meatTheory and practice of meat processingТеория и практика переработки мяса2414-438X2414-441XФГБНУ «Федеральный научный центр пищевых систем им. В.М. Горбатова» РАН10.21323/2414-438X-2020-5-4-13-17meat-149Research ArticleСтатьиThe methodology of food design. Part 1. The individual aspecthttps://orcid.org/0000-0002-5630-3196ProsekovA. Yu.

Aleksandr Yu. Prosekov — doctor of technical sciences, professor, rector, 650000, Kemerovo, Krasnaya str., 6 Tel.: +7–923–502–00–22

a.prosekov@rambler.ruKemerovo State UniversityRussian Federation202029122020541317Copyright © Prosekov A.Y., 20202020Prosekov A.Y.Prosekov A.Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.meatjournal.ru/jour/article/view/149

Innovative technologies for food raw material processing and food production are becoming globally important within the framework of modern biotechnology. The need to create a universal methodology for food design and the importance of its implementation in different lines of human life activity are obvious. Within the paradigm of modern biotechnology, personalized diets that take into consideration the genetic characteristics of consumers are becoming more and more popular. Nutrition science deals with the development of this direction. It is divided into nutrigenetics and nutrigenomics. Nutrigenetics investigates an effect of modifications in genes on absorption of metabolites, nutrigenomics investigates how food components affect the work of genes. In this work, we consider mutations that influence the assimilation of metabolites and contribute to nutrigenetic research. The work is aimed at finding and studying genes responsible for eating behavior. Methods of analysis of genetic polymorphisms and modern achievements of nutrigenetics in the development of personalized nutrition are considered. The review allowed us to find and describe the genes that influenced human eating behavior: the role of genes, their localization, polymorphisms affecting the metabolism of nutrients and food preferences are indicated. Thirty four genes that influence eating behavior were identified, and significant shortcomings of current methods / programs for developing personalized diets were indicated. Weaknesses in the development of nutrigenetics were identified (inconsistency of data on SNP genes, ignoring population genetics data, information that is hard for consumers to understand, etc.). Taking into consideration all shortcomings, an approximate model for selecting a personalized diet is proposed. In the future, it is planned to develop the proposed model for making up individual diets.

nutrigeneticseating behaviorgenotypepolymorphismfunctional productpersonalized nutritionReferencesBush, C.L., Blumberg, J.B., El-Sohemy, A., Minich, D.M., Ordovas, J.M., Reed, D.G., Behm, V.A.Y. (2020). Toward the definition of personalized nutrition: A proposal by the American nutrition association. Journal of the American College of Nutrition, 39(1), 5–15. https://doi.org/10.1080/07315724.2019.1685332Bush, C.L., Blumberg, J.B., El-Sohemy, A., Minich, D.M., Ordovas, J.M., Reed, D.G., Behm, V.A.Y. (2020). Toward the definition of personalized nutrition: A proposal by the American nutrition association. Journal of the American College of Nutrition, 39(1), 5–15. https://doi.org/10.1080/07315724.2019.1685332Lipatov, N.N. (1985). Methods for quantitative assessment and modeling of amino acid balance of meat products. XXXI European Congress of Scientific Workers of the Meat Industry, Sofia, 158.Lipatov, N.N. (1985). Methods for quantitative assessment and modeling of amino acid balance of meat products. XXXI European Congress of Scientific Workers of the Meat Industry, Sofia, 158.Martín-Hernández, R., Reglero, G., Ordovás, J.M., Dávalos, A. (2019). Nutri GenomeDB: A nutrigenomics exploratory and analytical platform. Database (Oxford), baz097. https://doi.org/10.1093/database/baz097Martín-Hernández, R., Reglero, G., Ordovás, J.M., Dávalos, A. (2019). Nutri