Genetic test for the prescription of diets in support of physical activity

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Zakira Naureen
Giacinto Abele Donato Miggiano
Barbara Aquilanti
Valeria Velluti
Giuseppina Matera
Lucilla Gagliardi
Alessandra Zulian
Roberta Romanelli
Matteo Bertelli


Nutrigenetics, nutrigenomics, direct to consumer test, personalized nutrition, obesity, physical activity


Owing to the fields of nutrigenetics and nutrigenomics today we can think of devising approaches to optimize health, delay onset of diseases and reduce its severity according to our genetic blue print. However this requires a deep understanding of nutritional impact on expression of genes that may result in a specific phenotype. The extensive research and observational studies during last two decades reporting interactions between genes, diet and physical activity suggest a cross talk between various genetic and environmental factors and lifestyle interventions. Although considerable efforts have been made in unraveling the mechanisms of gene-diet interactions the scientific evidences behind developing commercial genetic tests for providing personalized nutrition recommendations are still scarce. In this scenario the current mini-review aims to provide useful insights into salient feature of nutrition based genetic research and its commercial application and the ethical issue and concerns related to its outcome.


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1. 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.
2. 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.
3. Simopoulos AP. Nutrigenetics/nutrigenomics. Annu Rev Public Health 2010; 31: 53–68.
4. Corella D, Ordovas JM. Nutrigenomics in cardiovascular medicine. Circ Cardiovasc Genet 2009; 2: 637–51.
5. Trujillo E, Davis C, Milner J. Nutrigenomics, proteomics, metabolomics, and the practice of dietetics. J Am Diet Assoc 2006; 106: 403–13.
6. Ferguson LR. Nutrigenomics approaches to functional foods. J Am Diet Assoc 2009; 109: 452–8.
7. Kaput J. Nutrigenomics research for personalized nutrition and medicine. Curr Opin Biotechnol 2008; 19: 110–20.
8. Ordovas JM, Corella D. Nutritional genomics. Annu Rev Genomics Hum Genet 2004; 5: 71–118.
9. Fenech MF. Nutriomes and nutrient arrays – the key to personalised nutrition for DNA damage prevention and cancer growth control. Genome Integr 2010; 1: 11.
10. Simopoulos AP, Ordovas JM. World Review of Nutrition and Dietetics. In: Nutrigenetics and Nutrigenomics, Karger, Basel, 2004.
11. Frazer KA, Murray SS, Schork NJ, Topol EJ. Human genetic variation and its contribution to complex traits. Nat Rev Genet 2009; 10: 241–51.
12. Ordovas JM. Gender, a significant factor in the cross talk between genes, environment, and health. Gend Med 2007; 4: S111–22.
13. Jirtle RL, Skinner MK. Environmental epigenomics and disease susceptibility. Nat Rev Genet 2007; 8: 253–62.
14. Sharma S, Kelly TK, Jones PA. Epigenetics in cancer. Carcinogenesis 2010; 31: 27–36.
15. Frayling TM, Timpson NJ, Weedon MN, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 2007; 316: 889–94.
16. Farooqi IS, O’Rahilly S. Genetics of obesity in humans. Endocr Rev 2006; 10: 710–8.
17. Martinez JA, Parra MD, Santos JL, Moreno-Aliaga MJ, Marti A, Martinez-Gonzalez MA. Genotype-dependent response to energy-restricted diets in obese subjects: towards personalized nutrition. Asia Pacific J Clin Nutr 2008; 17: 119–22.
18. Razquin C, Marti A, Martinez JA. Evidences on three relevant obesogenes: MC4R, FTO and PPARgamma. Approaches for personalized nutrition. Mol Nutr Food Res 2011; 55: 136–49.
19. Wang J, Wang LJ, Zhong Y, et al. CETP gene polymorphisms and risk of coronary atherosclerosis in a Chinese population. Lipids Health Disease 2013; 12: 176.
20. Lu Y, Tayebi N, Li H, Saha N, Yang H, Heng CK. Association of CETP Taq1B and -629C > A polymorphisms with coronary artery disease and lipid levels in the multi-ethnic Singaporean population. Lipids Health Disease 2013; 12: 85.
21. Huang D, Xie X, Ma YT, Huang Y, Ma X. Endothelial lipase-384A/C polymorphism is associated with acute coronary syndrome and lipid status in elderly Uygur patients in Xinjiang. Genet Testing Mol Biomarkers 2014; 18: 781–4.
