Diabetes is increasing in prevalence worldwide, giving rise to a spectrum of associated comorbidities including cardiovascular, renal and liver complications and posing a significant global health crisis. These complications, along with the burden of living with a chronic illness, contribute to impaired quality of life and disability and ultimately increased mortality in people living with diabetes.1 Public health strategies have been unsuccessful at controlling the epidemic, and current pharmacological therapy is often suboptimal—focused largely on glycaemic regulation, with prescribing practice sometimes influenced by non-medical factors such as drug cost. As a result, opportunities to target therapy choice to underlying biological mechanisms of diabetes and relevant comorbidities may be overlooked.2

Recognising the highly heterogeneous nature of diabetes, the concept of precision diabetes medicine has garnered attention in modern diabetes care. Research breakthroughs in diabetes in recent decades create the potential for a revolution in care, incorporating a more comprehensive analysis of multiple biological factors, including genomic variations and metabolic pathways, with environmental risk factors for individual patients.2 For example, recent research has shed light on the heterogeneity of type 2 diabetes (T2D), which has traditionally been viewed as a uniform pathology. This heterogeneity exists as a continuum and is associated with both phenotypic and genotypic variations, resulting in differential disease progression, end-organ complications and treatment response.3 4 Additionally, predictive algorithms derived from patient-level characteristics within a UK-based population database demonstrated that this phenotypic heterogeneity could be used to guide treatment choices (eg, for choosing between sodium-glucose transporter 2 [SGLT2] inhibitor vs dipeptidyl peptidase-4 [DPP4] inhibitor) to optimise glycaemic control.5 More recently, clinical diagnostic models incorporating clinical features and biomarkers (such as pancreatic autoantibodies and Type 1 Diabetes (T1D) Genetic Risk Score) may allow enhanced differentiation between T1D and T2D at the time of diagnosis.6 Similarly, the identification of monogenic forms of diabetes has allowed for the cessation of unnecessary diabetes treatment in patients with GCK-MODY and spared patients with other forms, such as neonatal diabetes and HNF1A-MODY, from unnecessary insulin injections.7–9 However, incorporating these advances into routine clinical care, translating them to direct benefit to patients, is challenging and can be slow. For example, despite being well described in the literature, it has been estimated that approximately 80% of monogenic diabetes cases remain undiagnosed, highlighting the need for more effective ways to translate research outcomes into routine clinical care.10

Introducing precision medicine in diabetes care, as outlined in the consensus American Diabetes Association/European Association for the Study of Diabetes Precision Diabetes Medicine Initiative report, requires the transformation of the current care approaches.11 Using a range of data, a more refined diabetes care model should be developed, which encompasses all aspects of the disease—disease prevention, timely diagnosis, personalised treatment, individualised prognosis and appropriate monitoring of diabetes.

We aim to develop and assess an innovative ‘intelligent Diabetes’ (iDiabetes) platform to support and implement a precision diabetes care model, delivered at scale, to patients with diabetes managed in both primary and secondary care in the Tayside Health Board region of Scotland. The platform aims to facilitate precision diagnosis and promotes the selection of optimal treatments by healthcare professionals while ensuring that these diagnoses and treatment recommendations are effectively communicated to patients.

This study will compare two different arms of the iDiabetes platform to usual diabetes care. The iDiabetes (guideline support) arm will use routinely collected clinical and biochemical data to produce individualised treatment recommendations.

The iDiabetesPlus arm will combine routinely collected clinical and biochemical data with additional biochemical and genotypic testing to determine a patient’s risk of current and future end-organ diabetes complications and predicted glycaemic response to different pharmacotherapies. This information will be used to generate enhanced individualised treatment recommendations.

The treatment recommendations generated in both arms of the iDiabetes platform will allow healthcare professionals to make better-informed, individualised evidence-based treatment decisions, thereby improving the management for each patient. If demonstrated to improve patient care and to be cost-effective, the vision is for iDiabetes to be the standard of care for patients with diabetes across Scotland.