nutrients

Review
Personalized Nutrition: Tailoring Dietary Recommendations
through Genetic Insights
Saiful Singar 1 , Ravinder Nagpal 1 , Bahram H. Arjmandi 1 and Neda S. Akhavan 2, *

1 Department of Health, Nutrition, and Food Sciences, College of Education, Health, and Human Sciences,
Florida State University, Tallahassee, FL 32306, USA; ssingar@fsu.edu (S.S.); rnagpal@fsu.edu (R.N.);
barjmandi@fsu.edu (B.H.A.)
2 Department of Kinesiology and Nutrition Sciences, School of Integrated Health Sciences,
University of Nevada, Las Vegas, NV 89154, USA
* Correspondence: neda.akhavan@unlv.edu

Abstract: Personalized nutrition (PN) represents a transformative approach in dietary science, where
individual genetic profiles guide tailored dietary recommendations, thereby optimizing health
outcomes and managing chronic diseases more effectively. This review synthesizes key aspects of PN,
emphasizing the genetic basis of dietary responses, contemporary research, and practical applications.
We explore how individual genetic differences influence dietary metabolisms, thus underscoring
the importance of nutrigenomics in developing personalized dietary guidelines. Current research
in PN highlights significant gene–diet interactions that affect various conditions, including obesity
and diabetes, suggesting that dietary interventions could be more precise and beneficial if they are
customized to genetic profiles. Moreover, we discuss practical implementations of PN, including
technological advancements in genetic testing that enable real-time dietary customization. Looking
forward, this review identifies the robust integration of bioinformatics and genomics as critical for
advancing PN. We advocate for multidisciplinary research to overcome current challenges, such as
data privacy and ethical concerns associated with genetic testing. The future of PN lies in broader
adoption across health and wellness sectors, promising significant advancements in public health
and personalized medicine.
Citation: Singar, S.; Nagpal, R.;
Arjmandi, B.H.; Akhavan, N.S.
Keywords: personalized nutrition; nutrigenomics; genetic variability; dietary interventions; chronic
Personalized Nutrition: Tailoring disease management; bioinformatics in nutrition
Dietary Recommendations through
Genetic Insights. Nutrients 2024, 16,
2673. https://doi.org/
10.3390/nu16162673 1. Introduction
Academic Editor: Ahmad Personalized nutrition (PN) is a discipline that utilizes the unique characteristics
R. Heydari of individuals to formulate nutritional approaches aimed at preventing, managing, and
treating diseases, as well as enhancing overall health. According to the American Nutrition
Received: 19 July 2024 Association, this field is characterized by three interconnected components: the science and
Revised: 8 August 2024
data behind PN, professional education and training in PN, and the application of PN in
Accepted: 9 August 2024
guidance and therapeutic practices [1]. PN encompasses the use of genetic, phenotypic,
Published: 13 August 2024
biochemical, and nutritional data to analyze their effects on an individual’s health. It
also prepares healthcare providers to implement PN strategies in diverse environments
and customize interventions to meet specific personal requirements [1]. The International
Copyright: © 2024 by the authors.
Society of Nutrigenetics/Nutrigenomics (ISNN) offers insights into PN, highlighting how
Licensee MDPI, Basel, Switzerland. an individual’s genetic makeup and a range of biological and cultural differences, such
This article is an open access article as food intolerances, preferences, and allergies, influence their response to nutrients [2].
distributed under the terms and PN operates on the principle that individual genetic variations can influence how certain
conditions of the Creative Commons foods or amounts of nutrients modify one’s risk of disease [2]. The breadth of PN is
Attribution (CC BY) license (https:// enhanced by incorporating a variety of phenotypic data, including measurements of body
creativecommons.org/licenses/by/ composition, levels of physical activity, clinical indicators, and biochemical markers that
4.0/). assess nutritional status, along with genomic information, to deliver more customized

Nutrients 2024, 16, 2673. https://doi.org/10.3390/nu16162673 https://www.mdpi.com/journal/nutrients

Nutrients 2024, 16, 2673 2 of 18

dietary recommendations [3]. This approach is seen to exist at multiple levels, from internet-
delivered services to the use of genomic data in crafting personalized dietary advice [3].
The historical perspective on dietary recommendations reveals a shift in focus from
preventing nutrient deficiencies to addressing chronic diseases associated with dietary
excesses. Initially, dietary guidelines were developed to ensure nutritional adequacy and
prevent deficiencies, particularly in the context of food scarcity. As societies transitioned
from scarcity to abundance, the prevalence of chronic diseases such as heart disease, obesity,
and diabetes increased, prompting a change in dietary guidance toward the prevention
of these conditions [4–6]. Early dietary recommendations emphasized the consumption
of animal products and were less concerned with chronic disease prevention. However,
as evidence accumulated linking diet to chronic disease risk, there was a growing consen-
sus on the benefits of plant-based diets, including vegetarian, Mediterranean, and Asian
diets, which were associated with lower rates of chronic diseases [7]. The limitations of
historical dietary recommendations include a reliance on evidence that may not have been
robust or comprehensive, leading to guidelines that may not have been fully supported
by the available science (Table 1). For example, the initial Dietary Goals for Americans
proposed changes in macronutrient consumption without sufficient evidence to conclu-
sively recommend such shifts [5]. Additionally, the application of these guidelines to
diverse populations, including children, was often based on inferences rather than direct
evidence of benefit [4]. Furthermore, the methods used to assess dietary intake, such as
Food Frequency Questionnaires, have been criticized for their inaccuracy and the potential
for recall bias, which can undermine the validity of diet-disease relationships established
in epidemiological studies [8].

