Modeling the enigma of complex disease etiology.
Modeling the enigma of complex disease etiology.
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DOI：
10.1186/s12967-023-03987-x
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发表时间：
2023-02-25
影响因子：
7.4
通讯作者：
Greene, Carol
中科院分区：
文献类型：
--
作者：
Schriml, Lynn M. M.;Lichenstein, Richard;Bisordi, Katharine;Bearer, Cynthia;Baron, J. Allen;Greene, Carol
关键词：
复杂的疾病往往表现为一个诊断之谜，进一步复杂化的组合，多种表型和疾病的特征，其他疾病。为了加强对关键病因因素的确定，我们开发并测试了一种复杂的疾病模型，该模型包含了多种因素，这些因素结合在一起会导致复杂的疾病。该模型的开发是为了应对复杂疾病分类的挑战，因为对疾病的理解以及遗传，环境和社会因素的相互作用和贡献不断变化。在这里，我们提出了一种新的方法来模拟复杂的疾病，整合了多种贡献的遗传，表观遗传，环境，宿主和社会致病性的影响，导致疾病。开发该模型是为了为捕获复杂疾病的不同机制提供指导。对哮喘、糖尿病和胎儿酒精综合征的疾病驱动因素的评估测试了该模型。我们提供了一个详细的理论基础模型，代表复杂的疾病分类使用三个测试条件的哮喘，糖尿病和胎儿酒精综合征。通过模型评估，重新评估了三种复杂的疾病分类，并确定了驱动因素，从而改进了模型。随着对复杂疾病的了解的提高，该模型具有鲁棒性和灵活性，可以捕获新信息。人类疾病本体的复杂疾病模型提供了一种定义更准确疾病分类的机制，作为更精确临床诊断的工具。因此，复杂疾病的这种更广泛的代表性对临床医生和研究人员具有影响，他们的任务是创建基于证据和共识的建议，以及对复杂疾病的公共卫生跟踪。新模型有助于比较复杂、常见和罕见疾病之间的病因，可在人类疾病本体论网站上查阅。
Complex diseases often present as a diagnosis riddle, further complicated by the combination of multiple phenotypes and diseases as features of other diseases. With the aim of enhancing the determination of key etiological factors, we developed and tested a complex disease model that encompasses diverse factors that in combination result in complex diseases. This model was developed to address the challenges of classifying complex diseases given the evolving nature of understanding of disease and interaction and contributions of genetic, environmental, and social factors. Here we present a new approach for modeling complex diseases that integrates the multiple contributing genetic, epigenetic, environmental, host and social pathogenic effects causing disease. The model was developed to provide a guide for capturing diverse mechanisms of complex diseases. Assessment of disease drivers for asthma, diabetes and fetal alcohol syndrome tested the model. We provide a detailed rationale for a model representing the classification of complex disease using three test conditions of asthma, diabetes and fetal alcohol syndrome. Model assessment resulted in the reassessment of the three complex disease classifications and identified driving factors, thus improving the model. The model is robust and flexible to capture new information as the understanding of complex disease improves. The Human Disease Ontology’s Complex Disease model offers a mechanism for defining more accurate disease classification as a tool for more precise clinical diagnosis. This broader representation of complex disease, therefore, has implications for clinicians and researchers who are tasked with creating evidence-based and consensus-based recommendations and for public health tracking of complex disease. The new model facilitates the comparison of etiological factors between complex, common and rare diseases and is available at the Human Disease Ontology website.
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影响因子：
15.9
作者：
Broeer, Stefan;Bailey, Charles G.;Rasko, John E. J.
通讯作者：
Rasko, John E. J.
影响因子：
7.7
作者：
Buchanan, Anne V.;Weiss, Kenneth M.;Fullerton, Stephanie M.
通讯作者：
Fullerton, Stephanie M.
影响因子：
4.5
作者：
Anastassopoulou C;Gkizarioti Z;Patrinos GP;Tsakris A
通讯作者：
Tsakris A
影响因子：
2.1
作者：
Bookman, Ebony B.;McAllister, Kimberly;Gillanders, Elizabeth;Wanke, Kay;Balshaw, David;Rutter, Joni;Reedy, Jill;Shaughnessy, Daniel;Agurs-Collins, Tanya;Paltoo, Dina;Atienza, Audie;Bierut, Laura;Kraft, Peter;Fallin, M. Daniele;Perera, Frederica;Turkheimer, Eric;Boardman, Jason;Marazita, Mary L.;Rappaport, Stephen M.;Boerwinkle, Eric;Suomi, Stephen J.;Caporaso, Neil E.;Hertz-Picciotto, Irva;Jacobson, Kristen C.;Lowe, William L.;Goldman, Lynn R.;Duggal, Priya;Gunnar, Megan R.;Manolio, Teri A.;Green, Eric D.;Olster, Deborah H.;Birnbaum, Linda S.
通讯作者：
Birnbaum, Linda S.
影响因子：
3.5
作者：
Foil KE
通讯作者：
Foil KE