Cited by 143Metabolomics and Mass Spectrometry StudiesNutrition, Genetics, and DiseaseHealth, Environment, Cognitive Aging

Paving the Way to Precision Nutrition Through Metabolomics

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Tebani, Abdellah, Bekri, Soumeya·2019·Frontiers in Nutrition

Abstract

Nutrition is an interdisciplinary science that studies the interactions of nutrients with the body in relation to maintenance of health and well-being. Nutrition is highly complex due to the underlying various internal and external factors that could model it. Thus, hacking this complexity requires more holistic and network-based strategies that could unveil these dynamic system interactions at both time and space scales. The ongoing omics era with its high-throughput molecular data generation is paving the way to embrace this complexity and is deeply reshaping the whole field of nutrition. Understanding the future paths of nutrition science is of importance from both translational and clinical perspectives. Basic nutrients which might include metabolites are important in nutrition science. Moreover, metabolites are key biological communication channels and represent an appealing functional readout at the interface of different major influential factors that define health and disease. Metabolomics is the technology that enables holistic and systematic analyses of metabolites in a biological system. Hence, given its intrinsic functionality, its tight connection to metabolism and its high clinical actionability potential, metabolomics is a very appealing technology for nutrition science. The ultimate goal is to deliver a tailored and clinically relevant nutritional recommendations and interventions to achieve precision nutrition. This work intends to present an update on the applications of metabolomics to personalize nutrition in translational and clinical settings. It also discusses the current conceptual shifts that are remodeling clinical nutrition practices in this Precision Medicine era. Finally, perspectives of clinical nutrition in the ever-growing, data-driven healthcare landscape are presented.

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  • Permanent rather than transient changes to the core gut microbiota are likely required for long-term impact on health outcomes.

    “Permanent rather than transient changes to the core gut microbiota are likely required for long-term impact on health outcomes [16].”
  • Wide variations in individual responses to food intake contribute to many conflicting outcomes in this area of research with failures to find diet-specific effects.

    “These wide variations contribute to many conflicting outcomes in this area of research with failures to find diet-specific effects [16].”
  • Defining and stratifying those with a diet-responsive gut microbiota within an intervention cohort may enhance the predictable performance of dietary investigations and would likely reduce the number of conflicting observations between similar studies.

    “As seen in Figure 2, defining and stratifying those with a diet-responsive gut microbiota within an intervention cohort may enhance the predictable performance of these dietary investigations, and would likely reduce the number of conflicting observations between similar studies [16].”

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  1. [1]Oliver SG Winson MK Kell DB Baganz F. Systematic functional analysis of the yeast genome. Trends Biotechnol. (1998) 16: 373– 8. 10.1016/S0167-7799(98)01214-1 9744112 · DOI
