Top

Published in:

Open Access 01-12-2018 | Research article

Determinants of the urinary and serum metabolome in children from six European populations

Authors: Chung-Ho E. Lau, Alexandros P. Siskos, Léa Maitre, Oliver Robinson, Toby J. Athersuch, Elizabeth J. Want, Jose Urquiza, Maribel Casas, Marina Vafeiadi, Theano Roumeliotaki, Rosemary R. C. McEachan, Rafaq Azad, Line S. Haug, Helle M. Meltzer, Sandra Andrusaityte, Inga Petraviciene, Regina Grazuleviciene, Cathrine Thomsen, John Wright, Remy Slama, Leda Chatzi, Martine Vrijheid, Hector C. Keun, Muireann Coen

Published in: BMC Medicine | Issue 1/2018

Abstract

Background

Environment and diet in early life can affect development and health throughout the life course. Metabolic phenotyping of urine and serum represents a complementary systems-wide approach to elucidate environment–health interactions. However, large-scale metabolome studies in children combining analyses of these biological fluids are lacking. Here, we sought to characterise the major determinants of the child metabolome and to define metabolite associations with age, sex, BMI and dietary habits in European children, by exploiting a unique biobank established as part of the Human Early-Life Exposome project (http://www.projecthelix.eu).

Methods

Metabolic phenotypes of matched urine and serum samples from 1192 children (aged 6–11) recruited from birth cohorts in six European countries were measured using high-throughput ¹H nuclear magnetic resonance (NMR) spectroscopy and a targeted LC-MS/MS metabolomic assay (Biocrates AbsoluteIDQ p180 kit).

Results

We identified both urinary and serum creatinine to be positively associated with age. Metabolic associations to BMI z-score included a novel association with urinary 4-deoxyerythronic acid in addition to valine, serum carnitine, short-chain acylcarnitines (C3, C5), glutamate, BCAAs, lysophosphatidylcholines (lysoPC a C14:0, lysoPC a C16:1, lysoPC a C18:1, lysoPC a C18:2) and sphingolipids (SM C16:0, SM C16:1, SM C18:1). Dietary-metabolite associations included urinary creatine and serum phosphatidylcholines (4) with meat intake, serum phosphatidylcholines (12) with fish, urinary hippurate with vegetables, and urinary proline betaine and hippurate with fruit intake. Population-specific variance (age, sex, BMI, ethnicity, dietary and country of origin) was better captured in the serum than in the urine profile; these factors explained a median of 9.0% variance amongst serum metabolites versus a median of 5.1% amongst urinary metabolites. Metabolic pathway correlations were identified, and concentrations of corresponding metabolites were significantly correlated (r > 0.18) between urine and serum.

Conclusions

We have established a pan-European reference metabolome for urine and serum of healthy children and gathered critical resources not previously available for future investigations into the influence of the metabolome on child health. The six European cohort populations studied share common metabolic associations with age, sex, BMI z-score and main dietary habits. Furthermore, we have identified a novel metabolic association between threonine catabolism and BMI of children.

Available only for authorised users

Leon DA, Lithell HO, Vagero D, Koupilova I, Mohsen R, Berglund L, Lithell UB, McKeigue PM. Reduced fetal growth rate and increased risk of death from ischaemic heart disease: cohort study of 15 000 Swedish men and women born 1915-29. Br Med J. 1998;317(7153):241–5.CrossRef

Barker DJP, Gluckman PD, Godfrey KM, Harding JE, Owens JA, Robinson JS. Fetal nutrition and cardiovascular-disease in adult life. Lancet. 1993;341(8850):938–41.CrossRefPubMed

Wadhwa PD, Buss C, Entringer S, Swanson JM. Developmental origins of health and disease: brief history of the approach and current focus on epigenetic mechanisms. Semin Reprod Med. 2009;27(5):358–68.PubMedCrossRefPubMedCentral

Thacher JD, Gruzieva O, Pershagen G, Neuman A, Wickman M, Kull I, Melen E, Bergstrom A. Pre- and postnatal exposure to parental smoking and allergic disease through adolescence. Pediatrics. 2014;134(3):428–34.CrossRefPubMed

Berends LM, Ozanne SE. Early determinants of type-2 diabetes. Best Pract Res Clin Endocrinol Metab. 2012;26(5):569–80.CrossRefPubMed

