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Data-driven identification of complex disease phenotypes
Journal of The Royal Society Interface ( IF 3.9 ) Pub Date : 2021-07-28 , DOI: 10.1098/rsif.2020.1040
Markus J Strauss 1 , Thomas Niederkrotenthaler 2 , Stefan Thurner 1, 3, 4 , Alexandra Kautzky-Willer 5 , Peter Klimek 1, 3
Affiliation  

Disease interaction in multimorbid patients is relevant to treatment and prognosis, yet poorly understood. In the present work, we combine approaches from network science, machine learning and computational phenotyping to assess interactions between two or more diseases in a transparent way across the full diagnostic spectrum. We demonstrate that health states of hospitalized patients can be better characterized by including higher-order features capturing interactions between more than two diseases. We identify a meaningful set of higher-order diagnosis features that account for synergistic disease interactions in a population-wide (N = 9 M) medical claims dataset. We construct a generalized disease network where (higher-order) diagnosis features are linked if they predict similar diagnoses across the whole diagnostic spectrum. The fact that specific diagnoses are generally represented multiple times in the network allows for the identification of putatively different disease phenotypes that may reflect different disease aetiologies. At the example of obesity, we demonstrate the purely data-driven detection of two complex phenotypes of obesity. As indicated by a matched comparison between patients having these phenotypes, we show that these phenotypes show specific characteristics of what has been controversially discussed in the medical literature as metabolically healthy and unhealthy obesity, respectively. The findings also suggest that metabolically healthy patients show some progression towards more unhealthy obesity over time, a finding that is consistent with longitudinal studies indicating a transient nature of metabolically healthy obesity. The disease network is available for exploration at https://disease.network/.



中文翻译:

复杂疾病表型的数据驱动识别

多病患者的疾病相互作用与治疗和预后相关,但了解甚少。在目前的工作中,我们结合了网络科学、机器学习和计算表型的方法,以透明的方式在整个诊断范围内评估两种或多种疾病之间的相互作用。我们证明住院患者的健康状态可以通过包含高阶能更好地表征特征之间的相互作用捕捉不是两种疾病。我们确定了一组有意义的高阶诊断特征,这些特征解释了全人群 ( N = 9 M) 医疗索赔数据集中的协同疾病相互作用。我们构建了一个广义的疾病网络如果(高阶)诊断特征在整个诊断范围内预测相似的诊断,则其中(高阶)诊断特征是相互关联的。特定诊断通常在网络中多次表示的事实允许识别可能反映不同疾病病因的假定不同疾病表型。在肥胖的例子中,我们展示了对肥胖的两种复杂表型的纯数据驱动检测。正如具有这些表型的患者之间的匹配比较所表明的那样,我们表明这些表型显示出医学文献中分别被有争议地讨论为代谢健康和不健康肥胖的特定特征。研究结果还表明,随着时间的推移,代谢健康的患者会出现一些更不健康的肥胖症,这一发现与纵向研究一致,表明代谢健康肥胖的短暂性。疾病网络可在 https://disease.network/ 上进行探索。

更新日期:2021-07-28
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