There’s 99.9% similarity across human genomes, with the remaining 0.1% variation being what makes each person unique.1 In that tiny number, there is a tremendous amount of diversity, including in how an individual processes and responds to dietary components.
Nutrients from food directly impact health by influencing the expression of genes involved in metabolic processes. Nutrigenomics—the science of how genes interact with each other and with diet—while still in its infancy, is helping shape the future of personalized nutrition. The transcription and translation of genes can be altered by macronutrients, micronutrients, and phytochemicals, which go on to have profound effects on homeostatic processes throughout the body that influence disease progression. Diet may also affect genes through influencing the frequency of genetic mutations at the chromosomal or base sequence level. Additionally, the health effects of nutrients depend in part on the inherited genetic variants that alter the uptake and metabolism of nutrients themselves.2
Nutrigenomics vs Nutrigenetics
Nutrigenomics and nutrigenetics are terms often confused with one another, despite being distinct. Nutrigenomics explores how the nutrients we consume influence gene expression and interact with our genes’ activity. Nutrigenetics focuses on the effects of these genetic variations on food components, offering information about how an individual’s specific genes or body responds to food, and this can be considered their specific and unique nutrigenetic profile.2
To date, nutrigenetic research has primarily focused on the effects of single nucleotide polymorphisms (SNPs), epigenetic markers, and other genomic markers of biological responses to micronutrients and macronutrients. For example, a study comparing agricultural societies that ate high-starch diets with hunter-gatherer societies that ate low-starch diets found differences in the number of copies of the starch-digesting salivary amylase gene between the two groups. Researchers noted that as a result of the more robust salivary amylase presence in the high-starch consuming group, they appeared better equipped to digest the starch, with a corresponding reduced risk of obesity, despite the higher starch intake.2
Many studies explore the effects of polymorphisms located near or within genes regulating food intake, lipoprotein and lipid metabolism, glucose homeostasis, insulin signaling, circadian cycles, inflammatory responses, and amino acid metabolism on metabolic processes like detoxification and biotransformation processes, weight gain/loss, insulin resistance, and serum lipid levels, just to name a few. For instance, diets tailored to people with polymorphisms in the apolipoprotein E gene often aim to decrease the intake of saturated fats compared with the standard dietary advice, since these individuals may be at increased risk of myocardial infarction.3
Some researchers point out that we must look beyond these DNA sequence variants (like SNPs) alone and consider the copy number variants (CNVs). Copy number variation refers to a circumstance in which the number of copies of a specific segment of DNA varies among different genomes. These variants may be short or include thousands of bases. CNVs may come about through duplications, deletions, or other changes and can affect long stretches of DNA.4
Studies have reported an association between CNVs for small genome sections and the risk of disease. Examples include a lower copy number of the salivary amylase alpha 1A gene associated with altered carbohydrate metabolism and obesity predisposition, and a specific base deletion/insertion at a specific site on the leptin receptor gene associated with increased risk for type 2 diabetes.3 Current research has only touched the tip of the iceberg in terms of exploring the many levels of potential gene-diet interactions in relation to disease risk and dietary response.
Nutritional Epigenetics
Epigenetics involves reversible and heritable processes that regulate the expression of genes without causing actual changes in the coding sequence of DNA. Complex interactions between nutrients and processes like DNA methylation and other similar modifications contribute to obesity, type 2 diabetes, dyslipidemia, CVD, nonalcoholic fatty liver disease, and cancer. For instance, low-protein diets could alter lipid and glucose levels by disrupting histone modifications within major regulatory genes. Furthermore, deficiency of certain micronutrients like vitamin A, B group vitamins, selenium, potassium, and iron is linked with hypermethylation of tumor suppressor genes that play a crucial role in cancer.4
Nutriepigenetics is the study of nutritional interventions that alter epigenetics, significantly impacting treatment and prevention of chronic diseases. Research has demonstrated that the anti-inflammatory effects of the Mediterranean diet are linked to inhibitory hypermethylation of proinflammatory genes. Curcumin is also an important epigenetic regulator that exerts protective effects against heart failure and liver injury through the regulation of specific DNA methylation and histone modification patterns.4
The Role of AI
AI is a promising tool for the interpretation of complex omics-generated data and can process large amounts of data in a relatively short amount of time. Advanced computational methods and algorithms can identify patterns in omics data associated with diseases and their treatment. One such tool—Omics Integrator—processes diverse datasets to discover underlying molecular mechanisms and helps reveal the undiscovered molecular pathways not reported or present in conventional pathway databases. AI may also be able to predict how a patient will respond to different treatments and nutrition interventions by analyzing their genetic information.3
Challenges and Considerations
The science of nutritional medicine is ever-moving in the direction of personalization. This involves going beyond the use of traditional epidemiological surveys and generalized recommendations in favor of advanced omics tools like transcriptomics, proteomics, and metabolomics, combined with an appreciation for an individual’s unique history, current symptoms, and lifestyle to inform clinical decision making. However, we are still far from a standardized method for effectively using such genetic tools and information. The growing presence of online platforms and apps marketing cheaper, direct-to-consumer genetic testing with AI-driven “insights” for dietary changes without skilled expert oversight is a real concern.3
Another problem arises when inaccurate datasets lead to suboptimal or inappropriate recommendations. The excitement and novelty of this information as it emerges on the scene must be framed within the larger context of what is still unknown. Without understanding more about the complexity of gene-nutrient interactions, we may fail to appreciate the way certain SNPs, CNVs, or epigenetic changes interact together in one holistic genome (including phenotypic presentation), misleading efforts to “spot-treat” individual variants in ways that could be counterproductive in some cases.
There are also ethical considerations for how individual genetic data may be made available in public, nonprotected platforms.
All in all, while this aspect of personalized medicine is highly inspiring and will likely grow to offer groundbreaking improvements in delivering more effective and efficient care, dietitians may be among the many providers urging a cautious optimism and a tempered approach when incorporating it into current care models. A client who has pursued genetic testing to uncover their known SNPs and the supposed litany of corresponding dietary recommendations (perhaps delivered to them by an insufficiently trained algorithm) may still have little understanding of how to meet nutrient needs in an evidence-based, balanced, and appropriately personalized way. RDs are equipped to understand the limitations of the science at present while continuing to embrace curiosity and advocate for expert oversight in AI-driven prescriptive tools.
— Heather Davis, MS, RDN, LDN, editor
References
1. Bahinipati J, Sarangi R, Mishra S, Mahapatra S. Nutrigenetics and nutrigenomics: a brief review with future prospects. Biomedicine. 2021;41(4):714-719.
2. Chaudhary D, Guleria D, Aggarwal H, et al. Nutrigenomics and personalized diets – tailoring nutrition for optimal health. Applied Food Research. 2025;5(8):100980.
3. Kiani AK, Bonetti G, Donato K, et al. Polymorphisms, diet and nutrigenomics. J Prev Med Hyg. 2022;63(2 Suppl 3):E125-E141. 4. Copy number variation. National Human Genome Research Institute, National Institutes of Health website. https://www.genome.gov/genetics-glossary/Copy-Number-Variation-CNV. Updated October 7, 2025. Accessed October 3, 2025.