Burbank AJ, Sood AK, Kesic MJ, Peden DB, Hernandez ML. Environmental determinants of allergy and asthma in early life. J Allergy Clin Immunol. 2017;140(1):1–12.PubMedCrossRefPubMedCentral

Burke H, Leonardi-Bee J, Hashim A, Pine-Abata H, Chen YL, Cook DG, Britton JR, McKeever TM. Prenatal and passive smoke exposure and incidence of asthma and wheeze: systematic review and meta-analysis. Pediatrics. 2012;129(4):735–44.CrossRefPubMed

Deng QH, Lu C, Li YG, Sundell J, Norback D. Exposure to outdoor air pollution during trimesters of pregnancy and childhood asthma, allergic rhinitis, and eczema. Environ Res. 2016;150:119–27.CrossRefPubMed

Vafeiadi M, Roumeliotaki T, Myridakis A, Chalkiadaki G, Fthenou E, Dermitzaki E, Karachaliou M, Sarri K, Vassilaki M, Stephanou EG, et al. Association of early life exposure to bisphenol A with obesity and cardiometabolic traits in childhood. Environ Res. 2016;146:379–87.CrossRefPubMed

10.

Agay-Shay K, Martinez D, Valvi D, Garcia-Esteban R, Basagana X, Robinson O, Casas M, Sunyer J, Vrijheid M. Exposure to endocrine-disrupting chemicals during pregnancy and weight at 7 years of age: a multi-pollutant approach. Environ Health Perspect. 2015;123(10):1030–7.PubMedCrossRefPubMedCentral

11.

Boffetta P, Tredaniel J, Greco A. Risk of childhood cancer and adult lung cancer after childhood exposure to passive smoke: a meta-analysis. Environ Health Perspect. 2000;108(1):73–82.PubMedCrossRefPubMedCentral

12.

Gilmore JH, Jarskog LF, Vadlamudi S, Lauder J. Prenatal infection and risk for schizophrenia: IL-I beta, IL-6, and TNF alpha inhibit cortical neuron dendrite development. Neuropsychopharmacology. 2004;29(7):1221–9.CrossRefPubMed

13.

Braun JM, Kahn RS, Froehlich T, Auinger P, Lanphear BP. Exposures to environmental toxicants and attention deficit hyperactivity disorder in US children. Environ Health Perspect. 2006;114(12):1904–9.PubMedCrossRefPubMedCentral

14.

Heijmans BT, Tobi EW, Stein AD, Putter H, Blauw GJ, Susser ES, Slagboom PE, Lumey LH. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci U S A. 2008;105(44):17046–9.PubMedCrossRefPubMedCentral

15.

Ghantous A, Hernandez-Vargas H, Byrnes G, Dwyer T, Herceg Z. Characterising the epigenome as a key component of the fetal exposome in evaluating in utero exposures and childhood cancer risk. Mutagenesis. 2015;30(6):733–42.PubMedCrossRefPubMedCentral

16.

Lioy PJ, Rappaport SM. Exposure science and the exposome: an opportunity for coherence in the environmental health sciences. Environ Health Perspect. 2011;119(11):A466–7.PubMedCrossRefPubMedCentral

17.

Wild CP. The exposome: from concept to utility. Int J Epidemiol. 2012;41(1):24–32.PubMedCrossRef

18.

Vineis P, van Veldhoven K, Chadeau-Hyam M, Athersuch TJ. Advancing the application of omics-based biomarkers in environmental epidemiology. Environ Mol Mutagen. 2013;54(7):461–7.CrossRefPubMed

19.

Robinson O, Martinez D, Aurrekoetxea JJ, Estarlich M, Somoano AF, Iniguez C, Santa-Marina L, Tardon A, Torrent M, Sunyer J, et al. The association between passive and active tobacco smoke exposure and child weight status among Spanish children. Obesity. 2016;24(8):1767–77.CrossRefPubMed

20.

Robinson O, Basagana X, Agier L, de Castro M, Hernandez-Ferrer C, Gonzalez JR, Grimalt JO, Nieuwenhuijsen M, Sunyer J, Slama R, et al. The pregnancy exposome: multiple environmental exposures in the INMA-Sabadell birth cohort. Environ Sci Technol. 2015;49(17):10632–41.CrossRefPubMed

21.

