Shifts in gut microbiome and metabolome are associated with risk of recurrent atrial fibrillation.
Shifts in gut microbiome and metabolome are associated with risk of recurrent atrial fibrillation.
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DOI：
10.1111/jcmm.15959
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发表时间：
2020-11
影响因子：
5.3
通讯作者：
Yang X
中科院分区：
文献类型：
--
作者：
Li J;Zuo K;Zhang J;Hu C;Wang P;Jiao J;Liu Z;Yin X;Liu X;Li K;Yang X
心房颤动(房颤)患者肠道微生物区系(GM)的改变已经被描述过，其多样性、成分和功能紊乱。本研究旨在基于元基因组测序和代谢组学分析，评估GM成分与房颤消融后复发(RAF)的关系，并构建基于GM的房颤复发预测模型。与非房颤对照组(50例)相比，复发性房颤患者(17例)和非房颤患者(23例)的GM组成和代谢谱显著改变。值得注意的是，选择了非RAF和RAF类群之间的区分类群，包括亚硝酸单胞菌科和香菇科，Marinitoga属和Rufibacter属，以及Faecaliba spCAG：82，Bacillusgobiens和Desulfbacterales细菌PC51MH44，建立了基于套索分析的分类评分系统。在纳入RAF的临床因素后，分类评分与RAF的发生率仍有显著相关性(HR=2.647，P=0.041)。与传统的临床评分相比，预测RAF的AUC(0.954)和NRI(1.5601)显著升高(AUC=0.6918)。基于GM的分类评分系统在理论上提高了模型的性能，诺模图和决策曲线分析验证了预测模型的临床价值。这些数据为将GM因素纳入未来的复发风险分层提供了新的可能性。
Alternations of gut microbiota (GM) in atrial fibrillation (AF) with elevated diversity, perturbed composition and function have been described previously. The current work aimed to assess the association of GM composition with AF recurrence (RAF) after ablation based on metagenomic sequencing and metabolomic analyses and to construct a GM‐based predictive model for RAF. Compared with non‐AF controls (50 individuals), GM composition and metabolomic profile were significantly altered between patients with recurrent AF (17 individuals) and non‐RAF group (23 individuals). Notably, discriminative taxa between the non‐RAF and RAF groups, including the families Nitrosomonadaceae and Lentisphaeraceae, the genera Marinitoga and Rufibacter and the species Faecalibacterium spCAG:82, Bacillus gobiensis and Desulfobacterales bacterium PC51MH44, were selected to construct a taxonomic scoring system based on LASSO analysis. After incorporating the clinical factors of RAF, taxonomic score retained a significant association with RAF incidence (HR = 2.647, P = .041). An elevated AUC (0.954) and positive NRI (1.5601) for predicting RAF compared with traditional clinical scoring (AUC = 0.6918) were obtained. The GM‐based taxonomic scoring system theoretically improves the model performance, and the nomogram and decision curve analysis validated the clinical value of the predicting model. These data provide novel possibility that incorporating the GM factor into future recurrent risk stratification.
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