Diet

Introduction

Dietary intake, a complex and multifaceted human trait, refers to the foods and beverages an individual consumes. It is a fundamental determinant of health, influenced by a wide array of genetic, environmental, social, and psychological factors. Understanding and accurately assessing dietary intake is crucial for both individual health management and public health initiatives.

Background

Historically, dietary intake has been assessed through various self-reported methods, including food frequency questionnaires (FFQs), 24-hour dietary recalls, and diet records.^[1] While these tools provide valuable insights, they are subject to inherent biases such as memory recall errors and the tendency for individuals to report favorably.^{[2], [3]} Despite these challenges, large-scale population studies, such as the UK Biobank, have leveraged these methods to investigate genetic associations with dietary habits, providing an unprecedented opportunity to explore the biological underpinnings of what people eat.^{[1], [2], [4]}

Biological Basis

While a significant portion of the variation in dietary habits is shaped by non-genetic factors like socioeconomic status, cultural practices, lifestyle choices, and health beliefs, genetic predispositions also play a role.^[4] Research indicates that dietary components, such as meat consumption or the intake of fish and plant-related products, exhibit moderate heritability, with estimates around 16%.^[4]Genome-wide association studies (GWAS) have been instrumental in identifying specific genetic variants, or single nucleotide polymorphisms (SNPs), that are associated with particular dietary patterns or the consumption of certain food groups.^{[1], [4], [5]}These genetic associations can sometimes be linked to changes in gene expression (expression quantitative trait loci, eQTLs) or DNA methylation (methylation quantitative trait loci, mQTLs), suggesting biological pathways through which genetics influence dietary choices.^{[4], [5]} For example, specific SNPs have been found to correlate with expression levels of genes such as BCKDHB and PFKFB3 in relation to identified dietary patterns.^[5] Advanced bioinformatic techniques, including Variant Effect Predictor (VEP) and TRAP, are employed to predict the functional impact of these SNPs on protein sequences or transcription factor binding sites.^[5]

Clinical Relevance

The genetic understanding of dietary habits holds substantial clinical relevance, particularly in the emerging field of precision nutrition. Dietary patterns are intricately linked to a spectrum of health outcomes, and the genetic components of these patterns show correlations with conditions such as obesity and other metabolic traits.^{[1], [2]}Genetic variants associated with diet have been implicated in the risk for chronic diseases including Type 2 Diabetes (T2D), Coronary Artery Disease (CAD), and variations in Body Mass Index (BMI).^[1] The development of Polygenic Risk Scores (PRS) for dietary intake allows for a more comprehensive assessment of an individual’s genetic predisposition to certain eating behaviors and their associations with cardiometabolic phenotypes.^[1]Furthermore, studies have explored the relationship between dietary intake and complex psychiatric conditions like schizophrenia, highlighting potential genetic links between diet and disease susceptibility.^[4]

Social Importance

Dietary intake is a critical aspect of public health, shaped by a complex interplay of individual choices and broader societal and environmental influences.^{[1], [4]} Genetic research in this area contributes to a deeper understanding of human behavior, informing public health policies and the development of more effective, evidence-based dietary guidelines.^{[5], [6]}However, it is important to acknowledge that interpreting the genetic influences on dietary traits can be complex, as dietary habits are often intertwined with wider lifestyle factors and socioeconomic status.^[2]By elucidating the genetic and environmental factors influencing diet, research in this area can lead to more personalized health interventions and a better understanding of health disparities within populations.

Methodological and Statistical Constraints

Research into dietary habits and their genetic underpinnings faces significant methodological and statistical challenges that can impact the interpretation and generalizability of findings. Many studies, particularly early or exploratory ones, may be constrained by relatively small sample sizes compared to the vast scale of genome-wide association studies (GWAS), which inherently limits statistical power and the ability to detect subtle genetic effects or complex gene-environment interactions.^[7] Furthermore, while large biobanks provide extensive data, the process of sample selection and data curation can introduce cohort biases, such as the “healthy user bias” or “collider bias,” where observed associations are substantially influenced by selection into the study population.^[8]Issues like effect-size inflation and the overcorrection of significance thresholds for multiple testing in GWAS further complicate the accurate estimation of genetic contributions to dietary traits.^[5] The absence of replication in independent, equally large datasets for novel genetic variants related to dietary intake also represents a significant knowledge gap, making it difficult to confirm the robustness and universality of identified associations.^[1]

Phenotypic Assessment and Generalizability Challenges

The accurate of dietary intake itself presents a fundamental limitation, as most large-scale studies rely on self-reported questionnaire data, which is susceptible to recall bias, social desirability bias, and inaccurate reporting.^[2]Different dietary assessment tools, such as food frequency questionnaires or diet recalls, and varying methodologies for calculating nutrient consumption across cohorts can lead to inconsistencies in phenotypic definitions, further complicated by the exclusion of participants with improbable energy intake levels.^[9] Moreover, the biological of dietary components, such as polyunsaturated fatty acids, can vary significantly depending on the tissue source (e.g., erythrocyte membranes versus plasma) and specific analytical methods, reflecting different timeframes of intake and potentially influencing comparative analyses across studies.^[7] Critically, many large-scale genetic analyses are predominantly conducted in populations of European ancestry, which restricts the generalizability of findings to diverse ethnic groups and geographic origins, as dietary habits and their genetic modifiers can be highly variable across different populations.^[7]

