Abstract
Information derived from “omics” data in life science research are frequently limited by specific spatial or temporal scales these data describe. As a case study of integrating physiological and molecular data in human, here we study associations between the heart magnetic resonance images and serum lipidomic profiles. In the best case, such associations could help infer the physiologic state of the heart from a blood serum sample without need to use expensive imaging techniques. Strong marginal and partial correlations are found between the lipid profiles and parameters derived from the heart images. Regression analyses are applied to study these dependencies in more detail. This study demonstrates the feasibility of mapping lipid profiles to heart images, and thus combining information from two very different scales, small molecules and macroscopic physiologic features. Such mappings could be generalized to other “omics” data as well to complete our picture of the holistic function of a living organism.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fan, J.-B., Chee, M.S., Gunderson, K.L.: Highly parallel genomic assays. Nature Reviews Genetics 7(8), 632–644 (2006)
Tan, K., Ipcho, S.V.S., Trengove, R.D., Oliver, R.P., Solomon, P.S.: Assessing the impact of transcriptomics, proteomics and metabolomics on fungal phytopathology. Molecular Plant Pathology 10(5), 703–715 (2009)
Sreekumar, A., Poisson, L.M., Rajendiran, T.M., Khan, A.P., Cao, Q., Yu, J., Laxman, B., Mehra, R., Lonigro, R.J., Li, Y., Nyati, M.K., Ahsan, A., Kalyana-Sundaram, S., Han, B., Cao, X., Byun, J., Omenn, G.S., Ghosh, D., Pennathur, S., Alexander, D.C.: Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457(7231), 910–914 (2009)
Newman, J.R., Weissman, J.S.: Systems biology: many things from one. Nature 444(7119), 561–562 (2006)
Nicholson, J.K., Lindon, J.C.: Systems biology: Metabonomics. Nature 455(7216), 1054–1056 (2008)
Watson, A.D.: Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Lipidomics: a global approach to lipid analysis in biological systems. J. Lipid Res. 47(10), 2101–2111 (2006)
Karamitsos, T.D., Francis, J.M., Myerson, S., Selvanayagam, J.B., Neubauer, S.: The role of cardiovascular magnetic resonance imaging in heart failure. J. Am. Coll. Cardiol. 54(15), 1407–1424 (2009)
Koikkalainen, J.R., Antila, M., Lotjonen, J.M., Helio, T., Lauerma, K., Kivisto, S.M., Sipola, P., Kaartinen, M.A., Karkkainen, S.T., Reissell, E., Kuusisto, J., Laakso, M., Oresic, M., Nieminen, M.S., Peuhkurinen, K.J.: Early familial dilated cardiomyopathy: identification with determination of disease state parameter from cine MR image data. Radiology 249(1), 88–96 (2008)
Sun, J., Schnackenberg, L.K., Holland, R.D., Schmitt, T.C., Cantor, G.H., Dragan, Y.P., Beger, R.D.: Metabonomics evaluation of urine from rats given acute and chronic doses of acetaminophen using NMR and UPLC/MS. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 871(2), 328–340 (2008)
Lotjonen, J.M., Wolz, R., Koikkalainen, J.R., Thurfjell, L., Waldemar, G., Soininen, H., Rueckert, D.: The Alzheimer’s Disease Neuroimaging Initiative: Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage (2009)
Lotjonen, J., Kivisto, S., Koikkalainen, J., Smutek, D., Lauerma, K.: Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images. Med. Image Anal. 8(3), 371–386 (2004)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-Their training and Application. Comput. Vis. Image Underst. 61, 38–59 (1995)
Perperidis, D., Mohiaddin, R., Rueckert, D.: Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification. In: Int. Conf. Med. Image Comput. Comput. Assist. Interv., vol. 8(Pt 2), pp. 402–410 (2005)
Laaksonen, R., Katajamaa, M., Paiva, H., Sysi-Aho, M., Saarinen, L., Junni, P., Lutjohann, D., Smet, J., Van Coster, R., Seppanen-Laakso, T., Lehtimaki, T., Soini, J., Oresic, M.: A systems biology strategy reveals biological pathways and plasma biomarker candidates for potentially toxic statin-induced changes in muscle. PLoS One 1, e97 (2006)
Fahy, E., Subramaniam, S., Murphy, R.C., Nishijima, M., Raetz, C.R., Shimizu, T., Spener, F., van Meer, G., Wakelam, M.J., Dennis, E.A.: Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 50(Suppl. S9-14) (2009)
Castelo, R., Roverato, A.: Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J. Comput. Biol. 16(2), 213–227 (2009)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R Statist. Soc. B 67(2), 901–920 (2005)
Jain, R.K.: Ridge regression and its application to medical data. Comput. Biomed. Res. 18(4), 363–368 (1985)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Sysi-Aho, M. et al. (2010). Searching for Linear Dependencies between Heart Magnetic Resonance Images and Lipid Profiles. In: Elomaa, T., Mannila, H., Orponen, P. (eds) Algorithms and Applications. Lecture Notes in Computer Science, vol 6060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12476-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-12476-1_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12475-4
Online ISBN: 978-3-642-12476-1
eBook Packages: Computer ScienceComputer Science (R0)