Articles Vol. 66 No. 2 04/03/2025

12. APPLICATION OF GENETIC DECODING FOR PROACTIVE NUTRITIONAL SELECTION FOR HEALTH CARE

Tran Ba Kien1,2, Ngo Nguyen Quynh Anh1,2, Vu Van Anh3,4, Bui Thi Huyen3,4, Hoang Thi Ngoc Ha5,6, Vincent Nguyen7,8
1 Hai Duong Central College of Pharmacy
2 Trường Cao đẳng Dược Trung ương Hải Dương
3 Institute of Biotechnology, Vietnam Academy of Science and Technology
4 Viện Công nghệ Sinh học, Viện Hàn lâm Khoa học và Công nghệ Việt Nam
5 Hue University of Medicine and Pharmacy
6 Trường Đại học Y Dược, Đại học Huế
7 Advanced Genomics Research Inc
8 Công ty Advanced Genomics Research Inc
Corresponding author: qanhk63hup@gmail.com
DOI: 10.52163/yhc.v66i2.2057
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Keywords
genetic decoding proactive health care nutrition
Abstract

Objective: The study aims to clarify the relationship between genes and nutrition, enabling the selection of personalized diets and the proactive application of health care strategies from the time of good health, before the onset of any illness.

Method: Synthesizing and analyzing published domestic and foreign studies on the relationship between genes and nutrition, genetics related to health indicators such as BMI, digestive ability, risk of diabetes, obesity, cardiovascular disease.

Results: Gene decoding helps determine how each person's body reacts to food, from digestive ability to the risk of chronic diseases such as diabetes, obesity and cardiovascular disease. Studies show that people who adjust their diet according to genetic information can reduce the risk of disease.

Conclusion: The application of genetic decoding in nutrition emphasizes the role of genetic decoding in personalizing diet for proactive health care.

References
[1]
. Lê Đình Lương, Giáo trình Di truyền học (2009), NXB Đại học Quốc gia Hà Nội. Google Scholar
[2]
. Ellen Vogel, Ruth DeBusk, Milly Ryan-Harshman, Chapter 9 - Advancing Knowledge Translation in Nutritional Genomics by Addressing Knowledge, Skills and Confidence Gaps of Registered Dietitians, Nutrition and Genomics, Academic Press, 2009, Pages 161-186. Google Scholar
[3]
. Bouchard, C., & Rankinen, T. (2001). Individual differences in response to regular exercise. Medicine and Science in Sports and Exercise, 33(6), S446-S451. Google Scholar
[4]
. Nagata JM, Braudt DB, Domingue BW, Bibbins-Domingo K, Garber AK, Griffiths S, Murray SB. Genetic risk, body mass index, and weight control behaviors: Unlocking the triad. Int J Eat Disord. 2019 Jul;52(7):825-833. doi: 10.1002/eat.23083. Google Scholar
[5]
. Wang, L., et al. (2020). Application of artificial intelligence in nutritional research: a review. Frontiers in Nutrition, 7, 90. Google Scholar
[6]
. Frayling, T. M., et al. (2007). A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science, 316(5826), 889-894. Google Scholar
[7]
. Loos, R. J. F., & Yeo, G. S. H. (2008). The genetics of obesity: from discovery to biology. Nature Reviews Genetics, 9(1), 37-50. Google Scholar
[8]
. Möller, N. C., et al. (2019). Personalized nutrition in the prevention of chronic disease: a review of the evidence. Nutrients, 11(1), 1-12. Google Scholar
[9]
. Sicherer, S. H., et al. (2017). "Genetic Factors in Milk Allergy." Journal of Allergy and Clinical Immunology. Google Scholar
[10]
. Boukhalfa W, Jmel H, Kheriji N, Gouiza I, Dallali H, Hechmi M, Kefi R. Decoding the genetic relationship between Alzheimer's disease and type 2 diabetes: potential risk variants and future direction for North Africa. Front Aging Neurosci. 2023 Jun 5;15:1114810. doi: 10.3389/fnagi.2023.1114810. Google Scholar
[11]
. World Health Organization. Fact sheet no 311. WHO; 2012. Obesity and overweight. Google Scholar
[12]
. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA. 2014;311:806–814. doi: 10.1001/jama.2014.732. Google Scholar