# Nutrigenomics and personalized diets - Tailoring nutrition for optimal health

Divya Chaudhary, Diksha Guleria, Himanshi Aggarwal, Vaibhav Mishra, Abhishek Chauhan, Laurent Dufossé, Naveen Chandra Joshi

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Divya Chaudhary, Diksha Guleria, Himanshi Aggarwal, Vaibhav Mishra, Abhishek Chauhan, et al. Nutrigenomics and personalized diets - Tailoring nutrition for optimal health. Applied Food Research, 2025, 5 (1), pp.100980. 10.1016/j.afres.2025.100980. hal-05490891

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# Nutrigenomics and personalized diets - Tailoring nutrition for optimal health

Divya Chaudhary<sup>a</sup>, Diksha Guleria<sup>a</sup>, Himanshi Aggarwal<sup>a</sup>, Vaibhav Mishra<sup>a</sup>, Abhishek Chauhan<sup>b</sup>, Laurent Dufossé<sup>c, *</sup>, Naveen Chandra Joshi<sup>a, #</sup>

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

### Keywords:

Food bioactive compounds

Gene expression

Nutrigenomics

Omics approaches

Personalized nutrition

## ABSTRACT

Human health has long been understood to be severely affected by diet. Disturbed lifestyle and diets have led to an increase in metabolic disorders, resulting in high rates of mortality and morbidity. However, personalized nutrition, revolutionized by omics technologies, can address these disorders and enable doctors to anticipate the best treatment for patients. Omics along with nutrigenomics, explores how genes react to specific bioactive compounds in food within the human body. Since individuals vary in dietary response due to unique physiological and genetic factors, personalized nutrition considers gene polymorphisms to tailor diets. In addition, systems biology tools like proteomics, transcriptomics, and metabolomics work alongside statistical methods to analyse genetic variation and gut microflora differences that influence metabolic health through food bioactives. The blending of technologies like nutrigenomics and artificial intelligence is necessary to integrate multiple data sets, analyse numerous variables, build databases to support ethical guidelines and decision-making, identify underlying risk factors, and uncover biological mechanisms that enable the early diagnosis and prevention of complex diseases. Whereas personalized diets, shown to enhance the efficacy of medical treatments, offer a promising preventive strategy for conditions like obesity, cancer, and cardiovascular diseases. The nutrigenomic approach promotes "eating the food that not only your taste buds but your genes want," emphasizing diet's role in personalized health. This article explores the field of nutrigenomics and personalized nutrition, with a focus on how food's bioactive compounds influence genes to maintain health, prevent diseases, and optimize well-being. It highlights the relationship between genetic polymorphisms and conditions like cardiovascular disease and metabolic disorders, showcasing advancements from genome sequencing and omics technologies.

## 1. Introduction

It is an well-known fact that food intake is not only to provide energy to the human body; but also, it ensures optimal health, well-being and disease prevention. Moreover, the basis of human health, lifespan, and disease prevention are largely related to the biochemical diversity of the food compounds we eat. It is important to be mindful of dietary choices, as the bioactive elements in food can influence genes, potentially leading to significant outcomes (Mierzak et al., 2021). Each person is unique in their reaction to different bioactive chemicals and food amounts. After the completion of the human genome project, researchers developed a clearer concept of the relationships among genes, nutrition, and diseases. Sequencing of the human genome has uncovered numerous

single nucleotide polymorphisms (SNPs) associated with nutrition (Williams et al., 2008; Barrea et al., 2020). For instance, SNPs in the paraoxonase (PON1) gene have been connected to atherosclerosis and susceptibility to cardiovascular disease (CVD) (Shunmoogam et al., 2018). Similarly, polymorphisms in the apolipoprotein E (APO E) gene are linked to an increased risk of CVD (Xu et al., 2016). Other relevant SNPs include those in the fatty acid desaturase (FADS) gene family (FADS1 and FADS2), which play a role in polyunsaturated fatty acid synthesis, as well as genes like phospholipase A (PLA2G4) and arachidonate 5-lipoxygenase (ALOX5), which are involved in the metabolism of arachidonic acid. Additionally, fatty acid elongase (ELOVL2) SNPs have been identified (Chilton et al., 2014). Polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene (rs1801133 and

<footnote><sup>a</sup> Amity Institute of Microbial Technology, Amity University, Uttar Pradesh 201313, India</footnote>

<footnote><sup>b</sup> Amity Institute of Environmental Toxicology, Safety and Management, Amity University, Sector-125, Noida, Uttar Pradesh, India</footnote>

<footnote><sup>c</sup> CHEMBIOPRO Laboratory, Université de La Réunion, ESIROI agroalimentaire, 15 avenue René Cassin - CS 92003, 97744 Saint-Denis Cedex 09, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: laurent.dufosse@univ-reunion.fr (L. Dufossé), naveen.joshi.jnu@gmail.com (N.C. Joshi).</footnote>
rs1801131) and the 5-Methyltetrahydrofolate-Homocysteine Methyl-
transferase Reductase (MTRR) gene (rs1801394 and rs162036) are
connected to folate metabolism (Du et al., 2018). Further Cytochrome
p450 1A2 (CYP1A2) gene, which affects caffeine metabolism, and
various adenosine receptor genes (ADORA2A, ADRA1A, ADRA2B,
ADRB1, ADRB3) are exhibiting cardiovascular effects after coffee con-
sumption (Barrea et al., 2020). Furthermore, polymorphisms in the
insulin-induced gene 2 (INSIG2) have been associated with obesity (Dina
et al., 2007). In addition, the notion of personalized nutrition after
examining the interconnections between genes, nutrition, and diseases
to deliver more exact nutrition to everyone based on their genes and
body requirements. It is a concept that includes the analysis of an in-
dividual's genetic makeup, lifestyle, and environment to suggest
optimal and customized nutrition (Ordovas et al., 2018; Bush et al.,
2020). Researchers aim to advance the science of nutrition through
personalized approaches, moving beyond traditional epidemiological
surveys by using nutrigenomics to demonstrate the benefits of specific
diets for individuals.

Nutrigenomics is an “omic” science that combines fields such as genomics, proteomics, metabolomics, transcriptomics, and nutritional biochemistry to explain the interactions between genes and food-bioactive compounds and their effects at the molecular level (Dey & Kumar, 2022). All the analyses of these interactions provide information for the prescription of personalized diets to individuals according to their genotype. The omics research yields significantly large volumes of data. Nutrigenomics not only allows to combine the theories and concepts of these fields but also provides an opportunity to utilize high-throughput genomic tools in nutrition research. The usage of omics technologies in personalized nutrition offers valuable insights into metabolic disorders and revolutionizes their management. However, the advancement in technologies has led to the development of multi-omics approaches. Using these approaches numerous datasets can be merged for analysis of various prospects of human health. The genetic basis for explaining food bioactive compounds and individual responses to different bioactive compounds is understood throughout the world, and many initiatives have been taken to make this science a practical approach for daily life. Valuable information on metabolic pathways and protein expression profiling can be achieved by metabolome and proteome analysis respectively. This gives a deeper understanding of the mechanisms underlying the diseases and discovery of biomarkers. Biomarkers with high specificity and sensitivity can be identified using omics technologies that allow early detection and risk stratification. Omics-driven studies can be used for the development of predictive models to determine individual treatment strategies. Personalized nutrition is currently focused on people with metabolic disorders, newborns with inborn errors of metabolism, such as phenylketonuria, or adults with monogenic disorders (hypercholesterolemia, hypocatasia). Although it has also been proven successful in the treatment of life-threatening conditions such as obesity, and cardiovascular disease in a number of studies, scientists are given even more reasons to delve deeper into this field of study and to encourage large corporations and genetic panels to evaluate the full potential of the field (Lloyd-Jones et al., 2004; Celis-Morales et al., 2015; Vazquez-Vidal et al., 2019; Barrea et al., 2020).

While the concept of personalized nutrition holds significant prom-
ise, it remains an emerging field with several challenges that must be
addressed before its full potential can be realized. One major issue is the
necessity of making individuals' genetic information accessible through
direct-to-consumer genetic tests, raising concerns about both the val-
idity of such tests and the associated risks. The widespread use of online
platforms for marketing inexpensive, direct-to-consumer genetic testing
exacerbates these concerns, introducing ethical considerations (Ferrario
et al., 2021). The open nature of the internet further complicates mat-
ters, as it gives rise to various legal and public acceptance issues that
require resolution (Panacer, 2023). This review explores the field of
nutrigenomics and personalized nutrition, examining the fundamental

principles of nutrigenomics, the technologies driving personalized
nutrition, the differences between personalized and conventional diets,
their impact on disease, and the promising future of this domain.

## 2. Nutrigenomics and personalized diets

Personalized nutrition involves two major fields: nutrigenomics and nutrigenetics. Although these fields are closely related, they are completely different from each other and should not be confused with each other. The combined analysis of these two fields holds major importance in identifying an individual's personalized diet prescription. Therefore, to understand the concept of personalized diets or nutrition, it is essential to understand these two terms in detail. Despite a 99.9 % similarity across human genomes, the 0.1 % variation is what makes each individual unique (The 1000 Genomes Project Consortium, 2015; Bahinipati et al., 2021). This seemingly minor variation significantly impacts metabolic responses among individuals (Kabata & Thaldar, 2023). Nutrigenetics focuses on the effects of these genetic variations on food bioactive compounds, providing information about how an individual's specific genes or body respond to what ones eats, and this can be considered their specific nutrigenetic profile, which is unique to that particular person. Research has focused on the effects of single nucleotide polymorphisms (SNPs), epigenetic markers, copy number variations and other genomic markers on biological responses to micronutrients and macronutrients (De Guzman & Buhay, 2023). A study comparing agricultural societies with high-starch diets to hunter-gatherer societies with low-starch diets found differences in the number of copies of the salivary amylase gene (AMY1) between the two groups. The high-starch-consuming group showed a positive correlation between the AMY1 gene and the level of salivary amylase protein, having a greater average number of gene copies compared to the low-starch-consuming group. This study indicated that populations with high starch consumption could efficiently digest the excess starch, leading to reduced risks of obesity, diabetes, and cardiovascular diseases (Marquina et al., 2019; Higuchi et al., 2020).

