Chua, SereneMohaghegh, MahsaPaul, Sharad PMiranda, Victor2026-02-102026-02-102026-01-23Nutrition and Health, ISSN: 0260-1060 (Print); 2047-945X (Online), SAGE Publications, 2601060251415144-. doi: 10.1177/026010602514151440260-10602047-945Xhttp://hdl.handle.net/10292/20608Introduction: Advances in nutrigenomics have enabled exploration of how genetic variation may relate to nutrition and lifestyle traits. However, the extent to which demographic factors influence the distribution of such variants remains underexplored. Objective: This study examined gender- and region-specific variation in diet- and lifestyle-related genetic traits and described patterns of trait clustering within a cohort of direct-to-consumer gene-test clients. Methods: A cross-sectional analysis was conducted on 503 anonymised gene-test reports covering 41 nutrition- and lifestyle-linked genetic components. Chi-square tests assessed demographic differences in allele frequency distributions. Hierarchical clustering and principal component analysis were applied as exploratory tools to visualise trait patterns. Results: Most individuals exhibited typical genotype distributions, though some demographic differences were observed. Statistically significant gender variation was noted in omega-3/6 metabolism (p = 0.0378). Lactose intolerance showed the greatest regional disparity, disproportionately affecting Asian (p < 0.00001). Marked regional differences were also observed in vitamin-D status (p = 0.0137), omega-3 metabolism (p = 0.0215), pain tolerance (p = 0.0279), fat utilisation (p = 0.0406) and gluten sensitivity (p = 0.0411). Clustering grouped 41 components into 14 sets. Three principal clusters explained 44-80% of the variance. Predictive modelling was limited by incomplete data and class imbalance. Conclusion: This exploratory study highlights modest demographic differences in allele frequencies and demonstrates clustering of nutrition-related genetic traits within a direct-to-consumer dataset. Findings should be interpreted as descriptive signals rather than prescriptive guidance. Future research incorporating phenotypic, biomarker, and outcome data is needed to evaluate functional and clinical significance.© The Author(s) 2026. Creative Commons License (CC BY-NC 4.0). This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).demographic variationsgene-test reportsgenetic risk profilinghierarchical clusteringnutrigeneticsnutrigenomicsPersonalised nutritionprecision healthcare32 Biomedical and Clinical Sciences3210 Nutrition and DieteticsGeneticsNutrition3210 Nutrition and dieteticsJournal ArticleOpenAccess10.1177/02601060251415144