Wu Haoping, Zeng Rui, Li Lei, Li Mingqiang, Zhu Yuchen, Li Wenbo, Zhao Bin, Wen Chuanbiao, Feng Fei
State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.
College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 610036, China.
Analyst. 2025 Aug 4;150(16):3636-3642. doi: 10.1039/d5an00568j.
Exhaled gas detection offers a safe, convenient, and non-invasive clinical diagnostic method for preventing the progression of diabetes to complications. In this study, gas chromatography-mass spectrometry (GC-MS) analysis and statistical methods were employed to identify four volatile organic compounds (VOCs) that exhibit significant differences between patients with Type 2 Diabetes Mellitus (T2DM) and those with Diabetic Complications (DC). Compared with those in DC patients, the concentrations of isoprene, acetone, and isopropanol were found to be higher in T2DM patients, whereas the concentrations of tetradecane were lower. Based on the sets of these four VOCs, a voting classifier was constructed using three machine learning methods-Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbors (KNN). The accuracy, sensitivity, specificity, F1 score, and AUC value of the voting classifier are 90.8%, 92.1%, 89.5%, 0.909, and 0.988, respectively, in distinguishing between T2DM and DC. This diagnostic method of exhaled gas detection provides an important foundation for preventing DC and monitoring disease progression of DM.
呼出气体检测为预防糖尿病发展为并发症提供了一种安全、便捷且无创的临床诊断方法。在本研究中，采用气相色谱 - 质谱联用（GC-MS）分析和统计方法，以鉴定出2型糖尿病（T2DM）患者与糖尿病并发症（DC）患者之间存在显著差异的四种挥发性有机化合物（VOCs）。与DC患者相比，发现T2DM患者中异戊二烯、丙酮和异丙醇的浓度较高，而十四烷的浓度较低。基于这四种VOCs，使用支持向量机（SVM）、随机森林（RF）和K近邻（KNN）这三种机器学习方法构建了一个投票分类器。在区分T2DM和DC时，投票分类器的准确率、灵敏度、特异性、F1分数和AUC值分别为90.8%、92.1%、89.5%、0.909和0.988。这种呼出气体检测诊断方法为预防DC和监测DM疾病进展提供了重要依据。
