Metabolic changes in serum metabolome in response to a meal
The change in serum metabolic response from fasting state to postprandial state provides novel insights into the impact of a single meal on human metabolism. Therefore, this study explored changes in serum metabolite profile after a single meal.
Nineteen healthy postmenopausal women with normal glucose tolerance participated in the study. They received a meal consisting of refined wheat bread (50 g carbohydrates, 9 g protein, 4.2 g fat and 2.7 g dietary fibre), 40 g cucumber and 300 mL noncaloric orange drink. Blood samples were collected at fasting and five postprandial time points. Metabolic profile was measured by nuclear magnetic resonance and targeted liquid chromatography–mass spectrometry. Changes over time were assessed with multivariate models and ANOVA, with baseline as control.
The metabolomic analyses demonstrated alterations in phospholipids, amino acids and their breakdown products, glycolytic products, acylcarnitines and ketone bodies after a single meal. More specifically, phosphatidylcholines, lysophosphatidylcholines and citrate displayed an overall declining pattern, while leucine, isoleucine, methionine and succinate increased initially but declined thereafter. A sharp decline in acylcarnitines and ketone bodies and increase in glycolytic products postprandially suggest a switch in the body’s energy source from β-oxidation to glycolysis. Moreover, individuals with relatively high postprandial insulin responses generated a higher postprandial leucine responses compared to participants with lower insulin responses.
The study demonstrated complex changes from catabolic to anabolic metabolism after a meal and indicated that the extent of postprandial responses is different between individuals with high and low insulin response.
KeywordsMetabolomics Postprandial changes Insulin Amino acid Acylcarnitine Glycolytic products Phosphatidylcholine
The authors thank Dr. Claudia von Brömssen for her kind support regarding the statistical analyses and Erja Kinnunen for technical and laboratory assistance during the clinical phase of the study. This work was conducted as part of the Nordic Centre of Excellence ‘Nordic Health—Whole Grain Food’ (HELGA) project and was funded by the Swedish Research Council FORMAS, Dr. Håkansson’s foundation and SLUmat—a research fund allocated to food research at the Swedish University of Agricultural Sciences. In addition, the human trial was supported by Fazer Bakeries Ltd, Vaasan & Vaasan Oy, and the Technology Development Centre of Finland. Kaisa Poutanen gratefully acknowledges funding from the Academy of Finland.
Compliance with ethical standards
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
The present study was approved by the Ethical Committee of Kupio University and University Hospital Finland and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All participants gave their informed consent prior to inclusion in the study.
- 1.Zhao X, Peter A, Fritsche J, Elcnerova M, Fritsche A, Häring HU, Schleicher ED, Xu G, Lehmann R (2009) Changes of the plasma metabolome during an oral glucose tolerance test: is there more than glucose to look at? Am J Physiol: Endocrinol Metab 296(2):E384–E393Google Scholar
- 2.Shaham O, Wei R, Wang TJ, Ricciardi C, Lewis GD, Vasan RS, Carr SA, Thadhani R, Gerszten RE, Mootha VK (2008) Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol Syst Biol 4:214Google Scholar
- 9.Cavalot F, Petrelli A, Traversa M, Bonomo K, Fiora E, Conti M, Anfossi G, Costa G, Trovati M (2006) Postprandial blood glucose is a stronger predictor of cardiovascular events than fasting blood glucose in type 2 diabetes mellitus, particularly in women: lessons from the San Luigi Gonzaga diabetes study. J Clin Endocrinol Metab 91(3):813–819CrossRefGoogle Scholar
