Personalised nutrition and health

BMJ (Online)

Volumn 361, Issue , 2018, Pages

  Ordovas, Jose M ^a,b,c   Ferguson, Lynnette R ^d   Tai, E Shyong ^e   Mathers, John C ^f  

^a TUFTS UNIVERSITY   (United States)

^b CENTRO NACIONAL DE INVESTIGACIONES CARDIOVASCULARES CNIC   (Spain)

^c IMDEA FOOD INSTITUTE   (Spain)

^d UNIVERSITY OF AUCKLAND   (New Zealand)

^e NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^f NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BEHAVIOR CHANGE; DIETARY INTAKE; EATING HABIT; EPIGENETICS; FEEDING BEHAVIOR; GENETIC SUSCEPTIBILITY; HEALTH BEHAVIOR; HEALTH DISPARITY; HUMAN; METABOLOMICS; NUTRIGENOMICS; NUTRITIONAL ASSESSMENT; NUTRITIONAL SCIENCE; NUTRITIONAL SUPPORT; PERSONALIZED MEDICINE; PRIORITY JOURNAL; WELLBEING; DIET; DIETARY SUPPLEMENT; EVIDENCE BASED MEDICINE; GENETIC PREDISPOSITION; NUTRITION; NUTRITION POLICY; TRENDS;

DIET; DIETARY SUPPLEMENTS; EVIDENCE-BASED MEDICINE; GENETIC PREDISPOSITION TO DISEASE; HUMANS; NUTRIGENOMICS; NUTRITION ASSESSMENT; NUTRITION POLICY; NUTRITIONAL PHYSIOLOGICAL PHENOMENA; PRECISION MEDICINE;

EID: 85048546523     PISSN: 09598146     EISSN: 17561833     Source Type: Journal    
DOI: 10.1136/bmj.k2173     Document Type: Article

References (59)