Table 1. Comparison of traditional vs. personalized nutrition approaches.

Traditional Nutrition Personalized Nutrition

• West-established guidelines • Individualized approach
• Broad applicability • Potential for improved health outcomes
• Cost-effective Advantages
• Enhanced adherence
• Simplicity • Integration of advanced technologies

• Higher costs
• Lack of individualization
• Complexity
• Generalized recommendations
Limitations • Limited availability
• Outdated information
• Evolving science
• Limited flexibility
• Potential privacy concerns

The promise of genomics in enhancing dietary interventions lies in the potential to

tailor nutrition based on individual genetic variability. Nutrigenomics explores the in-
teraction between food and our genetic makeup, examining how our individual genetic
variations influence our response to nutrients in our diet. This field holds promise for
tailoring dietary guidelines to individual health needs, potentially enhancing health out-
comes. For instance, genome-wide single nucleotide polymorphism (SNP) data can be
used to create personalized dietary recommendations, taking into account an individual’s
genetic variability at multiple SNPs [9]. Incorporating genomics into the field of nutritional
sciences can enhance the efficacy of nutritional interventions. This approach allows for a
deeper understanding of the intricate relationships between dietary components and the
human genome across various states of health and illness [10]. This approach could pave
the way for creating dietary guidelines that are highly predictive, reducing the likelihood
of unforeseen outcomes and considering the impact of human genetic differences [10].
Moreover, the inclusion of genomic information in dietary interventions has been shown to
enhance the accuracy of weight loss models, indicating the efficacy of coaching informed
by participants’ genomic risk [11]. This suggests that genomics can play a role in adopting
Nutrients 2024, 16, 2673 3 of 18

changes beyond population-wide healthy eating guidelines, potentially mitigating the

prevalence of obesity and other chronic diseases [12]. However, it is important to note that
while the field of nutritional genomics holds great promise, it is still emerging, and more
research is needed to fully realize its potential in clinical practice. The ISNN recognizes the
ethical, practical, and scientific obstacles that need to be overcome to successfully apply
the findings from gene–nutrient interaction research into reliable practice guidelines for
PN [12,13].

2. Genetic Variability and Nutrient Metabolism

Human genetic variation encompasses the differences in DNA sequences among
individuals, which contribute to the diverse phenotypic characteristics observed in the
human population. Every human genome has more than 3 million single nucleotide
variants (SNVs) compared to the reference genome, and roughly 1% of a person’s genome
varies from this reference sequence [14]. These types of genetic variations encompass
SNPs, insertions and deletions (indels), copy number variations (CNVs), and structural
changes like inversions and complex rearrangements [14–16]. The average human gene
contains numerous biallelic polymorphisms, with a subset found in coding regions that
can affect protein function [17]. The distribution of these genetic variants varies among
populations, with some being common and others rare. Rare variants often show substantial
geographic differentiation, influenced by factors such as evolutionary conservation, coding
consequence, and purifying selection [15]. The 1000 Genomes Project has created a detailed
map of human genetic diversity, encompassing SNPs, short insertions and deletions, and
larger structural deletions. It covers as much as 98% of SNPs that are accessible and have
a frequency of at least 1% within populations that are closely related [15]. This variation
reflects the history of human migration, population dynamics, and adaptation to different
environments [18,19]. Understanding human genetic variation is crucial for the study
of genetic diseases, the development of personalized medicine, and the implementation
of genomic-informed dietary interventions (Figure 1). It enables the detection of genetic
factors that influence susceptibility to diseases and reactions to treatments, including
dietary interventions.
Genetic factors can significantly influence nutrient metabolism, and several exam-
ples are highlighted in the literature. Variants in the methylenetetrahydrofolate reductase
(MTHFR) gene can affect folate metabolism and are linked to cardiovascular disease and
diabetes. Individuals with certain SNPs in this gene may have altered responses to folate in-
take and could benefit from tailored folate supplementation [20–22]. Selenium metabolism
can also be influenced by genetic variation, with SNPs affecting the response of seleno-
protein expression or activity to selenium supplementation [20]. Genetic variations in the
beta-carotene oxygenase 1 (BCMO1) gene, which is critical for beta-carotene metabolism,
can lead to variability in plasma carotenoid levels and may have clinical significance, such
as the development of liver steatosis independent of dietary vitamin A [23,24]. Furthermore,
genetic variations in genes that play a role in lipid metabolism, including cholesteryl ester
transfer protein, lipoprotein lipase, low-density lipoprotein receptor, and apolipoprotein
E, have the potential to influence the risk of coronary artery disease [25–27]. Personalized
dietary recommendations that consider these genetic differences may prove beneficial [12].
Additionally, SNPs in genes related to choline and folate metabolism can determine the di-
etary requirement for choline, which is crucial for liver function and fetal development [28].
These examples underscore the importance of considering genetic factors when evaluating
nutrient metabolism and the potential for PN interventions to optimize health outcomes
based on individual genetic profiles.
 levels of HDL cholesterol following increased intake of long-chain omega-3 polyunsatu-
rated fatty acids (PUFAs), while those with the GG genotype do not demonstrate this ad-
vantage [12]. Additionally, people carrying significant risk alleles for serum- and gluco-
corticoid-inducible kinase 1 (SGK1) might show increased systolic blood pressure when
they consume a diet high in salt [12]. These examples underscore the importance of con-
Nutrients 2024, 16, 2673 4 of 18
sidering genetic variations when assessing dietary needs and the potential for PN to opti-
mize health outcomes based on individual genetic profiles (Table 2).

