Skip to main content
SpringerLink
Log in

Nutrimetabolomics: Metabolomics in Nutrition Research

  • Chapter
  • First Online:
Metabolomics

  • 538 Accesses

Abstract

Nutrimetabolomics is a field of study that focuses on the analysis of small molecules in biological samples such as blood, urine, and tissues, to understand the relationship between diet, metabolism, and health. It combines the disciplines of nutrition, metabolomics, and systems biology to identify and quantify the metabolic response of an individual to dietary interventions and to gain insights into the underlying mechanisms of how nutrients affect health and disease. It is a rapidly evolving field with potential applications in personalized nutrition, disease prevention, and the development of new therapeutic approaches. In this book chapter, we examine the most recent methods and approaches for multi-omics-based nutrimetabolomics investigations. Further, we have described the benefits of using machine learning techniques to improve the dynamics of nutrimetabolomics analysis. We have also included various statistical tools, functional tools, modeling tools for nutrimetabolomics, and tools for predicting chemical properties of nutrients and dietary biomarkers. Here, we offer R scripts for chemical molecule import and visualization utilizing R packages, which helps researchers interpret and preprocess mass spectrometry imaging (MSI) data easily. The ideas of physiological monitoring for diet and nutrition studies with food-related disorders are also important to comprehend. Additionally, it discusses the significance that nutrimetabolomics plays in precision nutrition’s most recent advancements.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

FAO:

Food and Agriculture Organization

FFQs:

Food frequency questionnaires

GNPS:

Global Natural Products Social Molecular Networking

IBRD:

International Bank for Reconstruction and Development

MS-DIAL:

Multiple-Stage Mass Spectrometry-Based Data Independent Acquisition Library

OPLS:

Orthogonal partial least squares

PLS-DA:

Partial least squares discriminant analysis

SIM:

Selected ion monitoring

WHO:

World Health Organization

XCMS:

XCentric mass spectrometry

XCMS:

X-chromatography/mass spectrometry

References

  1. Carlson, A.K., Rawle, R.A., Adams, E., Greenwood, M.C., Bothner, B. and June, R.K. (2018) Application of global metabolomic profiling of synovial fluid for osteoarthritis biomarkers. Biochem Biophys Res Commun 499, 182–188.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Carlson, A.K., Rawle, R.A., Wallace, C.W., Adams, E., Greenwood, M.C., Bothner, B. and June, R.K. (2019a) Global metabolomic profiling of human synovial fluid for rheumatoid arthritis biomarkers. Clin Exp Rheumatol 37, 393–399.

    PubMed  Google Scholar 

  3. Carlson, A.K., Rawle, R.A., Wallace, C.W., Brooks, E.G., Adams, E., Greenwood, M.C., Olmer, M., Lotz, M.K., Bothner, B. and June, R.K. (2019b) Characterization of synovial fluid metabolomic phenotypes of cartilage morphological changes associated with osteoarthritis. Osteoarthritis Cartilage 27, 1174–1184.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hahn, A.K., Batushansky, A., Rawle, R.A., Prado Lopes, E.B., June, R.K. and Griffin, T.M. (2021) Effects of long-term exercise and a high-fat diet on synovial fluid metabolomics and joint structural phenotypes in mice: an integrated network analysis. Osteoarthritis Cartilage 29, 1549–1563.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hislop, B.D., Devine, C., June, R.K. and Heveran, C.M. (2022) Subchondral bone structure and synovial fluid metabolism are altered in injured and contralateral limbs 7 days after non-invasive joint injury in skeletally-mature C57BL/6 mice. Osteoarthritis Cartilage.

