This document discusses the field of nutrigenomics, which examines the relationship between genes, nutrition, and health. It notes that nutrigenomics can provide personalized nutrition advice by understanding how genetic variants affect an individual's response to different foods. The document also discusses how epigenetics and early life nutrition can influence gene expression and lifelong health, and how nutrigenomics may help address inconsistent responses to general dietary recommendations and help develop more individualized diets.

1. ‫תזונתיות‬ ‫וגנטיקה‬ ‫גנומיקה‬ ‫של‬ ‫הרצף‬:
‫התקדמות‬,‫רלוונטיות‬,‫ויישום‬
‫שרון‬ ‫אסנת‬,‫שני‬ ‫תואר‬,R.D.
Continuum of Nutritional Genomics and
Genetics: Progress, Relevance, Application
Ossie (Osnat) Sharon, M.Sc., R.D.
‫תקציר‬
‫של‬ ‫העתיד‬ ‫נחשבת‬ ‫היא‬ ‫אישית‬ ‫מותאמת‬ ‫תזונה‬
‫התזונה‬ ‫תורת‬,‫ידי‬ ‫על‬ ‫שנוצר‬ ‫צורך‬ ‫על‬ ‫שמענה‬
‫בין‬ ‫וסתירות‬ ‫להתערבויות‬ ‫עקביות‬ ‫לא‬ ‫תגובות‬
‫המלצות‬.‫אישית‬ ‫מותאמת‬ ‫לרפואה‬ ‫במקביל‬,
‫ונוטריגנומיקה‬ ‫נוטריגנטיקה‬ ‫כולל‬ ‫זה‬ ‫תחום‬–‫איך‬
‫האורגניזם‬ ‫ועל‬ ‫לזו‬ ‫זו‬ ‫על‬ ‫משפיעות‬ ‫ותזונת‬ ‫גנטיקה‬
‫כולו‬–‫ואפי‬‫ואפיגנומיקה‬ ‫גנטיקה‬–‫על‬ ‫ההשפעה‬
‫הבאים‬ ‫הדורות‬.‫גנים‬ ‫זוהו‬ ‫מחקרים‬,‫פולימורפיזם‬,
‫וסביבתיים‬ ‫הללו‬ ‫הגורמים‬ ‫בין‬ ‫גומלין‬ ‫ויחסי‬
‫חומרים‬ ‫חילוף‬ ‫את‬ ‫המניעים‬,‫שמושפעת‬ ‫למידה‬
‫פנוטיפית‬ ‫גמישות‬ ‫ידי‬ ‫על‬.‫הזוהו‬ ‫כה‬ ‫שעד‬ ‫אלה‬
‫ופעילות‬ ‫לתזונה‬ ‫הקשר‬ ‫מבחינת‬ ‫אמינים‬ ‫כהכי‬
‫לאסטרטג‬ ‫רתומים‬ ‫גופנית‬‫התערבותיות‬ ‫יות‬,‫ועוד‬
‫להתברר‬ ‫ממשיכים‬ ‫אחרים‬.
‫מונע‬ ‫יוקר‬ ‫רקע‬ ‫על‬,‫בדיקות‬
‫נוטריגנטקיה‬/‫נתן‬ ‫ויותר‬ ‫יותר‬ ‫הן‬ ‫נוטריגנומיקה‬
‫לצרכן‬ ‫להשיג‬,‫העולם‬ ‫ברחבי‬ ‫ופופולריות‬.‫אתגר‬
‫תוצאות‬ ‫קבלת‬ ‫לאחר‬ ‫הכוונה‬ ‫חוסר‬ ‫היה‬ ‫נוסף‬
‫הצרכן‬ ‫ידי‬ ‫על‬ ‫הבדיקות‬,‫בסיכונים‬ ‫שכרוכים‬.
‫לפנו‬ ‫מקודדים‬ ‫שגנים‬ ‫העובדה‬‫מרובים‬ ‫טיפים‬–
‫הרחב‬ ‫לקהל‬ ‫ידועים‬ ‫שפחות‬ ‫אלה‬ ‫כולל‬–‫מציג‬
‫רב‬ ‫קליני‬ ‫פרופיל‬-‫דורש‬ ‫קרובות‬ ‫שלעתים‬ ‫גורמי‬
‫לכאורה‬ ‫סותרות‬ ‫ואפילו‬ ‫מורכבות‬ ‫התערבויות‬,‫ולכן‬
‫לספק‬ ‫יכול‬ ‫מקצוען‬ ‫שרק‬ ‫מאוזנת‬ ‫אסטרטגיה‬.‫זה‬
‫האמריקאי‬ ‫הבריאות‬ ‫משרד‬ ‫את‬ ‫הניע‬,‫גם‬ ‫כמו‬
‫אמריקאיות‬ ‫שאינן‬ ‫חברות‬ ‫כמה‬,‫בדיק‬ ‫לדרוש‬‫ה‬
‫בלבד‬ ‫מומחים‬ ‫ידי‬ ‫על‬ ‫מכוונים‬ ‫וייעוץ‬–‫של‬ ‫במקרה‬
‫לתזונה‬ ‫הקשורות‬ ‫אינדיקציות‬,‫דיאטנים‬ ‫ידי‬ ‫על‬/‫ות‬.
‫מכך‬ ‫כתוצאה‬,‫דיאטנים‬/‫בחוד‬ ‫שומו‬ ‫נמצאים‬ ‫ות‬
‫ידי‬ ‫על‬ ‫אישית‬ ‫מותאמת‬ ‫תזונה‬ ‫של‬ ‫החנית‬
‫נלווים‬ ‫מקצועות‬,‫גם‬ ‫עבור‬ ‫חשובות‬ ‫השלכות‬ ‫עם‬
‫הצרכן‬ ‫בריאות‬ ‫וגם‬ ‫מקצועי‬ ‫קידום‬.‫להמשך‬ ‫בנוסף‬
‫ב‬ ‫של‬‫בישראל‬ ‫נוטריגנומיקה‬ ‫מחקרי‬ ‫יצוע‬,‫בדיקות‬
‫לדיאטנים‬ ‫המיועדות‬ ‫אישיות‬/‫לכאן‬ ‫הגיעו‬ ‫ות‬
‫לאחרונה‬,‫להזדמנויות‬ ‫דלתות‬ ‫פתיחת‬ ‫לטובת‬
‫דומות‬.
