Achieving Good Metabolic Control Without Weight Gain with the Systematic Use of GLP-1-RAs and SGLT-2 Inhibitors in Type 2 Diabetes: A Machine-learning Projection Using Data from Clinical Practice

Abstract

PurposeRecently, the 2022 American Diabetes Association and European Association for the Study of Diabetes (ADA-EASD) consensus report stressed the importance of weight control in the management of patients with type 2 diabetes; weight control should be a primary target of therapy. This retrospective analysis evaluated, through an artificial-intelligence (AI) projection of data from the AMD Annals database—a huge collection of most Italian diabetology medical records covering 15 years (2005–2019)—the potential effects of the extended use of sodium–glucose co-transporter 2 inhibitors (SGLT-2is) and of glucose-like peptide 1 receptor antagonists (GLP-1-RAs) on HbA1c and weight. MethodsData from 4,927,548 visits in 558,097 patients were retrospectively extracted using these exclusion criteria: type 1 diabetes, pregnancy, age >75 years, dialysis, and lack of data on HbA1c or weight. The analysis revealed late prescribing of SGLT-2is and GLP-1-RAs (innovative drugs), and considering a time frame of 4 years (2014–2017), a paradoxic greater percentage of combined-goal (HbA1c <7% and weight gain <2%) achievement was found with older drugs than with innovative drugs, demonstrating aspects of therapeutic inertia. Through a machine-learning AI technique, a “what-if” analysis was performed, using query models of two outcomes: (1) achievement of the combined goal at the visit subsequent to a hypothetical initial prescribing of an SGLT-2i or a GLP-1-RA, with and without insulin, selected according to the 2018 ADA-EASD diabetes recommendations; and (2) persistence of the combined goal for 18 months. The precision values of the two models were, respectively, sensitivity, 71.1 % and 69.8%, and specificity, 67% and 76%. FindingsThe first query of the AI analysis showed a great improvement in achievement of the combined goal: 38.8% with prescribing in clinical practice versus 66.5% with prescribing in the “what-if” simulation. Addressing persistence at 18 months after the initial achievement of the combined goal, the simulation showed a potential better performance of SGLT-2is and GLP-1-RAs with respect to each antidiabetic pharmacologic class or combination considered. ImplicationsAI appears potentially useful in the analysis of a great amount of data, such as that derived from the AMD Annals. In the present study, an LLM analysis revealed a great potential improvement in achieving metabolic targets with SGLT-2i and GLP-1-RA utilization. These results underscore the importance of early, timely, and extended use of these new drugs.