Abstract
Changes in eating habits, food composition and processing are involved in the “nutritional transition” that accompanied the obesity pandemic and the burst of metabolic diseases. This study is one of the first to describe the metabolic trajectories that differentiate the responses of overweight (OW) from lean individuals during weight gain. Nineteen lean and 19 OW male volunteers were submitted to moderate weight gain using a lipid-enriched overfeeding protocol designed to add about 3,300 kJ per day in excess to their usual diet. Metabolic explorations in combination with plasma and urine metabolomic profiles using liquid chromatography coupled with mass spectrometry were determined along 8 weeks to compare metabolic trajectories and determine early changes in metabolic processes after identification of specific early responding markers. Urinary metabolomic profiles during overfeeding evidenced differences in metabolic trajectories between groups, characterized by an increase over time of short-, medium-chain acylcarnitines, and bile acids in overweight subjects. For most of the anthropometric, metabolic parameters and plasma metabolomics data, the time-course evolution of all subjects was similar with distinction between groups. Plasma abundances of unsaturated lysophosphosphatidylcholine (22:6) decreased over time more importantly in normal weight subjects while most of those of the saturated species increased in both groups. These findings not evidenced with classical parameters, indicate a differential response to overfeeding in urine metabolomes of subjects, suggesting different nutrient metabolic fate with weight status. Subtle plasma and urine metabolic changes, mostly related to differences in the adaptation of β-oxidation and inflammation indicate a lower metabolic flexibility of OW subjects facing weight gain induced by overfeeding.
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Adams, S. H., Hoppel, C. L., Lok, K. H., Zhao, L., Wong, S. W., Minkler, P. E., et al. (2009). Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid beta-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women. Journal of Nutrition, 139(6), 1073–1081. doi:10.3945/jn.108.103754.
Alligier, M., Gabert, L., Meugnier, E., Lambert-Porcheron, S., Chanseaume, E., Pilleul, F., et al. (2013). Visceral fat accumulation during lipid overfeeding is related to subcutaneous adipose tissue characteristics in healthy men. Journal of Clinical Endocrinology and Metabolism, 98(2), 802–810. doi:10.1210/jc.2012-3289.
Alligier, M., Meugnier, E., Debard, C., Lambert-Porcheron, S., Chanseaume, E., Sothier, M., et al. (2012). Subcutaneous adipose tissue remodeling during the initial phase of weight gain induced by overfeeding in humans. Journal of Clinical Endocrinology and Metabolism, 97(2), E183–E192. doi:10.1210/jc.2011-2314.
Alnouti, Y., Csanaky, I. L., & Klaassen, C. D. (2008). Quantitative-profiling of bile acids and their conjugates in mouse liver, bile, plasma, and urine using LC-MS/MS. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 873(2), 209–217. doi:10.1016/j.jchromb.2008.08.018.
Altmaier, E., Ramsay, S. L., Graber, A., Mewes, H. W., Weinberger, K. M., & Suhre, K. (2008). Bioinformatics analysis of targeted metabolomics–uncovering old and new tales of diabetic mice under medication. Endocrinology, 149(7), 3478–3489. doi:10.1210/en.2007-1747.
Benton, H. P., Wong, D. M., Trauger, S. A., & Siuzdak, G. (2008). XCMS2: Processing tandem mass spectrometry data for metabolite identification and structural characterization. Analytical Chemistry, 80(16), 6382–6389. doi:10.1021/ac800795f.
Caraux, G., & Pinloche, S. (2005). PermutMatrix: A graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics, 21(7), 1280–1281. doi:10.1093/bioinformatics/bti141.
D’Arrigo, P., & Servi, S. (2010). Synthesis of lysophospholipids. Molecules, 15(3), 1354–1377. doi:10.3390/molecules15031354.
Dettmer, K., Aronov, P. A., & Hammock, B. D. (2007). Mass spectrometry-based metabolomics. Mass Spectrometry Reviews, 26(1), 51–78. doi:10.1002/mas.20108.
Duranti, G., Boenzi, S., Rizzo, C., Rava, L., Di Ciommo, V., Carrozzo, R., et al. (2008). Urine acylcarnitine analysis by ESI-MS/MS: A new tool for the diagnosis of peroxisomal biogenesis disorders. Clinica Chimica Acta, 398(1–2), 86–89. doi:10.1016/j.cca.2008.08.018.
Eisenreich, W., & Bacher, A. (2007). Advances of high-resolution NMR techniques in the structural and metabolic analysis of plant biochemistry. Phytochemistry, 68(22–24), 2799–2815. doi:10.1016/j.phytochem.2007.09.028.
