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Published in: European Journal of Epidemiology 2/2011

01-02-2011 | Nutritional Epidemiology

Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics

Authors: Elisabeth Altmaier, Gabi Kastenmüller, Werner Römisch-Margl, Barbara Thorand, Klaus M. Weinberger, Thomas Illig, Jerzy Adamski, Angela Döring, Karsten Suhre

Published in: European Journal of Epidemiology | Issue 2/2011

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Abstract

Nutrition plays an important role in human metabolism and health. However, it is unclear in how far self-reported nutrition intake reflects de facto differences in body metabolite composition. To investigate this question on an epidemiological scale we conducted a metabolomics study analyzing the association of self-reported nutrition habits with 363 metabolites quantified in blood serum of 284 male participants of the KORA population study, aged between 55 and 79 years. Using data from an 18-item food frequency questionnaire, the consumption of 18 different food groups as well as four derived nutrition indices summarizing these food groups by their nutrient content were analyzed for association with the measured metabolites. The self-reported nutrition intake index “polyunsaturated fatty acids” associates with a decrease in saturation of the fatty acid chains of glycero-phosphatidylcholines analyzed in serum samples. Using a principal component analysis dietary patterns highly associating with serum metabolite concentrations could be identified. The first principal component, which was interpreted as a healthy nutrition lifestyle, associates with a decrease in the degree of saturation of the fatty acid moieties of different glycero-phosphatidylcholines. In summary, this analysis shows that on a population level metabolomics provides the possibility to link self-reported nutrition habits to changes in human metabolic profiles and that the associating metabolites reflect the self-reported nutritional intake. Moreover, we could show that the strength of association increases when composed nutrition indices are used. Metabolomics may, thus, facilitate evaluating questionnaires and improving future questionnaire-based epidemiological studies on human health.
Literature
1.
go back to reference Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40(5):638–45.CrossRefPubMed Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40(5):638–45.CrossRefPubMed
2.
go back to reference Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, Plagnol V, et al. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat Genet. 2007;39(7):857–64.CrossRefPubMed Todd JA, Walker NM, Cooper JD, Smyth DJ, Downes K, Plagnol V, et al. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat Genet. 2007;39(7):857–64.CrossRefPubMed
3.
go back to reference Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, et al. Genomewide association analysis of coronary artery disease. N Engl J Med. 2007;357(5):443–53.CrossRefPubMed Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, et al. Genomewide association analysis of coronary artery disease. N Engl J Med. 2007;357(5):443–53.CrossRefPubMed
4.
go back to reference Norat T, Bingham S, Ferrari P, Slimani N, Jenab M, Mazuir M, et al. Meat, fish, and colorectal cancer risk: the European Prospective Investigation into cancer and nutrition. J Natl Cancer Inst. 2005;97(12):906–16.CrossRefPubMed Norat T, Bingham S, Ferrari P, Slimani N, Jenab M, Mazuir M, et al. Meat, fish, and colorectal cancer risk: the European Prospective Investigation into cancer and nutrition. J Natl Cancer Inst. 2005;97(12):906–16.CrossRefPubMed
5.
go back to reference Van Dorsten FA, Daykin CA, Mulder TP, Van Duynhoven JP. Metabonomics approach to determine metabolic differences between green tea and black tea consumption. J Agric Food Chem. 2006;54(18):6929–38.CrossRefPubMed Van Dorsten FA, Daykin CA, Mulder TP, Van Duynhoven JP. Metabonomics approach to determine metabolic differences between green tea and black tea consumption. J Agric Food Chem. 2006;54(18):6929–38.CrossRefPubMed
6.
