Precision medicine in diabetes: an opportunity for clinical translation

Abstract

Metabolic disorders present a public health challenge of staggering proportions. In diabetes, there is an urgent need to better understand disease heterogeneity, clinical trajectories, and related comorbidities. A pressing and timely question is whether we are ready for precision medicine in diabetes. Some biological insights that have emerged during the last decade have already been used to direct clinical decision making, especially in monogenic forms of diabetes. However, much work is necessary to integrate high-dimensional explorations into complex disease architectures, less penetrant biological alterations, and broader phenotypes, such as type 2 diabetes. In addition, for precision medicine to take hold in diabetes, reproducibility, interpretability, and actionability remain key guiding objectives. In this review, we examine how mounting data sets generated during the last decade to understand biological variability are now inspiring new venues to clarify diabetes nosology and ultimately translate findings into more effective prevention and treatment strategies.

Keywords: diabetes; diabetes heterogeneity; omics; precision medicine.

Figure 1.

Heterogeneity of diabetes. The pie chart represents the multiple ways people can develop hyperglycemia and reach the diagnosis of diabetes. The size of each piece only represents an approximate proportion of prevalence in the population. IR, rare genetic forms of insulin resistance; MIDD, maternally inherited diabetes and deafness; MODY, maturity-onset diabetes of the young; type 1, type 1 diabetes; PGA, diabetes caused in the setting of polyglandular autoimmune syndrome; LADA, latent autoimmune diabetes of adults; KPD, ketosis-prone diabetes; type 2a–2e, hypothetical subgroups of type 2 diabetes.

Figure 2.

Implementing precision medicine in diabetes. A hypothetical example illustrating how precision medicine might deconstruct traditional clinical-based categories through the study and integration of the many axes of biological information that can serve to parse current heterogeneous syndromes into homogeneous clusters. The example suggests that the way to prevent new cases of diabetes or treat individuals with diabetes should be tailored to the specific molecular event or pathway that raises glycemia.