Plasma protein signatures of adult asthma

Author:

Smilnak Gordon J.1,Lee Yura2ORCID,Chattopadhyay Abhijnan1,Wyss Annah B.1,White Julie D.13,Sikdar Sinjini14,Jin Jianping5,Grant Andrew J.6,Motsinger‐Reif Alison A.7,Li Jian‐Liang8,Lee Mikyeong1ORCID,Yu Bing2,London Stephanie J.1

Affiliation:

1. Epidemiology Branch National Institute of Environmental Health Sciences, National Institutes of Health Research Triangle Park North Carolina USA

2. Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health The University of Texas Health Science Center at Houston Houston Texas USA

3. GenOmics and Translational Research Center, Biostatistics and Epidemiology Division RTI International Research Triangle Park North Carolina USA

4. Department of Mathematics and Statistics Old Dominion University Norfolk Virginia USA

5. Westat, Inc. Durham North Carolina USA

6. MRC Biostatistics Unit University of Cambridge Cambridge UK

7. Biostatistics and Computational Biology Branch National Institute of Environmental Health Sciences, National Institutes of Health Research Triangle Park North Carolina USA

8. Integrative Bioinformatics Support Group National Institute of Environmental Health Sciences, National Institutes of Health Research Triangle Park North Carolina USA

Abstract

AbstractBackgroundAdult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma.MethodsProtein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non‐case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non‐case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta‐analyzed using inverse‐variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n = 207), asthma without atopy (n = 554), atopy without asthma (n = 147), compared to neither (n = 948)).ResultsMeta‐analysis of 4860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC = 0.77, 95% CI = 0.75–0.79 in training set; AUC = 0.72, 95% CI = 0.69–0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma‐atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis.ConclusionThis first large‐scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management.

Funder

National Heart, Lung, and Blood Institute

National Institute of Environmental Health Sciences

National Cancer Institute

Publisher

Wiley

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3