NUTRITIONAL GENOMICS IN PERSONALIZED MEDICINE: DATA-DRIVEN CUSTOMIZED TREATMENTS AND LIFESTYLE-BASED DISEASE MANAGEMENT AND PREVENTION.
Individuals feature unique glycemic reactions to exactly the same foods. (Bashiardes et al., 2018) Unsatisfactory adjustment to public dietary guidance and separate health feedback are not adequately displayed by population averages, because nonspecific public health recommendations are low on importance for people, personally and medically. (Michel and Burbidge, 2019) The main target of public health advice associated with the hindrance of food-related chronic disease is the choosing of nutritious dietary models. (Laddu and Hauser, 2019)
2. Conceptual Framework and Literature Review
Genome and microbiome information may elucidate particular clinical characteristics. (Bashiardes et al., 2018) A person's regime and environment may shape disease predisposition by influencing the expression of genes related to critical metabolic routes. (Laddu and Hauser, 2019) The developments in the knowledge base of the intricate synergies among genotype, nutritional therapy, style of living, and environment bring about modifications (Androniceanu, 2017; Bratu, 2017; Caruso et al., 2017; Faggianelli et al., 2018; Ionescu, 2018; Massey et al., 2018; Nica, 2017; Ralston et al., 2018; Smith and Stirling, 2018) in present medical routine to eventually generate personalized nutrition advice for health and risk evaluation, as intakes and dietary supplements distinctively impact the good physical condition of persons. An individual's gut microbiota is decisive in encompassing the multiple-functional links with elaborate metabolic processes entailed in preserving cellular homeostasis. (Aruoma et al., 2019) The big data tools of personalized nutrition are facilitating the advancement of more customized and predictive dietary advice and interventions against energy imbalance and metabolic disease that may alter how individuals make food choices and significantly enhance population health. (O'Sullivan et al., 2018)
3. Methodology and Empirical Analysis
Using and replicating data from ABS, CCHS, CRN, Euromonitor International, Ipsos Public Affairs, Mintel, and Statista, I performed analyses and made estimates regarding U.S. people who take nutritional supplements and top reasons for taking them (%, by age group and sex), healthy habits of U.S. adult supplement users vs. non-users (%), U.S. adults who use an app to track their diet and nutrition (%), proportion of calories from fresh food, packaged food, soft drinks, and alcoholic drinks (% of total calories purchased), prevalence of inadequate fruit and vegetable intake (%, age groups), and prevalence of usual intakes of sodium exceeding the tolerable upper intake level (%, by sex and age groups). The data for this article were collected through an online survey questionnaire and were analyzed via structural equation modeling on a sample of 4,800 respondents.
4. Results and Discussion
Personalized algorithms facilitate the precise prediction of glycemic reactions to foods. (Bashiardes et al., 2018) Personalized nutrition can explain findings concerning heterogeneity in nutrient metabolism between subcategories and the interindividual inconsistency in the feedback to dietary interventions, providing particular, applicable dietary treatment contingent on distinct nutritional phenotype, generated from the synthesis of genetics, metabolic profile, and environmental components (Benedikter et al., 2017; Bratu, 2018; Douglas, 2018; Freeman-Moir, 2017; Lupu-Stanescu, 2018; Mihaila, 2017; Popescu Ljungholm, 2017; Schinckus, 2018; Tanankem Voufo et al., 2017) for the purpose of preventing and treating chronic disease. (Laddu and Hauser, 2019) User's genetic composition is employed in the manufacturing operation of hyper-personalized consumer products (Banerjee et al., 2016; Carter and Yeo, 2018; Drugau-Constantin, 2018; Hurd, 2016; Machan, 2016a, b; Mirica (Dumitrescu), 2018; Popescu Ljungholm, 2018; Sharp, 2016) that prioritize refinement and exceptionality, and reap high prices. (Rosenbaum et al., 2019) (Tables 1-9)
5. Conclusions and Implications
Personalized nutrition employs data on separate features to advance targeted nutritional guidance, foods, or services to help individuals in attaining a perpetual dietary alteration in behavior that is health-giving (Ordovas et al., 2018), improving dietary metabolic syndrome treatment. (Bashiardes et al., 2018) As a pretreatment gut microbiota biomarker, enterotypes may eventually be a relevant component in personalized nutrition and obesity management. (Christensen et al., 2018) The prevalence and acuteness of ordinary and expensive human diseases may be determined by nutritional vulnerabilities and inadequacies that alter the epigenome, and the predominant intervals of exposure are the periconception phase and early prepubescence. (Skinner et al., 2019)
This paper was supported by Grant GE-1386587 from the Social Analytics Laboratory, Los Angeles, CA.
