Vernon Suzanne D, Reeves William C
Centers for Disease Control and Prevention, National Center for Infectious Diseases, Atlanta, GA, USA.
Pharmacogenomics. 2006 Apr;7(3):345-54. doi: 10.2217/14622416.7.3.345.
Chronic fatigue syndrome (CFS) is a debilitating illness characterized by multiple unexplained symptoms including fatigue, cognitive impairment and pain. People with CFS have no characteristic physical signs or diagnostic laboratory abnormalities, and the etiology and pathophysiology remain unknown. CFS represents a complex illness that includes alterations in homeostatic systems, involves multiple body systems and results from the combined action of many genes, environmental factors and risk-conferring behavior. In order to achieve understanding of complex illnesses, such as CFS, studies must collect relevant epidemiological, clinical and laboratory data and then integrate, analyze and interpret the information so as to obtain meaningful clinical and biological insight. This issue of Pharmacogenomics represents such an approach to CFS. Data was collected during a 2-day in-hospital study of persons with CFS, other medically and psychiatrically unexplained fatiguing illnesses and nonfatigued controls identified from the general population of Wichita, KS, USA. While in the hospital, the participants' psychiatric status, sleep characteristics and cognitive functioning was evaluated, and biological samples were collected to measure neuroendocrine status, autonomic nervous system function, systemic cytokines and peripheral blood gene expression. The data generated from these assessments was made available to a multidisciplinary group of 20 investigators from around the world who were challenged with revealing new insight and algorithms for integration of this complex, high-content data and, if possible, identifying molecular markers and elucidating pathophysiology of chronic fatigue. The group was divided into four teams with representation from the disciplines of medicine, mathematics, biology, engineering and computer science. The papers in this issue are the culmination of this 6-month challenge, and demonstrate that data integration and multidisciplinary collaboration can indeed yield novel approaches for handling large, complex datasets, and reveal new insight and relevance to a complex illness such as CFS.
慢性疲劳综合征（CFS）是一种使人衰弱的疾病，其特征是出现多种不明原因的症状，包括疲劳、认知障碍和疼痛。患有慢性疲劳综合征的人没有特征性的体征或诊断性实验室异常，其病因和病理生理学仍然未知。慢性疲劳综合征是一种复杂的疾病，包括体内稳态系统的改变，涉及多个身体系统，由许多基因、环境因素和风险行为的共同作用导致。为了理解像慢性疲劳综合征这样的复杂疾病，研究必须收集相关的流行病学、临床和实验室数据，然后整合、分析和解释这些信息，以便获得有意义的临床和生物学见解。本期《药物基因组学》就代表了这样一种研究慢性疲劳综合征的方法。数据是在美国堪萨斯州威奇托市的普通人群中，对患有慢性疲劳综合征、其他医学和精神方面不明原因的疲劳性疾病的患者以及无疲劳症状的对照者进行为期两天的住院研究期间收集的。住院期间，对参与者的精神状态、睡眠特征和认知功能进行了评估，并采集了生物样本以测量神经内分泌状态、自主神经系统功能、全身细胞因子和外周血基因表达。从这些评估中产生的数据提供给了一个由来自世界各地的20名研究人员组成的多学科团队，他们面临着揭示新见解和整合这些复杂的、高含量数据的算法的挑战，并且如果可能的话，识别分子标记并阐明慢性疲劳的病理生理学。该团队分为四个小组，成员来自医学、数学、生物学、工程学和计算机科学等学科。本期的论文是这项为期6个月挑战的成果，表明数据整合和多学科合作确实可以产生处理大型复杂数据集的新方法，并揭示与慢性疲劳综合征等复杂疾病相关的新见解。
