Harnessing Diet and Gene Expression Insights Through a Centralized

Nutrigenomics Database to Improve Public Health

Shriya Samudrala

, Ijeoma Ezengwa

, Fahmida Hai

, Rubayat Khan

, Saif Nirzhor

and Don Roosan

College of Health Sciences, Western University of Health Sciences, Pomona, U.S.A.

Tekurai Inc., San Antonio, U.S.A.

University of Nebraska Medical Center, Omaha, U.S.A.

University of Texas Southwestern Medical Center, Dallas, U.S.A.

School of Engineering and Computational Sciences, Merrimack College, North Andover, U.S.A.

Keywords: Nutrigenomics, Nutrition, Public Health, Artificial Intelligence, Database Management.

Abstract: Nutrigenomics is an emerging field that explores the intricate interaction between genes and diet. This study

aimed to develop a comprehensive database to help clinicians and patients understand the connections

between genetic disorders, associated genes, and tailored nutritional recommendations. The database was built

through an extensive review of primary journal articles and includes detailed information on gene

characteristics, such as gene expression, location, descriptions, and their interactions with diseases and

nutrition. The data suggest that a patient's food intake can either increase or decrease the expression of genes

related to specific diseases. These findings underscore the potential of nutrition to modify gene expression

and reduce the risk of chronic diseases. The study highlights the transformative role nutrigenomics could play

in medicine by enabling clinicians to offer personalized dietary recommendations based on a patient’s genetic

profile. Future research should focus on validating the database in clinical counselling to further refine its

practical applications.

1 INTRODUCTION

The impact of nutrigenomics on daily life is profound

and far-reaching. Nutrigenomics, the study of the

relationship between diet and gene expression, is a

rapidly evolving field with tremendous potential for

growth, particularly through the use of advanced

databases. Given the current health challenges faced

by many communities, this area of research is

increasingly relevant, as nutrition plays a pivotal role

in the development and management of many chronic

disorders, such as colorectal cancer. Nutrigenomics

integrates diverse scientific and environmental

factors to explore the intricate connections between

nutrition and gene expression (Franzago et al, 2020).

The origins of nutrigenomics trace back to the

groundbreaking Human Genome Project of the late

20th century, which provided researchers with the

tools to investigate how diet influences gene activity

and contributes to the onset of diseases. As the field

continues to grow, nutrigenomics holds the promise

of advancing personalized nutrition and improving

public health outcomes.

Nutrigenomics offers valuable insights into how

individuals may respond differently to specific foods

or nutrients based on their genetic makeup. Food

serves as the primary source of nutrients essential for

the body to function effectively, supporting daily

activities and overall survival. Through

nutrigenomics, individuals can better understand how

modifying their diet can influence gene expression,

particularly in cases involving cancerous tissues.

The database empowers consumers by providing

knowledge to make informed dietary decisions that

can positively impact their health. Research has

identified numerous genes affected by dietary intake.

For instance, in colorectal cancer, consuming more

than 2 ounces of red meat per day has been linked to

the downregulation of COL1A1, a gene involved in

extracellular matrix (ECM) regulation and cell matrix

adhesion (Zeng et al, 2023; Aykan, 2015). In addition

to COL1A1, other genes such as the anti-metastasis

and angiogenesis-related gene COL4A2, the tumor

Samudrala, S., Ezengwa, I., Hai, F., Khan, R., Nirzhor, S., Roosan and D.

Harnessing Diet and Gene Expression Insights Through a Centralized Nutrigenomics Database to Improve Public Health.

DOI: 10.5220/0013457600003967

In Proceedings of the 14th International Conference on Data Science, Technology and Applications (DATA 2025), pages 291-298

ISBN: 978-989-758-758-0; ISSN: 2184-285X

291

suppressor gene TP53, and the cytokine signaling

regulation gene IL22RA1 have been implicated in

cancer pathogenesis (Hall et al., 2016; Abo El-Ella &

Bishayee, 2019; Nasir et al, 2019). Developing a

deeper understanding of these interactions opens new

opportunities for personalized dietary strategies to

prevent and manage diseases, underscoring the

transformative role of nutrition in promoting health

and well-being.