Franceschi, P., Masuero, D., Vrhovsek, U., Mattivi, F., & Wehrens, R. (2012). A benchmark spike-in data set for biomarker identification in metabolomics. Journal of Chemometrics, 26(1–2), 16–24. doi:10.1002/cem.1420.
Galgani, J. E., Moro, C., & Ravussin, E. (2008). Metabolic flexibility and insulin resistance. The American Journal of Physiology—Endocrinology and Metabolism, 295(5), E1009–E1017. doi:10.1152/ajpendo.90558.2008.
Gibney, M. J., Walsh, M., Brennan, L., Roche, H. M., German, B., & van Ommen, B. (2005). Metabolomics in human nutrition: Opportunities and challenges. American Journal of Clinical Nutrition, 82(3), 497–503.
Ha, C. Y., Kim, J. Y., Paik, J. K., Kim, O. Y., Paik, Y. H., Lee, E. J., & Lee, J. H. (2012). The association of specific metabolites of lipid metabolism with markers of oxidative stress, inflammation and arterial stiffness in men with newly diagnosed type 2 diabetes. Clinical Endocrinology (Oxford), 76(5), 674–682. doi:10.1111/j.1365-2265.2011.04244.x.
Hanhineva, K., Barri, T., Kolehmainen, M., Pekkinen, J., Pihlajamaki, J., Vesterbacka, A., et al. (2013). Comparative nontargeted profiling of metabolic changes in tissues and biofluids in high-fat diet-fed Ossabaw pig. Journal of Proteome Research, 12(9), 3980–3992. doi:10.1021/pr400257d.
Huang, L. S., Hung, N. D., Sok, D. E., & Kim, M. R. (2010). Lysophosphatidylcholine containing docosahexaenoic acid at the sn-1 position is anti-inflammatory. Lipids, 45(3), 225–236. doi:10.1007/s11745-010-3392-5.
Hung, N. D., Kim, M. R., & Sok, D. E. (2009). Anti-inflammatory action of arachidonoyl lysophosphatidylcholine or 15-hydroperoxy derivative in zymosan A-induced peritonitis. Prostaglandins & Other Lipid Mediators, 90(3–4), 105–111. doi:10.1016/j.prostaglandins.2009.10.001.
Hung, N. D., Kim, M. R., & Sok, D. E. (2011). 2-Polyunsaturated acyl lysophosphatidylethanolamine attenuates inflammatory response in zymosan A-induced peritonitis in mice. Lipids, 46(10), 893–906. doi:10.1007/s11745-011-3589-2.
Hung, N. D., Sok, D. E., & Kim, M. R. (2012). Prevention of 1-palmitoyl lysophosphatidylcholine-induced inflammation by polyunsaturated acyl lysophosphatidylcholine. Inflammation Research, 61(5), 473–483. doi:10.1007/s00011-012-0434-x.
Idle, J. R., & Gonzalez, F. J. (2007). Metabolomics. Cell Metabolism, 6(5), 348–351. doi:10.1016/j.cmet.2007.10.005.
Joyce, S. A., & Gahan, C. G. (2014). The gut microbiota and the metabolic health of the host. Current Opinion in Gastroenterology, 30(2), 120–127. doi:10.1097/MOG.0000000000000039.
Kaess, B. M., Pedley, A., Massaro, J. M., Murabito, J., Hoffmann, U., & Fox, C. S. (2012). The ratio of visceral to subcutaneous fat, a metric of body fat distribution, is a unique correlate of cardiometabolic risk. Diabetologia, 55(10), 2622–2630. doi:10.1007/s00125-012-2639-5.
Katz, A., Nambi, S. S., Mather, K., Baron, A. D., Follmann, D. A., Sullivan, G., & Quon, M. J. (2000). Quantitative insulin sensitivity check index: A simple, accurate method for assessing insulin sensitivity in humans. Journal of Clinical Endocrinology and Metabolism, 85(7), 2402–2410.
Kenny, L. C., Black, M. A., Poston, L., Taylor, R., Myers, J. E., Baker, P. N., et al. (2014). Early pregnancy prediction of preeclampsia in nulliparous women, combining clinical risk and biomarkers: The Screening for Pregnancy Endpoints (SCOPE) international cohort study. Hypertension, 64(3), 644–652. doi:10.1161/HYPERTENSIONAHA.114.03578.