    Google Scholar 

  6. Wang, Yang et al. “Enhanced MS/MS coverage for metabolite identification in LC-MS-based untargeted metabolomics by target-directed data dependent acquisition with time-staggered precursor ion list.” Analytica chimica acta vol. 992 (2017): 67–75. https://doi.org/10.1016/j.aca.2017.08.044

    Article  CAS  PubMed  Google Scholar 

  7. Ulaszewska, Marynka M et al. “Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies.” Molecular nutrition & food research vol. 63,1 (2019): e1800384. https://doi.org/10.1002/mnfr.201800384

    Article  CAS  Google Scholar 

  8. Hegeman, Adrian D. “Plant metabolomics--meeting the analytical challenges of comprehensive metabolite analysis.” Briefings in functional genomics vol. 9,2 (2010): 139–48. https://doi.org/10.1093/bfgp/elp053

    Article  CAS  PubMed  Google Scholar 

  9. Smith, Colin A et al. “XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.” Analytical chemistry vol. 78,3 (2006): 779–87. https://doi.org/10.1021/ac051437y

    Article  CAS  PubMed  Google Scholar 

  10. Gibb S, Strimmer K (2012). “MALDIquant: a versatile R package for the analysis of mass spectrometry data.” Bioinformatics, 28(17), 2270–2271. https://doi.org/10.1093/bioinformatics/bts447

    Article  CAS  PubMed  Google Scholar 

  11. Block G, Clifford C, Naughton MD, Henderson M, McAdam's M.A brief dietary screen for high fat intake. J Nutr Edu.1989;21:199–207.

    Article  Google Scholar 

  12. Tran KM, Johnson RK, Soultanakis RB, Mathhews DE. In person vs telephone-administered multiple-pass 24-hour recalls in women: validation with doubly labeled water. JAmDiet Assoc.2000; 100:777–83.11.

    Google Scholar 

  13. Johnson RK, Soultanakis RP, Matthews DE. Literacy and body fatness are associated with underreporting of energy intake in U.S. low-income women using the multiple-pass24-hour recall: a doubly labeled water study. J Am Diet Assoc.1998; 98:1136–40.12

    Article  CAS  PubMed  Google Scholar 

  14. Basiotis PP, Walsh SO, Cronin RJ, Kelsay JL, Mertz W. Number of days of food intake records required to estimate individual and group nutrient intakes with defined confidence. J Nutr.1987;117:1638–41.

    Article  CAS  PubMed  Google Scholar 

  15. G.F. Fletcher, C. Landolfo, J. Niebauer, C. Ozemek, R. Arena, C.J. Lavie Promoting physical activity and exercise: JACC health promotion series J Am Coll Cardiol, 72 (14) (2018), pp. 1622–1639

    Article  PubMed  Google Scholar 

  16. M.D. Jensen, D.H. Ryan, C.M. Apovian, et al. (2013) AHA/ACC/TOS’S guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society J Am Coll Cardiol, 63, (2013), pp. 2985–3023.

    Google Scholar 

  17. M.F. Piepoli, A.W. Hoes, S. Agewall, et al.2016 European guidelines on cardiovascular disease prevention in clinical practice Rev Esp Cardiol (Engl Ed), 69 (10) (2016), p. 939

    Google Scholar 

  18. C.W. Yancy, M. Jessup, B. Bozkurt, et al. (2013) ACCF/AHA guideline for the management of heart failure: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation, 128 (16) (2013), pp. 1810–1852

    Google Scholar 

  19. R. Ross, L. de Lannoy, P.J. Stotz. Separate effects of intensity and amount of exercise on interindividual cardiorespiratory fitness response. Mayo Clin Proc, 90 (11) (2015), pp. 1506–1514.

    Article  PubMed  Google Scholar 

  20. Ozemek C, Arena R. Precision in Promoting Physical Activity and Exercise with the Overarching Goal of Moving More. Prog Cardiovasc Dis. 2019 Jan-Feb;62(1):3–8. Epub 2018 Dec 7. PMID: 30529578, https://doi.org/10.1016/j.pcad.2018.12.001

  21. Alberti, K.G.; Zimmet, P.; Shaw, J.; IDF Epidemiology Task Force Consensus Group. The metabolic syndrome-a new worldwide definition. Lancet 2005, 366, 1059–1062.