Abstract
Personalized nutrition is considered to be the
future of dietetics, answering a need created
by inconsistent responses to interventions and
resultant contradictions within/between
recommendations. Parallel to personalized
medicine, it comprises nutrigenetics and
nutrigenomics – how genetics and nutrition
impact one another and the organism as a
whole – and epigenetics and epigenomics –
how this impact affects future generations.
Research has identified genes,
polymorphisms, and interactions between
these and environmental factors that drive
metabolism, to an extent influenced by
phenotypic plasticity. Those most reliably
correlating to nutrition and physical activity
are currently being harnessed for
interventional strategies, while others
continue to be clarified.
Once prohibitively expensive, consumer
nutrigenetic/nutrigenomic testing is becoming
increasingly affordable and popular globally.
An additional challenge has been inadequate
direction once results are received by the
consumer, with associated risks. The fact that
genes code for multiple phenotypes –
including those less publicized – presents a
multifactorial clinical profile that often
requires composite and even seemingly
contradictory interventions, and therefore a
balanced strategy that only a professional can
provide. This has prompted the United States
Food and Drug Administration, as well as
some non-American companies, to require
expert-directed testing and counseling – in the
case of nutrition-related indications, by
dietitians.
As a result, dietitians are being put on the
forefront of personalized nutrition by
ancillary professions, with important
implications for both professional
advancement and consumer health. In
addition to Israel continuing to host
nutrigenomics research, personal testing
technologies have recently become available
to practitioners here, opening the doors to
similar opportunities.

2. Summary
Nutrigenomics is a blanket term for a new scientific discipline that uses modern genomics
technology to study the relationship between genes, nutrition and health. It has long been
apparent that some people respond differently from others to certain foods. Nutrigenomics
allows us to understand how our genes affect the way we respond to the foods, beverages and
supplements we consume [1-3]. It is the position of the Academy of Nutrition and Dietetics that
“nutritional genomics provides insight into how diet and genotype interactions affect
phenotype.”
Key Definitions
Nutritional genomics The relationship between the human genome, nutrition, and health
Nutrigenetics How the genotype drives the phenotypic response to dietary intake
• Monogenic Result of alteration to one particular gene
• Polygenic: Result of alterations to more than one gene
Nutrigenomics How dietary intake drives genotypic activity
Phenotypic Plasticity How the phenotype adapts to environmental changes, including diet
SNP Single/simple nucleotide polymorphism: common variation of a
single nucleotide (A, T, C, G) in a common DNA sequence
Metabolomics The study of chemical processes involving metabolites
Nutritional epigenetics How phenotypic response to diet drives the genotype over generations
Nutritional epigenomics How nutritional epigenetic modifications affect the entire genome
DoHAD Developmental origins of health and disease
• Impact of experiences during initial phases of somatic
development on lifelong health, including chronic disease risk
• Models proposed: thrifty phenotype, programming, predictive
adaptive response theories, concept of match or mismatch
• Possible mechanisms: environmental effects on gene
expression, hormonal signals transmitted to fetus/infant via
placenta/lactation
Key Tenets
• Common dietary chemicals act on the human genome, either directly or indirectly, to
alter gene expression or structure.
• Under certain circumstances and in some individuals, diet can be a serious risk factor for
a number of diseases.
• Some diet-regulated genes (and their common variants, i.e. “SNP”s) are likely to play a
role in the onset, incidence, progression, and/or severity of chronic diseases of
individuals and their descendants.
• The degree to which diet influences the balance between healthy and disease states may
depend on an individual’s genetic makeup.
• Dietary intervention based on knowledge of nutritional requirement, nutritional status,
and genotype (i.e., “individualized nutrition”) can be used to prevent, mitigate or cure
chronic disease.

3. Introduction
Since completion of the human genome project, understanding of complex interactions between
environmental factors such as diet and genes has progressed considerably. The knowledge that
metabolic pathways may be altered in individuals with genetic variants in the presence of certain
dietary exposures offers great potential for personalized nutrition advice, and epigenetics and
nutrigenetics have been used to assess the need and status of specific nutrients. This has
dramatically increased the potential to individualize diets using dietary, phenotypic and
genotypic data, with improved outcomes [1-6].
Recent genetic interest and research focus surrounds the direct connections between metabolism
and developmental dynamics, which now represents an important conceptual challenge to
explain many aspects of metabolic dysfunction. Several components of the epigenetic machinery
require intermediates of cellular metabolism for enzymatic function. For example, specific
epigenetic influences of dietary glucose and lipid consumption, as well as undernutrition, are
observed across numerous organs and pathways associated with metabolism. Studies have started
to define the chromatin-dependent mechanisms underlying persistent and pathophysiological
changes induced by altered metabolism. Importantly, numerous recent studies demonstrate that
gene regulation underlying phenotypic determinants of adult metabolic health is influenced by
maternal and early postnatal diet. These emerging concepts open new perspectives to combat the
rising global epidemic of metabolic disorders [7-9].
Despite widespread promotion of population-based healthy eating guidelines over the half-
century, preventable diseases remain the leading cause of mortality in the developed world.
Moreover, the greatest numbers of disease events occur in individuals with a lower conventional
risk of disease [10-14].
Inconsistent research outcomes and real-world responses limit utility of recommendations for
health attainment and maintenance, particularly in the areas of weight and chronic disease such
as type 2 diabetes mellitus, cardiovascular disease, and cancer [15, 16]. Examples include the
following:
• Saturated fat risks
• Omega-3 fatty acid needs
• Low-fat vs. low-carbohydrate diet for weight loss
• Increased fiber needs
• Intolerances, e.g. to lactose, gluten
• Sodium restriction risk vs. advantage
• Increased need for specific micronutrients
• Animal vs. plant-based
• Relative risk from high-risk cooking methods, such as charring and frying
• Harmful vs. beneficial effects of diet-related habits, e.g. caffeine, alcohol
• Physical activity needs and injury risk
• Efforts required for healthy weight loss and maintenance
• Impact of the above on perinatal outcomes, including long-term in offspring
Personalized nutrition interventions may have greater potential for reducing the global burden of
preventable diseases and for promoting better health across the lifespan than the conventional
“one size fits all” approach [4-6, 10]. Further, such measures during the prenatal period –
including prior to conception – have implications not only for improved maternal outcomes and
future health, but also for the long-term health of the offspring and future generations [17-22].
