ArticleCardiovascular diabetology. Endocrinology reports2025
Real-world prescriptions of GLP-1RAs and SGLT2is in type 2 diabetes prioritise BMI and age over cardiorenal risk: a machine learning-based large cohort analysis.
Article in Cardiovascular diabetology. Endocrinology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The Possible Hematological Cost of Metabolic Success: Do Incretin-Based Therapies Silently Trigger Anemia?Medical sciences (Basel, Switzerland) · 2026Review
- Mortality and persistence in therapy in elderly subjects with type 2 diabetes receiving gliflozins or incretins.Journal of endocrinological investigation · 2026Article
- Integrated Evidence from VigiBase and Clinical Trials: A Comprehensive Pharmacovigilance Analysis of Seven Glucagon-Like Peptide 1 Receptor Agonists (GLP-1 RAs).Diabetes therapy : research, treatment and education of diabetes and related disorders · 2026Article
- Missing Opportunity for Nephroprotective Therapy in Patients With Non-Dialysis CKD Under Stable Nephrology Care.Kidney international reports · 2026Article
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14 authors.
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Abstract
backgroundUnderstanding how SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP-1RAs) are prescribed in relation to cardiorenal risk is crucial for assessing adherence to guidelines and optimize outcomes in type 2 diabetes (T2D). This study aimed to determine whether prescription patterns across different cardiorenal phenotypic subgroups reflect clinical risk or are affected by demographic factors. Explainable artificial intelligence (XAI) was used to identify the key factors influencing therapeutic decisions.
methodsWe analyzed 139,202 adults with T2D from the Italian AMD Annals registry (2023), stratified into four cardiorenal phenotypic subgroups based on ADA criteria: low risk, chronic kidney disease (CKD) without cardiovascular disease (CVD), atherosclerotic CVD without heart failure, and heart failure. Predictive models were developed using a Logic Learning Machine (XAI algorithm), with variable importance ranked by normalized relevance scores. Business intelligence tools and statistical analysis were used for validation.
resultsSGLT2i prescriptions were strongly associated with cardiorenal markers, including reduced eGFR (31–59 mL/min), intermediate HbA1c (5.5–8.1%), and lower BMI, with model accuracies ranging from 63.7% to 83.1%. Women were consistently less likely to receive SGLT2is across subgroups. GLP-1RA prescriptions were predominantly driven by higher BMI (> 30 kg/m²), younger age, and glycemic extremes (< 5.5% or > 8.1%), with lower model performance (31.6%–54.1%). Individuals with lower BMI were less frequently prescribed GLP-1RAs, even in the presence of renal or cardiovascular risk.
conclusionsWhile SGLT2i prescribing generally aligned with cardiorenal risk, sex-based disparities persist. GLP-1RA use was less consistently linked to clinical indications and more heavily influenced by BMI. These findings highlight missed opportunities to deliver proven cardiorenal protective therapies to high-risk individuals, emphasising the need for more equitable, phenotype-driven prescribing strategies in T2D.
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