Abstract

Objective

When studying any specific rare disease, heterogeneity and scarcity of affected individuals has historically hindered investigators from discerning on what to focus to understand and diagnose a disease. New nongenomic methodologies must be developed that identify similarities in seemingly dissimilar conditions.

Materials and Methods

This observational study analyzes 1042 patients from the Undiagnosed Diseases Network (2015-2019), a multicenter, nationwide research study using phenotypic data annotated by specialized staff using Human Phenotype Ontology terms. We used Louvain community detection to cluster patients linked by Jaccard pairwise similarity and 2 support vector classifier to assign new cases. We further validated the clusters’ most representative comorbidities using a national claims database (67 million patients).

Results

Patients were divided into 2 groups: those with symptom onset before 18 years of age (n = 810) and at 18 years of age or older (n = 232) (average symptom onset age: 10 [interquartile range, 0-14] years). For 810 pediatric patients, we identified 4 statistically significant clusters. Two clusters were characterized by growth disorders, and developmental delay enriched for hypotonia presented a higher likelihood of diagnosis. Support vector classifier showed 0.89 balanced accuracy (0.83 for Human Phenotype Ontology terms only) on test data.

Discussions

To set the framework for future discovery, we chose as our endpoint the successful grouping of patients by phenotypic similarity and provide a classification tool to assign new patients to those clusters.

Conclusion

This study shows that despite the scarcity and heterogeneity of patients, we can still find commonalities that can potentially be harnessed to uncover new insights and targets for therapy.

中文翻译：

---

通过未确诊疾病网络寻找罕见病的共性

摘要

客观的

在研究任何特定的罕见疾病时，受影响个体的异质性和稀缺性历来阻碍了研究人员辨别要了解和诊断疾病的重点。必须开发新的非基因组方法来识别看似不同条件下的相似性。

材料和方法

这项观察性研究分析了来自未确诊疾病网络 (2015-2019) 的 1042 名患者，这是一项多中心、全国性的研究，使用由专业人员使用人类表型本体论术语注释的表型数据。我们使用 Louvain 社区检测对由 Jaccard 成对相似性和 2 个支持向量分类器链接的患者进行聚类，以分配新病例。我们使用国家索赔数据库（6700 万患者）进一步验证了集群最具代表性的合并症。

结果

患者分为 2 组：症状在 18 岁之前（n = 810）和 18 岁或以上（n = 232）（平均症状发作年龄：10 [四分位距，0-14] 岁） ）。对于 810 名儿科患者，我们确定了 4 个具有统计学意义的集群。两个集群的特征是生长障碍，发育迟缓富含肌张力低下，诊断的可能性更高。支持向量分类器在测试数据上显示出 0.89 的平衡准确度（仅对于人类表型本体术语为 0.83）。

讨论

为了为未来的发现设置框架，我们选择了通过表型相似性成功对患者进行分组作为我们的终点，并提供了一个分类工具来将新患者分配到这些集群中。

结论

这项研究表明，尽管患者数量稀少且存在异质性，但我们仍然可以找到共性，可以利用这些共性来发现新的治疗见解和目标。