Xu Yun, Wilson Ian D, Goodacre Royston
Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, BioSciences Building, Crown St, Liverpool, L69 7ZB, UK.
Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London, W12 0NN, UK.
Metabolomics. 2025 Aug 29;21(5):126. doi: 10.1007/s11306-025-02331-2.
Untargeted metabolic phenotyping (metabolomics/metabonomics), also known as metabotyping, has been shown to be able to discriminate reliably between different physiological or clinical conditions. However, we believe that standard panels of routinely collected clinical and clinical chemistry data also have the potential to provide assay panels that complement metabotyping.
To test the above hypothesis and evaluate the use of multivariate statistical analyses to provided panels of clinical/clinical chemistry data measurements that predict the age, sex and body mass index (BMI) of 977 normal subjects and compare these predictions with results acquired by metabotyping on the same healthy individuals.
Metabotyping involved serum metabolomics using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) previously reported in our HUSERMET study (Dunn et al., 2015), while clinical chemistry data were obtained in clinic for 19 measurements assessing liver and kidney function, blood pressure, serum glucose, cations, as well as lipids. Multivariate analyses involved using support vector machines, random forest and partial least squares, to predict sex, age and BMI. These models used as inputs: (i) the clinical chemistry data alone; (ii) three metabolomics datasets; (iii) combinations of clinical chemistry with the metabolomics data. Model predictions were rigorously validated using 1,000 bootstrapping re-sampling coupled with permutation tests.
Multivariate statistical analyses on the clinical chemistry data obtained for these healthy participants could be used to predict: their sex, based on creatinine; their age, based on systolic blood pressure, total serum protein and serum glucose; as well as BMI using alanine transaminase, total cholesterol (Total-c) to high-density lipoprotein cholesterol (HDL-c) ratio and diastolic blood pressure. Combining clinical chemistry and metabolomics data sets enhanced the predictions of these characteristics. Moreover, this powerful combination allowed for quantitative predictions of age and BMI.
Multivariate statistical analysis on clinical chemistry data from the HUSERMET study obtained similar predictions of age, sex or BMI, compared to metabotyping using GC-MS and LC-MS. These predictions from clinical chemistry data were between 71 and 85% accurate (depending on the MVA used) and compared favourably with metabolomics (71-91 depending on analytical method). Combining clinical chemistry and metabolomics data sets enhanced the predictions of these characteristics to 77-93% accuracy, suggesting that this augmentation of methods may be a useful approach in the search for clinical biomarkers.
非靶向代谢表型分析（代谢组学/代谢物组学），也称为代谢分型，已被证明能够可靠地区分不同的生理或临床状况。然而，我们认为常规收集的临床和临床化学数据的标准面板也有可能提供补充代谢分型的检测面板。
检验上述假设，并评估使用多元统计分析来提供临床/临床化学数据测量面板，以预测977名正常受试者的年龄、性别和体重指数（BMI），并将这些预测结果与对同一健康个体进行代谢分型所获得的结果进行比较。
代谢分型涉及使用我们之前的HUSERMET研究（邓恩等人，2015年）中报道的气相色谱-质谱联用（GC-MS）和液相色谱-质谱联用（LC-MS）进行血清代谢组学分析，而临床化学数据是在诊所获取的，包括19项评估肝功能、肾功能、血压、血糖、阳离子以及血脂的测量数据。多元分析涉及使用支持向量机、随机森林和偏最小二乘法来预测性别、年龄和BMI。这些模型将以下内容用作输入：（i）仅临床化学数据；（ii）三个代谢组学数据集；（iii）临床化学数据与代谢组学数据的组合。使用1000次自助重采样结合置换检验对模型预测进行严格验证。
对这些健康参与者获得的临床化学数据进行多元统计分析可用于预测：基于肌酐预测其性别；基于收缩压、总血清蛋白和血糖预测其年龄；以及使用丙氨酸转氨酶、总胆固醇（Total-c）与高密度脂蛋白胆固醇（HDL-c）的比值和舒张压预测BMI。将临床化学和代谢组学数据集相结合可增强对这些特征的预测。此外，这种强大的组合允许对年龄和BMI进行定量预测。
与使用GC-MS和LC-MS进行代谢分型相比，对HUSERMET研究中的临床化学数据进行多元统计分析得到了类似的年龄、性别或BMI预测结果。这些基于临床化学数据的预测准确率在71%至
