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Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data

Diet design for vegetarian health is challenging due to the limited food repertoire of vegetarians. This challenge can be partially overcome by quantitative, data-driven approaches that utilise massive nutritional information collected for many different foods. Based on large-scale data of foods’ nu...

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Autores principales: Kim, Seunghyeon, Fenech, Michael F., Kim, Pan-Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5847509/
https://www.ncbi.nlm.nih.gov/pubmed/29531252
http://dx.doi.org/10.1038/s41598-018-22691-1
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author Kim, Seunghyeon
Fenech, Michael F.
Kim, Pan-Jun
author_facet Kim, Seunghyeon
Fenech, Michael F.
Kim, Pan-Jun
author_sort Kim, Seunghyeon
collection PubMed
description Diet design for vegetarian health is challenging due to the limited food repertoire of vegetarians. This challenge can be partially overcome by quantitative, data-driven approaches that utilise massive nutritional information collected for many different foods. Based on large-scale data of foods’ nutrient compositions, the recent concept of nutritional fitness helps quantify a nutrient balance within each food with regard to satisfying daily nutritional requirements. Nutritional fitness offers prioritisation of recommended foods using the foods’ occurrence in nutritionally adequate food combinations. Here, we systematically identify nutritionally recommendable foods for semi- to strict vegetarian diets through the computation of nutritional fitness. Along with commonly recommendable foods across different diets, our analysis reveals favourable foods specific to each diet, such as immature lima beans for a vegan diet as an amino acid and choline source, and mushrooms for ovo-lacto vegetarian and vegan diets as a vitamin D source. Furthermore, we find that selenium and other essential micronutrients can be subject to deficiency in plant-based diets, and suggest nutritionally-desirable dietary patterns. We extend our analysis to two hypothetical scenarios of highly personalised, plant-based methionine-restricted diets. Our nutrient-profiling approach may provide a useful guide for designing different types of personalised vegetarian diets.
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spelling pubmed-58475092018-03-19 Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data Kim, Seunghyeon Fenech, Michael F. Kim, Pan-Jun Sci Rep Article Diet design for vegetarian health is challenging due to the limited food repertoire of vegetarians. This challenge can be partially overcome by quantitative, data-driven approaches that utilise massive nutritional information collected for many different foods. Based on large-scale data of foods’ nutrient compositions, the recent concept of nutritional fitness helps quantify a nutrient balance within each food with regard to satisfying daily nutritional requirements. Nutritional fitness offers prioritisation of recommended foods using the foods’ occurrence in nutritionally adequate food combinations. Here, we systematically identify nutritionally recommendable foods for semi- to strict vegetarian diets through the computation of nutritional fitness. Along with commonly recommendable foods across different diets, our analysis reveals favourable foods specific to each diet, such as immature lima beans for a vegan diet as an amino acid and choline source, and mushrooms for ovo-lacto vegetarian and vegan diets as a vitamin D source. Furthermore, we find that selenium and other essential micronutrients can be subject to deficiency in plant-based diets, and suggest nutritionally-desirable dietary patterns. We extend our analysis to two hypothetical scenarios of highly personalised, plant-based methionine-restricted diets. Our nutrient-profiling approach may provide a useful guide for designing different types of personalised vegetarian diets. Nature Publishing Group UK 2018-03-12 /pmc/articles/PMC5847509/ /pubmed/29531252 http://dx.doi.org/10.1038/s41598-018-22691-1 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kim, Seunghyeon
Fenech, Michael F.
Kim, Pan-Jun
Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data
title Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data
title_full Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data
title_fullStr Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data
title_full_unstemmed Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data
title_short Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data
title_sort nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5847509/
https://www.ncbi.nlm.nih.gov/pubmed/29531252
http://dx.doi.org/10.1038/s41598-018-22691-1
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