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化学进展 DOI: 10.7536/PC231110   

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糖尿病呼出气体检测与分析研究进展

吴昊坪1,2, 李磊2, 曾睿1,2, 祝雨晨2, 赵斌2, 冯飞1,2,*   

  1. 1.成都中医药大学智能医学学院 成都 610036;
    2.中国科学院上海微系统与信息技术研究所传感技术国家重点实验室 上海 200050
  • 收稿日期:2023-11-08 修回日期:2024-02-29
  • 基金资助:
    国家重点研发计划(2018YFA0208504); 上海市“科技创新行动计划”医学创新研究专项(22Y11900600); 国家自然科学基金委员会面上项目(8217142522)
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Progress in the Study of Exhaled Gas Fingerprinting in Diabetes

Haoping Wu1,2, Lei Li2, Rui Zeng1,2, Yuchen Zhu2, Bin Zhao2, Fei Feng1,2,*   

  1. 1. College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 610036, China;
    2. State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystemand Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
  • Received:2023-11-08 Revised:2024-02-29
  • Supported by:
    National Key Research and Development Program of China(2018YFA0208504), Shanghai "Science and Technology Innovation Action Plan" Medical Innovation Research Special Program(22Y11900600), and General project of the National Natural Science Foundation of China(8217142522).
近年来,呼出气检测在糖尿病领域的研究引起了广泛关注。通过使用中英文学术搜索引擎的检索分析,综合了114篇相关文献,探讨了糖尿病与呼出气体检测之间的关联。糖尿病作为一种代谢性疾病,利用现代检测分析方法,如气相色谱、质谱、光谱和传感器检测等,实现了对糖尿病患者呼出气体的检测和监测。本综述概述了糖尿病患者呼出气体中一些挥发性有机化合物的成分及其来源,并评估了以机器学习为基础的算法在支持糖尿病及其并发症风险预测模型方面的应用。此外,对国内外糖尿病呼出气检测的发展与应用进行了探讨,并对其局限性和未来潜在应用进行了评价。
关键词： 呼出气体检测, 糖尿病, 疾病诊断, 无创, 丙酮, 挥发性有机化合物
In recent years, there has been a significant surge of interest in exploring exhaled gas detection within the context of diabetes research. This burgeoning field has attracted considerable attention due to its potential implications for the early detection and management of diabetes mellitus. Through a comprehensive synthesis of 114 pertinent scholarly works, researchers have delved into the intricate association between diabetes mellitus and exhaled gas detection. Leveraging state-of-the-art detection and analysis methodologies, including gas chromatography, mass spectrometry, spectroscopy, and sensor-based detection systems. This review provides an overview of the composition of some volatile organic compounds and their sources in the exhaled gas of diabetic patients. Furthermore, the application of machine learning-based algorithms has been scrutinized for its potential to facilitate predictive modeling of diabetes risk and associated complications. This comprehensive review also examines the national and international landscape of the development and application of exhaled gas detection methodologies in diabetes research, offering critical insights into current limitations and potential avenues for future research and application.
Key words: exhaled gas detection, diabetes mellitus, disease diagnosis, non-invasive, acetone, volatile organic compounds

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