Nutrigenomics also explores how nutrition

interacts with our genes to influence brain function

and mood, offering significant potential in the field of

mental health (Marcum, 2020). As an emerging

discipline, further research in nutrigenomics could

lead to innovative treatments for mental health

conditions, potentially minimizing the unpleasant

side effects and withdrawal symptoms often

associated with traditional medications. Key genes

implicated in depression and anxiety include the

serotonin transporter gene (SLC6A4/5HTT), the

serotonin transporter-linked promoter region (5-

HTTLPR), the serotonin receptor gene (HTR2A), and

brain-derived neurotrophic factor (BDNF). These

genes play critical roles in regulating mood and

emotional well-being (Birla et al., 2022). Notably, L-

tryptophan (TRP) supplements have shown promise

in improving affective states by promoting serotonin

synthesis, a neurotransmitter essential for mood

regulation, appetite control, and sleep. Serotonin is

synthesized from TRP, which can be obtained from

foods such as salmon, nuts and seeds, turkey and

poultry, and pineapple (Rodrigues et al., 2021).

Identifying the genetic pathways influenced by

nutrition, nutrigenomics leads to the potential to

revolutionize mental health care, paving the way for

personalized dietary strategies that enhance

emotional and cognitive well-being.

Consumers have access to various companies that

offer services to use genetic information to tailor

dietary recommendations to their unique genome.

However, the databases currently available tend to be

more generalized, offering insights into genes

associated with common diseases rather than

personalized, specific guidance (Jaskulski et al,

2023). Among these, NutrigenomeDB stands out as a

primary resource for finding nutrition-specific

articles related to various diseases. While other

databases exist, their focus is often limited to the

general connection between diet and genes in the field

of nutrigenomics. To improve the functionality and

precision of these databases, several key concepts

should be incorporated. First, databases should

specify the required intake amounts of specific foods

necessary to influence gene expression effectively.

Additionally, providing detailed descriptions of the

interactions between particular foods and genes,

along with the precise location of these genes, would

enhance their utility for consumers and researchers

alike (Jaskulski et al, 2023).

As a relatively new and emerging field,

nutrigenomics faces challenges in accessing

comprehensive information on the effects of nutrition

on gene expression. While existing studies have

explored a variety of nutrigenomic interactions, there

remains a lack of literature that delves deeply into

these interactions on a disease-specific level (Alegría-

Torres et al, 2011). Continued research is essential to

bridge this gap, as nutrigenomics has the potential to

significantly advance our understanding of these

mechanisms and contribute to the development of

targeted interventions and treatments. With increased

funding and resources, we anticipate a surge in

scientific studies and research in the near future,

enabling nutrigenomics to fulfill its potential as a

transformative tool in personalized medicine and

nutrition.

There is a pressing need for companies to provide

more detailed insights into how specific interactions

between food and genes result in positive health

outcomes. It is essential for consumers to have the

ability to track and understand the pathways involved

in these interactions, enhancing their knowledge of

how nutrigenomics works. Given the strong correlation

between nutrigenomics and diseases, understanding

the effects of excessive or insufficient intake of certain

foods is critical. For instance, increased consumption

of certain foods can lead to epigenetic changes, such as

methylation, which can alter gene sequences. An

example is the overconsumption of red meat, which

has been linked to the downregulation of the NCL gene

in colorectal cancer (Genkinger & Koushik, 2007).

Conversely, improper portion sizes or poor dietary

habits can lead to the overexpression or

underexpression of genes, further contributing to

disease risk (Zhang et al, 2019).

With the rising incidence of hospital admissions

related to poor nutrition and the rapid advancements

in nutrigenomics, there is hope that personalized

dietary strategies can help combat chronic diseases

linked to nutrition (Peña-Romero et al, 2017). By

emphasizing proper food intake and understanding

the genetic implications of diet, nutrigenomics has the

potential to transform healthcare and reduce the

burden of nutrition-related illnesses. Recognizing the

profound impact of nutrients on gene expression and

overall health has the potential to enhance the well-

being of entire communities, driving a new era of

precision health and disease prevention.

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2 METHODS

We identified a suitable database, Nutrigenetics, to

collect data on gene classification and the interaction

between genes and specific nutrients. The

Nutrigenetics database provides information on

various gene-food interactions and allowed us to

access relevant articles related to specific nutrients

(Martín-Hernández et al, 2019). During the selection

process, shown in Figure 1, we noted the database’s

limitations, including a lack of detailed explanations on

gene-nutrition interactions and recommended intake

amounts. These factors were considered as part of our

inclusion criteria. A total of 196 articles were

thoroughly reviewed to extract data on the effects of

nutrients on genes associated with chronic diseases.

We carefully examined the limitations and concerns

highlighted by researchers in the "gap" sections of

these articles, identifying commonalities among them.

