Tailored Nutrition The Science Behind Prescription Dog Diets
Genetic test for the prescription of diets in support of physical activity
Acta Biomed. 2020; 91(Suppl 13): e2020011.
Genetic test for the prescription of diets in support of physical activity
,1 ,2 ,3 ,3 ,3 ,3 ,4 ,4 and 4,5,6
Zakira Naureen
1 Department of Biological Sciences and Chemistry, College of Arts and Sciences, University of Nizwa, Nizwa, Oman
Giacinto Abele Donato Miggiano
2 Human Nutrition Research Center, Sacro Cuore Catholic University, Rome, Italy
Barbara Aquilanti
3 UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
Valeria Velluti
3 UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
Giuseppina Matera
3 UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
Lucilla Gagliardi
3 UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
Alessandra Zulian
4 MAGIS LAB, Rovereto (TN), Italy
Roberta Romanelli
4 MAGIS LAB, Rovereto (TN), Italy
Matteo Bertelli
4 MAGIS LAB, Rovereto (TN), Italy
5 MAGI EUREGIO, Bolzano, Italy
6 EBTNA-LAB, Rovereto (TN), Italy
1 Department of Biological Sciences and Chemistry, College of Arts and Sciences, University of Nizwa, Nizwa, Oman
2 Human Nutrition Research Center, Sacro Cuore Catholic University, Rome, Italy
3 UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
4 MAGIS LAB, Rovereto (TN), Italy
5 MAGI EUREGIO, Bolzano, Italy
6 EBTNA-LAB, Rovereto (TN), Italy
Received 2020 Sep 3; Accepted 2020 Oct 14.
Copyright: 2020 ACTA BIO MEDICA SOCIETY OF MEDICINE AND NATURAL SCIENCES OF PARMA
This work is licensed under a Creative Commons Attribution 4.0 International License
Abstract
Owing to the fields of nutrigenetics and nutrigenomics today we can think of devising approaches to optimize health, delay onset of diseases and reduce its severity according to our genetic blue print. However this requires a deep understanding of nutritional impact on expression of genes that may result in a specific phenotype. The extensive research and observational studies during last two decades reporting interactions between genes, diet and physical activity suggest a cross talk between various genetic and environmental factors and lifestyle interventions. Although considerable efforts have been made in unraveling the mechanisms of gene-diet interactions the scientific evidences behind developing commercial genetic tests for providing personalized nutrition recommendations are still scarce. In this scenario the current mini-review aims to provide useful insights into salient feature of nutrition based genetic research and its commercial application and the ethical issue and concerns related to its outcome. (www.actabiomedica.it)
Keywords: nutrigenetics, nutrigenomics, direct to consumer test, personalized nutrition, obesity, physical activity
Introduction
The notion that each of us has an exclusive nutrition blueprint within our genes is a scrumptious concept. Nutrients can indeed influence phenotypic expression of an individuals genotype by modifying gene expression at pre- and post-transcriptional and translational levels. This might result into improving health of an individual or, on the other hand, may affect the health in a negative manner. On the contrary there are certain genes that may give preferential benefit to intake of some of the nutrients while adversely affecting the consumptions of other. The complexity and girth of this scenario led to the development of two interesting scientific fields, nutrigenetics and nutrigenomics.
Nutrigenetics and nutrigenomics are defined as the branches of science dealing with the effect of genetic variation on nutritional response and the function of nutrients and bioactive food compounds in influencing gene expression, respectively (1-3). The wealth of genomic information coupled with high-throughput omics technologies facilitates the acquisition of in-depth knowledge of nutrient-gene interactions that ultimately lead to development of personalized nutrition approaches for good health and disease prevention (4-6). There are three fundamental factors underlying nutrigenetics and nutrigenomics. Genomic diversity with respect to ethnicity that affects nutrient bioavailability and metabolism, choice and availability of food depending on cultural, socioeconomical, geographical and sense of taste of an individual and malnutrition that can affect gene expression and pose threats to genome stability by paving the way for mutations in gene sequences or causing chromosomal aberrations resulting into abnormal gene dosage and corresponding adverse phenotypes (1-6).
Although the terms nutrigenomics and nutrigenetics look similar and are closely related, these terms are not interchangeable as nutrigenetics investigates the effect of inherited genetic traits or their variants and mutations on micronutrient uptake and metabolism while nutrigenomics is the interconnection between genome and diet which explains the nutritional effects on transcription, translation, proteomic, metabolomics changes and the variation in dietary factors due to individual genetic background (7, 8). Due to advancement in science now it is possible and affordable for individuals to get information about possibility of having alterations in genes involved in nutrient metabolism and pathways requiring micronutrients as cofactors, due to point or frameshift mutations or copy number variations (9). Age and gender may as well contribute to gene-diet interactions involved in micronutrient uptake for health maintenance (10). Hence the key challenge is to utilize this information to provide reliable and predictable personalized dietary recommendations for optimum health and wellbeing.
