Mustafina Malika, Silantyev Artemiy, Makarova Marina, Suvorov Aleksandr, Chernyak Alexander, Naumenko Zhanna, Pakhomov Pavel, Pershina Ekaterina, Suvorova Olga, Shmidt Anna, Gordeeva Anastasia, Vergun Maria, Bahankova Olesya, Gognieva Daria, Bykova Aleksandra, Belevskiy Andrey, Avdeev Sergey, Betelin Vladimir, Kopylov Philipp
Department of Cardiology, Functional and Ultrasound Diagnostics, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia.
Pulmonology Research Institute under the Federal Medical and Biological Agency of Russia, 115682 Moscow, Russia.
Int J Mol Sci. 2025 Jun 23;26(13):6005. doi: 10.3390/ijms26136005.
Lymphangioleiomyomatosis (LAM) is a rare progressive disease that affects women of reproductive age and is characterized by cystic lung destruction, airflow obstruction, and lymphatic dysfunction. Current diagnostic methods are costly or lack sufficient specificity, highlighting the need for novel non-invasive approaches. Exhaled breath analysis using real-time proton mass spectrometry (PTR-MS) presents a promising strategy for identifying disease-specific volatile organic compounds (VOCs). This cross-sectional study analyzed exhaled breath samples from 51 LAM patients and 51 age- and sex-matched healthy controls. PTR-time-of-flight mass spectrometry (PTR-TOF-MS) was employed to identify VOC signatures associated with LAM. Data preprocessing, feature selection, and statistical analyses were performed using machine learning models, including gradient boosting classifiers (XGBoost), to identify predictive biomarkers of LAM and its complications. We identified several VOCs as potential biomarkers of LAM, including / = 90.06 (lactic acid) and / = 113.13. VOCs predictive of disease complications included / = 49.00 (methanethiol), / = 48.04 (O-methylhydroxylamine), and / = 129.07, which correlated with pneumothorax, obstructive ventilation disorders, and radiological findings of lung cysts and bronchial narrowing. The classifier incorporating these biomarkers demonstrated high diagnostic accuracy (AUC = 0.922). This study provides the first evidence that exhaled breath VOC profiling can serve as a non-invasive additional tool for diagnosing LAM and predicting its complications. These findings warrant further validation in larger cohorts to refine biomarker specificity and explore their clinical applications in disease monitoring and personalized treatment strategies.
淋巴管平滑肌瘤病（LAM）是一种罕见的进行性疾病，影响育龄女性，其特征为肺囊性破坏、气流阻塞和淋巴功能障碍。目前的诊断方法成本高昂或缺乏足够的特异性，凸显了对新型非侵入性方法的需求。使用实时质子质谱（PTR-MS）进行呼出气分析为识别疾病特异性挥发性有机化合物（VOC）提供了一种有前景的策略。这项横断面研究分析了51例LAM患者和51例年龄及性别匹配的健康对照者的呼出气样本。采用飞行时间质子质谱（PTR-TOF-MS）来识别与LAM相关的VOC特征。使用包括梯度提升分类器（XGBoost）在内的机器学习模型进行数据预处理、特征选择和统计分析，以识别LAM及其并发症的预测生物标志物。我们鉴定出几种VOC作为LAM的潜在生物标志物，包括/ = 90.06（乳酸）和/ = 113.13。预测疾病并发症的VOC包括/ = 49.00（甲硫醇）、/ = 48.04（O-甲基羟胺）和/ = 129.07，它们与气胸、阻塞性通气障碍以及肺囊肿和支气管狭窄的影像学表现相关。纳入这些生物标志物的分类器显示出高诊断准确性（AUC = 0.922）。本研究首次证明呼出气VOC谱可作为诊断LAM和预测其并发症的非侵入性辅助工具。这些发现需要在更大的队列中进一步验证，以完善生物标志物的特异性，并探索其在疾病监测和个性化治疗策略中的临床应用。