1. Micha R, Peñalvo JL, Cudhea F, Imamura F, Rehm CD, Mozaffarian D. Association between dietary factors and mortality from heart disease, stroke, and type 2 diabetes in the United States. JAMA 2017;317:912-24. doi:10.1001/jama.2017.0947
2. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015;136:E359-86. doi:10.1002/ijc.29210
3. GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016;388:1659-724. doi:10.1016/S0140-6736(16)31679-8
4. Woolf SH, Purnell JQ. The good life: working together to promote opportunity and improve population health and well-being. JAMA 2016;315:1706-8. doi:10.1001/jama.2016.4263
5. Celis-Morales C, Lara J, Mathers JC. Personalising nutritional guidance for more effective behaviour change. Proc Nutr Soc 2015;74:130-8. doi:10.1017/S0029665114001633
6. Gibney M, Walsh M, Goosens J. Personalized nutrition: paving the way to better population health. In: Eggersdorfer M, Kraemer M, Vordaro JB, et al, eds. Good nutrition: perspectives for the 21st century. Karger Publishers, 2016: 235-48.
7. Pickering C, Kiely J. Are the current guidelines on caffeine use in sport optimal for everyone? Interindividual variation in caffeine ergogenicity, and a move towards personalised sports nutrition. Sports Med 2018;48:7-16. doi:10.1007/s40279-017-0776-1
8. Miller JZ, Weinberger MH, Daugherty SA, Fineberg NS, Christian JC, Grim CE. Blood pressure response to dietary sodium restriction in healthy normotensive children. Am J Clin Nutr 1988;47:113-9. doi:10.1093/ajcn/47.1.113
9. Morris C, O'Grada C, Ryan M, et al. Identification of differential responses to an oral glucose tolerance test in healthy adults. PLoS One 2013;8:e72890. doi:10.1371/journal.pone.0072890
10. Sweeney JS. Dietary factors that influence the dextrose tolerance test: a preliminary study. Arch Intern Med 1927;40:818-30. doi:10.1001/archinte.1927.00130120077005
11. Corella D, Ordovas JM. Nutrigenomics in cardiovascular medicine. Circ Cardiovasc Genet 2009;2:637-51. doi:10.1161/CIRCGENETICS.109.891366
12. Corella D, Coltell O, Mattingley G, Sorlí JV, Ordovas JM. Utilizing nutritional genomics to tailor diets for the prevention of cardiovascular disease: a guide for upcoming studies and implementations. Expert Rev Mol Diagn 2017;17:495-513. doi:10.1080/14737 159.2017.1311208
13. Frazier-Wood AC. Dietary patterns, genes, and health: challenges and obstacles to be overcome. Curr Nutr Rep 2015;4:82-7. doi:10.1007/s13668-014-0110-6
14. Carlsten C, Brauer M, Brinkman F, et al. Genes, the environment and personalized medicine: we need to harness both environmental and genetic data to maximize personal and population health. EMBO Rep 2014;15:736-9. doi:10.15252/embr.201438480
15. Siroux V, Agier L, Slama R. The exposome concept: a challenge and a potential driver for environmental health research. Eur Respir Rev 2016;25:124-9. doi:10.1183/16000617.0034-2016
16. Chiauzzi E, Rodarte C, DasMahapatra P. Patientcentered activity monitoring in the self-management of chronic health conditions. BMC Med 2015;13:77. doi:10.1186/s12916-015-0319-2
17. Patel MS, Asch DA, Volpp KG. Wearable devices as facilitators, not drivers, of health behavior change. JAMA 2015;313:459-60. doi:10.1001/jama.2014.14781
18. Forster H, Walsh MC, O'Donovan CB, et al. A dietary feedback system for the delivery of consistent personalized dietary advice in the webbased multicenter Food4Me Study. J Med Internet Res 2016;18:e150. doi:10.2196/jmir.5620
19. Zhou M, Fukuoka Y, Mintz Y, et al. Evaluating machine learning-based automated personalized daily step goals delivered through a mobile phone app: randomized controlled trial. JMIR Mhealth Uhealth 2018;6:e28. doi:10.2196/mhealth.9117
20. Chen CH, Karvela M, Sohbati M, Shinawatra T, Toumazou C. PERSON-personalized expert recommendation system for optimized nutrition. IEEE Trans Biomed Circuits Syst 2018;12:151-60. doi:10.1109/TBCAS.2017.2760504
21. Zeevi D, Korem T, Zmora N, et al. Personalized nutrition by prediction of glycemic responses. Cell 2015;163:1079-94. doi:10.1016/j. cell.2015.11.001
