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Open Access 01-09-2019 | Original Contribution

Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects

Authors: M. Armbruster, M. Rist, S. Seifert, L. Frommherz, C. Weinert, C. Mack, A. Roth, B. Merz, D. Bunzel, R. Krüger, S. Kulling, B. Watzl, A. Bub

Published in: European Journal of Nutrition | Issue 6/2019

Abstract

Purpose

Differences in resting energy expenditure (REE) between men and women mainly result from sex-related differences in lean body mass (LBM). So far, a little is known about whether REE and LBM are reflected by a distinct human metabolite profile. Therefore, we aimed to identify plasma and urine metabolite patterns that are associated with REE and LBM of healthy subjects.

Methods

We investigated 301 healthy male and female subjects (18–80 years) under standardized conditions in the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study. REE was determined by indirect calorimetry and LBM by dual X-ray absorptiometry. Fasting blood and 24 h urine samples were analyzed by targeted and non-targeted metabolomics methods using GC × GC–MS, GC–MS, LC–MS, and NMR. Data were evaluated by predictive modeling of combined data using different machine learning algorithms, namely SVM, glmnet, and PLS.

Results

When evaluating data of men and women combined, we were able to predict REE and LBM with high accuracy (> 90%). This, however, was a clear effect of sex, which is supported by the high degree of overlap in identified important metabolites for LBM, REE, and sex, respectively. The applied machine learning algorithms did not reveal a metabolite pattern predictive of REE or LBM, when analyzing data for men and women, separately.

Conclusions

We could not identify a sex independent predictive metabolite pattern for REE or LBM. REE and LBM have no impact on plasma and urine metabolite profiles in the KarMeN Study participants. Studies applying metabolomics in healthy humans need to consider sex specific data evaluation.

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Bader N, Bosy-Westphal A, Dilba B, Muller MJ (2005) Intra- and interindividual variability of resting energy expenditure in healthy male subjects—biological and methodological variability of resting energy expenditure. Br J Nutr 94(5):843–849CrossRefPubMed

Heymsfield SB, Bourgeois B, Thomas DM (2016) Assessment of human energy exchange: historical overview. Eur J Clin Nutr. https://doi.org/10.1038/ejcn.2016.221 CrossRefPubMed

Astrup A, Buemann B, Christensen NJ, Madsen J, Gluud C, Bennett P, Svenstrup B (1992) The contribution of body composition, substrates, and hormones to the variability in energy expenditure and substrate utilization in premenopausal women. J Clin Endocrinol Metab 74(2):279–286. https://doi.org/10.1210/jcem.74.2.1530952 CrossRefPubMed

Cunningham JJ (1991) Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation. Am J Clin Nutr 54(6):963–969CrossRefPubMed

Elia M (1992) Energy expenditure in the whole body. In: Kinney J, Tucker H (eds) Energy metabolism: tissue determinants and cellular corollaries. Raven Press, New York, pp 19–60

Ravussin E, Bogardus C (1989) Relationship of genetics, age, and physical fitness to daily energy expenditure and fuel utilization. Am J Clin Nutr 49(5):968–975CrossRefPubMed

Muller MJ, Wang Z, Heymsfield SB, Schautz B, Bosy-Westphal A (2013) Advances in the understanding of specific metabolic rates of major organs and tissues in humans. Curr Opin Clin Nutr Metab Care 16(5):501–508. https://doi.org/10.1097/MCO.0b013e328363bdf9 CrossRefPubMed

Moore FD, Olesen KH, McMurrey JD, Parker HV, Ball MR, Boyden CM (1963) The body cell mass and its supporting environment; body composition in health and disease. Saunders, Philadelphia

Newgard CB (2017) Metabolomics and metabolic diseases: where do we stand? Cell Metab 25(1):43–56. https://doi.org/10.1016/j.cmet.2016.09.018 CrossRefPubMed

10.

Gibbons H, O’Gorman A, Brennan L (2015) Metabolomics as a tool in nutritional research. Curr Opin Lipidol 26(1):30–34. https://doi.org/10.1097/mol.0000000000000140 CrossRefPubMed

11.

Bassini A, Cameron LC (2014) Sportomics: building a new concept in metabolic studies and exercise science. Biochem Biophys Res Commun 445(4):708–716. https://doi.org/10.1016/j.bbrc.2013.12.137 CrossRefPubMed

12.

