This study examines the information potential of comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOF MS) and variable ionization energy (i.e., Tandem Ionization™) to study changes in saliva metabolic signatures from a small group of obese individuals. The study presents a proof of concept for an effective exploitation of the complementary nature of tandem ionization data. Samples are taken from two sub-populations of severely obese (BMI > 40 kg/m²) patients, named metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). Untargeted fingerprinting, based on pattern recognition by template matching, is applied on single data streams and on fused data, obtained by combining raw signals from the two ionization energies (12 and 70 eV). Results indicate that at lower energy (i.e., 12 eV), the total signal intensity is one order of magnitude lower compared to the reference signal at 70 eV, but the ranges of variations for 2D peak responses is larger, extending the dynamic range. Fused data combine benefits from 70 eV and 12 eV resulting in more comprehensive coverage by sample fingerprints. Multivariate statistics, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) show quite good patient clustering, with total explained variance by the first two principal components (PCs) that increases from 54% at 70 eV to 59% at 12 eV and up to 71% for fused data. With PLS-DA, discriminant components are highlighted and putatively identified by comparing retention data and 70 eV spectral signatures. Within the most informative analytes, lactose is present in higher relative amount in saliva from MHO patients, whereas N-acetyl-D-glucosamine, urea, glucuronic acid γ-lactone, 2-deoxyribose, N-acetylneuraminic acid methyl ester, and 5-aminovaleric acid are more abundant in MUO patients. Visual feature fingerprinting is combined with pattern recognition algorithms to highlight metabolite variations between composite per-class images obtained by combining raw data from individuals belonging to different classes, i.e., MUO vs. MHO.

Graphical abstract

本研究考察了综合二维气相色谱结合飞行时间质谱 (GC×GC-TOF MS) 和可变电离能 (即串联电离™) 的信息潜力，以研究唾液代谢特征的变化。一小群肥胖者。该研究为有效利用串联电离数据的互补性提供了概念证明。样本取自严重肥胖的两个亚群（BMI > 40 kg/m ²) 患者，命名为代谢健康肥胖 (MHO) 和代谢不健康肥胖 (MUO)。基于模板匹配模式识别的非靶向指纹识别应用于单个数据流和融合数据，融合数据是通过组合来自两种电离能量（12 和 70 eV）的原始信号获得的。结果表明，在较低能量（即 12 eV）下，总信号强度比 70 eV 时的参考信号低一个数量级，但 2D 峰值响应的变化范围更大，从而扩展了动态范围。融合数据结合了 70 eV 和 12 eV 的优势，从而实现了更全面的样本指纹覆盖。多变量统计、主成分分析 (PCA) 和偏最小二乘判别分析 (PLS-DA) 显示出非常好的患者聚类，前两个主成分 (PC) 的总解释方差从 70 eV 时的 54% 增加到 12 eV 时的 59%，融合数据高达 71%。使用 PLS-DA，可通过比较保留数据和 70 eV 光谱特征来突出显示并推定识别判别组件。在信息量最大的分析物中，乳糖在 MHO 患者唾液中的相对含量较高，而 N-乙酰-D-葡糖胺、尿素、葡萄糖醛酸 γ-内酯、2-脱氧核糖、N-乙酰神经氨酸甲酯和 5- MUO 患者中氨基戊酸含量更高。视觉特征指纹与模式识别算法相结合，以突出显示通过组合属于不同类别的个体（即 MUO 与 MHO）的原始数据获得的复合每类图像之间的代谢物变化。

图形概要