Genetic testing is increasingly being embraced for the purpose of personalized nutrition
planning. Once available only to physicians through medical testing, provision of genetic
polymorphism (SNP) information is now widely available direct-to-consumer.
Method
Background
Over the past decade, several companies have formed partnerships with laboratories to provide
an increasing variety of genetic and immunologic tests that indicate predispositions for
physiologic imbalances that, left unchecked, could ultimately lead to serious disorders [23].
Most of these tests are provided by having the consumer send a saliva sample to a central
laboratory, which then performs the analysis for a predetermined group of genes,

4. polymorphisms, and alleles. The results are then provided either to the consumer or to a
healthcare practitioner.
Practitioners benefit because they have a new set of tools with potential to improve their ability
to prevent disease and help restore health, and patients benefit from new opportunities to avert
disorders before they cause significant disruptions, doing away with the hit-or-miss, trial-and-
error that characterizes the pursuit of the ideal health-oriented lifestyle [20].
However, challenges and questions remain regarding use and acceptance of nutrigenomic testing,
including the following:
• Accuracy of tests provided
Research into the many polymorphisms involved in nutrigenomics has yielded many
promising leads, but only some have been consistently linked to health conditions in a
manner that can benefit from specific interventions.
• Professional acceptance
It is not clear to some health care providers how nutrigenetic testing and related
interventions will yield benefits over current standards of care. However, nutrigenetic
screening has been associated with greater improvements towards and maintenance of
health goals when compared to conventional intervention [24-26].
• Government acceptance
Government bodies are concerned that consumers must be protected from unrealistic
claims and misinterpretations of complex genomic information, and that direct-to-
consumer test results are often not adequately understood by patients and thus raise
concerns when used to self-manage [27].
• Fraudulent practices of service providers
Impartial evaluations have found that many internet-based direct-to-consumer genetic
tests were fraudulent in some way [28].
• Quality of associated care
Many companies are linked to nutritional supplement companies, encouraging pills over
food, where research suggests a food-based approach to be superior [29-31].
• Public acceptance
Many consumers remain unaware of the potentially controllable nature of genetic
influence on health. When informed, research has identified health and clear consumer
benefits as key motivators in the uptake of genetic testing, with individuals
reporting personal experience of disease/symptoms being more willing to
undergo genetic testing for the purpose of personalized nutrition [20], particularly when
information is delivered by a qualified healthcare professional such as a dietitian [32]. A
perceived susceptibility to disease has also been suggested to improve motivation to
change behavior, which is a key barrier in the success of any nutrition intervention [19].
• Compliance with recommendations
Where widespread lack of compliance with conventional nutrition recommendations has
cast doubt on the efficacy of methods, the nutrigenetic approach has resulted in increased
compliance with lifestyle interventions [33].
Sources of Testing
Some companies that offer nutrigenetic testing products are designing programs that bypass
principal concerns of multiple authorities.
• Testing only of SNPs that have consistently been linked to health conditions in a manner
that can benefit from specific interventions.
• Provision of nutrigenetic testing through qualified professionals, specifically dietitians,
packaged with nutritional counseling.
Genes, SNPs, and Implications
The following are examples of SNPs meeting the above criteria, pointing to a strong indication
for professional guidance by a dietitian/nutritionist:
• MTHFR 677 CT/TT

5. Methylenetetrahydrofolate reductase (MTHFR) catalyzes the metabolism of folate and
nucleotides needed for DNA synthesis and repair, and as such gene plays an important
role in the genomic integrity and genetic stability. The most common SNP, MTHFR
C677T is known primarily for increasing the need for dietary folate and most frequently
linked to homocysteinemia and its impact on cardiovascular risk [34].
MTHFR SNPs have also been found in research to be associated with cellular functions
related to various cancer linked to key nutritional factors beyond folate (including breast,
gastric, colon, thyroid) [35-40], ovarian [41, 42] and sperm integrity [43], gestational
outcomes beyond neural tube defects [44, 45], thyroid function [46-48], and neurological
disorders, including Parkinson’s [49] and autism [50, 51], and metabolic risks related to
premature menopause, bone health [52], and development of type 2 diabetes, as well as
increased need for riboflavin, pyridoxine, and other nutritional interventions related to the
above issues [53].
While over-the-counter folic acid supplements have been recommended as a solution,
there are concerns that standard options are not adequate to meet the needs of SNP
carriers; research has even suggested that common supplements may actually increase
risk of methylation-related disorders [54-56]. In carriers of the polymorphism, either a
special (more expensive) supplemental form of folate and/or food-based intervention is
preferred – particularly relevant when considering the additional advantages of foods
high in folate, which tend also to be naturally high in fiber. When frequently concurrent
conditions of hypothyroid and coagulation disorders occur, this demands balancing intake
of the above foods with medications, i.e. thyroxine and warfarin, as well as other B-
vitamins with which folate performs its activities, also warranting intervention by a
qualified dietitian/nutritionist.
• GSTT1 and GSMT1
The GSTT1 and GSMT1 genes produce proteins for the phase II metabolizing
glutathione S-transferase enzyme family, responsible detoxifying numerous potentially
cytotoxic/genotoxic compounds and attenuating inflammatory cytokines such as
interleukin-6 (IL6). GSTT1 is also known for its key role in the utilization of vitamin C,
and a dysfunctional version of the gene results in a reduced ability to process vitamin C
and need to ensure the minimum recommended intake (Dietary Reference Intake, DRI) to
maintain normal levels, where the functional version enables individuals to thrive with
less [18].
In clinical research, IL-6 was inversely associated with total isothiocyanate excretion in
GSTM1-null/GSTT1-null individuals. Inadequate upregulation of these protective
enzymes has been linked to cancer, suggesting a need not only to decrease intake of
offending compounds, but also to increase intake of foods rich in indole antioxidants,,
such as those in cruciferous vegetables [57].
When occurring in combination with the above-noted MTHFR polymorphism, there may
be increased risk of both acute and chronic myelogenous leukemias and autism spectrum
disorders (ASD) [51, 58-60]. Given the implications not only for individuals but also
their offspring, there is a clear advantage to the increased compliance with a preventive
diet that can be attained with personalized dietetic counseling.
• TCF7L2
Studies have consistently shown that common variants in the TCF7L2 gene significantly
predict type 2 diabetes risk [61-63], due to a deleterious effect related to β-cell function
[64-66] and downregulation of gastric inhibitory polypeptide (GIP) and glucagon-like
peptide-1 (GLP-1) receptors [65]. This has been demonstrated to be relevant to
gestational diabetes as well [67-70].