To ensure the reliability of the findings, we analyzed

the methods and results sections of each study to

confirm the presence of robust and supporting data.

Based on the criteria listed in Table 1, the pool of

articles was narrowed down to eight. Two independent

reviewers conducted the data extraction process,

entered the extracted information into the database, and

cross-checked all inputs for accuracy. This rigorous

approach ensured the integrity and reliability of the

data collected for our study.

Gene location was included in the database to

provide patients with detailed information about

where each gene is positioned on their chromosomes.

This addition enhances patients’ understanding of

their genetic makeup. Gene descriptions were also

incorporated to explain each gene's function in its

natural state, uninfluenced by specific nutrients. This

information helps patients grasp the potential

mechanisms through which certain foods may

contribute to cancer development.

Figure 1: Flow chart illustrating the process of paper

selection for inclusion in the study.

Table 1: The inclusion and exclusion criteria were

implemented to create an adequate and proper

nutrigenomics database.

Inclusion Criteria Exclusion Criteria

Articles available in full

text

Articles is not available in

full text

Articles has correlation

between the nutrition

and the gene

Articles does not have

correlation between the

nutrition and the gene

Articles have measure of

consumption, whether

that be numerical intake

or frequency intake.

Articles does not have

measure of consumption,

whether that be numerical

intake or frequency intake.

Method and result

section of the paper has

proper and supporting

data

Method and result section

of the paper does not have

proper and supporting data

Food intake was measured using two different

approaches. The first method quantified the nutrient

intake in numeric values. The second method relied

on a Food Frequency Questionnaire (FFQ) to assess

consumption frequency. This questionnaire included

11 response categories, ranging from "never" to "2 or

more times a day," and reflected dietary habits from

the year prior to the study.

Data collected from various research articles and

studies were initially compiled into an Excel sheet

and subsequently transformed into a comprehensive

database. The database was implemented using

MySQL, a widely used relational database

management system, with data structured into tables

for genes, diseases, nutrients, and their interactions,

allowing for efficient querying and retrieval of

information.

3 RESULTS

The database results revealed critical insights into the

correlation between various nutritious foods and gene

expression. It offers a comprehensive depiction of

gene characteristics and their interactions with

specific foods. The data demonstrated that the intake

of certain foods can either upregulate or downregulate

the expression of genes associated with specific

diseases. The database is organized into ten sections:

disease, associated gene, P-value, gene expression,

location, species, description, function, interaction,

and intake amount. Each gene is linked to its

corresponding disease, along with the P-value that

quantifies the strength of its interaction with

particular foods. The inclusion of P-values provides a

clear understanding of the statistical significance of

these correlations.

Harnessing Diet and Gene Expression Insights Through a Centralized Nutrigenomics Database to Improve Public Health

293

Figure 2: Snapshot of a section of the database, showcasing detailed information on the relationship between nutritious foods,

gene expression, and disease prevention. This resource offers valuable insights for both clinicians and patients.

The gene expression section highlights the gene's

characteristics, including its type and a summary of

how it is expressed. The location section specifies the

gene's position on the chromosome, with the first

number representing the chromosome, the "p"

indicating the short arm, and the "q" denoting the long

arm. The database also includes concise descriptions

of each gene and their biological functions.

The interaction section outlines how specific

foods interact with genes to influence their

expression, offering insights into the metabolic

pathways involved. The intake amount category

provides a quantitative measure of the required

nutrient consumption to induce changes in gene

expression.

By integrating these elements, the database equips

clinicians and patients with a comprehensive

understanding of the intricate relationships between

nutritious foods, gene expression, and disease

prevention. This resource has the potential to inform

personalized nutrition strategies and enhance efforts

to prevent and manage chronic diseases. To evaluate

the functionality and utility of our database, we

conducted several test queries; for example, querying

the database for genes associated with colorectal

cancer and their interaction with red meat

consumption returned results including the

downregulation of COL1A1 with a recommended

intake limit of less than 2 ounces per day. In contrast,

a similar query in NutrigenomeDB provided general

information on gene-diet interactions but lacked

specific intake recommendations, highlighting the

enhanced detail our database offers. To demonstrate

the database's implementation and functionality, we

present a sample query output in Figure 3, which

shows the detailed information retrieved for the gene

MTHFR and its interaction with folate, including the

gene's location, description, and the specific intake

amount that influences its expression.