  2. [2]Nicholson JK Lindon JC Holmes E. “Metabonomics”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. (1999) 29: 1181– 9. 10598751
  3. [3]Fiehn O Metabolomics-the link between genotypes and phenotypes. Plant Mol Biol. (2002) 48: 155– 71. 10.1023/A:1013713905833 11860207 · DOI · Paper page
  4. [4]Beger RD Dunn W Schmidt MA Gross SS Kirwan JA Cascante M Metabolomics enables precision medicine:“a white paper, community perspective”. Metabolomics. (2016) 12: 149 10.1007/s11306-016-1094-6 27642271 PMC5009152 · DOI · Paper page
  5. [5]O'Donovan CB Walsh MC Gibney MJ Gibney ER Brennan L. Can metabotyping help deliver the promise of personalised nutrition? Proc Nutr Soc. (2015) 75: 106– 14. 10.1017/S0029665115002347 26259951 · DOI
  6. [6]Holmes E Wilson ID Nicholson JK. Metabolic phenotyping in health and disease. Cell. (2008) 134: 714– 7. 10.1016/j.cell.2008.08.026 18775301 · DOI
  7. [7]Celis-Morales C Livingstone KM Marsaux CF Macready AL Fallaize R O'Donovan CB. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int J Epidemiol. (2017) 46: 578– 88. 10.1093/ije/dyw186 27524815 · DOI · Paper page
  8. [8]Brennan L. Use of metabotyping for optimal nutrition. Curr Opin Biotechnol. (2017) 44: 35– 8. 10.1016/j.copbio.2016.10.008 27835796 · DOI
  9. [9]O'Gorman A Brennan L. The role of metabolomics in determination of new dietary biomarkers. Proc Nutr Soc. (2017) 76: 295– 302. 10.1017/S0029665116002974 28091346 · DOI
  10. [10]Ulaszewska MM Weinert CH Trimigno A Portmann R Andres Lacueva C Badertscher R. Nutrimetabolomics: an integrative action for metabolomic analyses in human nutritional studies. Mol Nutr Food Res. (2019) 63: e1800384. 10.1002/mnfr.201800384 30176196 · DOI
  11. [11]Tebani A Afonso C Bekri S. Advances in metabolome information retrieval: turning chemistry into biology. Part I: analytical chemistry of the metabolome. J Inherit Metabol Dis. (2018) 41: 379– 91. 10.1007/s10545-017-0074-y 28840392 PMC5959978 · DOI
  12. [12]Tebani A Afonso C Bekri S. Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery. J Inherit Metab Dis. (2018) 41: 393– 406. 10.1007/s10545-017-0080-0 28842777 PMC5959951 · DOI
  13. [13]Betts JA Gonzalez JT Personalised nutrition: what makes you so special? Nutr Bull. (2016) 41: 353– 9. 10.1111/nbu.12238 · DOI
  14. [14]Ohlhorst SD Russell R Bier D Klurfeld DM Li Z Mein JR. Nutrition research to affect food and a healthy life span. J Nutr. (2013) 143: 1349– 54. 10.3945/jn.113.180638 23784071 PMC6322830 · DOI
  15. [15]Heber D Berdanier CD Dwyer JT Handbook of Nutrition and Food. Boca Raton, FL: CRC Press (2016).
  16. [17]Garcia-Aloy M Llorach R Urpi-Sarda M Tulipani S Salas-Salvadó J Martínez-González MA Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort. Metabolomics. (2015) 11: 155– 65. 10.1007/s11306-014-0682-6 · DOI
  17. [18]Zeevi D Korem T Zmora N Israeli D Rothschild D Weinberger A. Personalized nutrition by prediction of glycemic responses. Cell. (2015) 163: 1079– 94. 10.1016/j.cell.2015.11.001 26590418 · DOI · Paper page
  18. [19]Trepanowski JF Ioannidis JPA. Perspective: limiting dependence on nonrandomized studies and improving randomized trials in human nutrition research: why and how. Adv Nutr. (2018) 9: 367– 77. 10.1093/advances/nmy014 30032218 PMC6054237 · DOI
