Sputum bacterial microbiota signature as a surrogate for predicting disease progression of nontuberculous mycobacterial lung disease

Hung-Ling Huang; Chieh-Hua Lin; Meng-Rui Lee; Wei-Chang Huang; Chau-Chyun Sheu; Meng-Hsuan Cheng; Po-Liang Lu; Cheng-Hsieh Huang; Yao-Tsung Yeh; Jinn-Moon Yang; Inn-Wen Chong; Yu-Chieh Liao; Jann-Yuan Wang

doi:10.1016/j.ijid.2024.107085

Sputum bacterial microbiota signature as a surrogate for predicting disease progression of nontuberculous mycobacterial lung disease

Int J Infect Dis. 2024 May 11:107085. doi: 10.1016/j.ijid.2024.107085. Online ahead of print.

Authors

Affiliations

¹ Division of Pulmonary and Critical Care Medicine, Kaohsiung, Taiwan; Department of Internal Medicine, Kaohsiung, Taiwan; Department of Internal Medicine, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan; Graduate Institute of Medicine, College of Medicine, Kaohsiung, Taiwan; Center for Liquid Biopsy and Cohort, Kaohsiung Medical University, Kaohsiung, Taiwan.
² Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli, Taiwan; Big Data Center, China Medical University Hospital, Taichung 404, Taiwan.
³ Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
⁴ Division of Chest Medicine, Department of Internal Medicine, Taichung, 407, Taiwan; Mycobacterial Center, Taichung Veterans General Hospital, Taichung, 407, Taiwan; Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, 402, Taiwan; School of Medicine, Chung Shan Medical University, Taichung, 402, Taiwan.
⁵ Division of Pulmonary and Critical Care Medicine, Kaohsiung, Taiwan; Department of Internal Medicine, Kaohsiung, Taiwan.
⁶ Division of Pulmonary and Critical Care Medicine, Kaohsiung, Taiwan; Department of Internal Medicine, Kaohsiung, Taiwan; Graduate Institute of Medicine, College of Medicine, Kaohsiung, Taiwan; Department of Respiratory Therapy, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
⁷ Department of Internal Medicine, Kaohsiung, Taiwan; Graduate Institute of Medicine, College of Medicine, Kaohsiung, Taiwan; Center for Liquid Biopsy and Cohort, Kaohsiung Medical University, Kaohsiung, Taiwan.
⁸ Aging and Disease Prevention Research Center, Kaohsiung, Taiwan; Department of Medical Laboratory Science and Biotechnology, Fooyin University, Kaohsiung, Taiwan.
⁹ Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
¹⁰ Division of Pulmonary and Critical Care Medicine, Kaohsiung, Taiwan; Department of Internal Medicine, Kaohsiung, Taiwan; Graduate Institute of Medicine, College of Medicine, Kaohsiung, Taiwan; Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
¹¹ Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli, Taiwan.
¹² Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan. Electronic address: jywang@ntu.edu.tw.

PMID: 38740280
DOI: 10.1016/j.ijid.2024.107085

Abstract

Objectives: Predicting progression of nontuberculous mycobacterial lung disease (NTM-LD) remains challenging. This study evaluated whether sputum bacterial microbiome diversity can be the biomarker and provide novel insights into related phenotypes and treatment timing.

Methods: We analyzed 126 sputum microbiomes of 126 patients with newly diagnosed NTM-LD due to Mycobacterium avium complex, M. abscessus complex, and M. kansasii between May 2020 and December 2021. Patients were followed for 2 years to determine their disease progression status. We identified consistently representative genera that differentiated the progressor and nonprogressor by using six methodologies. These genera were used to construct a prediction model using random forest with 5-fold cross validation.

Results: Disease progression occurred in 49 (38.6%) patients. Compared with nonprogressors, α-diversity was lower in the progressors. Significant compositional differences existed in the β-diversity between groups (p=0.001). The prediction model for NTM-LD progression constructed using seven genera (Burkholderia, Pseudomonas, Sphingomonas, Candidatus Saccharibacteria, Phocaeicola, Pelomonas, and Phascolarctobacterium) with significantly differential abundance achieved an area under curve of 0.871.

Conclusions: Identification of the composition of sputum bacterial microbiome facilitates prediction of the course of NTM-LD, and maybe used to develop precision treatment involving modulating the respiratory microbiome composition to ameliorate NTM-LD.

Keywords: lung disease; nontuberculous mycobacterium; prediction; progression; sputum microbiota.