22. Knoblauch H, Bauerfeind A, Krahenbuhl C, et al. Common haplotypes in five genes influence genetic variance of LDL and HDL cholesterol in the general population. Hum Mol Genet 2002; 11: 1477–85.
23. Lyssenko V, Lupi R, Marchetti P, et al. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 2155–63.
24. Shammas MA. Telomeres, lifestyle, cancer, and aging. Curr Opin Clin Nutr Metab Care 2011; 14: 28–34.
25. Mirabello L, Huang WY, Wong JY, et al. The association between leukocyte telomere length and cigarette smoking, dietary and physical variables, and risk of prostate cancer. Aging Cell 2009; 8: 405–13.
26. Liew SC, Gupta ED. Methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism: epidemiology, metabolism and the associated diseases. Eur J Med Genet 2015; 58: 1–10.
27. Cornelis MC, El-Sohemy A, Kabagambe EK, et al. Coffee, CYP1A2 genotype, and risk of myocardial infarction. JAMA 2006; 295: 1135-41.
28. 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.
29. 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.
30. Cahill LE, Fontaine-Bisson B, El-Sohemy A. Functional genetic variants of glutathione S-transferase protect against serum ascorbic acid deficiency. Am J Clin Nutr 2009; 90: 1411-7.
31. Slater NA, Rager ML, Havrda DE, et al. Genetic variation in CYP2R1 and GC genes associated with vitamin D deficiency status. J Pharm Pract 2017; 30: 31-6.
32. Tanwar VS, Chand MP, Kumar J, et al. Common variant in FUT2 gene is associated with levels of vitamin B(12) in Indian population. Gene 2013; 515: 224-8.
33. Benyamin B, Ferreira MA, Willemsen G, et al. Common variants in TMPRSS6 are associated with iron status and erythrocyte volume. Nat Genet 2009; 41: 1173-5.
34. Nielsen DE, El-Sohemy A. A randomized trial of genetic information for personalized nutrition. Genes Nutr 2012; 7: 559-66.
35. Nielsen DE, El-Sohemy A. Disclosure of genetic information and change in dietary intake: a randomized controlled trial. PLoS One 2014; 9: e112665.
36. De Toro-Martin J, Arsenault BJ, Despres JP, Vohl MC. Precision nutrition: a review of personalized nutritional approaches for the prevention and management of metabolic syndrome. Nutrients 2017; 9: 913.
37. Nizel AE. Personalized nutrition counseling. ASDC J Dent Child 1972; 39: 353–60.
38. Brug J, Campbell M, van Assema P. The application and impact of computer-generated personalized nutrition education: A review of the literature. Patient Educ Couns 1999; 36: 145–56.
39. Stewart-Knox B, Kuznesof S, Robinson J, et al. Factors influencing European consumer uptake of personalised nutrition. Results of a qualitative analysis. Appetite 2013; 66: 67–74.
40. Wang DD, Hu FB. Precision nutrition for prevention and management of type 2 diabetes. Lancet Diabetes Endocrinol 2018; 6: 416–26.
41. Van Ommen B, van den Broek T, de Hoogh I, et al. Systems biology of personalized nutrition. Nutr Rev 2017; 75: 579–99.
42. 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.
43. Bouteldja N, Timson DJ. The biochemical basis of hereditary fructose intolerance. J Inherit Metab Dis 2010; 33: 105–12.
44. de Baulny HO, Abadie V, Feillet F, de Parscau L. Management of phenylketonuria and hyperphenylalaninemia. J Nutr 2007; 137: 1561S–3S.
45. Novelli G, Reichardt JK. Molecular basis of disorders of human galactose metabolism: past, present, and future. Mol Genet Metab 2000; 71: 62–5.
46. Heap GA, van Heel DA. Genetics and pathogenesis of coeliac disease. Semin Immunol 2009; 21: 346–54.
47. Lactose intolerance: your guide to understanding genetic conditions. Available at: Accessed 05 June 2017.
48. Fallaize R, Macready AL, Butler LT, Ellis JA, Lovegrove JA. An insight into the public acceptance of nutrigenomic-based personalised nutrition. Nutr Res Rev 2013; 26: 39–48.
49. Atkinson SA. Defining the process of dietary reference intakes: frameworkfor the United States and Canada. Am J Clin Nutr 2011; 94: 655S–7S.