  19. [20]Combs GF Jr Trumbo PR McKinley MC Milner J Studenski S Kimura T. Biomarkers in nutrition: new frontiers in research and application. Ann N Y Acad Sci. (2013) 1278: 1– 10. 10.1111/nyas.12069 23488557 PMC3627310 · DOI
  20. [21]Raiten DJ Namasté S Brabin B Combs G Jr L'Abbe MR Wasantwisut E. Executive summary—biomarkers of nutrition for development: building a consensus. Am J Clin Nutr. (2011) 94: 633S− 50S. 10.3945/ajcn.110.008227 21733880 PMC3142731 · DOI
  21. [22]Gibney MJ Walsh MC. The future direction of personalised nutrition: my diet, my phenotype, my genes. Proc Nutr Soc. (2013) 72: 219– 25. 10.1017/S0029665112003436 23360849 · DOI
  22. [23]Ferguson LR De Caterina R Görman U Allayee H Kohlmeier M Prasad C. Guide and position of the international society of nutrigenetics/nutrigenomics on personalised nutrition: part 1 - fields of precision nutrition. Lifestyle Genomics. (2016) 9: 12– 27. 10.1159/000445350 27169401 · DOI
  23. [24]Park Y Dodd KW Kipnis V Thompson FE Potischman N Schoeller DA. Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers. Am J Clin Nutr. (2018) 107: 80– 93. 10.1093/ajcn/nqx002 29381789 PMC5972568 · DOI · Paper page
  24. [25]Garden L Clark H Whybrow S Stubbs RJ. Is misreporting of dietary intake by weighed food records or 24-hour recalls food specific? Eur J Clin Nutr. (2018) 72: 1026– 34. 10.1038/s41430-018-0199-6 29789710 · DOI
  25. [26]Yuan C Spiegelman D Rimm EB Rosner BA Stampfer MJ Barnett JB Validity of a dietary questionnaire assessed by comparison with multiple weighed dietary records or 24-hour recalls. Am J Epidemiol. (2017) 185: 570– 84. 10.1093/aje/kww104 28338828 PMC5859994 · DOI · Paper page
  26. [27]Thompson FE Subar AF Dietary Assessment Methodology, Nutrition in the Prevention and Treatment of Disease. 3rd ed Amsterdam: Elsevier (2013). p. 5– 46. 10.1016/B978-0-12-391884-0.00001-9 · DOI
  27. [28]Freedman LS Schatzkin A Midthune D Kipnis V. Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Institute. (2011) 103: 1086– 92. 10.1093/jnci/djr189 21653922 PMC3143422 · DOI
  28. [29]McClave SA Martindale RG. Why do current strategies for optimal nutritional therapy neglect the microbiome? Nutrition. (2019) 60: 100– 5. 10.1016/j.nut.2018.09.024 30551119 · DOI
  29. [30]Bekri S The role of metabolomics in precision medicine. Expert Rev Precis Med Drug Dev. (2016) 1: 517– 32. 10.1080/23808993.2016.1273067 · DOI
  30. [31]Tebani A Abily-Donval L Afonso C Marret S Bekri S. Clinical metabolomics: the new metabolic window for inborn errors of metabolism investigations in the post-genomic era. Int J Mol Sci. (2016) 17: 1167. 10.3390/ijms17071167 27447622 PMC4964538 · DOI
  31. [32]McMahon G Taylor AE Smith GD Munafo MR. Phenotype refinement strengthens the association of AHR and CYP1A1 genotype with caffeine consumption. PLoS ONE. (2014) 9: e103448. 10.1371/journal.pone.0103448 25075865 PMC4116211 · DOI · Paper page
  32. [33]Cornelis MC El-Sohemy A Campos H. Genetic polymorphism of the adenosine A2A receptor is associated with habitual caffeine consumption. Am J Clin Nutr. (2007) 86: 240– 4. 10.1093/ajcn/86.1.240 17616786 · DOI
  33. [34]Rudkowska I Pérusse L Bellis C Blangero J Després J-P Bouchard C. Interaction between common genetic variants and total fat intake on low-density lipoprotein peak particle diameter: a genome-wide association study. Lifestyle Genomics. (2015) 8: 44– 53. 10.1159/000431151 26112879 · DOI