Bisgaard H, Vissing NH, Carson CG, Bischoff AL, Folsgaard NV, Kreiner-Moller E, Chawes BLK, Stokholm J, Pedersen L, Bjarnadottir E, et al. Deep phenotyping of the unselected COPSAC2010 birth cohort study. Clin Exp Allergy. 2013;43(12):1384–94.PubMedCrossRefPubMedCentral

22.

Sarigiannis DA. Assessing the impact of hazardous waste on children’s health: the exposome paradigm. Environ Res. 2017;158:531–41.CrossRefPubMed

23.

Athersuch TJ, Keun HC. Metabolic profiling in human exposome studies. Mutagenesis. 2015;30(6):755–62.PubMed

24.

Athersuch TJ. The role of metabolomics in characterizing the human exposome. Bioanalysis. 2012;4(18):2207–12.CrossRefPubMed

25.

Baker MG, Simpson CD, Lin YS, Shireman LM, Seixas N. The use of metabolomics to identify biological signatures of manganese exposure. Ann Work Expo Health. 2017;61(4):406–15.PubMedCrossRefPubMedCentral

26.

Ladva CN, Golan R, Greenwald R, Yu TW, Sarnat SE, Flanders WD, Uppal K, Walker DI, Tran V, Liang DH, et al. Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection. J Breath Res. 2018;12(1):016008.CrossRef

27.

Ellis JK, Athersuch TJ, Thomas LDK, Teichert F, Perez-Trujillo M, Svendsen C, Spurgeon DJ, Singh R, Jarup L, Bundy JG, et al. Metabolic profiling detects early effects of environmental and lifestyle exposure to cadmium in a human population. BMC Med. 2012;10:61.PubMedCrossRefPubMedCentral

28.

Wilson K, Hawken S, Ducharme R, Potter BK, Little J, Thebaud B, Chakraborty P. Metabolomics of prematurity: analysis of patterns of amino acids, enzymes, and endocrine markers by categories of gestational age. Pediatr Res. 2014;75(2):367–73.CrossRefPubMed

29.

Maitre L, Villanueva CM, Lewis MR, Ibarluzea J, Santa-Marina L, Vrijheid M, Sunyer J, Coen M, Toledano MB. Maternal urinary metabolic signatures of fetal growth and associated clinical and environmental factors in the INMA study. BMC Med. 2016;14:177.PubMedCrossRefPubMedCentral

30.

Overgaard AJ, Kaur S, Pociot F. Metabolomic biomarkers in the progression to type 1 diabetes. Curr Diab Rep. 2016;16(12):127.CrossRefPubMed

31.

Smolinska A, Klaassen EMM, Dallinga JW, van de Kant KDG, Jobsis Q, Moonen EJC, van Schayck OCP, Dompeling E, van Schooten FJ. Profiling of volatile organic compounds in exhaled breath as a strategy to find early predictive signatures of asthma in children. PLoS One. 2014;9(4):e95668.PubMedCrossRefPubMedCentral

32.

James SJ, Cutler P, Melnyk S, Jernigan S, Janak L, Gaylor DW, Neubrander JA. Metabolic biomarkers of increased oxidative stress and impaired methylation capacity in children with autism. Am J Clin Nutr. 2004;80(6):1611–7.CrossRefPubMed

33.

Yu ZH, Zhai GJ, Singmann P, He Y, Xu T, Prehn C, Roemisch-Margl W, Lattka E, Gieger C, Soranzo N, et al. Human serum metabolic profiles are age dependent. Aging Cell. 2012;11(6):960–7.PubMedCrossRefPubMedCentral

34.

Elliott P, Posma JM, Chan Q, Garcia-Perez I, Wijeyesekera A, Bictash M, Ebbels TMD, Ueshima H, Zhao LC, van Horn L, et al. Urinary metabolic signatures of human adiposity. Sci Transl Med. 2015;7(285):285ra62.CrossRefPubMed

35.

Guertin KA, Moore SC, Sampson JN, Huang WY, Xiao Q, Stolzenberg-Solomon RZ, Sinha R, Cross AJ. Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. Am J Clin Nutr. 2014;100(1):208–17.PubMedCrossRefPubMedCentral

36.