4 DISCUSSIONS

Nutrigenomics is an emerging and rapidly evolving

field, but current research remains limited and

fragmented. While our database underscores the

significant impact of nutrition on gene expression and

its potential to influence disease risk, it is crucial to

acknowledge that nutrition is one of several factors

contributing to chronic disease management. Genetic

predispositions, medical treatments, and lifestyle

choices all play integral roles, and thus, the nutritional

recommendations provided should be considered as

part of a holistic healthcare approach. While valuable

information exists, compiling a cohesive and

comprehensive understanding of disorders, their

associated genes, and corresponding nutritional

recommendations often requires navigating multiple,

often inaccessible, sources. To address this gap, we

have developed a curated database that systematically

organizes diseases, associated genes, and specific

nutritional interventions that can upregulate or

downregulate these genes.

This database serves as a valuable resource for

nutrition counselors and healthcare clinicians,

offering evidence-based recommendations to support

patient health. Currently, our research focuses on

specific cancers and neuropsychiatric disorders—

relatively new areas of study within nutrigenomics.

As the field continues to grow, we anticipate an

expanding body of knowledge that will enable the

identification of additional targets and further

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enhance personalized approaches to nutrition and

disease prevention.

The potential applications of this database extend

across various domains, including nutritional

counselling, informatics, and public health. Insights

derived from the database enable clinicians to offer

personalized, gene-based dietary recommendations

tailored to an individual’s unique genetic profile

(Agrawal et al, 2023; Mullins et al, 2020). Such

personalized nutrition plans not only enhance the

effectiveness of dietary interventions but also reduce

the risk of nutrition-related diseases, presenting a

transformative approach to preventive healthcare

(Meng et al., 2019).

Current research in nutrigenomics has significant

implications for informatics. The database serves as a

centralized, well-organized resource that researchers

can easily access to support the discovery of new

genes and dietary interventions. Moreover, the

nutrigenomics database can integrate with advanced

technologies, such as artificial intelligence (AI), to

unlock deeper insights from large datasets of genetic

and nutritional information. AI applications have the

potential to analyze these datasets, identify patterns

and associations, and develop predictive models to

assist healthcare clinicians in making informed

decisions about nutrition and health (Gao & Chen,

2017; Marcum, 2020). Currently, healthcare

applications utilizing AI, augmented and virtual

reality have been increasing rapidly. This technology

is being utilized for patient counselling, medication

reminders, and even to aide in surgery (Roosan, 2024;

Li et al., 2023). For example, AI could generate

personalized nutrition plans tailored to an

individual’s unique genetic profile that the provider

reviews (Roosan, 2024). By analyzing genetic data

and identifying variations that influence how an

individual metabolizes specific nutrients or responds

to certain diets, AI can provide highly targeted dietary

recommendations (Malle, 2021). This integration of

AI with nutrigenomics could revolutionize the field,

paving the way for more precise and effective

healthcare solutions.

Integrating the nutrigenomics database with

electronic health records (EHRs) could significantly

enhance its clinical utility. With access to

comprehensive patient data, including genetic

profiles and dietary habits, clinicians can develop

more precise and tailored nutrition recommendations

(Roosan et al, 2019). Additionally, using AI-powered

tools like ChatGPT alongside the database could

enable patients to monitor and refine their dietary

intake in real time, empowering them to achieve their

nutritional goals (Roosan et al, 2023; Roosan et al,

2024).

The applications of genomic knowledge extend

far beyond healthcare. Genomics forms the

foundation for “personalized medicine,” offering

unique, patient-specific clinical interventions that

improve outcomes. Beyond healthcare, genomics has

transformative potential in agriculture, environmental

science, forensic science, and drug development

(Senthil et al., 2019).

From a public health perspective, the database

could enhance the effectiveness of interventions by

providing tailored nutritional recommendations based

on genetic profiles and dietary needs (Alegría-Torres

et al, 2011; Roosan et al, 2024). Targeted strategies

could address specific subpopulations with unique

genetic traits and nutritional risk factors, enabling the

development of more effective public health policies

and programs (Wu et al., 2024; Roosan et al, 2024).

The database also provides valuable insights into the

relationships between genetics, nutrients, and health

outcomes, offering opportunities to identify

therapeutic targets and improve disease prevention at

the population level (Fenech et al., 2011).

