呼吸系统
医学
计算生物学
计算机科学
生物
内科学
作者
Nicole Prince,Sofina Begum,Kevin Mendez,Lourdes G. Ramirez,Yulu Chen,Qingwen Chen,Su H. Chu,Priyadarshini Kachroo,Ofer Levy,Joann Diray‐Arce,Paolo Palma,Augusto A. Litonjua,Scott T. Weiss,Rachel S. Kelly,Jessica Lasky‐Su
标识
DOI:10.1101/2024.06.14.599044
摘要
Abstract Background The first year of life is a period of rapid immune development that can impact health trajectories and the risk of developing respiratory-related diseases, such as asthma, recurrent infections, and eczema. However, the biology underlying subsequent disease development remains unknown. Methods Using weighted gene correlation network analysis (WGCNA), we derived modules of highly correlated immune-related proteins in plasma samples from children at age 1 year (N=294) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART). We applied regression analyses to assess relationships between protein modules and development of childhood respiratory diseases up to age 6 years. We then characterized genomic, environmental, and metabolomic factors associated with modules. Results WGCNA identified four protein modules at age 1 year associated with incidence of childhood asthma and/or recurrent wheeze (P adj range: 0.02-0.03), respiratory infections (P adj range: 6.3×10-9-2.9×10-6), and eczema (P adj =0.01) by age 6 years; three modules were associated with at least one environmental exposure (P adj range: 2.8×10-10-0.03) and disrupted metabolomic pathway(s) (P adj range: 2.8×10-6-0.04). No genome-wide SNPs were identified as significant genetic risk factors for any protein module. Relationships between protein modules with clinical, environmental, and ‘omic factors were temporally sensitive and could not be recapitulated in protein profiles at age 6 years. Conclusion These findings suggested protein profiles as early as age 1 year predicted development of respiratory-related diseases through age 6 and were associated with changes in pathways related to amino acid and energy metabolism. These may inform new strategies to identify vulnerable individuals based on immune protein profiling.
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