insights

Despite recent advances in our understanding of nutrigenomics and the role of the microbiome in energy extraction, the idea that a given food will have the same effect for all individuals is still widespread. However, one recent study found that after ingesting identical foods, changes in blood glucose levels could vary by up to 20% in the same individual and up to 25% across individuals.¹

Another study also suggests that individuals may have dramatically different glucose responses to the same meal.² Using continuous glucose monitoring and standardized meals, the researchers found that identical meals led to very different physiologic effects. As a result, any approach that grades dietary ingredients as either “good” or “bad” based on their average postprandial glycemic responses tends to be of little use to an individual patient.²

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 responses³ and overall dietary impact.⁴ Differences in the microbiome across individuals likely also play a role.⁵ The exciting and relatively new discipline of metabolomics is currently being applied to nutritional research to help understand all the factors that affect a person’s individual response to diet.^6,7 Because metabolomics identifies the small molecules and metabolites found in the body that may vary between diets,⁸ researchers suspect it could be used to determine potential biomarkers of disease risk⁹ and to track effects of specific foods.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

While metabolomic testing of every patient is not yet practical, 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 we as clinicians should use all the available data we have to recommend a personalized dietary plan for each patient. It also means that we should also be willing to change that plan 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 The Institute for Functional Medicine’s foundational online 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.

References

1. 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
2. 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
3. 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. Published online August 20, 2019. doi:1002/mnfr.201900226
4. 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
5. Utzschneider KM, Kratz M, Damman CJ, Hullar M. Mechanisms linking the gut microbiome and glucose metabolism. J Clin Endocrinol Metab. 2016;101(4):1445-1454. doi:1210/jc.2015-4251
6. Astarita G, Langridge J. An emerging role for metabolomics in nutrition science. J Nutrigenet Nutrigenomics. 2013;6(4-5):181-200. doi:1159/000354403
7. Zeisel SH. Nutrigenomics and metabolomics will change clinical nutrition and public health practice: insights from studies on dietary requirements for choline. Am J Clin Nutr. 2007;86(3):542-548. doi:1093/ajcn/86.3.542
8. Gibney MJ, Walsh M, Brennan L, Roche HM, German B, van Ommen B. Metabolomics in human nutrition: opportunities and challenges. Am J Clin Nutr. 2005;82(3):497-503. doi:1093/ajcn.82.3.497
9. O’Sullivan A, Gibney MJ, Brennan L. Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am J Clin Nutr. 2011;93(2):314-321. doi:3945/ajcn.110.000950
10.  Heinzmann SS, Brown IJ, Chan Q, et al. Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am J Clin Nutr. 2010;92(2):436-443. doi:3945/?ajcn.2010.29672
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. Published online September 16, 2019. 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. Published online August 28, doi:10.1093/jn/nxz198