Why Personalised Nutrition Is Replacing Generic Diet Advice

By Dr Adam Jameson, Precision Health Executive at REVIV Global

We know from the literature that variability between individuals in response to nutrition is real. Two people can eat the same meal and get different outcomes. One feels energised and steady, while the other can crash an hour later, feel hungry again, and observe a spike in glucose levels. This isn’t due to a lack of discipline or willpower, its routed in biology. 

For decades, nutrition has relied on one-size-fits-all guidance based on population-level data to provide generic rules for everyone to follow. But in reality, one size fits none. Two bodies can ingest the same ingredients and produce different outcomes. Factors at play include genetics, metabolism, gut microbiome, hormones, activity levels, substances, and medicines. 

The rise and fall of glucose, fats, and hormones after a eating a meal is called the postprandial response. Research has shown that interindividual variability in postprandial response is stark. In the landmark ‘PREDICT 1’ study (1), identical meals produced strikingly different postprandial response across participants. The study also found that person-specific factors mattered more than the macronutrient composition of meals. These findings demonstrate that the same diet can have different outcomes across a population, and even when people eat the same meals, food matters, but who you are matters too. 

Research is now uncovering patterns across populations in their response to foods. Coining the term ‘metabotypes’, a recent study (2) found that people can be grouped based on how they respond to specific foods, with some being described as ‘rice-spikers’ and others ‘potato-spikers’ to describe how different carbohydrate sources affect glucose levels between individuals. So if you’ve ever wondered why you feel different to others following certain meals – you’re not imagining it. Your body is giving you data. 

Genetics does not fully determine all health outcomes, but it certainly influences and shapes how you respond to specific food types and where you may have genetic predispositions in nutrient pathways. Genetics can nudge how you metabolise carbohydrates and fats, and how your body uses different micronutrients. 

Large genome-wide studies show that some genetic variants are associated with macronutrient intake patterns and metabolism (3,4). The 10X REVIV Precision Genetic Test (PGT) explores specific genes that have been associated with different aspects of nutrition and weight management. Genes like FTO sit in the conversation around appetite regulation and energy balance, while PPARG, TCF7L2, and KCNJ11 connect more closely to pathways involved in insulin signalling and glucose handling. On their own, single variants for these genes do not necessarily determine which foods you should or should not eat. But when insights are combined together, they can help explain why some people feel and function better with different macro- and micro-nutrient balances. 

Fat handling is another area where genetics can matter. In one analysis, combinations of variants across multiple lipid-related genes (including APOA5 and APOB) explained a large proportion of variance in post-meal triglyceride response to dietary fat (5). Other research highlights that APOA5 is strongly linked to non-fasting triglycerides and cardiovascular disease risk, with environment and obesity playing a role in addition to genetics (6). In essence, genetic variation between individuals can influence how your body processes fats, and lifestyle can amplify or soften these predispositions. 

This is why generic population-level guidance can often leave people short in terms of meeting their metabolic and nutritional health goals. Although one-size-fits-all guidance is directionally correct, it can be incomplete and not account for interindividual differences in response to nutrition. The real question is not “what is healthy?” but “what works best for me?”

If you want simple, everyday proof about the role of genes in response to specific compounds, look no further than caffeine. Variation in the CYP1A2 gene is associated with differences in caffeine metabolism and measurable differences in the function of the CYP1A2 enzyme [5]. Some people clear caffeine faster and may tolerate it later in the day, while others are slower metabolisers and can be left feeling jittery, anxious, or sleep-disrupted from the same dose. This is a clear example of a gene–environment interaction shaping daily behaviour and self-regulation (7,8).

Alcohol is another illustration of how genetics affects everyday activities, particularly in East Asian populations, where a variant in the ALDH2 gene can markedly slow acetaldehyde breakdown, driving the flushing reaction associated with East Asian populations when consuming alcohol (9–11). It’s one of the strongest gene–behaviour links we are aware of, because it forces behaviour change and alters how the body experiences exposure to alcohol.

