Published date: Mar 14 2024

Journal Title: West Kazakhstan Medical Journal

Issue title: West Kazakhstan Medical Journal: Volume 66 Issue 1

Pages: 84–93

DOI: 10.18502/wkmj.v66i1.15681

Elmira KhairullinaDepartment of General Hygiene, West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan

Akmaral Baspakovabaspakova@zkgmu.kzDepartment for Scientific Work, West Kazakhstan Marat Ospanov Medical University, Aktobe, 030012, Kazakhstan

Saltanat ZhumagaliyevaDepartment for Scientific Work, West Kazakhstan Marat Ospanov Medical University, Aktobe, 030012, Kazakhstan

This mini review delves into the transformative role of empirical nutrition models in the field of public health. By leveraging large-scale datasets and advanced statistical analyses, these models offer a more precise understanding of individual dietary needs, moving beyond traditional one-size-fits-all approaches. Their ability to identify and analyze complex dietary patterns, such as the Mediterranean diet or plant-based lifestyles, contributes to a more comprehensive assessment of overall dietary quality. Moreover, empirical nutrition models facilitate early intervention and prevention strategies by identifying risk factors for chronic diseases at an early stage. This proactive approach holds significant promise in reducing the burden of prevalent health issues, including obesity, diabetes, and cardiovascular disorders. The article also highlights the crucial role of these models in guiding policy development and influencing public health campaigns, providing evidence-based recommendations that address the diverse dietary needs of populations. Despite their promise, challenges such as data quality, standardization, and the dynamic nature of nutritional science are acknowledged. The abstract underscores the importance of continuous technological advancements and collaborative efforts among researchers, policymakers, and healthcare professionals to address these challenges and refine the accuracy of nutrition models. In conclusion, empirical nutrition models represent a paradigm shift toward more personalized and effective public health strategies. As these models continue to evolve with technological advancements, their impact on public health is poised to grow. They can contribute to creating healthier communities and foster a more nuanced understanding of the complex relationship between diet and overall well-being.

Keywords: empirical nutrition models, public health, dietary patterns, early intervention, evidence-based recommendations

[1] Bassaganya-Riera J, Berry EM, Blaak EE, Burlingame B, le Coutre J, van Eden W, et al. Goals in nutrition science 2020-2025. Front Nutr. 2020;7:606378.

[2] Carey CC, Doyle L, Lucey A. Nutritional priorities, practices and preferences of athletes and active individuals in the context of new product development in the sports nutrition sector. Front Sports Act Living. 2023;5:1088979.

[3] Stoumpos AI, Kitsios F, Talias MA. Digital transformation in healthcare: Technology acceptance and its applications. Int J Environ Res Public Health. 2023;20(4).

[4] Verma M, Hontecillas R, Tubau-Juni N, Abedi V, Bassaganya-Riera J. Challenges in personalized nutrition and health. Front Nutr. 2018;5:117.

[5] Chen PJ, Antonelli M. Conceptual models of food choice: Influential factors related to foods, individual differences, and society. Foods. 2020;9(12).

[6] Amiri M, Li J, Hasan W. Personalized flexible meal planning for individuals with dietrelated health concerns: System design and feasibility validation study. JMIR Form Res. 2023;7:e46434.

[7] Mattes RD, Rowe SB, Ohlhorst SD, Brown AW, Hoffman DJ, Liska DJ, et al. Valuing the diversity of research methods to advance nutrition science. Adv Nutr. 2022;13(4):1324-1393.

[8] Silva P, Araújo R, Lopes F, Ray S. Nutrition and food literacy: Framing the challenges to health communication. Nutrients. 2023;15(22).

[9] Lee BY, Ordovás JM, Parks EJ, Anderson CAM, Barabási AL, Clinton SK, et al. Research gaps and opportunities in precision nutrition: An NIH workshop report. Am J Clin Nutr. 2022;116(6):1877-1900.