Nutrigenomics, on other hand, explores how the nutrients we
consume influence gene expression and interact with our genes' activity
(Bhadra & Banerjee, 2020). Nutrigenomic data are predicted on the
assumption that individuals have different responses to long and
frequent exposures to particular nutrients or blends of nutrients. Since
ancient times, diets have affected genetic expression, resulting in
different phenotypes that are more suitable and appropriate for envi-
ronmental changes and further facilitating survival in different envi-
ronments. This field incorporates transcriptomics, proteomics,
metabolomics, and other more advanced approaches to determine the
precise effects of nutrients at the molecular, tissue, and organ levels
(Brennan & de Roos, 2021). A recent report indicates that after five
months of following a low-calorie diet, humans experienced reduced
plasma leptin levels, which enhanced the effectiveness of obesity
treatment by boosting energy expenditure (Ebbeling et al., 2018).
Nutrigenomics, nutrigenetics, and personalized nutrition research are
based on two main hypotheses. First, diet can directly impact health
outcomes by influencing the expression of genes involved in crucial
metabolic pathways (Fig. 1). It can also indirectly affect health outcomes
by influencing the frequency of genetic mutations at the chromosomal or
base sequence level, which in turn affects the expression of genes. Sec-
ond, the health effects of nutrients depend on inherited genetic variants
that alter the uptake and metabolism of nutrients and enzyme in-
teractions at the molecular level with nutrient cofactors, thereby
altering biochemical reactions. Health benefits can be maximized by
adjusting nutritional diets and necessities to match an individual's ge-
netic characteristics and taking into account their lifestyle, diet, and
health status (Gupta et al., 2020). Epigenetics is another crucial field
that is considered in personalized nutrition analysis. It focuses on how
modifications to DNA and proteins, histone interactions with DNA, DNA
methylation, and changes in chromatin structure can occur without
<figure><img src="image_3.png" /><figcaption>**Fig. 1. Gene-Nutrient interactions:** gene-nutrient interaction refers to the complex relationship between a living organism's genetic makeup (genome) and the nutrients it consumes from its environment. These interactions play a crucial role in how nutrients are absorbed, metabolized, and utilized by an organism, ultimately affecting growth, development, health, and disease susceptibility.</figcaption></figure>

altering the nucleotide sequences (Pal & Pal, 2022). These various disciplines collectively form the foundation of nutrigenomics and its application to personalized nutrition.

## 2.1. Personalized versus general diets

Primarily, nutritionists based their dietary recommendations on information collected from large groups of people. The data from these studies were aggregated to form general nutritional guidelines. Because the data came from diverse populations, these recommendations were intended to be relevant to a wide range of demographics. General diets remain popular choices in today's world due to their global acceptance. They are easier for nutritionists to study and monitor on a large scale, as it allows them to track effects across a broader population rather than focusing on individuals. Additionally, conveying general nutritional information is more straightforward through dietary guidelines or food labels (Donini et al., 2022).

Since nearly all humans share a 99.9 % similarity in their genetic codes, they generally have similar dietary needs. These needs include essential macro- and micronutrients necessary to provide calories for basic bodily functions. However, slight variations in human genetic codes, known as SNPs, along with larger differences in metabolism, microbiomes, and lifestyles, lead to significant differences in nutritional requirements. These variations can occur at the individual level or

within specific population groups such as population homozygous for methylenetetrahydrofolate reductase C677T polymorphism have high dietary requirements for folate as they have impaired folate metabolism (Shelnutt et al., 2003; Matusheski et al., 2021). A comparative study of personalized dietary advice with general advice on increasing fibre intake suggested that personalized diets are more suitable and cost efficient method for improving dietary fibre uptake in healthy individuals (Rijnaarts et al., 2021). Personalized dietary advice successfully boosted fibre intake, leading to a reduction in constipation issues, suggesting that dietary modifications are both practical and essential in managing mild constipation (Rijnaarts et al., 2022). These nuanced differences contribute to the development of personalized diets, making them increasingly sophisticated and effective, as depicted in Fig. 2. The aim of personalized diets is to use an individual's metabolomic information in addition to other fields that will improve an individual's health. The main principle behind personalized nutrition addresses the fact that each individual is unique and has different nutritional requirements; hence, the approach of one-food-fits all is overly randomized (McClements & McClements, 2019). Increasing humans clinical evidences suggest that standard dietary guidelines may not be suitable after a cancer diagnosis, as targeted dietary interventions—such as short-term fat and carbohydrate restrictions—can impact tumor metabolism (Mittelman, 2020; Xiao et al., 2024). The most effective approach to disrupt cancer cells at all stages is through personalized nutrition

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Effect of dietary bioactive compounds on individual genotypes: food contains many bioactive compounds, such as terpenoids, polsterols, polyunsaturated fatty acids, alkaloids, flavonoids and saponins, that cause SNPs (single nucleotide polymorphisms) in certain nucleotides in the genomes of different individuals, leading to different genotypes. SNPs can lead to epigenetic changes and genetic variations that are linked to cancer and cardiovascular disease prevention.</figcaption></figure>
(Wallace et al., 2019).

### 3. Personalized nutrition versus epidemiological-based dietary guidelines

Personalized nutrition and epidemiologically based dietary guidelines offer contrasting approaches to dietary recommendations, each with distinct strengths and limitations shaped by current scientific knowledge and technological capabilities. Personalized nutrition focuses on tailoring dietary advice to individual biological, genetic, and lifestyle factors utilizing technologies such as genomic testing and metabolomics to provide personalized insights into nutrient metabolism and dietary responses (Rahman & Muhammad, 2023). This approach aims to optimize health outcomes by considering genetic predispositions, metabolic profiles, microbiota composition, and personal food preferences. However, personalized nutrition faces challenges such as high costs associated with data collection and analysis, limited accessibility to comprehensive genetic and microbiome data, and the complexity of accurately interpreting multisource data (Bush et al., 2020; Yang et al., 2024). Moreover, robust evidence of its long-term effectiveness in large-scale clinical trials is lacking compared to that of traditional methods. In contrast, epidemiologically based dietary guidelines derive recommendations from population-level data from studies and clinical trials (Papanikolaou et al., 2020). These guidelines, issued by health agencies and organizations, aim to improve public health by addressing common dietary patterns linked to chronic diseases. They provide evidence-based advice emphasizing certain foods and limiting others based on observed associations with health outcomes. However, epidemiological guidelines rely on observational data (EFSA Scientific Committee et al., 2024) that establish correlations rather than causation, potentially overlooking individual dietary responses influenced by genetic, metabolic, cultural, and socioeconomic factors. They also face challenges in updating recommendations promptly with evolving scientific knowledge, potentially leading to outdated advice. In conclusion, both personalized nutrition and epidemiological-based guidelines contribute valuable perspectives to dietary recommendations. Personalized nutrition holds promise for tailored health benefits but requires overcoming practical challenges (Zeisel, 2020). Moreover, epidemiological guidelines provide broad, evidence-based advice but may not sufficiently account for individual variability. Integrating these approaches and advancing research methods can enhance the effectiveness and inclusivity of dietary recommendations, addressing both population-wide health goals and individual needs in a diverse global context.

### 4. Nutrigenomic tools

Systems biology disciplines such as transcriptomics, metabolomics, and proteomics serve as the foundation for nutrigenomics, and their use of high-throughput molecular technologies allows us to gain a detailed understanding of the interaction between genes and nutrition (Fig. 3).

Nutrigenomics presents an astounding opportunity for researchers to leverage various technologies to enhance the accuracy and precision of nutrition research, which is one of its most remarkable applications. With regard to health and illness prevention, these technologies enable us to better understand food metabolism and identify biomarkers. The use of these technologies has made outstanding contributions in disease-oriented studies, molecular diagnosis and treatment. Doctors and clinicians are leveraging large scale molecular profiling that studies the genes, proteins and metabolites of the patients. Deploying the omics technology, insights into molecular mechanism of disease can be gained and treatments can be provided according to needs of the patients. Integration of these nutrigenomics tools to personalized nutrition holds a greater potential for development of personalized diets.

#### 4.1. Transcriptomics

As the name indicates, transcriptomics focuses on RNA transcripts. The goal of transcriptomics is to determine the expression level of the whole genome or a subset of genes in a sample using RNA (Lowe et al., 2017). Transcriptomics is employed in nutrigenomic studies to assist in the dissemination of knowledge regarding the mechanics or causal effects of a specific vitamin or diet on genes. It also helps in identifying and understanding the mechanisms influenced by bioactive compounds or nutrients in diets, as well as in classifying proteins, genes, and metabolites that affect the progression of pre-disease conditions (Balaji Ganesh & Sugumar, 2020). The regulation of the transcription rate by food constituents is interesting for regulating the phenotype of an individual. Important regulators of gene expression patterns can be found in a variety of critical nutrients and in biologically active food components. The transcription and translation of genes can be altered by macronutrients, micronutrients, vitamins, minerals and phytochemicals that affect homeostatic responses, such as metabolism and cell growth, which are significant in disease progression (Kiani et al., 2022). The mRNAs generated by a specific gene at a particular time show that gene expression changes under various conditions. Therefore, gene expression profiling can be utilized for several purposes in nutrition research (Uthpala et al., 2020). Transcriptomics was used to analyze feed-deprived goats, profiling genes responsible for reducing milk, protein, and lactose, as well as those involved in cell proliferation and differentiation (Ollier et al., 2007; Osorio et al., 2017). Some of the studies performed using transcriptomics, such as micro-RNA (miRNA) assays in nutrient biology, have described the effects of diets rich in polyunsaturated fatty acids on nervous system development and functions in rodents, along with potential consequences for neurodegenerative disorders (Chakraborty et al., 2017). Transcriptomic analysis revealed that 1.2 % taurine supplementation in the diet of juvenile turbot (Scopthalmus maximus) increased the gene expression of the muscle growth-stimulating proteins TauT, MyoD, myogenin, follistatin and Myf5 (Hays et al., 2023).

Individual gene expression has been evaluated for years using Northern blotting to quantify mRNA. This traditional method has

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** "Omics" approaches in nutrigenomics: the nutrients in food (fats, sugars, water, vitamins and minerals) may affect gene expression at the DNA level, which in turn influences changes in mRNA. mRNA modifications were studied via a transcriptomic approach involving nutrigenomics. After transcription, mRNA undergoes translation to produce the desired protein. Proteome studies are called proteomics, which is the second most important technique of nutrigenomics. Metabolomics involves the study and identification of metabolic compounds that are formed from posttranslational modifications of proteins and carry out body functions.</figcaption></figure>
progressively been supplanted by many methods, such as real-time PCR, which are sensitive. Since the use of both of these technologies allows for the examination of only a limited number of genes of a candidate at a given time, their application in nutrigenomic research may not offer necessary information on the association of biologically active micro-nutrients or their bioeffects. Moreover, studies of worldwide genome expression provide better prospects for determining the effects of micronutrients on various metabolic pathways, as well as how they might be altered in the progression of specific long-lasting disorders like diabetes 2 (Bao et al., 2021; Clemente-Suárez et al., 2023). Microarray technology can measured the expression levels of up to 50,000 transcripts in parallel using a single array, and tens of samples per day can be screened. This method uses unique nucleic acid hybridization for simultaneous quantification of the relative amounts of mRNAs in more than one sample of thousands of genes. Although gene microarrays are effective, uncertainty in the measurement procedure can conceal genes of interest. In microarray studies, data collection and pre-processing are essential steps in post-processing. However, the most challenging aspect is analysing the vast amount of microarray data to extract meaningful physiological insights.