50. Taylor C L. Framework for DRI development: components ‘known’ andcomponents ‘to be explored.’ 2008, Available at:
51. Timotijevic L, Brown KA, Lähteenmäki L, et al. EURRECA––a framework for considering evidence in public health nutrition policy development. Crit Rev Food Sci Nutr 2013; 53: 1124–34.
52. Claessens M, Contor L, Dhonukshe-Rutten R, et al. EURRECA––principles and future for deriving micronutrient recommendations. Crit Rev Food Sci Nutr 2013; 53: 1135–46.
53. Hruby A, Manson JE, Qi L, et al. Determinants and consequences of obesity. Am J Public Health 2016; 106: 1656–62.
54. Qi Q, Chu AY, Kang JH, et al. Sugar-sweetened beverages and genetic risk of obesity. N Engl J Med 2012; 367: 1387–96.
55. Brunkwall L, Chen Y, Hindy G, et al. Sugar-sweetened beverage consumption and genetic predisposition to obesity in 2 Swedish cohorts. Am J Clin Nutr 2016; 104: 809–15.
56. Olsen NJ, Angquist L, Larsen SC, et al. Interactions between genetic variants associated with adiposity traits and soft drinks in relation to longitudinal changes in body weight and waist circumference. Am J Clin Nutr 2016; 104: 816–26.
57. Qi Q, Chu AY, Kang JH, et al. Fried food consumption, genetic risk, and body mass index: Gene-Diet interaction analysis in three us cohort studies. BMJ 2014; 348: g1610.
58. Ahmad S, Rukh G, Varga TV, et al. Gene × physical activity interactions in obesity: Combined analysis of 111,421 individuals of european ancestry. PLoS Genet 2013; 9: e1003607.
59. Tyrrell J, Wood AR, Ames RM, et al. Gene-obesogenic environment interactions in the UK biobank study. Int J Epidemiol 2017; 46: 559-75.
60. Alfredo Martínez J. Perspectives on personalized nutrition for obesity. J Nutrigenet Nutrigenomics 2014; 7: I–III.
61. Marreiro Ddo N, de Sousa AF, Nogueira Ndo N, Oliveira FE. Effect of zinc supplementation on thyroid hormone metabolism of adolescents with Down syndrome. Biol Trace Elem Res 2009; 129: 20–7.
62. Pogribna M, Melnyk S, Pogribny I, Chango A, Yi P, James SJ. Homocysteine metabolism in children with Down syndrome: in vitro modulation. Am J Hum Genet 2001; 69: 88–95.
63. Ordovas JM, Mooser V: Nutrigenomics and nutrigenetics. Curr Opin Lipidol 2004; 15: 101–8.
64. Rao AD, Sun B, Saxena A, et al. Polymorphisms in the serum- and glucocorticoid-inducible kinase 1 gene are associated with blood pressure and renin response to dietary salt intake. J Hum Hypertens 2013; 27: 176–80.
65. Mustalahti K, Catassi C, Reunanen A, et al. The prevalence of celiac disease in Europe: results of a centralized, international mass screening project. Ann Med 2010; 42: 587–95.
66. Fenech M, El-Sohemy A, Cahill L, et al: Nutrigenetics and nutrigenomics: viewpoints on the current status and applications in nutrition research and practice. J Nutrigenet Nutrigenomics 2011; 4: 69–89.
67. Burkhardt R, Kirsten H, Beutner F, et al. Integration of genome-wide SNP data and gene-expression profiles reveals six novel loci and regulatory mechanisms for amino acids and acylcarnitines in whole blood. PLoS Genet 2015; 11: e1005510.
68. Goni L, Cuervo M, Milagro FI, Martínez A. Future perspectives of personalized weight loss interventions based on nutrigenetic, epigenetic, and metagenomic data. J Nutr 2015; 146: 905S–12S.
69. Precone V, Beccari T, Stuppia L, et al. Taste, olfactory and texture related genes and food choices: Implications on health status. Eur Rev Med Pharmacol Sci 2019; 23: 1305–21.
70. Grimaldi K. Nutrigenetics and personalized nutrition: Are we ready for DNA-based dietary advice? Pers Med 2014; 11: 297–307.
71. De Caterina R, El-Sohemy A. Moving towards specific nutrigenetic recommendation algorithms: caffeine, genetic variation and cardiovascular risk. J Nutr Nutr 2016; 9: 106–15.
