Lifestyle and precision diabetes medicine: will genomics help optimise the prediction, prevention and treatment of type 2 diabetes through lifestyle therapy?

Abstract

Precision diabetes medicine, the optimisation of therapy using patient-level biomarker data, has stimulated enormous interest throughout society as it provides hope of more effective, less costly and safer ways of preventing, treating, and perhaps even curing the disease. While precision diabetes medicine is often framed in the context of pharmacotherapy, using biomarkers to personalise lifestyle recommendations, intended to lower type 2 diabetes risk or to slow progression, is also conceivable. There are at least four ways in which this might work: (1) by helping to predict a person's susceptibility to adverse lifestyle exposures; (2) by facilitating the stratification of type 2 diabetes into subclasses, some of which may be prevented or treated optimally with specific lifestyle interventions; (3) by aiding the discovery of prognostic biomarkers that help guide timing and intensity of lifestyle interventions; (4) by predicting treatment response. In this review we overview the rationale for precision diabetes medicine, specifically as it relates to lifestyle; we also scrutinise existing evidence, discuss the barriers germane to research in this field and consider how this work is likely to proceed.

Keywords: Biomarkers; Lifestyle; Precision medicine; Review; Type 2 diabetes.

Fig. 1

Type 2 diabetes results from the complex interplay between environmental and genomic factors. The model is thus one of primers and catalysts, whereby environmental triggers act against a backdrop of genetic susceptibility to affect the transcriptional and regulatory processes that cause diabetes (e.g. through methylation, chromatin remodelling or histone modifications). The figure shows the key lifestyle risk factors, candidate loci (with evidence of gene–lifestyle interactions) and target organs purported to affect adiposity and/or glycaemic control

Fig. 2

Precision medicine for type 2 diabetes. A schematic showing key time points for intervention in the course of type 2 diabetes (T2D) pathophysiology where precision lifestyle medicine might play a role

Fig. 3

Estimating the required power for clinical trials focused on interaction effects of diabetes risk factors that have been previously reported in epidemiological studies. The figure compares two core studies focused on the interaction of FTO variants and lifestyle in obesity. We sought to estimate the power that the sample size reported by Livingstone et al [40] had to detect the interaction of the FTO variant and physical activity in obesity, as previously described in Kilpeläinen et al [32]. The conclusions regarding power and sample size in trials in this figure are predicated on the assumption that the interaction effect reported in the cross-sectional epidemiological analysis [32] can be applied to the setting of a randomised lifestyle intervention meta-analysis. However, there are several factors that are likely to confound this comparison; these are outlined in the figure. The given estimates of power and sample size are intended only to illustrate that the trials are likely to be substantially underpowered to observe previously reported interaction effects, rather than provide precise estimates of these variables