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The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples

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Abstract

Data quality is critical for epidemiology, and as scientific understanding expands, the range of data available for epidemiological studies and the types of tools used for measurement have also expanded. It is essential for the epidemiologist to have a grasp of the issues involved with different measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS “omics” approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding of the possibilities and possible problems related to MS-based measurements will help the epidemiologist in their discussions with analytical chemists and lead to the use of the most appropriate statistical tools for these data.

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Abbreviations

ELISA:

Enzyme-linked immunosorbent assay

GC:

Gas chromatography

LC:

Liquid chromatography

MS:

Mass spectrometry

References

  1. Cheng S, Larson MG, McCabe EL, et al. Distinct metabolomic signatures are associated with longevity in humans. Nat Commun. 2015;6:6791.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Wang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17:448-U83.

    Google Scholar 

  3. Kyro C, Olsen A, Landberg R, et al. Plasma alkylresorcinols, biomarkers of whole-grain wheat and rye intake, and incidence of colorectal cancer. JNCI Natl Cancer Inst. 2014;106:djt352.

    Article  CAS  Google Scholar 

  4. Fedirko V, Duarte-Salles T, Bamia C, et al. Prediagnostic circulating vitamin D levels and risk of hepatocellular carcinoma in European populations: a nested case-control study. Hepatology. 2014;60:1222–30.

    Article  CAS  PubMed  Google Scholar 

  5. Calafat AM, Ye X, Wong L, Reidy JA, Needham LL. Exposure of the US population to bisphenol A and 4-tertiary-octylphenol: 2003–2004. Environ Health Perspect. 2008;116:39–44.

    Article  CAS  PubMed  Google Scholar 

  6. Li S, Zhao J, Wang G, et al. Urinary triclosan concentrations are inversely associated with body mass index and waist circumference in the US general population: experience in NHANES 2003–2010. Int J Hyg Environ Health. 2015;218:401–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Floegel A, Stefan N, Yu Z, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62:639–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ganna A, Salihovic S, Sundstrom J, et al. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease. PLoS Genet. 2014;10:e1004801.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Jaremek M, Yu Z, Mangino M, et al. Alcohol-induced metabolomic differences in humans. Transl Psychiatry. 2013;3:e276.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jourdan C, Petersen A, Gieger C, et al. Body fat free mass is associated with the serum metabolite profile in a population-based study. PLoS ONE. 2012;7:e40009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Floegel A, von Ruesten A, Drogan D, et al. Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam. Eur J Clin Nutr. 2013;67:1100–8.

    Article  CAS  PubMed  Google Scholar 

  12. Guertin KA, Moore SC, Sampson JN, et al. 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;100:208–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jenab M, Slimani N, Bictash M, Ferrari P, Bingham SA. Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet. 2009;125:507–25.

    Article  PubMed  Google Scholar 

  14. Ross AB, Bourgeois A, Macharia HN, et al. Plasma alkylresorcinols as a biomarker of whole-grain food consumption in a large population: results from the WHOLEheart Intervention Study. Am J Clin Nutr. 2012;95:204–11.

    Article  CAS  PubMed  Google Scholar 

  15. Andersen MS, Rinnan A, Manach C, et al. Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern. J Proteome Res. 2014;13:1405–18.

    Article  CAS  PubMed  Google Scholar 

  16. Scalbert A, Brennan L, Manach C, et al. The food metabolome: a window over dietary exposure. Am J Clin Nutr. 2014;99:1286–308.

    Article  CAS  PubMed  Google Scholar 

  17. Clarke Hillyer G, Neugut AI, Crew KD, et al. Use of a urine anastrozole assay to determine treatment discontinuation among women with hormone-sensitive breast cancer: a pilot study. J Oncol Pract. 2012;8:e100–4.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Mandal B, Ogra Y, Suzuki K. Speciation of arsenic in human nail and hair from arsenic-affected area by HPLC-inductively coupled argon plasma mass spectrometry. Toxicol Appl Pharmacol. 2003;189:73–83.

    Article  CAS  PubMed  Google Scholar 

  19. Hanhineva K, Lankinen MA, Pedret A, et al. Nontargeted metabolite profiling discriminates diet-specific biomarkers for consumption of whole grains, fatty fish, and bilberries in a randomized controlled trial. J Nutr. 2015;145:7–17.