    Article  PubMed  Google Scholar 

  22. Micha, R.; Peñalvo, J.L.; Cudhea, F.; Imamura, F.; Rehm, C.D.; Mozaffarian, D. Association Between Dietary Factors and Mortality from Heart Disease, Stroke, and Type 2 Diabetes in the United States. JAMA 2017, 317, 912–924.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wang, Q.; Afshin, A.; Yakoob, M.Y.; Singh, G.M.; Rehm, C.D.; Khatibzadeh, S.; Micha, R.; Shi, P.; Mozaffarian, D.; Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). Impact of Nonoptimal Intakes of Saturated, Polyunsaturated, and Trans Fat on Global Burdens of Coronary Heart Disease. J. Am. Heart Assoc. 2016, 5, e002891.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Olatona, F.A.; Onabanjo, O.O.; Ugbaja, R.N.; Nnoaham, K.E.; Adelekan, D.A. Dietary habits and metabolic risk factors for non-communicable diseases in a university undergraduate population. J. Health Popul Nutr. 2018, 37, 21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Popkin, B.M.; Adair, L.S.; Ng, S.W. Global nutrition transition and the pandemic of obesity in developing countries. Nutr. Rev. 2012, 70, 3–21.

    Article  PubMed  Google Scholar 

  26. World Health Organization. Healthy Diet. Available online: https://www.who.int/news-room/fact-sheets/detail/healthy-diet (accessed on 21 July 2022).

  27. Grzelak-Kostulska, E.; Sypion-Dutkowska, N.; Michalski, T. Changes in the health situation of the population of Poland following the accession to the European Union (compared to Central and Eastern European countries). J. Geogr. Politics Soc. 2017, 7, 24–38.

    Google Scholar 

  28. Stos, K.; Rychlik, E.; WoĹşniak, A.; OĹ‚tarzewski, M.; Jankowski, M.; Gujski, M.; Juszczyk, G. Prevalence and Sociodemographic Factors Associated with Overweight and Obesity among Adults in Poland: A 2019/2020 Nationwide Cross-Sectional Survey. Int. J. Environ. Res. Public Health 2022, 19, 1502.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Eurostat. Overweight and Obesity-BMI Statistics. Available online: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Overweight_and_obesity_-_BMI_statistics (accessed on 24 November 2022).

  30. Wojtyniak, B.; Gorynski, P. Health Status of Polish Population and Its Determinants 2020. Available online: https://www.pzh.gov.pl/sytuacja-zdrowotna-ludnosci-polski-i-jej-uwarunkowania-raport-za-2020-rok/ (accessed on 24 November 2022).

  31. Acosta A, Camilleri M, Abu Dayyeh B, et al. Selection of anti-obesity medications based on phenotypes enhances weight loss: a pragmatic trial in an obesity clinic. Obesity. 2021; 29:662–671.

    Article  PubMed  Google Scholar 

  32. Emwas A.-H.M., Salek R.M., Griffin J.L., Merzaban J.S. NMR-Based Metabolomics in Human Disease Diagnosis: Applications, Limitations, and Recommendations. Metabolomics. 2013; 9:1048–1072. https://doi.org/10.1007/s11306-013-0524-y.

    Article  CAS  Google Scholar 

  33. Gowda G.A.N., Zhang S., Gu H., Asiago V., Shanaiah N., Raftery D. Metabolomics-Based Methods for Early Disease Diagnostics. Expert Rev. Mol. Diagn. 2008; 8:617–633. https://doi.org/10.1586/14737159.8.5.617.

    Article  CAS  PubMed  Google Scholar 

  34. Guo, Y.; Bian, X.; Liu, J.; Zhu, M.; Li, L.; Yao, T.; Tang, C.; Ravichandran, V.; Liao, P.; Papadimitriou, K.; et al. Dietary Components, Microbial Metabolites and Human Health: Reading between the Lines. Foods 2020, 9, 1045.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Defernez, M.; Gunning, Y.M.; Parr, A.J.; Shepherd, L.V.T.; Davies, H.V.; Colquhoun, I.J. NMR and HPLC-UV Profiling of Potatoes with Genetic Modifications to Metabolic Pathways. J. Agric. Food Chem. 2004, 52, 6075–6085.

