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DOI 10.1016/j.hrthm.2022.07.030

Медицинская библиотека · Heart rhythm · 2022

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.

Cui Chang, Qin Huiyuan, Zhu Xiyu, Lu Xiaohu, Wang Bing, Wang Xingyao, Wang Junxia, Jiao Jincheng, Chu Ming, Wang Cheng, Li Mingfang, Wang Xiaowei, Wang Dongjin, Chen Minglong

СтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

BACKGROUND

Epicardial 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.

OBJECTIVE

The purpose of this study was to identify and characterize EAT subgroups in the persistent atrial fibrillation (PeAF) cohorts.

METHODS

EATs 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.

RESULTS

The 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.

CONCLUSION

Machine 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.

Для цитирования: Cui Chang, Qin Huiyuan, Zhu Xiyu, Lu Xiaohu, Wang Bing, Wang Xingyao, Wang Junxia, Jiao Jincheng, Chu Ming, Wang Cheng, Li Mingfang, Wang Xiaowei, Wang Dongjin, Chen Minglong. 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.. Heart rhythm. 2022. DOI: 10.1016/j.hrthm.2022.07.030.

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