    Article  CAS  PubMed  Google Scholar 

  20. Andersson A, Marklund M, Diana M, Landberg R. Plasma alkylresorcinol concentrations correlate with whole grain wheat and rye intake and show moderate reproducibility over a 2-to 3-month period in free-living Swedish adults. J Nutr. 2011;141:1712–8.

    Article  CAS  PubMed  Google Scholar 

  21. Kristensen M, Toubro S, Jensen MG, et al. Whole grain compared with refined wheat decreases the percentage of body fat following a 12-week, energy-restricted dietary intervention in postmenopausal women. J Nutr. 2012;142:710–6.

    Article  CAS  PubMed  Google Scholar 

  22. Bailey LB, Stover PJ, McNulty H, et al. Biomarkers of nutrition for development—folate review. J Nutr. 2015;145:1636S–80S.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Yetley EA, Pfeiffer CM, Phinney KW, et al. Biomarkers of vitamin B-12 status in NHANES: a roundtable summary. Am J Clin Nutr. 2011;94:313S–21S.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ueland PM, Midttun O, Windelberg A, Svardal A, Skalevik R, Hustad S. Quantitative profiling of folate and one-carbon metabolism in large-scale epidemiological studies by mass spectrometry. Clin Chem Lab Med. 2007;45:1737–45.

    Article  CAS  PubMed  Google Scholar 

  25. Herrmann W, Obeid R. Utility and limitations of biochemical markers of vitamin B12 deficiency. Eur J Clin Invest. 2013;43:231–7.

    Article  CAS  PubMed  Google Scholar 

  26. Lankinen M, Schwab U. Biomarkers of dairy fat. Am J Clin Nutr. 2015;101:1101–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ratnayake WMN. Concerns about the use of 15:0, 17:0, and trans-16:1n-7 as biomarkers of dairy fat intake in recent observational studies that suggest beneficial effects of dairy food on incidence of diabetes and stroke. Am J Clin Nutr. 2015;101:1102–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Santaren ID, Watkins SM, Hanley AJ. Concerns about the use of 15:0, 17:0, and trans-16:1n-7 as biomarkers of dairy fat intake in recent observational studies that suggest beneficial effects of dairy food on incidence of diabetes and stroke Reply. Am J Clin Nutr. 2015;101:1103–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang-Sattler R, Yu Z, Herder C, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012;8:615.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Wang Z, Klipfell E, Bennett BJ, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011;472:57-U82.

    Google Scholar 

  31. Shah SH, Sun J, Stevens RD, et al. Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease. Am Heart J. 2012;163(844–850):e1.

    PubMed  Google Scholar 

  32. Sreekumar A, Poisson LM, Rajendiran TM, et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature. 2009;457:910–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Patti GJ, Yanes O, Siuzdak G. Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13:263–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pan Z, Raftery D. Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal Bioanal Chem. 2007;387:525–7.

    Article  CAS  PubMed  Google Scholar 

  35. Soininen P, Kangas AJ, Wuertz P, Suna T, Ala-Korpela M. Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circ Cardiovasc Genet. 2015;8:192–206.

    Article  CAS  PubMed  Google Scholar 

  36. Pere-Trepat E, Ross AB, Martin F, et al. Chemometric strategies to assess metabonomic imprinting of food habits in epidemiological studies. Chemom Intell Lab Syst. 2010;104:95–100.

    Article  CAS  Google Scholar 

  37. Dudley E, Yousef M, Wang Y, Griffiths WJ. Targeted metabolomics and mass spectrometry. Adv Protein Chem Struct Biol. 2010;80:45–83.

    Article  CAS  PubMed  Google Scholar 

  38. Mayeux R. Biomarkers: potential uses and limitations. NeuroRx. 2004;1:182–8.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Grebe SK, Singh RJ. LC-MS/MS in the clinical laboratory—Where to from here? Clin Biochem Rev. 2011;32:5–31.

    PubMed  PubMed Central  Google Scholar 

  40. Buescher JM, Czernik D, Ewald JC, Sauer U, Zamboni N. Cross-platform comparison of methods for quantitative metabolomics of primary metabolism. Anal Chem. 2009;81:2135–43.