Measurement of gene expression can give us insights into an organism's biology and infer the functions of genes that were unannotated previously. RNA sequencing (Next Generation Sequencing based) is one of the most powerful techniques that allows the assessment of gene expression in thousands of individual cells (Negi et al., 2022). It is possible to pinpoint key genes and pathways involved in the onset and progression of metabolic disorders using transcriptomics. Researchers can use this information for the development of personalized treatment plans and nutrition for individuals. For example, a glycoprotein, Sex hormone-binding globulin (SHBG), is responsible for the regulation of sex hormones. Polymorphisms in the SHBG gene are linked to Polycystic ovarian syndrome (PCOS) and the decreased expression of this gene could serve as a potential biomarker for disrupted glucose metabolism and insulin resistance in people with PCOS (Bhimwal and Priyadarshini, 2023). So clinicians can use this data for preparing personalized diets for people with PCOS. Integration of transcriptomics with genome-wide association studies (TWAS) is a promising tool for the identification of disease-causing genes. Identification of genetic signatures can predict the disease progression so by using transcriptomics data clinicians can potentially target the use of medications and nutrition to those people who need them the most. Whole genome transcriptome sequencing (WGTS) is an advanced approach that helps detect of numerous tumor types. It is economical and aids in the identifying a growing number of clinically significant abnormalities, as well as developing intricate therapeutic biomarkers (Cuppen et al., 2022). WGTS could be used in nutrigenomics to effectively target nutrient-gene interactions, and this may help in recommending of personalized nutrition based on nutrient-gene interactions.

## 4.2. Proteomics

Dietary components are found in complex combinations; thus, not only the quantities of individual compounds but also their interactions have an impact on the bioavailability and bioefficacy of the final product. Proteins play a crucial role in almost all biological processes in the human body. The set of proteins that are expressed at a given time under defined conditions is called the proteome (Mir et al., 2023). It is dynamic and varies according to the type of cell and its functional state. Based on the composition and stoichiometry of protein complexes, as well as their functional organization within pathways, it has developed into a powerful tool for understanding the molecular foundations of cellular and physiological processes (Rinschen et al., 2018). Several proteomic techniques have proven useful in the analysis of the proteome for nutritional research.

Recently developed proteomic technology that is widely used in nutrition research is mass spectrometry (MS). This technology is used to

address food health, sensory, quality, and safety concerns. In nutrition research, mass spectrometry is used to discover and analyse biomarkers for predisposition, exposure, and efficacy, as well as other health-related applications. Some of the prevailing advantages of this MS proteomic technology are that it allows both the identification and quantification of specific proteins for biological purposes and provides further information about their location in cells, protein interactions, and protein modifications by co or posttranslational modifications (Aebersold & Mann, 2016; Feng et al., 2023). Proteins are subjected to proteolytic breakdown, resulting in peptide collections that are then examined via MS (peptide mass fingerprinting) (Jiang et al., 2023). MS-MS tandem mass spectrometry is used to obtain information not only about the molecular weight but also about the structure of peptides by fragment analysis. Posttranslational modifications of proteins, such as phosphorylation, have been revealed by such investigations (Crotti et al., 2023). A study has been performed in the field of cancer research and diagnosis using isobaric tags for relative and absolute quantification (iTRAQ) coupled with 2D liquid chromatography (2D-LC) and mass spectrometry for the detection of protein biomarkers associated with cancer (Cardoso et al., 2023). To quantify the protein expression levels, two-dimensional gel electrophoresis was initially performed. The two-dimensional (2D) gel electrophoresis can be combined with MS for visualization, separation and identification of proteins (Gygi et al., 2000).

Another approach used is also called shotgun proteomics, an advanced technique wherein protein mixtures that are complex in nature are processed with endoproteinases which provides wide-ranging, rapid and automatic identification of these complex protein mixtures. Shotgun proteomics relies on the use of powerful separation procedures that are highly resolved in nature prior to MS examination to analyse complex biological materials while avoiding difficulties with suppressing ionization (García-Canas et al., 2010). With the development of protein array technology, further developments in proteomics have been made. One of the approaches for protein quantification is ELISA. On a solid carrier, antibodies are coated that are used to pull down the targeted protein from a sample, and for quantitative staining of this protein, a secondary antibody is used (Marques et al., 2024). Because proteomics attempts to analyse a large number of proteins at the same time, spotted antibody arrays have become a novel method for protein profiling. Protein arrays have the same methodology as DNA microarrays. Protein microarray technologies have progressed dramatically in the past decade like development of cell-free protein array that enables the in situ protein synthesis from RNA or DNA samples via in vitro transcription and translation using cell lysates (Yu et al., 2018). Additional methods, such as Protein In Situ Array (PISA) (He & Taussig, 2001), Nucleic Acid Programmable Protein Array (NAPPA) (Ramachandran et al., 2004), and DNA Array to Protein Array (DAPA) (He et al., 2008), are considered less accurate or reproducible compared to DNA microarrays. Consequently, they are now recognized as diagnostic platforms ranging from low to high density (Yu et al., 2016). On the basis of protein complement, chemistry, and active surface reactivity, multiple protein array technologies have been established, and there are many available platforms that can monitor protein changes; however, the use of protein-based arrays specially Companion Diagnostics (CDx) is somewhat restricted in oncology research (Poulsen et al., 2020). Quantitative "biomic" techniques are required for most biomic queries, which necessitate comparing two biological samples, often case vs. control. Quantitative transcriptomics has become a commercialized and partially standardized platform (Han et al., 2015); however, quantitative proteomics is still in its infancy, with few standards in place. Understanding of the dynamics of the proteome is critical for developing biomarkers that can be used to indicate nutrient-gene relationships and may help in recommending personalized nutrition on an individual basis. Proximity Extension Assay (PEA) is one such technique that translates protein information by linking protein-specific antibodies to DNA encoded tags. This technique is a multiplexed assay with high specificity and sensitivity, covering a broad dynamic range
using a minimal amount of sample (Wik et al., 2021). PEA has potential in the discovery of new biomarkers of disease prediction and contributes to the development of rapidly evolving field of nutrigenomics and personalised nutrition.

## 4.3. Metabolomics

Metabolomics is the comprehensive study and analysis of metabolites, which are the small molecules produced during cellular processes in various organisms, including microbes, animals, and plants (Patti et al., 2012; Astarita & Langridge, 2013). A clear picture of metabolism and molecular fingerprinting can be obtained by analysing all of the metabolites. As a result, such a characterization can serve as a biomarker or indication of an organism's biological status. Patterns of variation between diverse sets can be established by comparison of 'metabotypes' by metabolic phenotyping that allows classification of phenotypic state of person's that is related to disease diagnosis at individual level and disease related risk factors at population level (Nicholson et al., 2012). It is emerging as an "omics" science because of the development of new diagnostic tests for implementation in medicine. Complete metabolite profiles can provide a degree of biological system explanation that goes beyond pure genetic data and more accurately reflects eventual phenotypes (Trifonova et al., 2023). Using metabolomics techniques, it is becoming more common to analyse food components, detect their metabolites in body fluids and biological tissues, evaluate their metabolism and bioavailability, examine the role of the gut microbiota, and quantify the body's reaction to a particular food, diet, or nutraceutical (Herrero et al., 2012; Jones et al., 2012; Astarita & Langridge, 2013).

Two main analytical techniques used in metabolomics are mass spectrometry and nuclear magnetic resonance (NMR) for the identification of a large number of metabolites. However, MS-based techniques are highly sensitive (Letertre et al., 2020). Time-of-flight (TOF), triple quadrupole (QqQ), quadrupole 48 linear ion trap (Q-TRAP) and Fourier transform ion cyclotron resonance (FT-ICR) are advanced mass spectrometers that are able to characterize metabolites with structural diversity and wide concentration ranges (Aksenov et al., 2017). Gu et al. (2015), identified approximately 160 endogenous metabolites in serum via the LC-QqQ MS technique. Cao et al. (2020), developed a metabolomics method for the unbiased identification of metabolites in human tissues and body fluids. This method uses high-resolution orbitrap mass spectrometry (HRMS) coupled with ultrahigh-performance liquid chromatography (UHPLC). This method enabled the profiling of over 92 metabolic pathways and 400 metabolites in humans for lung cancer biomarker discovery. It offers strong reproducibility, linearity, biomarker discovery potential, flexibility and sensitivity, making it a valuable tool in metabolomics research.

Until recently, food analysis was limited to estimating its dietary value on the basis of the composition of six basic types: carbohydrates, proteins, minerals, water, lipids and vitamins. Metabolomics is revolutionizing nutrition investigations by examining beyond a simple list of nutrients, the number of calories, and the protein-to-carbohydrate or fat-to-protein ratio. Indeed, evidence is mounting that subclasses of metabolites within large nutritional groups may have opposing effects on human health. Moreover, metabolomics is assisting in the investigation of hundreds and thousands of compounds that are needed for human survival and affect human health and well-being (Menichetti et al., 2024). For pre-processing of the high dimensional data dimensionality reduction techniques like Principal Component Analysis (PCA) and Projection to latent structures regression (PLS) are used. Many researchers utilize PCA method for the detection of the relationship between a given intervention and a measured metabolome. Principal component analysis lowers multidimensional datasets to fewer dimensions for analysis by focusing on the dataset's attributes that contribute the most variance. This helps investigators to recognize changing patterns, even if they may not be certain peak identification that contributes to this pattern. Food-specific biomarkers play a crucial role in metabolome analysis, as they can provide insights into dietary intake. Urinary biomarkers, in particular, can indicate the consumption of specific nutrients or food components. Examples include menadione as a marker for vitamin K, hippuric acid for fruit and vegetable intake, 1-methylhistidine for oily fish, and methylmalonic acid for vitamin B12 (Price & Preedy, 2015). Similar quantitative metabolite screening could make food consumption monitoring easier for studies in epidemiology or dietary interventions, bolstering the practice of diet surveys (Tucker et al., 2013).

