Evidence map›Paper›PMID 42436899›Full record

ArticleThe World Allergy Organization journal2026

Machine learning-based MPO and Lp-PLA2 profiling reveals asthma-predominant inflammatory signature in asthma-COPD overlap.

Mingtao Liu, Jiaxi Chen, Jiani Yao, Yueying Zheng, Xiangyu Li, Haoming Zhong, Jiahui Liu, Jiating Zheng, Rui Ye, Huimin Huang and 2 more

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Article in The World Allergy Organization journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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12 authors.

Mingtao LiuDepartment of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.
Jiaxi ChenMedical laboratory technology, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou 511495, China.
Jiani YaoMedical laboratory technology, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou 511495, China.
Yueying ZhengDepartment of Clinical Medicine, The First Clinical College of Guangzhou Medical University, Guangzhou 511495, China.
Xiangyu LiMedical laboratory technology, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou 511495, China.
Haoming ZhongMedical laboratory technology, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou 511495, China.
Jiahui LiuMedical laboratory technology, The First Clinical Medical College of Guangdong Pharmaceutical University, Guangzhou 510006, China.
Jiating ZhengMedical laboratory technology, The First Clinical Medical College of Guangdong Pharmaceutical University, Guangzhou 510006, China.
Rui YeMedical laboratory technology, The First Clinical Medical College of Guangdong Pharmaceutical University, Guangzhou 510006, China.
Huimin HuangDepartment of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.
Peiyan ZhengDepartment of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.
Baoqing SunDepartment of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Asthma-COPD overlap (ACO) remains poorly characterized at the molecular level, leading to diagnostic uncertainty and suboptimal treatment. We hypothesized that machine learning analysis of myeloperoxidase (MPO) and lipoprotein-associated phospholipase A2 (Lp-PLA2) could help reveal ACO as a distinct inflammatory endotype. Methods: We prospectively enrolled 609 patients with obstructive airway diseases (asthma n = 255, COPD n = 100, AECOPD n = 182, ACO n = 72) at First Hospital of Guangzhou Medical University (National Center for Respiratory Medicine) between July 2023 and August 2025. Serum MPO and Lp-PLA2 were quantified using chemiluminescence immunoassay. Five machine learning algorithms (XGBoost, LightGBM, CatBoost, Neural Network, Random Forest) were employed for supervised classification, while unsupervised clustering (hierarchical, k-means, DBSCAN) defined molecular phenotypes. Model interpretability utilized SHAP analysis and dimensionality reduction techniques (PCA, UMAP, t-SNE). Results: MPO/Lp-PLA2 ratio emerged as a powerful discriminatory biomarker, with ACO patients exhibiting nominally lower ratios (1.66, IQR 1.19-2.27) resembling asthma (1.47, IQR 1.07-2.06) rather than COPD (2.26, IQR 1.98-2.56, FDR-adjusted Conclusion: Machine learning reveals ACO exhibits predominantly asthma-like inflammatory characteristics. MPO/Lp-PLA2 ratio and ACORN score enable precision phenotyping with potential therapeutic implications, advocating for biomarker-guided treatment strategies in obstructive airway diseases.

Indexed as

Asthma–COPD overlap syndromeInflammatory endotypeLipoprotein–associated phospholipase A2Machine learningMyeloperoxidase

Identifiers

PMID42436899
PMCPMC13355028

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.