    Article  CAS  PubMed  Google Scholar 

  36. Al-Talla, Z.; Akrawi, S.H.; Emwas, A.-H.M. Solid State NMR and Bioequivalence Comparison of the Pharmacokinetic Parameters of Two Formulations of Clindamycin. Int. J. Clin. Pharmacol. Ther. 2011, 49, 469–476.

    Article  CAS  PubMed  Google Scholar 

  37. Ahmed, A.E.-S.I.; Wardell, J.N.; Thumser, A.E.; Avignone-Rossa, C.A.; Cavalli, G.; Hay, J.N.; Bushell, M.E. Metabolomic Profiling can differentiate between Bactericidal Effects of Free and Polymer Bound Halogen. J. Appl. Polym. Sci. 2011, 119, 709–718.

    Article  CAS  Google Scholar 

  38. Lin, S.; Liu, N.; Yang, Z.; Song, W.; Wang, P.; Chen, H.; Lucio, M.; Schmitt-Kopplin, P.; Chen, G.; Cai, Z. GC/MS-Based Metabolomics Reveals Fatty Acid Biosynthesis and Cholesterol Metabolism in Cell Lines Infected with Influenza A Virus. Talanta 2010, 83, 262–268.

    Article  CAS  PubMed  Google Scholar 

  39. Riccio, M.F.; Saraiva, S.A.; Marques, L.A.; Alberici, R.; Haddad, R.; Möller, J.C.; Eberlin, M.N.; Catharino, R.R. Easy Mass Spectrometry for Metabolomics and Quality Control of Vegetable and Animal Fats. Eur. J. Lipid Sci. Technol. 2010, 112, 434–438.

    Article  CAS  Google Scholar 

  40. Emwas, A.-H.M. The Strengths and Weaknesses of NMR Spectroscopy and Mass Spectrometry with Particular Focus on Metabolomics Research. In Metabonomics: Methods and Protocols; Bjerrum, J.T., Ed.; Springer: New York, NY, USA, 2015; pp. 161–193.

    Chapter  Google Scholar 

  41. Emwas, A.-H.; Roy, R.; McKay, R.T.; Tenori, L.; Saccenti, E.; Gowda, G.A.N.; Raftery, D.; AlAhmari, F.; Jaremko, L.; Jaremko, M.; et al. NMR Spectroscopy for Metabolomics Research. Metabolites 2019, 9, 123.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Roager, H.M.; Dragsted, L.O. Diet-Derived Microbial Metabolites in Health and Disease. Nutr. Bull. 2019, 44, 216–227.

    Article  Google Scholar 

  43. Qin, J.; Li, R.; Raes, J.; Arumugam, M.; Burgdorf, K.S.; Manichanh, C.; Nielsen, T.; Pons, N.; Levenez, F.; Yamada, T.; et al. A Human Gut Microbial Gene Catalogue Established by Metagenomic Sequencing. Nature 2010, 464, 59–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Farag, M.A.; Abdelwareth, A.; Sallam, I.E.; Shorbagi, M.; Jehmlich, N.; Fritz-Wallace, K.; Schäpe, S.S.; Rolle-Kampczyk, U.; Ehrlich, A.; Wessjohann, L.A.; et al. Metabolomics Reveals Impact of Seven Functional Foods on Metabolic Pathways in a Gut Microbiota Model. J. Adv. Res. 2020, 23, 47–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. LeBlanc, J.G.; Milani, C.; de Giori, G.S.; Sesma, F.; van Sinderen, D.; Ventura, M. Bacteria as Vitamin Suppliers to Their Host: A Gut Microbiota Perspective. Curr. Opin. Biotechnol. 2013, 24, 160–168.

    Article  CAS  PubMed  Google Scholar 

  46. Gu, Q.; Li, P. Biosynthesis of Vitamins by Probiotic Bacteria. In Probiotics and Prebiotics in Human Nutrition and Health; Rao, V., Rao, L., Eds.; Intech Open: London, UK, 2016

    Google Scholar 

  47. Hasan, N.; Yang, H. Factors Affecting the Composition of the Gut Microbiota, and Its Modulation. PeerJ 2019, 7, e7502.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Posma, J.M.; Garcia-Perez, I.; Frost, G.; Aljuraiban, G.S.; Chan, Q.; Van Horn, L.; Daviglus, M.; Stamler, J.; Holmes, E.; Elliott, P.; et al. Nutriome–Metabolome Relationships Provide Insights into Dietary Intake and Metabolism. Nat. Food 2020, 1, 426–436.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Berkow, S.E.; Barnard, N.; Eckart, J.; Katcher, H. Four Therapeutic Diets: Adherence and Acceptability. Can. J. Diet. Pr. Res. 2010,71, 199–204.