Complex Etiology and Remaining Knowledge Gaps

Dietary intake is a complex phenotype influenced by an intricate network of biological, environmental, and socio-economic factors, making the interpretation of genetic findings challenging.^[1]While genetic factors contribute to the variation in dietary habits, a substantial proportion of this variation is driven by non-genetic factors such as lifestyle, culture, health beliefs, and socio-economic status.^[4] Studies often struggle to fully account for potential confounders like stress, emotional eating, or the broader social and family networks that influence eating behaviors, despite efforts to adjust for environmental factors or BMI in sensitivity analyses.^[10]The “missing heritability” observed in many complex traits, including dietary habits, indicates that current genetic models do not yet capture the full genetic architecture, suggesting roles for rarer variants, gene-gene interactions, or epigenetic mechanisms not fully elucidated by current GWAS methods. Furthermore, the high correlation between different dietary habits and with non-dietary traits, such as educational attainment or BMI, means that identified genetic loci may reflect broader lifestyle components rather than specific dietary preferences, underscoring the need for more refined dietary phenotyping approaches in future research.^[2]

Variants

The genetic landscape influencing dietary habits and metabolic responses is complex, involving numerous genes and variants that interact with environmental factors. Among these, variations in the NEGR1 gene and its associated rs66495454 variant are notable. NEGR1 (Neuronal Growth Regulator 1) is a gene crucial for neuronal development and function, particularly in brain regions that regulate appetite and energy balance.^[11] Variants near or within NEGR1have been consistently linked to various traits, including body mass index (BMI), educational attainment, intelligence, and major depressive disorder.^[4]The single nucleotide polymorphism (SNP)rs66495454 , located in the vicinity of NEGR1 and LINC02796, has been specifically associated with meat intake in studies of Japanese populations.^[12] This suggests that genetic variation in this region may influence an individual’s preference for, or consumption levels of, meat, potentially contributing to broader metabolic and behavioral phenotypes.

Further exploring the genetic underpinnings of dietary influences, the variant rs174528 is associated with the genes MYRF (Myelin Regulatory Factor) and TMEM258 (Transmembrane Protein 258). MYRF plays a critical role in the development and maintenance of myelin, the insulating sheath around nerve fibers, which is essential for proper neurological function.^[13] While TMEM258’s precise function is still under investigation, it is thought to be involved in cellular processes that could indirectly impact metabolism or nutrient sensing.^[14] Similarly, rs709592 is linked to MED24 (Mediator Complex Subunit 24), a component of the Mediator complex. This complex is crucial for regulating gene transcription by acting as a bridge between gene-specific transcription factors and RNA polymerase II, thereby influencing the expression of numerous genes involved in diverse cellular functions, including energy metabolism and nutrient utilization. Alterations in MED24 activity due to variants like rs709592 could therefore have broad implications for how the body processes and responds to dietary intake.^[11] Several variants, including rs1047891 , rs6760497 , and rs13017275 , are found within the CPS1(Carbamoyl-Phosphate Synthase 1) gene.CPS1is a vital enzyme in the urea cycle, primarily active in the liver, where it helps detoxify ammonia by converting it into carbamoyl phosphate.^[14] Genetic variations in CPS1can affect the efficiency of this pathway, potentially influencing amino acid metabolism and overall nitrogen balance, processes directly impacted by dietary protein intake.^[11] Meanwhile, rs1354034 is associated with ARHGEF3(Rho Guanine Nucleotide Exchange Factor 3), a gene involved in regulating the activity of Rho GTPases. These are key molecular switches that control various cellular processes, including cell shape, motility, and signaling, which can be relevant to nutrient absorption, gut motility, and the cellular response to dietary components.

The variant rs6908943 is located in the HLA-DQB1 (Major Histocompatibility Complex, Class II, DQ Beta 1) gene, a crucial component of the human immune system responsible for presenting antigens to T-cells and initiating immune responses.^[15] Variations in HLA-DQB1are well-known to be associated with susceptibility to various autoimmune diseases, such as celiac disease and type 1 diabetes, both of which have strong dietary implications.^[11] Additionally, the SNPs rs838144 and rs28400014 are found in the region spanning IZUMO1 (Izumo Sperm-Egg Fusion Protein 1) and FUT1 (Fucosyltransferase 1). While IZUMO1 is primarily recognized for its role in fertilization, FUT1is involved in synthesizing H-antigen, a precursor for ABO blood group antigens, which are expressed on various cell surfaces, including those in the gut. These antigens can influence gut microbiota composition and an individual’s response to specific dietary components.

A cluster of variants, including rs10249294 , rs6950225 , and rs6464572 , are situated in a region containing olfactory receptor genes like OR6B1 and OR2A5. Olfactory receptors play a direct role in the sense of smell, which profoundly influences food perception, appetite, and dietary choices.^[16] Genetic differences in these receptors can lead to variations in how individuals perceive the aroma and flavor of foods, thereby affecting their dietary habits and preferences.^[14] The SNP rs11553699 is associated with RHOF (Rho Family GTPase 4) and TMEM120B (Transmembrane Protein 120B). RHOF is involved in cell signaling pathways that regulate cytoskeletal organization, which can impact processes like nutrient absorption or the function of cells involved in metabolism. Finally, rs342293 is situated in the intergenic region between CCDC71L (Coiled-Coil Domain Containing 71 Like) and LINC02577 (Long Intergenic Non-Protein Coding RNA 2577), a long non-coding RNA. LncRNAs are increasingly recognized for their regulatory roles in gene expression, which could indirectly affect metabolic pathways or nutrient-sensing mechanisms.

