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ORIGINAL RESEARCH article
Front. Med.
Sec. Pulmonary Medicine
Volume 11 - 2024 |
doi: 10.3389/fmed.2024.1410051
Metabolic Profiling of Idiopathic Pulmonary Fibrosis in a Mouse Model: Implications for Pathogenesis and Biomarker Discovery
Provisionally accepted- 1 Department of Geriatrics, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China
- 2 Department of Respiratory and Critical Care Medicine, Qingdao Municipal Hospital, Qingdao, Shandong Province, China
- 3 The Affiliated Hospital of Qingdao University, Qingdao, China
Alterations in metabolites and metabolic pathways are thought to be important triggers of idiopathic pulmonary fibrosis (IPF), but our lack of a comprehensive understanding of this process has hampered the development of IPF-targeted drugs. To fully understand the metabolic profile of unsaturated fatty acids, pentose phosphate pathway, and alanine, aspartate, and glutamate metabolism. Seven metabolites were screened by machine learning LASSO models and evaluated as ideal diagnostic tools by receiver operating characteristic curves (ROCs). In conclusion, the serum metabolic disorders found to be associated with pulmonary fibrosis formation will help to deepen our understanding of the pathogenesis of pulmonary fibrosis.
Keywords: Pulmonary Fibrosis, Metabolites, machine learning, biomarkers, Mice
Received: 05 Apr 2024; Accepted: 19 Jul 2024.
Copyright: © 2024 Zhang, Jia, Xu, Ding, Wang, Chi, Hu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiao-hui Yang, The Affiliated Hospital of Qingdao University, Qingdao, China
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