Figure1.1.Workflow
Figure Workflow of
of aa personalized
personalized nutrition
nutritionprogram.
program.

Case studies in the literature illustrate the impact of genetics on dietary needs through
the lens of nutrigenetics. For example, individuals with the homozygous mutation (TT) in
the MTHFR gene (MTHFRAla222Val, C > T polymorphism) have increased requirements
for folate due to altered folate metabolism. These individuals might require a greater intake
of folate than the suggested dietary allowances (RDAs) to reduce their susceptibility to
diseases associated with folate deficiency [12]. Similarly, individuals with Down syndrome,
who possess an additional copy of chromosome 21, demonstrate higher needs for zinc and
folate because of the heightened expression of cystathionine-β-synthase, a key enzyme in
folate metabolism [12]. Another case study examines the APOA1 polymorphism (G > A).
It reveals that individuals with the A allele experience elevated levels of HDL cholesterol
following increased intake of long-chain omega-3 polyunsaturated fatty acids (PUFAs),
while those with the GG genotype do not demonstrate this advantage [12]. Additionally,
people carrying significant risk alleles for serum- and glucocorticoid-inducible kinase 1
(SGK1) might show increased systolic blood pressure when they consume a diet high in
salt [12]. These examples underscore the importance of considering genetic variations
when assessing dietary needs and the potential for PN to optimize health outcomes based
on individual genetic profiles (Table 2).
Nutrients 2024, 16, 2673 5 of 18

Table 2. Overview of key genes involved in nutrigenomics.

Gene Name Function Associated Nutritional Influence Relevant Studies and Findings
Variations affect folate metabolism and
MTHFR Methylenetetrahydrofolate reductase Folate metabolism
cardiovascular risk [20–22]
Influences lipid levels and cardiovascular
APOE Apolipoprotein E Lipid metabolism
disease risk [25–27]
Associated with increased risk of type 2
TCF7L2 Transcription factor 7-like 2 Type 2 diabetes risk diabetes and response to dietary
carbohydrates [29,30]
Variations affect vitamin A levels and
BCMO1 Beta-carotene oxygenase 1 Beta-carotene metabolism
carotenoid metabolism [23,24]
Linked to increased risk of obesity and
FTO Fat mass and obesity-associated protein Obesity, energy balance
response to dietary fats [31,32]

3. Current Research in Nutrigenomics

Key studies have identified gene–diet interactions that influence the risk of developing
conditions such as obesity and type 2 diabetes mellitus (T2DM). For instance, dietary pat-
terns that emphasize whole grains, vegetables, and fruits, and limit total and saturated fats,
such as the Mediterranean and DASH diets, have demonstrated the potential to lower the
risk of obesity for people with high genetic predisposition scores for obesity, especially in
those with risk alleles of FTO rs9939609, rs1121980, and rs1421085 [29]. However, findings
related to other SNPs in genes like MC4R, APOA5, and PPARG were inconclusive [29].
In the context of T2DM, gene–macronutrient interactions have been studied, with several
genetic variants in or near genes like TCF7L2, GIPR, CAV2, and PEPD showing poten-
tial interactions with macronutrients such as carbohydrates, fats, saturated fats, dietary
fiber, and glycemic load. However, the large-scale EPIC-InterAct study did not replicate
these interactions [30]. Research on gene–diet interactions has extended into the realm of
maternal–child health, examining issues such as gestational diabetes, pregnancy-induced
hypertension, recurrent miscarriages, iron deficiency anemia, and excessive weight gain
during pregnancy. Findings from these studies indicate that insights into gene–diet interac-
tions might contribute to tailored nutritional strategies for mothers and their children [33].
Overall, while there is evidence of gene–diet interactions affecting metabolic health, the
findings are often specific to certain populations or genetic variants and may not be univer-
sally applicable (Table 3). Further research is needed to better understand these interactions
and to develop PN strategies based on genetic profiles [29,30,33].

Table 3. Summary of personalized interventions.

Genetic Markers
Intervention Type Target Population Dietary Adjustments Health Outcomes
Used
Reduced fat intake, Weight loss, improved
Dietary modification Obese individuals FTO, MC4R
increased physical activity metabolic health
Individuals with folate
Supplementation MTHFR Increased folate intake Reduced cardiovascular risk
deficiency
Modified fat intake,
People at risk of
Lifestyle changes APOE increased omega-3 fatty Improved lipid profile
cardiovascular disease
acids
Controlled carbohydrate
Medical nutrition therapy Diabetics TCF7L2, PPARG Better blood sugar control
intake
Personalized meal plans
Technological integration General population Various Overall health improvement
based on genetic tests

Technological advancements in genetic testing and bioinformatics have significantly

enhanced the ability to identify genetic biomarkers that inform on disease susceptibility,
progression, and response to therapy. The development of molecular point-of-care tests
Nutrients 2024, 16, 2673 6 of 18