    Article  CAS  Google Scholar 

  41. Pitt JJ. Principles and applications of liquid chromatography-mass spectrometry in clinical biochemistry. Clin Biochem Rev. 2009;30:19–34.

    PubMed  PubMed Central  Google Scholar 

  42. Want EJ, Nordström A, Morita H, Siuzdak G. From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics. J Proteome Res. 2007;6:459–68.

    Article  CAS  PubMed  Google Scholar 

  43. Villas-Bôas SG, Mas S, Åkesson M, Smedsgaard J, Nielsen J. Mass spectrometry in metabolome analysis. Mass Spectrom Rev. 2005;24:613–46.

    Article  PubMed  CAS  Google Scholar 

  44. Scalbert A, Brennan L, Fiehn O, et al. Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics. 2009;5:435–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Annesley T. Ion suppression in mass spectrometry. Clin Chem. 2003;49:1041–4.

    Article  CAS  PubMed  Google Scholar 

  46. Chambers E, Wagrowski-Diehl DM, Lu Z, Mazzeo JR. Systematic and comprehensive strategy for reducing matrix effects in LC/MS/MS analyses. J Chromatogr B. 2007;852:22–34.

    Article  CAS  Google Scholar 

  47. Dunn WB, Broadhurst D, Begley P, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011;6:1060–83.

    Article  CAS  PubMed  Google Scholar 

  48. de Hoffmann E. Tandem mass spectrometry: a primer. J Mass Spectrom. 1996;31:129–37.

    Article  Google Scholar 

  49. Picotti P, Aebersold R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat Methods. 2012;9:555–66.

    Article  CAS  PubMed  Google Scholar 

  50. Sheehan TL, Yost RA. What’s the most meaningful standard for mass spectrometry: instrument detection limit or signal-to-noise ratio. Curr Trends Mass Spectrometry. 2015;13:16–22.

    Google Scholar 

  51. Savolainen OI, Sandberg A, Ross AB. A simultaneous metabolic profiling and quantitative multimetabolite metabolomic method for human plasma using gas-chromatography tandem mass spectrometry. J Proteome Res. 2016;15:259–65. doi:10.1021/acs.jproteome.5b00790.

    Article  PubMed  CAS  Google Scholar 

  52. Kellogg MD, Ellervik C, Morrow D, Hsing A, Stein E, Sethi AA. Preanalytical considerations in the design of clinical trials and epidemiological studies. Clin Chem. 2015;61:797–803.

    Article  CAS  PubMed  Google Scholar 

  53. Vuckovic D. Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry. Anal Bioanal Chem. 2012;403:1523–48.

    Article  CAS  PubMed  Google Scholar 

  54. Dunn WB, Wilson ID, Nicholls AW, Broadhurst D. The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis. 2012;4:2249–64.

    Article  CAS  PubMed  Google Scholar 

  55. United States Food and Drug Administration. Bioanalytical method validation (draft guidance). 2013. http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm368107.pdf. Accessed 24 May 2016.

  56. Gika HG, Theodoridis GA, Wingate JE, Wilson ID. Within-day reproducibility of an HPLC-MS-Based method for metabonomic analysis: application to human urine. J Proteome Res. 2007;6:3291–303.

    Article  CAS  PubMed  Google Scholar 

  57. Gika HG, Theodoridis GA, Wilson ID. Liquid chromatography and ultra-performance liquid chromatography-mass spectrometry fingerprinting of human urine—sample stability under different handling and storage conditions for metabonomics studies. J Chromatogr A. 2008;1189:314–22.

    Article  CAS  PubMed  Google Scholar 

  58. Breier M, Wahl S, Prehn C, et al. Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples. PLoS ONE. 2014;9:e89728.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Zimmerman LJ, Li M, Yarbrough WG, Slebos RJC, Liebler DC. Global stability of plasma proteomes for mass spectrometry-based analyses. Mol Cell Proteomics. 2012;11:M111.014340. doi:10.1074/mcp.M111.014340.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Midttun O, Townsend MK, Nygard O, et al. Most blood biomarkers related to vitamin status, one-carbon metabolism, and the kynurenine pathway show adequate preanalytical stability and within-person reproducibility to allow assessment of exposure or nutritional status in healthy women and cardiovascular patients. J Nutr. 2014;144:784–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Denery JR, Nunes AAK, Dickerson TJ. Characterization of differences between blood sample matrices in untargeted metabolomics. Anal Chem. 2011;83:1040–7.