Data analysis presents various challenges, necessitating the adoption of more effective approaches. One such approach, Projection to latent structures regression (PLS) has proven successful in solving numerous problems in data calibration and integration, and it has become a widely used tool of metabolomic studies. PLS is an algorithmic approach designed for the modelling of relationships between two data metrics. This technique has successfully solved a large number of problems and became a basic tool of metabolomics (Stocchero et al. (2019). This approach excels in incorporating class labels in data analysis, biomarker identification, and distinguishing between physiological states of samples based on their metabolic profiles. PLS effectively filters out noise, focusing mainly on metabolites, and provides impactful data features with the potential to elevate nutrigenomics studies to a new level.

## 5. Meta-omics in the characterization of gut microbiome

The complex and dynamic community of microbes that resides in the human gastrointestinal tract is collectively called as gut microbiota (GM). Infections, inflammations, and metabolic disorders can result from disruptions in the gut microbiota because individual differences in the microbiome's composition and metabolic capability affect how people interact with their surroundings (Singh et al., 2023). Environmental factors significantly impact the microbiome by altering the proportions and types of microbes present and their metabolic activities, making it essential to integrate microbiome studies into nutrigenomics due to its importance in human health. Microbiome characterization involves various techniques including marker gene analysis that identify variable regions in conserved 16 s rRNA. In order to gain deeper insights into microbiome potential and expression of bioactive molecules additional approaches like meta-genomics, proteomics, transcriptomics are utilized that allows the analysis of all genes, genomes, metabolites and proteins (Kashyap et al., 2017). The meta-omics is emerging as a powerful tool to support personalized nutrition. The GM is related to obesity and other metabolic disorders, with six common GM markers (Parabacteriods, Bacteriods, Roseburia, Alistipes, Blautia and Romboutstia) identified among obesity patients, which have the potential to predict metabolic abnormalities related to obesity (Zeng et al., 2019). In nutrigenomics, it is essential to incorporate the concept of microbial fingerprints as precise, accessible and cost-effective tools in disease diagnosis and recommending the personalized nutrition to patient. Genetic engineering can be used to develop of precision probiotics tailored to an individual's microbial system, which when administered, can enhance liver function and mitigate oxidative stress. *Lactobacillus acidophilus* promotes the proliferation of short-chain fatty acids (SCFA)-producing bacteria and significantly impacts the gut microbiota of mice, promoting it as a novel candidate for antidiabetic drug development (Yan et al., 2019). Understanding the human genome, proteome, gut microbiome, metabolome, and transcriptome offers a comprehensive picture of how an individual's health can aid in the development of personalised nutrition.

## 6. Artificial intelligence (AI) in nutrigenomics

Omics technologies, imaging protocols, DNA sequencing, and wireless health monitoring devices are generating enormous amount of complex datasets, and they must be interpreted with sophisticated analytical tools. Artificial intelligence (AI) is a promising tool for nutrigenomics.
interpretation of omics generated data and provides valuable insights that could be used in the development personalized nutrition strategies. In analysis of complex diseases, the integration of big data and AI is transforming the healthcare via high throughput analysis. Advanced computational methods and algorithms can identify patterns in omics data associated with diseases and their treatment. One such tool, called ‘Omics Integrator’, processes diverse omic datasets to uncover underlying molecular mechanisms. By integrating epigenetics data, gene expression, and proteomics through protein-protein interactions, Omics Integrator employs network optimization algorithms to analyse extensive molecular interaction networks. This approach identifies reliable and interpretable subnetwork that provide the best explanations for complex biological data (Tuncbag et al., 2016). These interpretable subnetworks fills the gaps between changes observed in expression of genes, levels of proteins or other assays that are not directly measured due to measurement biases. This approach reveals the undiscovered molecular pathways that are not reported or present in conventional pathway databases. AI plays an important role in nutrigenomics by identifying effective intervention targets, enabling clinicians to quickly and accurately analyse large amounts of patient data. Through such analyses, AI algorithms can uncover molecular pathways linked to metabolic disorders. Another versatile tool, MiBiOmics constructs omics- based networks to unravel the internal biological systems. It helps in identification of biomarkers that are associated with specific biological conditions (Zoppi et al., 2021). AI has significant potential in the developing predictive models by analysing large datasets of patient information to identify the patterns that predict the likelihood of the development. Some examples of AI algorithms that can develop predictive models used in the field of nutrigenomics and precision medicine are SVM, hidden Maekov, discriminant analysis, K-nearest neighbor, decision tree, logistic regression, etc. (Quazi, 2022). The generated models can identify high risk patients to prevent onset of the disease. Targeted therapies and nutrition can be developed using AI technologies to provide better treatment with fewer side effects. AI can also predict how a patient will respond to different treatments and nutrition by analysing their genetic information. There are several AI based framework for recommending personalized nutrition, with ChatDiet, is one such framework for nutrition-oriented food recommendation chatbots that orchestrate and integrate personal and population models to deliver tailored food recommendations, as demonstrated in a case study with a 92 % effective food recommendation rate, showcasing its strengths in personalized nutrition (Yang et al., 2024). While AI offers significant benefits in nutrigenomics, several challenges need to be addressed. These includes the development of large, comprehensive datasets and ensuring healthcare professionals can effectively apply the insight obtained by AI algorithms. Additionally, concerns regarding data confidentiality and the potential for AI to amplify existing biases in healthcare must be carefully considered.

## 7. Impact of personalized nutrition on human health

Nutrigenomics helps in the determination of an individual's nutritional requirements on the basis of a specific genetic makeup, which is known as a personalized diet, and provides information about the association between diet and chronic diseases. Information about these associations has helped professionals determine the etiology of long-lasting diseases such as type 2 diabetes, cardiovascular disease, obesity and cancer. Poor food quality, excess weight, and alcohol consumption were all linked to an elevated risk of cancer-related death (Molina-Montes et al., 2021; Rehm & Shield, 2021). Lifestyle factors, in conjunction with genetic predispositions, can substantially elevate the risk of developing pulmonary and cardiovascular disorders, hypertension, diabetes, and malignancies, which collectively may contribute to increased mortality. According to the World Health Organization (WHO), 40 % of cancers can be prevented by lifestyle changes (Bode et al., 2016).

### 7.1. Diet in cancer prevention

Cancer occurs when cells divide in an uncontrolled manner, resulting in tumor formation (Sharma et al., 2023). Genetic variations may contribute to the onset of carcinogenesis, and these genetic factors can also influence the incidence rate (Wang et al., 2020). Food is recognized as a source of cancer-preventive components, including omega-3 fatty acids (present in flaxseed oil and fatty fish), vitamins A, C, and B12 (from green leafy vegetables), folate (found in green leafy vegetables, nuts, and cereals), and antioxidants (Khanashyam et al., 2023). Bioactive compounds can induce single nucleotide polymorphisms (SNPs) by interacting directly with DNA, leading to mutations, or by impairing DNA repair mechanisms, increasing the likelihood of replication errors (Azad & Wright, 2012; Clarke & Mostoslavsky, 2022). SNPs can then contribute to epigenetic changes by altering chromatin structure or affecting microRNA (miRNA) binding, thereby influencing gene expression (Park et al., 2009). These epigenetic modifications, such as DNA methylation or histone modification, can lead to altered gene expression patterns, contributing to phenotypic variation without altering the DNA sequence (Chhichholiya et al., 2021). Diet-gene interactions can significantly influence the risk of cancer development, particularly in the case of breast cancer (Cao et al., 2021). Omega 3-fatty acids may have a beneficial effect on colorectal cancer prevention. Gholamalizadeh et al. (2022), stated that ALOX gene SNPs (rs4986832, rs2228065 and rs6413416) and COX gene SNP (rs689465) may influence the effects of dietary fatty acids on risk of colorectal cancer. Dietary compounds have demonstrated to affect DNA methylation by targeting epigenetic enzymes (Carlos-Reyes et al., 2019). A phytoestrogen named genistein decrease DNA methyltransferase (DNMT) activity that transcriptionally activates RAR-β-2 in cervical cancer (Mukherjee et al., 2015). Additionally, feedback loops between epigenetic changes and SNPs can stabilize certain genetic variants, leading to heritable changes in genetic variation (Kabekkodu et al., 2017) (Fig. 2). Precision nutrition during radiation therapy in breast cancer may enhance outcomes by addressing metabolic disruptions (Shastri et al., 2021). Similarly, personalized nutrition interventions have shown beneficial effects on the nutritional status and quality of life of colorectal cancer survivors (Wang et al., 2022). Tailored dietary recommendations, based on nutrigenomic testing and blood test results, may complement medical treatment and improve outcomes in prostate cancer patients, with no disease recurrence and normalized lipid levels (Brinkman et al., 2024). Diet and nutrition can have a long-term impact on the epigenome and perhaps have transgenerational consequences. Several food components, such as antioxidants, beta-carotene, flavonoids, and foods rich in omega-3 fatty acids, have been recommended to aid in the battle against HPV infection (Medina-Contreras et al., 2020).

### 7.2. Dietary interventions on cardiovascular health

Cardiovascular diseases (CVDs) are the major health concerns worldwide, influenced by complex interplay between genetics and environmental factors such as diet (Barrea et al., 2020). Among the different dietary components, total fat and certain fatty acids have received significant research attention. A person's dietary fat intake is associated with lipid metabolism, which, in turn, is linked to metabolic risk factors and the risk of CVDs (Musunuru, 2010; Zeng et al., 2023). However, this link might be influenced by variations in the genes related to fatty acid metabolism. Despite their multifaceted complexity, CVDs have been the disease group in which the most progress has been made in identifying genetic risk factors, both through traditional models based on protein function and more recent genome-wide association studies aimed at gaining more knowledge about gene variants that confer genetic susceptibility to these diseases (Vrablik et al., 2021). In addition, the interplay between environmental factors and genetic susceptibility needs to be taken into account when exploring genetic susceptibility (Kiani et al., 2022). Thus, while a genetic mutation may not always
increase the likelihood of an illness, other factors, such as food habits and physical fitness, might also have an impact on its consequences. Among certain aspects to consider, diet plays an important role in genetic variations in diverse transitional and ultimate phenotypes of CVDs (Corella & Ordovas, 2009).

Poor diet quality increases CVDs risk, highlighting the need for personalized nutrition based on gene-diet interaction (Konstantinidou et al., 2014). Despite all the progress made in numerous fields on the subject, it is important to remember that genomics related to nutrition, i. e., nutritional genomics, is in its early stages, and much more work is required before its practice to prevent and cure CVDs. The normalization of transformed intermediate CVD phenotypes, which will include data on statistically important and biological diet-gene correlations, will most likely be the initial application of nutrigenomics in the field of cardiovascular medicine (Corella & Ordovas, 2009). Hence, gathering a set of well-verified gene variants associated with CVDs among symptoms across the board is essential. It facilitates the identification of genetically susceptible individuals with whom to concentrate studies on nutritional changes intended to effectively reverse their genetic predisposition.