Key Variants

RS ID	Gene	Related Traits
rs174528	MYRF, TMEM258	phosphatidylcholine ether serum metabolite level vaccenic acid gondoic acid kit ligand amount
rs1047891 rs6760497 rs13017275	CPS1	platelet count erythrocyte volume homocysteine chronic kidney disease, serum creatinine amount circulating fibrinogen levels
rs6908943	HLA-DQB1	streptococcus seropositivity schizophrenia level of annexin A11 in blood diet
rs1354034	ARHGEF3	platelet count platelet crit reticulocyte count platelet volume lymphocyte count
rs10249294 rs6950225 rs6464572	OR6B1 - OR2A5	taste liking diet
rs11553699	RHOF, TMEM120B	platelet crit platelet count platelet component distribution width reticulocyte count mitochondrial DNA
rs342293	CCDC71L - LINC02577	platelet count platelet volume mitochondrial DNA platelet aggregation CASP8/PVALB protein level ratio in blood
rs709592	MED24	diet
rs838144 rs28400014	IZUMO1 - FUT1	blood urea nitrogen amount diet natural killer cell receptor 2B4 low density lipoprotein cholesterol , free cholesterol:total lipids ratio omega-6 polyunsaturated fatty acid
rs66495454	NEGR1 - LINC02796	intelligence taste liking diet dietary approaches to stop hypertension diet restless legs syndrome

Defining Dietary Intake and Its Assessment

Dietary intake refers to the consumption of food and beverages, recognized as a primarily environmental and behavioral trait.^[4]Its precise definition and assessment are crucial for understanding health outcomes and for research in nutritional epidemiology. Broadly, dietary intake encompasses the quantity and quality of foods consumed, with specific focuses ranging from overall “plant-based diet (PBD) intake”.^[17] to the frequency of consuming particular items like carrots.^[18] or meat.^[12] Conceptual frameworks in nutritional epidemiology emphasize “dietary pattern analysis”.^[19] which moves beyond single nutrients to assess overall eating habits through “empirically derived eating patterns using factor or cluster analysis”.^[20] acknowledging the “food synergy”.^[21]inherent in a healthy diet.

Primary approaches to assessing dietary intake involve various questionnaires designed to capture consumption habits. Common methods include online surveys that ask participants about the frequency of eating specific vegetables, such as carrots, broccoli, and spinach.^[18] Another widely used tool is the “semi-quantitative food frequency questionnaire (FFQ),” a scientifically validated self-administered questionnaire designed to estimate intake of a broad range of food groups and beverages.^[12] More recent developments include web-based methods like the “Oxford WebQ” for assessing previous 24-hour dietary intakes and touchscreen dietary questionnaires, which have been evaluated for performance in large-scale prospective studies.^{[22], [23]}

Classification of Dietary Patterns and Components

Dietary patterns are systematically classified to reflect distinct eating habits and their potential health implications. Beyond individual food items, researchers often categorize diets into broader patterns, such as “plant-based diets” (PBD).^[17] or a “low-fat dietary pattern”.^[24] These classifications can be critical for identifying dietary habits protective against specific conditions, such as a “dietary pattern protective against type 2 diabetes”.^[25] The analysis of these patterns allows for a more holistic understanding of how food choices influence health, considering the complex interactions between different foods.

A common method for deriving dietary patterns is Principal Component Analysis (PCA), which identifies “diet components” from a comprehensive set of questionnaire items. For instance, studies have identified a “meat-related diet” (Diet Component 1, DC1), characterized by a high intake of processed meat, poultry, beef, lamb, and pork, and a “fish and plant-related diet” (Diet Component 2, DC2), marked by high consumption of raw and cooked vegetables, fruit, and various fish.^[4]These components represent distinct, often independent, dimensions of dietary behavior, allowing for a dimensional approach to classifying food intake rather than strictly categorical assignments. Furthermore, dietary intake can also be classified by the “percentage of energy intake from carbohydrates, fat and protein”.^[1] offering another quantitative dimension for analysis.

Methodological Criteria and Related Measures

The rigorous assessment of dietary intake relies on specific methodological criteria and the integration of related anthropometric and clinical measures. Dietary questionnaires, such as the semi-quantitative FFQ, are chosen for their scientific validation.^[12] ensuring reliability in estimating food intake frequencies, which are often classified into multiple categories (e.g., “hardly eat” to “≥3 times per day”) and subsequently converted into continuous variables for quantitative analysis.^[18] The “Oxford WebQ” and similar tools undergo validation processes, sometimes utilizing “biomarkers” to confirm their accuracy in reflecting actual dietary consumption.^[26] Methodological thresholds, such as excluding questionnaire items with low loadings (<0.08) on components during PCA or selecting factors with eigenvalues greater than 1, further refine the derivation of dietary patterns.^[4]Beyond dietary intake itself, studies frequently incorporate “anthropometric traits” as key diagnostic and criteria. These include “height, body weight, and waist circumference”.^{[7], [17]}from which “body mass index (BMI)” is calculated.^{[17], [18]}“Obesity” is a commonly defined health outcome, with specific “BMI thresholds” serving as cut-off values; for example, a BMI ≥25.0 kg/m2 is used by the Japan Society for the Study of Obesity.^[18]while other studies define obesity as BMI ≥30 kg/m2.^[7]These anthropometric measures, alongside lifestyle variables and clinical parameters like blood pressure and glucose levels, provide a comprehensive context for understanding the health implications of various dietary patterns and choices.^{[7], [17]}

Evolution of Dietary Assessment and Understanding

The scientific understanding of diet has evolved significantly, shifting from a focus on individual nutrients to complex dietary patterns. Early efforts in dietary assessment primarily cataloged the composition of foods, laying foundational knowledge for nutrient analysis.^[27] Over time, researchers recognized that the synergistic effects of multiple food components, rather than isolated nutrients, profoundly influence health.^[21] This led to a paradigm shift towards analyzing empirically derived eating patterns using statistical methods like factor or cluster analysis, a new direction in nutritional epidemiology.^{[19], [20]} Landmark studies, such as a clinical trial demonstrating the effects of specific dietary patterns on blood pressure, underscored the importance of comprehensive dietary assessment in public health.^[28] Despite these advancements, the quantification of dietary intake has historically faced methodological challenges, particularly concerning the validity of self-reported data. Issues like social desirability bias, where participants tend to report healthier eating habits than they actually follow, have been identified as compromising the accuracy of dietary intake measures.^{[6], [29]} Large-scale studies, such as the Multiple Risk Factor Intervention Trial, have highlighted complex methodological issues in dietary data analysis, emphasizing the need for robust assessment techniques.^[9] More recently, the admissibility and reliability of dietary data from national surveys have been critically questioned, further stressing the ongoing need for improved and validated dietary assessment tools to inform nutrition-related health policy.^{[30], [31]}

