Nutrigenomics: Personalized Diets to Meet Patient Needs

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Nutritional interventions tailored to the 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 developing components of therapeutic dietary interventions. Despite recent advances in our understanding of 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), for example, vary considerably between individuals.1-3

Nutrigenomics, the science that explores how individual genetic differences play a role in the way an individual responds to diet, may be at least partially responsible for the variability in glucose responses4 and overall dietary impact.5 Differences in the microbiome and gut microbial genetic expression across individuals likely also play a role.6,7 The exciting and relatively new discipline of nutritional metabolomics is currently being applied to nutrigenomics research to help understand all the factors that affect a person’s individual response to diet.8 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 specific foods.8-10 It also serves as a readout of the combined genetic and epigenetic effects that impact response to diet, the latter of which are responsible for a significant proportion of glucose metabolism.11

A major clinical takeaway from this recent nutritional research is that each patient is unique and may not respond in the same way to a particular food plan at different times, or in the same way as other patients. This means that clinicians should use all the available data to recommend a personalized dietary plan for each patient. It also means that a treatment strategy may need to be changed or 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 can achieve amazing outcomes by leveraging this cutting-edge research to assess and treat patients according to their individual needs.12,13

IFM’s foundational course, Applying Functional Medicine in Clinical Practice (AFMCP), connects practitioners to personalized evaluations and clinical tools that can be tailored to each patient’s specific physiology, including genetics, lifestyle, and behavior change. AFMCP gives you 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 simple tools to customize patient nutrition and lifestyle recommendations.

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  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. 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
  5. 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
  6. 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
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  8. 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
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  10.  Ruskovska T, Budic-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
  11.  Sharma S, Kriebel J, Grallert H. Epigenetic regulation of glucose metabolism. Curr Opin Clin Nutr Metab Care. 2017;20(4):266-271. doi:1097/MCO.0000000000000375
  12.  Hassapidou M, Tziomalos K, Lazaridou S, et al. The Nutrition Health Alliance (NutriHeAl) study: a randomized, controlled, nutritional intervention based on Mediterranean diet in Greek municipalities. J Am Coll Nutr. 2020;39(4):338-344. doi:1080/07315724.2019.1660928
  13.  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

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