Kim, J. Y., Park, J. Y., Kim, O. Y., Ham, B. M., Kim, H. J., Kwon, D. Y., et al. (2010). Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). Journal of Proteome Research, 9(9), 4368–4375. doi:10.1021/pr100101p.
Kootte, R. S., Vrieze, A., Holleman, F., Dallinga-Thie, G. M., Zoetendal, E. G., de Vos, W. M., et al. (2012). The therapeutic potential of manipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes, Obesity & Metabolism, 14(2), 112–120. doi:10.1111/j.14631326.2011.01483.x.
Koves, T. R., Ussher, J. R., Noland, R. C., Slentz, D., Mosedale, M., Ilkayeva, O., et al. (2008). Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metabolism, 7(1), 45–56. doi:10.1016/j.cmet.2007.10.013.
Lefebvre, P., Cariou, B., Lien, F., Kuipers, F., & Staels, B. (2009). Role of bile acids and bile acid receptors in metabolic regulation. Physiological Reviews, 89(1), 147–191. doi:10.1152/physrev.00010.2008.
Libert, R., Van Hoof, F., Thillaye, M., Vincent, M. F., Nassogne, M. C., de Hoffmann, E., & Schanck, A. (2000). Identification of undescribed medium-chain acylcarnitines present in urine of patients with propionic and methylmalonic acidemias. Clinica Chimica Acta, 295(1–2), 87–96.
Meugnier, E., Bossu, C., Oliel, M., Jeanne, S., Michaut, A., Sothier, M., et al. (2007). Changes in gene expression in skeletal muscle in response to fat overfeeding in lean men. Obesity (Silver Spring), 15(11), 2583–2594. doi:10.1038/oby.2007.310.
Mihalik, S. J., Goodpaster, B. H., Kelley, D. E., Chace, D. H., Vockley, J., Toledo, F. G., & DeLany, J. P. (2010). Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity. Obesity (Silver Spring), 18(9), 1695–1700. doi:10.1038/oby.2009.510.
Mihalik, S. J., Michaliszyn, S. F., de las Heras, J., Bacha, F., Lee, S., Chace, D. H., et al. (2012). Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2 diabetes: evidence for enhanced mitochondrial oxidation. Diabetes Care, 35(3), 605–611. doi:10.2337/DC11-1577.
Moco, S., Bino, R. J., De Vos, R. C. H., & Vervoort, J. (2007). Metabolomics technologies and metabolite identification. TrAC Trends in Analytical Chemistry, 26(9), 855–866. doi:10.1016/j.trac.2007.08.003.
Momken, I., Stevens, L., Bergouignan, A., Desplanches, D., Rudwill, F., Chery, I., et al. (2011). Resveratrol prevents the wasting disorders of mechanical unloading by acting as a physical exercise mimetic in the rat. FASEB J., 25(10), 3646–3660. doi:10.1096/fj.10-177295.
Nazare, J. A., Normand, S., Oste Triantafyllou, A., Brac de la Perriere, A., Desage, M., & Laville, M. (2009). Modulation of the postprandial phase by beta-glucan in overweight subjects: Effects on glucose and insulin kinetics. Molecular Nutrition & Food Research, 53(3), 361–369. doi:10.1002/mnfr.200800023.
Newgard, C. B. (2012). Interplay between lipids and branched-chain amino acids in development of insulin resistance. Cell Metabolism, 15(5), 606–614. doi:10.1016/j.cmet.2012.01.024.
Newgard, C. B., An, J., Bain, J. R., Muehlbauer, M. J., Stevens, R. D., Lien, L. F., et al. (2009). A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metabolism, 9(4), 311–326. doi:10.1016/j.cmet.2009.02.002.
Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). ‘Metabonomics’: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29(11), 1181–1189. doi:10.1080/004982599238047.
Nieuwdorp, M., Gilijamse, P. W., Pai, N., & Kaplan, L. M. (2014). Role of the microbiome in energy regulation and metabolism. Gastroenterology, 146(6), 1525–1533. doi:10.1053/j.gastro.2014.02.008.
Pan, Z., & Raftery, D. (2007). Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Analytical and Bioanalytical Chemistry, 387(2), 525–527. doi:10.1007/s00216-006-0687-8.
Pereira, H., Martin, J. F., Joly, C., Sebedio, J. L., & Pujos-Guillot, E. (2010). Development and validation of a UPLC/MS method for a nutritional metabolomic study of human plasma. Metabolomics, 6(2), 207–218. doi:10.1007/s11306-009-0188-9.