GenomeDB: A nutrigenomics exploratory and analytical platform. Database (Oxford), baz097. https://doi.org/10.1093/database/baz097Neeha, V.S., Kinth, P. (2013). Nutrigenomics research: A review. Journal of Food Science and Technology, 50(3), 415–428. https://doi.org/10.1007/s13197–012–0775-zNeeha, V.S., Kinth, P. (2013). Nutrigenomics research: A review. Journal of Food Science and Technology, 50(3), 415–428. https://doi.org/10.1007/s13197–012–0775-zVesnina, A., Prosekov, A., Kozlova, O., Atuchin, V. (2020).Genes and Eating Preferences, Their Roles in Personalized Nutrition. Genes, 11(4), 357. https://doi.org/10.3390/genes11040357.Vesnina, A., Prosekov, A., Kozlova, O., Atuchin, V. (2020).Genes and Eating Preferences, Their Roles in Personalized Nutrition. Genes, 11(4), 357. https://doi.org/10.3390/genes11040357.Prosekov, A. Yu. (2005). Scientific foundations of food production. Kemerovo: KemTIPP. — 234 p. ISBN 5–89289–324–3 (in Russian)Prosekov, A. Yu. (2005). Scientific foundations of food production. Kemerovo: KemTIPP. — 234 p. ISBN 5–89289–324–3 (in Russian)Prosekov, A. Yu. (2019). Fundamentals of food technology Kemerovo: Kemerovo State University. — 498 p. ISBN 978–5–83532–275–6 (in Russian)Prosekov, A. Yu. (2019). Fundamentals of food technology Kemerovo: Kemerovo State University. — 498 p. ISBN 978–5–83532–275–6 (in Russian)Vesnina, A. D. Research of genes responsible for eating behavior (Master’s thesis in biotechnology) Kemerovo State University, Kemerovo, 2020. — 185 p. (accepted for publication). (in Russian)Vesnina, A. D. Research of genes responsible for eating behavior (Master’s thesis in biotechnology) Kemerovo State University, Kemerovo, 2020. — 185 p. (accepted for publication). (in Russian)Lisitsyn, A.B., Nikitina, M.A., Zakharov, A.N., Sus, E.B., Nasonova, V.V., Lebedeva, L.I. (2016). Prediction of meat product quality by the mathematical programming methods. Theory and practice of meat processing, 1(1), 75—90. https://doi.org/10.21323/2414–438X-2016–1–1–75–90 (in Russian)Lisitsyn, A.B., Nikitina, M.A., Zakharov, A.N., Sus, E.B., Nasonova, V.V., Lebedeva, L.I. (2016). Prediction of meat product quality by the mathematical programming methods. Theory and practice of meat processing, 1(1), 75—90. https://doi.org/10.21323/2414–438X-2016–1–1–75–90 (in Russian)Krasulya, O.N., Nikolaeva, S.V., Tokarev, A.V., Krasnov, A.E., IPanin, I.G. (2015). Modeling of recipes for food products and technologies for their production: theory and practice. Saint Petersburg: GIORD. — 320 p. ISBN978–5–98879–164–5 (in Russian)Krasulya, O.N., Nikolaeva, S.V., Tokarev, A.V., Krasnov, A.E., IPanin, I.G. (2015). Modeling of recipes for food products and technologies for their production: theory and practice. Saint Petersburg: GIORD. — 320 p. ISBN978–5–98879–164–5 (in Russian)Koneva, M.S., Usatikov, S.V., Bugaets, N.A., Tamova, M.Y. (2017). Neu ral Network and Regression Analysis of the Dependence of the Ranking Score of Organoleptic Characteristics on the Food System Composition. Asian journal of pharmaceutics, 11(2), Appl. S., S308-S319. https://doi.org/10.22377/ajp.v11i02.1270Koneva, M.S., Usatikov, S.V., Bugaets, N.A., Tamova, M.Y. (2017). Neu ral Network and Regression Analysis of the Dependence of the Ranking Score of Organoleptic Characteristics on the Food System Composition. Asian journal of pharmaceutics, 11(2), Appl. S., S308-S319. https://doi.org/10.22377/ajp.v11i02.1270Berezina, N.A., Artemov, A.V., Nikitin, I.A., Budnik, A. A. (2019). The Method of Computer-Aided Design of a Bread Composition with Regard to Biomedical Requirements. International Journal of Advanced Computer Science and Applications, 10(5), 137–143. https://doi.org/10.14569/ijacsa.2019.0100517Berezina, N.A., Artemov, A.V., Nikitin, I.A., Budnik, A. A. (2019). The Method of Computer-Aided Design of a Bread Composition with Regard to Biomedical