  34. [35]Tremblay BL Cormier H Rudkowska I Lemieux S Couture P Vohl M-C Association between polymorphisms in phospholipase A 2 genes and the plasma triglyceride response to an n-3 PUFA supplementation: a clinical trial. Lipids Health Dis. (2015) 14: 12 10.1186/s12944-015-0009-2 25889305 PMC4342012 · DOI
  35. [36]Ferguson LR. Genome-wide association studies and diet. World Rev Nutr Diet. (2010) 101: 8– 14. 10.1159/000314505 20436248 · DOI
  36. [37]Ahmadi KR Andrew T. Opportunism: a panacea for implementation of whole-genome sequencing studies in nutrigenomics research? Genes Nutr. (2014) 9: 387. 10.1007/s12263-014-0387-5 24535715 PMC3968294 · DOI
  37. [38]Vockley J Andersson HC Antshel KM Braverman NE Burton BK Frazier DM. Phenylalanine hydroxylase deficiency: diagnosis and management guideline. Genet Med. (2014) 16: 188. 10.1038/gim.2013.157 24385074 · DOI
  38. [39]Camp KM Trujillo E. Position of the Academy of Nutrition and Dietetics: nutritional genomics. J Acad Nutr Diet. (2014) 114: 299– 312. 10.1016/j.jand.2013.12.001 24439821 · DOI
  39. [40]Atkinson W Downer P Lever M Chambers ST George PM. Effects of orange juice and proline betaine on glycine betaine and homocysteine in healthy male subjects. Eur J Nutr. (2007) 46: 446– 52. 10.1007/s00394-007-0684-5 18060588 · DOI
  40. [41]Cross AJ Major JM Sinha R. Urinary biomarkers of meat consumption. Cancer Epidemiol Prevent Biomarkers. (2011) 20: 1107– 11. 10.1158/1055-9965.EPI-11-0048 21527577 PMC3111815 · DOI
  41. [42]Heinzmann SS Holmes E Kochhar S Nicholson JK Schmitt-Kopplin P. 2-Furoylglycine as a candidate biomarker of coffee consumption. J Agric Food Chem. (2015) 63: 8615– 21. 10.1021/acs.jafc.5b03040 26357997 · DOI
  42. [43]van Velzen EJ Westerhuis JA van Duynhoven JP van Dorsten FA Grun CH Jacobs DM. Phenotyping tea consumers by nutrikinetic analysis of polyphenolic end-metabolites. J Proteome Res. (2009) 8: 3317– 30. 10.1021/pr801071p 19374449 · DOI
  43. [44]Gibbons H McNulty BA Nugent AP Walton J Flynn A Gibney MJ. A metabolomics approach to the identification of biomarkers of sugar-sweetened beverage intake. Am J Clin Nutr. (2015) 101: 471– 7. 10.3945/ajcn.114.095604 25733631 · DOI
  44. [45]Jacobs DM Fuhrmann JC van Dorsten FA Rein D Peters S van Velzen EJ. Impact of short-term intake of red wine and grape polyphenol extract on the human metabolome. J Agric Food Chem. (2012) 60: 3078– 85. 10.1021/jf2044247 22372405 · DOI
  45. [46]Lloyd AJ Beckmann M Haldar S Seal C Brandt K Draper J. Data-driven strategy for the discovery of potential urinary biomarkers of habitual dietary exposure. Am J Clin Nutr. (2012) 97: 377– 89. 10.3945/ajcn.112.048033 23269817 · DOI
  46. [47]Garcia-Aloy M Llorach R Urpi-Sarda M Tulipani S Estruch R Martínez-González MA. Novel multimetabolite prediction of walnut consumption by a urinary biomarker model in a free-living population: the PREDIMED study. J Proteome Res. (2014) 13: 3476– 83. 10.1021/pr500425r 24882253 · DOI
  47. [48]Krishnan S Newman JW Hembrooke TA Keim NL. Variation in metabolic responses to meal challenges differing in glycemic index in healthy women: is it meaningful? Nutr Metabol. (2012) 9: 26. 10.1186/1743-7075-9-26 22458475 PMC3352098 · DOI
  48. [49]Wang TT Edwards AJ Clevidence BA. Strong and weak plasma response to dietary carotenoids identified by cluster analysis and linked to beta-carotene 15, 15'-monooxygenase 1 single nucleotide polymorphisms. J Nutr Biochem. (2013) 24: 1538– 46. 10.1016/j.jnutbio.2013.01.001 23517913 · DOI
