ArticleHeart rhythm2022
Unsupervised machine learning reveals epicardial adipose tissue subtypes with distinct atrial fibrosis profiles in patients with persistent atrial fibrillation: A prospective 2-center cohort study.
Article in Heart rhythm, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 18 citations in OpenAlex.
- Clinical subtypes and prognosis of new-onset atrial fibrillation in critically ill patients.International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- Oxidative Stress in Diabetic Cardiomyopathy: Molecular Mechanisms and Emerging Therapeutic Targets.Biomolecules · 2026Review
- Patients with persistent atrial fibrillation and metabolic comorbidities have an altered inflammatory state of atrial epicardial adipose tissue, which is linked to CT-attenuation.Cardiovascular diabetology · 2025Article
- Changes in Epicardial Adipose Tissue Assessed by Chest CT in Breast Cancer Patients Receiving Adjuvant Chemotherapy with Anthracyclines and Trastuzumab.Reviews in cardiovascular medicine · 2024Article
- CT radiomic features reproducibility of virtual non-contrast series derived from photon-counting CCTA datasets using a novel calcium-preserving reconstruction algorithm compared with standard non-contrast series: focusing on epicardial adipose tissue.The international journal of cardiovascular imaging · 2024Article
- Use of a two-handed model to improve comprehension of ventricular outflow tract anatomy.BMC medical education · 2023Article
- Machine learning of adipose tissue in atrial fibrillation.Heart rhythm · 2022Article
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14 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundEpicardial adipose tissue (EAT) accumulation is associated with the progression of atrial fibrillation. However, the histological features of EATs are poorly defined and their correlation with atrial fibrosis is unclear.
objectiveThe purpose of this study was to identify and characterize EAT subgroups in the persistent atrial fibrillation (PeAF) cohorts.
methodsEATs and the corresponding left atrial appendage samples were obtained from patients with PeAF via surgical intervention. Adipocyte markers, that is, Uncoupling Protein 1, Transcription Factor 21, and CD137, were examined. On the basis of expression of adipocyte markers, patients with PeAF were categorized into subgroups by using unsupervised clustering analysis. Clinical characteristics, histological analyses, and outcomes were subsequently compared across the clusters. External validation was performed in a validation cohort.
resultsThe ranking of feature importance revealed that the 3 adipocyte markers were the most relevant factors for atrial fibrosis compared with other clinical indicators. On the k-medoids analysis, patients with PeAF could be categorized into 3 clusters in the discovery cohort. The histological studies revealed that patients in cluster 1 exhibited statistically larger size of adipocytes in EATs and severe atrial fibrosis in left atrial appendages. Findings were replicated in the validation cohort, where severe atrial fibrosis was noted in cluster 1. Moreover, in the validation cohort, there was a high degree of overlap between the supervised classification results and the unsupervised cluster results from the k-medoids method.
conclusionMachine learning-based cluster analysis could identify subtypes of patients with PeAF having distinct atrial fibrosis profiles. Additionally, EAT whitening (increased proportion of white adipocytes) may be involved in the process of atrial fibrosis.
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