(POCTs) has been particularly notable, with microfluidic technologies and novel amplifica-
tion methods enabling rapid genetic testing at the point of care, which is expected to further
the adoption of personalized medicine methods [34]. Advances in next-generation sequenc-
ing (NGS) technologies, such as massively parallel sequencing, have significantly decreased
the costs and accelerated the process of DNA sequencing. This advancement enables both
whole-genome sequencing (WGS) and the targeted sequencing of particular genomic areas.
As a result, NGS has begun to integrate into clinical settings, offering improvements in
the diagnosis, prognosis, and treatment selection for numerous diseases [35–37]. Artificial
intelligence (AI) is being integrated into clinical laboratory genomics to manage the “big
data” generated by these technologies. AI enhances genetic research by helping identify
variants in DNA sequences, predicting how these variants could affect protein structure
and function, and linking genomic data with clinical insights. This support bolsters the
ability of geneticists to convert intricate data into practical information for managing patient
care [38]. Advancements in long-read sequencing and long-range mapping technologies
are enhancing genomic diagnostics by enabling the identification of a broader range of
variants and providing a more holistic perspective of transcriptomes and epigenomes.
To fully capitalize on these technologies’ distinct features and tackle their complex error
profiles, new bioinformatics strategies are essential [39].
In nutritional research, several genetic markers have been identified as significant in
understanding gene–diet interactions. Variants of the fat mass and obesity-associated (FTO)
gene, including rs9939609, rs1121980, and rs1421085, have been linked to an increased risk
of obesity. These genetic markers have shown interactions with dietary patterns that are
high in whole grains, vegetables, and fruits, and low in both total and saturated fats [29].
Other genes commonly studied include MC4R, PPARG, and APOA5, although findings
related to their interaction with diet on overnutrition have been inconclusive [29]. The gene
responsible for fibroblast growth factor 21 (FGF21) has been correlated with macronutrient
intake. Specifically, a variation located at the chromosome 19 locus (rs838145) has been
associated with increased carbohydrate intake and decreased fat consumption [40]. Addi-
tionally, the FTO variant (rs1421085) is associated with higher protein intake, independent
of body mass index [40]. The ISNN emphasizes the significance of providing dietary rec-
ommendations customized to genetic variations, including those found in genes crucial
for lipid metabolism (such as cholesteryl ester transfer protein, lipoprotein lipase, low-
density lipoprotein receptor, and apolipoprotein E), which could impact the susceptibility
to coronary artery disease [2]. Furthermore, specific polymorphisms have been shown
to interact with nutrient intake, influencing obesity and abdominal obesity. For instance,
the presence of the minor allele (A) of the Ca binding protein 39 (CAB39) rs6722579 gene
variant is linked to an increased susceptibility to abdominal obesity in those who exceed
the Dietary Reference Intakes (DRIs) for fat consumption [41]. Conversely, individuals
carrying the minor allele (T) of the carboxypeptidase Q (CPQ) rs59465035 gene tend to
exhibit reduced susceptibility to abdominal obesity, particularly in cases of higher vitamin
C consumption [41]. These genetic markers are instrumental in advancing the field of
PN, where dietary recommendations can be tailored to an individual’s genetic makeup to
optimize health outcomes.

4. Methods in Personalized Nutrition Research

The methodologies used in studying nutrigenomics involve a range of high-throughput
omics technologies. Transcriptomics, proteomics, and metabolomics are key approaches
employed to assess the responses of biological systems to dietary interventions and to
understand the interactions between diet and genes [42–45]. Transcriptomics analyzes
genome-wide gene expression changes and has been the most frequently applied technique
in nutrigenomics research [45]. Proteomics explores the comprehensive array of proteins
produced by a genome, cell, tissue, or organism, a profile that can be influenced by nutri-
tional intake [43]. Metabolomics delves into the distinct chemical imprints left by cellular
activities, focusing on the analysis of their profiles of small-molecule metabolites [43,44].
Nutrients 2024, 16, 2673 7 of 18

Microarray technology is another powerful tool used in nutrigenomics to evaluate gene

expression profiles globally and to understand the regulation of gene transcription by
nutrients or dietary bioactive compounds [46]. Additionally, advances in bioinformatics are
crucial for the integration and interpretation of the vast amounts of data generated by these
omics technologies, allowing for the investigation of complex gene–nutrient interactions
and the development of PN strategies [43,47]. These methodologies collectively contribute
to the understanding of how dietary components can influence gene regulation, protein
expression, and metabolite production, which are central to the field of nutrigenomics and
the pursuit of PN.
Designing studies to evaluate genetic and dietary interactions presents several chal-
lenges. One major issue is the complexity of gene–nutrient interactions, which increases
the dimensionality of the problem, making it difficult to approach these interactions at
the population level [48]. Additionally, accurately assessing dietary intake in population
studies is complex due to the limitations of current dietary assessment tools like food
frequency questionnaires, 24 h food recalls, and diet records. These tools may not be
reliable or sensitive enough to capture long-term intake accurately, which is necessary to
establish gene–nutrient associations [49]. Another challenge is the small effect size of com-
mon genetic variations and the complexity of establishing associations between lifestyle
factors and the likelihood of developing obesity in the future. This requires an analytical
method that relies on clearly defined prior probabilities to minimize the risk of erroneous
findings [50]. Moreover, gene–environment interaction studies must address design and
analytical challenges such as confounding and selection bias, measurement accuracy of
exposures and genotypes, and the assumptions surrounding biological factors [51]. Further-
more, the genetic architecture of nutrition-related diseases is complex, involving multiple
genes and interactions that cannot be explained by single polymorphisms. This complexity
requires the use of whole-genome analysis to understand gene interactions and pathways
influencing nutritional metabolism [12]. Finally, the use of Mendelian randomization can
help to strengthen causality in nutrition research, but it requires careful selection of genetic
markers to avoid biases present in observational studies [52].
Analyzing complex genetic data to evaluate gene–diet interactions involves employing
a variety of statistical approaches that address the high dimensionality and complexity
of the data. Various methodologies such as multifactor dimensionality reduction, gram-
matical evolution neural networks, random forests, focused interaction testing framework,
step-wise logistic regression, and explicit logistic regression are employed for identify-
ing main effects and gene–gene interactions [53]. These methods have their strengths
and weaknesses, and their relative success is context-dependent. Methods that leverage
summary association data, such as those developed to analyze genome-wide association
studies (GWAS) data, are also employed. These methods are particularly useful when
individual-level genetic data are not accessible due to privacy concerns or other logisti-
cal considerations [54]. In research that deals with pedigrees or data that is structured
by population, statistics known as “burden” and kernel statistics have been formulated.
These statistics are designed to adjust for the effects of shared genetic heritage and the
linkage disequilibrium present among genetic markers [55]. These techniques enhance
traditional approaches for unrelated case-control data by incorporating known familial
connections. Family-based association studies that utilize next-generation sequencing data
can leverage various kinds of family and unrelated individual data collected from diverse
population structures [56]. This method is beneficial for conditions influenced by both
common and rare genetic variations. It is also essential to employ statistical techniques
to analyze complex correlation patterns in extensive pharmacogenomic datasets. These
techniques encompass the estimation of large covariance matrices, conducting broad-scale
simultaneous tests to identify genes that show significant differential expression, and se-
lecting variables in high-dimensional spaces [57]. The statistical approaches for analyzing
complex genetic data in nutrigenomics research are diverse and must be carefully chosen
Nutrients 2024, 16, 2673 8 of 18