  49. [50]Li K Brennan L McNulty BA Bloomfield JF Duff DJ Devlin NF. Plasma fatty acid patterns reflect dietary habits and metabolic health: a cross-sectional study. Mol Nutr Food Res. (2016) 60: 2043– 52. 10.1002/mnfr.201500711 27028111 · DOI
  50. [51]Moazzami AA Shrestha A Morrison DA Poutanen K Mykkänen H. Metabolomics reveals differences in postprandial responses to breads and fasting metabolic characteristics associated with postprandial insulin demand in postmenopausal women. J Nutr. (2014) 144: 807– 14. 10.3945/jn.113.188912 24717363 · DOI
  51. [52]Andersen M-BS Kristensen M Manach C Pujos-Guillot E Poulsen SK Larsen TM. Discovery and validation of urinary exposure markers for different plant foods by untargeted metabolomics. Anal Bioanal Chem. (2014) 406: 1829– 44. 10.1007/s00216-013-7498-5 24390407 · DOI
  52. [53]Bouchard-Mercier A Rudkowska I Lemieux S Couture P Vohl M-C. The metabolic signature associated with the Western dietary pattern: a cross-sectional study. Nutr J. (2013) 12: 158. 10.1186/1475-2891-12-158 24330454 PMC3874609 · DOI
  53. [54]Brennan L. Metabolomics in nutrition research: current status and perspectives. Biochem Soc Trans. (2013) 41: 670– 3. 10.1042/BST20120350 23514174 · DOI
  54. [55]Praticò G Gao Q Scalbert A Vergères G Kolehmainen M Manach C. Guidelines for Biomarker of Food Intake Reviews (BFIRev): how to conduct an extensive literature search for biomarker of food intake discovery. Genes Nutr. (2018) 13: 3. 10.1186/s12263-018-0592-8 29484030 PMC5819202 · DOI
  55. [56]Myint T Fraser GE Lindsted KD Knutsen SF Hubbard RW Bennett HW. Urinary 1-methylhistidine is a marker of meat consumption in Black and in White California Seventh-day Adventists. Am J Epidemiol. (2000) 152: 752– 5. 10.1093/aje/152.8.752 11052553 · DOI · Paper page
  56. [57]Paquette M Larqué ASM Weisnagel S Desjardins Y Marois J Pilon G. Strawberry and cranberry polyphenols improve insulin sensitivity in insulin-resistant, non-diabetic adults: a parallel, double-blind, controlled and randomised clinical trial. Br J Nutr. (2017) 117: 519– 31. 10.1017/S0007114517000393 28290272 PMC5426341 · DOI
  57. [58]Allam-Ndoul B Guénard F Garneau V Cormier H Barbier O Pérusse L. Association between metabolite profiles, metabolic syndrome and obesity status. Nutrients. (2016) 8: 324. 10.3390/nu8060324 27240400 PMC4924165 · DOI
  58. [59]Bakker GC Van Erk MJ Pellis L Wopereis S Rubingh CM Cnubben NH. An antiinflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: a nutrigenomics approach. Am J Clin Nutr. (2010) 91: 1044– 59. 10.3945/ajcn.2009.28822 20181810 · DOI · Paper page
  59. [61]Peré-Trepat E Ross AB Martin F-P Rezzi S Kochhar S Hasselbalch AL Chemometric strategies to assess metabonomic imprinting of food habits in epidemiological studies. Chemometrics Intell Lab Syst. (2010) 104: 95– 100. 10.1016/j.chemolab.2010.06.001 · DOI
  60. [62]Menni C Zhai G MacGregor A Prehn C Römisch-Margl W Suhre K. Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics. (2013) 9: 506– 14. 10.1007/s11306-012-0469-6 23543136 PMC3608890 · DOI
  61. [63]O'Gorman A Morris C Ryan M O'Grada C Roche H Gibney E. Habitual dietary intake impacts on the lipidomic profile. J Chromatogr B. (2014) 966: 140– 6. 10.1016/j.jchromb.2014.01.032 24565891 · DOI
  62. [64]Floegel A Von Ruesten A Drogan D Schulze M Prehn C Adamski J. Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam. Eur J Clin Nutr. (2013) 67: 1100. 10.1038/ejcn.2013.147 23942179 · DOI
