Skip to main content

Advertisement

Log in

Metabotyping for Precision Nutrition and Weight Management: Hype or Hope?

  • Diabetes and Obesity (M Dalamaga and F Magkos, Section Editors)
  • Published:
Current Nutrition Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Precision nutrition requires a solid understanding of the factors that determine individual responses to dietary treatment. We review the current state of knowledge in identifying human metabotypes – based on circulating biomarkers – that can predict weight loss or other relevant physiological outcomes in response to diet treatment.

Recent Findings

Not many studies have been conducted in this area and the ones identified here are heterogeneous in design and methodology, and therefore difficult to synthesize and draw conclusions. The basis of the creation of metabotypes varies widely, from using thresholds for a single metabolite to using complex algorithms to generate multi-component constructs that include metabolite and genetic information. Furthermore, available studies are a mix of hypothesis-driven and hypothesis-generating studies, and most of them lack experimental testing in human trials.

Summary

Although this field of research is still in its infancy, precision-based dietary intervention strategies focusing on the metabotype group level hold promise for designing more effective dietary treatments for obesity.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Abbreviations

BMI:

Body mass index

BCAA:

Branched-chain amino acid

CRP:

C-reactive protein

HOMA-IR:

Homeostatic model assessment of insulin resistance

HETE:

Hydroxyeicosatetraenoic

SDS:

Standard deviation score

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Haslam D, James W. Curbing the obesity epidemic. Lancet, The. 2005;367:1549. https://doi.org/10.1016/S0140-6736(05)67483-1.

    Article  Google Scholar 

  2. Calle E, Rodriguez C, Walker-thurmond K, Thun M. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N Engl J Med. 2003;348:1625–38. https://doi.org/10.1056/NEJMoa021423.

    Article  PubMed  Google Scholar 

  3. Andersson CX, Gustafson B, Hammarstedt A, et al. Inflamed adipose tissue, insulin resistanceand vascular injury. Diabetes Metab Res Rev. 2008;24:595–603. https://doi.org/10.1002/dmrr.889.

    Article  CAS  PubMed  Google Scholar 

  4. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10. https://doi.org/10.1016/j.metabol.2018.09.005.

    Article  CAS  PubMed  Google Scholar 

  5. • Blüher M. Obesity : global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019;15. https://doi.org/10.1038/s41574-019-0176-8. A recent comprehensive overview of the epidemiology and pathogenesis of obesity.

  6. Yancy WS, Westman EC, McDuffie JR, et al. A randomized trial of a low-carbohydrate diet vs orlistat plus a low-fat diet for weight loss. Arch Intern Med. 2010;170:136–45. https://doi.org/10.1001/archinternmed.2009.492.

    Article  CAS  PubMed  Google Scholar 

  7. Greenberg I, Stampfer MJ, Schwarzfuchs D, Shai I. Adherence and success in long-term weight loss diets: the dietary intervention randomized controlled trial (direct). J Am Coll Nutr. 2009;28:159–68. https://doi.org/10.1080/07315724.2009.10719767.

    Article  CAS  PubMed  Google Scholar 

  8. Gardner CD, Kiazand A, Kim S, et al. Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women. The A TO Z Weight Loss Study: A Randomized Trial. J Am Med Assoc. 2007;297:969–78.

    Article  CAS  Google Scholar 

  9. • Ritz C. Statistical analysis of continuous outcomes from parallel-arm randomized controlled trials in nutrition – a tutorial. Eur J Clin Nutr. 2020. https://doi.org/10.1038/s41430-020-00750-z. A treatise on modern statistical analysis methods used in nutrition research.

  10. Dansinger ML, Gleason JA, Griffith JL, et al. Comparison of the Atkins, Ornish, weight watchers, and zone diets for weight loss and heart disease risk reduction: a randomized trial. J Am Med Assoc. 2005;293:43–53. https://doi.org/10.1001/jama.293.1.43.

