The Continuing Evolution of Precision Health in Type 2 Diabetes: Achievements and Challenges

Abstract

Purpose of Review The purpose of this review was to summarize recent advances in the genomics of type 2 diabetes (T2D) and to highlight current initiatives to advance precision health. Recent Findings Generation of multi-omic data to measure each of the 'biologic layers', developments in describing genomic function and annotation in T2D relevant tissue, along with the increasing recognition that T2D is a heterogeneous disease, and large-scale collaborations have all contributed to advancing our understanding of the molecular basis of T2D. Summary Substantial advances have been made in understanding the molecular basis of T2D pathogenesis, such that precision health diabetes is increasingly becoming a reality. For precision diabetes to become routine in clinical and public health, additional large-scale multiomic initiatives are needed along with better assessment of our environment to delineate an individual's diabetes subtype for improved detection and management.

Current Progress in Pharmacogenetics of Second-Line Antidiabetic Medications: Towards Precision Medicine for Type 2 Diabetes

“…Since most studies have been conducted based on very limited study designs and sample size and thus somewhat inconsistent outcomes have been shown, additional large-scale clinical trials and GWAS approaches are needed to clarify the clinical implications of these genes and to identify novel genetic biomarkers in the prevention and treatment of T2D and its complications. More recently, “multiomic” applications integrating genetic, metabolomic and/or proteomic data are emerging in the field of disease risk prediction model, providing information on the mutual impacts of genetic variations and metabolic differences for the early diagnosis, treatment, and prevention of various complex diseases including T2D [164,165].…”

Disaggregating diabetes

“…Most analyses of cluster-related data have been conducted in wealthy nations (Ahlqvist et al 2018;Dahl et al 2019;Li et al 2019;Udler et al 2018), although evidence from China also suggests subtype variation (Tuomi et al 2014). Where investigators have not derived clusters to directly account for genetic variance, they point to genetic variation as key (Ahlqvist et al 2018;Lin and Wessel 2019).…”