Jourdan C, Petersen AK, Gieger C, Doring A, Illig T, Wang-Sattler R, Meisinger C, Peters A, Adamski J, Prehn C, et al. Body fat free mass is associated with the serum metabolite profile in a population-based study. PLoS One. 2012;7(6):e40009.PubMedCrossRefPubMedCentral

37.

Kochhar S, Jacobs DM, Ramadan Z, Berruex F, Fuerhoz A, Fay LB. Probing gender-specific metabolism differences in humans by nuclear magnetic resonance-based metabonomics. Anal Biochem. 2006;352(2):274–81.CrossRefPubMed

38.

Moore SC, Matthews CE, Sampson JN, Stolzenberg-Solomon RZ, Zheng W, Cai QY, Tan YT, Chow WH, Ji BT, Liu DK, et al. Human metabolic correlates of body mass index. Metabolomics. 2014;10(2):259–69.CrossRefPubMed

39.

Stella C, Beckwith-Hall B, Cloarec O, Holmes E, Lindon JC, Powell J, van der Ouderaa F, Bingham S, Cross AJ, Nicholson JK. Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res. 2006;5(10):2780–8.CrossRefPubMed

40.

Holmes E, Loo RL, Stamler J, Bictash M, Yap IKS, Chan Q, Ebbels T, De Iorio M, Brown IJ, Veselkov KA, et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature. 2008;453(7193):396–U350.CrossRefPubMed

41.

Wurtz P, Wang Q, Kangas AJ, Richmond RC, Skarp J, Tiainen M, Tynkkynen T, Soininen P, Havulinna AS, Kaakinen M, et al. Metabolic signatures of adiposity in young adults: Mendelian randomization analysis and effects of weight change. PLoS Med. 2014;11(12):e1001765.PubMedCrossRefPubMedCentral

42.

Dunn WB, Lin WC, Broadhurst D, Begley P, Brown M, Zelena E, Vaughan AA, Halsall A, Harding N, Knowles JD, et al. Molecular phenotyping of a UK population: defining the human serum metabolome. Metabolomics. 2015;11(1):9–26.CrossRefPubMed

43.

McCormack SE, Shaham O, McCarthy MA, Deik AA, Wang TJ, Gerszten RE, Clish CB, Mootha VK, Grinspoon SK, Fleischman A. Circulating branched-chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents. Pediatric Obesity. 2013;8(1):52–61.CrossRefPubMed

44.

Knip M, Virtanen SM, Akerblom HK. Infant feeding and the risk of type 1 diabetes. Am J Clin Nutr. 2010;91(5):1506S–13S.CrossRefPubMed

45.

Freemark M. Metabolomics in nutrition research: biomarkers predicting mortality in children with severe acute malnutrition. Food Nutr Bull. 2015;36:S88–92.CrossRefPubMed

46.

Chiu CY, Yeh KW, Lin G, Chiang MH, Yang SC, Chao WJ, Yao TC, Tsai MH, Hua MC, Liao SL, et al. Metabolomics reveals dynamic metabolic changes associated with age in early childhood. PLoS One. 2016;11(2):e0149823.PubMedCrossRefPubMedCentral

47.

Playdon MC, Sampson JN, Cross AJ, Sinha R, Guertin KA, Moy KA, Rothman N, Irwin ML, Mayne ST, Stolzenberg-Solomon R, et al. Comparing metabolite profiles of habitual diet in serum and urine. Am J Clin Nutr. 2016;104(3):776–89.PubMedCrossRefPubMedCentral

48.

Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, Thomsen C, Wright J, Athersuch TJ, Avellana N, et al. The human early-life exposome (HELIX): project rationale and design. Environ Health Perspect. 2014;122(6):535–44.PubMedCrossRefPubMedCentral

49.

Maitre L, de Bont J, Casas M, Robinson O, Aasvang GM, Agier L, Andrušaitytė S, Ballester F, Basagaña X, Borràs E, et al. Human Early Life Exposome (HELIX) study: a European population-based exposome cohort. BMJ Open. 2018;8(9):e021311.PubMedCrossRefPubMedCentral

50.

Wright J, Small N, Raynor P, Tuffnell D, Bhopal R, Cameron N, Fairley L, Lawlor DA, Parslow R, Petherick ES, et al. Cohort profile: the Born in Bradford multi-ethnic family cohort study. Int J Epidemiol. 2013;42(4):978–91.CrossRefPubMed

51.

