Diabetic Atrial Cardiomyopathy: Pathogenesis, Diagnosis, Management, AI-Driven Diagnosis, and Risk Prediction.

BACKGROUND

Diabetic atrial cardiomyopathy, a component of diabetic cardiomyopathy, is increasingly recognized. However, atrial-focused mechanistic and clinical frameworks remain less developed than ventricular paradigms.

MAIN BODY: This review synthesizes the pathogenesis of diabetic atrial cardiomyopathy from conventional, multiomics, and translational perspectives. We highlight how diabetes-related metabolic stress, inflammation, gut microbiota dysregulation, electrophysiological remodeling, genetic susceptibility, and epigenetic regulation converge on atrial fibrosis, conduction heterogeneity, contractile dysfunction, and thrombogenicity. These processes increase susceptibility to atrial fibrillation, heart failure, and embolic events. We integrate diagnostic strategies, including electrocardiographic indices, biomarkers, and multimodality imaging, with emphasis on left atrial size, strain, and fibrosis assessment. We also appraise emerging artificial intelligence approaches using electrocardiograms, imaging, and wearable signals while emphasizing that most models are not yet validated in DAtCM-specific cohorts. Finally, we outline an integrated management framework that combines cardiometabolic optimization, lifestyle and rehabilitation strategies, guideline-directed anticoagulation when indicated, and cautious development of upstream disease-modifying interventions.

CONCLUSION

Integrating mechanistic, multiomics, and artificial intelligence-enabled approaches may improve early identification and risk stratification of diabetic atrial cardiomyopathy. Dedicated atrial phenotyping cohorts and prospective trials are needed before these concepts can be translated into routine care.