based on the specific context of the study, including the genetic architecture of the trait of
interest, the type of dietary exposure, and the population structure.

5. Practical Applications of Personalized Nutrition

PN is being implemented in clinical settings through the development and use of
tools that integrate individual genetic, phenotypic, and lifestyle data to tailor dietary ad-
vice. Clinical Nutritional Information Systems (CNIS) have been implemented to assist
hospital dietitians in providing PN assessments, particularly for inpatients requiring cancer
nutrition counseling [58]. These systems enhance the standardization of nutritional inter-
vention and monitoring, improve the quality of nutritional interventions through precise
calculations and patient information verification, and decrease the time needed for tasks
like manual documentation [58]. Additionally, the Academy of Nutrition and Dietetics,
along with the American Council on Exercise, have established evidence-based practice
guidelines that advocate for tailored nutrition and physical activity interventions. These
guidelines underscore the significance of customizing interventions to fit the specific needs
of individuals, taking into account comprehensive client data such as genetic information,
to guide care [59]. Furthermore, nutrigenomics technology interface tools are being com-
pared and developed to facilitate the translation of nutrition-related gene test information
into actionable dietary plans. These tools aim to support health professionals in delivering
PN interventions that are more effective in improving health outcomes compared to stan-
dard approaches [60]. In critical care settings, PN therapy recommendations incorporate
recent literature and guidelines from American/European societies, suggesting the use
of indirect calorimetry to measure energy expenditure and guide the personalization of
nutrition therapy, including the timing and dosing of macronutrients [61].
Commercially available genetic testing for nutritional advice has expanded with the
advent of direct-to-consumer (DTC) nutrigenetics testing services. These services offer
nutritional recommendations tailored to a person’s genetic makeup, determining dietary
plans from a limited set of genetic markers [62]. Companies such as 23andMe have re-
ceived FDA authorization to market DTC genetic tests, including reports on carrier status,
wellness, traits, and ancestry, although they are clear that their tests are not intended
to diagnose health conditions or provide dietary advice [12]. Although debates persist
about the effectiveness of individual genetic variants compared to genetic risk scores, and
whether DNA-based dietary guidance can truly inspire beneficial behavioral changes, there
is supportive evidence that genetic testing can inform dietary suggestions to improve health
and performance [63]. However, it is important to note that the scientific evidence support-
ing gene–diet interactions and the effectiveness of gene-based dietary recommendations
is still evolving, and more research is needed to establish clinical evidence for these PN
approaches [31]. Physicians should be aware that while these tests can provide insights
into genetic predispositions, they should be interpreted with caution and in the context of
a comprehensive approach to patient care that considers the totality of genetic, phenotypic,
and environmental factors influencing health.
The ethical, legal, and social implications of PN are multifaceted (Figure 2). Ethically,
the use of genetic profiles in nutritional advice raises concerns about privacy, informed
consent, and the potential psychological impacts of genetic information. Legally, there is a
lack of specific regulations for genetic testing in the context of PN, which can lead to con-
sumer protection issues, particularly with direct-to-consumer (DTC) genetic tests. Socially,
there is the risk of exacerbating health inequalities if PN services are not accessible to all
segments of the population. The ISNN has discussed these issues, noting the importance of
integrating nutrigenetic approaches with cultural, emotional, and ethical aspects of food,
and the need for individually tailored nutritional advice to extend beyond single nutrient
recommendations [12]. Furthermore, there is a need for healthcare professionals, including
registered dietitian nutritionists (RDNs), to be involved in the delivery of PN to ensure
that ethical and legal standards are met. This involvement includes protecting individual
privacy, transparently communicating effects, and identifying potential beneficiaries of
Nutrients 2024, 16, x FOR PEER REVIEW 9 of 18