- 16.Moazzami AA, Shrestha A, Morrison DA, Poutanen K, Mykkänen H (2014) Metabolomics reveals differences in postprandial responses to breads and fasting metabolic characteristics associated with postprandial insulin demand in postmenopausal women. J Nutr 144(6):807–814. doi: 10.3945/jn.113.188912 CrossRefGoogle Scholar
- 18.Illig T, Gieger C, Zhai G, Römisch-Margl W, Wang-Sattler R, Prehn C, Altmaier E, Kastenmüller G, Kato BS, Mewes HW, Meitinger T, De Angelis MH, Kronenberg F, Soranzo N, Wichmann HE, Spector TD, Adamski J, Suhre K (2010) A genome-wide perspective of genetic variation in human metabolism. Nat Genet 42(2):137–141CrossRefGoogle Scholar
- 19.Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M, Holzapfel C, Thorand B, Grallert H, Xu T, Bader E, Huth C, Mittelstrass K, Döring A, Meisinger C, Gieger C, Prehn C, Roemisch-Margl W, Carstensen M, Xie L, Yamanaka-Okumura H, Xing G, Ceglarek U, Thiery J, Giani G, Lickert H, Lin X, Li Y, Boeing H, Joost H-G, de Angelis MH, Rathmann W, Suhre K, Prokisch H, Peters A, Meitinger T, Roden M, Wichmann HE, Pischon T, Adamski J, Illig T (2012) Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. doi: 10.1038/msb.2012.43 Google Scholar
- 22.Vanhove JLK, Zhang W, Kahler SG, Roe CR, Chen YT, Terada N, Chace DH, Iafolla AK, Ding JH, Millington DS (1993) Medium-chain acyl-coa dehydrogenase (Mcad) deficiency: diagnosis by acylcarnitine analysis in blood. Am J Hum Genet 52(5):958–966Google Scholar
- 23.Krug S, Kastenmüller G, Stückler F, Rist MJ, Skurk T, Sailer M, Raffler J, Römisch-Margl W, Adamski J, Prehn C, Frank T, Engel KH, Hofmann T, Luy B, Zimmermann R, Moritz F, Schmitt-Kopplin P, Krumsiek J, Kremer W, Huber F, Oeh U, Theis FJ, Szymczak W, Hauner H, Suhre K, Daniel H (2012) The dynamic range of the human metabolome revealed by challenges. FASEB J 26(6):2607–2619CrossRefGoogle Scholar
- 25.Floegel A, Stefan N, Yu Z, Mühlenbruch K, Drogan D, Joost HG, Fritsche A, Häring HU, De Angelis MH, Peters A, Roden M, Prehn C, Wang-Sattler R, Illig T, Schulze MB, Adamski J, Boeing H, Pischon T (2013) Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62(2):639–648CrossRefGoogle Scholar
- 26.Dashti M, Kulik W, Hoek F, Veerman EC, Peppelenbosch MP, Rezaee F (2011) A Phospholipidomic analysis of all defined human plasma lipoproteins. Sci Rep 1. http://www.nature.com/srep/2011/111107/srep00139/abs/srep00139.html#supplementary-information
- 30.Tai ES, Tan MLS, Stevens RD, Low YL, Muehlbauer MJ, Goh DLM, Ilkayeva OR, Wenner BR, Bain JR, Lee JJM, Lim SC, Khoo CM, Shah SH, Newgard CB (2010) Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia 53(4):757–767CrossRefGoogle Scholar
- 31.McCormack SE, Shaham O, McCarthy MA, Deik AA, Wang TJ, Gerszten RE, Clish CB, Mootha VK, Grinspoon SK, Fleischman A (2013) Circulating branched-chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents. Pediatric Obes 8(1):52–61. doi: 10.1111/j.2047-6310.2012.00087.x CrossRefGoogle Scholar
- 35.Garvey WT, Kwon S, Zheng D, Shaughnessy S, Wallace P, Hutto A, Pugh K, Jenkins AJ, Klein RL, Liao Y (2003) Effects of insulin resistance and type 2 diabetes on lipoprotein subclass particle size and concentration determined by nuclear magnetic resonance. Diabetes 52(2):453–462. doi: 10.2337/diabetes.52.2.453 CrossRefGoogle Scholar
- 38.Heimbürger O, Lönnqvist F, Danielsson A, Nordenström J, Stenvinkel P (1997) Serum immunoreactive leptin concentration and its relation to the body fat content in chronic renal failure. J Am Soc Nephrol 8(9):1423–1430Google Scholar
- 39.Pelleymounter MA, Cullen MJ, Healy D, Hecht R, Winters D, McCaleb M (1998) Efficacy of exogenous recombinant murine leptin in lean and obese 10- to 12-mo-old female CD-1 mice. Am J Physiol: Regul Integr Comp Physiol 275(4):R950–R959Google Scholar