    Article  CAS  PubMed  Google Scholar 

  62. Liu L, Aa J, Wang G, et al. Differences in metabolite profile between blood plasma and serum. Anal Biochem. 2010;406:105–12.

    Article  CAS  PubMed  Google Scholar 

  63. Barri T, Dragsted LO. UPLC-ESI-QTOF/MS and multivariate data analysis for blood plasma and serum metabolomics: effect of experimental artefacts and anticoagulant. Anal Chim Acta. 2013;768:118–28.

    Article  CAS  PubMed  Google Scholar 

  64. Bruce SJ, Guy PA, Rezzi S, Ross AB. Quantitative measurement of betaine and free choline in plasma, cereals and cereal products by isotope dilution LC-MS/MS. J Agric Food Chem. 2010;58:2055–61.

    Article  CAS  PubMed  Google Scholar 

  65. Yue B, Pattison E, Roberts WL, et al. Choline in whole blood and plasma: sample preparation and stability. Clin Chem. 2008;54:590–3.

    Article  CAS  PubMed  Google Scholar 

  66. Stabler S, Allen R. Quantification of serum and urinary S-adenosylmethionine and S-adenosylhomocysteine by stable-isotope-dilution liquid chromatography-mass spectrometry. Clin Chem. 2004;50:365–72.

    Article  CAS  PubMed  Google Scholar 

  67. Gellekink H, Van Oppenraaij-Emmerzaal D, van Rooij A, Struys E, den Heijer M, Blom H. Stable-isotope dilution liquid chromatography-electrospray injection tandem mass spectrometry method for fast, selective measurement of S-adenosylmethionine and S-adenosylhomocysteine in plasma. Clin Chem. 2005;51:1487–92.

    Article  CAS  PubMed  Google Scholar 

  68. Bruce SJ, Tavazzi I, Parisod V, Rezzi S, Kochhar S, Guy PA. Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Anal Chem. 2009;81:3285–96.

    Article  CAS  PubMed  Google Scholar 

  69. Luque-Garcia JL, Neubert TA. Sample preparation for serum/plasma profiling and biomarker identification by mass spectrometry. J Chromatogr A. 2007;1153:259–76.

    Article  CAS  PubMed  Google Scholar 

  70. Courant F, Antignac J, Dervilly-Pinel G, Le Bizec B. Basics of mass spectrometry based metabolomics. Proteomics. 2014;14:2369–88.

    Article  CAS  PubMed  Google Scholar 

  71. Ross AB, Svelander C, Savolainen OI, et al. A high throughput method for LC-MS/MS determination of plasma alkylresorcinols, biomarkers of whole grain wheat and rye intake. Anal Biochem. 2016;499:1–7. doi:10.1016/j.ab.2015.12.023.

  72. Holm P, Ueland P, Kvalheim G, Lien E. Determination of choline, betaine, and dimethylglycine in plasma by a high-throughput method based on normal-phase chromatography-tandem mass spectrometry. Clin Chem. 2003;49:286–94.

    Article  CAS  PubMed  Google Scholar 

  73. Nilsson ME, Vandenput L, Tivesten A, et al. Measurement of a comprehensive sex steroid profile in rodent serum by high-sensitive gas chromatography-tandem mass spectrometry. Endocrinology. 2015;156:2492–502.

    Article  CAS  PubMed  Google Scholar 

  74. Warrack BM, Hnatyshyn S, Ott K, et al. Normalization strategies for metabonomic analysis of urine samples. J Chromatogr B. 2009;877:547–52.

    Article  CAS  Google Scholar 

  75. Chen Y, Shen G, Zhang R, et al. Combination of injection volume calibration by creatinine and ms signals’ normalization to overcome urine variability in LC-MS-based metabolomics studies. Anal Chem. 2013;85:7659–65.