Requirements. International Journal of Advanced Computer Science and Applications, 10(5), 137–143. https://doi.org/10.14569/ijacsa.2019.0100517Nikitina, M.A., Chernukha, I.M. (2018). Multi–criteria optimization of a product recipe composition. Theory and practice of meat processing, 3(3), 89—98. https://doi.org/10.21323/2414–438X-2018–3–3–89–98 (in Russian)Nikitina, M.A., Chernukha, I.M. (2018). Multi–criteria optimization of a product recipe composition. Theory and practice of meat processing, 3(3), 89—98. https://doi.org/10.21323/2414–438X-2018–3–3–89–98 (in Russian)Musina, O., Putnik, P., Koubaa, M., Barba, F.J., Greiner, R., Granato, D., Roohinejad, S. (2017). Application of modern computer algebra systems in food formulations and development: a case study. Trends in food science & technology, 64, 48—59. https://doi.org/10.1016/j.tifs.2017.03.011Musina, O., Putnik, P., Koubaa, M., Barba, F.J., Greiner, R., Granato, D., Roohinejad, S. (2017). Application of modern computer algebra systems in food formulations and development: a case study. Trends in food science & technology, 64, 48—59. https://doi.org/10.1016/j.tifs.2017.03.011Ruggiero, J.E., Northrup, H., Au, K.S. (2015). Association of facilitated glucose transporter 2 gene variants with the myelomeningocele phenotype. Birth Defects Research Part A: Clinical and Molecular Teratology, 103(6), 479–487. https://doi.org/10.1002/bdra.23358Ruggiero, J.E., Northrup, H., Au, K.S. (2015). Association of facilitated glucose transporter 2 gene variants with the myelomeningocele phenotype. Birth Defects Research Part A: Clinical and Molecular Teratology, 103(6), 479–487. https://doi.org/10.1002/bdra.23358Hashimoto, M., Watanabe, M., Uematsu, Y., Hattori, S., Miyai, N., Utsumi, M., Oka, M., Hayashida, M., Kinoshita, K., Arita, M., Takeshita, T. (2016). Relationships of alcohol dehydrogenase 1B (ADH1B) and aldehyde dehydrogenase 2 (ALDH2) genotypes with alcohol sensitivity, drinking behavior and problem drinking in Japanese older men. Environmental Health and Preventive Medicine, 21(3), 138–148. https://doi.org/10.1007/s12199–016–0507–5Hashimoto, M., Watanabe, M., Uematsu, Y., Hattori, S., Miyai, N., Utsumi, M., Oka, M., Hayashida, M., Kinoshita, K., Arita, M., Takeshita, T. (2016). Relationships of alcohol dehydrogenase 1B (ADH1B) and aldehyde dehydrogenase 2 (ALDH2) genotypes with alcohol sensitivity, drinking behavior and problem drinking in Japanese older men. Environmental Health and Preventive Medicine, 21(3), 138–148. https://doi.org/10.1007/s12199–016–0507–5Mozafarizadeh, M., Mohammadi, M., Sadeghi, S., Hadizadeh, M., Talebzade, T., Houshmand, M. (2019). Evaluation of FTO rs9939609 and MC4R rs17782313 polymorphisms as prognostic biomarkers of obesity: A population-based cross-sectional study. Oman Medical Journal, 34(4), 56–62. https://doi.org/10.5001/omj.2019.09Mozafarizadeh, M., Mohammadi, M., Sadeghi, S., Hadizadeh, M., Talebzade, T., Houshmand, M. (2019). Evaluation of FTO rs9939609 and MC4R rs17782313 polymorphisms as prognostic biomarkers of obesity: A population-based cross-sectional study. Oman Medical Journal, 34(4), 56–62. https://doi.org/10.5001/omj.2019.09Drabsch, T., Gatzemeier, J., Pfadenhauer, L., Hauner, H., Holzapfel, C. (2018). Associations between single nucleotide polymorphisms and total energy, carbohydrate, and fat intakes: A systematic review. Advances in Nutrition, 9(4), 425–453. https://doi.org/10.1093/advances/nmy024Drabsch, T., Gatzemeier, J., Pfadenhauer, L., Hauner, H., Holzapfel, C. (2018). Associations between single nucleotide polymorphisms and total energy, carbohydrate, and fat intakes: A systematic review. Advances in Nutrition, 9(4), 425–453. https://doi.org/10.1093/advances/nmy024Sun, X., Luquet, S., Small, D.M. (2017). DRD2: Bridging the genome and ingestive behavior. Trends in Cognitive Sciences, 21(5), 372–384. https://doi.org/10.1016/j.tics.2017.03.004Sun, X., Luquet, S., Small, D.M. (2017). DRD2: Bridging the genome and ingestive behavior. Trends in Cognitive Sciences, 21(5), 372–384. https://doi.org/10.1016/j.tics.2017.03.004Sevgi, M., Rigoux, L., Kühn, A.B., Mauer, J., Schilbach, L., Hess, M.E., Gruendler, T.O.J., Ullsperger, M., Stephan, K.E., Brüning, J.C., Tittgemeyer, M. (2015). An obesity-predisposing variant of the FTO gene regulates D2R‑dependent reward learning. Journal of Neuroscience, 35(36), 12584–12592. https://doi.org/10.1523/jneurosci.1589–15.2015Sevgi, M., Rigoux, L., Kühn, A.B., Mauer, J., Schilbach, L., Hess, M.E., Gruendler, T.O.J., Ullsperger, M., Stephan, K.E., Brüning, J.C., Tittgemeyer, M. (2015). An obesity-predisposing variant of the FTO gene regulates D2R‑dependent reward learning. Journal of Neuroscience, 35(36), 12584–12592. https://doi.org/10.1523/jneurosci.1589–15.2015NutriGenomeDB platform. [Electronic resource: http://nutrigenomedb.org/home Access date 24.02.2020]NutriGenomeDB platform. [Electronic resource: http://nutrigenomedb.org/home Access date 24.02.2020]Layman, D.K. (2014). Eating patterns, diet quality and energy balance. Physiology & Behavior, 134, 126–130. https://doi.org/10.1016/j.physbeh.2013.12.005Layman, D.K. (2014). Eating patterns, diet quality and energy balance. Physiology & Behavior, 134, 126–130. https://doi.org/10.1016/j.physbeh.2013.12.005Sukhikh, S., Astakhova, L., Golubcova, Yu., Lukin, A., Prosekova, E., Milent`eva, I., Kostina, N., Rasshchepkin, A. (2019). Functional dairy products enriched with plant ingredients. Foods and Raw Materials, 7(2), 428–438. https://doi.org/10.21603/2308–4057–2019–2–428–438Sukhikh, S., Astakhova, L., Golubcova, Yu., Lukin, A., Prosekova, E., Milent`eva, I., Kostina, N., Rasshchepkin, A. (2019). Functional dairy products enriched with plant ingredients. Foods and Raw Materials, 7(2), 428–438. https://doi.org/10.21603/2308–4057–2019–2–428–438Volobuev, V.V., Polunovskiy, V.V., Tsvetkovich, A.V., Seledtsova, L.A. The method of forming individual dietary recommendations based on DNA analysis. Patent RF, no.2691145, 2019.Volobuev, V.V., Polunovskiy, V.V., Tsvetkovich, A.V., Seledtsova, L.A. The method of forming individual dietary recommendations based on DNA analysis. Patent RF, no.2691145, 2019.Matsuo, T., Nakata, Y., Katayama, Y., Iemitsu, M., Maeda, S., Okura, T., Kim, M.K., Ohkubo, H., Hotta, K., Tanaka, K. (2009). PPARG genotype accounts for part of individual variation in body weight reduction in response to calorie restriction. Obesity, 17(10), 1924–1931. https://doi.org/10.1038/oby.2009.199Matsuo, T., Nakata, Y., Katayama, Y., Iemitsu, M., Maeda, S., Okura, T., Kim, M.K., Ohkubo, H., Hotta, K., Tanaka, K. (2009). PPARG genotype accounts for part of individual variation in body weight reduction in response to calorie restriction. Obesity, 17(10), 1924–1931. https://doi.org/10.1038/oby.2009.199Arkadianos, I., Valdes, A.M., Marinos, E., Florou, A., Gill, R.D., Grimaldi, K.A. (2007). Improved weight management using genetic information to personalize a calorie controlled diet. Nutrition Journal, 6(1), 29. https://doi.org/10.1186/1475–2891–6–29Arkadianos, I., Valdes, A.M., Marinos, E., Florou, A., Gill, R.D., Grimaldi, K.A. (2007). Improved weight management using genetic information to personalize a calorie controlled diet. Nutrition Journal, 6(1), 29. https://doi.org/10.1186/1475–2891–6–29Trihina, V.V., Spirichev, V.B., Koltun, V.Z., Avstrievskih, A. N. Nutritional factor in ensuring health and reliability increase of professional activities of industrial workers. Foods and Raw Materials, 3(1), 86–96. https://doi.org/10.12737/11242Trihina, V.V., Spirichev, V.B., Koltun, V.Z., Avstrievskih, A. N. Nutritional factor in ensuring health and reliability increase of professional activities of industrial workers. Foods and Raw Materials, 3(1), 86–96. https://doi.org/10.12737/11242

The authors declare that there are no conflicts of interest present.