    Article  Google Scholar 

  50. Jin, Q., Black, A., Kales, S. N., Vattem, D., Ruiz-Canela, M., & Sotos-Prieto, M. (2019). Metabolomics and Microbiomes as Potential Tools to Evaluate the Effects of the Mediterranean Diet. Nutrients, 11(1), 207. https://doi.org/10.3390/nu11010207.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yue, Y., Chu, GX., Liu, XS. et al. TMDB: A literature-curated database for small molecular compounds found from tea. BMC Plant Biol 14, 243 (2014). https://doi.org/10.1186/s12870-014-0243-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Forte, G.; Bocca, B.; Senofonte, O.; Petrucci, F.; Brusa, L.; Stanzione, P.; Zannino, S.; Violante, N.; Alimonti, A.; Sancesario, G. Trace and Major Elements in Whole Blood, Serum, Cerebrospinal Fluid and Urine of Patients with Parkinson’s Disease. J. Neural Transm. 2004, 111, 1031–1040.

    Article  CAS  PubMed  Google Scholar 

  53. Ferguson, D.J., Gonnermann, H.M., Ruprecht, P. et al. Magma decompression rates during explosive eruptions of KÄ«lauea volcano, Hawaii, recorded by melt embayments. Bull Volcanol 78, 71 (2016). https://doi.org/10.1007/s00445-016-1064-x

    Article  Google Scholar 

  54. Sahu N, Mishra S., Minu K, Kanchan S, Sinha RP. 2022. Identification of cyanobacteria-based natural inhibitors against SARS-CoV-2 druggable target ACE2 using molecular docking study, ADME and toxicity analysis. Indian Journal of Clinical Biochemistry. https://doi.org/10.1007/s12291-022-01056-6.

  55. Kesheri M, Kanchan S, Sinha RP. 2022. Responses of antioxidants for resilience to temporal variations in the cyanobacterium Microcystis aeruginosa. South African Journal of Botany 148:190–199. https://doi.org/10.1016/j.sajb.2022.04.017

    Article  CAS  Google Scholar 

  56. Kesheri M, Kanchan S, Sinha RP. 2021. Isolation and in-silico analysis of antioxidants in response to temporal variations in the cyanobacterium Oscillatoria sp. Gene Reports. 23:101023. https://doi.org/10.1016/j.genrep.2021.101023.

    Article  CAS  Google Scholar 

  57. Kanchan S., Sharma P., and Chowdhury, S. 2019. Evolution of endonuclease IV protein family: an in-silico analysis. 3 Biotech. 9:168. https://doi.org/10.1007/s13205-019-1696-6 PMID: 30997305

    Article  PubMed  PubMed Central  Google Scholar 

  58. Singla S. Kesheri M., Kanchan S., Aswath S. 2019. Current Status and Data Analysis of Diabetes in India. Internal Journal of Innovative Technology and Exploring Engineering (IJITEE). 8(9):1920–1934. https://doi.org/10.35940/ijitee.I8403.078919

    Article  Google Scholar 

  59. Kumari A., Kesheri M., Sinha R.P., Kanchan S. 2018. Integration of soft computing approach in plant biology and its applications in agriculture. In: Soft computing in biological sciences (Eds. H.J. Purohit, D.V. Raje, R. P. More, V. C. Kalia), pp. 265–278, Springer Nature Singapore. https://doi.org/10.1007/978-981-10-7455-4_16.

    Chapter  Google Scholar 

  60. Kanchan S, Mehrotra R, Chowdhury S. 2015. In-silico study of endonuclease III protein family identifies key residues and processes during evolution. Journal of molecular evolution. 81:54–67. https://doi.org/10.1007/s00239-015-9689-5.