  63. [65]Vázquez-Fresno R Llorach R Perera A Mandal R Feliz M Tinahones FJ. Clinical phenotype clustering in cardiovascular risk patients for the identification of responsive metabotypes after red wine polyphenol intake. J Nutr Biochem. (2016) 28: 114– 20. 10.1016/j.jnutbio.2015.10.002 26878788 · DOI
  64. [66]Zubair N Kuzawa CW Lee NR McDade TW Adair LS. Clustering and determinants of cardiometabolic risk factors among Filipino young adults. Asia Pacific J Clin Nutr. (2014) 23: 148– 58. 10.6133/apjcn.2014.23.1.06 24561983 PMC4100915 · DOI
  65. [67]Zubair N Kuzawa CW McDade TW Adair LS. Cluster analysis reveals important determinants of cardiometabolic risk patterns in Filipino women. Asia Pacific J Clin Nutr. (2012) 21: 271. 22507615 PMC3469259
  66. [68]van Bochove K van Schalkwijk DB Parnell LD Lai C-Q Ordovás JM de Graaf AA. Clustering by plasma lipoprotein profile reveals two distinct subgroups with positive lipid response to fenofibrate therapy. PLoS ONE. (2012) 7: e38072. 10.1371/journal.pone.0038072 22719863 PMC3373573 · DOI
  67. [69]O'sullivan A Gibney MJ Connor AO Mion B Kaluskar S Cashman KD. Biochemical and metabolomic phenotyping in the identification of a vitamin D responsive metabotype for markers of the metabolic syndrome. Mol Nutr Food Res. (2011) 55: 679– 90. 10.1002/mnfr.201000458 21240901 · DOI
  68. [70]Frazier-Wood AC Glasser S Garvey WT Kabagambe EK Borecki IB Tiwari HK. A clustering analysis of lipoprotein diameters in the metabolic syndrome. Lipids Health Dis. (2011) 10: 237. 10.1186/1476-511X-10-237 22182248 PMC3260106 · DOI
  69. [71]Tzeng C-R Chang Y-cI Chang Y-c Wang C-W Chen C-H Hsu M-I. Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age. Fertil Steril. (2014) 101: 1404– 10. e1. 10.1016/j.fertnstert.2014.01.023 24534286 · DOI
  70. [72]Wilcox M Li Q Sun Y Stang P Berlin J Wang D. Genome-wide association study for empirically derived metabolic phenotypes in the Framingham Heart Study offspring cohort. BMC Proc. (2009) 3(Suppl. 7): S53. 20018046 PMC2795953
  71. [73]Wilcox MA Wyszynski DF Panhuysen CI Ma Q Yip A Farrell J. Empirically derived phenotypic subgroups–qualitative and quantitative trait analyses. BMC Genet. 4(Suppl 1): S15. 10.1186/1471-2156-4-S1-S15 14975083 PMC1866449 · DOI
  72. [74]Mäkinen VP Soininen P Forsblom C Parkkonen M Ingman P Kaski K. 1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death. Mol Syst Biol. (2008) 4: 167. 10.1038/msb4100205 18277383 PMC2267737 · DOI
  73. [75]Morris C O'Grada C Ryan M Roche HM Gibney MJ Gibney ER. Identification of differential responses to an oral glucose tolerance test in healthy adults. PLoS ONE. (2013) 8: e72890. 10.1371/journal.pone.0072890 23991163 PMC3749984 · DOI · Paper page
  74. [76]Ventura AK Loken E Birch LL. Risk profiles for metabolic syndrome in a nonclinical sample of adolescent girls. Pediatrics. (2006) 118: 2434– 42. 10.1542/peds.2006-1527 17142529 PMC2548300 · DOI
  75. [77]Bouwman J Vogels JT Wopereis S Rubingh CM Bijlsma S van Ommen B. Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes. BMC Med Genomics. (2012) 5: 1. 10.1186/1755-8794-5-1 22221319 PMC3271030 · DOI
  76. [78]Houston M. The role of noninvasive cardiovascular testing, applied clinical nutrition and nutritional supplements in the prevention and treatment of coronary heart disease. Ther Adv Cardiovasc Dis. (2018) 12: 85– 108. 10.1177/1753944717743920 29316855 PMC5933539 · DOI
  77. [79]Riedl A Gieger C Hauner H Daniel H Linseisen J. Metabotyping and its application in targeted nutrition: an overview. Br J Nutr. (2017) 117: 1631– 44. 10.1017/S0007114517001611 28720150 · DOI