    Article  CAS  PubMed  Google Scholar 

  76. Webb-Robertson B, Kim Y, Zink EM, et al. A statistical analysis of the effects of urease pre-treatment on the measurement of the urinary metabolome by gas chromatography-mass spectrometry. Metabolomics. 2014;10:897–908.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Want EJ, Wilson ID, Gika H, et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc. 2010;5:1005–18.

    Article  CAS  PubMed  Google Scholar 

  78. Snyder NW, Khezam M, Mesaros CA, Worth A, Blair IA. Untargeted metabolomics from biological sources using ultraperformance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). J Vis Exp. 2013;20:e50433.

    Google Scholar 

  79. Dunn WB, Broadhurst D, Ellis DI, et al. 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.

    Article  Google Scholar 

  80. Yin P, Lehmann R, Xu G. Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal Bioanal Chem. 2015;407:4879–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Mitchell BL, Yasui Y, Li CI, Fitzpatrick AL, Lampe PD. Impact of freeze-thaw cycles and storage time on plasma samples used in mass spectrometry based biomarker discovery projects. Cancer Inf. 2005;1:98–104.

    CAS  Google Scholar 

  82. Saude EJ, Sykes BD. Urine stability for metabolomic studies: effects of preparation and storage. Metabolomics. 2007;3:19–27.

    Article  CAS  Google Scholar 

  83. Leu M, Mehlig K, Hunsberger M, et al. Quality assessment of 25(OH)D, insulin, total cholesterol, triglycerides, and potassium in 40-year-old frozen serum. Epidemiol Res Int. 2015;2015:8.

    Article  Google Scholar 

  84. Kirsch SH, Knapp J, Herrmann W, Obeid R. Quantification of key folate forms in serum using stable-isotope dilution ultra performance liquid chromatography-tandem mass spectrometry. J Chromatogr B. 2010;878:68–75.

    Article  CAS  Google Scholar 

  85. Sumner LW, Amberg A, Barrett D, et al. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007;3:211–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. European Medicines Agency. Guideline on bioanalytical method validation. 2011. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2011/08/WC500109686.pdf. Accessed 24 May 2016.

  87. Cuadros-Rodriguez L, Bagur-Gonzalez MG, Sanchez-Vinas M, Gonzalez-Casado A, Gomez-Saez AM. Principles of analytical calibration/quantification for the separation sciences. J Chromatogr A. 2007;1158:33–46.

    Article  CAS  PubMed  Google Scholar 

  88. Temmerman L, Livera AMD, Bowne JB, et al. Cross-platform urine metabolomics of experimental hyperglycemia in type 2 diabetes. J Diabetes Metab. 2012;S6:002. doi:10.4172/2155-6156.S6-002.

  89. van der Kloet FM, Bobeldijk I, Verheij ER, Jellema RH. Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. J Proteome Res. 2009;8:5132–41.

    Article  PubMed  CAS  Google Scholar 

  90. Sangster T, Major H, Plumb R, Wilson AJ, Wilson ID. A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst. 2006;131:1075–8.

    Article  CAS  PubMed  Google Scholar 

  91. De Livera AM, Dias DA, De Souza D, et al. Normalizing and integrating metabolomics data. Anal Chem. 2012;84:10768–76.

    Article  PubMed  CAS  Google Scholar 

  92. Dolan JW. Calibration curves, part V: curve weighting. LC GC N Am. 2009;27:534.

    CAS  Google Scholar 

  93. Bictash M, Ebbels TM, Chan Q, et al. Opening up the “Black Box”: metabolic phenotyping and metabolome-wide association studies in epidemiology. J Clin Epidemiol. 2010;63:970–9.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Benton HP, Ivanisevic J, Mahieu NG, et al. Autonomous metabolomics for rapid metabolite identification in global profiling. Anal Chem. 2015;87:884–91.

    Article  CAS  PubMed  Google Scholar 

  95. Dunn WB, Erban A, Weber RJM, et al. Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics. 2013;9:S44–66.

    Article  CAS  Google Scholar 

  96. Lynn K, Cheng M, Chen Y, et al. Metabolite identification for mass spectrometry-based metabolomics using multiple types of correlated ion information. Anal Chem. 2015;87:2143–51.

    Article  CAS  PubMed  Google Scholar 

  97. Kuhl C, Tautenhahn R, Boettcher C, Larson TR, Neumann S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem. 2012;84:283–9.