Nutrients 2024, 16, 2673 professionals, including registered dietitian nutritionists (RDNs), to be involved in9 of the
18
delivery of PN to ensure that ethical and legal standards are met. This involvement in-
cludes protecting individual privacy, transparently communicating effects, and identify-
ing
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the context of cardiovascular disease prevention, the PREDIMED study has demonstrated
genetic variability [64]. In the context of cardiovascular disease prevention, the PRED-
the clinical relevance of gene–diet interactions, suggesting that PN could be a more effective
IMED study has demonstrated the clinical relevance of gene–diet interactions, suggesting
tool for chronic disease prevention than traditional one-size-fits-all recommendations [65].
that PN could be a more effective tool for chronic disease prevention than traditional one-
Additionally, the concept of metabotyping involves customizing dietary recommendations
size-fits-all recommendations [65]. Additionally, the concept of metabotyping involves
based on the metabolic characteristics of different groups, which could be especially significant
customizing dietary recommendations based on the metabolic characteristics of different
in preventing cardiometabolic diseases [66]. The ISNN supports the notion that PN, which
groups, which could be especially significant in preventing cardiometabolic diseases [66].
includes the consideration of genotype, can be more effective in improving public health and
The ISNN supports the notion that PN, which includes the consideration of genotype, can
may be beneficial in long-term weight control [12,67]. However, the use of nutrigenomics
be more effective in improving public health and may be beneficial in long-term weight
and nutrigenetics in managing specific diseases such as cardiovascular disease remains in a
control [12,67]. However, the use of nutrigenomics and nutrigenetics in managing specific
nascent phase. An integrated strategy that incorporates lifestyle changes and tailored dietary
diseases
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Nutrigenomics plays a pivotal role in weight management and obesity prevention variability
[12,64–68].
by elucidating the interplay between diet and an individual’s genetic makeup. The field
has identified specific genetic markers that influence an individual’s response to dietary
components, which can be leveraged to tailor weight management strategies. For instance,
variations in the FTO gene have been associated with differential responses to macronutrient
intake and susceptibility to obesity [69,70]. Nutrigenomic analysis has also demonstrated
Nutrients 2024, 16, 2673 10 of 18

that certain dietary supplements, such as hydroxycitric acid and niacin-bound chromium,
can alter gene expression related to adipogenic and lipolytic pathways, potentially offering
new avenues for obesity management [71]. Furthermore, the integration of nutrigenetic
and nutrigenomic approaches can enhance the precision of obesity care by considering
both genetic predispositions and gene expression changes in response to diet [70]. This
includes the assessment of polymorphisms that influence energy homeostasis and body
composition, as well as the analysis of epigenetic mechanisms such as DNA methyla-
tion and microRNA expression profiles [72]. Clinical trials, such as the Nutrigenomics,
Overweight/Obesity and Weight Management (NOW) trial, have provided evidence that
nutrigenomics interventions can lead to greater reductions in body fat percentage compared
to standard interventions [73]. This supports the potential of nutrigenomics to optimize
weight management strategies. Nutrigenomics offers a promising approach to weight
management and obesity prevention by providing insights into how genetic and epigenetic
factors interact with dietary intake, thereby enabling the development of personalized
dietary strategies that can improve obesity-related outcomes.
In the context of PN for weight management and obesity prevention, a study by Arka-
dianos et al. showed that integrating genetic data to tailor dietary plans enhances long-term
weight management and prevents obesity [74]. Patients who underwent a nutrigenetic test
screening for 24 variants across 19 metabolism-related genes and received tailored dietary
guidance demonstrated enhanced adherence, more substantial long-term reductions in BMI,
and better blood glucose outcomes compared to those without genetic testing. Another
study by Ramos-Lopez et al. developed a model that integrates genetic, phenotypic, and
environmental data to customize low-calorie diets with varying macronutrient composi-
tions [75]. The study found that various genetic, phenotypic, and external factors influence
the reduction in BMI based on whether a moderately high-protein hypocaloric diet or a
low-fat diet is followed. Using a comprehensive approach could enhance the customization
of dietary recommendations for controlling obesity through the use of precision nutrition
variables. Additionally, the Food4Me study investigated the relationships and possible
interactions between adherence to the Mediterranean Diet and genetic predispositions
during an online nutritional intervention [76]. The study revealed that greater adherence
to the Mediterranean Diet leads to positive effects on metabolic markers, which may be
influenced by genetic factors in certain specific indicators. These studies illustrate the
successful application of nutrigenetic testing and PN interventions in clinical settings,
leading to improved outcomes in weight management and obesity prevention.

7. Challenges and Controversies

The scientific and technological limitations include the complexity of gene–nutrient
interactions and the challenges in accurately assessing long-term dietary intake. Many
diseases linked to nutrition are intricate and involve multiple genes, indicating that single
polymorphisms are insufficient to comprehensively elucidate the associated conditions and
traits. Whole-genome analysis could aid in grasping how genes interact and how pathways
affect nutritional metabolism. However, continual enhancements in genetic testing and
analysis are essential to underpin the forthcoming advancements in nutrigenomics [12].
Additionally, the tools for assessing food intake, such as 24 h food recalls, diet recalls, and
food frequency questionnaires, have not developed as rapidly as omics technologies and
often lack reliability [12]. Furthermore, there is a need for robust and reproducible results,
economical omics technologies, and improvements in research methodology, including rig-
orous study design, alongside advanced analyses and comprehension of high-dimensional
data [64]. The field also faces challenges in defining optimal responses due to a lack of key
health biomarkers and signatures [77]. These limitations highlight the need for continued
research and development to enhance the efficacy and practical application of PN in clinical
and public health settings.
Concerns about privacy and data security are significant, as the integration of genetic
data with dietary information increases the sensitivity of the data being handled. The ISNN
Nutrients 2024, 16, 2673 11 of 18