72. Frayling TM, Timpson NJ, Weedon M, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 2007; 316: 889–94.
73. Hetherington MM, Cecil JE. Gene-environment interactions in obesity. Forum Nutr 2009; 63: 195–203.
74. Ortega-Azorín C, Sorlí JV, Asensio EM, et al. Associations of the FTO rs9939609 and the MC4R rs17782313 polymorphisms with type 2 diabetes are modulated by diet, being higher when adherence to the Mediterranean diet pattern is low. Cardiovasc Diabetol 2012; 11: 137.
75. Fisher E, Boeing H, Fritsche A, Doering F, Joost HG, Schulze MB. Whole-grain consumption and transcription factor-7-like 2 (TCF7L2) rs7903146: Gene–diet interaction in modulating type 2 diabetes risk. Br J Nutr 2008; 101: 478–81.
76. Hindy G, Sonestedt E, Ericson U, et al. Role of TCF7L2 risk variant and dietary fibre intake on incident type 2 diabetes. Diabetologia 2012; 55: 2646–54.
77. Wirström T, Hilding A, Gu HF, Ostenson CG, Björklund A. Consumption of whole grain reduces risk of deteriorating glucose tolerance, including progression to prediabetes. Am J Clin Nutr 2012; 97: 179–87.
78. López-Ortiz MM, Garay-Sevilla ME, Tejero ME, Pérez-Luque E. Analysis of the interaction between transcription factor 7-like 2 genetic variants with nopal and wholegrain fibre intake: Effects on anthropometric and metabolic characteristics in type 2 diabetes patients. Br J Nutr 2016; 116: 969–78.
79. Franzago M, Fraticelli F, Nicolucci A, et al. Molecular analysis of a genetic variants panel related to nutrients and metabolism: association with susceptibility to gestational diabetes and cardiometabolic risk in affected women. J Diabetes Res 2017; 2017: 4612623.
80. Franzago M, Fraticelli F, Marchetti D, et al. Nutrigenetic variants and cardio-metabolic risk in women with or without gestational diabetes. Diabetes Res Clin Pract 2018; 137: 64–71.
81. Franzago M, Fraticelli F, Di Nicola M, et al. Early subclinical atherosclerosis in gestational diabetes: the predictive role of routine biomarkers and nutrigenetic variants. J Diabetes Res 2018; 2018: 9242579.
82. Dhurandhar NV, Schoeller D, Brown AW, et al. Energy balance measurement: When something is not better than nothing. Int J Obes 2014; 39: 1109–13.
83. Archer E, Marlow ML, Lavie CJ. Controversy and debate: memory based methods paper 3: nutrition’s ‘Black Swans’: Our reply. J Clin Epidemiol 2018; 104: 130–5.
84. Matsuo T, Nakata Y, Murotake Y, Hotta K, Tanaka K. Effects of FTO genotype on weight loss and metabolic risk factors in response to calorie restriction among Japanese women. Obesity (Silver Spring) 2012; 20: 1122–6.
85. Zheng Y, Huang T, Zhang X, et al. Dietary fat modifies the effects of FTO genotype on changes in insulin sensitivity. J Nutr 2015; 145: 977–82.
86. Grau K, Hansen T, Holst C, et al. Macronutrient-specific effect of FTO rs9939609 in response to a 10-week randomized hypo-energetic diet among obese Europeans. Int J Obes (Lond) 2009; 33: 1227–34.
87. Zhang X, Qi Q, Zhang C, et al. FTO genotype and 2-year change in body composition and fat distribution in response to weight-loss diets: the POUNDS LOST Trial. Diabetes 2012; 61: 3005–11.
88. Mitchell JA, Church TS, Rankinen T, Earnest CP, Sui X, Blair SN. FTO genotype and the weight loss benefits of moderate intensity exercise. Obesity (Silver Spring) 2010; 18: 641–3.
89. Labayen I, Margareto J, Maldonado-Martin S, et al. Independent and combined influence of the FTO rs9939609 and MC4Rrs17782313 polymorphisms on hypocaloric diet induced changes in body mass and composition and energy metabolism in non-morbid obese premenopausal women. Nutr Hosp 2015; 31: 2025–32.
90. De Luis DA, Izaola O, Primo D, et al. Role of rs1501299 variant in the adiponectin gene on total adiponectin levels, insulin resistance and weight loss after a Mediterranean hypocaloric diet. Diabetes Res Clin Pract 2019; 148: 262–7.