The TCF7L2 has been closely linked to the role of fiber in maintenance of normal
glucose metabolism [71-73], including prevention of progression to the prediabetic state
in high-risk individuals [74].
In addition, compelling evidence has suggested important roles of TCF7L2 in the
regulation of body weight and adiposity including 1) taking part in the Wnt signaling
cascade, which inhibits adipogenesis and stimulates leptin production in mature
adipocytes in response to nutritional status [75], 2) gene expression in adipose tissue
differing by genotype after caloric restriction, 3) the association with hunger-satiety
hormones that influence weight loss, and 4) the promotion of the transcription of

6. proglucagon, which induces the synthesis of glucagon-like peptide, which is a regulator
of insulin and glucagon secretion, appetite, and food intake [76-78].
Despite this evidence, reports on the association of TCF7L2 variants with body weight
and composition as well as diabetes risk have been contradictory. One possible
explanation for the discrepancies might be interactions between TCF7L2 genotypes and
environmental factors such as dietary intake [67, 73, 79]. Such interactions have been
reported in observational studies for dietary fat, carbohydrate intake, and animal protein,
and are largely dependent on specific SNPs. For example, a recent randomized trial
reported that obese individuals with the rs7903146 or rs12255372 risk allele have better
weight/adiposity loss responses in weight loss with a low- rather than high-fat dietary
intervention [79] and additional genes have been observed to interact with prenatal
nutrition in relation to T2D risk and glucose levels in later life [73].
Given that a possible predisposition to type 2 diabetes generally indicates a diet low in
carbohydrates and attainment and maintenance of a healthy weight, and that efforts to
reduce fat intake have historically resulted in high carbohydrate intake, guidance by a
dietitian is indicated to assist in constructing a balanced diet that can be sustained long-
term.
• NOS3
The NOS3 gene upregulates nitric oxide synthase and production of nitric oxide, which
plays an important role in the function of the vascular epithelium. New research has
shown that variations in the NOS3 gene interact with omega-3 fatty acids in different
ways to impact how the body processes triglycerides, and the degree to which elevated
triglycerides impair circulation. Previous studies have produced mixed results relating to
the effects of omega-3 fat on triglyceride levels between individuals. Some people
experience a significant reduction in triglyceride levels in response to increasing omega-3
fat intake, whereas others experience little benefit. The reasons for these differences have
been unclear until a recent breakthrough study* showed that the effect of omega-3 fat on
triglyceride levels depends on variations in a gene called NOS3. Those who have the GT
or TT variant of the gene are at greater risk of elevated triglyceride levels when
consuming a diet low in omega-3 fats, compared to those who have the GG variant.
Beyond this function, NOS3 SNPs such as rs1799983 have been and continue to be
investigated for influence on cancers, obesity, and perinatal disorders such as recurrent
miscarriage and pre-eclampsia. Pre-eclampsia is a well-known example of a potentially
preventable/manageable condition that in addition to omega-3 intake [80], nutrition-
related prevention and management of pre-eclampsia is multi-factorial [81, 82], and is an
accepted indication for dietitian involvement. Pre-eclampsia in turn increases
• APOA2
Saturated fats have been associated with health conditions such as diabetes,
cardiovascular disease, and obesity. However, the connection between saturated fats and
obesity, until recently, has been inconsistent. A number of studies have now shown that
the effect of saturated fat on obesity can be influenced by variations in APOA2 [83-87].
The APOA2 gene upregulates production of apolipoprotein A-II, which impacts dietary
fat utilization. Scientists now understand that there are different variations in the APOA2
gene present in the human population and that these different versions of the gene interact
with saturated fat in unique ways to influence energy balance and ultimately the risk of
obesity [83, 86]. Those people who have the CC variant of the gene are at a higher risk of
developing obesity when consuming a diet high in saturated fats than those possessing
the TT or TC variant of the gene [85].
Additionally, the CC polymorphism has been associated with elevated levels of visceral
adipose tissue and development of insulin resistance, independent of gender, age and
body mass index [87]. Related concerns point to the influence of APOA2 SNPs on
inflammation and blood lipids [88].
The above issues require guidance regarding a diet that balances low saturated fat with
low simple carbohydrate intake, while protecting against the oxidation risk [84].
• HLA DQ2/8
The human leukocyte antigen (HLA) gene is the most important genetic predictor of
gluten intolerance. Nearly all individuals with celiac disease [89-91] and more than half

7. of those with non-celiac gluten sensitivity (NCGS) [92, 93] have the DQ2 or DQ8 (“high
risk”) version of the HLA gene, compared to only 25% of the general population.
Celiac disease represents the most severe form of gluten intolerance and affects about 1%
of the population. Another 5-10% of the population have non-celiac gluten sensitivity
NCGS, which results in various gastrointestinal problems, and still others have wheat
sensitivities related to its carbohydrate fractions rather than gluten, which in turn may be
indicative of multiple sensitivities, e.g. to FODMAPs [94, 95], requiring guidance by a
dietitian [96-98].
Recent research has suggested that in predisposed individuals, celiac may represent the
final step in a continuum of severity of gluten intolerance following cumulative exposure
[99, 100]. Testing for the relevant HLA forms may enable preventive dietary measures
and/or redirect focus to other disorders with similar symptoms but differing interventions.
It can also rule-out a need to undergo an invasive biopsy procedure, currently standard of
care to rule out celiac in individuals experiencing characteristic symptom.
Additional genes with SNPs indicating a need for nutritional management and currently being
offered for direct-to-dietitian testing include APOE, ACE, CYP1A2, ADH1C, VDR, IL6, TNF,
SOD2, CAT, GPX1, LCT, and FOT, each with complexities requiring intervention by a qualified
professional.
The above polymorphisms can occur in combination, potentially magnifying risk not only of
characteristic disorders, but also to diseases specifically associated with concurrence of alleles,
again pointing to the need for qualified dietetic guidance for both prevention and management,
for the individual and future generations.
Conclusions
Personalized interventions that take into account genetic variations in response to nutritional
factors may have greater potential for disease prevention and management than a “one size fits
all” approach, and may be better accepted by patients.