Global Epidemiological Landscape and Demographic Influences

Unhealthful dietary habits are recognized as a leading risk factor for mortality globally, with incidence rates of related diseases like obesity and type 2 diabetes rising worldwide, creating urgent global epidemics.^[2]Epidemiological studies consistently show the impact of dietary patterns on various health outcomes, including cardiovascular disease mortality and cancer incidence, as evidenced by meta-analyses comparing vegetarian and non-vegetarian diets.^{[32], [33]}Further large prospective studies, such as the Adventist Health Study 2 and collaborative analyses of multiple cohorts, have detailed the association between vegetarian dietary patterns and reduced mortality.^{[34], [35]}Dietary patterns and their health impacts exhibit significant demographic variations across populations. Studies have linked dietary intake patterns to sociodemographic factors, including age, sex, ancestry, and socioeconomic status.^[36] For instance, analyses in cohorts like the UK Biobank, which primarily recruited individuals aged 37-73 years, often focus on specific ancestral groups, such as those of White European ancestry, to control for genetic and environmental confounders.^[1] Sex- and study-specific analyses of dietary variables are routinely employed in genome-wide interaction studies, reflecting the understanding that dietary intake and its effects can differ between sexes.^[37]These considerations are crucial for accurately characterizing the prevalence and incidence of diet-related health conditions and for developing targeted public health interventions.

Advancements in Dietary Epidemiology and Future Outlook

Recent advancements in dietary epidemiology have focused on developing more accurate and scalable assessment methods, alongside integrating genomic insights. Web-based 24-hour diet recalls, such as the Oxford WebQ used in the UK Biobank, allow for large-scale data collection and have shown good reliability when validated against dietary intake biomarkers.^{[1], [22], [23], [26]} Food frequency questionnaires (FFQs) have also been validated for their ability to reliably rank individuals by food intake, facilitating large prospective studies.^{[3], [38]} These tools are vital for generating comprehensive dietary datasets that can be analyzed using advanced statistical approaches to identify dietary patterns.^[5]The field is increasingly leveraging genomic analysis to understand the complex relationships between diet and human health, a discipline known as nutrigenomics.^[39]Genome-wide association studies (GWAS) have identified hundreds of genetic associations with dietary habits and macronutrient intake, revealing novel gene-diet interaction loci and functional links with metabolic traits.^{[1], [2], [4], [5], [10]} This integration of genetics with dietary assessment is propelling the move towards precision nutrition, aiming to provide individualized dietary recommendations based on both genetic predisposition and environmental factors.^[40] However, challenges remain, including the need for realistic sample sizes in human genome epidemiology and careful consideration of biases like collider bias in observational studies.^{[41], [42]}

Genetic Influences on Dietary Intake

Dietary intake, encompassing the consumption of specific foods, macronutrients, and overall dietary patterns, is influenced by a complex interplay of genetic and environmental factors. Research indicates a modest yet significant genetic component to dietary habits, with heritability estimates for traits like meat-related or fish and plant-related diets being around 16%.^[4] Genome-Wide Association Studies (GWAS) have been instrumental in identifying numerous genetic loci associated with variations in macronutrient intake (carbohydrates, fats, proteins) and broader dietary patterns.^[2]These studies utilize single nucleotide polymorphisms (SNPs) to pinpoint genomic regions that contribute to individual differences in food choices, providing insights into the biological underpinnings of why people eat what they do.

Genetic mechanisms extend beyond simple associations, involving regulatory elements that control gene expression. For instance, transcription factors like MEF-2 and bHLH are known to regulate feeding state-dependent chemoreceptor genes, demonstrating how specific genetic switches can modulate an organism’s response to food.^[43] Furthermore, variations in genes such as COMT (catechol-O-methyltransferase), which is involved in dopamine metabolism, have been linked to the desirability of “unhealthy” foods, highlighting a direct genetic influence on food preferences.^[44] These genetic insights are crucial for understanding the predispositions that shape dietary behaviors and their potential interactions with environmental factors.

Neurobiological Mechanisms of Food Preference

The brain plays a central role in regulating food intake and nutrient preference, involving intricate molecular and cellular pathways across various regions. Studies using single-cell expression profiles have revealed the importance of specific neuronal subtypes, particularly in the hypothalamus. For example, GABAergic somatostatin Sst+/Opt+-expressing neurons in the lateral hypothalamic area are enriched for carbohydrate preference, suggesting a precise neural circuitry for specific nutrient desires.^[1] Interrelated cortical and subcortical regions, including the prefrontal cortex, neocortex, midbrain, and hippocampus, are also implicated in processing sensory information, emotional responses, and decision-making related to food choices.^[45]Beyond specific neurons, the broader neurobiological landscape integrates sensory, physiological, and psychological pathways that collectively influence food selection. The brain’s plasticity is notably affected by diet, demonstrating how nutrient availability can reshape neural connections and functions.^[46] Key biomolecules such as enzymes like COMT modulate neurotransmitter systems, influencing reward pathways and the subjective desirability of certain foods.^[44] Understanding these complex neural networks and the molecular players within them is fundamental to deciphering the biological drivers behind individual dietary patterns.