91. Zhang Y, Proenca R, Maffei M, Barone M, Leopold L, Friedman JM. Positional cloning of the mouse obese gene and its human homologue. Nature 1994; 372: 425–32.
92. De Luis Roman D, de la Fuente RA, Sagrado MG, Izaola O, Vicente RC. Leptin receptor Lys656Asn polymorphism is associated with decreased leptin response and weight loss secondary to a lifestyle modification in obese patients. Arch Med Res 2006; 37: 854–9.
93. Walley AJ, Asher JE, Froguel P. The genetic contribution to non-syndromic human obesity. Nat Rev Genet 2009; 10: 431-42.
94. Choquet H, Meyre D. Genetics of obesity: What have we learned? Curr Genomics 2011; 12: 169-79.
95. Labayen I, Margareto J, Maldonado-Martin S, et al. Independent and combined influence of the FTO rs9939609 and MC4R rs17782313 polymorphisms on hypocaloric diet induced changes in body mass and composition and energy metabolism in non-morbid obese premenopausal women. Nutr Hosp 2015; 31: 2025–32.
96. Pan Q, Delahanty LM, Jablonski KA, et al. Variation at the melanocortin 4 receptor gene and response to weight-loss interventions in the diabetes prevention program. Obesity (Silver Spring) 2013; 21: E520–6.
97. Huang T, Huang J, Qi Q, et al. PCSK7genotype modifies effect of a weight-loss diet on 2-year changes of insulin resistance: The pounds lost trial. Diabetes Care 2015; 38: 439–44.
98. Tonjes A, Scholz M, Loeffler M, Stumvoll M. Association of Pro12Ala polymorphism in peroxisome proliferator-activated receptor gamma with pre-diabetic phenotypes: meta-analysis of 57 studies on nondiabetic individuals. Diabetes Care 2006; 29: 2489-97.
99. Meirhaeghe A, Amouyel P. Impact of genetic variation of PPAR gamma in humans. Mol Genet Metab 2004; 83: 93-102.
100. Ruiz JR, Larrarte E, Margareto J, Ares R, Labayen I. Role of β₂-adrenergic receptor polymorphisms on body weight and body composition response to energy restriction in obese women: preliminary results. Obesity (Silver Spring) 2011; 19: 212–5.
101. Saliba LF, Reis RS, Brownson RC, et al. Obesity-related gene ADRB2, ADRB3 and GHRL polymorphisms and the response to a weight loss diet intervention in adult women. Genet Mol Biol 2014; 37: 15–22.
102. Gardner CD, Trepanowski JF, Del Gobbo LC, et al. Effect of low-fat vs low-carbohydrate diet on 12-Month weight loss in overweight adults and the association with genotype pattern or insulin secretion: the DIETFITS randomized clinical trial. JAMA 2018; 319: 667–79.
103. Tahara A, Osaki Y, Kishimoto T. Effect of the β3-adrenergic receptor gene polymorphism Trp64Arg on BMI reduction associated with an exercise-based intervention program in Japanese middle-aged males. Environ Health Prev Med 2010; 15: 392–7.
104. Shiwaku K, Nogi A, Anuurad E, et al. Difficulty in losing weight by behavioral intervention for women with Trp64Arg polymorphism of the beta3-adrenergic receptor gene. Int J Obes Relat Metab Disord 2003; 27: 1028–36.
105. Korbonits M, Gueorguiev M, O'Grady E, et al. A variation in the ghrelin gene increases weight and decreases insulin secretion in tall, obese children. J Clin Endocrinol Metab 2002; 87: 4005-8.
106. Zarebska A, Jastrzebski Z, Cieszczyk P, et al. The Pro12Ala polymorphism of the peroxisome proliferator-activated receptor gamma gene modifies the association of physical activity and body mass changes in Polish women. PPAR Res 2014; 2014: 373782.
107. Mocking RJT, Lok A, Assies J, et al. Ala54Thr fatty acid-binding protein 2 (FABP2) polymorphism in recurrent depression: associations with fatty acid concentrations and waist circumference. PLoS One 2012; 8: e82980.
108. Zhang X, Qi Q, Bray GA, Hu FB, Sacks FM, Qi L. APOA5 genotype modulates 2-y changes in lipid profile in response to weight-loss diet intervention: the Pounds Lost Trial. Am J Clin Nutr 2012; 96: 917–22.