The fact that genes code for multiple phenotypic effects presents a unique, multifactorial picture
for individuals that require the type of balancing between lifestyle factors that only a professional
can provide. This is highly likely to improve compliance and quality of results and in a much
shorter timeframe than are associated with conventional interventions.
As a result, dietitians are being put on the forefront of personalized nutrition by ancillary
professions, with important implications for both professional advancement and consumer
health. In addition to Israel continuing to host nutrigenomics research, personal testing
technologies have recently become available to practitioners here, opening the doors to similar
opportunities.

8. References
1. Isaak CK, Siow YL. The evolution of nutrition research. Can J Physiol Pharmacol 2013;
91 (4):257-267.
2. Mocchegiani E, Costarelli L, Giacconi R, Malavolta M, Basso A, Piacenza F, Ostan R,
Cevenini E, Gonos ES, Monti D. Micronutrient-gene interactions related to
inflammatory/immune response and antioxidant activity in ageing and inflammation. A
systematic review. Mech Ageing Dev 2014; 136-137:29-49.
3. Perez-Martinez P, Phillips CM, Delgado-Lista J, Garcia-Rios A, Lopez-Miranda J, Perez-
Jimenez F. Nutrigenetics, metabolic syndrome risk and personalized nutrition. Curr Vasc
Pharmacol 2013; 11 (6):946-953.
4. Bordoni A, Capozzi F. Foodomics for healthy nutrition. Curr Opin Clin Nutr Metab Care
2014; 17 (5):418-424.
5. Gibney MJ, McNulty BA, Ryan MF, Walsh MC. Nutritional phenotype databases and
integrated nutrition: from molecules to populations. Adv Nutr 2014; 5 (3):352S-357S.
6. Jones ML, Ganopolsky JG, Martoni CJ, Labbe A, Prakash S. Emerging science of the
human microbiome. Gut Microbes 2014; 5 (4):446-457.
7. Desai M, Jellyman JK, Ross MG. Epigenomics, gestational programming and risk of
metabolic syndrome. Int J Obes (Lond) 2015; 39 (4):633-641.
8. Keating ST, El-Osta A. Epigenetics and metabolism. Circ Res 2015; 116 (4):715-736.
9. Tarrade A, Panchenko P, Junien C, Gabory A. Placental contribution to nutritional
programming of health and diseases: epigenetics and sexual dimorphism. J Exp Biol
2015; 218 (Pt 1):50-58.
10. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the
21st century: elimination of the leading preventable causes of premature death and
disability in the USA. Lancet 2014; 384 (9937):45-52.
11. Dwyer JT, Melanson KJ, Ms US, Cross P, Wilson M. Dietary Treatment of Obesity.
2000.
12. Kwon BJ, Kim DW, Her SH, Kim DB, Jang SW, Cho EJ, Ihm SH, Kim HY, Youn HJ,
Seung KB, Kim JH, Rho TH. Metabolically obese status with normal weight is associated
with both the prevalence and severity of angiographic coronary artery disease.
Metabolism 2013; 62 (7):952-960.
13. Karelis AD, St-Pierre DH, Conus F, Rabasa-Lhoret R, Poehlman ET. Metabolic and body
composition factors in subgroups of obesity: what do we know? J Clin Endocrinol Metab
2004; 89 (6):2569-2575.
14. Nowak M, Grzywa M. Metabolic obesity in normal weight individuals and
cardiovascular disease. Pol Merkur Lekarski 2011; 31 (182):118-121.
15. Alexander DD, Weed DL, Miller PE, Mohamed MA. Red Meat and Colorectal Cancer: A
Quantitative Update on the State of the Epidemiologic Science. J Am Coll Nutr 2010:1-
23.
16. Jenkins DJ, Josse AR, Dorian P, Burr ML, LaBelle Trangmar R, Kendall CW, Cunnane
SC. Heterogeneity in randomized controlled trials of long chain (fish) omega-3 fatty
acids in restenosis, secondary prevention and ventricular arrhythmias. J Am Coll Nutr
2008; 27 (3):367-378.
17. Bouchard C, Ordovas JM. Fundamentals of nutrigenetics and nutrigenomics. Prog Mol
Biol Transl Sci 2012; 108:1-15.

9. 18. Cahill LE, Fontaine-Bisson B, El-Sohemy A. Functional genetic variants of glutathione
S-transferase protect against serum ascorbic acid deficiency. Am J Clin Nutr 2009; 90
(5):1411-1417.
19. Nielsen DE, El-Sohemy A. A randomized trial of genetic information for personalized
nutrition. Genes Nutr 2012; 7 (4):559-566.
20. Ronteltap A, van Trijp JC, Renes RJ. Consumer acceptance of nutrigenomics-based
personalised nutrition. Br J Nutr 2009; 101 (1):132-144.
21. Subbiah MT. Nutrigenetics and nutraceuticals: the next wave riding on personalized
medicine. Transl Res 2007; 149 (2):55-61.
22. Millis RM. Epigenetics and hypertension. Curr Hypertens Rep 2011; 13 (1):21-28.
23. Preuss C, Das MK, Pathak YV. Genomics and natural products: role of bioinformatics
and recent patents. Recent Pat Biotechnol; 8 (2):144-151.
24. Rizkalla SW, Prifti E, Cotillard A, Pelloux V, Rouault C, Allouche R, Laromiguiere M,
Kong L, Darakhshan F, Massiera F, Clement K. Differential effects of macronutrient
content in 2 energy-restricted diets on cardiovascular risk factors and adipose tissue cell
size in moderately obese individuals: a randomized controlled trial. Am J Clin Nutr 2012;
95 (1):49-63.
25. Roy N, Barnett M, Knoch B, Dommels Y, McNabb W. Nutrigenomics applied to an
animal model of Inflammatory Bowel Diseases: transcriptomic analysis of the effects of
eicosapentaenoic acid- and arachidonic acid-enriched diets. Mutat Res 2007; 622 (1-
2):103-116.
26. Sanchez-Muniz FJ, Canales A, Nus M, Bastida S, Guillen M, Corella D, Olmedilla-
Alonso B, Granado-Lorencio F, Benedi J. The antioxidant status response to low-fat and
walnut paste-enriched meat differs in volunteers at high cardiovascular Risk carrying
different PON-1 polymorphisms. J Am Coll Nutr 2012; 31 (3):194-205.
