Daily Immune Monitoring in Chronic Fatigue Syndrome

描述(申请人提供)：肌痛性脑脊髓炎/慢性疲劳综合症(ME/CFS)是一种衰弱的疾病，其特征是严重的慢性疲劳，无法通过休息缓解，以及疼痛、运动后的不适以及记忆力和注意力的损害。ME/CFS在美国影响了100多万妇女，在受影响的个人中造成了严重的痛苦和功能丧失，并给社会带来了重大的经济负担，因为ME/CFS的基本病理生理学还没有得到很好的了解，没有专门针对这种疾病开发的有效治疗方法，许多患者对现有的治疗方案不满意。以前的研究为炎症参与ME/CFS提供了强有力的证据，尽管在所有研究中没有免疫因素一致地预测疲劳。在低水平或非典型炎症的情况下，传统的横断面研究方法对于识别ME/CFS生物标志物可能不够敏感。我们观察到，患有ME/CFS的女性在疲劳严重程度上表现出相当大的每日变异性，这种变异性可能反映了潜在疾病机制的快速变化。通过将每天的疲劳变异性视为一个重要的信号，并每天采集血样，我们已经确定了一小部分与ME/CFS1疲劳变化密切相关的血清细胞因子。在这项拟议的研究中，我们计划在更大的样本中确认我们关于免疫-疲劳关系的初步发现。我们将连续25天采集70名ME/CFS女性的血液样本，以及20名健康对照和20名积极疲劳对照(甲状腺功能减退症患者)。血液样本将被分析51种与炎症相关的不同免疫因素。此外，参与者将在掌上电脑上提交疲劳严重程度的每日报告。通过纵向分析疲劳评分和细胞因子浓度，我们可以识别出“跟踪”疲劳严重程度逐日波动的细胞因子。这种方法将使我们能够开发出一种区别于 从低疲劳天数到高疲劳天数，提供了关于ME/CFS机制的重要信息。在目标1中，我们将开发一个生理模型，该模型使用血清细胞因子水平来准确预测疲劳严重程度的日常波动。在目标2中，我们将根据细胞因子-疲劳关系定义重要的ME/CFS亚群。在目标3中，我们将开发免疫因素和疲劳之间的时间路径，以识别疲劳的早期驱动因素。此外，我们还将开发一个血液样本样本库，供其他感兴趣的研究人员使用。密集的纵向免疫监测是了解ME/CFS病理生理学的唯一途径。这项研究揭示的生物标记物将作为开发ME/CFS诊断测试的工具，并将为开发改进的治疗方法提供极好的靶点。

DESCRIPTION (provided by applicant): Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating condition characterized by profound, chronic fatigue that is not alleviated by rest, as well as pain, post-exertional malaise, and impairments in memory and concentration. ME/CFS affects over one million women in the United States, causing significant distress and loss of function in affected individuals and a significant financial burden on society Because the underlying pathophysiology of ME/CFS is not well-understood, there are no effective treatments developed specifically for the condition, and many patients are unsatisfied with existing treatment options. Previous research provides a strong case for inflammatory involvement in ME/CFS, though no immune factors have been consistently predictive of fatigue across studies. Conventional cross-sectional research approaches may not be sufficiently sensitive for identifying ME/CFS biomarkers in cases of low-level or atypical inflammation. We have observed that women with ME/CFS demonstrate considerable day-to-day variability in their fatigue severity, and this variability may reflect rapid shifts in underlying disease mechanisms. By viewing the daily fatigue variability as an important signal, and collecting blood samples daily, we have identified a small set of serum cytokines that are strongly correlated with changes in ME/CFS 1fatigue. In this proposed study, we plan to confirm our preliminary findings of immune-fatigue relationships in a larger sample. We will collect blood samples for 25 consecutive days in 70 women with ME/CFS, as well as 20 healthy controls and 20 active fatigue controls (individuals with hypothyroidism). Blood samples will be analyzed for 51 different immune factors associated with inflammation. In addition, participants will submit daily reports of fatigue severity on handheld computers. By analyzing fatigue scores and cytokine concentrations longitudinally, we can identify cytokines that "track" day-to-day fluctuations in fatigue severity. This approach will allow us to develop a physiological profile that distinguishes high fatigue days from low fatigue days, providing important information about ME/CFS mechanisms. In Aim 1, we will develop a physiological model that uses serum cytokine levels to accurately predict day-to-day fluctuations in fatigue severity. In Aim 2, we will define important ME/CFS subgroups based on cytokine-fatigue relationships. In Aim 3, we will develop a temporal pathway between immune factors and fatigue that identifies early drivers of fatigue. Additionally, we will develop a specimen bank of blood samples that can be made available to other interested researchers. Intensive longitudinal immune monitoring is a unique approach to understanding ME/CFS pathophysiology. Biomarkers revealed by this research will serve as tools in the development of ME/CFS diagnostic tests, and will provide excellent targets for developing improved therapies.