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NUTRITIONAL GENOMICS IN PERSONALIZED MEDICINE: DATA-DRIVEN CUSTOMIZED TREATMENTS AND LIFESTYLE-BASED DISEASE MANAGEMENT AND PREVENTION.

1. Introduction

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)

Funding

This paper was supported by Grant GE-1386587 from the Social Analytics Laboratory, Los Angeles, CA.

Author Contributions

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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SOFIA BRATU

sofiabratu@yahoo.com

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

doi:10.22381/LPI18201910
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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