Abstract Bexagliflozin, approved in the United States in 2023, is the fifth selective sodium-glucose cotransporter 2 (SGLT2) inhibitor for type 2 diabetes mellitus (T2DM). Unlike other class members, it lacks an event-driven cardiovascular outcomes trial (CVOT) and trials with hard kidney or heart failure endpoints. This narrative mini-review separates bexagliflozin-specific evidence from class benefits. PubMed and Google Scholar were searched for English-language articles (January 2009–June 2026) using “bexagliflozin,” “Brenzavvy,” and “SGLT2 inhibitor,” plus prescribing information and pivotal class trials. Randomized trials and meta-analyses were prioritized. Bexagliflozin reduced glycated hemoglobin (HbA1c), body weight, and systolic blood pressure vs. placebo and was noninferior to sitagliptin, glimepiride, and, in one 24-week trial, dapagliflozin for HbA1c. In stage 3a/3b chronic kidney disease, it reduced albuminuria by 20.1 % vs. placebo, a surrogate finding. A cardiovascular safety trial and meta-analysis excluded excess cardiovascular risk but were not powered for benefit. Adverse events were consistent with the class. Bexagliflozin is an effective glucose-lowering drug, but its cardiovascular, kidney, and heart failure benefits remain unproven and cannot be assumed from class data. Outcome-proven agents are preferred for cardiorenal protection. An adequately powered CVOT and hard kidney endpoint trials are the key research priorities.
Abstract Sodium-glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) improve cardiovascular (CV) outcomes in type 2 diabetes mellitus, and several agents benefit patients without diabetes, yet their mechanisms remain incompletely understood. This narrative review examines whether these benefits arise from direct cardiac actions, systemic hemodynamic and metabolic effects, or both. We performed a structured PubMed search for CV outcome trials, mediation analyses, human mechanistic and imaging studies, and preclinical studies, classifying each proposed mechanism by its supporting evidence. Outcome trials consistently show that SGLT2 inhibitors reduce heart failure (HF) hospitalization and slow kidney disease progression, whereas GLP-1 RAs reduce atherosclerotic events, but these trials cannot identify the responsible mechanisms. Human data support contributions from natriuresis, decongestion, and renal hemodynamics for SGLT2 inhibitors and from glycemic, vascular, and anti-inflammatory effects for GLP-1 RAs, although mediation analyses explain only part of either class’s benefit. Direct myocardial actions remain largely hypothetical in humans. The CV effects of both classes are best viewed as overlapping, interacting hemodynamic, renal, metabolic, vascular, and neurohormonal mechanisms rather than a direct-versus-systemic dichotomy. Clinically, the evidence supports prioritizing SGLT2 inhibitors for HF and chronic kidney disease and GLP-1 RAs for atherosclerotic CV disease and obesity-related risk.
Abstract Introduction RCDs comprise a diverse group of inherited and acquired disorders characterized by low prevalence, substantial genetic and phenotypic heterogeneity, delayed diagnosis, and limited evidence-based therapeutic options. These challenges contribute to poor clinical outcomes, including progressive heart failure, malignant arrhythmias, and sudden cardiac death. Content This review summarizes recent advances in artificial intelligence (AI) for the diagnosis and management of RCDs. AI technologies, including machine learning, deep learning, and multimodal data integration, enable comprehensive analysis of electrocardiography, echocardiography, cardiac magnetic resonance imaging, computed tomography, genomic and multi-omics data, biomarkers, wearable devices, and electronic health records. These approaches improve early disease detection, phenotypic classification, risk stratification, prognostic prediction and personalized treatment planning. Summary Current evidence suggests that AI significantly enhances diagnostic accuracy, prognostic assessment, and individualized clinical decision-making in patients with RCDs. Outlook Future clinical implementation requires standardized multicenter datasets, prospective validation, transparent and explainable algorithms, robust regulatory and ethical frameworks, and seamless integration into healthcare systems. Continued interdisciplinary collaboration will accelerate AI-driven precision cardiovascular medicine and improve outcomes for patients with rare cardiovascular diseases.
Abstract Metabolically healthy obesity (MHO) is a heterogeneous phenotype characterized by preserved traditional metabolic markers despite excess adiposity. However, accumulating evidence suggests that MHO may not be uniformly benign and may confer increased long-term cardiovascular risk through inflammatory and adipose tissue–mediated mechanisms. This narrative review synthesizes contemporary evidence on cardiovascular risk in MHO, focusing on visceral adiposity, ectopic fat, and immune metabolic pathways. Literature published between January 2010 and December 2025 was identified through PubMed/MEDLINE, Embase, Web of Science, and Cochrane Library. Across the reviewed evidence, MHO is frequently associated with the progression to metabolically unhealthy obesity, although transition rates and cardiovascular risk estimates vary according to MHO definitions. Visceral and ectopic fat are associated with subclinical atherosclerosis, coronary calcification, and cardiovascular events, including in individuals without overt metabolic abnormalities. Evidence regarding heart failure and ventricular dysfunction remains limited and inconsistent. Low-grade inflammation and adipose immune remodeling, including NLRP3 inflammasome activation, provide plausible mechanisms, although human evidence remains predominantly observational. Key gaps include inconsistent MHO definitions, limited longitudinal imaging and biomarker integration, population diversity, and lack of validated MHO-specific risk models. Standardized phenotyping and prospective validation of inflammation-based risk stratification are required.
Abstract Objectives Insulinoma is a rare pancreatic neuroendocrine tumor causing endogenous hyperinsulinemic hypoglycemia. Its heterogeneous clinical presentation often delays diagnosis. Dietary patterns, particularly ketogenic or high-carbohydrate regimens, may influence glucose homeostasis and modify the clinical manifestations of insulinoma. Case presentation A 70-year-old woman was admitted with recurrent documented hypoglycemia. She reported a 10-year history of seizure-like episodes and intermittent neurological symptoms that began after starting a ketogenic diet. Symptoms later became less frequent during a high-calorie, carbohydrate-rich diet but recurred after adopting a Mediterranean diet. Biochemical evaluation confirmed endogenous hyperinsulinemic hypoglycemia. A supervised fasting test documented symptomatic hypoglycemia with plasma glucose of 17 mg/dL, fulfilling Whipple’s triad. Magnetic resonance imaging revealed a 4 cm pancreatic tail lesion, and ˆ68 Ga-DOTATATE PET/CT showed intense uptake consistent with a somatostatin receptor-positive neuroendocrine tumor. As the patient declined surgery, diazoxide and dietary modifications were started, improving symptoms and glycemic control. Conclusions This case suggests that dietary habits may influence the clinical presentation of insulinoma. Marked carbohydrate restriction may unmask hypoglycemia, whereas high-carbohydrate intake may attenuate symptoms and contribute to delayed diagnosis. Careful assessment of dietary habits should be part of the evaluation of patients with recurrent unexplained hypoglycemia suggestive of insulinoma.
