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Am J Respir Crit Care Med
Am J Respir Crit Care Med
ajrccm
American Journal of Respiratory and Critical Care Medicine
1073-449X
1535-4970
American Thoracic Society

202404-0748LE
10.1164/rccm.202404-0748LE
Correspondence
Classifying Interstitial Lung Disease: Omics Are in the Air
https://orcid.org/0000-0001-5566-8129
van der Sar Iris G.
https://orcid.org/0000-0002-5295-2877
Moor Catharina C.
https://orcid.org/0000-0002-4527-6962
Wijsenbeek Marlies S.
Center for Interstitial Lung Diseases and Sarcoidosis, Department of Respiratory Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
Correspondence and requests for reprints should be addressed to Marlies S. Wijsenbeek, M.D., Ph.D., 40, 3015 GD Rotterdam, The Netherlands Email: m.wijsenbeek-lourens@erasmusmc.nl.
3 5 2024
1 9 2024
3 5 2024
210 5 690691
Copyright © 2024 by the American Thoracic Society
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0. For commercial usage and reprints, please e-mail Diane Gern (dgern@thoracic.org).
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pmcTo the Editor:

We currently stand at the beginning of an era in which artificial intelligence will likely become integrated into medical practice and health care on a broad scale. In the scientific quest for novel diagnostic tests and biomarkers, vast amounts of data are collected. Data originating from specific biological sources are known as “omics data” and may include genes (i.e., genomics), serum proteins (i.e., proteomics), or breath molecules (i.e., breathomics). Machine learning algorithms are often used for analyzing these extensive datasets, revealing patterns in data that exceed human capabilities. This approach is particularly useful to extract insightful information for better diagnosis of complex rare diseases such as interstitial lung diseases (ILDs).

In this context, we find Huang and colleagues’ report on serum plasma proteomics for diagnosing patients with pulmonary fibrosis especially interesting (1). They aimed to design a proteomic classifier for differentiating patients with idiopathic pulmonary fibrosis (IPF) and connective tissue disease–related ILD (CTD-ILD) by including an impressively large cohort (n = 1,247 IPF; n = 352 CTD-ILD) representing 42 clinical centers across the United States. The developed classifier for differentiation consisted of 37 proteins. Subsequently, the accuracy of four algorithms for classifying patients with IPF or CTD-ILD was calculated. All algorithms yielded high accuracies (77.3–82.5%) in an independent dataset. Considering the high performance of the classifier and noninvasive nature of blood sampling, integrating proteomics analysis in medical practice holds promise. However, the present analysis process is still too elaborative and should be converted into an easily applicable and affordable method to enable integration in medical practice.

We would like to challenge Huang and colleagues that exhaled breath data resulting from eNose analysis (i.e., breathomics data) has greater potential to serve as an easily integrated diagnostic tool for ILD: an eNose using multiple sensors to analyze the >2,000 types of volatile organic compounds present in breath. The technology can provide individual results in real time and comes with low costs. Besides, the procedure is patient-friendly because testing takes less than 2 minutes and involves slow breathing without forced maneuvers. Opposite to invasive procedures such as tissue biopsies, an eNose test can be repeated during the disease course allowing one to monitor or reclassify an ILD diagnosis. Importantly, studies reported high performance for differentiating types of ILD, pulmonary fibrosis in particular. Two single-center studies from 2019 and 2021 compared IPF and CTD-ILD cohorts, similar to that of Huang and colleagues, and reported area under the curve values for classifying breath profiles using different type of eNoses ranging from 0.84 to 0.96 (2, 3).

The examples of proteomics and breathomics research show that the analysis of high-volume omics data analyzed with machine learning algorithms can support diagnosing rare diseases. One could envision that combining results from the plasma proteomics classifier and an eNose breathomics profile could lead to improved diagnostic confidence of ILD multidisciplinary team discussions and limit the need for invasive biopsies. Given the limited number of expertise centers for ILD and potential differences in ILDs across the world, international collaboration is essential to guarantee the collection of sufficient data for robust models. Thus, we need to start sharing omics data in secured and constantly updated cloud-connected databases to facilitate the development and use of high-quality, clinically applicable diagnostic models. Because omics are in the air, we look forward to a bright future in which we use biological data to its full potential to enable accurate, fast, and noninvasive diagnostic trajectories for most patients with ILD worldwide.

Originally Published in Press as DOI: 10.1164/rccm.202404-0748LE on May 3, 2024

Author disclosures are available with the text of this letter at www.atsjournals.org.
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References

1. Huang Y Ma SF Oldham JM Adegunsoye A Zhu D Murray S et al. Machine learning of plasma proteomics classifies diagnosis of interstitial lung disease Am J Respir Crit Care Med 2024 210
2. Krauss E Haberer J Maurer O Barreto G Drakopanagiotakis F Degen M et al. Exploring the ability of electronic nose technology to recognize interstitial lung diseases (ILD) by non-invasive breath screening of exhaled volatile compounds (VOC): a pilot study from the European IPF Registry (eurIPFreg) and Biobank J Clin Med 2019 8 1698 31623141
3. Moor CC Oppenheimer JC Nakshbandi G Aerts JGJV Brinkman P Maitland-van der Zee AH et al. Exhaled breath analysis by use of eNose technology: a novel diagnostic tool for interstitial lung disease Eur Respir J 2021 57 2002042 32732331