go back to reference Walsh MC, Brennan L, Pujos-Guillot E, Sebedio JL, Scalbert A, Fagan A, et al. Influence of acute phytochemical intake on human urinary metabolomic profiles. Am J Clin Nutr. 2007;86(6):1687–93.PubMed Walsh MC, Brennan L, Pujos-Guillot E, Sebedio JL, Scalbert A, Fagan A, et al. Influence of acute phytochemical intake on human urinary metabolomic profiles. Am J Clin Nutr. 2007;86(6):1687–93.PubMed
7.
go back to reference Solanky KS, Bailey NJ, Beckwith-Hall BM, Bingham S, Davis A, Holmes E, et al. Biofluid 1H NMR-based metabonomic techniques in nutrition research—metabolic effects of dietary isoflavones in humans. J Nutr Biochem. 2005;16(4):236–44.CrossRefPubMed Solanky KS, Bailey NJ, Beckwith-Hall BM, Bingham S, Davis A, Holmes E, et al. Biofluid 1H NMR-based metabonomic techniques in nutrition research—metabolic effects of dietary isoflavones in humans. J Nutr Biochem. 2005;16(4):236–44.CrossRefPubMed
8.
go back to reference Kimura Y, Kono S, Toyomura K, Nagano J, Mizoue T, Moore MA, et al. Meat, fish and fat intake in relation to subsite-specific risk of colorectal cancer: the Fukuoka Colorectal Cancer Study. Cancer Sci. 2007;98(4):590–7.CrossRefPubMed Kimura Y, Kono S, Toyomura K, Nagano J, Mizoue T, Moore MA, et al. Meat, fish and fat intake in relation to subsite-specific risk of colorectal cancer: the Fukuoka Colorectal Cancer Study. Cancer Sci. 2007;98(4):590–7.CrossRefPubMed
9.
go back to reference Chao A, Thun MJ, Connell CJ, McCullough ML, Jacobs EJ, Flanders WD, et al. Meat consumption and risk of colorectal cancer. JAMA. 2005;293(2):172–82.CrossRefPubMed Chao A, Thun MJ, Connell CJ, McCullough ML, Jacobs EJ, Flanders WD, et al. Meat consumption and risk of colorectal cancer. JAMA. 2005;293(2):172–82.CrossRefPubMed
10.
go back to reference Cross AJ, Leitzmann MF, Gail MH, Hollenbeck AR, Schatzkin A, Sinha R. A prospective study of red and processed meat intake in relation to cancer risk. PLoS Med. 2007;4(12):e325.CrossRefPubMed Cross AJ, Leitzmann MF, Gail MH, Hollenbeck AR, Schatzkin A, Sinha R. A prospective study of red and processed meat intake in relation to cancer risk. PLoS Med. 2007;4(12):e325.CrossRefPubMed
11.
go back to reference Stella C, Beckwith-Hall B, Cloarec O, Holmes E, Lindon JC, Powell J, et al. Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res. 2006;5(10):2780–8.CrossRefPubMed Stella C, Beckwith-Hall B, Cloarec O, Holmes E, Lindon JC, Powell J, et al. Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res. 2006;5(10):2780–8.CrossRefPubMed
12.
go back to reference Gieger C, Geistlinger L, Altmaier E, Hrabe de Angelis M, Kronenberg F, Meitinger T, et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 2008;4(11):e1000282.CrossRefPubMed Gieger C, Geistlinger L, Altmaier E, Hrabe de Angelis M, Kronenberg F, Meitinger T, et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 2008;4(11):e1000282.CrossRefPubMed
13.
go back to reference Altmaier E, Ramsay SL, Graber A, Mewes HW, Weinberger KM, Suhre K. Bioinformatics analysis of targeted metabolomics—uncovering old and new tales of diabetic mice under medication. Endocrinology. 2008;149(7):3478–89.CrossRefPubMed Altmaier E, Ramsay SL, Graber A, Mewes HW, Weinberger KM, Suhre K. Bioinformatics analysis of targeted metabolomics—uncovering old and new tales of diabetic mice under medication. Endocrinology. 2008;149(7):3478–89.CrossRefPubMed
14.
go back to reference Wang-Sattler R, Yu Y, Mittelstrass K, Lattka E, Altmaier E, Gieger C, et al. Metabolic profiling reveals distinct variations linked to nicotine consumption in humans—first results from the KORA study. PLoS One. 2008;3(12):e3863.CrossRefPubMed Wang-Sattler R, Yu Y, Mittelstrass K, Lattka E, Altmaier E, Gieger C, et al. Metabolic profiling reveals distinct variations linked to nicotine consumption in humans—first results from the KORA study. PLoS One. 2008;3(12):e3863.CrossRefPubMed
15.
go back to reference Altmaier E, Kastenmuller G, Romisch-Margl W, Thorand B, Weinberger KM, Adamski J, et al. Variation in the human lipidome associated with coffee consumption as revealed by quantitative targeted metabolomics. Mol Nutr Food Res. 2009;53:1357–65.CrossRefPubMed Altmaier E, Kastenmuller G, Romisch-Margl W, Thorand B, Weinberger KM, Adamski J, et al. Variation in the human lipidome associated with coffee consumption as revealed by quantitative targeted metabolomics. Mol Nutr Food Res. 2009;53:1357–65.CrossRefPubMed
16.