Heude B, Forhan A, Slama R, Douhaud L, Bedel S, Saurel-Cubizolles MJ, Hankard R, Thiebaugeorges O, De Agostini M, Annesi-Maesano I, et al. Cohort profile: the EDEN mother-child cohort on the prenatal and early postnatal determinants of child health and development. Int J Epidemiol. 2016;45(2):353–63.CrossRefPubMed

52.

Guxens M, Ballester F, Espada M, Fernandez MF, Grimalt JO, Ibarluzea J, Olea N, Rebagliato M, Tardon A, Torrent M, et al. Cohort profile: the INMA-INfancia y Medio Ambiente-(environment and childhood) project. Int J Epidemiol. 2012;41(4):930–40.CrossRefPubMed

53.

Grazuleviciene R, Nieuwenhuijsen MJ, Vencloviene J, Kostopoulou-Karadanelli M, Krasner SW, Danileviciute A, Balcius G, Kapustinskiene V. Individual exposures to drinking water trihalomethanes, low birth weight and small for gestational age risk: a prospective Kaunas cohort study. Environ Health. 2011;10:32.PubMedCrossRefPubMedCentral

54.

Magnus P, Birke C, Vejrup K, Haugan A, Alsaker E, Daltveit AK, Handal M, Haugen M, Hoiseth G, Knudsen GP, et al. Cohort profile update: the Norwegian mother and child cohort study (MoBa). Int J Epidemiol. 2016;45(2):382–8.CrossRefPubMed

55.

Chatzi L, Leventakou V, Vafeiadi M, Koutra K, Roumeliotaki T, Chalkiadaki G, Karachaliou M, Daraki V, Kyriklaki A, Kampouri M, et al. Cohort profile: the mother-child cohort in Crete, Greece (Rhea study). Int J Epidemiol. 2017;46(5):1392–1393k.CrossRefPubMed

56.

de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85(9):660–7.PubMedCrossRefPubMedCentral

57.

Maitre L, Lau CE, Vizcaino E, Robinson O, Casas M, Siskos AP, Want EJ, Athersuch T, Slama R, Vrijheid M, et al. Assessment of metabolic phenotypic variability in children’s urine using 1H NMR spectroscopy. Sci Rep. 2017;7:46082.PubMedCrossRefPubMedCentral

58.

Dona AC, Jimenez B, Schafer H, Humpfer E, Spraul M, Lewis MR, Pearce JTM, Holmes E, Lindon JC, Nicholson JK. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal Chem. 2014;86(19):9887–94.CrossRefPubMed

59.

Karaman I, Ferreira DLS, Boulange CL, Kaluarachchi MR, Herrington D, Dona AC, Castagne R, Moayyeri A, Lehne B, Loh M, et al. Workflow for integrated processing of multicohort untargeted H-1 NMR metabolomics data in large-scale metabolic epidemiology. J Proteome Res. 2016;15(12):4188–94.CrossRefPubMed

60.

Veselkov KA, Lindon JC, Ebbels TMD, Crockford D, Volynkin VV, Holmes E, Davies DB, Nicholson JK. Recursive segment-wise peak alignment of biological H-1 NMR spectra for improved metabolic biomarker recovery. Anal Chem. 2009;81(1):56–66.CrossRefPubMed

61.

Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in H-1 NMR metabonomics. Anal Chem. 2006;78(13):4281–90.CrossRefPubMed

62.

Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, Cheng D, Jewell K, Arndt D, Sawhney S. HMDB: the human metabolome database. Nucleic Acids Res. 2007;35(Database):D521–6.PubMedCrossRefPubMedCentral

63.

Cloarec O, Dumas ME, Craig A, Barton RH, Trygg J, Hudson J, Blancher C, Gauguier D, Lindon JC, Holmes E, et al. Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic H-1 NMR data sets. Anal Chem. 2005;77(5):1282–9.CrossRefPubMed

64.

User Manual UM_p180_AB SCIEX_9. Biocrates Life Sciences AG. Innsbruck; 2014.

65.

Siskos AP, Jain P, Romisch-Margl W, Bennet M, Achaintre D, Asad Y, Marney L, Richardson L, Koulman A, Griffin JL, et al. Interlaboratory reproducibility of a targeted metabolomics platform for analysis of human serum and plasma. Anal Chem. 2017;89(1):656–65.CrossRefPubMed

66.

