Painting a new picture of personalised medicine for diabetes

Abstract

The current focus on delivery of personalised (or precision) medicine reflects the expectation that developments in genomics, imaging and other domains will extend our diagnostic and prognostic capabilities, and enable more effective targeting of current and future preventative and therapeutic options. The clinical benefits of this approach are already being realised in rare diseases and cancer but the impact on management of complex diseases, such as type 2 diabetes, remains limited. This may reflect reliance on inappropriate models of disease architecture, based around rare, high-impact genetic and environmental exposures that are poorly suited to our emerging understanding of type 2 diabetes. This review proposes an alternative 'palette' model, centred on a molecular taxonomy that focuses on positioning an individual with respect to the major pathophysiological processes that contribute to diabetes risk and progression. This model anticipates that many individuals with diabetes will have multiple parallel defects that affect several of these processes. One corollary of this model is that research efforts should, at least initially, be targeted towards identifying and characterising individuals whose adverse metabolic trajectory is dominated by perturbation in a restricted set of processes.

Keywords: Biomarkers; Complex disease; Diabetes; Environment; Genetics; Pathogenesis; Review; Risk; Taxonomy.

Fig. 1

The ‘palette’ model of type 2 diabetes. The concepts are illustrated using a model of six diabetes component pathways (‘base colours’) and four individuals, three of whom have diabetes. The grids display the range of trait variation for each of these component pathways and the position of each individual on each of those spectra. The pathophysiology of individual ‘a’ is dominated by a single process (shown in red), that of individuals ‘b’ and ‘c’ reflects contributions from multiple processes (resulting in an aggregated brown or grey colour, respectively). Individual ‘d’ does not have diabetes but shows type 2 diabetes-associated risk in the blue process. To the right, data from a larger cohort have been used to position individuals in a multidimensional space (reduced to two dimensions here for illustrative purposes) with respect to the status of each of the component pathways (with hue denoting the mixture of type 2 diabetes-associated contributions and saturation broadly reflecting diabetic status). Some individuals, such as individual ‘a’, lie at the extremes and represent ‘archetypes’ for particular component pathways, whereas individuals ‘b’ and ‘c’ lie more centrally, reflecting a more complex pathophysiology. Individual ‘d’ is currently not diabetic but the red dotted line describes their past and future path through this multidimensional space, up to and beyond the point at which diabetes is diagnosed. To convert values for HbA_1c in % into mmol/mol, subtract 2.15 and multiply by 10.929