Pneumology Group, Department of Integrated Traditional Chinese and Western Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China.
Department of Respiratory and Critical Care Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China.
Nutrients. 2022 Jun 17;14(12):2525. doi: 10.3390/nu14122525.
Previous studies have indicated the limitations of body mass index for defining disease phenotypes. The description of asthma phenotypes based on body composition (BC) has not been largely reported.
To identify and characterize phenotypes based on BC parameters in patients with asthma.
A study with two prospective observational cohorts analyzing adult patients with stable asthma ( = 541 for training and = 179 for validation) was conducted. A body composition analysis was performed for the included patients. A cluster analysis was conducted by applying a 2-step process with stepwise discriminant analysis. Logistic regression models were used to evaluate the association between identified phenotypes and asthma exacerbations (AEs). The same algorithm for cluster analysis in the independent validation set was used to perform an external validation.
Three clusters had significantly different characteristics associated with asthma outcomes. An external validation identified the similarity of the participants in training and the validation set. In the training set, cluster Training (T) 1 (29.4%) was "patients with undernutrition", cluster T2 (18.9%) was "intermediate level of nutrition with psychological dysfunction", and cluster T3 (51.8%) was "patients with good nutrition". Cluster T3 had a decreased risk of moderate-to-severe and severe AEs in the following year compared with the other two clusters. The most important BC-specific factors contributing to being accurately assigned to one of these three clusters were skeletal muscle mass and visceral fat area.
We defined three distinct clusters of asthma patients, which had distinct clinical features and asthma outcomes. Our data reinforced the importance of evaluating BC to determining nutritional status in clinical practice.
先前的研究表明，体重指数在定义疾病表型方面存在局限性。基于身体成分（BC）描述哮喘表型尚未得到广泛报道。
鉴定并描述哮喘患者基于 BC 参数的表型。
对两项前瞻性观察队列研究进行分析，纳入稳定期哮喘成年患者（训练队列=541，验证队列=179）。对纳入患者进行身体成分分析。采用逐步判别分析的 2 步流程进行聚类分析。采用逻辑回归模型评估鉴定表型与哮喘加重（AE）之间的相关性。在独立验证队列中，使用相同的聚类分析算法进行外部验证。
三个聚类具有显著不同的特征，与哮喘结局相关。外部验证确认了训练集和验证集参与者的相似性。在训练集中，聚类 1（T1）（29.4%）为“营养不良患者”，聚类 2（T2）（18.9%）为“营养水平中等伴心理功能障碍”，聚类 3（T3）（51.8%）为“营养良好的患者”。与其他两个聚类相比，T3 聚类在随后的一年中，中度至重度和重度 AE 的风险降低。对准确分配到这三个聚类之一具有重要贡献的 BC 特定因素是骨骼肌质量和内脏脂肪面积。
我们定义了三种不同的哮喘患者聚类，它们具有不同的临床特征和哮喘结局。我们的数据强化了在临床实践中评估 BC 以确定营养状况的重要性。
