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Achieving Good Metabolic Control Without Weight Gain with the Systematic Use of GLP-1-RAs and SGLT-2 Inhibitors in
Carlo Bruno Giorda1, Antonio Rossi2, Fabio Baccetti3
1Metabolism and Diabetes Unit, ASL, Torino, Italy.
Clinical Therapeutics
|July 14, 2023
Summary
Artificial intelligence analysis suggests improved type 2 diabetes management with early use of sodium-glucose co-transporter 2 inhibitors (SGLT-2is) and GLP-1 receptor agonists (GLP-1-RAs). These drugs show potential for better achievement and persistence of metabolic goals compared to older treatments.
Area of Science:
- Endocrinology and Metabolism
- Artificial Intelligence in Healthcare
- Pharmacological Research
Background:
- The 2022 ADA-EASD consensus emphasizes weight control as a primary therapeutic target for type 2 diabetes.
- Sodium-glucose co-transporter 2 inhibitors (SGLT-2is) and GLP-1 receptor agonists (GLP-1-RAs) are innovative drug classes with potential benefits for glycemic control and weight management.
- Therapeutic inertia, characterized by delayed adoption of newer, more effective treatments, can hinder optimal patient outcomes.
Purpose of the Study:
- To evaluate the potential effects of extended use of SGLT-2is and GLP-1-RAs on HbA1c and weight in type 2 diabetes patients.
- To assess the impact of these newer agents versus older antidiabetic drugs on achieving combined glycemic and weight goals.
- To leverage artificial intelligence for projecting treatment outcomes based on real-world data.
Main Methods:
- Retrospective analysis of 4,927,548 visits from 558,097 patients in the AMD Annals database (2005-2019).
- Exclusion criteria included type 1 diabetes, pregnancy, age >75 years, dialysis, and incomplete HbA1c or weight data.
- Machine-learning AI 'what-if' analysis was employed to simulate the achievement and 18-month persistence of combined goals (HbA1c <7% and weight gain <2%) with SGLT-2is and GLP-1-RAs.
Main Results:
- AI simulation projected a significant increase in achieving combined goals (66.5%) compared to actual clinical practice (38.8%).
- SGLT-2is and GLP-1-RAs demonstrated superior potential for sustained achievement of combined goals over 18 months compared to other antidiabetic drug classes.
- The analysis identified therapeutic inertia, with older drugs showing a paradoxically greater achievement of combined goals in a 4-year timeframe (2014-2017).
Conclusions:
- Artificial intelligence is a valuable tool for analyzing large-scale real-world data in diabetology.
- Early and extended utilization of SGLT-2is and GLP-1-RAs holds significant potential for improving metabolic outcomes in type 2 diabetes.
- Timely adoption of these newer pharmacotherapies is crucial for optimizing patient management and achieving treatment targets.
Keywords:
Artificial intelligence what-if projectionHbA(1c) and weight controlReal-life therapeutic inertiaUse of SGLT2i and GLP1-RAMore Related Videos
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