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Nutritional interventions tailored to each individual patient are foundational in the functional medicine model for chronic disease treatments and health strategies. A person’s gene-nutrient interactions as well as variations of gut microbiome composition between populations and individuals are important components of therapeutic dietary interventions. Despite recent advances in our understanding of nutrigenetics and nutrigenomics and the role of the gut microbiome in energy extraction, the idea that a given food will have the same effect for all individuals is still widespread. However, studies continue to demonstrate that after ingesting identical foods, postprandial metabolic responses (including blood triglyceride, glucose, and insulin responses) vary considerably between individuals,^1-3 and personalized approaches may benefit metabolic health.^4,5

Nutrigenetics, Nutrigenomics, & Response to Diet

Nutrigenetics investigates how a person’s genetic profile impacts their body’s response to food, influencing absorption, metabolism, and potentially how they respond to nutritional interventions. In tandem, nutrigenomics is the science that explores how foods affect someone’s genes. In other words, what effects do nutrients have on a person’s gene regulation and expression through epigenetic mechanisms?^6,7 Variability in how individuals’ genes respond to the foods they consume and how foods interact with someone’s genes may be at least partially responsible for the variability in glucose responses⁸ and overall dietary impact.⁹ Differences in the microbiome and gut microbial genetic expression across individuals likely also play a role.^10,11

The exciting and relatively new discipline of nutritional metabolomics is currently being applied to nutrigenetic and nutrigenomic research to help understand all the factors that affect a person’s individual response to diet.^12,13 Because metabolomics identifies the small molecules and metabolites found in the body that may vary between diets, researchers suspect it could be used to determine potential biomarkers of disease risk and to track effects of dietary plans and even specific foods.^12-15

Clinical Considerations & the Functional Medicine Model

A major clinical takeaway from this recent nutritional research is that each patient is unique in how they respond to nutrients and how nutrients impact their genes, and an individual patient may not respond in the same way to a particular therapeutic food plan in the same way as other patients, or even in the same way at different times in their lives. This suggests that, if possible, all the available data may be valuable for a clinician when recommending a personalized dietary plan for each patient. It also suggests that a treatment strategy may need to be adjusted if the patient does not respond to it, or if their response to the dietary plan changes. In an era in which more personalized data is available than ever before, healthcare practitioners may choose to leverage this cutting-edge research to assess and treat patients according to their individual needs.^4,5,7,16

IFM’s foundational course, Applying Functional Medicine in Clinical Practice (AFMCP)^TM, connects practitioners to personalized evaluations and clinical tools that can be tailored to each patient’s specific physiology, including genetics, lifestyle, and behavior change. The collaborative therapeutic partnership with patients is a cornerstone of the functional medicine model to support patients and contribute to personalized interventions. AFMCP gives clinicians the tools to prescribe effective treatment plans customized to individual patients’ needs and the flexibility to adapt those plans as needed to match the dynamic physiology of each patient. Join us at AFMCP and learn how to apply these tools to customize patient nutrition and lifestyle recommendations.

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References

1. Zeevi D, Korem T, Zmora N, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-1094. doi:1016/j.cell.2015.11.001
2. Matthan NR, Ausman LM, Meng H, Tighiouart H, Lichtenstein AH. Estimating the reliability of glycemic index values and potential sources of methodological and biological variability. Am J Clin Nutr. 2016;104(4):1004-1013. doi:3945/ajcn.116.137208
3. Berry SE, Valdes AM, Drew DA, et al. Human postprandial responses to food and potential for precision nutrition [published correction appears in Nat Med. 2020;26(11):1802]. Nat Med. 2020;26(6):964-973. doi:1038/s41591-020-0934-0
4. Rein M, Ben-Yacov O, Godneva A, et al. Effects of personalized diets by prediction of glycemic responses on glycemic control and metabolic health in newly diagnosed T2DM: a randomized dietary intervention pilot trial. BMC Med. 2022;20(1):56. doi:1186/s12916-022-02254-y
5. Ben-Yacov O, Godneva A, Rein M, et al. Personalized postprandial glucose response-targeting diet versus Mediterranean diet for glycemic control in prediabetes. Diabetes Care. 2021;44(9):1980-1991. doi:2337/dc21-0162
6. 15th Congress of the International Society of Nutrigenetics & Nutrigenomics (ISNN). Lifestyle Genom. 2023;16(1):35-60. doi:1159/000527546
7. Dallio M, Romeo M, Gravina AG, et al. Nutrigenomics and nutrigenetics in metabolic- (dysfunction) associated fatty liver disease: novel insights and future perspectives. Nutrients. 2021;13(5):1679. doi:3390/nu13051679
8. Murphy AM, Smith CE, Murphy LM, et al. Potential interplay between dietary saturated fats and genetic variants of the NLRP3 inflammasome to modulate insulin resistance and diabetes risk: insights from a meta-analysis of 19 005 individuals. Mol Nutr Food Res. 2019;63(22):e1900226. doi:1002/mnfr.201900226
9. Zweers H, Smit D, Leij S, Wanten G, Janssen MC. Individual dietary intervention in adult patients with mitochondrial disease due to the m.3243 A>G mutation. Nutrition. 2019;69:110544. doi:1016/j.nut.2019.06.025
10.  Tily H, Patridge E, Cai Y, et al. Gut microbiome activity contributes to prediction of individual variation in glycemic response in adults. Diabetes Ther. 2022;13(1):89-111. doi:1007/s13300-021-01174-z
11.  Hoefer CC, Hollon LK, Campbell JA. The role of the human gutome on chronic disease: a review of the microbiome and nutrigenomics. Clin Lab Med. 2022;42(4):627-643. doi:1016/j.cll.2022.09.015
12.  Kiani AK, Bonetti G, Donato K, et al. Polymorphisms, diet and nutrigenomics. J Prev Med Hyg. 2022;63(2 Suppl 3):E125-E141. doi:15167/2421-4248/jpmh2022.63.2S3.2754
13.  Singh V. Current challenges and future implications of exploiting the omics data into nutrigenetics and nutrigenomics for personalized diagnosis and nutrition-based care. Nutrition. 2023;110:112002. doi:1016/j.nut.2023.112002
14.  Srivastava S, Dubey AK, Madaan R, et al. Emergence of nutrigenomics and dietary components as a complementary therapy in cancer prevention. Environ Sci Pollut Res Int. 2022;29(60):89853-89873. doi:1007/s11356-022-24045-x
15.  Ruskovska T, Budić-Leto I, Corral-Jara KF, et al. Systematic analysis of nutrigenomic effects of polyphenols related to cardiometabolic health in humans – evidence from untargeted mRNA and miRNA studies. Ageing Res Rev. 2022;79:101649. doi:1016/j.arr.2022.101649
16.  Christensen L, Vuholm S, Roager HM, et al. Prevotella abundance predicts weight loss success in healthy, overweight adults consuming a whole-grain diet ad libitum: a post hoc analysis of a 6-wk randomized controlled trial. J Nutr. 2019;149(12):2174-2181. doi:1093/jn/nxz198