## 8. Public perspective and challenges in the adoption of personalized nutrition

The public's perception of personalized nutrition is just as significant as the current body of scientific evidence supporting it. The public needs to feel at ease and supportive of the various forms of testing that are a part of the process for the personalized nutrition concept to become a reality. According to a Swedish survey, people are interested in using tailored nutrition services. Seventy percent of respondents said they would be willing to have a DNA test conducted to receive this type of nutritional guidance. Sixty-five percent of those polled said they would be willing to have their minor children examined. Younger respondents (aged 16-45 years) expressed greater interest in personalized nutritional recommendations based on genetic tests than older respondents (aged 46-79 years) (Ahlgren et al., 2013; Szakály et al., 2021). Studies on consumer acceptance have focused on the impact of sociodemographic traits, individual health histories, and perceived factors like data privacy, security, reliability, and the role of healthcare professionals in the testing process (Fallaize et al., 2013). Despite the general consensus on the health benefits of nutrigenetic testing, adoption rate is low, with few people pursuing such tests to align with personalized diets.

A common delivery method for personalized nutrition involves genetic data-based counselling or direct-to-consumer genetic testing (DTC-GT) (Floris et al., 2020; Pratelli et al., 2024). While this approach has shown promise as a lucrative business model, it has had limited success so far. Genetic testing is a necessary part of personalized nutrition yet concerns over data privacy, cybersecurity risks, and potential misuse by companies, employers, insurers, or government agencies (Szakály et al., 2019). Experts remain divided on the future trajectory of nutrigenomics, particularly regarding factors influencing its market success. Although the path from basic research to consumer applications remains uncertain, opportunities exist in dietary counselling, customized food products, or other avenues altogether. As a relatively new field with vast potential, nutrigenomics continues to evolve driven by ongoing scientific advancement and public interest.

## 9. Overcoming challenges in personalized nutrition

Nutrients from food directly impact the health of an individual by affecting the expression of genes involved in various metabolic processes inside the human body (Uthpala et al., 2020). How genes interact with each other and with diet could be studied by nutrigenomic approaches. The interpretation of genetic tests and disease susceptibility assessment data in nutrigenomics raises legal and ethical issues about privacy, data ownership, and potential misuse (Bahinipati et al., 2021). To avoid misuse of individual data we need to implement robust data security

measures and develop user friendly consent policies that explain how data will be used and stored and also to comply with international and local data protection law.

It is an ongoing challenge to develop personalized treatment plans, which call for the development of large-scale data infrastructure, integration of multi-omics, bioinformatics expertise and sophisticated computational tools. A serious problem arises when inaccurate datasets can lead to suboptimal recommendations. This challenge can be resolved by using standardized protocols for genetic, biochemical, and microbiome analysis to ensure data accuracy. Additionally, improving data collection tools such as wearable devices or AI based applications can enable real-time tracking of dietary intake, further improving data reliability. Scientists urge humans to be the main focus of research because findings from in vitro cultured cells, tissue-specific cells and animal studies cannot be applied directly to humans. Nevertheless, there is a need for public acceptance of these technologies. Moreover, legal issues surrounding the use of individual genetic information and the careful management of this data present challenges; while consumers view nutrigenomic testing as an empowering tool, the lack of support from general practitioners or genetic experts limits the potential for health improvements (Vyas, 2022). Implementation of nutrigenomics requires efforts such as the involvement of scientific communities, experts, and data analysts. Therefore, certain useful databases and strategies need to be developed to address this concern. The future appears to be promising, with potential to benefit people by aligning their genetic makeup with their diet.

## 10. Conclusion and future prospects

As the ability of scientific tools to discern key physiological variations unites with industrial means to supply unique solutions, nutrition and health research and its implementation in food products will become increasingly individualized. Food personalization that focuses on tested nutritional solutions for known segments of the population will benefit most customers. Consumers may obtain value from information on their personal genetic code, as nutrigenomics and nutrigenetics lay a scientific foundation for individualized nutrition and genotyping technologies becoming more widely available. Current knowledge of diet-gene interactions opens the door to consistent, actionable advice that may be offered to an individual to lessen the risk aspects related to dietary factors. Advancements in the medical field have made it possible to develop personalized therapies, along with personalized nutrition, that have ability to maximize the effectiveness of treatment which is obtained through the use of patient profiles as well as using 'omics' data relevant to metabolic disorders, particularly. Omics approaches have helped clinicians to understand the molecular mechanism of metabolic disorders. In the future, the use of omics tools in nutrigenomics is likely to continue to expanding. Although still in its infancy, personalized nutrition is nevertheless a reality, with consumers frequently reporting considerable advantages. Here, it is essential to create firm and transparent procedures for data collection and handling, although guaranteeing that the confidentiality of each individual's information is protected, as some intriguing ethics-related issues are developing. Finally, we must persuade the food industry that the time has arrived to embrace evidence-based nutrition using nutrigenomics, which has the potential to revolutionize the treatment, and prevention of diseases.

## Funding

DST-SERB-SRG provided funding for this research to Naveen Joshi (File no SRG/2020/002237).

## Studies in humans and animals

Authors must follow ethical guidelines for studies carried out in humans and animals.
## Studies in humans

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## Studies in animals

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## CRediT authorship contribution statement

**Divya Chaudhary:** Writing – original draft. **Diksha Guleria:** Writing – review & editing. **Himanshi Aggarwal:** Writing – review & editing. **Vaibhav Mishra:** Writing – review & editing. **Abhishek Chauhan:** Writing – review & editing. **Laurent Dufossé:** Writing – review & editing, Validation, Supervision. **Naveen Chandra Joshi:** Writing – review & editing, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data Availability Statement

No data were used in the manuscript.

## Data availability

No data was used for the research described in the article.

## References

Aebersold, R., & Mann, M. (2016). Mass-spectrometric exploration of proteome structure and function. *Nature*, 537, 347–355. https://doi.org/10.1038/nature19949

Ahlgren, J., Nordgren, A., Perrudin, M., Ronteltap, A., Savigny, J., van Trijp, H., Nordström, K., & Görman, U. (2013). Consumers on the internet: Ethical and legal aspects of commercialization of personalized nutrition. *Genes & Nutrition*, 8, 349–355. https://doi.org/10.1007/s12263-013-0331-0

Aksenov, A. A., da Silva, R., Knight, R., Lopes, N. P., & Dorrestein, P. C. (2017). Global chemical analysis of biology by mass spectrometry. *Nature Reviews Chemistry*, 1, 0054. https://doi.org/10.1038/s41570-017-0054-1

Astarita, G., & Langridge, J. (2013). An emerging role for metabolomics in nutrition science. *Journal of Nutrigenetics and Nutrigenomics*, 6, 181–200. https://doi.org/10.1159/000354403

Azad, M. A., & Wright, G. D. (2012). Determining the mode of action of bioactive compounds. *Bioorganic & Medicinal Chemistry*, 20, 1929–1939. https://doi.org/10.1016/j.bmc.2011.10.088

Bahinipati, J., Sarangi, R., Mishra, S., & Mahapatra, S. (2021). Nutrigenetics and nutrigenomics: A brief review with future prospects. *Biomedicine*, 41, 714–719. https://doi.org/10.51248/.v41i4.445

Balaji Ganesh, S., & Sugumar, K. (2020). Nutrigenomics: A new direction in periodontics. *IJADS*, 6, 146–149.

Bao, K., Cui, Z., Wang, H., Xiao, H., Li, T., Kong, X., & Liu, T. (2021). Pseudotime ordering single-cell transcriptomic of β cells pancreatic islets in health and type 2 diabetes. *Phenomics*, 1, 199–210. https://doi.org/10.1007/s43657-021-00024-z

Barrea, L., Annunziata, G., Bordoni, L., Muscogiuri, G., Colao, A., Savastano, S., & Obesity Programs of Nutrition, Education, Research and Assessment (OPERA) Group. (2020). Nutrigenetics—Personalized nutrition in obesity and cardiovascular diseases. *International Journal of Obesity Supplements*, 10, 1–13. https://doi.org/10.1038/s41367-020-0014-4

Bhadra, P., & Banerjee, P. (2020). The role of personalized nutrition in Human physiological disorders. *Bioscience Biotechnology Research Communications*, 13, 31–38.

Bode, A. M., Dong, Z., & Wang, H. (2016). Cancer prevention and control: Alarming challenges in China. *National Science Review*, 3, 117–127. https://doi.org/10.1093/nsr/nwv054

Brennan, L., & de Roos, B. (2021). Nutrigenomics: Lessons learned and future perspectives. *The American Journal of Clinical Nutrition*, 113, 503–516. https://doi.org/10.1093/ajcn/nqaa366

Brinkman, M., Green, H., Crofts, S., Jones, B., Walsh, S., & Smith, M. (2024). Personalized dietary management of advanced prostate cancer using nutrigenomics: A case report. *European Journal of Clinical Nutrition*, 78, 356–359. https://doi.org/10.1038/s41430-023-01377-6

Bush, C. L., Blumberg, J. B., El-Sohemy, A., Minich, D. M., Ordovás, J. M., Reed, D. G., & Behm, V. A. Y. (2020). Toward the definition of personalized nutrition: A proposal by the American Nutrition Association. *Journal of the American College of Nutrition*, 39, 5–15. https://doi.org/10.1080/07315724.2019.1685332

Cao, G., Song, Z., Hong, Y., Yang, Z., Song, Y., Chen, Z., Chen, Z., & Cai, Z. (2020). Large-scale targeted metabolomics method for metabolite profiling of human samples. *Analytica Chimica Acta*, 1125, 144–151. https://doi.org/10.1016/j.aca.2020.05.053

Cao, S., Zhu, Z., Zhou, J., Li, W., Dong, Y., Qian, Y., Wei, P., & Wu, M. (2021). Associations of one-carbon metabolism-related gene polymorphisms with breast cancer risk are modulated by diet, being higher when adherence to the Mediterranean dietary pattern is low. *Breast Cancer Research and Treatment*, 187, 793–804. https://doi.org/10.1007/s10549-021-06108-8

Cardoso, C. L., Moraes, M. C. D., & Cass, Q. B. (2023). The versatility of two-dimensional liquid chromatography. *Journal of the Brazilian Chemical Society*, 34, 1565–1580. https://doi.org/10.21577/0103-5053.20230118