Trabado S, Al-Salameh A, Croixmarie V, Masson P, Corruble E, Feve B, Colle R, Ripoll L, Walther B, Boursier-Neyret C, et al. The human plasma-metabolome: reference values in 800 French healthy volunteers; impact of cholesterol, gender and age. PLoS One. 2017;12(3):e0173615.PubMedCrossRefPubMedCentral

67.

Merz B, Nothlings U, Wahl S, Haftenberger M, Schienkiewitz A, Adamski J, Suhre K, Wang-Sattler R, Grallert H, Thorand B, et al. Specific metabolic markers are associated with future waist-gaining phenotype in women. PLoS One. 2016;11(6):e0157733.PubMedCrossRefPubMedCentral

68.

Yet I, Menni C, Shin SY, Mangino M, Soranzo N, Adamski J, Suhre K, Spector TD, Kastenmuller G, Bell JT. Genetic influences on metabolite levels: a comparison across metabolomic platforms. PLoS One. 2016;11(4):e0153672.PubMedCrossRefPubMedCentral

69.

Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431–2.PubMedCrossRef

70.

Basu S, Duren W, Evans CR, Burant CF, Michailidis G, Karnovsky A. Sparse network modeling and MetScape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics. 2017;33(10):1545–53.PubMedPubMedCentral

71.

Sakia RM. The Box-Cox transformation technique - a review. J R Stat Soc Ser D. 1992;41(2):169–78.

72.

Wei RM, Wang JY, Su MM, Jia E, Chen SQ, Chen TL, Ni Y. Missing value imputation approach for mass spectrometry-based metabolomics data. Sci Rep. 2018;8:663.PubMedCrossRefPubMedCentral

73.

Vanholder R, Schepers E, Pletinck A, Nagler EV, Glorieux G. The uremic toxicity of Indoxyl sulfate and p-Cresyl sulfate: a systematic review. J Am Soc Nephrol. 2014;25(9):1897–907.PubMedCrossRefPubMedCentral

74.

Darling PB, Grunow J, Rafii M, Brookes S, Ball RO, Pencharz PB. Threonine dehydrogenase is a minor degradative pathway of threonine catabolism in adult humans. Am J Physiol Endocrinol Metab. 2000;278(5):E877–84.CrossRefPubMed

75.

Carayol M, Licaj I, Achaintre D, Sacerdote C, Vineis P, Key TJ, Moret NCO, Scalbert A, Rinaldi S, Ferrari P. Reliability of serum metabolites over a two-year period: a targeted metabolomic approach in fasting and non-fasting samples from EPIC. PLoS One. 2015;10(8):e0135437.PubMedCrossRefPubMedCentral

76.

Anton G, Wilson R, Yu ZH, Prehn C, Zukunft S, Adamski J, Heier M, Meisinger C, Romisch-Margl W, Wang-Sattler R, et al. Pre-analytical sample quality: metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples. PLoS One. 2015;10(3):e0121495.PubMedCrossRefPubMedCentral

77.

Dunn WB, Broadhurst D, Ellis DI, Brown M, Halsall A, O'Hagan S, Spasic I, Tseng A, Kell DB. A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. Int J Epidemiol. 2008;37:23–30.CrossRef

78.

Barton RH, Nicholson JK, Elliott P, Holmes E. High-throughput H-1 NMR-based metabolic analysis of human serum and urine for large-scale epidemiological studies: validation study. Int J Epidemiol. 2008;37:31–40.CrossRef

79.

Shrestha A, Mullner E, Poutanen K, Mykkanen H, Moazzami AA. Metabolic changes in serum metabolome in response to a meal. Eur J Nutr. 2017;56(2):671–81.CrossRefPubMed

80.

Baxmann AC, Ahmed MS, Marques NC, Menon VB, Pereira AB, Kirsztajn GM, Heilberg IP. Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin J Am Soc Nephrol. 2008;3(2):348–54.PubMedCrossRefPubMedCentral

81.

Savory DJ. Reference ranges for serum creatinine in infants, children and adolescents. Ann Clin Biochem. 1990;27:99–101.CrossRefPubMed

82.

