The Future of Nutrigenomics
October 2017 Issue
The Future of Nutrigenomics
By Carrie Dennett, MPH, RDN, CD
Vol. 19, No. 10, P. 30
Dietitians know genes and diet interact, but is nutrition counseling based on genetic makeup ready for prime time?
Why have public health efforts to prevent obesity and metabolic diseases been relatively unsuccessful? It's likely because making one-size-fits-all nutritional strategies often miss the mark. It's clear that not all people respond to diet equally, and it's becoming more and more clear that, as nutrition science evolves, nutrition professionals need to consider how genes interact with an individual's diet and physical activity patterns.
Nutrigenomics may have the potential to prevent and treat diet-related chronic disease and conditions in a way that nutrition recommendations based on epidemiologic research and physiology can't by using genetics and molecular biology to predict individual risks, explain why those risks are present based on genotype, and allow personalization of nutrition therapy.1,2
What Is Nutrigenomics?
Nutrigenomics and nutrigenetics fall under the umbrella of nutritional genomics—so what's the difference between the two? Both study how individual genetic makeup contributes to observed differences in response to diet and how that gene-diet interaction contributes to predisposition to disease.3 Nutrigenomics goes deeper, using molecular tools to identify how nutrients and bioactive food compounds alter the DNA transcription and translation process, affecting the expression of genes that regulate critical metabolic pathways, which may ultimately affect health outcomes.1,3
Ahmed El-Sohemy, PhD, Canada research chair in nutrigenomics in the department of nutritional sciences at the University of Toronto, says that if you ask five different experts in the field to define nutrigenetics vs nutrigenomics, you'll get five different answers. "There really is no technical official definition of one over the other," El-Sohemy says. "I've moved to using nutrigenomics as the umbrella term to cover gene-diet interactions. 'How does my diet affect my genes?' is what most people want to know."
Personalized Nutrition: Is the Future Here?
Once the Human Genome Project published the full sequence of the human genome in 2003, the push for personalized or precision medicine and nutrition began.2 Not all people respond equally to diet, and nutrigenomics looks at how single-nucleotide polymorphisms (SNPs) interact—alone or in combination—with diet, disease, and other health conditions.2 An SNP is a variation in one nucleotide (the building blocks of DNA) in a specific position in the genome.
On a molecular level, nutrients transmit signals that can be translated into changes in gene, protein, and metabolite expression. In other words, nutrigenomics looks at what happens in our cells when we eat, don't eat, or eat too much.2 Applying nutrigenomics to everyday life as the future of nutrition science offers new tools for dietitians to design and prescribe diets for individuals based on their genome and their genetic variations.
At the end of the day, El-Sohemy, who's founder, president, and chief scientific officer of Nutrigenomix, a biotechnology company that works with dietitians to offer testing for nutrition-related genetic variants, says nutrigenomics is about consumer genetic testing for personalized nutrition. "What does it mean to your patient on a day-to-day basis? Do we have scientific evidence that can tell us, based on your genes, how you should eat?"
Genetic variation likely explains why there are inconsistencies in research investigating the role of diet in disease, and why some individuals don't have "average" responses to nutrition interventions.4 "We used to call these people outliers, but more and more we find that these so-called outliers are consistently there," El-Sohemy says.