    Article  CAS  PubMed  Google Scholar 

  98. Wishart DS, Jewison T, Guo AC, et al. HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res. 2013;41:D801–7.

    Article  CAS  PubMed  Google Scholar 

  99. National Institute of Standards and Technology. NIST mass spectral library. 2015. http://www.nist.gov/srd/nist1a.cfm. Accessed 24 May 2016.

  100. Scripps Center for Metabolomics. METLIN databse. 2015. https://metlin.scripps.edu/index.php. Accessed 24 May 2016.

  101. Kirkwood JS, Maier C, Stevens JF. Simultaneous, untargeted metabolic profiling of polar and nonpolar metabolites by LC-Q-TOF mass spectrometry. Curr Protoc Toxicol. 2013;Chapter 4:Unt4.39. doi:10.1002/0471140856.tx0439s56.

  102. Gurdeniz G, Kristensen M, Skov T, Dragsted LO. The effect of LC-MS data preprocessing methods on the selection of plasma biomarkers in fed vs. fasted rats. Metabolites. 2012;2:77–99.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Smith C, Want E, O’Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006;78:779–87.

    Article  CAS  PubMed  Google Scholar 

  104. Kind T, Tolstikov V, Fiehn O, Weiss RH. A comprehensive urinary metabolomic approach for identifying kidney cancer. Anal Biochem. 2007;363:185–95.

    Article  CAS  PubMed  Google Scholar 

  105. Gromski P, Xu Y, Hollywood K, Turner M, Goodacre R. The influence of scaling metabolomics data on model classification accuracy. Metabolomics. 2015;11:684–95.

    Article  CAS  Google Scholar 

  106. van den Berg RA, Hoefsloot HCJ, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genom. 2006;7:142.

    Article  CAS  Google Scholar 

  107. Huan T, Li L. Counting missing values in a metabolite-intensity data set for measuring the analytical performance of a metabolomics platform. Anal Chem. 2015;87:1306–13.

    Article  CAS  PubMed  Google Scholar 

  108. Helsel DR. Fabricating data: how substituting values for nondetects can ruin results, and what can be done about it. Chemosphere. 2006;65:2434–9.

    Article  CAS  PubMed  Google Scholar 

  109. Hrydziuszko O, Viant MR. Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline. Metabolomics. 2012;8:S161–74.

    Article  CAS  Google Scholar 

  110. Goodacre R, Broadhurst D, Smilde AK, et al. Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics. 2007;3:231–41.

    Article  CAS  Google Scholar 

  111. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Introduction, measurement error in nonlinear models a modern perspective. Boca Raton: Chapman and Hall/CRC; 2006. p. 1–24.

    Google Scholar 

  112. Keogh RH, White IR. A toolkit for measurement error correction, with a focus on nutritional epidemiology. Stat Med. 2014;33:2137–55.

    Article  PubMed  PubMed Central  Google Scholar 

  113. Zou KH, O’Malley AJ, Mauri L. Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation. 2007;115:654–7.

    Article  PubMed  Google Scholar 

  114. Perkins NJ, Schisterman EF, Vexler A. Generalized ROC curve inference for a biomarker subject to a limit of detection and measurement error. Stat Med. 2009;28:1841–60.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Perkins N, Schisterman E. The Youden index and the optimal cut-point corrected for measurement error. Biom J. 2005;47:428–41.

    Article  PubMed  Google Scholar 

  116. White MT, Xie SX. Adjustment for measurement error in evaluating diagnostic biomarkers by using an internal reliability sample. Stat Med. 2013;32:4709–25.

    Article  PubMed  Google Scholar 

  117. Van Batenburg MF, Coulier L, van Eeuwijk F, Smilde AK, Westerhuis JA. New figures of merit for comprehensive functional genomics data: the metabolomics case. Anal Chem. 2011;83:3267–74.

    Article  PubMed  CAS  Google Scholar 

  118. Smilde AK, van der Werf MJ, Schaller J, Kistemaker C. Characterizing the precision of mass-spectrometry-based metabolic profiling platforms. Analyst. 2009;134:2281–5.