The author confirms being the sole contributor of this work and approved it for publication.
Conflict of Interest Statement
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Spiru Haret University, Bucharest, Romania
How to cite: Bratu, Sofia (2019). "Nutritional Genomics in Personalized Medicine: Data-driven Customized Treatments and Lifestyle-based Disease Management and Prevention," Linguistic and Philosophical Investigations 18: 140-146.
Received 22 December 2018 * Received in revised form 24 March 2019
Accepted 27 March 2019 * Available online 18 April 2019
Table 1 Proportion of launches carrying an organic claim (top 10 countries) Germany 23 Sweden 21 Netherlands 19 Czech Republic 18 Denmark 17 Austria 17 France 16 USA 14 Norway 12 Switzerland 11 Sources: Mintel; my survey among 4,800 individuals conducted October 2018. Table 2 Prevalence of inadequate fruit and vegetable intake (%, age groups) Age group Inadequate fruit Inadequate vegetables 18-24 54 91 25-34 52 88 35-44 49 87 45-54 47 86 55-64 45 86 65-74 44 85 75+ 42 84 Sources: ABS; my survey among 4,800 individuals conducted October 2018. Table 3 Most popular supplements among U.S. adults (%) Multivitamin 58 Vitamin D 31 Vitamin C 26 Calcium 24 Vitamin B/B Complex 24 Protein 23 Omega 3/Fatty Acids 21 Probiotics 18 Magnesium 17 Vitamin E 16 Fiber 14 Green Tea 13 Sources: CRN; Ipsos Public Affairs; my survey among 4,800 individuals conducted October 2018. Table 4 U.S. people who take nutritional supplements (%, by age group and sex) Age group Male Female 1-3 43 4-8 48 9-13 37 14-18 35 19-30 32 31-50 38 51-70 44 71+ 52 Sources: CCHS; my survey among 4,800 individuals conducted October 2018. Table 5 Top reasons for taking supplements (%) Overall health/wellness 52 Fill nutrient gaps in diet 36 Energy 29 Immune health 27 Bone health 25 Heart health 25 Healthy aging 24 Sources: CRN; Ipsos Public Affairs; my survey among 4,800 individuals conducted October 2018. Table 6 Healthy habits of U.S. adult supplement users vs. non-users (%) Users Non-users Exercise regularly 72 60 Visit doctor regularly 78 64 Try to eat a balanced diet 89 72 Get a good night's sleep 77 63 Maintain a healthy weight 67 61 Do not smoke/use tobacco 79 64 Sources: CRN; Ipsos Public Affairs; my survey among 4,800 individuals conducted October 2018. Table 7 Proportion of calories from Fresh Food (FF), Packaged Food (PF), Soft Drinks (SD), and Alcoholic Drinks (AD) (% of total calories purchased) Area FF PF SD AD Asia Pacific 57 37 2 4 Australasia 29 56 7 8 Eastern Europe 36 51 4 9 Latin America 34 52 6 8 Middle East and Africa 33 56 5 6 North America 24 63 7 6 Western Europe 21 64 6 9 Sources: Euromonitor International; my survey among 4,800 individuals conducted October 2018. Table 8 U.S. adults who use an app to track their diet and nutrition (%) Age group 18-29 30-45 46-60 61+ I use it regularly 29 26 22 14 I use it occasionally 22 22 19 13 I have used it once 14 14 16 20 I can imagine using it 27 31 39 50 I will not use it 8 7 4 3 Sources: Statista; my survey among 4,800 individuals conducted October 2018. Table 9 Prevalence of usual intakes of sodium exceeding the tolerable upper intake level (%, by sex and age groups) Age group Males Females 2-3 98 94 4-8 97 96 9-13 84 74 14-18 87 52 19-30 82 44 31-50 78 33 51-70 59 27 71+ 47 14 Sources: ABS; my survey among 4,800 individuals conducted October 2018.
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|Date:||Jan 1, 2019|
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