[10] Berciano S, Figueiredo J, Brisbois TD, Alford S, Koecher K, Eckhouse S, et al. Precision nutrition: Maintaining scientific integrity while realizing market potential. Front Nutr. 2022;9:979665.

[11] Schulz CA, Oluwagbemigun K, Nöthlings U. Advances in dietary pattern analysis in nutritional epidemiology. Eur J Nutr. 2021;60(8):4115-4130.

[12] Tapsell LC, Neale EP, Satija A, Hu FB. Foods, nutrients, and dietary patterns: Interconnections and implications for dietary guidelines. Adv Nutr. 2016;7(3):445- 454.

[13] Figueroa C, Echeverría G, Villarreal G, Martínez X, Ferreccio C, Rigotti A. Introducing plant-based Mediterranean diet as a lifestyle medicine approach in Latin America: Opportunities Within the Chilean Context. Front Nutr. 2021;8:680452.

[14] Zhao J, Li Z, Gao Q, Zhao H, Chen S, Huang L, et al. A review of statistical methods for dietary pattern analysis. Nutr J. 2021;20(1):37.

[15] Agedew E, Abebe Z, Ayelign A. Dietary patterns in relation with nutritional outcomes and associated factors among adolescents: Implications for contextspecific dietary intervention for the Agrarian Community, Northwest Ethiopia. Front Nutr. 2023;10:1274406.

[16] Martínez-González MA, Gea A, Ruiz-Canela M. The Mediterranean diet and cardiovascular health. Circ Res. 2019;124(5):779-798.

[17] Maksimov SA, Karamnova NS, Shalnova SA, Drapkina OM. [Empirical dietary patterns and their influence on health in epidemiological studies]. Vopr Pitan. 2020;89(1):6-18.

[18] Meijaard E, Abrams JF, Slavin JL, Sheil D. Dietary fats, human nutrition and the environment: Balance and sustainability. Front Nutr. 2022;9:878644.

[19] Schulze MB, Martínez-González MA, Fung TT, Lichtenstein AH, Forouhi NG. Food based dietary patterns and chronic disease prevention. Bmj. 2018;361:k2396.

[20] Paoli A, Tinsley G, Bianco A, Moro T. The influence of meal frequency and timing on health in humans: The role of fasting. Nutrients. 2019;11(4).

[21] Hess JM, Comeau ME, Casperson S, Slavin JL, Johnson GH, Messina M, et al. Dietary guidelines meet NOVA: Developing a menu for a healthy dietary pattern using ultraprocessed foods. J Nutr. 2023;153(8):2472-2481.

[22] Mersha TB, Abebe T. Self-reported race/ethnicity in the age of genomic research: Its potential impact on understanding health disparities. Hum Genomics. 2015;9(1):1.

[23] Mozaffarian D, Angell SY, Lang T, Rivera JA. Role of government policy in nutritionbarriers to and opportunities for healthier eating. Bmj. 2018;361:k2426.

[24] Field P, Gauld R, Lawrence M. Enhancing evidence use in public health nutrition policymaking: Theoretical insights from a New Zealand case study. Health Res Policy Syst. 2016;14(1):84.

[25] Denniss E, Lindberg R, McNaughton SA. Quality and accuracy of online nutritionrelated information: A systematic review of content analysis studies. Public Health Nutr. 2023;26(7):1345-1357.

[26] Federici C, Detzel P, Petracca F, Dainelli L, Fattore G. The impact of food reformulation on nutrient intakes and health, a systematic review of modelling studies. BMC Nutr. 2019;5:2.

[27] Döring F, Ströhle A. Nutritional biology: A neglected basic discipline of nutritional science. Genes Nutr. 2015;10(6):55.

[28] Ganson KT, Nagata JM, Vanderlee L, Rodgers RF, Lavender JM, Hazzard VM, et al. Weight gain attempts and diet modification efforts among adults in five countries: A cross-sectional study. Nutr J. 2022;21(1):30.