There is growing evidence linking genetics, poor

nutrition, and disease risk. For example, individuals

with genetic variations affecting folate metabolism

are at an increased risk of colorectal cancer when

consuming low-folate diets (Kim, 2006; Zeng et al,

2023). Variations in the MTHFR gene can impair

folic acid metabolism, and excessive folic acid

consumption in these individuals may mask vitamin

B12 deficiency, leading to adverse health outcomes

(Birla et al., 2022). Diets high in processed and red

meat have similarly been associated with a

heightened risk of colorectal cancer, particularly in

individuals with genetic predispositions (Aykan,

2015; Bertucci et al, 2004). Additionally, genetic

variations have been shown to influence

susceptibility to mental health disorders such as

depression and anxiety, with poor dietary quality

exacerbating these conditions. These findings

highlight the intricate interplay between genetics,

nutrition, and health, emphasizing the importance of

personalized dietary strategies to mitigate risks and

improve outcomes.

The database is designed primarily for clinicians,

offering a robust tool to assist in creating personalized

treatment plans. By analyzing genetic profiles,

clinicians can identify variations that influence

nutrient metabolism and recommend tailored dietary

changes or supplements to optimize patient health.

Equally important is educating patients about the

benefits of the database, which can be achieved

Harnessing Diet and Gene Expression Insights Through a Centralized Nutrigenomics Database to Improve Public Health

295

through online resources and consultations with

healthcare professionals. Empowering patients with

this knowledge enables them to take an active role in

managing their health through informed dietary and

lifestyle changes (Roosan et al, 2022; Roosan, 2022).

It is important to note that while nutrigenomics

provides valuable insights into potential areas of

concern and personalized strategies, it is not a

diagnostic tool. Instead, it serves as a resource to

enhance understanding and promote proactive health

management. Although nutrigenomics is still in its

early stages, it represents the beginning of a deeper

understanding of the complex relationship between

genetics and nutrition. This field has already made

significant strides in identifying how genetic factors

and nutrients interact to impact health and reduce

disease risk. As research continues to advance, we

anticipate a growing body of knowledge that will

pave the way for more personalized approaches to

medicine, tailoring healthcare interventions to

individual genetic profiles. To ensure the

nutrigenomics database can be effectively integrated

into clinical workflows and patient care, it is crucial

to adhere to established health data standards (Roosan

et al., 2020b), explore the development of AI-

powered applications for personalized dietary

guidance (Roosan et al., 2020a), and consider

advanced data management solutions like blockchain

for secure and efficient access (Roosan et al., 2022).

While many companies currently focus on

genomic testing to raise awareness of genetic

diseases, there is a growing need for these companies

to provide customers with more comprehensive

information on the connection between genes and

nutrition. Enhancing access to such insights would

enable customers to develop a deeper understanding

of nutrigenomics. Achieving this goal may involve

initiatives such as education programs, accessible

genetic testing, consultations with healthcare

providers, and greater transparency from companies

(Guasch-Ferré et al., 2018). These studies

collectively highlight the multifaceted nature of

clinical decision-making, exploring cognitive

strategies, complexity measurement, expert

heuristics, and disease-specific reasoning to inform

the design of effective decision support systems

((Islam et al, 2014; Islam et al, 2015; Islam et al,

2016a; Islam et al, 2016b). By expanding awareness

and accessibility, nutrigenomics has the potential to

transform how individuals approach health and

nutrition, fostering a deeper understanding of

personalized wellness.

5 CONCLUSION

In conclusion, the nutrigenomics database offers a

comprehensive understanding of the relationship

between nutritious foods, gene expression, and

disease prevention. By cataloging diseases,

associated genes, and specific nutritional

interventions that can upregulate or downregulate

gene activity, this resource serves as a valuable tool

for nutrition counselors and healthcare clinicians

seeking evidence-based recommendations to support

their patients' health. The database has far-reaching

implications for nutritional counseling, informatics,

and public health. It holds the potential to enable the

creation of personalized nutrition plans tailored to an

individual’s genetic makeup, integrate seamlessly

with AI applications for data analysis and predictive

modeling, and enhance clinical practice through

integration with EHRs.

Furthermore, genomics knowledge provides

opportunities for personalized medicine, agriculture

environment, forensic science, and drug

development. The nutrigenomics database plays a

crucial role in preventing or managing disease

through diet and recognizing the significant impact of

nutrients on overall health, which can notably

enhance the health and well-being of the entire

community. Lastly, the nutrigenomics database could

help identify targeted interventions for subgroups of

the population with specific genes and risk factors of

nutritional needs and offer valuable insights into

disease pathogenesis and potential targets for

interventions, ultimately optimizing health in

individuals and communities through nutrigenomics.

ACKNOWLEDGEMENTS

We are grateful to Merrimack College for support.

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