109. Suchanek P, Lorenzova A, Poledne R, Hubacek JA. Changes of plasma lipids during weight reduction in females depends on APOA5 variants. Ann Nutr Metab 2008; 53: 104–8.
110. De Luis DA, Izaola O, Primo D, Aller R. Role of rs670 variant of APOA1 gene on lipid profile, insulin resistance and adipokine levels in obese subjects after weight loss with a dietary intervention. Diabetes Res Clin Pract 2018; 142: 139–45.
111. Xu M, Ng SS, Bray GA, et al. Dietary fat intake modifies the effect of a common variant in the LIPC gene on changes in serum lipid concentrations during a long-term weight-loss intervention trial. J Nutr 2015; 145: 1289–94.
112. Qi Q, Durst R, Schwarzfuchs D, et al. CETP genotype and changes in lipid levels in response to weight loss diet intervention in the POUNDS LOST and DIRECT randomized trials. J Lipid Res 2015; 56: 713–21.
113. De Luis DA, Izaola O, Primo D, Aller R. Association of the rs10830963 polymorphism in melatonin receptor type 1B (MTNR1B) with metabolic response after weight loss secondary to a hypocaloric diet based in Mediterranean style. Clin Nutr 2018; 37: 1563–8.
114. Goni L, Sun D, Heianza Y, et al. Macronutrient-specific effect of the MTNR1B genotype on lipid levels in response to 2 year weight-loss diets. J Lipid Res 2018; 59: 155–61.
115. Lin X, Qi Q, Zheng Y, et al. Neuropeptide Y genotype, central obesity, and abdominal fat distribution: the POUNDS LOST trial. Am J Clin Nutr 2015; 102: 514–9.
116. Qi Q, Bray GA, Hu FB, Sacks FM, Qi L. Weight-loss diets modify glucose-dependent insulinotropic polypeptide receptor rs2287019 genotype effects on changes in body weight, fasting glucose, and insulin resistance: the Preventing Overweight Using Novel Dietary Strategies trial. Am J Clin Nutr 2012; 95: 506–13.
117. Qi Q, Bray GA, Smith SR, Hu FB, Sacks FM, Qi L. Insulin receptor substrate 1 gene variation modifies insulin resistance response to weight-loss diets in a 2-year randomized trial: the Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial. Circulation 2011; 124: 563–71.
118. Qi Q, Xu M, Wu H, et al. IRS1 genotype modulates metabolic syndrome reversion in response to 2-year weight-loss diet intervention: the POUNDS LOST trial. Diabetes Care 2013; 36: 3442–7.
119. Grau K, Cauchi S, Holst C, et al. TCF7L2 rs7903146macronutrient interaction in obese individuals’ responses to a 10-wk randomized hypoenergetic diet. Am J Clin Nutr 2010; 91: 472–9.
120. Löffler D, Behrendt S, Creemers JWM, et al. Functional and clinical relevance of novel and known PCSK1 variants for childhood obesity and glucose metabolism. Mol Metab 2016; 6: 295-305.
121. Huang T, Huang J, Qi Q, et al. PCSK7 genotype modifies effect of a weight-loss diet on 2-year changes of insulin resistance: the POUNDS LOST trial. Diabetes Care 2015; 38: 439–44.
122. Rankinen T, Bouchard C. Gene-physical activity interactions: overview of human studies. Obesity (Silver Spring) 2008; 16: S47-50.
123. Lin X, Eaton CB, Manson JE, Liu S. The genetics of physical activity. Curr Cardiol Rep 2017; 19: 119.
124. Lin X, Zhang X, Guo J, et al. Effects of exercise training on cardiorespiratory fitness and biomarkers of cardiometabolic health: a systematic review and meta-analysis of randomized controlled trials. J Am Heart Assoc 2015; 4: e002014.
125. Goryakin Y, Aldea A, Lerouge A, et al. Promoting sport and physical activity in Italy: a cost-effectiveness analysis of seven innovative public health policies. Ann Ig 2019; 31: 614-25.
126. Carlson SA, Fulton JE, Pratt M, Yang Z, Adams EK. Inadequate physical activity and health care expenditures in the United States. Prog Cardiovasc Dis 2015; 57: 315–23.
127. De Geus EJ, De Moor MH. Genes, exercise, and psychological factors. In: Genetic and molecular aspects of sport performance. Oxford: Blackwell Publishing; 2011.