  78. [80]Sébédio J-L. Metabolomics, nutrition, and potential biomarkers of food quality, intake, and health status. Adv Food Nutr Res. (2017) 82: 83– 116. 10.1016/bs.afnr.2017.01.001 28427537 · DOI
  79. [82]Kang JX. Gut microbiota and personalized nutrition. Lifestyle Genomics. (2013) 6: I– II. 10.1159/000353144 23774142 · DOI
  80. [85]Byndloss MX Baumler AJ. The germ-organ theory of non-communicable diseases. Nat Rev Microbiol. (2018) 16: 103– 10. 10.1038/nrmicro.2017.158 29307890 · DOI
  81. [86]den Besten G van Eunen K Groen AK Venema K Reijngoud DJ Bakker BM. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res. (2013) 54: 2325– 40. 10.1194/jlr.R036012 23821742 PMC3735932 · DOI · Paper page
  82. [87]Velazquez OC Lederer HM Rombeau JL. Butyrate and the colonocyte. Production, absorption, metabolism, and therapeutic implications. Adv Exp Med Biol. (1997) 427: 123– 34. 9361838
  83. [88]Mathews AT Famodu OA Olfert MD Murray PJ Cuff CF Downes MT. Efficacy of nutritional interventions to lower circulating ceramides in young adults: FRUVEDomic pilot study. Physiol Rep. (2017) 5: e13329. 10.14814/phy2.13329 28694327 PMC5506522 · DOI
  84. [89]Koeth RA Wang Z Levison BS Buffa JA Org E Sheehy BT. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. (2013) 19: 576. 10.1038/nm.3145 23563705 PMC3650111 · DOI · Paper page
  85. [93]Bokulich NA Blaser MJ. A bitter aftertaste: unintended effects of artificial sweeteners on the gut microbiome. Cell Metabol. (2014) 20: 701– 3. 10.1016/j.cmet.2014.10.012 25440050 PMC4494042 · DOI
  86. [94]Feehley T Nagler CR. Health: the weighty costs of non-caloric sweeteners. Nature. (2014) 514: 176. 10.1038/nature13752 25231865 PMC4449731 · DOI
  87. [95]Gibson GR Hutkins R Sanders ME Prescott SL Reimer RA Salminen SJ. Expert consensus document: the International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of prebiotics. Nat Rev Gastroenterol Hepatol. (2017) 14: 491– 502. 10.1038/nrgastro.2017.75 28611480 · DOI · Paper page
  88. [97]B.Beserra TS Fernandes R do Rosario VA Mocellin MC Kuntz MGF Trindade EBSM A systematic review and meta-analysis of the prebiotics and synbiotics effects on glycaemia, insulin concentrations and lipid parameters in adult patients with overweight or obesity. Clin Nutr. (2015) 34: 845– 58. 10.1016/j.clnu.2014.10.004 25456608 · DOI
  89. [99]Rosario D Benfeitas R Bidkhori G Zhang C Uhlen M Shoaie S. Understanding the representative gut microbiota dysbiosis in metformin-treated type 2 diabetes patients using genome-scale metabolic modeling. Front Physiol. (2018) 9: 775. 10.3389/fphys.2018.00775 29988585 PMC6026676 · DOI
  90. [100]Kovatcheva-Datchary P Nilsson A Akrami R Ying Lee S De Vadder F Arora T. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of prevotella. Cell Metabol. (2015) 22: 971– 82. 10.1016/j.cmet.2015.10.001 26552345 · DOI · Paper page
  91. [101]Flint HJ Duncan SH Louis P. The impact of nutrition on intestinal bacterial communities. Curr Opin Microbiol. (2017) 38: 59– 65. 10.1016/j.mib.2017.04.005 28486162 · DOI · Paper page
  92. [102]Suez J Elinav E. The path towards microbiome-based metabolite treatment. Nat Microbiol. (2017) 2: 17075. 10.1038/nmicrobiol.2017.75 28540921 · DOI
  93. [103]Shoaie S Ghaffari P Kovatcheva-Datchary P Mardinoglu A Sen P Pujos-Guillot E. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metabol. (2015) 22: 320– 31. 10.1016/j.cmet.2015.07.001 26244934 · DOI