has highlighted the need for a better understanding of ethical issues and the development of
a robust regulatory framework before PN can become commercially viable for everyone [12].
The concerns are centered around the secure handling of health data, particularly with
the rise of DTC genetic tests and online service delivery systems. Technological progress
is crucial to safeguarding the privacy of online service delivery systems and securing the
data collected during the creation of tailored nutrition therapies [78]. Consumers have
reservations about the ability of service providers to ensure data security, and there is a
demand for an efficacious, transparent, and trustworthy regulatory framework to alleviate
these concerns [79]. Additionally, further advances in data interpretation tools are necessary
to ensure the effective delivery of information acquired from tests and technologies to
consumers [12]. Protecting individual data privacy and acting responsibly are paramount
as PN services develop and become more widely available [80].
Skepticism in the medical community and among the public concerning is multifaceted.
While there is considerable interest in PN, controversies arise regarding the extent of genetic
influence on individual responses to diet, the effectiveness of single genetic markers versus
genetic risk scores, the capacity of DNA-informed dietary guidance to promote beneficial
behavioral shifts and health outcomes, and the impact of genetic insights on the type
of dietary recommendations provided [63]. Despite these controversies, a solid body
of evidence shows that genetic testing for PN can guide dietary recommendations to
improve health and performance [63]. Public acceptance of PN is influenced by concerns
about the secure handling of health data and the ability of service providers to ensure
privacy [78]. There is also a general agreement on the promise of nutrigenomics for
better understanding diet–health relationships, but less consensus exists on the potential
of consumer applications such as PN [81]. Negative consumer opinion and concerns
about how genetic information is used and held pose potential barriers to the application of
nutrigenomic interventions [82]. The ethical and legal issues around PN are recognized, and
there is a need for guidance on how to address these issues and regulate practice [83,84].
The North American division of the International Life Sciences Institute has put forth
guiding principles for adopting PN strategies. These underscore the significance of utilizing
personalized data based on scientific evidence to encourage dietary changes that could
lead to measurable improvements in health [80].
Regulatory issues and standardization of practices are evolving areas with significant
implications for clinical implementation. In the United States, the Food and Drug Adminis-
tration (FDA), the Centers for Medicare & Medicaid Services (CMS), and the Federal Trade
Commission (FTC) are involved in regulating genetic tests, which are integral to PN. The
FDA has broad authority under the Federal Food, Drug, and Cosmetic Act and regulates
tests sold as kits to multiple laboratories. However, laboratory-developed tests (LDTs),
which are more common, are not currently regulated by the FDA, although there has been
discussion about future regulation [12]. The CMS ensures clinical laboratories comply
with the Clinical Laboratory Improvement Amendments (CLIA) of 1988, focusing on the
qualifications of technicians and the quality control of lab processes rather than the clinical
meaningfulness of genetic tests [12]. In Europe, the regulatory framework is less clear, with
no specific legal instruments dealing with PN. Different countries have various regulations
regarding genetic tests, with some requiring informed consent and genetic counseling,
while others have no specific legislation for direct-to-consumer tests but require medical
physician involvement if the test is of a medical nature [12]. These regulatory nuances
highlight the need for standardized practices and guidelines to ensure the responsible
delivery of PN services, protect consumer privacy, and maintain data security. The ISNN
emphasizes the integration of nutrigenetic approaches with cultural, emotional, ethical,
and sensual understandings of food, suggesting that PN should extend beyond single
nutrient recommendations to include meals and recipes [12].
Nutrients 2024, 16, 2673 12 of 18

8. Future Perspectives
Emerging trends in research and technology of PN are being driven by advancements
in various “omics” technologies and digital health tools (Table 4). The integration of
genomics, proteomics, and metabolomics, alongside wearable technologies for tracking
dietary intakes and physiological parameters, is advancing the concept of PN [85]. The
detailed characterization of food molecules, including their chemical makeup, physical-
chemical structure, and biological characteristics, is gaining importance. This process is
crucial for aligning human genetic and physical traits with specific foods that promote
better physiological health outcomes [86]. The use of AI and machine learning (ML)
algorithms is also a growing trend, enabling the integration of large datasets from omic
profiles with personal and clinical measures to provide PN advice [87]. Wearable and
mobile sensors are being developed to monitor nutritional status at the molecular level,
offering non-invasive, real-time dietary information that supports dietary behavior change
toward managed nutritional balance [88]. However, there are challenges in implementing
these technologies in clinical and public health settings, including the need for robust and
reproducible results, cost-effectiveness, and methodological improvements in study design
and data interpretation [64]. Despite these challenges, these technologies hold the potential
to transform dietary decision-making and improve population health through precision
nutrition [89].

Table 4. Technologies used in personalized nutrition.

Application in Nutritional
Technology Type Description Advantages and Limitations
Assessment
Genomic Sequencing Analyzing genetic variations Identifying genetic predispositions High accuracy, cost-intensive
Monitoring physical activity and
Wearable Sensors Tracking lifestyle factors Real-time data, privacy concerns
health metrics
Comprehensive analysis, complex
Bioinformatics Tools Interpreting genetic data Integrating multi-omics data
interpretation
Predictive capabilities, ethical
AI and Machine Learning Predicting health outcomes Customizing nutrition plans
issues
Providing dietary
Mobile Health Apps Engaging users in dietary changes User-friendly, variable reliability
recommendations

