Institute for Science and Technology in Medicine, School of Medicine, Keele University, Thornburrow Drive, Hartshill, Stoke-on-Trent ST4 7QB, UK.
J Breath Res. 2007 Sep;1(1):014004. doi: 10.1088/1752-7155/1/1/014004. Epub 2007 Sep 4.
The data obtained for the concentration distributions of the most abundant volatile metabolites in exhaled breath determined in two independent studies are reviewed, the first limited study involving five healthy volunteers providing daily breath samples over a month, and the subsequent study involving 30 healthy volunteers providing breath samples weekly over six months. Both studies were carried out using selected ion flow tube mass spectrometry, SIFT-MS, to obtain on-line, real-time analyses of single breath exhalations, avoiding the complications associated with sample collection. The distributions of the metabolites from the larger more comprehensive study are mostly seen to be log normal with the median values (in parts per billion, ppb) being ammonia (833), acetone (477), methanol (461), ethanol (112), propanol (18), acetaldehyde (22), isoprene (106) with the geometric standard deviation being typically 1.6, except for ethanol which was larger (3.24) due to the obvious increase of breath ethanol following the ingestion of sugar. These were the first well-defined concentration distributions of breath metabolites obtained and they are the essential requirement for recognizing abnormally high levels that are associated with particular diseases. The associations of each metabolite with known diseased states are alluded to. These SIFT-MS studies reveal the promise of breath analysis as a valuable addition to the tools for clinical diagnosis and therapeutic monitoring.
本文回顾了两项独立研究中确定的呼气中最丰富挥发性代谢物浓度分布的相关数据，第一项研究涉及 5 名健康志愿者，他们在一个月内每天提供呼吸样本；第二项研究涉及 30 名健康志愿者，他们在六个月内每周提供呼吸样本。这两项研究均使用选择离子流管质谱（SIFT-MS）进行，以进行在线实时分析单次呼气，避免了与样本收集相关的复杂问题。较大、更全面研究中的代谢物分布大多呈对数正态分布，中位数（十亿分之几，ppb）分别为氨（833）、丙酮（477）、甲醇（461）、乙醇（112）、丙醇（18）、乙醛（22）、异戊二烯（106），几何标准差通常为 1.6，除了乙醇，其值较大（3.24），因为摄入糖后呼气中的乙醇明显增加。这些是首次明确获得的呼吸代谢物浓度分布，这是识别与特定疾病相关的异常高水平的必要条件。本文还提到了每种代谢物与已知疾病状态的关联。这些 SIFT-MS 研究表明，呼吸分析有望成为临床诊断和治疗监测工具的重要补充。