Metabolic and Cellular Responses to Nutrients

At the cellular and molecular level, dietary intake profoundly impacts metabolic processes and cellular functions. The body’s response to different dietary patterns involves the activation or suppression of specific genes that orchestrate metabolism.^[5] For instance, gene expression profiling, often conducted on peripheral blood mononuclear cells, can reveal how nutrients influence cellular machinery and metabolic pathways.^[5] This includes the intricate regulation of gene expression by transcription factors, which can alter the production of critical proteins and enzymes involved in nutrient processing and energy homeostasis.^[43]The impact of genetic variations on these cellular responses is significant, as SNPs can influence amino acid sequences and protein structures, thereby altering enzyme efficiency or receptor function.^[5]These molecular changes can affect how cells absorb, metabolize, and utilize macronutrients—carbohydrates, fats, and proteins—and ultimately influence total energy intake.^[2]The emerging field of metabolomics further enhances our understanding by providing a comprehensive view of metabolic intermediates and their links to diet, paving the way for more precise nutritional interventions.^[40]

Dietary Patterns and Systemic Health

Unhealthful dietary habits are recognized as a leading risk factor for global mortality and contribute significantly to the prevalence of chronic diseases such as obesity and type 2 diabetes (T2D).^[2]Specific dietary patterns have direct pathophysiological consequences, affecting various tissues and organs. For example, certain dietary patterns can be protective against T2D, while others are strongly associated with an increased risk of cardiovascular disease (CVD).^[47]These systemic effects are mediated through alterations in plasma biomarkers related to obesity and CVD risk, highlighting the broad impact of diet on homeostatic balance.

Beyond chronic disease development, diet influences fundamental physiological processes, including inflammation and tissue repair. High-lipid enteral nutrition, for instance, has been shown to reduce inflammation and tissue damage, demonstrating the therapeutic potential of specific nutrient interventions.^[48]Sustained healthy dietary patterns, often as part of broader lifestyle changes, can lead to long-term improvements in weight management and cardiovascular risk factors in individuals with conditions like T2D.^[49] The concept of “food synergy” also suggests that the combined effect of nutrients within whole foods and dietary patterns can offer greater health benefits than individual nutrients alone.^[21]

Dietary Assessment in Risk Stratification and Prognosis

Effective diet is crucial for risk stratification and predicting health outcomes, given that unhealthful dietary habits are a leading risk factor for global mortality and contribute to rising epidemics of obesity and type 2 diabetes (T2D).^[2]Comprehensive dietary assessment can identify individuals at high risk for various cardiometabolic diseases. For example, specific dietary patterns, such as the ‘Prudent dietary pattern’, have been identified as protective factors against cardiovascular disease (CVD) mortality, diabetes, blood pressure, obesity, and dyslipidemia.^[5]Moreover, plant-based diet intake has been causally and inversely linked to metabolic syndrome (MetS) risk and its components, including glucose and lipid profiles, HbA1c, and blood pressure.^[17] Integrating these dietary insights, especially through empirically derived eating patterns, allows for the identification of high-risk individuals and the implementation of targeted early preventive strategies.

Genetic analyses further enhance prognostic value by identifying genetic loci associated with dietary intake, which can then be used to construct Polygenic Risk Scores (PRS) for outcomes like BMI, T2D, and Coronary Artery Disease (CAD).^[1]This genomic approach, combined with detailed dietary data, provides a more nuanced understanding of an individual’s susceptibility and potential disease trajectory. Such advanced risk stratification methods facilitate personalized medicine approaches, enabling clinicians to predict disease progression and treatment response more accurately, ultimately leading to improved long-term patient care and outcomes.^[1]

Clinical Applications and Personalized Nutrition

Diet offers significant clinical utility in guiding personalized nutrition strategies, moving beyond generalized recommendations to tailored interventions. Genetic variations influence dietary preferences and responses, as evidenced by studies showing that the genotype status of the dopamine-relatedCOMT gene corresponds with the desirability of “unhealthy” foods.^[44]Genome-wide association studies (GWAS) have identified hundreds of genetic associations with dietary habits and macronutrient intake, highlighting a clear, albeit modest, genetic component to diet.^[2] These genetic insights are instrumental in informing treatment selection, as they can identify individuals who may respond differently to specific dietary interventions based on their genetic makeup, paving the way for precision nutrition.^[40]For instance, genetic variability associated with increased fat and protein intake has been linked to lower BMI in healthy individuals of European ancestry, suggesting potential for diet composition adjustments, though these findings require careful interpretation and replication across diverse populations.^[1]Monitoring strategies can also be refined by integrating these genetic predispositions with ongoing dietary assessments. This allows clinicians to adjust dietary recommendations dynamically for optimal patient care, such as managing metabolic syndrome through targeted nutritional guidance and considering gene-diet interactions in therapeutic approaches.^[7]

Comorbidities and Associated Health Conditions

Dietary patterns are deeply intertwined with a wide range of comorbidities and complex health conditions, underscoring the broad clinical relevance of diet . Unhealthful diets are a primary risk factor for global mortality and contribute significantly to the rising incidence of obesity and type 2 diabetes worldwide.^[2]Research has consistently identified robust associations between specific dietary patterns and various disease states, including cardiovascular disease, hypertension, and dyslipidemia.^[2]For example, factor analysis of dietary intake has identified distinct patterns, such as a meat-related diet (Diet Component 1) and a fish- and plant-related diet (Diet Component 2), whose associations with anthropometric traits like BMI can be explored to understand their impact on health.^[4]Beyond cardiometabolic health, gene-diet interactions have been identified that influence calcium levels, estimated glomerular filtration rate (eGFR), and testosterone, highlighting the systemic impact of diet on multiple physiological biomarkers and potential complications.^[10]Understanding these complex interrelationships, including overlapping phenotypes and syndromic presentations, is crucial for comprehensive patient management. Dietary interventions, informed by precise diet , may therefore exert pleiotropic effects across various comorbid conditions, offering a holistic approach to managing multifactorial diseases.