27. Food and Drug Administration (FDA). Warning Letter: 23andMe, Inc. 11/22/13. 2013.
28. Saukko P. State of play in direct-to-consumer genetic testing for lifestyle-related
diseases: market, marketing content, user experiences and regulation. Proc Nutr Soc
2013; 72 (1):53-60.
29. Fortmann SP, Burda BU, Senger CA, Lin JS, Whitlock EP. Vitamin and mineral
supplements in the primary prevention of cardiovascular disease and cancer: An updated
systematic evidence review for the U.S. Preventive Services Task Force. Ann Intern Med
2013; 159 (12):824-834.
30. Jacobs DR, Jr., Gross MD, Tapsell LC. Food synergy: an operational concept for
understanding nutrition. Am J Clin Nutr 2009; 89 (5):1543S-1548S.
31. Ward E. Addressing nutritional gaps with multivitamin and mineral supplements. Nutr J
2014; 13:72.
32. Nielsen DE, Shih S, El-Sohemy A. Perceptions of genetic testing for personalized
nutrition: a randomized trial of DNA-based dietary advice. J Nutrigenet Nutrigenomics
2014; 7 (2):94-104.
33. Nielsen DE, El-Sohemy A. Disclosure of genetic information and change in dietary
intake: a randomized controlled trial. PLoS One 2014; 9 (11):e112665.
34. Trimmer EE. Methylenetetrahydrofolate reductase: biochemical characterization and
medical significance. Curr Pharm Des 2013; 19 (14):2574-2593.

10. 35. Slaoui M, Razine R, Ibrahimi A, Attaleb M, Mzibri ME, Amrani M. Breast cancer in
Morocco: a literature review. Asian Pac J Cancer Prev 2014; 15 (3):1067-1074.
36. Xia LZ, Liu Y, Xu XZ, Jiang PC, Ma G, Bu XF, Zhang YJ, Yu F, Xu KS, Li H.
Methylenetetrahydrofolate reductase C677T and A1298C polymorphisms and gastric
cancer susceptibility. World J Gastroenterol 2014; 20 (32):11429-11438.
37. Nassiri M, Kooshyar MM, Roudbar Z, Mahdavi M, Doosti M. Genes and SNPs
associated with non-hereditary and hereditary colorectal cancer. Asian Pac J Cancer Prev
2013; 14 (10):5609-5614.
38. Chen Y, Wang B, Yan S, Wang YG. Significant association between MTHFR C677T
polymorphism and thyroid cancer risk: evidence from a meta-analysis. Genet Test Mol
Biomarkers 2014; 18 (10):695-702.
39. Yan Y, Han F, Fu H, Xia W, Qin X. Association between MTHFR C677T polymorphism
and thyroid cancer risk: a meta-analysis. Tumour Biol 2014; 35 (8):7707-7712.
40. Yang YM, Zhang TT, Yuan L, Ren Y. The association between the C677T
polymorphism in MTHFR gene and the risk of thyroid cancer: a meta-analysis. Eur Rev
Med Pharmacol Sci 2014; 18 (15):2097-2101.
41. Casarini L, Simoni M. Gene polymorphisms in female reproduction. Methods Mol Biol
2014; 1154:75-90.
42. Rah H, Jeon YJ, Choi Y, Shim SH, Yoon TK, Choi DH, Cha SH, Kim NK. Association
of methylenetetrahydrofolate reductase (MTHFR 677C>T) and thymidylate synthase
(TSER and TS 1494del6) polymorphisms with premature ovarian failure in Korean
women. Menopause 2012; 19 (11):1260-1266.
43. Singh K, Jaiswal D. One-carbon metabolism, spermatogenesis, and male infertility.
Reprod Sci 2012; 20 (6):622-630.
44. Lewis SJ. Commentary: One-carbon metabolism has major implications for fetal growth
and development beyond neural tube defects. Int J Epidemiol 2014; 43 (5):1498-1499.
45. Yajnik CS, Chandak GR, Joglekar C, Katre P, Bhat DS, Singh SN, Janipalli CS, Refsum
H, Krishnaveni G, Veena S, Osmond C, Fall CH. Maternal homocysteine in pregnancy
and offspring birthweight: epidemiological associations and Mendelian randomization
analysis. Int J Epidemiol 2014; 43 (5):1487-1497.
46. Arakawa Y, Watanabe M, Inoue N, Sarumaru M, Hidaka Y, Iwatani Y. Association of
polymorphisms in DNMT1, DNMT3A, DNMT3B, MTHFR and MTRR genes with
global DNA methylation levels and prognosis of autoimmune thyroid disease. Clin Exp
Immunol 2012; 170 (2):194-201.
47. Kakkoura MG, Demetriou CA, Loizidou MA, Loucaides G, Neophytou I, Marcou Y,
Hadjisavvas A, Kyriacou K. Single-nucleotide polymorphisms in one-carbon metabolism
genes, Mediterranean diet and breast cancer risk: a case-control study in the Greek-
Cypriot female population. Genes Nutr 2015; 10 (2):453.
48. Lambrinoudaki I, Kaparos G, Rizos D, Galapi F, Alexandrou A, Sergentanis TN, Creatsa
M, Christodoulakos G, Kouskouni E, Botsis D. Apolipoprotein E and paraoxonase 1
polymorphisms are associated with lower serum thyroid hormones in postmenopausal
women. Clin Endocrinol (Oxf) 2009; 71 (2):284-290.
49. Wu YL, Ding XX, Sun YH, Yang HY, Sun L. Methylenetetrahydrofolate reductase
(MTHFR) C677T/A1298C polymorphisms and susceptibility to Parkinson's disease: a
meta-analysis. J Neurol Sci 2013; 335 (1-2):14-21.

11. 50. Frustaci A, Neri M, Cesario A, Adams JB, Domenici E, Dalla Bernardina B, Bonassi S.
Oxidative stress-related biomarkers in autism: systematic review and meta-analyses. Free
Radic Biol Med 2012; 52 (10):2128-2141.
51. James SJ, Melnyk S, Jernigan S, Cleves MA, Halsted CH, Wong DH, Cutler P, Bock K,
Boris M, Bradstreet JJ, Baker SM, Gaylor DW. Metabolic endophenotype and related
genotypes are associated with oxidative stress in children with autism. Am J Med Genet
B Neuropsychiatr Genet 2006; 141B (8):947-956.
52. Clarke M, Ward M, Strain JJ, Hoey L, Dickey W, McNulty H. B-vitamins and bone in
health and disease: the current evidence. Proc Nutr Soc 2014; 73 (2):330-339.