Nutrigenomics and Obesity
CVD, type 2 diabetes, and obesity are major public health focus areas. Accordingly, they're also points of focus for nutrigenomic research. Many cellular functions related to energy balance are regulated by gene expression and gene-environment interactions. Genetic variation may affect appetite, calorie intake, and macronutrient preference,5,6 as well as insulin signaling, inflammation, adipogenesis (the formation of fat cells), and lipid metabolism.5 This means that the individual variation seen in body weight and composition likely is influenced by genetic makeup as well as diet and activity patterns.7,8
It's been thought that 25% to 70% of variation in BMI may be due to genetic makeup, but this number is hard to ascertain.7,8 A 2015 genomewide association study (GWAS) and meta-analysis of BMI by the Genetic Investigation of Anthropometric Traits, or GIANT, consortium published in Nature identified 97 chromosomal regions, or loci, accounting for about 2.7% of variation in BMI. The authors say this suggests that common genetic variations account for more than 20% of BMI variation, even though most of that variability remains unexplained.5 So far, the fat mass and obesity-associated (FTO) gene explains the majority of this variance, with individuals who inherited the less common variation from both parents weighing on average 3 kg more and having a 1.7-fold increased odds of being in the obese BMI range than those who inherited two copies of the lower-risk variation.9
Ginger Hultin, MS, RDN, CSO, a spokesperson for the Academy of Nutrition and Dietetics and dietitian at Arivale, a biotechnology company that incorporates genes and blood data into nutrition and health assessments, says there are two different sets of evidence, one for genes related to obesity risk and another for genes that help establish links between dietary factors and body weight. "Keep in mind that it's critical to always assess the interaction between an individual's genetics and environment," she says. "A person could have obesity-related variants and not be overweight, or an individual could have an obese BMI without a strong obesity-related genetic profile."
Diet-Gene Interactions and Weight Loss
Diet interventions in clinical trials universally see variation in response between participants.6,8 Why do some people gain weight in an "obesogenic" food environment while others don't? Why do certain individuals respond differently to diet or physical activity interventions? Why do some people lose weight on a high-protein diet, while others regain? The same question can be asked of low-fat and low-carb diets, and that's a question that Christopher Gardner, PhD, a professor of medicine at Stanford University, has been working hard to answer.10,11
Ten years ago, Gardner had just completed the A to Z study—which compared the Atkins, Zone, Ornish, and LEARN diets and found huge variation in weight outcomes within each12—when a company that was working on genetic testing contacted him and asked if he wanted to look at the participants genotypes. "They had looked at 200 candidate SNPs and found only three that met their stringent criteria," he says. "They specifically made sure that the SNPs selected were connected to functional proteins that were involved in fat and carb metabolism."
Grouping the three SNPs as a multilocus genotype, Gardner got usable samples from 134 of the original 311 women in the study. The question was who fit a low-carb genotype, who fit a low-fat genotype, and who fit neither? Most participants fit one of the two genotypes, nearly a 50-50 split, but because there were four diet groups, some of the specific diet-genotype subgroup sample sizes were small. To explain the variability Gardner and his colleagues were seeing, they needed to replicate it.
Gardner's National Institutes of Health-funded DIETFITS study enrolled more than 600 people to test the primary hypotheses that whoever did best on a low-fat or low-carb diet would depend on insulin resistance and genotype.13 Neither hypothesis panned out. Not only that, but in the newer study, fewer of the participants fit as neatly into the two main genotype patterns. "We tried it and it didn't work," Gardner says. "We followed the scientific method of trying to replicate preliminary findings, and those findings were not replicated." However, their work isn't done: because it was less expensive to test for 850,000 SNPs than for only the three SNPs of interest, Gardner's group still has much genotype data to sift through.
SNP vs GRS: The Controversy
El-Sohemy says many skeptics say that using single SNPs to make nutrition recommendations is useless and that researchers need to wait until the less-robust science on genetic risk scores (GRSs)—which aggregate information from multiple risk-related SNPs—further evolves. El-Sohemy disagrees: "We're not trying to predict obesity, we're trying to look at what is a marker that's actionable," he says. "You can do that, with even a single gene."
For example, a 2012 Harvard study found that individuals with a specific FTO variation lost significantly more body fat on a high-protein (20% to 30% of calories) diet than those on a low-protein diet,14 possibly due to reduction in appetite and cravings.15 Protein intake made little difference for participants who didn't have that variation.14 El-Sohemy replicated these findings, looking at BMI and waist circumference in an East Asian population, and the results also were replicated in a study involving 16,000 children.16
"If you're looking at the right metabolic gene, it should function the same way in different populations, and there's evidence that it does," El-Sohemy says. "There are other SNPs that don't factor in, but based on the evidence we can use the information from that one single SNP to guide advice on protein." The important question, he says, is whether that bit of information is better than the current standard of care.