Pietilainen, K. H., Sysi-Aho, M., Rissanen, A., Seppanen-Laakso, T., Yki-Jarvinen, H., Kaprio, J., & Oresic, M. (2007). Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects–a monozygotic twin study. PLoS One, 2(2), e218. doi:10.1371/journal.pone.0000218.
Popkin, B. M. (2011). Is the obesity epidemic a national security issue around the globe? Current opinion in Endocrinology, Diabetes, and Obesity, 18(5), 328–331. doi:10.1097/MED.0b013e3283471c74.
Rimbert, V., Boirie, Y., Bedu, M., Hocquette, J. F., Ritz, P., & Morio, B. (2004). Muscle fat oxidative capacity is not impaired by age but by physical inactivity: Association with insulin sensitivity. FASEB J, 18(6), 737–739. doi:10.1096/fj.03-1104fje.
Rubio-Aliaga, I., Roos, Bd, Sailer, M., McLoughlin, G. A., Boekschoten, M. V., van Erk, M., et al. (2011). Alterations in hepatic one-carbon metabolism and related pathways following a high-fat dietary intervention. Physiological Genomics, 43(8), 408–416. doi:10.1152/physiolgenomics.00179.2010.
Saccenti, E., Hoefsloot, H. C. J., Smilde, A. K., Westerhuis, J. A., & Hendriks, M. M. W. B. (2014). Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics, 10, 361–374. doi:10.1007/s11306-013-0598-6.
Sevastou, I., Kaffe, E., Mouratis, M. A., & Aidinis, V. (2013). Lysoglycerophospholipids in chronic inflammatory disorders: The PLA(2)/LPC and ATX/LPA axes. Biochimica et Biophysica Acta, 1831(1), 42–60. doi:10.1016/j.bbalip.2012.07.019.
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78(3), 779–787. doi:10.1021/ac051437y.
Steiber, A., Kerner, J., & Hoppel, C. L. (2004). Carnitine: A nutritional, biosynthetic, and functional perspective. Molecular Aspects of Medicine, 25(5–6), 455–473. doi:10.1016/j.mam.2004.06.006.
Sumner, L. W., Amberg, A., Barrett, A., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics, 3(9), 211–221.
Taylor, D. R., Alaghband-Zadeh, J., Cross, G. F., Omar, S., le Roux, C. W., & Vincent, R. P. (2014). Urine bile acids relate to glucose control in patients with type 2 diabetes mellitus and a body mass index below 30 kg/m2. PLoS One, 9(4), e93540. doi:10.1371/journal.pone.0093540.
Vrieze, A., Out, C., Fuentes, S., Jonker, L., Reuling, I., Kootte, R. S., et al. (2014). Impact of oral vancomycin on gut microbiota, bile acid metabolism, and insulin sensitivity. Journal of Hepatology, 60(4), 824–831. doi:10.1016/j.jhep.2013.11.034.
Wang, C., Feng, R. N., Sun, D. J., Li, Y., Bi, X. X., & Sun, C. H. (2011). Metabolic profiling of urine in young obese men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC/Q-TOF MS). Journal of Chromatography B, 879(27), 2871–2876. doi:10.1016/j.jchromb.2011.08.014.
Wishart, D. S. (2007). Current progress in computational metabolomics. Briefings in Bioinformatics, 8(5), 279–293. doi:10.1093/bib/bbm030.
Zeng, M., Liang, Y., Li, H., Wang, M., Wang, B., Chen, X., et al. (2010). Plasma metabolic fingerprinting of childhood obesity by GC/MS in conjunction with multivariate statistical analysis. Journal of Pharmaceutical and Biomedical Analysis, 52(2), 265–272. doi:10.1016/j.jpba.2010.01.002.
Acknowledgments
This research was supported by the Agence Nationale pour la Recherche (Project, PNRA-007, 2007-2010), Danone (18 months of a post-doctorate position), the Actions Incitatives from the Hospices Civils de Lyon and the Programme Hospitalier de Recherche Clinique Inter-régional. We would also like to thank H. Pereira for LC–MS analyses of plasma samples.
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None of the authors have a conflict of interest relevant to this work.
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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all participants for being included in the study.
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Beatrice Morio and Blandine Comte have contributed equally to the study.
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Morio, B., Comte, B., Martin, JF. et al. Metabolomics reveals differential metabolic adjustments of normal and overweight subjects during overfeeding. Metabolomics 11, 920–938 (2015). https://doi.org/10.1007/s11306-014-0750-y
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DOI: https://doi.org/10.1007/s11306-014-0750-y