53. Reilly R, McNulty H, Pentieva K, Strain JJ, Ward M. MTHFR 677TT genotype and
disease risk: is there a modulating role for B-vitamins? Proc Nutr Soc 2014; 73 (1):47-56.
54. Christensen KE, Mikael LG, Leung KY, Levesque N, Deng L, Wu Q, Malysheva OV,
Best A, Caudill MA, Greene ND, Rozen R. High folic acid consumption leads to pseudo-
MTHFR deficiency, altered lipid metabolism, and liver injury in mice. Am J Clin Nutr
2015; 101 (3):646-658.
55. Scaglione F, Panzavolta G. Folate, folic acid and 5-methyltetrahydrofolate are not the
same thing. Xenobiotica 2014; 44 (5):480-488.
56. Shorter KR, Felder MR, Vrana PB. Consequences of dietary methyl donor supplements:
Is more always better? Prog Biophys Mol Biol 2015.
57. Navarro SL, Schwarz Y, Song X, Wang CY, Chen C, Trudo SP, Kristal AR, Kratz M,
Eaton DL, Lampe JW. Cruciferous vegetables have variable effects on biomarkers of
systemic inflammation in a randomized controlled trial in healthy young adults. J Nutr
2014; 144 (11):1850-1857.
58. Cummings AM, Kavlock RJ. Gene-environment interactions: a review of effects on
reproduction and development. Crit Rev Toxicol 2004; 34 (6):461-485.
59. Ding H, Liu W, Yu X, Wang L, Shao L, Yi W. Risk association of meningiomas with
MTHFR C677T and GSTs polymorphisms: a meta-analysis. Int J Clin Exp Med 2014; 7
(11):3904-3914.
60. Lordelo GS, Miranda-Vilela AL, Akimoto AK, Alves PC, Hiragi CO, Nonino A,
Daldegan MB, Klautau-Guimaraes MN, Grisolia CK. Association between methylene
tetrahydrofolate reductase and glutathione S-transferase M1 gene polymorphisms and
chronic myeloid leukemia in a Brazilian population. Genet Mol Res 2012; 11 (2):1013-
1026.
61. Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J,
Helgason A, Stefansson H, Emilsson V, Helgadottir A, Styrkarsdottir U, Magnusson KP,
Walters GB, Palsdottir E, Jonsdottir T, Gudmundsdottir T, Gylfason A, Saemundsdottir J,
Wilensky RL, Reilly MP, Rader DJ, Bagger Y, Christiansen C, Gudnason V, Sigurdsson
G, Thorsteinsdottir U, Gulcher JR, Kong A, Stefansson K. Variant of transcription factor
7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet 2006; 38 (3):320-323.
62. Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, Manning AK, Florez
JC, Wilson PW, D'Agostino RB, Sr., Cupples LA. Genotype score in addition to common
risk factors for prediction of type 2 diabetes. N Engl J Med 2008; 359 (21):2208-2219.
63. Tong Y, Lin Y, Zhang Y, Yang J, Liu H, Zhang B. Association between TCF7L2 gene
polymorphisms and susceptibility to type 2 diabetes mellitus: a large Human Genome
Epidemiology (HuGE) review and meta-analysis. BMC Med Genet 2009; 10:15.
64. Migliorini A, Lickert H. Beyond association: A functional role for Tcf7l2 in beta-cell
development. Mol Metab 2015; 4 (5):365-366.

12. 65. Shu L, Matveyenko AV, Kerr-Conte J, Cho JH, McIntosh CH, Maedler K. Decreased
TCF7L2 protein levels in type 2 diabetes mellitus correlate with downregulation of GIP-
and GLP-1 receptors and impaired beta-cell function. Hum Mol Genet 2015; 24
(10):3004.
66. Yao DD, Yang L, Wang Y, Liu C, Wei YJ, Jia XB, Yin W, Shu L. Geniposide promotes
beta-cell regeneration and survival through regulating beta-catenin/TCF7L2 pathway.
Cell Death Dis 2015; 6:e1746.
67. Pagan A, Sabater-Molina M, Olza J, Prieto-Sanchez MT, Blanco-Carnero JE, Parrilla JJ,
Gil A, Larque E. A gene variant in the transcription factor 7-like 2 (TCF7L2) is
associated with an increased risk of gestational diabetes mellitus. Eur J Obstet Gynecol
Reprod Biol 2014; 180:77-82.
68. Reyes-Lopez R, Perez-Luque E, Malacara JM. Metabolic, hormonal characteristics and
genetic variants of TCF7L2 associated with development of gestational diabetes mellitus
in Mexican women. Diabetes Metab Res Rev 2014; 30 (8):701-706.
69. Shi X, Cai Q, Zou M, Shen Y. [Correlation between TCF7L2 gene polymorphism and
genetic susceptibility in women with gestational diabetes mellitus]. Zhonghua Fu Chan
Ke Za Zhi 2014; 49 (8):588-593.
70. Zhang C, Bao W, Rong Y, Yang H, Bowers K, Yeung E, Kiely M. Genetic variants and
the risk of gestational diabetes mellitus: a systematic review. Hum Reprod Update 2013;
19 (4):376-390.
71. Heni M, Herzberg-Schafer S, Machicao F, Haring HU, Fritsche A. Dietary fiber intake
modulates the association between variants in TCF7L2 and weight loss during a lifestyle
intervention. Diabetes Care 2012; 35 (3):e24.
72. Hindy G, Sonestedt E, Ericson U, Jing XJ, Zhou Y, Hansson O, Renstrom E, Wirfalt E,
Orho-Melander M. Role of TCF7L2 risk variant and dietary fibre intake on incident type
2 diabetes. Diabetologia 2012; 55 (10):2646-2654.
73. Qi L, Liang J. Interactions between genetic factors that predict diabetes and dietary
factors that ultimately impact on risk of diabetes. Curr Opin Lipidol 2010; 21 (1):31-37.
74. Wirstrom T, Hilding A, Gu HF, Ostenson CG, Bjorklund A. Consumption of whole grain
reduces risk of deteriorating glucose tolerance, including progression to prediabetes. Am
J Clin Nutr 2012; 97 (1):179-187.