Mittelstrass K, Ried JS, Yu Z, Krumsiek J, Gieger C, Prehn C, Roemisch-Margl W, Polonikov A, Peters A, Theis FJ, Meitinger T, Kronenberg F, Weidinger S, Wichmann HE, Suhre K, Wang-Sattler R, Adamski J, Illig T (2011) Discovery of sexual dimorphisms in metabolic and genetic biomarkers. PLoS Genet 7(8):e1002215. https://doi.org/10.1371/journal.pgen.1002215 CrossRefPubMedPubMedCentral

13.

Kochhar S, Jacobs DM, Ramadan Z, Berruex F, Fuerholz A, Fay LB (2006) Probing gender-specific metabolism differences in humans by nuclear magnetic resonance-based metabonomics. Anal Biochem 352(2):274–281. https://doi.org/10.1016/j.ab.2006.02.033 CrossRefPubMed

14.

Bertram HC, Duus JO, Petersen BO, Hoppe C, Larnkjaer A, Schack-Nielsen L, Molgaard C, Michaelsen KF (2009) Nuclear magnetic resonance-based metabonomics reveals strong sex effect on plasma metabolism in 17-year-old Scandinavians and correlation to retrospective infant plasma parameters. Metabolism 58(7):1039–1045. https://doi.org/10.1016/j.metabol.2009.03.011 CrossRefPubMed

15.

Ruoppolo M, Campesi I, Scolamiero E, Pecce R, Caterino M, Cherchi S, Mercuro G, Tonolo G, Franconi F (2014) Serum metabolomic profiles suggest influence of sex and oral contraceptive use. Am J Transl Res 6(5):614–624PubMedPubMedCentral

16.

Slupsky CM, Rankin KN, Wagner J, Fu H, Chang D, Weljie AM, Saude EJ, Lix B, Adamko DJ, Shah S, Greiner R, Sykes BD, Marrie TJ (2007) Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Anal Chem 79(18):6995–7004. https://doi.org/10.1021/ac0708588 CrossRefPubMed

17.

Krumsiek J, Mittelstrass K, Do KT, Stuckler F, Ried J, Adamski J, Peters A, Illig T, Kronenberg F, Friedrich N, Nauck M, Pietzner M, Mook-Kanamori DO, Suhre K, Gieger C, Grallert H, Theis FJ, Kastenmuller G (2015) Gender-specific pathway differences in the human serum metabolome. Metabolomics 11(6):1815–1833. https://doi.org/10.1007/s11306-015-0829-0 CrossRefPubMedPubMedCentral

18.

Psihogios NG, Gazi IF, Elisaf MS, Seferiadis KI, Bairaktari ET (2008) Gender-related and age-related urinalysis of healthy subjects by NMR-based metabonomics. NMR Biomed 21(3):195–207. https://doi.org/10.1002/nbm.1176 CrossRefPubMed

19.

Rist MJ, Roth A, Frommherz L, Weinert CH, Kruger R, Merz B, Bunzel D, Mack C, Egert B, Bub A, Gorling B, Tzvetkova P, Luy B, Hoffmann I, Kulling SE, Watzl B (2017) Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study. PLoS One 12(8):e0183228. https://doi.org/10.1371/journal.pone.0183228 CrossRefPubMedPubMedCentral

20.

Korostishevsky M, Steves CJ, Malkin I, Spector T, Williams FM, Livshits G (2015) Genomics and metabolomics of muscular mass in community-based sample of UK females. Eur J Hum Genet. https://doi.org/10.1038/ejhg.2015.85 CrossRefPubMedPubMedCentral

21.

Lustgarten MS, Price LL, Logvinenko T, Hatzis C, Padukone N, Reo NV, Phillips EM, Kirn D, Mills J, Fielding RA (2013) Identification of serum analytes and metabolites associated with aerobic capacity. Eur J Appl Physiol 113(5):1311–1320. https://doi.org/10.1007/s00421-012-2555-x CrossRefPubMed

22.

Lustgarten MS, Price LL, Phillips EM, Kirn D, Mills J, Fielding RA (2013) Serum predictors of percent lean mass in young adults. J Strength Cond Res. https://doi.org/10.1519/JSC.0b013e31829eef24 CrossRef

23.

Jourdan C, Petersen AK, Gieger C, Doring A, Illig T, Wang-Sattler R, Meisinger C, Peters A, Adamski J, Prehn C, Suhre K, Altmaier E, Kastenmuller G, Romisch-Margl W, Theis FJ, Krumsiek J, Wichmann HE, Linseisen J (2012) Body fat free mass is associated with the serum metabolite profile in a population-based study. PLoS One 7(6):e40009. https://doi.org/10.1371/journal.pone.0040009 CrossRefPubMedPubMedCentral

24.

