Machine learning-based MPO and Lp-PLA2 profiling reveals asthma-predominant inflammatory signature in asthma-COPD
Mingtao Liu1,2,3, Jiaxi Chen4, Jiani Yao4
1Department 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.
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 q = 0.031). Unsupervised clustering revealed that 72.2% of clinically diagnosed ACO patients clustered with asthma, challenging traditional disease conceptualization. Ensemble machine learning model achieved exceptional performance (AUC 0.954, 95% CI: 0.926-0.982), significantly outperforming individual biomarkers (MPO/Lp-PLA2: AUC 0.782/0.714) and traditional clinical criteria (AUC 0.694). Clinically applicable ACORN (ACO recognition by neutrophil-eosinophil) score demonstrated 91.2% positive predictive value (PPV).
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.
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