And then there are micronutrients. Vitamin D is another great example because response to supplementation varies meaningfully between individuals. Variants in vitamin D metabolism and signalling genes, such as the VDR gene, have been shown to modify how much blood vitamin D levels rise from supplementation, even at the same dose (12,13). This highlights how two people doing the recommended thing by supplementing vitamin D but see different results.

The evidence to support gene-led diets is mixed and there is difficulty replicating the findings from studies exploring this approach. Does that mean we should ignore the influence of genetics on nutrition? No. But it does point to a more complicated landscape of how individuals should apply precision nutrition as part of a broader approach to health and wellness. Combining genetics with phenotypic data like biomarkers, body composition data, habits, sleep, stress, and personal preferences is key to making precision nutrition truly personalised. The more layers of data added in, the more difficult it can be to replicate findings. Genetics helps explain why certain approaches to nutrition are preferable, but real-world data is feedback that helps you to understand what is working. 

When personalisation is done well, the data points towards improved outcomes. One trial found that personalised dietary programmes led to improved outcomes in triglyceride levels, bodyweight, waist circumference, HbA1c, diet quality, and microbiome diversity, when compared against standard guidance (14). Another study showed that precision nutrition advice led to more appropriate dietary behaviour changes compared with generic advice (15). In summary, personalisation, when done correctly, may help to make healthy change more actionable and sustainable in the long-term.

Generic supplementation and population-based nutrition guidance are starting to feel outdated. As the science continues to evolve, more people will leverage genetics (alongside real-world health data) to personalise how they eat, train, and support their bodies. 

That’s the thinking behind the 10X REVIV Precision Nutrition System. It looks at the variation in key genes linked to nutrition and weight management, and then translates those insights into a practical, personalised plan. The goal isn’t ‘more supplements’ it’s the right support, at the right time, combining tailored nutrition guidance, exercise recommendations, and precision supplementation and IV therapy. 

Understanding your genetic blueprint is a foundational for building a nutrition and lifestyle routine that actually works for your biology. 

Precision nutrition is built on a simple reality: two people can follow the same nutrition and exercise routine and get different outcomes. Understanding why this is, and applying this knowledge in practice is exactly what precision nutrition offers. 

Genetics does not determine your destiny, but it can nudge how you respond to carbohydrates, fats, stimulants like caffeine, and different nutrient pathways like vitamin D and the B-vitamins. On its own, these insights are useful. But combing these insights, along with biomarkers, body composition, habits, sleep, stress, and performance data can become a powerful way to reduce guesswork.

Precision nutrition is a shift away from traditional one-size-fits-all approaches, towards person-centred, genetic-informed guidance, helping you prioritise what’s most likely to work for your biology, and making healthy change more achievable and sustainable over time.

Disclaimer: This content is for educational purposes only and is not medical advice. Genetics is one input alongside biomarkers, lifestyle, preferences, and clinician guidance where appropriate.

1. Berry SE, Valdes AM, Drew DA, Asnicar F, Mazidi M, Wolf J, et al. Human postprandial responses to food and potential for precision nutrition. Nat Med [Internet]. 2020 Jun 1 [cited 2026 Mar 5];26(6):964–73. Available from: https://pubmed.ncbi.nlm.nih.gov/32528151/

2. Wu Y, Ehlert B, Metwally AA, Perelman D, Park H, Brooks AW, et al. Individual variations in glycemic responses to carbohydrates and underlying metabolic physiology. Nat Med [Internet]. 2025 Jul 1 [cited 2026 Mar 5];31(7):2232–43. Available from: https://pubmed.ncbi.nlm.nih.gov/40467897/

3. Merino J, Dashti HS, Li SX, Sarnowski C, Justice AE, Graff M, et al. Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium. Mol Psychiatry [Internet]. 2019 Dec 1 [cited 2026 Mar 5];24(12):1920–32. Available from: https://pubmed.ncbi.nlm.nih.gov/29988085/

4. Tanaka T, Ngwa JS, Van Rooij FJA, Zillikens MC, Wojczynski MK, Frazier-Wood AC, et al. Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. American Journal of Clinical Nutrition [Internet]. 2013 Jun 1 [cited 2026 Mar 5];97(6):1395–402. Available from: https://pubmed.ncbi.nlm.nih.gov/23636237/

