Precision Medicine in Type 2 Diabetes: Using Individualized Prediction Models to Optimize Selection of Treatment

Abstract

Despite the known heterogeneity of type 2 diabetes and variable response to glucose lowering medications, current evidence on optimal treatment is predominantly based on average effects in clinical trials rather than individual-level characteristics. A precision medicine approach based on treatment response would aim to improve on this by identifying predictors of differential drug response for people based on their characteristics and then using this information to select optimal treatment. Recent research has demonstrated robust and clinically relevant differential drug response with all noninsulin treatments after metformin (sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 [DPP-4] inhibitors, glucagon-like peptide 1 [GLP-1] receptor agonists, and sodium–glucose cotransporter 2 [SGLT2] inhibitors) using routinely available clinical features. This Perspective reviews this current evidence and discusses how differences in drug response could inform selection of optimal type 2 diabetes treatment in the near future. It presents a novel framework for developing and testing precision medicine–based strategies to optimize treatment, harnessing existing routine clinical and trial data sources. This framework was recently applied to demonstrate that “subtype” approaches, in which people are classified into subgroups based on features reflecting underlying pathophysiology, are likely to have less clinical utility compared with approaches that combine the same features as continuous measures in probabilistic “individualized prediction” models.

Prototype of an evidence‐based tool to aid individualized treatment for type 2 diabetes

“…As research continues to advance the goal of offering probabilistic, individualized predictions for diabetes treatment, there may be opportunities to integrate novel statistical methods to maximize robustness while minimizing bias. 4 In conclusion, this prototype illustrates how evidence-based individualized treatment selection may be realized in the clinic for people with T2D. Such an instrument would be welcomed to assist physicians in optimizing individualized treatment for their patients.…”

“…4 An expert perspective on precision medicine in T2D emphasized that for most people without high-risk co-morbidities, there is often no clear choice to suggest the 'best' antihyperglycaemic medication. 4 Clinical tools for individualized care have been developed and implemented for other diseases. For example, the Framingham Cardiovascular Risk Score allows for the input of a range of patientspecific characteristics, and provides a risk score for developing heart disease over the next 10 years, with associated indications for different preventative strategies.…”

TriMaster: randomised double-blind crossover study of a DPP4 inhibitor, SGLT2 inhibitor and thiazolidinedione as second-line or third-line therapy in patients with type 2 diabetes who have suboptimal glycaemic control on metformin treatment with or without a sulfonylurea—a MASTERMIND study protocol

“… 5–8 However, to be most useful for stratification a marker needs to predict differential response between therapies. 9 Work by the MASTERMIND consortium using routine and trial data has strengthened the evidence that clinical features are associated with differential glycaemic response to dipeptidyl peptidase‐4 (DPP4)-inhibitors, sodium-glucose co-transporter-2 (SGLT2)-inhibitors and thiazolidinediones. 10–12 Analysis of data from the UK Clinical Practice Research Datalink (CPRD) and a Diabetes Outcome Progression Trial (ADOPT) trial showed that sex and body mass index (BMI) above and below 30 were associated with differential glycaemia response between sulfonylureas and thiazolidinediones.…”

Data Mining Classification Techniques for Diabetes Prediction

“…Nowadays, machine learning (ML) used in every area of computational work where algorithms are designed, and performance is increased [1] [2]. In the last years, learning from unbalanced data sets has become a critical problematic in machine learning and is frequently found in several applications such as computer security, Swarm Intelligence [3] [4] , remote sensing [5], biomedicine [6] .…”

“…opening up a window of relatively stronger funds, this effective technique increases the sensitivity and/or accuracy of disease identification and diagnosis. The cost of unnecessary and costly diagnostic examinations often declines substantially [2] [3]. For several years' extensive experiments have been carried out in connection with diabetes prediction [4] [5] [6].…”