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临床试验/NCT03310177
NCT03310177已完成不适用

Compartmental Analysis of Metabolite Profiles Associated With Disease Phenotype in Smokers With and Without Chronic Obstructive Pulmonary Disease

Peking University Third Hospital1 个研究点 分布在 1 个国家目标入组 167 人开始时间: 2015年12月10日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
入组人数
167
试验地点
1
主要终点
Metabolites that can predict the progress of lung function

研究概览

简要总结

Despite the high prevalence of chronic obstructive pulmonary disease (COPD), there continues to be a large gap in our understanding of disease pathogenesis and mechanisms accounting for large variability in disease phenotype. Untargeted metabolomics is an ideal approach to uncover the metabolic basis of disease, as well as discover unique drug target opportunities aimed at these nodal metabolic drivers of disease. There are very limited data from metabolomics studies from plasma/serum and exhaled breath condensate that suggest certain metabolic pathways or metabolites might predict the presence and/or severity of COPD phenotypes.

Here, the investigators hope to generate comprehensive, compartment specific (blood and lung) metabolite profiles that will be correlated with various clinical phenotypes of COPD, using a complementary approach of untargeted nuclear magnetic resonance (NMR) and liquid chromatography (LC)- mass spectroscopy (MS) -based metabolomics.

详细描述

Despite the high prevalence of chronic obstructive pulmonary disease (COPD), there continues to be a large gap in our understanding of disease pathogenesis and mechanisms accounting for large variability in disease phenotype. Untargeted metabolomics is an ideal approach to uncover the metabolic basis of disease, as well as discover unique drug target opportunities aimed at these nodal metabolic drivers of disease. There are very limited data from metabolomics studies from plasma/serum and exhaled breath condensate that suggest certain metabolic pathways or metabolites might predict the presence and/or severity of COPD phenotypes.

The investigators hypothesize that: 1) smokers with COPD will have a metabolomics signature that is distinct from healthy non-COPD smokers; 2) this signature will be associated with clinically relevant manifestations of disease (e.g., GOLD classification, PFT).

The availability of biosamples from a well-characterized population of smokers with and without COPD, combined with our established in-house metabolomics expertise, will robustly allow to test these novel hypotheses. The investigators hope to generate comprehensive, compartment specific (blood and lung) metabolite profiles that will be correlated with various clinical phenotypes of COPD, using a complementary approach of untargeted nuclear magnetic resonance (NMR) and liquid chromatography (LC)- mass spectroscopy (MS) -based metabolomics. Moreover, this strategy may identify previously unrecognized metabolic pathways that are dysregulated in COPD. Collectively, these data will be used to direct a prospective clinical study to determine the association between metabolomics signatures and clinical outcomes.

研究设计

研究类型
Observational
观察模型
Case Control
时间视角
Prospective

入排标准

年龄范围
40 Years 至 80 Years(Adult, Older Adult)
性别
Male
接受健康志愿者

入选标准

  • males aged 40-80;
  • diagnosed with COPD according to the GOLD guidelines;
  • clinically stable patients without medication changes or exacerbation in two months;
  • smoking history of more than 10 pack years

排除标准

  • diagnosed with unstable cardiovascular diseases, significant renal or hepatic dysfunction or mental incompetence;
  • diagnosed with asthma, active pulmonary tuberculosis, diffuse panbronchiolitis, cystic fibrosis, clinically significant bronchiectasis, exacerbation of COPD or pneumonia in two months;
  • prescribed immunosuppressive medications.

结局指标

主要结局

Metabolites that can predict the progress of lung function

时间窗: 3 months

The study is aimed to investigate the relationship between the metabolites and the progress of lung function in COPD

次要结局

  • Metabolites that are associated with inflammatory mediators(3 months)
  • Metabolites that can predict the severity of emphysema(3 months)

研究者

申办方类型
Other
责任方
Principal Investigator
主要研究者

Bei He

Professor and Chief in Department of Respiratory Medicine

Peking University Third Hospital

研究点 (1)

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