Carlos-Reyes, Á., López-González, J. S., Meneses-Flores, M., Gallardo-Rincón, D., Ruíz-García, E., Marchat, L. A., Astudillo-De La Vega, H., Hernández de la Cruz, O. N., & López-Camarillo, C. (2019). Dietary compounds as epigenetic modulating agents in cancer. *Frontiers in Genetics*, 10, 79. https://doi.org/10.3389/fgenet.2019.00079

Celis-Morales, C., Livingstone, K. M., Marsaux, C. F., Forster, H., O'Donovan, C. B., Woolhead, C., Macready, A. L., Fallaize, R., Navas-Carretero, S., San-Cristobal, R., & Kolossa, S., C. (2015). Design and baseline characteristics of the Food4Me study: A web-based randomised controlled trial of personalised nutrition in seven European countries. *Genes and Nutrition*, 10, 450. https://doi.org/10.1007/s12263-014-0450-2

Chakraborty, N., Muhie, S., Kumar, R., Gautam, A., Srinivasan, S., Sowe, B., Dimitrov, G., Miller, S. A., Jett, M., & Hammamieh, R. (2017). Contributions of polyunsaturated fatty acids (PUFA) on cerebral neurobiology: An integrated omics approach with epigenomic focus. *The Journal of Nutritional Biochemistry*, 42, 84–94. https://doi.org/10.1016/j.jnutbio.2016.12.006

Chhichholiya, Y., Suryan, A. K., Suman, P., Munshi, A., & Singh, S. (2021). SNPs in miRNAs and target sequences: Role in cancer and diabetes. *Frontiers in Genetics*, 12, Article 793523. https://doi.org/10.3389/fgene.2021.793523
Chilton, F. H., Murphy, R. C., Wilson, B. A., Sergeant, S., Ainsworth, H., Seeds, M. C., & Mathias, R. A. (2014). Diet-gene interactions and PUFA metabolism: A potential contributor to health disparities and human diseases. *Nutrients*, *6*, 1993–2022.
https://doi.org/10.3390/nu6051993

Clarke, T. L., & Mostoslavsky, R. (2022). DNA repair as a shared hallmark in cancer and ageing. *Molecular Oncology*, *16*, 3352–3379. https://doi.org/10.1002/1878-0261.13285

Clemente-Suárez, V. J., Martín-Rodríguez, A., Redondo-Flórez, L., López-Mora, C., Yáñez-Sepúlveda, R., & Tornero-Aguilera, J. F. (2023). New insights and potential therapeutic interventions in metabolic diseases. *International Journal of Molecular Sciences*, *24*, Article 10672. https://doi.org/10.3390/ijms241310672

Corella, D., & Ordovas, J. M. (2009). *Nutrition in cardiovascular medicine*. *Circulation: Cardiovascular Genetics*, *2*, 637–651. https://doi.org/10.1161/CIRGENETICS.109.891366

Crotti, S., Menicatti, M., Pallecchi, M., & Bartolucci, G. (2023). Tandem mass spectrometry approaches for recognition of isomeric compounds mixtures. *Mass Spectrometry Reviews*, *42*, 1244–1260. https://doi.org/10.1002/mas.21757

Cuppen, E., Elemento, O., Rosenquist, R., Nikic, S., Lijzerman, M., Zaleski, I. D., Frederix, G., Levin, L.A., Mullighan, C. G., Buettner, R., & Pugh, T. J. (2022). Implementation of whole-genome and transcriptome sequencing into clinical cancer care. *JCO Precision Oncology*, *6*, Article 2200245. https://doi.org/10.1200/PO.22.00245

De Guzman, M. B., & Buhay, M. N. M. (2023). *Nutrigenomics and nutrigenetics: Importance in health and diseases. Role of nutrigenomics in modern-day healthcare and drug discovery* (pp. 19–34). Elsevier. https://doi.org/10.1016/B978-0-12-824412-8.00011-4

Dey, S., & Kumar, Y. (2022). *Nutrigenomics research: A review. Bioactive Components: A Sustainable System for Good Health and Well-Being*, 359–379. https://doi.org/10.1007/978-981-19-2366-1_21

Dina, C., Meyre, D., Samson, C., Tichet, J., Marre, M., Jouret, B., Charles, M. A., Balkau, B., & Froguel, P. (2007). Comment on “A common genetic variant is associated with adult and childhood obesity”. *Science*, *315*. https://doi.org/10.1126/science.1129402, 187-187.

Donini, L. M., Penzavecchia, C., Muzzioli, L., Poggiogalle, E., Giusti, A. M., Lenzi, A., & Pinto, A. (2022). Efficacy of front-of-pack nutrition labels in improving health status. *Nutrition*, *102*, Article 111770. https://doi.org/10.1016/j.nut.2022.111770

Du, B., Tian, H., Tian, D., Cang, W., Wang, W., Wang, L., Ge, M., Hou, Q., & Zhang, W. (2018). Genetic polymorphisms of key enzymes in folate metabolism affect the efficacy of folate therapy in patients with hyperhomocysteinaemia. *British Journal of Nutrition*, *119*, 887–895. https://doi.org/10.1017/S0007114518000508

Ebbeling, C. B., Feldman, H. A., Klein, G. L., Wong, J. M., Bielak, L., Steltz, S. K., Luo, P. K., Wolfe, R. R., Wong, W. W., & Ludwig, D. S. (2018). Effects of a low carbohydrate diet on energy expenditure during weight loss maintenance: Randomized trial. *BMJ*, *363*. https://doi.org/10.1136/bmj.k4583

EFSA Scientific Committee, More, S., Bampidis, V., Benford, D., Bragard, C., Hernandez-Jerez, A., Bennekou, S. H., Koutsoumanis, K., Lambré, C., Machera, K., & Mennes, W. (2024). Scientific Committee guidance on appraising and integrating evidence from epidemiological studies for use in EFSA’s scientific assessments. *EFSA Journal, 22*, 8866. https://doi.org/10.2903/j.efsa.2024.8866

Fallaize, R., Macready, A. L., Butler, L. T., Ellis, J. A., & Lovegrove, J. A. (2013). An insight into the public acceptance of nutrigenomic-based personalised nutrition. *Nutrition Research Reviews*, *26*, 39–48. https://doi.org/10.1017/S0954422413000024

Feng, F., Zhang, W., Chai, Y., Guo, D., & Chen, X. (2023). Label-free target protein characterization for small molecule drugs: Recent advances in methods and applications. *Journal of Pharmaceutical and Biomedical Analysis*, *223*, Article 115107. https://doi.org/10.1016/j.jpba.2022.115107

Ferrario, P. G., Watzl, B., Møller, G., & Ritz, C. (2021). What is the promise of personalised nutrition? *Journal of Nutritional Science*, *10*, e23. https://doi.org/10.1017/jns.2021.13

Floris, M., Cano, A., Porru, L., Addis, R., Cambedda, A., Idda, M. L., Steri, M., Ventura, C., & Maiolì, M. (2020). Direct-to-consumer nutrigenetics testing: An overview. *Nutrients*, *12*, 566. https://doi.org/10.3390/nu12020566

García-Canas, V., Simó, C., León, C., & Cifuentes, A. (2010). Advances in nutrigenomics research: Novel and future analytical approaches to investigate the biological activity of natural compounds and food functions. *Journal of Pharmaceutical and Biomedical Analysis*, *51*, 290–304. https://doi.org/10.1016/j.jpba.2009.04.019

Gholamalizadeh, M., Majidi, N., Tajaddod, S., Abdollahi, S., Poorhosseini, S. M., Ahmadzadeh, M., Naimi Joubani, M., Mirzaei Dahka, S., Shafeei, H., Hajiesmaeil, M., & Alizadeh, A. (2022). Interactions of colorectal cancer, dietary fats, and polymorphisms of arachidonate lipoxygenase and cyclooxygenase genes: A literature review. *Frontiers in Oncology*, *12*, Article 865208. https://doi.org/10.3389/fonc.2022.865208

Gu, H., Zhang, P., Zhu, J., & Raftery, D. (2015). Globally optimized targeted mass spectrometry: Reliable metabolomics analysis with broad coverage. *Analytical chemistry*, *87*, 12355–12362. https://doi.org/10.1021/acs.analchem.5b03812

Gupta, E., Shyam, H., Devi, S., & Maurya, N. K. (2020). *Nutrigenomics in functional foods and personalized nutrition. Gedrag Organ Усерднєтг Utgev Lemma BV*, 33, 670–686.

Gygi, S. P., Corthals, G. L., Zhang, Y., Rochon, Y., & Aebersold, R. (2000). Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. *Proceedings of the National Academy of Sciences*, *97*, 9390–9395. https://doi.org/10.1073/pnas.160270797

Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015). Advanced applications of RNA sequencing and challenges. *Bioinformatics and Biology Insights*, *9*, BBI-S28991. https://doi.org/10.4137/BBI.S28991

Hays, H., Gu, Z., Mai, K., & Zhang, W. (2023). Transcriptome-based nutrigenomics analysis reveals the roles of dietary taurine in the muscle growth of juvenile turbot (Scophthalmus maximus). *Comparative Biochemistry and Physiology Part D: Genomics and Proteomics*, *47*, Article 101120. https://doi.org/10.1016/j.cbd.2023.101120

He, M., Stoevesandt, O., Palmer, E. A., Khan, F., Ericsson, O., & Taussig, M. J. (2008). Printing protein arrays from DNA arrays. *Nature Methods*, *5*, 175–177. https://doi.org/10.1038/nmeth.1178

He, M., & Taussig, M. J. (2001). Single step generation of protein arrays from DNA by cell-free expression and in situ immobilisation (PISA method). *Nucleic Acids Research*, *29*. https://doi.org/10.1093/nar/29.15.e73. e73-e73.