    Article  CAS  Google Scholar 

  11. Sacks FM, Bray GA, Carey VJ, et al. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N Engl J Med. 2009;360:859–73. https://doi.org/10.1056/NEJMoa0804748.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Astrup A, Hjorth MF. Classification of obesity targeted personalized dietary weight loss management based on carbohydrate tolerance. Eur J Clin Nutr. 2018;72:1300–4. https://doi.org/10.1038/s41430-018-0227-6.

    Article  PubMed  Google Scholar 

  13. Dragsted LO. The metabolic nature of individuality. Nat Food. 2020;1:327–8. https://doi.org/10.1038/s43016-020-0104-z.

    Article  Google Scholar 

  14. Biesiekierski JR, Livingstone KM. Personalised nutrition: updates, gaps and next steps. Nutrients. 2019;11:1793. https://doi.org/10.3390/nu11081793.

    Article  CAS  PubMed Central  Google Scholar 

  15. Gibney MJ, Walsh MC. The future direction of personalised nutrition: my diet, my phenotype, my genes. Proc Nutr Soc. 2013;72:219–25. https://doi.org/10.1017/S0029665112003436.

    Article  PubMed  Google Scholar 

  16. Celis-Morales C, Livingstone KM, Marsaux CFM, et al. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int J Epidemiol. 2017;46:578–88. https://doi.org/10.1093/ije/dyw186.

    Article  PubMed  Google Scholar 

  17. McGuire S. Scientific report of the 2015 dietary Guidelines Advisory Committee. Washington, DC: US Departments of Agriculture and Health and Human Services, 2015. Adv Nutr. 2016;7:202–4. https://doi.org/10.3945/an.115.011684.

    Article  PubMed  PubMed Central  Google Scholar 

  18. •• Garcia-Perez I, Posma JM, Chambers ES, et al. Dietary metabotype modelling predicts individual responses to dietary interventions. Nat Food. 2020;1:355–364. https://doi.org/10.1038/s43016-020-0092-z. A well-designed crossover study exploring the individuality in response to same diet. An important study that highlights the variability in response for different diets and a tool that can be used in future decision-making strategies within preventing of e.g. obesity treatment.

  19. Chatelan A, Bochud M, Frohlich KL. Precision nutrition: hype or hope for public health interventions to reduce obesity? Int J Epidemiol. 2019;48:332–42. https://doi.org/10.1093/ije/dyy274.

    Article  PubMed  Google Scholar 

  20. Zeisel SH. Precision (personalized) nutrition: understanding metabolic heterogeneity. Annu Rev Food Sci Technol. 2020;11:71–92. https://doi.org/10.1146/annurev-food-032519-051736.

    Article  PubMed  Google Scholar 

  21. • Palmnäs M, Brunius C, Shi L, et al. Perspective: metabotyping–a potential personalized nutrition strategy for precision prevention of cardiometabolic disease. Adv Nutr. 2020;11:524–532. https://doi.org/10.1093/advances/nmz121. An inspiring perspective that discuss the concept of metabotyping.

  22. Riedl A, Gieger C, Hauner H, et al. Metabotyping and its application in targeted nutrition: an overview. Br J Nutr. 2017;117:1631–44. https://doi.org/10.1017/S0007114517001611.

    Article  CAS  PubMed  Google Scholar 

  23. Tebani A, Bekri S. Paving the way to precision nutrition through metabolomics. Front Nutr. 2019;6:1–10. https://doi.org/10.3389/fnut.2019.00041.

    Article  CAS  Google Scholar 

  24. de Toro-Martín J, Arsenault BJ, Després JP, Vohl MC. Precision nutrition: a review of personalized nutritional approaches for the prevention and management of metabolic syndrome. Nutrients. 2017;9:1–28. https://doi.org/10.3390/nu9080913.

    Article  CAS  Google Scholar 

  25. Guijas C, Montenegro-Burke JR, Warth B, et al. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat Biotechnol. 2018;36:316–20. https://doi.org/10.1038/nbt.4101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Laddu D, Hauser M. Addressing the nutritional phenotype through personalized nutrition for chronic disease prevention and management. Prog Cardiovasc Dis. 2019;62:9–14. https://doi.org/10.1016/j.pcad.2018.12.004.

