First, the palette model is clearly consistent with the current understanding of type 2 diabetes as a multisystem disease with evident abnormalities in the pancreas, fat, muscle, liver and, most likely, the brain.
Second, this approach is supported by the emerging genetic data. Amongst the >150 common variant signals for diabetes (all types) identified in genome-wide association studies (GWAS), there are examples of loci that influence each of the following processes: HLA loci for autoimmunity, HNF1A and WFS1 for islet development, CDKN2A for islet senescence, KLF14 and PPARG for adipogenesis, FTO for obesity, KCNJ11 for islet function and so on [15, 16]. Carriers of risk alleles for each of these association signals are shifted a little rightwards in the respective trait spectrum, towards higher saturation (see Fig. 1). However, irrespective of the process directly involved, the consequence is only a subtle increase in diabetes risk. With better regulatory maps for key diabetes-relevant tissues, it is now possible to tie these genetic variants to tissue-specific effects on component pathways, not only on the basis of their impact on whole-body physiology , but also through their relationships with tissue-specific patterns of genome regulation and gene transcription . For example, the type 2 diabetes GWAS-identified variant near KLF14 is now known to exert its effects exclusively via the regulation of gene transcription in adipose tissue .
Third, this view of diabetes pathogenesis is consistent with the growing portfolio of available therapies. We have agents and interventions that can prevent or ameliorate diabetes through, for example, beneficial effects on islet function (e.g. sulfonylureas), obesity (weight loss), insulin resistance (e.g. exercise), fuel partitioning (e.g. thiazolidinediones) and microbiome content (metformin, possibly). Just as diabetes risk alleles influence metabolic phenotype through pushing individual positions on particular component spectra to the right, these interventions drive them to the left, towards a reduction in diabetes risk (see Fig. 1). The fact that most patients with diabetes respond to most treatments (admittedly to different degrees) irrespective of the specific processes through which they act fits the notion that diabetes represents the aggregate effect of multiple parallel contributions to disease. It should be noted, however, that type 1 diabetes is a special case in this regard: the absolute and permanent loss of insulin secretion clearly ‘breaks’ the system and limits the potential for beneficial interventions in other components to compensate and return the patient towards metabolic health.
As a framework for the taxonomy of diabetes, this model has greater compatibility with clinical observation and, in particular, the graded heterogeneity of the diabetic phenotype. It imagines individuals (both those with and without diabetes) positioned within a multidimensional space (reduced to two dimensions in Fig. 1, for illustrative purposes), the axes of which reflect the various processes that contribute to diabetes pathogenesis. For most people with diabetes (such as individual ‘c’ in Fig. 1), diabetes is not the consequence of marked failure in any one process; instead it results from the aggregate impact of several contributions to risk, any one of which may be entirely unremarkable in isolation. We could imagine that individual ‘c’ has a genetic risk profile and a history of life-course exposures that means they are overweight with a somewhat adverse distribution of excess fat. In conjunction with a modest reduction in insulin secretory reserve, reflecting a below-average complement of slightly underperforming beta cells, this configuration of metabolic characteristics suffices to drive that person towards diabetes. In the palette analogy, such an individual would end up a nondescript shade of taupe (between grey and brown), reflecting the concomitant contribution from multiple pathogenetic processes.
In contrast, there will be some individuals with diabetes for whom one of the component pathways has played a predominant role in the development of disease. Most obviously, this applies to those with monogenic disease (MODY, neonatal diabetes, lipodystrophies). But even in those families in which high-impact ‘causal’ alleles may be segregating, individual phenotypes will remain subject to the usual population variation in diabetes-related genetic risk and environmental exposures, introducing opportunities for phenotypic heterogeneity. Nevertheless, we would still expect such individuals, by virtue of their high-impact alleles, to cluster near the edges and corners of the multidimensional space. This would also be true for those with type 1 diabetes (especially early-onset type 1 diabetes), in whom there is a dominant contribution of islet autoimmunity. Other individuals with ‘asymmetric’ patterns of exposure across diabetes component pathways may also gravitate to the corners and edges of this space, such as those with extreme obesity perhaps, or those with ‘lean type 2 diabetes’ (in whom beta cell insufficiency is likely to be particularly influential). In the palette analogy, such individuals would be represented by a ‘purer’ hue (for example, individual ‘a’ in Fig. 1).
This model, which is based around a pathophysiological taxonomy, lends itself to the generation of a diagnostic classification system that, for the subset of individuals who map to the edges and corners, is superficially compatible with the pigeonhole model. At the same time however, through explicit recognition that for most people with diabetes multiple parallel processes (both primary and secondary) have contributed to disease development, the palette model makes it clear that aetiological complexity is not compatible with simplistic diagnostic categorisation.
A model based on a pathophysiological or molecular taxonomy, rather than rigid diagnostic classification, also has the advantage of more easily encompassing a dynamic, life-course view of disease evolution. Using this approach, one should be able to plot an individual’s path through multidimensional space as they transition from health to diabetes (or not). In early life, that individual’s location in multidimensional ‘metabolic’ space would be dominated by the impact of their genetic profile and early life environment on the function of the various diabetes component processes. As the individual matures and ages, diverse environmental exposures (related to employment, medication or other external circumstances) may increasingly leave their mark. If the net effect of these is to nudge the individual away from a position of compensated homeostasis, they will start to track along a path that leads towards others with diabetes, along the way adding reactive effects to the phenotypic mix that are secondary to disease progression (e.g. islet glucotoxicity) or interventions (e.g. addition of statins).