  94. [106]Thaiss CA Itav S Rothschild D Meijer MT Levy M Moresi C. Persistent microbiome alterations modulate the rate of post-dieting weight regain. Nature. (2016) 540: 544. 10.1038/nature20796 27906159 · DOI · Paper page
  95. [107]Fito M Konstantinidou V. Nutritional genomics and the mediterranean diet's effects on human cardiovascular health. Nutrients. (2016) 8: 218. 10.3390/nu8040218 27089360 PMC4848687 · DOI
  96. [108]Konstantinidou V Covas MI Sola R Fito M. Up-to date knowledge on the in vivo transcriptomic effect of the Mediterranean diet in humans. Mol Nutr Food Res. (2013) 57: 772– 83. 10.1002/mnfr.201200613 23417868 · DOI
  97. [110]Lee S Park Y Zuidema MY Hannink M Zhang C. Effects of interventions on oxidative stress and inflammation of cardiovascular diseases. World J Cardiol. (2011) 3: 18– 24. 10.4330/wjc.v3.i1.18 21286214 PMC3030733 · DOI
  98. [111]Berdasco M Esteller M. Clinical epigenetics: seizing opportunities for translation. Nat Rev Genet. (2019) 20: 109– 27. 10.1038/s41576-018-0074-2 30479381 · DOI
  99. [114]Lillycrop KA Hoile SP Grenfell L Burdge GC. DNA methylation, ageing and the influence of early life nutrition. Proc Nutr Soc. (2014) 73: 413– 21. 10.1017/S0029665114000081 25027290 · DOI
  100. [116]Badimon L Vilahur G Padro T. Systems biology approaches to understand the effects of nutrition and promote health. Br J Clin Pharmacol. (2017) 83: 38– 45. 10.1111/bcp.12965 27062443 PMC5338131 · DOI
  101. [117]Panagiotou G Nielsen J. Nutritional systems biology: definitions and approaches. Ann Rev Nutr. (2009) 29: 329– 39. 10.1146/annurev-nutr-080508-141138 19575602 · DOI
  102. [118]Zheng T Ni Y Li J Chow BKC Panagiotou G. Designing dietary recommendations using system level interactomics analysis and network-based inference. Front Physiol. (2017) 8: 753. 10.3389/fphys.2017.00753 29033850 PMC5625024 · DOI
  103. [119]Jensen K Ni Y Panagiotou G Kouskoumvekaki I. Developing a molecular roadmap of drug-food interactions. PLoS Comput Biol. (2015) 11: e1004048. 10.1371/journal.pcbi.1004048 25668218 PMC4323218 · DOI
  104. [121]Ordovas JM Ferguson LR Tai ES Mathers JC Personalised nutrition and health. BMJ. (2018) 2018: 361 10.1136/bmj.k2173 PMC6081996 29898881 · DOI
  105. [122]Ozdemir V Kolker E. Precision nutrition 4.0: a big data and ethics foresight analysis–convergence of agrigenomics, nutrigenomics, nutriproteomics, and nutrimetabolomics. Omics. (2016) 20: 69– 75. 10.1089/omi.2015.0193 26785082 · DOI
  106. [123]Tebani A Afonso C Marret S Bekri S Omics-based strategies in precision medicine: toward a paradigm shift in inborn errors of metabolism investigations. Int J Mol Sci. (2016) 2016: 17 10.3390/ijms17091555 PMC5037827 27649151 · DOI

Cited by

Sorted by citations

Authors
Tebani, A., & Bekri, S.
Affiliations
  • Centre Hospitalier Universitaire de Rouen (FR)
  • Inserm (FR)
Corresponding author
Tebani, Abdellah
Venue
Frontiers in Nutrition
Published
2019-04-09
Volume
6
Pages
41-41
ISSN
2296-861X (ISSN-L)
OpenAlex
W2937827778
Cited by
143
References
17
Citations by year
2026: 10 · 2025: 23 · 2024: 19 · 2023: 22 · 2022: 25 · 2021: 20 · 2020: 20 · 2019: 4
FWCI
1.52
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Nutrition (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
Keywords
personalized nutrition, metabolomics, metabolites, clinical nutrition, precision medicine, omics, dietary intervention, data-driven healthcare

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