128. Perusse L, Tremblay A, Leblanc C, Bouchard C. Genetic and environmental influences on level of habitual physical activity and exercise participation. Am J Epidemiol 1989; 129: 1012–22.
129. Simonen RL, Perusse L, Rankinen T, Rice T, Rao DC, Bouchard C. Familial aggregation of physical activity levels in the Quebec Family Study. Med Sci Sports Exerc 2002; 34: 1137–42.
130. Sallis JF, Patterson TL, Buono MJ, Atkins CJ, Nader PR. Aggregation of physical activity habits in Mexican-American and Anglo families. J Behav Med 1988; 11: 31–41.
131. Choh AC, Demerath EW, Lee M, et al. Genetic analysis of self-reported physical activity and adiposity: the Southwest Ohio Family Study. Public Health Nutr 2009; 12: 1052–60.
132. Butte NF, Cai G, Cole SA, Comuzzie AG. Viva la Familia Study: genetic and environmental contributions to childhood obesity and its comorbidities in the Hispanic population. Am J Clin Nutr 2006; 84: 646–54.
133. Moore LL, Lombardi DA, White MJ, Campbell JL, Oliveria SA, RC. Influence of parents’ physical activity levels on activity levels of young children. J Pediatr 1991; 118: 215–9.
134. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science 1996; 273: 1516–7.
135. Altmuller J, Palmer LJ, Fischer G, Scherb H, Wjst M. Genome-wide scans of complex human diseases: true linkage is hard to find. Am J Hum Genet 2001; 69: 936–50.
136. Simonen RL, Rankinen T, Perusse L, et al. Genome-wide linkage scan for physical activity levels in the Quebec Family Study. Med Sci Sports Exerc 2003; 35: 1355–9.
137. Cai G, Cole SA, Butte N, et al. A quantitative trait locus on chromosome 18q for physical activity and dietary intake in Hispanic children. Obesity (Silver Spring) 2006; 14: 1596–604.
138. De Moor MH, Posthuma D, Hottenga JJ, Willemsen G, Boomsma DI, De Geus EJ. Genome-wide linkage scan for exercise participation in Dutch sibling pairs. Eur J Hum Genet 2007; 15: 1252–9.
139. Stefan N, Vozarova B, Del Parigi A, et al. The Gln223Arg polymorphism of the leptin receptor in Pima Indians: influence on energy expenditure, physical activity and lipid metabolism. Int J Obes Relat Metab Disord 2002; 26: 1629–32.
140. Richert L, Chevalley T, Manen D, Bonjour JP, Rizzoli R, Ferrari S. Bone mass in prepubertal boys is associated with a Gln223Arg amino acid substitution in the leptin receptor. J Clin Endocrinol Metab 2007; 92: 4380–6.
141. De Moor MH, Liu YJ, Boomsma DI, et al. Genome-wide association study of exercise behavior in Dutch and American adults. Med Sci Sports Exerc 2009; 41: 1887–95.
142. Voisin S, Eynon N, Yan X, Bishop DJ. Exercise training and DNA methylation in humans. Acta Physiol (Oxf) 2015; 213: 39–59.
143. Krieger JW, Sitren HS, Daniels MJ, Langkamp-Henken B. Effects of variation in protein and carbohydrate intake on body mass and composition during energy restriction: a meta-regression Am J Clin Nutr 2006; 83: 260–74.
144. Knapik JJ. The importance of physical fitness for injury prevention: part 2. J Spec Oper Med 2015; 15: 112–5.
145. Van Deveire KN, Scranton SK, Kostek MA, et al. Variants of the ankyrin repeat domain 6 gene (ANKRD6) and muscle and physical activity phenotypes among European-derived American adults. J Strength Cond Res 2012; 26: 1740-8.
146. Bruneau M Jr, Walsh S, Selinsky E, et al. A genetic variant in IL-15Ra correlates with physical activity among European-American adults. Mol Genet Genomic Med 2018; 6: 401–8.
147. Gielen M, Westerterp-Plantenga MS, Bouwman FG, et al. Heritability and genetic etiology of habitual physical activity: a twin study with objective measures. Genes Nutr 2014; 9: 415.
148. Akhmetov II, Astranenkova IV, Rogozkin VA. Mol Biol (Mosk) 2007; 41: 852-7.
149. Stefan N, Vozarova B, Del Parigi A, et al. The Gln223Arg polymorphism of the leptin receptor in Pima Indians: influence on energy expenditure, physical activity and lipid metabolism. Int J Obes Relat Metab Disord 2002; 26: 1629–32.
