Anderson Lisa M, Lim Kelvin O, Kummerfeld Erich, Crosby Ross D, Crow Scott J, Engel Scott G, Forrest Lauren, Wonderlich Stephen A, Peterson Carol B
Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota, USA.
Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
Int J Eat Disord. 2023 Nov;56(11):2012-2021. doi: 10.1002/eat.24040. Epub 2023 Aug 7.
Precision medicine (i.e., individually tailored treatments) represents an optimal goal for treating complex psychiatric disorders, including eating disorders. Within the eating disorders field, most treatment development efforts have been limited in their ability to identify individual-level models of eating disorder psychopathology and to develop and apply an individually tailored treatment for a given individual's personalized model of psychopathology. In addition, research is still needed to identify causal relationships within a given individual's model of eating disorder psychopathology. Addressing this limitation of the current state of precision medicine-related research in the field will allow us to progress toward advancing research and practice for eating disorders treatment.
We present a novel set of analytic tools, causal discovery analysis (CDA) methods, which can facilitate increasingly fine-grained, person-specific models of causal relations among cognitive, behavioral, and affective symptoms.
CDA can advance the identification of an individual's causal model that maintains that individuals' eating disorder psychopathology.
In the current article, we (1) introduce CDA methods as a set of promising analytic tools for developing precision medicine methods for eating disorders including the potential strengths and weaknesses of CDA, (2) provide recommendations for future studies utilizing this approach, and (3) outline the potential clinical implications of using CDA to generate personalized models of eating disorder psychopathology.
CDA provides a novel statistical approach for identifying causal relationships among variables of interest for a given individual. Person-specific causal models may offer a promising approach to individualized treatment planning and inform future personalized treatment development efforts for eating disorders.
精准医学（即个性化定制治疗）是治疗包括饮食失调在内的复杂精神疾病的理想目标。在饮食失调领域，大多数治疗开发工作在识别饮食失调精神病理学的个体层面模型以及为特定个体的个性化精神病理学模型开发和应用个性化治疗方面能力有限。此外，仍需要开展研究以确定特定个体饮食失调精神病理学模型中的因果关系。解决该领域精准医学相关研究当前状态的这一局限性，将使我们能够在饮食失调治疗的研究和实践推进方面取得进展。
我们提出了一套新颖的分析工具，即因果发现分析（CDA）方法，它能够促进对认知、行为和情感症状之间因果关系的更精细、针对个体的模型构建。
CDA能够推动对维持个体饮食失调精神病理学的个体因果模型的识别。
在本文中，我们（1）将CDA方法作为一套有前景的分析工具引入，用于开发饮食失调的精准医学方法，包括CDA的潜在优势和劣势，（2）为未来使用该方法的研究提供建议，以及（3）概述使用CDA生成饮食失调精神病理学个性化模型的潜在临床意义。
CDA为识别特定个体感兴趣变量之间的因果关系提供了一种新颖的统计方法。针对个体的因果模型可能为个性化治疗规划提供一种有前景的方法，并为未来饮食失调的个性化治疗开发工作提供参考。