22. Korem T, Zeevi D, Zmora N, et al. Bread affects clinical parameters and induces gut microbiomeassociated personal glycemic responses. Cell Metab 2017;25:1243-1253.e5. doi:10.1016/j. cmet.2017.05.002.
23. Price ND, Magis AT, Earls JC, et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol 2017;35:747-56. doi:10.1038/nbt.3870
24. 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. doi:10.1017/S0007114517001611
25. Macready AL, Fallaize R, Butler LT, et al. Application of behavior change techniques in a personalized nutrition electronic health intervention study: protocol for the web-based Food4Me randomized controlled trial. JMIR Res Protoc 2018;7:e87. doi:10.2196/resprot.8703
26. Horne J, Madill J, O'Connor C, Shelley J, Gilliland J. A systematic review of genetic testing and lifestyle behaviour change: are we using highquality genetic interventions and considering behaviour change theory? Lifestyle Genom 2018. doi:10.1159/000488086.
27. Celis-Morales C, Livingstone KM, Marsaux CF, et al, Food4Me Study. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int J Epidemiol 2017;46:578-88. doi:10.1093/ije/dyw186.
28. Lara J, Evans EH, O'Brien N, et al. Association of behaviour change techniques with effectiveness of dietary interventions among adults of retirement age: a systematic review and meta-analysis of randomised controlled trials. BMC Med 2014;12:177. doi:10.1186/s12916-014-0177-3
29. Stenne R, Hurlimann T, Godard B. Are research papers reporting results from nutrigenetics clinical research a potential source of biohype? Account Res 2012;19:285-307. doi:10.1080/08989621.20 12.718681
30. Joost HG, Gibney MJ, Cashman KD, et al. Personalised nutrition: status and perspectives. Br J Nutr 2007;98:26-31. doi:10.1017/S0007114507685195
31. Stenne R, Hurlimann T, Godard B. Benefits associated with nutrigenomics research and their reporting in the scientific literature: researchers' perspectives. Account Res 2013;20:167-83. doi:10.1080/08989621.2013.788381
32. Hurlimann T, Menuz V, Graham J, Robitaille J, Vohl MC, Godard B. Risks of nutrigenomics and nutrigenetics? What the scientists say. Genes Nutr 2014;9:370. doi:10.1007/s12263-013-0370-6
33. Ramos-Lopez O, Milagro FI, Allayee H, et al. Guide for current nutrigenetic, nutrigenomic, and nutriepigenetic approaches for precision nutrition involving the prevention and management of chronic diseases associated with obesity. J Nutrigenet Nutrigenomics 2017;10:43-62. doi:10.1159/000477729
34. Ferguson LR, De Caterina R, Görman U, et al. Guide and position of the International Society of Nutrigenetics/Nutrigenomics on personalised nutrition: Part 1-Fields of precision nutrition. J Nutrigenet Nutrigenomics 2016;9:12-27. doi:10.1159/000445350
35. Ferguson JF, Allayee H, Gerszten RE, et al, American Heart Association Council on Functional Genomics and Translational Biology, Council on Epidemiology and Prevention, and Stroke Council. Nutrigenomics, the microbiome, and gene-environment interactions: new directions in cardiovascular disease research, prevention, and treatment: a scientific statement from the American Heart Association. Circ Cardiovasc Genet 2016;9:291-313. doi:10.1161/HCG.0000000000000030
36. Camp KM, Trujillo E. Position of the Academy of Nutrition and Dietetics: nutritional genomics. J Acad Nutr Diet 2014;114:299-312. doi:10.1016/j. jand.2013.12.001
37. Kohlmeier M, De Caterina R, Ferguson LR, et al. Guide and position of the International Society of Nutrigenetics/Nutrigenomics on personalized nutrition: Part 2-Ethics, challenges, and endeavors of precision nutrition. J Nutrigenet Nutrigenomics 2016;9:28-46. doi:10.1159/000446347
38. Grimaldi KA, van Ommen B, Ordovas JM, et al. Proposed guidelines to evaluate scientific validity and evidence for genotype-based dietary advice. Genes Nutr 2017;12:35. doi:10.1186/s12263-017-0584-0
39. Corella D, Carrasco P, Sorlí JV, et al. Mediterranean diet reduces the adverse effect of the TCF7L2-rs7903146 polymorphism on cardiovascular risk factors and stroke incidence: a randomized controlled trial in a high-cardiovascular-risk population. Diabetes Care 2013;36:3803-11. doi:10.2337/dc13-0955
