Patterns of time since last meal revealed by sparse PCA in an observational LC–MS based metabolomics study
- 583 Downloads
In metabolomics studies, liquid chromatography mass spectrometry (LC–MS) provides comprehensive information on biological samples. However, extraction of few relevant metabolites from this large and complex data is cumbersome. To resolve this issue, we have employed sparse principal component analysis (SPCA) to capture the underlying patterns and select relevant metabolites from LC–MS plasma profiles. The study involves a small pilot cohort with 270 subjects where each subject’s time since last meal (TSLM) has been recorded prior to plasma sampling. Our results have demonstrated that both PCA and SPCA can capture the TSLM patterns. Nevertheless, SPCA provides more easily interpretable loadings in terms of selection of relevant metabolites, which are identified as amino acids and lyso-lipids. This study demonstrates the utility of SPCA as a pattern recognition and variable selection tool in metabolomics. Furthermore, amino acids and lyso-lipids are determined as dominating compounds in response to TSLM.
KeywordsMetabolomics SPCA LC–MS Plasma Time since last meal Observational study
This work is carried out as a part of the research program of the Danish Obesity Research Centre (DanORC, see www.danorc.dk), funded by the Danish Strategic Research Council. This work is also supported by Nordic Centre of Excellence (NCoE) programme (Systems biology in controlled dietary interventions and cohort studies—SYSDIET, P no. 070014).
- Acar, E., Gurdeniz, G., Rasmussen, M. A., Rago, D., Dragsted, L. O., & Bro, R. (2012). Coupled matrix factorization with sparse factors to identify potential biomarkers in metabolomics. Proceedings of the 2012 IEEE International Conference on Data Mining Workshops, pp. 1–8.Google Scholar
- Barri, T., Holmer-Jensenb, J., Hermansen, K., & Dragsted, L. O. (2012). Metabolic fingerprinting of high-fat plasma samples processed by centrifugation and filtration-based protein precipitation delineates significant differences in metabolite information coverage. Analytica Chimica Acta, 718, 47–57.PubMedCrossRefGoogle Scholar
- Boirie, Y., Dangin, M., Gachon, P., Vasson, M. P., Maubois, J. L., & Beaufrere, B. (1997). Slow and fast dietary proteins differently modulate postprandial protein accretion. Proceedings of the National Academy of Sciences of the United States of America, 94, 14930–14935.PubMedCrossRefGoogle Scholar
- Fukagawa, N. K., Minaker, K. L., Rowe, J. W., Goodman, M. N., Matthews, D. E., Bier, D. M., et al. (1985). Insulin-mediated reduction of whole-body protein breakdown—Dose-response effects on leucine metabolism in post-absorptive men. Journal of Clinical Investigation, 76, 2306–2311.PubMedCrossRefGoogle Scholar
- Tjonneland, A., Olsen, A., Boll, K., Stripp, C., Christensen, J., Engholm, G., et al. (2007). Study design, exposure variables, and socioeconomic determinants of participation in Diet, Cancer and Health: a population-based prospective cohort study of 57,053 men and women in Denmark. Scandinavian Journal of Public Health, 35, 432–441.PubMedCrossRefGoogle Scholar
- Zhao, X., Peter, A., Fritsche, J., Elcnerova, M., Fritsche, A., Haring, H. U., et al. (2009). Changes of the plasma metabolome during an oral glucose tolerance test: is there more than glucose to look at? American Journal of Physiology–Endocrinology and Metabolism, 296, E384–E393.PubMedCrossRefGoogle Scholar