Large-Scale Human Metabolomics Studies:  A Strategy for Data (Pre-) Processing and Validation

• Published in: Analytical chemistry (Washington) Vol. 78; no. 2; pp. 567 - 574
• Main Authors: Bijlsma, Sabina, Bobeldijk, Ivana, Verheij, Elwin R, Ramaker, Raymond, Kochhar, Sunil, Macdonald, Ian A, van Ommen, Ben, Smilde, Age K
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 15.01.2006
• Subjects: Analytical chemistry; Chemistry; Chromatographic methods and physical methods associated with chromatography; Chromatography; Chromatography, Liquid; Data Interpretation, Statistical; Data processing; Dietary Fats - administration & dosage; Dietary Fats - blood; Discriminant analysis; Europe; Exact sciences and technology; Humans; Least-Squares Analysis; Lipids - blood; Mass Spectrometry; Metabolism; Obesity - blood; Other chromatographic methods; Plasma; Postprandial Period - physiology; Spectrometric and optical methods; Spectrum analysis; Validation studies; Europe; Human; Statistical method; PLS regression; Discriminant analysis; Data analysis; Coupled method; Statistical model; Fat; Data processing; Liquid chromatography; Mass spectrometry
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac051495j

A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography−mass spectrometry (LC−MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.