Sugita O, Uchiyama K, Yamada T, Sato T, Okada M, Takeuchi K. Reference values of serum and urine creatinine, and of creatinine clearance by a new enzymatic method. Ann Clin Biochem. 1992;29:523–8.CrossRefPubMed

83.

Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, Bjorndahl TC, Krishnamurthy R, Saleem F, Liu P, et al. The human urine metabolome. PLoS One. 2013;8(9):e73076.PubMedCrossRefPubMedCentral

84.

Richmond W, Colgan G, Simon S, Stuart-Hilgenfeld M, Wilson N, Alon US. Random urine calcium/osmolality in the assessment of calciuria in children with decreased muscle mass. Clin Nephrol. 2005;64(4):264–70.CrossRefPubMed

85.

Must A, Anderson SE. Body mass index in children and adolescents: considerations for population-based applications. Int J Obes. 2006;30(4):590–4.CrossRef

86.

Mangge H, Zelzer S, Pruller F, Schnedl WJ, Weghuber D, Enko D, Bergsten P, Haybaeck J, Meinitzer A. Branched-chain amino acids are associated with cardiometabolic risk profiles found already in lean, overweight and obese young. J Nutr Biochem. 2016;32:123–7.CrossRefPubMed

87.

Perng W, Gillman MW, Fleisch AF, Michalek RD, Watkins SM, Isganaitis E, Patti ME, Oken E. Metabolomic profiles and childhood obesity. Obesity. 2014;22(12):2570–8.PubMed

88.

Lynch CJ, Adams SH. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol. 2014;10(12):723–36.PubMedCrossRefPubMedCentral

89.

Robinson O, Keski-Rahkonen P, Chatzi L, Kogevinas M, Nawrot T, Pizzi C, Plusquin M, Richiardi L, Robinot N, Sunyer J, et al. Cord blood metabolic signatures of birth weight: a population-based study. J Proteome Res. 2018;17(3):1235–47.CrossRefPubMed

90.

Carayol M, Leitzmann MF, Ferrari P, Zamora-Ros R, Achaintre D, Stepien M, Schmidt JA, Travis RC, Overvad K, Tjonneland A, et al. Blood metabolic signatures of body mass index: a targeted metabolomics study in the EPIC cohort. J Proteome Res. 2017;16(9):3137–46.PubMedCrossRefPubMedCentral

91.

Edgar AJ. The human L-threonine 3-dehydrogenase gene is an expressed pseudogene. BMC Genet. 2002;3:18.PubMedCrossRefPubMedCentral

92.

Van Winkle LJ, Galat V, Iannaccone PM. Threonine appears to be essential for proliferation of human as well as mouse embryonic stem cells. Front Cell Dev Biol. 2014;2:18.PubMedCrossRefPubMedCentral

93.

Diaz SO, Barros AS, Goodfellow BJ, Duarte IF, Carreira IM, Galhano E, Pita C, Almeida MD, Gil AM. Following healthy pregnancy by nuclear magnetic resonance (NMR) metabolic profiling of human urine. J Proteome Res. 2013;12(2):969–79.CrossRefPubMed

94.

Thompson JA, Markey SP, Fennessey PV. Gas-chromatographic-mass-spectrometric identification and quantitation of tetronic and deoxytetronic acids in urine from normal adults and neonates. Clin Chem. 1975;21(13):1892–8.PubMed

95.

Kassel DB, Martin M, Schall W, Sweeley CC. Urinary metabolites of L-threonine in type-1 diabetes determined by combined gas-chromatography chemical ionization mass-spectrometry. Biomed Environ Mass Spectrom. 1986;13(10):535–40.CrossRefPubMed

96.

Darling PB, Dunn M, Sarwar G, Brookes S, Ball RO, Pencharz PB. Threonine kinetics in preterm infants fed their mothers’ milk or formula with various ratios of whey to casein. Am J Clin Nutr. 1999;69(1):105–14.CrossRefPubMed

97.

Matafome P, Sena C, Seica R. Methylglyoxal, obesity, and diabetes. Endocrine. 2013;43(3):472–84.CrossRefPubMed

98.

Ravichandran M, Priebe S, Grigolon G, Rozanov L, Groth M, Laube B, Guthke R, Platzer M, Zarse K, Ristow M. Impairing L-threonine catabolism promotes healthspan through methylglyoxal-mediated proteohormesis. Cell Metab. 2018;27(4):914–25 e915.CrossRefPubMed

99.