Herrero, M., Simó, C., García-Canas, V., Ibáñez, E., & Cifuentes, A. (2012). Foodomics: MS-based strategies in modern food science and nutrition. *Mass Spectrometry Reviews*, *31*, 49–69. https://doi.org/10.1002/mas.20335

Higuchi, R., Iwane, T., Iida, A., & Nakajima, K. (2020). Copy number variation of the salivary amylase gene and glucose metabolism in healthy young Japanese women. *Journal of Clinical Medicine Research*, *12*, 184. https://doi.org/10.14740/jocmr4082

Jiang, Y., Rex, D. A. B., Schuster, D., Neely, B. A., Rosano, G. L., Volkmar, N., Momenzadeh, A., Peters-Clarke, T. M., Egbert, S. B., Kreimer, S., & Doud, E. H. (2023). Comprehensive overview of bottom-up proteomics using mass spectrometry. *ACS Measurement Science Au*, https://doi.org/10.1021/acsmeasursciua.3c00068

Jones, D. P., Park, Y., & Ziegler, T. R. (2012). Nutritional metabolomics: Progress in addressing complexity in diet and health. *Annual Review of Nutrition*, *32*, 183–202. https://doi.org/10.1146/annurev-nutr-072610-145159

Kabata, F., & Thaldar, D. (2023). Regulating human genomic research in Africa: Why a human rights approach is a more promising conceptual framework than genomic sovereignty. *Frontiers in Genetics*, *14*. https://doi.org/10.3389/fgene.2023.1208606

Kabekkodu, S. P., Chakrabarty, S., Ghosh, S., Brand, A., & Satyamoorthy, K. (2017). Epigenomics, pharmacoepigenomics, and personalized medicine in cervical cancer. *Public Health Genomics*, *20*, 100–115. https://doi.org/10.1159/000475804

Kashyap, P. C., Chia, N., Nelson, H., Segal, E., & Elinav, E. (2017). Microbiome at the frontier of personalized medicine. *In Mayo Clinic Proceedings (Vol. 92, No. 12, pp. 1855-1864)*. Elsevier. https://doi.org/10.1016/j.mayocp.2017.10.004

Khanashyam, A. C., Thorakkattu, P., Babu, K. S., Shah, K., Rajput, M. S., Nirmal, S., & Nirmal, N. P. (2023). Diet and supplements in cancer prevention. *Cancer Screening and Prevention*, *2*, 138–147. https://doi.org/10.14218/CSP.2023.00001

Kiani, A. K., Bonetti, G., Donato, K., Kaftalli, B., Herbst, K. L., Stuppia, L., Fioretti, F.,NDari, S., Perrone, M., Chirurazzi, P., & Bellinato, F. (2022). Polymorphisms, diet and nutrigenomics. *Journal of Preventive Medicine and Hygiene*, *63*, E125. https://doi.org/10.15167/2421-4248/jpmh2022.63.253.2754

Konstantinidou, V., Ruiz, L. A. D., & Ordovás, J. M. (2014). Personalized nutrition and cardiovascular disease prevention: From Framingham to PREDIMED. *Advances in Nutrition*, *5*, 368S–371S. https://doi.org/10.13945/an.113.005686

Letertre, M. P., Dervilly, G., & Giraudeau, P. (2020). Combined nuclear magnetic resonance spectroscopy and mass spectrometry approaches for nutrigenomics. *Analytical Chemistry*, *93*, 500–518. https://doi.org/10.1021/acs.analchem.0c04371

Lloyd-Jones, D. M., Nam, B. H., D'Agostino, R. B., Sr, Levy, D., Murabito, J. M., Wang, T. J., Wilson, P. W., & O'Donnell, C. J (2004). Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: A prospective study of parents and offspring. *JAMA*, *291*, 2204–2211. https://doi.org/10.1001/jama.291.18.2204

Lowe, R., Shirley, N., Bleackley, M., Dolan, S., & Shafee, T. (2017). Transcriptomics technologies. *PLO S Computational Biology*, *13*, Article 1005457. https://doi.org/10.1371/journal.pcbi.1005457

Marques, A. C., Costa, P. C., Velho, S., & Amaral, M. H. (2024). Analytical techniques for characterizing tumor-targeted antibody-functionalized nanoparticles. *Life*, *14*, 489. https://doi.org/10.3390/life14040489

Marquina, C., Mousa, A., Belski, R., Banaharis, H., Naderpoor, N., & de Courten, B. (2019). Increased inflammation and cardiometabolic risk in individuals with low AMY1 copy numbers. *Journal of Clinical Medicine*, *8*, 382. https://doi.org/10.3390/jcm8030382

Matusheski, N. V., Caffrey, A., Christensen, L., Mezegz, S., Surendran, S., Hjorth, M. F., McNulty, H., Pentieva, K., Roager, H. M., Seljak, B. K., & Vimaleswaran, K. S. (2021). Diets, nutrients, genes and the microbiome: Recent advances in personalised nutrition. *British Journal of Nutrition*, *126*, 1489–1497. https://doi.org/10.1017/S0007114521000374

McClements, D. J., & McClements, D. J. (2019). Personalized nutrition: Customizing your diet for better health. *Future Foods: How Modern Science Is Transforming the Way We Eat*, 233–260. https://doi.org/10.1007/978-0-303-12995-8_8

Medina-Contreras, O., Luviani-Morales, J., Valdez-Palomares, F., Flores-Cisneros, L., Sánchez-López, M. S., Soto-Lugo, J. H., & Castro-Eguiluz, D. (2020). Immunomodulation in cervical cancer: Immune response modulation by diet. *Revista de investigación clínica*, *72*, 219–230. https://doi.org/10.24875/ric.2000062

Menichetti, G., Barabási, A. L., & Localzo, J. (2024). Decoding the foodome: Molecular networks connecting diet and health. *Annual review of nutrition*, *44*, 257–288. https://doi.org/10.1146/annurev-nutr-062322-030557

Mierziak, J., Kostyn, K., Boba, A., Czemplik, M., Kulma, A., & Wojtasik, W. (2021). Influence of the bioactive diet components on the gene expression regulation. *Nutrients*, *13*, 3673. https://doi.org/10.3390/nu13113673

Mir, M. A., Qayoom, H., Sofi, S., & Jan, N. (2023). Proteomics: A groundbreaking development in cancer biology. *Proteomics* (pp. 31–53). Academic Press. https://doi.org/10.1016/B978-0-323-95072-5.00004-3

Mittelman, S. D. (2020). The role of diet in cancer prevention and chemotherapy efficacy. *Annual Review of Nutrition*, *40*, 273–297. https://doi.org/10.1146/annurev-nutr-013120-041149

Molina-Montes, E., Ubago-Guisado, E., Petrova, D., Amiano, P., Chirlaque, M. D., Agudo, A., & Sánchez, M. J. (2021). The role of diet, alcohol, BMI, and physical
activity in cancer mortality: Summary findings of the EPIC study. *Nutrients*, **13**, 4293.
https://doi.org/10.3390/nu13124293

Mukherjee, N., Kumar, A. P., & Ghosh, R. (2015). DNA methylation and flavonoids in genitourinary cancers. *Current Pharmacology Reports*, **1**, 112–120. https://doi.org/10.1007/s40495-014-0004-8

Musunuru, K. (2010). Atherogenic dyslipidemia: Cardiovascular risk and dietary intervention. *Lipids*, **45**, 907–914. https://doi.org/10.1007/s11745-010-3408-1

Negi, A., Shukla, A., Jaiswar, A., Shrinet, J., & Jasrotia, R. S. (2022). Applications and challenges of microarray and RNA-sequencing. *Bioinformatics*, **91**, 91–103. https://doi.org/10.1016/j/b978-0-323-89775-5-00016-x

Nicholson, J. K., Holmes, E., Kinross, J. M., Darzi, A. W., Takats, Z., & Lindon, J. C. (2012). Metabolic phenotyping in clinical and surgical environments. *Nature*, **491**, 384–392. https://doi.org/10.1038/nature11708

Ollier, S., Robert-Granié, C., Bernard, L., Chilliard, Y., & Leroux, C. (2007). Mammary transcriptome analysis of food-deprived lactating goats highlights genes involved in milk secretion and programmed cell death, 2. *The Journal of Nutrition*, **137**, 560–567. https://doi.org/10.1093/jn/137.3.560

Ordovas, J. M., Ferguson, L. R., Tai, E. S., & Mathers, J. C. (2018). Personalised nutrition and health. *BMJ*, **361**. https://doi.org/10.1136/bmj.k2173

Osorio, J. S., Vailati-Riboni, M., Palladino, A., Luo, J., & Loor, J. J. (2017). Application of nutrigenomics in small ruminants: Lactation, growth, and beyond. *Small Ruminant Research*, **154**, 29–44. https://doi.org/10.1016/j smallrumres.2017.06.021

Pal, A., & Pal, A. (2022). Epigenetics and DNA methylation. *Protocols in Advanced Genomics and Allied Techniques*, **245–278**. https://doi.org/10.1007/978-1-0716-1818-9_12

Panacer, K. S. (2023). Ethical issues associated with direct-to-consumer genetic testing. *Disease, Cure, 15*. https://doi.org/10.7759/cureus.39918

Papanikolaou, Y., Slavin, J. L., Clemens, R., Brenna, J. T., Hayes, D., Gaesser, G. A., & Fulgoni, V. L. III (2020). Do refined grains have a place in a healthy dietary pattern? *Perspectives from an expert panel consensus meeting. In Current developments in nutrition*, **4** p. nzaa125). https://doi.org/10.1093/cdn/nzaa125

Park, J. Y., Huang, Y., & Sellers, T. A. (2009). Single nucleotide polymorphisms in DNA repair genes and prostate cancer risk. *Cancer Epidemiology*, **361**–385. https://doi.org/10.1007/978-1-59745-416-2_18

Patti, G. J., Yanes, O., & Siuzdak, G. (2012). Metabolomics: The apogee of the omics trilogy. *Nature Reviews Molecular Cell Biology*, **13**, 263–269. https://doi.org/10.1038/nrm3314

Poulsen, T. B., Karamehmedovic, A., Aboo, C., Jørgensen, M. M., Yu, X., Fang, X., Blackburn, J. M., Nielsen, C. H., Kragstrup, T. W., & Stensballe, A. (2020). Protein array-based companion diagnostics in precision medicine. *Expert Review of Molecular Diagnostics*, **20**, 1183–1198. https://doi.org/10.1080/14737159.2020.1857734

Pratelli, G., Tamburini, B., Badami, G. D., Lo Pizzo, M., De Blasio, A., Carlisi, D., & Di Liberto, D. (2024). Cow's milk: A benefit for Human health? Omics tools and precision nutrition for lactose intolerance management. *Nutrients*, **16**, 320. https://doi.org/10.3390/nu16020320

Price, M. Y., & Preedy, V. R. (2015). Urinary markers in nutritional studies. *General methods in biomarker research and their applications* (pp. 547–566). Springer International Publishing Switzerland. https://doi.org/10.1007/978-94-007-7696-8_29

Quazi, S. (2022). Artificial intelligence and machine learning in precision and genomic medicine. *Medical Oncology*, **39**(8), 120. https://doi.org/10.1007/s12032-022-01711-1

Rahman, M. N. A., & Muhammad, N. H. (2023). Precision nutrition: Using nutrigenetic and nutrigenomic concepts in personalised nutrition. *Nutrition*, **6**, 7.

