Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study.
Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study.
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DOI：
10.1371/journal.pone.0183228
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Watzl B
中科院分区：
文献类型：
--
作者：
Rist MJ;Roth A;Frommherz L;Weinert CH;Krüger R;Merz B;Bunzel D;Mack C;Egert B;Bub A;Görling B;Tzvetkova P;Luy B;Hoffmann I;Kulling SE;Watzl B
生理和机能参数，如身体成分或身体健康，已知在男性和女性之间存在差异，并随着年龄的变化而变化。这项研究的目的是调查与性别和年龄相关的生理状况如何反映在健康人的代谢组中，以及是否可以根据血浆和尿代谢物的谱来预测性别和年龄。在卡门(卡尔斯鲁厄代谢和营养学)横断面研究中，招募了301名年龄在18-80岁之间的健康男性和女性。所有测量包括人体测量、临床和功能参数的标准操作程序对参与者进行了详细的描述。采用靶向代谢组学和非靶向代谢组学方法分析空腹血和24 h尿样，即一维或全二维气相色谱或液相色谱联用的质谱学分析方法和核磁共振波谱分析方法。这总共产生了400多种血浆分析物和500多种尿样分析物。使用不同的机器学习算法对代谢组学数据集进行预测建模。根据尿液和血浆的代谢物图谱，可以识别代谢物模式，根据性别对参与者进行分类，准确率为90%。对正确分类很重要的血浆代谢物包括肌酸、支链氨基酸和肌氨酸。根据男性和女性的代谢物特征预测年龄也是可能的。可以确定几种对这一预测很重要的代谢物，包括血浆中的胆碱和尿液中的七硫代谢物。对于女性来说，根据代谢组数据对她们的绝经状态进行分类是可能的，准确率为80%。人体尿液和血浆的代谢物图谱可以高精度地预测性别和年龄，这意味着在健康的人中，性别和年龄与歧视性代谢物特征有关，因此在代谢组学研究中应该始终考虑到这一点。
Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles. In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18–80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one- or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms. Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy. The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies.
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女性血浆代谢特征与更年期相关
影响因子：
3.7
作者：
Ke C;Hou Y;Zhang H;Yang K;Wang J;Guo B;Zhang F;Li H;Zhou X;Li Y;Li K
通讯作者：
Li K
影响因子：
3.6
作者：
Dunn, Warwick B.;Lin, Wanchang;Broadhurst, David;Begley, Paul;Brown, Marie;Zelena, Eva;Vaughan, Andrew A.;Halsall, Antony;Harding, Nadine;Knowles, Joshua D.;Francis-McIntyre, Sue;Tseng, Andy;Ellis, David I.;O'Hagan, Steve;Aarons, Gill;Benjamin, Boben;Chew-Graham, Stephen;Moseley, Carly;Potter, Paula;Winder, Catherine L.;Potts, Catherine;Thornton, Paula;McWhirter, Catriona;Zubair, Mohammed;Pan, Martin;Burns, Alistair;Cruickshank, J. Kennedy;Jayson, Gordon C.;Purandare, Nitin;Wu, Frederick C. W.;Finn, Joe D.;Haselden, John N.;Nicholls, Andrew W.;Wilson, Ian D.;Goodacre, Royston;Kell, Douglas B.
通讯作者：
Kell, Douglas B.
DOI：
10.1002/bms.1200131004
发表时间：
1986-10-01
期刊：
BIOMEDICAL AND ENVIRONMENTAL MASS SPECTROMETRY
影响因子：
--
作者：
KASSEL, DB;MARTIN, M;SWEELEY, CC
通讯作者：
SWEELEY, CC
影响因子：
3.7
作者：
Ishikawa M;Maekawa K;Saito K;Senoo Y;Urata M;Murayama M;Tajima Y;Kumagai Y;Saito Y
通讯作者：
Saito Y
影响因子：
4.4
作者：
Do, Kieu Trinh;Kastenmueller, Gabi;Krumsiek, Jan
通讯作者：
Krumsiek, Jan