Integrated Nanosystems Development Institute, Indiana University-Purdue University Indianapolis, IN, United States of America. Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, IN, United States of America.
J Breath Res. 2017 Jun 1;11(2):026007. doi: 10.1088/1752-7163/aa6ac6.
Diabetes is a disease that involves dysregulation of metabolic processes. Patients with type 1 diabetes (T1D) require insulin injections and measured food intake to maintain clinical stability, manually tracking their results by measuring blood glucose levels. Low blood glucose levels, hypoglycemia, can be extremely dangerous and can result in seizures, coma, or even death. Canines trained as diabetes alert dogs (DADs) have demonstrated the ability to detect hypoglycemia from breath, which led us to hypothesize that hypoglycemia, a metabolic dysregulation leading to low blood glucose levels, could be identified through analyzing volatile organic compounds (VOCs) contained within breath. We hoped to replicate the canines' detection ability and success by analytically using gas chromatography/mass spectrometry of VOCs in 128 breath samples collected from 52 youths with T1D at two different diabetes camps. We used different tests for significance including Ranksum, Student's T-test, and difference between means, and found a subset of 56 traces of potential metabolites. Principle component and linear discriminant analysis (LDA) confirmed a hypoglycemic signature likely resides within this group. Supervised machine learning combined with LDA narrowed the list of likely components to seven. The technique of leave one out cross validation demonstrated the model thus developed has a sensitivity of 91% (95% confidence interval (CI) [57.1, 94.7]) and a specificity of 84% (95% CI [73.0, 92.7]) at identifying hypoglycemia. Confidence intervals were obtained by bootstrapping. These results demonstrate that it is possible to differentiate breath samples obtained during hypoglycemic events from all other breath samples by analytical means and could lead to developing a simple analytical monitoring device as an alternative to using DADs.
糖尿病是一种涉及代谢过程失调的疾病。1 型糖尿病（T1D）患者需要胰岛素注射和定量的食物摄入来维持临床稳定，通过测量血糖水平手动跟踪他们的结果。低血糖，即低血糖症，可能非常危险，可导致癫痫发作、昏迷甚至死亡。经过训练的糖尿病警示犬（DAD）已证明能够从呼吸中检测到低血糖症，这使我们假设，低血糖症是一种导致低血糖的代谢失调，可以通过分析呼吸中包含的挥发性有机化合物（VOC）来识别。我们希望通过在两个不同的糖尿病营地中，从 52 名 T1D 青少年收集的 128 个呼吸样本中，通过气相色谱/质谱法分析 VOC 来复制犬类的检测能力和成功。我们使用不同的显著性测试，包括秩和检验、学生 t 检验和均值差异检验，并找到了潜在代谢物的 56 个潜在痕迹子集。主成分和线性判别分析（LDA）证实，该组中可能存在低血糖特征。有监督的机器学习与 LDA 相结合，将可能的成分列表缩小到七个。留一法交叉验证技术表明，由此开发的模型在识别低血糖症方面具有 91%的灵敏度（95%置信区间（CI）[57.1, 94.7]）和 84%的特异性（95% CI [73.0, 92.7]）。置信区间通过自举法获得。这些结果表明，通过分析手段，可以区分在低血糖事件期间获得的呼吸样本和所有其他呼吸样本，这可能导致开发出一种简单的分析监测设备，作为使用 DAD 的替代方法。
