Predictive Molecular Markers of Lung Function Decline

肺功能下降的预测分子标志物

• 批准号: 9922985
• 负责人: ASSEM G ZIADY
• 金额: $ 43.61万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9922985
• 关键词: Adherence; Adolescence; Affect; Algorithms; Behavior; Biological Markers; Biometry; Blood; Blood Proteins; Body mass index; Caregivers; Caring; Child; Chronic lung disease; Clinical; Clinical Data; Clinical Research; Clinical Trials; Cystic Fibrosis; Cystic Fibrosis Transmembrane Conductance Regulator; Data; Data Analyses; Disease; Disease Marker; Disease Progression; Doctor of Philosophy; Forced expiratory volume function; Future; Individual; Infection Control; Inflammation; Intervention; Length; Letters; Longitudinal Studies; Lung; Lung diseases; Measurement; Measures; Modeling; Molecular; Monitor; Pathway interactions; Patients; Persons; Post-Translational Protein Processing; Protein Isoforms; Proteins; Proteome; Proteomics; Pseudomonas Infections; Pulmonary Cystic Fibrosis; Research Personnel; Respiratory physiology; Risk; Sampling; Serum; Serum Proteins; Severity of illness; Stable Disease; Statistical Models; Testing; Therapeutic Intervention; Time; Translating; base; clinical translation; cohort; cost; cystic fibrosis patients; design; disease natural history; evidence base; exhaustion; high risk; improved; individual patient; individualized medicine; longitudinal analysis; molecular marker; novel; novel diagnostics; novel marker; outcome prediction; personalized medicine; prediction algorithm; predictive marker; predictive modeling; prevent; protein biomarkers; protein expression; response; success; tool

ABSTRACT The identification of molecules that reflect and/or predict disease progression and can be easily measured in chronic lung disease is highly desirable. Such biomarkers that can be rapidly and reliably measured to establish change in the course of the disease over time or in response to therapy would significantly improve care. For example, measurement of short term markers that track with or predict long term changes in FEV1 (a clinical measure of lung function) may shorten the length, significantly reduce the cost of clinical trials, and accelerate the examination of potential therapies allowing faster delivery to the patients. Furthermore, the behavior of molecular biomarkers can inform therapy and increase success of alleviating disease progression. In preliminary studies, we analyzed differences in pathways in serum samples from 44 children with mild lung disease (FEV1>80th percentile) and 44 with more severe lung disease (FEV1<45th percentile). We found significant differences in a number of proteins and pathways associated with disease. The top markers in our analysis correlated with disease progression and significantly improved novel disease progression prediction models. Here we propose to integrate the longitudinal behavior of novel disease markers with novel Functional Data (FD) analysis of FEV1 and other clinical data to develop an algorithm that models lung function decline. To achieve this we propose to; 1) identify and validate serum proteome changes in banked samples collected from patients with stable and declining FEV1, 2) develop a dynamic prediction model that integrates validated proteomic biomarkers with Functional Data (FD) analysis of longitudinal FEV1 values to produce a novel diagnostic algorithm that identifies individuals at risk of lung function decline, and 3) test the capacity of dynamic prediction modeling to identify modulator treated CF patients who demonstrate rapid pulmonary decline compared to highly responsive individuals in banked longitudinal samples. If we are successful, we will clinically translate a novel lung function prediction model that will significantly improve therapeutic intervention and accelerate clinical studies.

抽象的 鉴定反映和/或预测疾病进展的分子，可以轻松测量 在慢性肺部疾病中是非常可取的。可以迅速可靠地测量的生物标志物 随着时间的推移或对治疗的响应，建立疾病过程中的变化将大大改善 关心。例如，测量跟踪或预测FEV1长期变化或预测长期变化的短期标记 （肺功能的临床指标）可能会缩短长度，大大降低临床试验的成本，并 加速对潜在疗法的检查，从而可以更快地向患者分娩。此外， 分子生物标志物的行为可以为治疗提供信息，并增加减轻疾病进展的成功。 在初步研究中，我们分析了44名轻度肺儿童的血清样品途径差异 疾病（FEV1> 80％）和44个患有更严重的肺部疾病（FEV1 <45％）。我们发现 与疾病相关的许多蛋白质和途径的显着差异。我们的最高标记 分析与疾病进展相关，并显着改善了新的疾病进展预测 型号。在这里，我们建议将新型疾病标记的纵向行为与新颖 FEV1和其他临床数据的功能数据（FD）分析，以开发建模肺功能的算法 衰退。为了实现这一目标，我们建议； 1）识别和验证库样品中的血清蛋白质组变化 从稳定且下降FEV1的患者收集，2）开发一个动态预测模型 通过功能数据（FD）分析纵向FEV1值的蛋白质组学生物标志物进行了验证 新型诊断算法确定有肺功能下降风险的个体，3）测试能力 动态预测建模，以识别调节剂治疗的CF患者，这些患者表现出快速肺部 与在纵向纵向样本中响应高度敏感的个体相比，下降。如果我们成功，我们将 临床翻译新的肺功能预测模型，该模型将显着改善治疗干预措施 并加速临床研究。