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.

Autor: Giorda CB; Metabolism and Diabetes Unit, ASL, Torino, Italy. Electronic address: carlogiordaposta@gmail.com., Rossi A; Metabolism and Diabetes Unit, ASST Fatebenefratelli, Milan, Italy., Baccetti F; ASL Nordovest Toscana, Massa Carrara, Italy., Zilich R; Mix-x Partner, Milan, Italy., Romeo F; Metabolism and Diabetes Unit, ASL, Torino, Italy., Besmir N; Diabetes Unit, Careggi Hospital, Firenze, Italy., Di Cianni G; Diabetes and Metabolic Diseases Unit, Health Local Unit Nord-West Tuscany, Livorno Hospital, Italy., Guaita G; Diabetes and Endocrinology Unit, ASLSULCIS Carbonia-Iglesias, Italy., Morviducci L; Diabetes and dietetics Unit, Santo Spirito Hospital, ASL Rome, Italy., Muselli M; Rulex Innovation Labs, Genova, Italy; Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genoa, Italy., Ozzello A; AIAMD National Group, Bruino, Italy., Pisani F; artificial intelligence consultancy, Ivrea, Italy., Ponzani P; Metabolism and Diabetes Unit, ASL4, Chiavari, Italy., Santin P; Data Scientist Deimos, Udine, Italy., Verda D; Rulex Innovation Labs, Genova, Italy., Musacchio N; AIAMD National Group, Bruino, Italy.
Jazyk: angličtina
Zdroj: Clinical therapeutics [Clin Ther] 2023 Aug; Vol. 45 (8), pp. 754-761. Date of Electronic Publication: 2023 Jul 12.
DOI: 10.1016/j.clinthera.2023.06.006
Abstrakt: Purpose: Recently, 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 HbA 1c and weight.
Methods: Data 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 HbA 1c 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 (HbA 1c <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%.
Findings: The 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.
Implications: AI 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.
Competing Interests: Declaration of Competing Interest The authors have indicated that they have no conflicts of interest with regard to the content of this article.
(Copyright © 2023. Published by Elsevier Inc.)
Databáze: MEDLINE
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