Genotype-based nutrition and dietary guidance

Numerous common genetic variants and other complex biological factors influence what we should eat and what we actually end up eating. As the evidence for adjustments based on genomic and -omic information for setting individual intake targets becomes clearer, the development of individualized meal plans remains challenging. We have developed effective strategies that generate detailed food combinations that meet very complex intake targets with high fidelity. The intake targets for individual users are set taking into account estimated energy requirements, body size, sex, age, genotypes and other sufficiently evidence-supported characteristics. By focusing on intake targets, an unlimited number of algorithms can adjust for multiple factors acting simultaneously on the same intake target. The next step is then to use parabolic functions weighted separately by intake target to calculate the sum of normalized deviations from the optimum for all nutrients in a potential menu. The settings ensure the selection of menus that closely meet all intake targets without allowing any major deviation of individual targets. As more evidence becomes available, these target-setting algorithms can be readily adapted. Currently implemented applications use a wide range of genetic algorithms for which there is sufficient evidence support. In addition to macronutrients und micronutrients, the algorithmic meal plan generation can also take into account individual food sensitivities, intolerances and preferences. Important examples are gluten-free options, vegan choices and low-carbohydrate combinations. The addition of carbon dioxide targets can help consumers to make climate-conscientious food choices that align with their nutritional and health needs.