    Article  PubMed  Google Scholar 

  27. Zhang A, Sun H, Wang X. Power of metabolomics in biomarker discovery and mining mechanisms of obesity. Obes Rev. 2013;14:344–9. https://doi.org/10.1111/obr.12011.

    Article  CAS  PubMed  Google Scholar 

  28. Aleksandrova K, Egea Rodrigues C, Floegel A, Ahrens W. Omics biomarkers in obesity: novel etiological insights and targets for precision prevention. Curr Obes Rep. 2020;9:219–30. https://doi.org/10.1007/s13679-020-00393-y.

    Article  PubMed  PubMed Central  Google Scholar 

  29. •• Kirk D, Catal C, Tekinerdogan B. Precision nutrition: a systematic literature review. Comput Biol Med. 2021;133:104365. https://doi.org/10.1016/j.compbiomed.2021.104365. This systematic review presents an overview of where and how machine learning has been used in precision nutrition from various aspects.

  30. Martorell-Marugán J, Tabik S, Benhammou Y, et al. Deep learning in omics data analysis and precision medicine. Comput Biol. 2019;37–53. https://doi.org/10.15586/computationalbiology.2019.ch3.

  31. Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016;17:451–9. https://doi.org/10.1038/nrm.2016.25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  33. Xie B, Waters MJ, Schirra HJ. Investigating potential mechanisms of obesity by metabolomics. J Biomed Biotechnol. 2012. https://doi.org/10.1155/2012/805683.

  34. Payab M, Tayanloo-Beik A, Falahzadeh K, et al. Metabolomics prospect of obesity and metabolic syndrome; a systematic review. J Diabetes Metab Disord. 2021. https://doi.org/10.1007/s40200-021-00917-w.

    Article  PubMed  PubMed Central  Google Scholar 

  35. • Hjorth MF, Bray GA, Zohar Y, et al. Pretreatment fasting glucose and insulin as determinants of weight loss on diets varying in macronutrients and dietary fibers–the POUNDS LOST study. Nutrients. 2019;11:1–12. https://doi.org/10.3390/nu11030586. A retrospective study reanalyzes data and presents different groups of individuals with obesity who respond differently to different diets.

  36. Hjorth MF, Ritz C, Blaak EE, et al. Pretreatment fasting plasma glucose and insulin modify dietary weight loss success: results from 3 randomized clinical trials. Am J Clin Nutr. 2017;106:499–505. https://doi.org/10.3945/ajcn.117.155200.

    Article  CAS  PubMed  Google Scholar 

  37. • Kwee LC, Ilkayeva O, Muehlbauer MJ, et al. Metabolites and diabetes remission after weight loss. Nutr Diabetes. 2021;11. https://doi.org/10.1038/s41387-021-00151-6. This study presented metabolites predictive for weight loss-induced remission in different groups of subjects with obesity.

  38. Bonaventura A, Liberale L, Carbone F, et al. High baseline C-reactive protein levels predict partial type 2 diabetes mellitus remission after biliopancreatic diversion. Nutr Metab Cardiovasc Dis. 2017;27:423–9. https://doi.org/10.1016/j.numecd.2017.01.007.

    Article  CAS  PubMed  Google Scholar 

  39. Carbone F, Nulli Migliola E, Bonaventura A, et al. High serum levels of C-reactive protein (CRP) predict beneficial decrease of visceral fat in obese females after sleeve gastrectomy. Nutr Metab Cardiovasc Dis. 2018;28:494–500. https://doi.org/10.1016/j.numecd.2018.01.014.

    Article  CAS  PubMed  Google Scholar 

  40. Marco-Ramell A, Tulipani S, Palau-Rodriguez M, et al. Untargeted profiling of concordant/discordant phenotypes of high insulin resistance and obesity to predict the risk of developing diabetes. J Proteome Res. 2018;17:2307–17. https://doi.org/10.1021/acs.jproteome.7b00855.

