AI-Enabled Chest X-Ray Detects Subclinical Diastolic Dysfunction in Diabesity and its Therapeutic Responses to GLP-1 or GLP-1/GIP Receptor Agonists

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Abstract

Introduction The evaluation of diastolic function in diabesity patients undergoing incretin-based therapy could facilitate early detection and help prevent progression to heart failure. Methodology A group of 15 diabesity cases with chest x-ray examinations (< 1 month) before and after ≥ 12 months of incretin-based therapy (absent ventricular/valvular dysfunction) were identified. Standard diabesity descriptors and predictions of pulmonary venous hypertension (aka “pulmonary congestion”) [None; Stage 1: vascular distention/redistribution but minimal interstitial edema; or Stage ≥ 2: vascular congestion with ≥ mild interstitial or alveolar edema] by validated AI-enabled chest x-ray staging, representing mean left atrial pressure in diastolic dysfunction, were evaluated pre- and post-therapy. Results By weight loss, cases clustered into equal-sized subgroups: 1. Significant Responders (9–30% decreases); 2. Insignificant Responders (0–2% decreases); and 3. Non-Responders (2–12% increases). Regarding hemoglobin A1c changes: 1. Significant Responders collectively decreased; 2. Insignificant Responders varied; and 3. Non-Responders were largely stable. Pre-therapy, all 15 cases demonstrated AI-enabled chest x-ray evidence of diastolic dysfunction; post-therapy, 4 improved (especially cases of greatest weight loss or hemoglobin A1c reduction), 5 were stable, and 6 worsened. Per subgroup, AI-enabled chest x-ray indications therapeutic response were: 1. Significant Responders (all demonstrating unequivocally decreased obesity and improved diabetes) collectively showed stable-decreased dysfunction; 2. Insignificant Responders (all demonstrating minimally decreased obesity but stable-worsening diabetes in most) reflected stable-increased dysfunction in 60%; and 3. Non-Responders (all demonstrating increased obesity but stable-improved diabetes) showed increased dysfunction in 80%. Conclusion AI-enabled CXR staging detects background subclinical diastolic dysfunction in diabesity and monitors its response to incretin-based therapy.

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