The potential for integrating AI and ML in PN is substantial, as these technologies can

manage and analyze the large and complex datasets characteristic of nutrigenomics and
dietary patterns. AI and ML can improve screening and assessment in clinical nutrition,
predict clinical events and outcomes, and integrate diverse data sources such as microbiota
and metabolomics profiles with clinical conditions [90]. These technologies introduce
innovative approaches in dietary assessment, recognizing food items, predicting models
for disease prevention, and diagnosing and monitoring diseases [91]. ML, in particular,
has shown promise in developing predictive models suitable for precision nutrition, fa-
cilitating the incorporation of complex features and allowing for high-performance PN
approaches [92]. Moreover, AI and ML can support the design of personalized dietary
plans based on individual genetic data, potentially improving patient outcomes in various
health conditions, including weight management and chronic disease prevention [93,94].
However, ethical considerations and the limitations of AI must be considered, including
data privacy, security, and the need for transparency in algorithmic decision-making [90].
The incorporation of AI into nutrition offers considerable potential. However, it is crucial to
manage these challenges with care to improve individual nutritional results and effectively
refine dietary recommendations.
The potential for widespread implementation of PN in the healthcare and wellness
sectors is encouraging. This approach allows for dietary interventions to be customized
based on individual genetic, metabolic, and environmental profiles. The ISNN suggests
that PN, which includes the consideration of genotype, can be more effective in improving
Nutrients 2024, 16, 2673 13 of 18

public health and may be beneficial in long-term weight control [12]. The use of omics
technologies could enable health professionals to provide tailored dietary recommendations
and personalized advice, potentially revolutionizing public health guidelines through
the incorporation of PN [12]. However, there are challenges to be addressed for the
successful implementation of PN. This calls for continued progress in the development
of tools for interpreting data, ensuring that the information derived from various tests
and technologies is effectively communicated to consumers [12]. Additionally, there is an
urgent need for the development of ethical and legal frameworks to facilitate the adoption
and implementation of the newly introduced concepts of precision nutrition and medicine,
ensuring their broad and individualized application [12]. The International Life Sciences
Institute’s North American Branch has suggested a set of guiding principles for applying
PN strategies. These guidelines highlight the importance of using individual-specific,
evidence-based information to encourage changes in dietary behaviors, which could lead
to measurable health improvements [80]. These guidelines aim to lay the groundwork
for responsible, evidence-based research and practice in PN, and they encourage ongoing
public discussion [80]. In conclusion, although PN presents potential benefits for public
health, there is a need for additional research to enhance the accuracy of dietary intake
assessments, improve and standardize systems approaches, and refine the application and
dissemination of findings [80]. It is crucial to integrate the evolving area of PN with public
health nutrition approaches to enhance diet quality and prevent chronic diseases.

9. Conclusions
The potential of PN to revolutionize dietary recommendations is profound and multi-
faceted. By integrating genetic, phenotypic, and environmental data, PN offers a nuanced
approach to diet and health, promising improved outcomes by tailoring dietary advice
to individual biological profiles. This individualized approach is especially pertinent in
addressing chronic diseases such as obesity, cardiovascular disease, and diabetes, where
one-size-fits-all dietary guidelines have shown limitations. As evidenced in the studies
highlighted, PN not only enhances the precision of dietary interventions but also aligns
with the increasing consumer demand for customized health solutions.
However, the realization of PN’s full potential necessitates sustained collaborative
efforts across various domains. There is a pressing need for further research to refine our
understanding of gene–diet interactions and to validate the efficacy of PN interventions
in diverse populations. This research should be underpinned by advances in genomics,
bioinformatics, and biotechnology which continue to evolve at a rapid pace. Collaboration
among scientists to share data and insights, clinicians to apply these findings responsibly in
patient care, and policymakers to create supportive regulatory frameworks is crucial. These
combined efforts will ensure that PN not only progresses scientifically but also becomes
ethically and practically implementable on a wide scale.
Reflecting on the balance between the benefits and risks of personalized dietary advice,
it is clear that while the benefits hold significant promise, the risks cannot be overlooked
(Table 5). The ethical, legal, and social implications, particularly concerning data privacy,
informed consent, and the potential for exacerbating health disparities, must be rigorously
managed. It is also imperative to critically evaluate the clinical relevance of genetic testing
in nutrition and to ensure that such interventions do not oversimplify the complexities of
human nutrition and health.
In conclusion, as we stand on the brink of a dietary revolution fueled by PN, it is
essential to navigate this emerging field with a balanced perspective, acknowledging its
transformative potential while conscientiously addressing the associated risks. The future
of PN will significantly depend on our ability to integrate scientific innovation with ethical
responsibility and inclusivity.
Nutrients 2024, 16, 2673 14 of 18

Table 5. Risk and benefits of Personalized Nutrition.

Potential Benefits Associated Risks Mitigation Strategies Ethical Considerations

Optimized health outcomes Data privacy concerns Robust data security measures Informed consent
Reduced risk of chronic Potential for misuse of genetic
Strict regulatory frameworks Transparency in data use
diseases information
Improved dietary adherence Ethical issues Ethical guidelines Fair access to PN services
Avoidance of genetic
Tailored interventions Accessibility and cost Insurance coverage, subsidies
discrimination
Comprehensive health
Enhanced patient engagement Over-reliance on genetic data Holistic approach to health
assessment

Author Contributions: Conceptualization, S.S. and N.S.A.; writing—original draft preparation, S.S.;
writing—review and editing, S.S., N.S.A., R.N. and B.H.A.; visualization, S.S. All authors have read
and agreed to the published version of the manuscript.
Funding: The APC was funded by the UNLV University Libraries Open Article Fund.
Institutional Review Board Statement: Not applicable.
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Acknowledgments: I would like to express my heartfelt gratitude to my major supervisors and the
members of my laboratory, whose insights and feedback were invaluable throughout the process of
writing this manuscript. Their support and collaborative spirit significantly enriched my research
experience. Additionally, I am deeply thankful for the staff of our department and college, whose
dedication and assistance facilitated my research activities and contributed to the completion of this
work. Their ongoing support and professionalism have been essential to my academic endeavors.
Conflicts of Interest: The authors declare no conflicts of interest.

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