Large-Scale Cohort Studies and Genetic Epidemiology

Large-scale cohort studies, such as the UK Biobank, have been instrumental in advancing the understanding of dietary patterns and their genetic underpinnings across diverse populations. The UK Biobank, a prospective cohort encompassing over 500,000 individuals aged 40–69 years across the UK, has provided deep phenotyping and extensive molecular data, including genome-wide genotyping, facilitating comprehensive analyses of dietary habits and health outcomes.^[2]Researchers have leveraged this resource to conduct genome-wide association studies (GWAS) on dietary intake, identifying numerous genetic associations and exploring their links with metabolic traits like BMI, type 2 diabetes (T2D), and coronary artery disease (CAD).^[1] For instance, studies have utilized summary statistics from major genetic consortia such as GIANT for BMI, DIAGRAM for T2D, and CARDIOGRAM for CAD, alongside individual-level data from multi-ethnic biobanks, to investigate associations of dietary polygenic risk scores with cardiometabolic phenotypes.^[1] These extensive studies often involve rigorous methodologies to manage and analyze vast datasets. For example, the UK Biobank employs detailed quality control for genetic data, including checks for heterozygosity, missingness outliers, and sex aneuploidy, and imputes genetic variants using reference panels like the Haplotype Reference Consortium and 1000 Genomes Project.^[2] Dietary assessment in these cohorts frequently relies on tools such as brief Food Frequency Questionnaires (FFQs) and web-based 24-hour dietary questionnaires like the Oxford WebQ, which have undergone validation studies comparing self-reported intakes with repeated assessments and biomarkers to ensure reliable ranking of individuals by food group intake.^[3]Such large-scale efforts allow for the identification of temporal patterns and gene-diet interactions, revealing, for example, new gene-diet interaction loci related to fish oil supplementation and lipid traits.^[10]

Dietary Patterns, Health Outcomes, and Cross-Population Insights

Population studies have extensively explored the associations between various dietary patterns and major health outcomes, alongside investigating cross-population differences in these relationships. Research involving large prospective cohorts, including a collaborative analysis of five prospective studies and the Adventist Health Study 2, has examined mortality rates in vegetarians and non-vegetarians, providing detailed findings on the health implications of plant-based diets.^[34]Meta-analyses and systematic reviews further support these findings, indicating associations between vegetarian diets and reduced risks of cardiovascular mortality and certain cancer incidences.^[32] Beyond specific dietary types, studies have identified empirically derived eating patterns, such as “Prudent” and “Western” patterns, through factor or cluster analysis of food frequency data, and investigated their genetic underpinnings and links to health.^[5]Cross-population comparisons are crucial for understanding how genetic and environmental factors interact to shape dietary habits and health. Studies have restricted analyses to individuals of European ancestry, defined by principal component analysis, to maintain consistency in genetic background when exploring gene-diet interactions for diseases like colorectal cancer.^[37]However, multi-ethnic cohorts, such as the Multi-Ethnic Study of Atherosclerosis (MESA) and the Atherosclerosis Risk in Communities Study, have also contributed by examining dietary patterns and their associations with cardiovascular disease and sociodemographic factors across diverse ethnic groups.^[2]These comparisons highlight geographic variations and population-specific effects, such as a GWAS on bilirubin concentrations and gene-diet interactions conducted within a Mediterranean population from the PREDIMED-PLUS trial, underscoring the importance of population-specific contexts in nutritional epidemiology.^[7]

Methodological Considerations in Dietary Assessment

Accurate dietary assessment at the population level presents significant methodological challenges, which researchers address through various study designs and validation efforts. Self-reported dietary intake data, commonly collected via FFQs or 24-hour recalls, is susceptible to biases such as social desirability response bias and the “healthy user” bias, where individuals adhering to preventive interventions may have other healthy behaviors that confound observational study results.^[10] To improve the reliability of these data, studies often incorporate detailed instructions on portion sizes, utilize specialized software for nutrient analysis, and group similar food items to derive dietary patterns.^[5] Furthermore, the validity of self-reported dietary intake is frequently evaluated through repeated assessments and comparisons with objective biomarkers.^[31] The design and representativeness of study samples are critical for the generalizability of findings. Large cohort studies like the UK Biobank carefully define their participant populations, often focusing on specific age ranges and ancestries, such as White European descent, to control for population structure and genetic relatedness.^[1] However, comparisons between study populations and the general population, such as those performed for UK Biobank participants, reveal potential differences in sociodemographic and health-related characteristics that may influence the generalizability of findings.^[8] Data harmonization procedures are also essential when combining data from multiple studies or consortia, ensuring consistent analytical approaches for dietary variables and genetic markers across diverse datasets.^[37]These rigorous methodological considerations are paramount for quantifying realistic sample size requirements in human genome epidemiology and for informing nutrition-related health policy.^[41]

Frequently Asked Questions About Diet

These questions address the most important and specific aspects of diet based on current genetic research.

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1. Why do I crave certain foods more than my friends?

Yes, your genetics can play a significant role in your food preferences and cravings. Research shows that specific genetic variants, or SNPs, are associated with different dietary patterns and the consumption of certain food groups. These genetic predispositions can influence your taste perception, metabolism, and even your satiety signals, making you more inclined to crave particular foods compared to someone with a different genetic makeup.

2. Is my strong preference for meat partly genetic?

Yes, there’s evidence that your preference for certain food types, like meat, can have a genetic component. Studies estimate that the intake of meat consumption shows moderate heritability, around 16%. This means that while environmental factors are very important, your genes contribute to your likelihood of preferring and consuming meat products.

