Identification of Three Different Phenotypes in Anti-Melanoma Differentiation-Associated Gene 5 Antibody-Positive Dermatomyositis Patients: Implications for Prediction of Rapidly Progressive Interstitial Lung Disease
Identification of Three Different Phenotypes in Anti-Melanoma Differentiation-Associated Gene 5 Antibody-Positive Dermatomyositis Patients: Implications for Prediction of Rapidly Progressive Interstitial Lung Disease
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DOI：
10.1002/art.42308
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发表时间：
2023-03-17
影响因子：
13.3
通讯作者：
Tan, Wenfeng
中科院分区：
文献类型：
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作者：
Xu, Lingxiao;You, Hanxiao;Tan, Wenfeng
目的抗黑色素瘤分化相关基因5抗体阳性（anti-MDA 5+）皮肌炎（DM）患者的表型存在很大的异质性，阻碍了疾病的评估和治疗。本研究的目的是确定不同的表型组在抗-MDA 5 + DM患者，并确定这些表型在预测患者outcome.Methods的效用，共265例抗-MDA 5 + DM患者的回顾性研究。一个无监督的层次聚类分析进行表征不同的phenotype.Results患者分为3个集群，其特点是显着不同的功能和结果。第1组（n = 108例患者）的特征为快速进展性间质性肺病（RPILD）的轻度风险，非RPILD的累积发生率为85.2%。第2组（n = 72例患者）的特征是RPILD的中度风险，非RPILPD的累积发生率为73.6%。以RPILD高风险和累积非RPILD发生率32.9%为特征的第3组患者（n = 85例患者）比其他2个亚组的患者更可能具有抗Ro 52抗体和高滴度抗MDA 5抗体。聚类3、2和1的全因死亡率分别为60%、9.7%和3.7%（P < 0.0001）。决策树分析导致开发了用于抗-MDA 5 + DM患者分类的简单算法，其包括以下8个变量：年龄> 50岁、病程< 3个月、肌无力（近端肌无力）、关节炎、C-反应蛋白水平、肌酸激酶水平、抗-Ro 52抗体滴度和抗-MDA 5抗体滴度。该算法将患者放置在适当的集群与78.5%的准确性，在发展队列和70.0%的准确性在外部validationcoherent.Conclusion聚类分析确定了3个不同的临床模式和结果在我们的大队列抗MDA 5 + DM患者。将糖尿病患者分为具有预后价值的表型亚组可能有助于医生提高临床决策的有效性。
Objective There is substantial heterogeneity among the phenotypes of patients with anti-melanoma differentiation-associated gene 5 antibody-positive (anti-MDA5+) dermatomyositis (DM), hindering disease assessment and management. This study aimed to identify distinct phenotype groups in patients with anti-MDA5+ DM and to determine the utility of these phenotypes in predicting patient outcomes.Methods A total of 265 patients with anti-MDA5+ DM were retrospectively enrolled in the study. An unsupervised hierarchical cluster analysis was performed to characterize the different phenotypes.Results Patients were stratified into 3 clusters characterized by markedly different features and outcomes. Cluster 1 (n = 108 patients) was characterized by mild risk of rapidly progressive interstitial lung disease (RPILD), with the cumulative incidence of non-RPILD being 85.2%. Cluster 2 (n = 72 patients) was characterized by moderate risk of RPILD, with the cumulative incidence of non-RPILPD being 73.6%. Patients in cluster 3 (n = 85 patients), which was characterized by a high risk of RPILD and a cumulative non-RPILD incidence of 32.9%, were more likely than patients in the other 2 subgroups to have anti-Ro 52 antibodies in conjunction with high titers of anti-MDA5 antibodies. All-cause mortality rates of 60%, 9.7%, and 3.7% were determined for clusters 3, 2, and 1, respectively (P < 0.0001). Decision tree analysis led to the development of a simple algorithm for anti-MDA5+ DM patient classification that included the following 8 variables: age > 50 years, disease course of < 3 months, myasthenia (proximal muscle weakness), arthritis, C-reactive protein level, creatine kinase level, anti-Ro 52 antibody titer, and anti-MDA5 antibody titer. This algorithm placed patients in the appropriate cluster with 78.5% accuracy in the development cohort and 70.0% accuracy in the external validation cohort.Conclusion Cluster analysis identified 3 distinct clinical patterns and outcomes in our large cohort of anti-MDA5+ DM patients. Classification of DM patients into phenotype subgroups with prognostic values may help physicians improve the efficacy of clinical decision-making.