40. Corella D, Asensio EM, Coltell O, et al. CLOCK gene variation is associated with incidence of type-2 diabetes and cardiovascular diseases in type-2 diabetic subjects: dietary modulation in the PREDIMED randomized trial. Cardiovasc Diabetol 2016;15:4. doi:10.1186/s12933-015-0327-8
41. Caslake MJ, Miles EA, Kofler BM, et al. Effect of sex and genotype on cardiovascular biomarker response to fish oils: the FINGEN Study. Am J Clin Nutr 2008;88:618-29. doi:10.1093/ajcn/88.3.618
42. Corella D, Peloso G, Arnett DK, et al. APOA2, dietary fat, and body mass index: replication of a gene-diet interaction in 3 independent populations. Arch Intern Med 2009;169:1897-906. doi:10.1001/archinternmed.2009.343
43. Corella D, Tai ES, Sorlí JV, et al. Association between the APOA2 promoter polymorphism and body weight in Mediterranean and Asian populations: replication of a gene-saturated fat interaction. Int J Obes (Lond) 2011;35:666-75. doi:10.1038/ijo.2010.187
44. Schork NJ, Goetz LH. Single-subject studies in translational nutrition research. Annu Rev Nutr 2017;37:395-422. doi:10.1146/annurevnutr-071816-064717
45. Chen R, Mias GI, Li-Pook-Than J, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012;148:1293-307. doi:10.1016/j.cell.2012.02.009
46. Vieira R, McDonald S, Araújo-Soares V, Sniehotta FF, Henderson R. Dynamic modelling of n-of-1 data: powerful and flexible data analytics applied to individualised studies. Health Psychol Rev 2017;11:222-34. doi:10.1080/17437199.201 7.1343680
47. Celis-Morales C, Livingstone KM, Woolhead C, et al. How reliable is internet-based self-reported identity, socio-demographic, and obesity measures in European adults? Genes Nutr 2015;10:28. doi:10.1007/s12263-015-0476-0
48. O'Brien N, Heaven B, Teal G, et al. Integrating evidence from systematic reviews, qualitative research, and expert knowledge using co-design techniques to develop a web-based intervention for people in the retirement transition. J Med Internet Res 2016;18:e210. doi:10.2196/jmir.5790
49. Nielsen DE, El-Sohemy A. Disclosure of genetic information and change in dietary intake: a randomized controlled trial. PLoS One 2014;9:e112665. doi:10.1371/journal. pone.0112665
50. Hollands GJ, French DP, Griffin SJ, et al. The impact of communicating genetic risks of disease on riskreducing health behaviour: systematic review with meta-analysis. BMJ 2016;352:i1102. doi:10.1136/bmj.i1102
51. Langenberg C, Sharp SJ, Franks PW, et al. Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study. PLoS Med 2014;11:e1001647. doi:10.1371/journal. pmed.1001647
52. Saukko P. State of play in direct-to-consumer genetic testing for lifestyle-related diseases: market, marketing content, user experiences, and regulation. Proc Nutr Soc 2013;72:53-60. doi:10.1017/S0029665112002960
53. Guasch-Ferré M, Dashti HS, Merino J. Nutritional genomics and direct-to-consumer genetic testing: an overview. Adv Nutr 2018;9:128-35. doi:10.1093/advances/nmy001
54. Ahlgren J, Nordgren A, Perrudin M, et al. Consumers on the internet: ethical and legal aspects of commercialization of personalized nutrition. Genes Nutr 2013;8:349-55. doi:10.1007/s12263-013-0331-0
55. Kanoni S, Nettleton JA, Hivert MF, et al, MAGIC Investigators. Total zinc intake may modify the glucose-raising effect of a zinc transporter (SLC30A8) variant: a 14-cohort meta-analysis. Diabetes 2011;60:2407-16. doi:10.2337/db11-0176
56. McAuley E, McNulty H, Hughes C, Strain JJ, Ward M. Riboflavin status, MTHFR genotype and blood pressure: current evidence and implications for personalised nutrition. Proc Nutr Soc 2016;75:405-14. doi:10.1017/S0029665116000197
57. Jaeger B, Bosch AM. Clinical presentation and outcome of riboflavin transporter deficiency: mini review after five years of experience. J Inherit Metab Dis 2016;39:559-64. doi:10.1007/s10545-016-9924-2
58. Celis-Morales C, Livingstone KM, Marsaux CF, 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;10:450. doi:10.1007/s12263-014-0450-2
59. Bouchard C, Tremblay A, Després JP, et al. The response to long-term overfeeding in identical twins. N Engl J Med 1990;322:1477-82. doi:10.1056/NEJM199005243222101