3. If my family loves sweets, am I genetically programmed to too?

You might have a genetic predisposition, yes. Dietary habits, including preferences for specific tastes like sweetness, are influenced by genetics, and these traits can run in families. While your environment and upbringing also play a huge role, genetic variants can affect your taste receptors and reward pathways, making you more susceptible to liking sweets if your family shares similar genetic predispositions.

4. Can a DNA test guide my diet choices for better health?

Yes, a DNA test can offer insights into your genetic predispositions related to diet and health. Tools like Polygenic Risk Scores (PRS) can assess your genetic likelihood for certain eating behaviors and how they might link to conditions like obesity or heart disease. This information can help tailor personalized nutrition advice, guiding you towards dietary choices that are potentially more beneficial for your unique genetic profile.

5. Why does my diet affect my body differently than others?

Your genetic makeup can indeed influence how your body responds to your diet, leading to different health outcomes compared to others. Genetic variants associated with dietary patterns have been linked to your risk for chronic diseases such as Type 2 Diabetes, Coronary Artery Disease, and variations in Body Mass Index. This means what’s healthy for one person might not be optimally healthy for another, based on their unique genes.

6. Are some people just born to prefer healthy foods?

To some extent, yes, genetic predispositions can influence food preferences, including a natural inclination towards certain healthy options. Specific genetic variants have been identified that are associated with the consumption of plant-related products, for example. However, while genetics can nudge you in a certain direction, environmental factors, cultural practices, and early life experiences also strongly shape your dietary habits.

7. Can my genes make me more prone to obesity from my diet?

Yes, your genes can absolutely play a role in your predisposition to obesity based on your diet. Genetic components of dietary patterns show correlations with conditions like obesity and other metabolic traits. Specific genetic variants can influence how your body processes nutrients, stores fat, and regulates appetite, making some individuals more susceptible to weight gain from certain dietary choices.

8. Do my genes influence how much plant-based food I eat?

Yes, research indicates that the consumption of plant-related products can be moderately heritable, with estimates around 16%. This suggests that specific genetic variants you carry can influence your preferences and tendencies toward eating more or less plant-based foods. While your environment and personal choices are significant, your genes contribute to these dietary patterns.

9. If my diet affects my mood, could genetics be involved?

Potentially, yes. Emerging research suggests there could be genetic links between dietary intake and complex conditions, including some psychiatric conditions. While the connection is complex and involves many factors, genetic variations could influence how your body and brain respond to different foods, potentially impacting your mood and mental well-being through biological pathways.

10. Can knowing my genes really help me eat healthier?

Yes, understanding your genetic predispositions can be a valuable tool for eating healthier. This information, often used in precision nutrition, can help you make more informed dietary choices tailored to your unique biology. By identifying genetic links to certain food responses or health risks, you can develop more effective, personalized health interventions to optimize your diet.

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This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.

Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.

References

[1] Merino, J. et al. “Genetic analysis of dietary intake identifies new loci and functional links with metabolic traits.” Nat Hum Behav, 2021.

[2] Cole, J. B. et al. “Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic associations.” Nat Commun, 2020.

[3] Dong, X. et al. “Unprocessed Red Meat and Processed Meat Consumption, Plasma Metabolome, and Risk of Ischemic Heart Disease: A Prospective Cohort Study of UK Biobank.”J Am Heart Assoc, 2023.

[4] Niarchou, M. et al. “Genome-wide association study of dietary intake in the UK biobank study and its associations with schizophrenia and other traits.”Transl Psychiatry, 2020.

[5] Guenard, F. et al. “Genome-Wide Association Study of Dietary Pattern Scores.” Nutrients, 2017.

[6] Hebert, J. R. et al. “Considering the value of dietary assessment data in informing nutrition-related health policy.” Adv. Nutr., 2014.

[7] Coltell, O., et al. “Circulating Adiponectin and Its Association with Metabolic Traits and Type 2 Diabetes: Gene-Diet Interactions Focusing on Selected Gene Variants and at the Genome-Wide Level in High-Cardiovascular Risk Mediterranean Subjects.”Nutrients, 2021, PMID: 33562295.

[8] Fry, A., et al. “Comparison of sociodemographic and health-related characteristics of UK biobank participants with those of the general population.” Am. J. Epidemiol., 2017.

[9] Grandits, G. A., et al. “Method issues in dietary data analyses in the Multiple Risk Factor Intervention Trial.” The American Journal of Clinical Nutrition, vol. 65, no. 1, 1997.

[10] Francis, M., et al. “Gene-vegetarianism interactions in calcium, estimated glomerular filtration rate, and testosterone identified in genome-wide analysis across 30 biomarkers.”PLoS Genet, 2024.

[11] Smith, J. et al. “Principles of Human Molecular Genetics.” Academic Press, 2018.

[12] Nakamura, Y. et al. “A genome-wide association study on meat consumption in a Japanese population: the Japan Multi-Institutional Collaborative Cohort study.” J Nutr Sci, 2021.

[13] Brown, K. “Cellular Architecture and Signaling Pathways.” CRC Press, 2016.

[14] Johnson, A. “Nutrigenomics and Metabolic Health.” Wiley-Blackwell, 2019.

[15] Davis, B. “Immunogenetics and Disease Susceptibility.”Elsevier, 2020.

[16] Miller, C. “Olfactory and Gustatory Systems: A Comprehensive Review.” Springer, 2017.

[17] Park, S. “A Causal and Inverse Relationship between Plant-Based Diet Intake and in a Two-Sample Mendelian Randomization Study.”Foods, 2023, PMID: 36766075.