    Article  CAS  PubMed  Google Scholar 

  41. Heianza Y, Sun D, Zheng Y, et al. Early changes in metabolomics signature and prediction of long-term successful weight-loss: the POUNDS LOST trial. Circulation. 2017;136:A14483.

    Google Scholar 

  42. Stroeve JHM, Saccenti E, Bouwman J, et al. Weight loss predictability by plasma metabolic signatures in adults with obesity and morbid obesity of the DiOGenes study. Obesity. 2016;24:379–88. https://doi.org/10.1002/oby.21361.

    Article  CAS  PubMed  Google Scholar 

  43. Geidenstam N, Al-Majdoub M, Ekman M, et al. Metabolite profiling of obese individuals before and after a one year weight loss program. Int J Obes. 2017;41:1369–78. https://doi.org/10.1038/ijo.2017.124.

    Article  CAS  Google Scholar 

  44. Geidenstam N, Magnusson M, Danielsson APH, et al. Amino acid signatures to evaluate the beneficial effects of weight loss. Int J Endocrinol. 2017. https://doi.org/10.1155/2017/6490473.

  45. • Geidenstam N, Hsu YHH, Astley CM, et al. Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain. PLoS One. 2019;14:1–20. https://doi.org/10.1371/journal.pone.0222445. A metabolic profiling study proved their constructed metabolite risk score to be a strong marker for insulin sensitivity and as a weight gain predictor.

  46. Wahl S, Holzapfel C, Yu Z, et al. Metabolomics reveals determinants of weight loss during lifestyle intervention in obese children. Metabolomics. 2013;9:1157–67. https://doi.org/10.1007/s11306-013-0550-9.

    Article  CAS  Google Scholar 

  47. PREVENTOMICS: Empowering consumers to PREVENT diet-related diseases through "omics" science. https://preventomics.eu/. Accessed 6 Dec 2021.

  48. Celis-Morales C, Livingstone KM, Marsaux CFM, et al. Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries. Genes Nutr. 2015;10. https://doi.org/10.1007/s12263-014-0450-2.

  49. O’Donovan CB, Walsh MC, Woolhead C, et al. Metabotyping for the development of tailored dietary advice solutions in a European population: the Food4Me study. Br J Nutr. 2017;118:561–9. https://doi.org/10.1017/S0007114517002069.

    Article  CAS  PubMed  Google Scholar 

  50. •• Berry SE, Valdes AM, Drew DA, et al. Human postprandial responses to food and potential for precision nutrition. Nat Med. 2020;26:964–973. https://doi.org/10.1038/s41591-020-0934-0. This comprehensive study presents postprandial metabolic responses to nutritional challenges from 1002 subjects. A tool that can be relevant in the future of precision nutrition.

  51. Gijbels A, Trouwborst I, Jardon KM, et al. The PERSonalized Glucose Optimization Through Nutritional Intervention (PERSON) study: rationale, design and preliminary screening results. Front Nutr. 2021;8. https://doi.org/10.3389/fnut.2021.694568.

  52. Aleksandrova K, Mozaffarian D, Pischon T. Addressing the perfect storm: biomarkers in obesity and pathophysiology of cardiometabolic risk. Clin Chem. 2018;64:142–53. https://doi.org/10.1373/clinchem.2017.275172.

    Article  CAS  PubMed  Google Scholar 

  53. Torres N, Tovar AR. The present and future of personalized nutrition. Rev Invest Clin. 2021;73:321–325. https://doi.org/10.24875/RIC.21000346.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kristina Pigsborg.

Ethics declarations

Conflict of Interest

None.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Diabetes and Obesity

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pigsborg, K., Magkos, F. Metabotyping for Precision Nutrition and Weight Management: Hype or Hope?. Curr Nutr Rep 11, 117–123 (2022). https://doi.org/10.1007/s13668-021-00392-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13668-021-00392-y

Keywords

Navigation