adapted to the characteristics of each patient. With regard to DM2, PM manages a wealth of "omics" data (genomic, metabolic, proteomic, environmental, clinical and paraclinical) to increase the number of clinically validated biomarkers in order to identify patients in early stage even before the prediabetic phase.

Significance: In this paper, we discuss the epidemic dimension of metabolic disorders like type 2 diabetes mellitus (DM2) and the urgent demand for novel biomarkers to reduce the incidence or even delay the onset of DM2. Recent research data produced by "multi-omics" technologies (genomics/epigenomics, transcriptomics, proteomics and metabolomics), suggest that many potential biomarkers might be helpful in the prediction and early diagnosis of DM2. Predictive, Preventive and Personalized Medicine (PPPM) manages and integrates these data to apply personalized, preventive, and therapeutic approaches. This is significant because there is an emerging need for establishing channels for communication and personalized consultation between systems research and precision medicine, as the medicine of the future.

Keywords: Diabetes mellitus 2; Metabolic diseases; Microbiota; Omics; Precision; Predictive preventive personalized medicine; Preventive biomarkers; Proteomics.