    Article  CAS  PubMed  Google Scholar 

  119. Guo Y, Little RJ. Regression analysis with covariates that have heteroscedastic measurement error. Stat Med. 2011;30:2278–94.

    Article  PubMed  Google Scholar 

  120. Pollack AZ, Perkins NJ, Mumford SL, Ye A, Schisterman EF. Correlated biomarker measurement error: an important threat to inference in environmental epidemiology. Am J Epidemiol. 2013;177:84–92.

    Article  CAS  PubMed  Google Scholar 

  121. Chen H, Quandt SA, Grzywacz JG, Arcury TA. A distribution-based multiple imputation method for handling bivariate pesticide data with values below the limit of detection. Environ Health Perspect. 2011;119:351–6.

    Article  PubMed  Google Scholar 

  122. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Important concepts, measurement error in nonlinear models a modern perspective. Boca Raton: Chapman and Hall/CRC; 2006. p. 25–39.

    Google Scholar 

  123. Bro R, Smilde AK. Principal component analysis. Anal Methods. 2014;6:2812–31.

    Article  CAS  Google Scholar 

  124. Fonville JM, Richards SE, Barton RH, et al. The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping. J Chemometrics. 2010;24:636–49.

    Article  CAS  Google Scholar 

  125. Hendriks MMWB, van Eeuwijk FA, Jellema RH, et al. Data-processing strategies for metabolomics studies. TrAC Trends Anal Chem. 2011;30:1685–98.

    Article  CAS  Google Scholar 

  126. Tzoulaki I, Ebbels TMD, Valdes A, Elliott P, Ioannidis JPA. Design and analysis of metabolomics studies in epidemiologic research: a primer on -omic technologies. Am J Epidemiol. 2014;180:129–39.

    Article  PubMed  Google Scholar 

  127. Kjeldahl K, Bro R. Some common misunderstandings in chemometrics. J Chemom. 2010;24:558–64.

    Article  CAS  Google Scholar 

  128. Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M. Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis. Curr Bioinform. 2012;7:96–108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Xia J, Broadhurst DI, Wilson M, Wishart DS. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics. 2013;9:280–99.

    Article  CAS  PubMed  Google Scholar 

  130. Broadhurst DI, Kell DB. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics. 2006;2:171–96.

    Article  CAS  Google Scholar 

  131. Storey J. A direct approach to false discovery rates. J R Stat Soc Ser B Stat Methodol. 2002;64:479–98.

    Article  Google Scholar 

  132. Benjamini Y, Hochberg Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300.

    Google Scholar 

  133. Saccenti E, Hoefsloot HCJ, Smilde AK, Westerhuis JA, Hendriks MMWB. Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics. 2014;10:361–74.

    Article  CAS  Google Scholar 

  134. Dunn WB, Lin W, Broadhurst D, et al. Molecular phenotyping of a UK population: defining the human serum metabolome. Metabolomics. 2015;11:9–26.

    Article  CAS  PubMed  Google Scholar 

  135. Sampson JN, Boca SM, Shu XO, et al. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomarkers Prev. 2013;22:631–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Ferreia JA, Zwinderman A. Approximate power and sample size calculations with the Benjamini–Hochberg method. Int J Biostat. 2006;2:1–36.

    Google Scholar 

  137. Glueck DH, Mandel J, Karimpour-Fard A, Hunter L, Muller KE. Exact calculations of average power for the Benjamini–Hochberg procedure. Int J Biostat. 2008;4:1–20.

    Google Scholar 

  138. Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell. 2008;134:714–7.

    Article  CAS  PubMed  Google Scholar 

  139. Bro R, Kamstrup-Nielsen M, Engelsen S, et al. Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics 2015;11:1376–80.

  140. Bro R, Nielsen HJ, Savorani F, et al. Data fusion in metabolomic cancer diagnostics. Metabolomics. 2013;9:3–8.

    Article  CAS  PubMed  Google Scholar 

  141. Freedman LS, Kipnis V, Schatzkin A, Tasevska N, Potischman N. Can we use biomarkers in combination with self-reports to strengthen the analysis of nutritional epidemiologic studies? Epidemiol Perspect Innov. 2010;7:2.