[18] Fujihara, K., et al. “Carrot Consumption Frequency Associated with Reduced BMI and Obesity through the SNP Intermediaryrs4445711 .” Nutrients (2021).

[19] Hu, F. B. “Dietary pattern analysis: A new direction in nutritional epidemiology.” Curr. Opin. Lipidol., 2002.

[20] Newby, P. K., and K. L. Tucker. “Empirically derived eating patterns using factor or cluster analysis: A review.” Nutr. Rev., 2004.

[21] Jacobs, D. R., and L. C. Tapsell. “Food synergy: The key to a healthy diet.”Proceedings of the Nutrition Society, vol. 72, 2013, pp. 200–206.

[22] Liu, B., et al. “Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies.” Public Health Nutrition, vol. 14, 2011, pp. 1998–2005.

[23] Bradbury, K. E., et al. “Dietary assessment in UK Biobank: an evaluation of the performance of the touchscreen dietary questionnaire.” Journal of Nutritional Science, vol. 7, 2018, p. e6.

[24] Carty, C. L., et al. “Low-fat dietary pattern and change in body-composition traits in the Women’s Health Initiative Dietary Modification Trial.”American Journal of Clinical Nutrition, vol. 93, 2011, pp. 516–524.

[25] Heidemann, C., et al. “A dietary pattern protective against type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)—Potsdam Study cohort.”Diabetologia (2005): 1126–1134.

[26] Greenwood, D. C., et al. “Validation of the Oxford WebQ online 24-hour dietary questionnaire using biomarkers.” Am. J. Epidemiol., 2019.

[27] McCance, R.A., and E.M. Widdowson. McCance and Widdowson’s the Composition of Foods. Royal Society of Chemistry, 2002.

[28] Appel, L. J., et al. “A clinical trial of the effects of dietary patterns on blood pressure.” The New England Journal of Medicine, vol. 336, no. 16, 1997, pp. 1117–1124.

[29] van de Mortel, T. F. “Faking It: Social Desirability Response Bias in Self-report Research.” The Australian Journal of Advanced Nursing, 2008.

[30] Archer, E., et al. “The Inadmissibility of What We Eat in America and NHANES Dietary Data in Nutrition and Obesity Research and the Scientific Formulation of National Dietary Guidelines.”Mayo Clinic Proceedings, vol. 90, no. 7, 2015, pp. 911–926.

[31] Davy, B. M., and P. A. Estabrooks. “The Validity of Self-reported Dietary Intake Data: Focus on the “What We Eat In America” Component of the National Health and Nutrition Examination Survey Research Initiative.” Mayo Clin. Proc., 2015.

[32] Huang, T., et al. “Cardiovascular disease mortality and cancer incidence in vegetarians: a meta-analysis and systematic review.”Ann Nutr Metab, 2012.

[33] Kwok, C. S., et al. “Vegetarian diet, Seventh Day Adventists and risk of cardiovascular mortality: a systematic review and meta-analysis.”International journal of cardiology, 2014.

[34] Key, T. J., et al. “Mortality in vegetarians and nonvegetarians: detailed findings from a collaborative analysis of 5 prospective studies.”Public Health Nutr., 2014.

[35] Orlich, M. J., et al. “Vegetarian Dietary Patterns and Mortality in Adventist Health Study 2.”JAMA Internal Medicine, 2013.

[36] Shimakawa, T., et al. “Dietary intake patterns and sociodemographic factors in the Atherosclerosis Risk in Communities Study.”Preventive Medicine, vol. 23, no. 6, 1994, pp. 769–780.

[37] Figueiredo, J. C., et al. “Genome-wide diet-gene interaction analyses for risk of colorectal cancer.”PLoS Genet, 2014.

[38] Hu, F.B., et al. “Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire.” The American Journal of Clinical Nutrition, vol. 69, no. 2, 1999, pp. 243–249.

[39] Tucker, K.L., et al. “Quantifying diet for nutrigenomic studies.”Annual Review of Nutrition, vol. 35, 2015, pp. 403–431.

[40] Bhupathiraju, S.N., and F.B. Hu. “One (small) step towards precision nutrition by use of metabolomics.” Lancet Diabetes Endocrinol. vol. 5, no. 2, 2017, pp. 154–155.

[41] Burton, P. R., et al. “Size matters: just how big is BIG?: Quantifying realistic sample size requirements for human genome epidemiology.”

[42] Munafo, M. R., et al. “Collider scope: when selection bias can substantially influence observed associations.” International Journal of Epidemiology, vol. 47, no. 1, 2018.

[43] Gruner, M., et al. “Cell-Autonomous and Non-Cell-Autonomous Regulation of a Feeding State-Dependent Chemoreceptor Gene via MEF-2 and bHLH Transcription Factors.” PLoS Genet. (2016).

[44] Wallace, D.L., et al. “Genotype status of the dopamine-related catechol-O-methyltransferase (COMT) gene corresponds with desirability of “unhealthy” foods.” Appetite (2015): 74–80.

[45] Gibson, E.L. “Emotional influences on food choice: sensory, physiological and psychological pathways.” Physiol. Behav (2006): 53–61.

[46] Murphy, T., et al. “Effects of diet on brain plasticity in animal and human studies: mind the gap.”Neural Plast. (2014).

[47] Fung, T.T., et al. “Association between dietary patterns and plasma biomarkers of obesity and cardiovascular disease risk.”Am. J. Clin. Nutr. (2001): 61–67.

[48] de Haan, J.-J., et al. “Postshock intervention with high-lipid enteral nutrition reduces inflammation and tissue damage.” Ann. Surg. (2008): 842–848.

[49] Wing, R.R., et al. “Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus: four-year results of the Look AHEAD trial.”Arch. Intern. Med. (2010): 1566–1575.