One BMI-related gene can have many mutations, with different effects,17 and this has been demonstrated with the FTO gene. A 2016 meta-analysis published in The American Journal of Clinical Nutrition found that individuals with the obesity-predisposing variation of the FTO gene were more likely to lose weight through diet and lifestyle interventions than noncarriers.18 Various SNPs in the FTO gene are associated with different dietary interactions. For example, one SNP is associated with increased risk of type 2 diabetes if there's low adherence to a Mediterranean diet and with obesity if a high-fat diet is consumed. A different FTO SNP is associated with obesity when a high-carb diet is consumed.1 Individuals with the SNP assessed in the Harvard study also can counteract the effect of their genotype by increasing physical activity.19,20
SNPs affecting other genes can reduce resting metabolic rate21 or determine whether an individual will lose weight on a low- to moderate-fat diet,22,23 or a diet that includes more monounsaturated fats.24 Various SNPs may increase the odds of weight gain on a high-dairy diet or a diet low in vitamin D. Another SNP is associated with enhanced weight loss on a high-fiber diet.1
Data from the Nurses' Health Study and the Health Professionals Follow-Up Study found that individuals who have a higher GRS for obesity showed a greater association between fried food intake and weight gain than those with a lower GRS.25 Similar results were found for sugar-sweetened beverages.26 A high GRS also may interact aversely with a diet high in dietary fat, especially saturated fat,27 inducing gene expression profiles related to inflammation, glucose intolerance, and fatty liver, as well as weight gain.1
Does Knowing Your Genotype Matter?
Hultin points out that standard recommendations for dietary macronutrients that encourage weight loss are based on practitioner or client preferences since there's often no clear evidence for when to use a low-fat or low-carbohydrate diet for a specific patient. As more nutrition-related SNPs are identified, we can move beyond generalized recommendations. "Incorporating genetic data into this equation allows for personalization and often increases motivation in the patient to adhere to a specific dietary plan," she says.
As every dietitian knows, motivation is key. El-Sohemy says that while simply telling someone they're at increased risk of developing a disease or health condition isn't helpful, there's evidence that when an individual has that information—and there's a defined action they can take—it increases motivation.28,29 A 2014 randomized controlled trial coauthored by El-Sohemy found that when participants were given DNA-based dietary advice coupled with personalized recommendations, they were more likely to make—and maintain—changes in diet.28 "Not only did that increase compliance, but it increased compliance in those who needed it the most," he says.
State of the Science
It's becoming increasingly clear that the traditional assumption of nutrition research—that the influence of diet on disease risk is universal—isn't accurate.8 We all live in the same food environment, but not all people gain weight or develop chronic disease. As technology improves, GWAS and large meta-analyses are making it possible to examine the interactions between millions of SNPs, dietary factors, and specific phenotypes (observable traits based on gene-environment interactions).4
In spite of that, El-Sohemy constantly hears from skeptics that it's too soon to offer personalized nutrition. "We've gone from one-size-fits-all to an ideal of perfect precision nutrition with no chance of error. We can drag our feet, but at the end of the day we have to give nutrition advice today, because we eat today." He says health care practitioners do give advice, but that's based on science from maybe 20 years ago. "We now have better science that can give us more precise advice. Why not do that?"
