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Updated: Aug 5, 2026
06:22
Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
Published on: November 29, 2024
Diabetic Atrial Cardiomyopathy: Pathogenesis, Diagnosis, Management, AI-Driven Diagnosis, and Risk Prediction
Helin Yang1,2,3,4, Kexiao Yu5, Bing Liang2,3,4
1Chongqing Medical University-University of Leicester Joint Institute, Chongqing Medical University, Chongqing, China, cqmu.edu.cn.
Journal of Diabetes Research
|July 30, 2026
Summary
Diabetic atrial cardiomyopathy (DAtCM) involves metabolic stress, inflammation, and genetic factors, leading to heart issues. Integrating multiomics and AI may improve DAtCM identification and management.
Area of Science:
- Cardiology
- Diabetology
- Biomedical Engineering
Background:
- Diabetic atrial cardiomyopathy (DAtCM) is a recognized complication of diabetes.
- Atrial-specific frameworks for DAtCM lag behind ventricular paradigms.
Purpose of the Study:
- To synthesize the pathogenesis of DAtCM from multiple perspectives.
- To review diagnostic and management strategies for DAtCM.
- To explore the role of artificial intelligence in DAtCM.
Main Methods:
- Review of conventional, multiomics, and translational research on DAtCM.
- Integration of diagnostic tools including ECG, biomarkers, and imaging.
- Appraisal of AI applications in DAtCM detection and risk stratification.
Main Results:
- Diabetes induces atrial fibrosis, dysfunction, and thrombogenicity via metabolic stress, inflammation, and genetic factors.
- These changes increase risks for atrial fibrillation, heart failure, and embolic events.
- AI models show promise but require validation in DAtCM cohorts.
Conclusions:
- Mechanistic, multiomics, and AI approaches can enhance DAtCM identification and risk stratification.
- Further research with dedicated cohorts and trials is necessary for clinical translation.
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