5. Desmarchelier C, Martin JC, Planells R, Gastaldi M, Nowicki M, Goncalves A, et al. The postprandial chylomicron triacylglycerol response to dietary fat in healthy male adults is significantly explained by a combination of single nucleotide polymorphisms in genes involved in triacylglycerol metabolism. J Clin Endocrinol Metab [Internet]. 2014 [cited 2026 Mar 5];99(3). Available from: https://pubmed.ncbi.nlm.nih.gov/24423365/

6. Rosenson RS, Davidson MH, Hirsh BJ, Kathiresan S, Gaudet D. Genetics and causality of triglyceride-rich lipoproteins in atherosclerotic cardiovascular disease. J Am Coll Cardiol [Internet]. 2014 [cited 2026 Mar 5];64(23):2525–40. Available from: https://pubmed.ncbi.nlm.nih.gov/25500239/

7. van Dam RM, Hu FB, Willett WC. Coffee, Caffeine, and Health. New England Journal of Medicine [Internet]. 2020 Jul 23 [cited 2026 Mar 5];383(4):369–78. Available from: https://www.nejm.org/doi/full/10.1056/NEJMra1816604

8. Cornelis MC, Kacprowski T, Menni C, Gustafsson S, Pivin E, Adamski J, et al. Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior. Hum Mol Genet [Internet]. 2016 [cited 2026 Mar 5];25(24):5472–82. Available from: https://pubmed.ncbi.nlm.nih.gov/27702941/

9. Millwood IY, Walters RG, Mei XW, Guo Y, Yang L, Bian Z, et al. Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in China. The Lancet [Internet]. 2019 May 4 [cited 2026 Mar 5];393(10183):1831–42. Available from: https://pubmed.ncbi.nlm.nih.gov/30955975/

10. Hurley TD, Edenberg HJ. Genes encoding enzymes involved in ethanol metabolism. Alcohol Res [Internet]. 2012 [cited 2026 Mar 5];34(3):339–44. Available from: https://pubmed.ncbi.nlm.nih.gov/23134050/

11. Edenberg HJ, McClintick JN. Alcohol Dehydrogenases, Aldehyde Dehydrogenases, and Alcohol Use Disorders: A Critical Review. Alcohol Clin Exp Res [Internet]. 2018 Dec 1 [cited 2026 Mar 5];42(12):2281–97. Available from: https://pubmed.ncbi.nlm.nih.gov/30320893/

12. Ammar M, Heni S, Tira MS, Khalij Y, Hamdouni H, Amor D, et al. Variability in response to vitamin D supplementation according to vitamin D metabolism related gene polymorphisms in healthy adults. Eur J Clin Nutr [Internet]. 2023 Feb 1 [cited 2026 Mar 5];77(2):189–94. Available from: https://pubmed.ncbi.nlm.nih.gov/36167979/

13. Barry EL, Rees JR, Peacock JL, Mott LA, Amos CI, Bostick RM, et al. Genetic variants in CYP2R1, CYP24A1, and VDR modify the efficacy of vitamin D3 supplementation for increasing serum 25-hydroxyvitamin D levels in a randomized controlled trial. J Clin Endocrinol Metab [Internet]. 2014 Oct 1 [cited 2026 Mar 5];99(10):E2133–7. Available from: https://pubmed.ncbi.nlm.nih.gov/25070320/

14. Bermingham KM, Linenberg I, Polidori L, Asnicar F, Arrè A, Wolf J, et al. Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial. Nat Med [Internet]. 2024 Jul 1 [cited 2026 Mar 5];30(7):1888–97. Available from: https://pubmed.ncbi.nlm.nih.gov/38714898/

15. Celis-Morales C, Livingstone KM, Marsaux CFM, Macready AL, Fallaize R, O’Donovan CB, et al. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int J Epidemiol [Internet]. 2017 [cited 2026 Mar 5];46(2):578–88. Available from: https://pubmed.ncbi.nlm.nih.gov/27524815/