Nair S, O'Brien SV, Hayden K, Pandya B, Lisboa PJG, Hardy KJ, Wilding JPH. Effect of a cooked meat meal on serum creatinine and estimated glomerular filtration rate in diabetes-related kidney disease. Diabetes Care. 2014;37(2):483–7.CrossRefPubMed

100.

Krupp D, Doberstein N, Shi LJ, Remer T. Hippuric acid in 24-hour urine collections is a potential biomarker for fruit and vegetable consumption in healthy children and adolescents. J Nutr. 2012;142(7):1314–20.CrossRefPubMed

101.

Edmands WMB, Beckonert OP, Stella C, Campbell A, Lake BG, Lindon JC, Holmes E, Gooderham NJ. Identification of human urinary biomarkers of cruciferous vegetable consumption by metabonomic profiling. J Proteome Res. 2011;10(10):4513–21.CrossRefPubMed

102.

Heinzmann SS, Brown IJ, Chan Q, Bictash M, Dumas ME, Kochhar S, Stamler J, Holmes E, Elliott P, Nicholson JK. Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am J Clin Nutr. 2010;92(2):436–43.PubMedCrossRefPubMedCentral

103.

Glaser C, Demmelmair H, Koletzko B. High-throughput analysis of fatty acid composition of plasma glycerophospholipids. J Lipid Res. 2010;51(1):216–21.PubMedCrossRefPubMedCentral

104.

Careagahouck M, Sprecher H. Effect of a fish oil diet on the composition of rat neutrophil lipids and the molecular-species of choline and ethanolamine glycerophospholipids. J Lipid Res. 1989;30(1):77–87.

105.

Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, et al. The human serum metabolome. PLoS One. 2011;6(2):e16957.PubMedCrossRefPubMedCentral

106.

Vogt S, Wahl S, Kettunen J, Breitner S, Kastenmuller G, Gieger C, Suhre K, Waldenberger M, Kratzsch J, Perola M, et al. Characterization of the metabolic profile associated with serum 25-hydroxyvitamin D: a cross-sectional analysis in population-based data. Int J Epidemiol. 2016;45(5):1469–81.PubMedCrossRefPubMedCentral

107.

Nicholson G, Rantalainen M, Maher AD, Li JV, Malmodin D, Ahmadi KR, Faber JH, Hallgrimsdottir IB, Barrett A, Toft H, et al. Human metabolic profiles are stably controlled by genetic and environmental variation. Mol Syst Biol. 2011;7:525.PubMedCrossRefPubMedCentral

Title: Determinants of the urinary and serum metabolome in children from six European populations
Authors: Chung-Ho E. Lau
Alexandros P. Siskos
Léa Maitre
Oliver Robinson
Toby J. Athersuch
Elizabeth J. Want
Jose Urquiza
Maribel Casas
Marina Vafeiadi
Theano Roumeliotaki
Rosemary R. C. McEachan
Rafaq Azad
Line S. Haug
Helle M. Meltzer
Sandra Andrusaityte
Inga Petraviciene
Regina Grazuleviciene
Cathrine Thomsen
John Wright
Remy Slama
Leda Chatzi
Martine Vrijheid
Hector C. Keun
Muireann Coen
Publication date: 01-12-2018
Publisher: BioMed Central
Published in: BMC Medicine / Issue 1/2018
Electronic ISSN: 1741-7015
DOI: https://doi.org/10.1186/s12916-018-1190-8

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Determinants of the urinary and serum metabolome in children from six European populations

Abstract

Background

Methods

Results

Conclusions

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2018

Association of longitudinal alcohol consumption trajectories with coronary heart disease: a meta-analysis of six cohort studies using individual participant data

Second-line treatment for advanced NSCLC without actionable mutations: is immunotherapy the ‘panacea’ for all patients?

Reporting guidelines: doing better for readers

A longitudinal, observational study of the features of transitional healthcare associated with better outcomes for young people with long-term conditions

Antidepressant use during pregnancy and risk of autism spectrum disorder and attention deficit hyperactivity disorder: systematic review of observational studies and methodological considerations

Childhood trauma and the enduring consequences of forcibly separating children from parents at the United States border