    Article  CAS  PubMed  Google Scholar 

  61. Kesheri M, Kanchan S, Richa, and Sinha RP. 2014. Isolation and in-silico analysis of Fe-superoxide dismutase in Nostoc commune. Gene, 553(2), pp. 117–125. https://doi.org/10.1016/j.gene.2014.10.010.

    Article  CAS  PubMed  Google Scholar 

  62. Priya P, Kesheri M, Sinha RP, Kanchan S. 2017. Molecular dynamics simulations for Biological Systems. In: Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes (Karâa W. B. A., Dey N. (eds.), 286–313. IGI Global, USA. https://doi.org/10.4018/978-1-4666-8811-7.ch014.

    Chapter  Google Scholar 

  63. Kumari A, Kanchan S, Kesheri M. 2016. Applications of Bio-Molecular Databases in Bioinformatics. In: Advancements in Bio-Medical Sensing, Imaging, Measurements and Instrumentation (N. Dey, V. Bhateja, A.E. Hassanien (eds.)), Vol. 651, Ch 15, (pp. 329–351). Springer-Verlag, Germany. https://doi.org/10.1007/978-3-319-33793-7_15.

  64. Kesheri M, Kanchan S*, Chowdhury S, Sinha RP. 2015. Secondary and Tertiary Structure Prediction of Proteins: A Bioinformatic Approach. In: Complex system modelling and control through intelligent soft computations (Q. Zhu, A.T Azar (eds.)), 319:541–569, Springer-Verlag, Germany. https://doi.org/10.1007/978-3-319-12883-2_19. (Scopus indexed)

  65. Ghai R., Nagarajan K., Singh J., Swarup S., Kesheri M. 2016. Evaluation of anti-oxidant status in-vitro and in-vivo in hydro-alcoholic extract of Eugenia caryophyllus. International Journal of Pharmacology and Toxicology, 4(1), 19–24. https://doi.org/10.14419/ijpt.v4i1.5880

    Article  Google Scholar 

  66. Gahoi S, Mandal RS, Ivanisenko N, Shrivastava P, Jain S, Singh AK, Raghunandanan MV, Kanchan S, Taneja B, Mandal C, Ivanisenko VA, Kumar A, Kumar R, Open Source Drug Discovery Consortium, Ramachandran S. 2013. Computational screening for new inhibitors of M. tuberculosis mycolyltransferases antigen 85 group of proteins as potential drug targets. Journal of Biomolecular Structure and Dynamics. 31(1):30–43. https://doi.org/10.1080/07391102.2012.691343.

  67. Srivastava, U., Singh, S. (2022). Approaches of Single-Cell Analysis in Crop Improvement. In: Wani, S.H., Kumar, A. (eds) Genomics of Cereal Crops. Springer Protocols Handbooks. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2533-0_14

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of California, San Diego, CA, USA

    Upasna Srivastava

  2. School of Medicine, Yale University, New Haven, CT, USA

    Upasna Srivastava

  3. INRIA, Bordeaux Sud-Ouest, Talence, France

    Swarna Kanchan

  4. Boise State University, Boise, ID, USA

    Swarna Kanchan & Minu Kesheri

  5. Department of Computational Biology and Bioinformatics JSSB, Sam Higginbottom Institute of Agriculture, Technology and Sciences (Formerly Allahabad Agriculture Institute), Allahabad, Uttar Pradesh, India

    Satendra Singh

Authors
  1. Upasna Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swarna Kanchan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minu Kesheri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Satendra Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Upasna Srivastava .

Editor information

Editors and Affiliations

  1. Division of Infectious Diseases, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA

    Vijay Soni

  2. Division of Infectious Diseases, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA

    Travis E. Hartman

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Srivastava, U., Kanchan, S., Kesheri, M., Singh, S. (2023). Nutrimetabolomics: Metabolomics in Nutrition Research. In: Soni, V., Hartman, T.E. (eds) Metabolomics. Springer, Cham. https://doi.org/10.1007/978-3-031-39094-4_8

Download citation

Publish with us

Policies and ethics

Search

Navigation