Ramachandran, N., Hainsworth, E., Bhullar, B., Eisenstein, S., Rosen, B., Lau, A. Y., Walter, J. C., & LaBaer, J. (2004). Self-assembling protein microarrays. *Science*, **305**, 86–90. https://doi.org/10.1126/science.1097639

Rehm, J., & Shield, K. D. (2021). Alcohol use and cancer in the European Union. *European Addiction Research*, **27**, 1–8. https://doi.org/10.1159/000507017

Rijnaarts, I., De Roos, N. M., Wang, T., Zoetendal, E. G., Top, J., Timmer, M., Bouwman, E. P., Hogenelst, K., Witteman, B., & De Wit, N. (2021). Increasing dietary fibre intake in healthy adults using personalised dietary advice compared with general advice: A single-blind randomised controlled trial. *Public Health Nutrition*, **24**, 1117–1128. https://doi.org/10.1017/S1368980020002980

Rijnaarts, I., de Roos, N. M., Wang, T., Zoetendal, E. G., Top, J., Timmer, M., Hogenelst, K., Bouwman, E. P., Witteman, B., & de Wit, N. (2022). A high-fibre personalised dietary advice given via a web tool reduces constipation complaints in adults. *Journal of Nutritional Science*, **11**, e31. https://doi.org/10.1017/jns.2022.27

Rinschen, M. M., Limbutara, K., Knepper, M. A., Payne, D. M., & Pitsikun, T. (2018). From molecules to mechanisms: Functional proteomics and its application to renal tubule physiology. *Physiological Reviews*, **98**, 2571–2606. https://doi.org/10.1152/physrev.00057.2017

Sharma, R., Perez, M. R., da Silva, V. A., Thomsen, J., Bhardwaj, L., Andrade, T. A., Alhussan, A., & Willerth, S. M. (2023). 3D bioprinting complex models of cancer. *Biomaterials Science*, **11**, 3414–3430. https://doi.org/10.1039/D2BM02060B

Shastri, A. A., Lombardo, J., Okere, S. C., Higgins, S., Smith, B. C., DeAngelis, T., Palagani, A., Hines, K., Monti, D. A., Volpe, S., & Mitchell, E. P. (2021). Personalized nutrition as a key contributor to improving radiation response in breast cancer. *International Journal of Molecular Sciences*, **23**, 175. https://doi.org/10.3390/ijms23010175

Shelnutt, K. P., Kauwell, G. P., Chapman, C. M., Gregory, J. F., III, Maneval, D. R., Browdy, A. A., Bailey, L. B., & Theriaque, D. W (2003). Folate status response to controlled folate intake is affected by the methylenetetrahydrofolate reductase 677C→ T polymorphism in young women. *The Journal of Nutrition*, **133**, 4107–4111. https://doi.org/10.1093/jn/133.12.4107

Shunmoogam, N., Naidoo, P., & Chilton, R. (2018). Paraoxonase (PON)-1: A brief overview on genetics, structure, polymorphisms and clinical relevance. *Vascular Health and Risk Management*, **13**, 143. https://doi.org/10.2147/VHRM.S165173

Stocchero, M., Locci, E., d'Aloja, E., Nioi, M., Baraldi, E., & Giordano, G. (2019). PLS2 in metabolomics. *Metabolites*, **9**, 51. https://doi.org/10.3390/metabo09030051

Szakály, Z., Fehér, A., & Kiss, M. (2019). Consumer acceptance of personalized nutrition. *Trends in Personalized Nutrition*, **22**, 225–260. https://doi.org/10.1016/j.b978-0-12-816403-7-00009-x

Szakály, Z., Kovács, B., Szakály, M., Nagy-Pető, T., Popovics, P. D., & Kiss, M. (2021). Consumer acceptance of genetic-based personalized nutrition in Hungary. *Genes & Nutrition*, **16**, 1–12. https://doi.org/10.1186/s12263-021-00683-7

The 1000 Genomes Project Consortium. (2015). A global reference for human genetic variation. *Nature*, **526**, 68–74. https://doi.org/10.1038/nature15393

Trifonova, O. P., Maslov, D. L., Balashova, E. E., & Lokhov, P. G. (2023). Current state and future perspectives on personalized metabolomics. *Metabolites*, **13**, 67. https://doi.org/10.3390/metabol013010067

Tucker, K. L., Smith, C. E., Lai, C. Q., & Ordovas, J. M. (2013). Quantifying diet for nutrigenomic studies. *Annual Review of Nutrition*, **33**, 349–371. https://doi.org/10.1146/annurev-nutr-072610-145203

Tuncbag, N., Gosline, S. J., Kedaigle, A., Soltis, A. R., Gitter, A., & Fraenkel, E. (2016). Network-based interpretation of diverse high-throughput datasets through the omics integrator software package. *PLoS Computational Biology*, **12**, Article 1004879. https://doi.org/10.1371/journal.pcbi.1004879

Uthpala, T.G., Fernando, H.N., Thibbotuwawa, A., & Jayasinghe, M. (2020). Importance of nutrigenomics and nutrigenetics in food science. https://doi.org/10.15406/mojft.2020.08.00250.

Vazquez-Vidal, I., Desmarchelier, C., & Jones, P. J. (2019). Metagenomics of blood cholesterol concentrations: Towards personalized nutrition. *Current Cardiology Reports*, **21**, 1–5. https://doi.org/10.1007/s11886-019-1124-x

Vrablik, M., Dlouha, D., Todorova, V., Stefler, D., & Hubacek, J. A. (2021). Genetics of cardiovascular disease: How far are we from personalized CVD risk prediction and management? *International Journal of Molecular Sciences*, **22**, 4182. https://doi.org/10.3390/ijms22084182

Vyas, S. (2022). Advances in nutrigenomics and applications in public health: A recent update. *Current Research in Nutrition and Food Science Journal*, **10**, 1092–1104. https://doi.org/10.12944/CRNFJSJ.10.3.23

Wallace, T. C., Bultman, S., D'Adamo, C., Daniel, C. R., Debelius, J., Ho, E., Eliassen, H., Lemanne, D., Mukherjee, P., Seyfried, T. N., & Tian, Q. (2019). Personalized nutrition in disrupting cancer—Proceedings from the 2017 American College of Nutrition Annual Meeting. *Journal of the American College of Nutrition*, **38**, 1–14. https://doi.org/10.1080/07315724.2018.1500499

Wang, S., Yang, J., You, L., Dai, M., & Zhao, Y. (2020). GSTM3 function and polymorphism in cancer: Emerging but promising. *Cancer Management and Research*, **10**.377–10388. https://doi.org/10.2147/CMAR.S272467

Wang, X., Zeng, H., Li, L., Fang, Z., Xu, L., Shi, W., Li, J., Qian, J., Tan, X., Li, J., & Qian, Y. (2022). Personalized nutrition intervention improves nutritional status and quality of life of colorectal cancer survivors in the community: A randomized controlled trial. *Nutrition*, **103**, Article 111835. https://doi.org/10.1016/j.nut.2022.111835

Wik, L., Nordberg, N., Broberg, J., Björkesten, J., Assarsson, E., Henriksson, S., Grundberg, I., Pettersson, E., Westerberg, C., Liljeroth, E., & Falck, A. (2021). Proximity extension assay in combination with next-generation sequencing for high-throughput proteome-wide analysis. *Molecular & Cellular Proteomics*, **20**, https://doi.org/10.1016/j.mcpro.2021.100168

Williams, C. M., Ordovas, J. M., Lairon, D., Hesketh, J., Lietz, G., Gibney, M., & van Ommen, B. (2008). The challenges for molecular nutrition research 1: Linking genotype to healthy nutrition. *Genes & Nutrition*, **3**, 41–49. https://doi.org/10.1007/s12263-008-0086-1

Xiao, Y., Xiao, X., Zhang, X., Yi, D., Li, T., Hao, Q., Zhang, F., Li, X., & Wang, N. (2024). Mediterranean diet in the targeted prevention and personalized treatment of chronic diseases: Evidence, potential mechanisms, and prospects. *EPMA Journal*, **1**–14. https://doi.org/10.1007/s13167-024-00360-w

Xu, M., Zhao, J., Zhang, Y., Ma, X., Dai, Q., Zhi, H., Wang, B., & Wang, L. (2016). Apolipoprotein E gene variants and risk of coronary heart disease: A meta-analysis. *BioMed Research International*, **20**, Article 3912175. https://doi.org/10.1155/2016/3912175

Yan, F., Li, N., Shi, J., Li, H., Yue, Y., Jiao, W., Wang, N., Song, Y., Huo, G., & Li, B. (2019). *Lactobacillus acidophilus* alleviates type 2 diabetes by regulating hepatic glucose, lipid metabolism and gut microbiota in mice. *Food & Function*, **10**, 5804–5815. https://doi.org/10.1039/C9FO01062A

Yang, Z., Zhu, J., Lu, W., Tian, F., Zhang, H., & Chen, W. (2024). Integrating artificial intelligence in exploring multiscale gut microbiota and diet relations for health promotion: A comprehensive review. *Food Bioscience*, **61**, Article 104541. https://doi.org/10.1016/j.fbio.2024.104541

Yu, X., Petritis, B., Duan, H., Xu, D., & LaBaer, J. (2018). Advances in cell-free protein array methods. *Expert Review of Proteomics*, **15**, 1–11. https://doi.org/10.1080/14789450.2018.1415146

Yu, X., Petritis, B., & Labaer, J. (2016). Advancing translational research with next-generation protein microarrays. *Proteomics*, **16**, 1238–1250. https://doi.org/10.1002/pmic.201500374

Zeisel, S. H. (2020). Precision (personalized) nutrition: Understanding metabolic heterogeneity. *Annual review of food science and technology*, **11**, 71–92. https://doi.org/10.1146/annurev-food-032519-051736

Zeng, Q., Li, D., He, Y., Li, Y., Yang, Z., Zhao, X., Liu, Y., Wang, Y., Sun, J., Feng, X., & Wang, F. (2019). Discrepant gut microbiota markers for the classification of obesity.
related metabolic abnormalities. *Scientific reports*, 9, Article 13424. https://doi.org/
10.1038/s41598-019-49462-w

Zeng, W., Jin, Q., & Wang, X. (2023). Reassessing the effects of dietary fat on cardiovascular disease in China: A review of the last three decades. *Nutrients*, 15, 4214. https://doi.org/10.3390/nu15194214

Zoppi, J., Guillaume, J. F., Neunlist, M., & Chaffron, S. (2021). MiBiOmics: An interactive web application for multi-omics data exploration and integration. *BMC Bioinformatics*, 22, 1-14. https://doi.org/10.1186/s12859-020-03921-8