While several SNPs have been identified that can more accurately guide recommendations for specific micro- or macronutrients, the research isn't at the point where nutrition professionals or other health care practitioners can precisely tailor someone's entire diet to their genes. Researching gene-diet interactions generally requires large sample sizes, compared with GWAS simply looking for association, because collecting accurate measures of diet and physical activity is difficult.30 Large-scale collaborations of multiple weight loss trials may be needed to identify new genetic loci.31 Gardner says the National Institutes of Health has started a consortium of researchers who have weight loss and genotype data, in an effort to pool the study data. "We might not be able to answer this individually," he says. "The potential is certainly still there, but the complexity of it is massive. We are getting better tools."
"Using genetic information to help create diet recommendations with dietitian guidance is a useful, ready-for-primetime option because—for gene variants where the evidence is consistent and strong—genetic data can be incorporated in a way that's aligned with a person's unique physiology and lifestyle," Hultin says.
Other factors in personalizing nutrition recommendations for weight and health include not just genotype and phenotype but also the microbiome—there are genetic factors that regulate the gut microbiome, which in turn affects how we respond to diet—gender, and other aspects of the individual's environment.1,32,33 Social and economic considerations also can't be ruled out.33,34
Incorporating Nutrigenomics Into Nutrition Counseling
"Obesity is a complex condition. Genes play a role, but we can't underestimate the potential impact of a person's lifestyle and environment," Hultin says. "Stating a person should eat a certain diet based on genes alone is too simplistic." She points out that while using obesity genetics in clinical practice is highly promising, it's not a stand-alone tactic and always should include data translation and personalized guidance from a dietitian or other qualified nutrition practitioner.35 "A dietitian can help coach patients into realistic strategies for reducing their fat or carb intake—instead of completely overhauling the diet—therefore achieving weight loss in a sustainable way."
Even though the ability to give actionable nutrition recommendations based on genotype—say, for example, sugar intake—may be powerful, dietitians also know that excessive sugar intake isn't good for anyone. So in some ways, nutrition counseling will be the same. "At the end of the day, Americans eat too much added sugar, too much refined flour, and not enough vegetables. I don't care what your genotype is. You're going to have to address those three things," Gardner says. "Of course, that's not sexy. Maybe we need to refocus on elevating the unapologetic deliciousness of good food."
— Carrie Dennett, MPH, RDN, CD, is the nutrition columnist for The Seattle Times and speaks frequently on nutrition-related topics. She also provides nutrition counseling via the Menu for Change program in Seattle.
1. 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(1-2):43-62.
2. Pavlidis C, Patrinos GP, Katsila T. Nutrigenomics: a controversy. Appl Transl Genom. 2015;4:50-53.
3. 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(1):28-46.
4. Ordovás Muñoz JM. Predictors of obesity: the 'power' of the omics. Nutr Hosp. 2013;28(Suppl 5):63-72.
5. Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197-206.
6. Heianza Y, Qi L. Gene-diet interaction and precision nutrition in obesity. Int J Mol Sci. 2017;18(4):E787.
7. Alfredo Martínez J. Perspectives on personalized nutrition for obesity. J Nutrigenet Nutrigenomics. 2014;7(1):I-III.
8. Qi L. Personalized nutrition and obesity. Ann Med. 2014;46(5):247-252.
9. Livingstone KM, Celis-Morales C, Papandonatos GD, et al. FTO genotype and weight loss: systematic review and meta-analysis of 9563 individual participant data from eight randomised controlled trials. BMJ. 2016;354:i4707.
10. Gardner CD. Tailoring dietary approaches for weight loss. Int J Obes Suppl. 2012;2(Suppl 1):S11-S15.
11. Gardner CD, Offringa LC, Hartle JC, Kapphahn K, Cherin R. Weight loss on low-fat vs. low-carbohydrate diets by insulin resistance status among overweight adults and adults with obesity: a randomized pilot trial. Obesity (Silver Spring). 2016;24(1):79-86.
12. Gardner CD, Kiazand A, Alhassan 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. JAMA. 2007;297(9):969-977.
13. 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;53:151-161.
14. 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(11):3005-3011.