    Article  PubMed  PubMed Central  Google Scholar 

  142. Ried JS, Shin S, Krumsiek J, et al. Novel genetic associations with serum level metabolites identified by phenotype set enrichment analyses. Hum Mol Genet. 2014;23:5847–57.

    Article  PubMed  PubMed Central  Google Scholar 

  143. Dharuri H, Demirkan A, van Klinken JB, et al. Genetics of the human metabolome, What is next? Biochim Biophys Acta Mol Basis Dis. 2014;1842:1923–31.

    Article  CAS  Google Scholar 

  144. Shin S, Petersen A, Wahl S, et al. Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids. Genom Med. 2014;6:25.

    Article  CAS  Google Scholar 

  145. Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in utero and early-life conditions on adult health and disease. N Engl J Med. 2008;359:61–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Godfrey KM, Lillycrop KA, Burdge GC, Gluckman PD, Hanson MA. Epigenetic mechanisms and the mismatch concept of the developmental origins of health and disease. Pediatr Res. 2007;61:5R–10R.

    Article  PubMed  Google Scholar 

  147. Petersen A, Zeilinger S, Kastenmueller G, et al. Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits. Hum Mol Genet. 2014;23:534–45.

    Article  CAS  PubMed  Google Scholar 

  148. Huang H, Lin S, Garcia BA, Zhao Y. Quantitative proteomic analysis of histone modifications. Chem Rev. 2015;115:2376–418.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Darwanto A, Curtis MP, Schrag M, et al. A modified, “Cross-talk” between histone H2B Lys-120 ubiquitination and H3 Lys-79 methylation. J Biol Chem. 2010;285:21868–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Nicholson JK, Holmes E, Kinross J, et al. Host-gut microbiota metabolic interactions. Science. 2012;336:1262–7.

    Article  CAS  PubMed  Google Scholar 

  151. Flint HJ, Scott KP, Louis P, Duncan SH. The role of the gut microbiota in nutrition and health. Nat Rev Gastroenterol Hepatol. 2012;9:577–89.

    Article  CAS  PubMed  Google Scholar 

  152. Griffin JL, Wang X, Stanley E. does our gut microbiome predict cardiovascular risk? A review of the evidence from metabolomics. Circ Cardiovasc Genet. 2015;8:187–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Wikoff WR, Anfora AT, Liu J, et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci USA. 2009;106:3698–703.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. O’Donovan CB, Walsh MC, Nugent AP, et al. Use of metabotyping for the delivery of personalised nutrition. Mol Nutr Food Res. 2015;59:377–85.

    Article  PubMed  CAS  Google Scholar 

  155. Fuhrer T, Zamboni N. High-throughput discovery metabolomics. Curr Opin Biotechnol. 2015;31:73–8.

    Article  CAS  PubMed  Google Scholar 

  156. Allwood JW, Erban A, de Koning S, et al. Inter-laboratory reproducibility of fast gas chromatography-electron impact-time of flight mass spectrometry (GC-EI-TOF/MS) based plant metabolomics. Metabolomics. 2009;5:479–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Klavins K, Neubauer S, Al Chalabi A, et al. Interlaboratory comparison for quantitative primary metabolite profiling in Pichia pastoris. Anal Bioanal Chem. 2013;405:5159–69.

    Article  CAS  PubMed  Google Scholar 

  158. Weiner M, Tröndle J, Schmideder A, et al. Parallelized small-scale production of uniformly 13C-labeled cell extract for quantitative metabolome analysis. Anal Biochem. 2015;478:134–40.

    Article  CAS  PubMed  Google Scholar 

  159. Pinto RC, Gerber L, Eliasson M, Sundberg B, Trygg J. Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis. Anal Chem. 2012;84:8675–81.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

We thank Innovation Fund Denmark and Chalmers Area of Advance Life Sciences for funding that has supported writing this review.

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MVL, OIS and ABR wrote the manuscript and all authors provided input and approved the final version.

Funding

MVL is funded by the Innovation Fund Denmark, grant no. 0603-00487B (11-116163). ABR and OIS are funded by a Chalmers University of Technology Area of Advance Life Sciences Grant.

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Lind, M.V., Savolainen, O.I. & Ross, A.B. The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples. Eur J Epidemiol 31, 717–733 (2016). https://doi.org/10.1007/s10654-016-0166-2

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