75. Chen ZL, Shao WJ, Xu F, Liu L, Lin BS, Wei XH, Song ZL, Lu HG, Fantus IG, Weng
JP, Jin TR. Acute Wnt pathway activation positively regulates leptin gene expression in
mature adipocytes. Cell Signal 2015; 27 (3):587-597.
76. Helgason A, Palsson S, Thorleifsson G, Grant SF, Emilsson V, Gunnarsdottir S,
Adeyemo A, Chen Y, Chen G, Reynisdottir I, Benediktsson R, Hinney A, Hansen T,
Andersen G, Borch-Johnsen K, Jorgensen T, Schafer H, Faruque M, Doumatey A, Zhou
J, Wilensky RL, Reilly MP, Rader DJ, Bagger Y, Christiansen C, Sigurdsson G,
Hebebrand J, Pedersen O, Thorsteinsdottir U, Gulcher JR, Kong A, Rotimi C, Stefansson
K. Refining the impact of TCF7L2 gene variants on type 2 diabetes and adaptive
evolution. Nat Genet 2007; 39 (2):218-225.
77. Lee YS, Jun HS. Anti-diabetic actions of glucagon-like peptide-1 on pancreatic beta-
cells. Metabolism 2014; 63 (1):9-19.
78. Xiong X, Shao W, Jin T. New insight into the mechanisms underlying the function of the
incretin hormone glucagon-like peptide-1 in pancreatic beta-cells: the involvement of the
Wnt signaling pathway effector beta-catenin. Islets 2012; 4 (6):359-365.

13. 79. Mattei J, Qi Q, Hu FB, Sacks FM, Qi L. TCF7L2 genetic variants modulate the effect of
dietary fat intake on changes in body composition during a weight-loss intervention. Am
J Clin Nutr 2012; 96 (5):1129-1136.
80. Fereidooni B, Jenabi E. The use of omega 3 on pregnancy outcomes: a single-center
study. J Pak Med Assoc 2014; 64 (12):1363-1365.
81. Dodd JM, O'Brien C, Grivell RM. Preventing pre-eclampsia - are dietary factors the key?
BMC Med 2014; 12:176.
82. Endeshaw M, Abebe F, Bedimo M, Asart A. Diet and Pre-eclampsia: A Prospective
Multicentre Case-Control Study in Ethiopia. Midwifery 2015; 31 (6):617-624.
83. Corella D, Tai ES, Sorli JV, Chew SK, Coltell O, Sotos-Prieto M, Garcia-Rios A, Estruch
R, Ordovas JM. Association between the APOA2 promoter polymorphism and body
weight in Mediterranean and Asian populations: replication of a gene-saturated fat
interaction. Int J Obes (Lond) 2011; 35 (5):666-675.
84. Martinez-Gonzalez MA, Salas-Salvado J, Estruch R, Corella DD, Fito M, Ros E. Benefits
of the Mediterranean Diet: Insights from the PREDIMED Study. Prog Cardiovasc Dis
2015.
85. Smith CE, Tucker KL, Arnett DK, Noel SE, Corella D, Borecki IB, Feitosa MF,
Aslibekyan S, Parnell LD, Lai CQ, Lee YC, Ordovas JM. Apolipoprotein A2
polymorphism interacts with intakes of dairy foods to influence body weight in 2 U.S.
populations. J Nutr 2013; 143 (12):1865-1871.
86. Zaki ME, Amr KS, Abdel-Hamid M. APOA2 Polymorphism in Relation to Obesity and
Lipid Metabolism. Cholesterol 2011; 2013:289481.
87. Zaki ME, Amr KS, Abdel-Hamid M. Evaluating the association of APOA2
polymorphism with insulin resistance in adolescents. Meta Gene 2014; 2:366-373.
88. Peloso GM, Demissie S, Collins D, Mirel DB, Gabriel SB, Cupples LA, Robins SJ,
Schaefer EJ, Brousseau ME. Common genetic variation in multiple metabolic pathways
influences susceptibility to low HDL-cholesterol and coronary heart disease. J Lipid Res
2010; 51 (12):3524-3532.
89. Koning F. The molecular basis of celiac disease. J Mol Recognit 2003; 16 (5):333-336.
90. Meresse B, Ripoche J, Heyman M, Cerf-Bensussan N. Celiac disease: from oral tolerance
to intestinal inflammation, autoimmunity and lymphomagenesis. Mucosal Immunol 2009;
2 (1):8-23.
91. Schuppan D, Zimmer KP. The diagnosis and treatment of celiac disease. Dtsch Arztebl
Int 2013; 110 (49):835-846.
92. Brottveit M, Beitnes AC, Tollefsen S, Bratlie JE, Jahnsen FL, Johansen FE, Sollid LM,
Lundin KE. Mucosal cytokine response after short-term gluten challenge in celiac disease
and non-celiac gluten sensitivity. Am J Gastroenterol 2013; 108 (5):842-850.
93. Kabbani TA, Vanga RR, Leffler DA, Villafuerte-Galvez J, Pallav K, Hansen J,
Mukherjee R, Dennis M, Kelly CP. Celiac disease or non-celiac gluten sensitivity? An
approach to clinical differential diagnosis. Am J Gastroenterol 2014; 109 (5):741-746;
quiz 747.
94. Fasano A, Sapone A, Zevallos V, Schuppan D. Nonceliac gluten sensitivity.
Gastroenterology 2014; 148 (6):1195-1204.
95. Shepherd SJ, Halmos E, Glance S. The role of FODMAPs in irritable bowel syndrome.
Curr Opin Clin Nutr Metab Care 2014; 17 (6):605-609.

14. 96. Ashat M, Kochhar R. Non-celiac gluten hypersensitivity. Trop Gastroenterol 2014; 35
(2):71-78.
97. Mansueto P, Seidita A, D'Alcamo A, Carroccio A. Non-celiac gluten sensitivity:
literature review. J Am Coll Nutr 2014; 33 (1):39-54.
98. Nijeboer P, Bontkes HJ, Mulder CJ, Bouma G. Non-celiac gluten sensitivity. Is it in the
gluten or the grain? J Gastrointestin Liver Dis 2013; 22 (4):435-440.
99. Meresse B, Malamut G, Cerf-Bensussan N. Celiac disease: an immunological jigsaw.
Immunity 2012; 36 (6):907-919.
100. Uibo R, Tian Z, Gershwin ME. Celiac disease: a model disease for gene-environment
interaction. Cell Mol Immunol 2011; 8 (2):93-95.