Cvijovic Marija, Polster Annikka
Department of Applied Mathematics and Statistics, University of Gothenburg, Gothenburg, Sweden.
Division of Systems and Synthetic Biology, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden.
Front Bioinform. 2023 May 24;3:1163445. doi: 10.3389/fbinf.2023.1163445. eCollection 2023.
Complex diseases are prevalent medical conditions which are characterized by inter-patient heterogeneity with regards to symptom profiles, disease trajectory, comorbidities, and treatment response. Their pathophysiology involves a combination of genetic, environmental, and psychosocial factors. The intricacies of complex diseases, encompassing different levels of biological organization in the context of environmental and psychosocial factors, makes them difficult to study, understand, prevent, and treat. The field of network medicine has progressed our understanding of these complex mechanisms and highlighted mechanistic overlap between diagnoses as well as patterns of symptom co-occurrence. These observations call into question the traditional conception of complex diseases, where diagnoses are treated as distinct entities, and prompts us to reconceptualize our nosological models. Thus, this manuscript presents a novel model, in which the individual disease burden is determined as a function of molecular, physiological, and pathological factors simultaneously, and represented as a state vector. In this conceptualization the focus shifts from identifying the underlying pathophysiology of diagnosis cohorts towards identifying symptom-determining traits in individual patients. This conceptualization facilitates a multidimensional approach to understanding human physiology and pathophysiology in the context of complex diseases. This may provide a useful concept to address both the significant interindividual heterogeneity of diagnose cohorts as well as the lack of clear distinction between diagnoses, health, and disease, thus facilitating the progression towards personalized medicine.
复杂疾病是常见的医学病症,其特征在于患者之间在症状特征、疾病轨迹、合并症和治疗反应方面存在异质性。它们的病理生理学涉及遗传、环境和心理社会因素的综合作用。复杂疾病的复杂性,包括在环境和心理社会因素背景下不同层次的生物组织,使得它们难以研究、理解、预防和治疗。网络医学领域增进了我们对这些复杂机制的理解,并突出了不同诊断之间的机制重叠以及症状共现模式。这些观察结果对复杂疾病的传统概念提出了质疑,在传统概念中,诊断被视为不同的实体,并促使我们重新构建我们的疾病分类模型。因此,本手稿提出了一种新颖的模型,其中个体疾病负担同时根据分子、生理和病理因素来确定,并表示为一个状态向量。在这种概念化中,重点从识别诊断队列的潜在病理生理学转向识别个体患者中决定症状的特征。这种概念化有助于在复杂疾病背景下采用多维度方法来理解人体生理学和病理生理学。这可能提供一个有用的概念,以解决诊断队列中显著的个体间异质性以及诊断、健康和疾病之间缺乏明确区分的问题,从而促进向个性化医学的发展。