15. Huang T, Qi Q, Li Y, et al. FTO genotype, dietary protein and change in appetite: the Preventing Overweight using Novel Dietary Strategies trial. Am J Clin Nutr. 2014;99(5):1126-1130.
16. Qi Q, Downer MK, Kilpeläinen TO, et al. Dietary intake, FTO genetic variants, and adiposity: a combined analysis of over 16,000 children and adolescents. Diabetes. 2015;64(7):2467-2476.
17. Hebebrand J, Volckmar AL, Knoll N, Hinney A. Chipping away the 'missing heritability': GIANT steps forward in the molecular elucidation of obesity — but still lots to go. Obes Facts. 2010;3(5):294-303.
18. Xiang L, Wu H, Pan A, et al. FTO genotype and weight loss in diet and lifestyle interventions: a systematic review and meta-analysis. Am J Clin Nutr. 2016;103(4):1162-1170.
19. Andreasen CH, Stender-Petersen KL, Mogensen MS, et al. Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation. Diabetes. 2008;57(1):95-101.
20. Graff M, Scott RA, Justice AE, et al. Genome-wide physical activity interactions in adiposity — a meta-analysis of 200,452 adults. PLoS Genet. 2017;13(4):e1006528.
21. Nagai N, Sakane N, Tsuzaki K, Moritani T. UCP1 genetic polymorphism (-3826 A/G) diminishes resting energy expenditure and thermoregulatory sympathetic nervous system activity in young females. Int J Obes (Lond). 2011;35(8):1050-1055.
22. Grau K, Cauchi S, Holst C, et al. TCF7L2 rs7903146-macronutrient interaction in obese individuals' responses to a 10-wk randomized hypoenergetic diet. Am J Clin Nutr. 2010;91(2):472-479.
23. Mattei J, Qi Q, Hu FB, Sacks FM, Qi L. TCF7L2 genetic variants modulate the effect of dietary fat intake on changes in body composition during a weight-loss intervention. Am J Clin Nutr. 2012;96(5):1129-1136.
24. Garaulet M, Smith CE, Hernández-González T, Lee YC, Ordovás JM. PPARγ Pro12Ala interacts with fat intake for obesity and weight loss in a behavioural treatment based on the Mediterranean diet. Mol Nutr Food Res. 2011;55(12):1771-1779.
25. 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.
26. Qi Q, Chu AY, Kang JH, et al. Sugar-sweetened beverages and genetic risk of obesity. N Engl J Med. 2012;367(15):1387-1396.
27. Casas-Agustench P, Arnett DK, Smith CE, et al. Saturated fat intake modulates the association between an obesity genetic risk score and body mass index in two US populations. J Acad Nutr Diet. 2014;114(12):1954-1966.
28. Nielsen DE, El-Sohemy A. Disclosure of genetic information and change in dietary intake: a randomized controlled trial. PLoS One. 2014;9(11):e112665.
29. Celis-Morales C, Marsaux CF, Livingstone KM, et al. Can genetic-based advice help you lose weight? Findings from the Food4Me European randomized controlled trial. Am J Clin Nutr. 2017;105(5):1204-1213.
30. Day FR, Loos RJ. Developments in obesity genetics in the era of genome-wide association studies. J Nutrigenet Nutrigenomics. 2011;4(4):222-238.
31. Qi L. Gene-diet interaction and weight loss. Curr Opin Lipidol. 2014;25(1):27-34.
32. Turpin W, Espin-Garcia O, Xu W, et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat Genet. 2016;48(11):1413-1417.
33. 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(1):12-27.
34. Shao A, Drewnowski A, Willcox DC, et al. Optimal nutrition and the ever-changing dietary landscape: a conference report. Eur J Nutr. 2017;56(Suppl 1):1-21.
35. Camp KM, Trujillo E. Position of the Academy of Nutrition and Dietetics: nutritional genomics. J Acad Nutr Diet. 2014;114(2):299-312.