150. De Moor MH, Posthuma D, Hottenga JJ, Willemsen G, Boomsma DI, De Geus EJ. Genome-wide linkage scan for exercise participation in Dutch sibling pairs. Eur J Hum Genet 2007; 15: 1252–9.
151. Lorentzon M, Lorentzon R, Lerner UH, Nordstrom P. Calcium sensing receptor gene polymorphism, circulating calcium concentrations and bone mineral density in healthy adolescent girls. Eur J Endocrinol 2001; 144: 257–61.
152. Winnicki M, Accurso V, Hoffmann M, et al. Physical activity and angiotensin-converting enzyme gene polymorphism in mild hypertensives. Am J Med Genet A 2004; 125A: 38–44.
153. Loos RJ, Lindgren CM, Li S, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 2008; 40: 768–75.
154. Kilpeläinen TO, Qi L, Brage S, et al. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med 2011; 8: e1001116.
155. Rankinen T, Rice T, Teran-Garcia M, Rao DC, Bouchard C. FTO genotype is associated with exercise training-induced changes in body composition. Obesity 2010; 18: 322–6.
156. Cassidy S, Chau JY, Catt M, et al. Cross-sectional study of diet, physical activity, television viewing and sleep duration in 233 110 adults from the UK Biobank; the behavioural phenotype of cardiovascular disease and type 2 diabetes. BMJ Open 2016; 6: e010038.
157. Franks PW, Jablonski KA, Delahanty LM, et al. Assessing gene-treatment interactions at the FTO and INSIG2 loci on obesity-related traits in the Diabetes Prevention Program. Diabetologia 2008; 51: 2214–23.
158. Liaw YC, Liaw YP, Lan TH. Physical activity might reduce the adverse impacts of the FTO gene variant rs3751812 on the body mass index of adults in Taiwan. Genes (Basel) 2019; 10: 354.
159. Bruneau M Jr, Angelopoulos TJ, Gordon P, et al. The angiotensin-converting enzyme insertion/deletion polymorphism rs4340 associates with habitual physical activity among European American adults. Mol Genet Genomic Med 2017; 5: 524-30.
160. Khoury MJ, Gwinn M, Bowen MS, Dotson WD. Beyond base pairs to bedside: a population perspective on how genomics can improve health. Am J Public Health 2012; 102: 34–7.
161. San-Cristobal R, Milagro FI, Martinez JA. Future challenges and present ethical considerations in the use of personalized nutrition based on genetic advice. J Acad Nutr Diet 2013; 113: 1447–54.
162. Bergmann MM, Gorman U, Mathers JC. Bioethical considerations for human nutrigenomics. Annu Rev Nutr 2008; 28: 447–67.
163. Arab L. Individualized nutritional recommendations: do we have the measurements needed to assess risk and make dietary recommendations? Proc Nutr Soc 2004; 63: 167–72.
164. The NuGO Bioethics Guidelines on Human Studies: - Oslo, September 17th, 2007.
166. Human Genetic Commission (UK HGC): A common framework of principles for direct-to-consumer genetic testing services. London, Human Genome Commission, 2010.
167. The German Human Genetic Examination Act (Gesetz über genetische Untersuchungen bei Menschen Gendiagnostikgesetz-GenDG), 2009.
168. Superior Health Council: Publication of the Superior Health Council No. 8714. Brussels, Superior HealthCouncil, 2012.
169. Borry P, van Hellemondt RE, Sprumont D, et al. Legislation on direct-to consumer genetic testing in seven European countries. Eur J Hum Genet 2012; 20: 715–21.
170. Gibney MJ, Walsh M. Personalised nutrition: an integrated analysis of opportunities and challenges white paper by Food4Me partners. Available at:
171. Berry S, Valdes A, Davies R, et al. Predicting personal metabolic responses to food using multi-omics machine learning in over 1000 twins and singletons from the UK and US: The PREDICT I Study (OR31-01-19). Curr Dev Nutr 2019; 3.
172. Stanton MV, Robinson JL, Kirkpatrick SM, et al. DIETFITS study (diet intervention examining the factors interacting with treatment success) - Study design and methods. Contemp Clin Trials 2017; 53: 151-61.
173. Gorman U. Ethical issues raised by personalized nutrition based on genetic information. Genes Nutr 2006; 1: 13–22.