go back to reference Dowell SA, Welch JL. Use of electronic self-monitoring for food and fluid intake: a pilot study. Nephrol Nurs J. 2006;33(3):271–7.PubMed Dowell SA, Welch JL. Use of electronic self-monitoring for food and fluid intake: a pilot study. Nephrol Nurs J. 2006;33(3):271–7.PubMed
17.
go back to reference Kikunaga S, Tin T, Ishibashi G, Wang DH, Kira S. The application of a handheld personal digital assistant with camera and mobile phone card (Wellnavi) to the general population in a dietary survey. J Nutr Sci Vitaminol (Tokyo). 2007;53(2):109–16.CrossRef Kikunaga S, Tin T, Ishibashi G, Wang DH, Kira S. The application of a handheld personal digital assistant with camera and mobile phone card (Wellnavi) to the general population in a dietary survey. J Nutr Sci Vitaminol (Tokyo). 2007;53(2):109–16.CrossRef
18.
go back to reference Subar AF, Thompson FE, Potischman N, Forsyth BH, Buday R, Richards D, et al. Formative research of a quick list for an automated self-administered 24-h dietary recall. J Am Diet Assoc. 2007;107(6):1002–7.CrossRefPubMed Subar AF, Thompson FE, Potischman N, Forsyth BH, Buday R, Richards D, et al. Formative research of a quick list for an automated self-administered 24-h dietary recall. J Am Diet Assoc. 2007;107(6):1002–7.CrossRefPubMed
19.
go back to reference Slimani N, Valsta L. Perspectives of using the EPIC-SOFT programme in the context of pan-European nutritional monitoring surveys: methodological and practical implications. Eur J Clin Nutr. 2002;56(Suppl 2):S63–74.CrossRefPubMed Slimani N, Valsta L. Perspectives of using the EPIC-SOFT programme in the context of pan-European nutritional monitoring surveys: methodological and practical implications. Eur J Clin Nutr. 2002;56(Suppl 2):S63–74.CrossRefPubMed
20.
go back to reference Wang DH, Kogashiwa M, Kira S. Development of a new instrument for evaluating individuals’ dietary intakes. J Am Diet Assoc. 2006;106(10):1588–93.CrossRefPubMed Wang DH, Kogashiwa M, Kira S. Development of a new instrument for evaluating individuals’ dietary intakes. J Am Diet Assoc. 2006;106(10):1588–93.CrossRefPubMed
21.
go back to reference Michels KB. The role of nutrition in cancer development and prevention. Int J Cancer. 2005;114(2):163–5.CrossRefPubMed Michels KB. The role of nutrition in cancer development and prevention. Int J Cancer. 2005;114(2):163–5.CrossRefPubMed
22.
go back to reference Jenab M, Slimani N, Bictash M, Ferrari P, Bingham SA. Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet. 2009;125(5–6):507–25.CrossRefPubMed Jenab M, Slimani N, Bictash M, Ferrari P, Bingham SA. Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet. 2009;125(5–6):507–25.CrossRefPubMed
23.
go back to reference Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13(1):3–9.CrossRefPubMed Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13(1):3–9.CrossRefPubMed
24.
go back to reference Fung TT, Schulze M, Manson JE, Willett WC, Hu FB. Dietary patterns, meat intake, and the risk of type 2 diabetes in women. Arch Intern Med. 2004;164(20):2235–40.CrossRefPubMed Fung TT, Schulze M, Manson JE, Willett WC, Hu FB. Dietary patterns, meat intake, and the risk of type 2 diabetes in women. Arch Intern Med. 2004;164(20):2235–40.CrossRefPubMed
25.
go back to reference Liu L, Nettleton JA, Bertoni AG, Bluemke DA, Lima JA, Szklo M. Dietary pattern, the metabolic syndrome, and left ventricular mass and systolic function: the Multi-Ethnic Study of Atherosclerosis. Am J Clin Nutr. 2009;90:362–8.CrossRefPubMed Liu L, Nettleton JA, Bertoni AG, Bluemke DA, Lima JA, Szklo M. Dietary pattern, the metabolic syndrome, and left ventricular mass and systolic function: the Multi-Ethnic Study of Atherosclerosis. Am J Clin Nutr. 2009;90:362–8.CrossRefPubMed
26.
go back to reference Winkler G, Doring A. Validation of a short qualitative food frequency list used in several German large scale surveys. Z Ernahrungswiss. 1998;37(3):234–41.PubMed Winkler G, Doring A. Validation of a short qualitative food frequency list used in several German large scale surveys. Z Ernahrungswiss. 1998;37(3):234–41.PubMed
27.
28.
go back to reference Weinberger KM, Graber A. Using comprehensive metabolomics to identify novel biomarkers. Screen Trends Drug Discov. 2005;6:42–5. Weinberger KM, Graber A. Using comprehensive metabolomics to identify novel biomarkers. Screen Trends Drug Discov. 2005;6:42–5.