Stretch C, Eastman T, Mandal R, Eisner R, Wishart DS, Mourtzakis M, Prado CM, Damaraju S, Ball RO, Greiner R, Baracos VE (2012) Prediction of skeletal muscle and fat mass in patients with advanced cancer using a metabolomic approach. J Nutr 142(1):14–21. https://doi.org/10.3945/jn.111.147751 CrossRefPubMed

25.

Bub A, Kriebel A, Dörr C, Bandt S, Rist M, Roth A, Hummel E, Kulling S, Hoffmann I, Watzl B (2016) The Karlsruhe Metabolomics and Nutrition (KarMeN) study: protocol and methods of a cross-sectional study to characterize the metabolome of healthy men and women. JMIR Res Protoc 5(3):e146. https://doi.org/10.2196/resprot.5792 CrossRefPubMedPubMedCentral

26.

Compher C, Frankenfield D, Keim N, Roth-Yousey L (2006) Best practice methods to apply to measurement of resting metabolic rate in adults: a systematic review. J Am Diet Assoc 106(6):881–903. https://doi.org/10.1016/j.jada.2006.02.009 CrossRefPubMed

27.

Weir JB (1949) New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol 109(1–2):1–9CrossRefPubMedPubMedCentral

28.

Jakobsen J, Ovesen L, Fagt S, Pedersen AN (1997) Para-aminobenzoic acid used as a marker for completeness of 24 hour urine: assessment of control limits for a specific HPLC method. Eur J Clin Nutr 51(8):514–519CrossRefPubMed

29.

Weinert CH, Egert B, Kulling SE (2015) On the applicability of comprehensive two-dimensional gas chromatography combined with a fast-scanning quadrupole mass spectrometer for untargeted large-scale metabolomics. J Chromatogr A 1405:156–167. https://doi.org/10.1016/j.chroma.2015.04.011 CrossRefPubMed

30.

Ecker J, Scherer M, Schmitz G, Liebisch G (2012) A rapid GC–MS method for quantification of positional and geometric isomers of fatty acid methyl esters. J Chromatogr B 897(0):98–104. https://doi.org/10.1016/j.jchromb.2012.04.015 CrossRef

31.

Romisch-Margl W, Prehn C, Bogumil R, Röhring C, Suhre K, Adamski J (2012) Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics. Metabolomics 8(1):133–142. https://doi.org/10.1007/s11306-011-0293-4 CrossRef

32.

Weinert CH, Empl MT, Kruger R, Frommherz L, Egert B, Steinberg P, Kulling SE (2016) The influence of a chronic l-carnitine administration on the plasma metabolome of male Fischer 344 rats. Mol Nutr Food Res. https://doi.org/10.1002/mnfr.201600651 CrossRef

33.

Frommherz L, Bub A, Hummel E, Rist MJ, Roth A, Watzl B, Kulling SE (2016) Age-related changes of plasma bile acid concentrations in healthy adults-results from the cross-sectional KarMeN study. PLoS One 11(4):e0153959. https://doi.org/10.1371/journal.pone.0153959 CrossRefPubMedPubMedCentral

34.

Rist MJ, Muhle-Goll C, Gorling B, Bub A, Heissler S, Watzl B, Luy B (2013) Influence of freezing and storage procedure on human urine samples in NMR-based metabolomics. Metabolites 3(2):243–258. https://doi.org/10.3390/metabo3020243 CrossRefPubMedPubMedCentral

35.

Barbano DM, Clark JL (1990) Kjeldahl method for determination of total nitrogen content of milk: collaborative study. J Assoc Off Anal Chem 73(6):849–859

36.

Lynch JM, Barbano DM, Healy PA, Fleming JR (1997) Performance evaluation of direct forced-air total solids and Kjeldahl total nitrogen methods: 1990 through 1995. J AOAC Int 80(5):1038–1043PubMed

37.

Egert B, Weinert CH, Kulling SE (2015) A peaklet-based generic strategy for the untargeted analysis of comprehensive two-dimensional gas chromatography mass spectrometry data sets. J Chromatogr A 1405:168–177. https://doi.org/10.1016/j.chroma.2015.05.056 CrossRefPubMed

38.

De Livera AM, Sysi-Aho M, Jacob L, Gagnon-Bartsch JA, Castillo S, Simpson JA, Speed TP (2015) Statistical methods for handling unwanted variation in metabolomics data. Anal Chem 87(7):3606–3615. https://doi.org/10.1021/ac502439y CrossRefPubMedPubMedCentral

39.

Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, Nicholls AW, Wilson ID, Kell DB, Goodacre R (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6(7):1060–1083. https://doi.org/10.1038/nprot.2011.335 CrossRefPubMed

40.

Kamleh MA, Ebbels TM, Spagou K, Masson P, Want EJ (2012) Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies. Anal Chem 84(6):2670–2677. https://doi.org/10.1021/ac202733q CrossRefPubMed

41.

Skov T, van den Berg F, Tomasi G, Bro R (2006) Automated alignment of chromatographic data. J Chemom 20(11–12):484–497. doi:https://doi.org/10.1002/Cem.1031 CrossRef

42.

Gromski P, Xu Y, Hollywood K, Turner M, Goodacre R (2015) The influence of scaling metabolomics data on model classification accuracy. Metabolomics 11(3):684–695. https://doi.org/10.1007/s11306-014-0738-7 CrossRef

43.

Hochrein J, Klein MS, Zacharias HU, Li J, Wijffels G, Schirra HJ, Spang R, Oefner PJ, Gronwald W (2012) Performance evaluation of algorithms for the classification of metabolic ¹H NMR fingerprints. J Proteome Res 11(12):6242–6251. https://doi.org/10.1021/pr3009034 CrossRefPubMed

44.

Frisard MI, Broussard A, Davies SS, Roberts LJ II, Rood J, de Jonge L, Fang X, Jazwinski SM, Deutsch WA, Ravussin E (2007) Aging, resting metabolic rate, and oxidative damage: results from the Louisiana Healthy Aging Study. J Gerontol A Biol Sci Med Sci 62(7):752–759CrossRefPubMed

45.

Lustgarten MS, Price LL, Chale A, Phillips EM, Fielding RA (2014) Branched chain amino acids are associated with muscle mass in functionally limited older adults. J Gerontol A Biol Sci Med Sci 69(6):717–724. https://doi.org/10.1093/gerona/glt152 CrossRefPubMed

46.

Eisner R, Stretch C, Eastman T, Xia J, Hau B, Damaraju S, Greiner R, Wishart DS, Baracos VE (2010) Learning to predict cancer-associated skeletal muscle wasting from ¹H-NMR profiles of urinary metabolites. Metabolomics 7:25–34. https://doi.org/10.1007/s11306-010-0232-9 CrossRef

47.

Rasmussen LG, Savorani F, Larsen TM, Dragsted LO, Astrup A, Engelsen SB (2011) Standardization of factors that influence human urine metabolomics. Metabolomics 7(1):71–83CrossRef

48.

Xiao Q, Moore SC, Boca SM, Matthews CE, Rothman N, Stolzenberg-Solomon RZ, Sinha R, Cross AJ, Sampson JN (2014) Sources of variability in metabolite measurements from urinary samples. PLoS One 9(5):e95749. https://doi.org/10.1371/journal.pone.0095749 CrossRefPubMedPubMedCentral

49.

Margolis LM, Pasiakos SM, Karl JP, Rood JC, Cable SJ, Williams KW, Young AJ, McClung JP (2012) Differential effects of military training on fat-free mass and plasma amino acid adaptations in men and women. Nutrients 4(12):2035–2046. https://doi.org/10.3390/nu4122035 CrossRefPubMedPubMedCentral

50.

Kassel DB, Martin M, Schall W, Sweeley CC (1986) Urinary metabolites of l-threonine in type 1 diabetes determined by combined gas chromatography/chemical ionization mass spectrometry. Biomed Environ Mass Spectrom 13(10):535–540CrossRefPubMed

51.

Saude EJ, Adamko D, Rowe BH, Marrie T, Sykes BD (2007) Variation of metabolites in normal human urine. Metabolomics 3(4):439–451. https://doi.org/10.1007/s11306-007-0091-1 CrossRef

52.

Zuppi C, Messana I, Forni F, Rossi C, Pennacchietti L, Ferrari F, Giardina B (1997) ¹H NMR spectra of normal urines: reference ranges of the major metabolites. Clin Chim Acta 265(1):85–97CrossRefPubMed

Title: Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects
Authors: M. Armbruster
M. Rist
S. Seifert
L. Frommherz
C. Weinert
C. Mack
A. Roth
B. Merz
D. Bunzel
R. Krüger
S. Kulling
B. Watzl
A. Bub
Publication date: 01-09-2019
Publisher: Springer Berlin Heidelberg
Published in: European Journal of Nutrition / Issue 6/2019
Print ISSN: 1436-6207
Electronic ISSN: 1436-6215
DOI: https://doi.org/10.1007/s00394-018-1767-1

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