30.
go back to reference Storey JD. The positive false discovery rate: a Bayesian interpretation and the q value. Ann Stat. 2003;31(6):2013–35.CrossRef Storey JD. The positive false discovery rate: a Bayesian interpretation and the q value. Ann Stat. 2003;31(6):2013–35.CrossRef
31.
go back to reference Sprecher H, Luthria DL, Mohammed BS, Baykousheva SP. Reevaluation of the pathways for the biosynthesis of polyunsaturated fatty acids. J Lipid Res. 1995;36(12):2471–7.PubMed Sprecher H, Luthria DL, Mohammed BS, Baykousheva SP. Reevaluation of the pathways for the biosynthesis of polyunsaturated fatty acids. J Lipid Res. 1995;36(12):2471–7.PubMed
32.
go back to reference Winkler G, Doring A, Keil U. Trends in dietary sources of nutrients among middle-aged men in southern Germany. Results of the MONICA project Augsburg: dietary surveys 1984/1985 and 1994/1995. Monitoring trends and determinants in cardiovascular disease. Appetite. 2000;34(1):37–45.CrossRefPubMed Winkler G, Doring A, Keil U. Trends in dietary sources of nutrients among middle-aged men in southern Germany. Results of the MONICA project Augsburg: dietary surveys 1984/1985 and 1994/1995. Monitoring trends and determinants in cardiovascular disease. Appetite. 2000;34(1):37–45.CrossRefPubMed
33.
go back to reference Rubba P, Fidanza F, Gautiero G, Leccia G, Cozzolino G, Mancini M. Influence of dietary intake of energy and carbohydrate on the proportion of saturated and monounsaturated fatty acids in adipose tissue of middle aged men. Int J Vitam Nutr Res. 1990;60(4):383–91.PubMed Rubba P, Fidanza F, Gautiero G, Leccia G, Cozzolino G, Mancini M. Influence of dietary intake of energy and carbohydrate on the proportion of saturated and monounsaturated fatty acids in adipose tissue of middle aged men. Int J Vitam Nutr Res. 1990;60(4):383–91.PubMed
34.
go back to reference Day N, McKeown N, Wong M, Welch A, Bingham S. Epidemiological assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. Int J Epidemiol. 2001;30(2):309–17.CrossRefPubMed Day N, McKeown N, Wong M, Welch A, Bingham S. Epidemiological assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. Int J Epidemiol. 2001;30(2):309–17.CrossRefPubMed
35.
go back to reference Kaaks R, Ferrari P, Ciampi A, Plummer M, Riboli E. Uses and limitations of statistical accounting for random error correlations, in the validation of dietary questionnaire assessments. Public Health Nutr. 2002;5(6A):969–76.CrossRefPubMed Kaaks R, Ferrari P, Ciampi A, Plummer M, Riboli E. Uses and limitations of statistical accounting for random error correlations, in the validation of dietary questionnaire assessments. Public Health Nutr. 2002;5(6A):969–76.CrossRefPubMed
36.
go back to reference Sugar EA, Wang CY, Prentice RL. Logistic regression with exposure biomarkers and flexible measurement error. Biometrics. 2007;63(1):143–51.CrossRefPubMed Sugar EA, Wang CY, Prentice RL. Logistic regression with exposure biomarkers and flexible measurement error. Biometrics. 2007;63(1):143–51.CrossRefPubMed
37.
38.
go back to reference Potischman N, Freudenheim JL. Biomarkers of nutritional exposure and nutritional status: an overview. J Nutr. 2003;133(Suppl 3):873S–4S.PubMed Potischman N, Freudenheim JL. Biomarkers of nutritional exposure and nutritional status: an overview. J Nutr. 2003;133(Suppl 3):873S–4S.PubMed
39.
go back to reference Tasevska N, Runswick SA, McTaggart A, Bingham SA. Urinary sucrose and fructose as biomarkers for sugar consumption. Cancer Epidemiol Biomarkers Prev. 2005;14(5):1287–94.CrossRefPubMed Tasevska N, Runswick SA, McTaggart A, Bingham SA. Urinary sucrose and fructose as biomarkers for sugar consumption. Cancer Epidemiol Biomarkers Prev. 2005;14(5):1287–94.CrossRefPubMed
Metadata
Title
Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics
Authors
Elisabeth Altmaier
Gabi Kastenmüller
Werner Römisch-Margl
Barbara Thorand
Klaus M. Weinberger
Thomas Illig
Jerzy Adamski
Angela Döring
Karsten Suhre
Publication date
01-02-2011
Publisher
Springer Netherlands
Published in
European Journal of Epidemiology / Issue 2/2011
Print ISSN: 0393-2990
Electronic ISSN: 1573-7284
DOI
https://doi.org/10.1007/s10654-010-9524-7

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