Another important emerging facet of gene-diet-interaction is involvement of epigenetic with the potential for both intra- and transgenerational effects (11,12). Epigenetics refers to regulatory mechanisms that play an important role in switching genes on and off thus having a strong impact on growth and development with epigenetic controls being lost during diseases such as cancer. Whereas epigenomics involves analysis of epigenetic changes in a cell or entire organism. Diet has a special perspective in this regard in the sense that either alone or in combination with confounding environmental factors it may switch certain genes on or off. Consequently , this may either lead to uncontrolled growth leading to cancer or predisposition to and susceptibility towards developing diseases later on in life (13,14). Thence the epigenome is the global epigenetic pattern that is heritable and modifiable by diet. The gene-specific modifications like DNA methylation, histone modifications and chromatin remodeling regulate the expression of house-keeping genes and suppress the expression of parasitic transposons (1,2,14).
This mini review aims to provides various perspectives on nutrigenomics and nutrigenetics and the use of technologies to unravel gene diet-interactions and their underlying mechanisms that result into inter-individual variations in response to the same nutritional intakes.
Genetic variation/polymorphisms affecting response to dietary intake
The term personalized nutrition is not a new concept. We have examples from inborn errors of metabolism that the nutrition regime can be customized for a healthy life. These inborn errors of metabolism are a consequence of genetic variations in the genes coding specific metabolic enzymes. Unavailability or non-functionality of these enzymes leads to metabolic problems. These variations can result in gene-diet-interactions and alter the nutrient requirements and the metabolism (4,5).
Classical examples of such single gene- diet- interactions affecting the response to dietary intake are phenylketonuria and lactose intolerance. Phenylketonuria is an autosomal recessive disorder caused by mutations in gene encoding a key hepatic enzyme phenyl alanine hydroxylase. Individuals having phenylketonuria need to avoid foods containing amino acid phenyl alanine as they lack or have very low activity of phenyl alanine hydroxylase to metabolize this amino acid (6). Another common example is the autosomal recessive lactose intolerance. This phenotype is caused by a point mutation in the lactase gene (LCT) that results into a T to C transition leading to the malfunctioning or absent lactase. Consequently, individuals that harbor this mutation are unable to digest lactose.
Besides these straight forward genetic interactions involving single genes and diet there are more complex polygenic interactions and multifactorial etiologies, such as obesity, cardiovascular disease (CVD), type 2 diabetes and cancer (1,2). Such disorders involve interactions among many genes and can be influenced by several dietary exposures. For instance multiple genetic variants have been linked with an increased risk of obesity such as fat mass and obesity associated gene (FTO, rs9939609), genes encoding peroxisome proliferator-activated receptor protein, uncoupling proteins (UCP1 and UCP3), leptin receptor and melanocortin 4 receptor (15-18).
In case of coronary artery disease variations in genes involved in lipid metabolism such as cholesteryl ester transfer protein (CETP); lipoprotein lipase (LPL), apolipoprotein E (APOE), low density lipoprotein receptor (LDLR) affect the uptake and catabolism of cholesterol and other lipids thus causing fat deposition in the arteries (atherosclerosis) that can be dangerous for health (19-22). Other genetic variants may be responsible for diabetes, cancer and other diseases (23-25). Another important example of metabolic pathways affected by genetic variants in genes encoding metabolic enzymes regards the variants that can be present in methyl tetrahydrofolate reductase gene (MTHFR). Two common variants in MTHFR C677T and A1298C have a minor allele frequency of 25% in Hispanic people and 10% in North American Caucasians. Both cause homocystenemia that eventually might result in birth defects, encephalopathies and glaucoma (26).
At present, there are many examples of how nutrigenomics is studying these complex disorders. One of those studies analyzed the involvement of genetic variants in the gene CYP1A2 encoding a caffeine-metabolizing enzyme in CVD (27). The study reported that high caffeine consumption may be associated with an increased risk of CVD in individuals carrying the slow caffeine metabolizing variant of the gene. On the contrary, individuals having fast coffee metabolizing genotype have a protective effect on moderate coffee consumption (27).
Substantial efforts have also been made in determining genetic variants in metabolic enzymes to devise effective optimal dietary regimes to prevent obesity, which may be a major contributory factor in chronic diseases such as CVD and type 2 diabetes. One example are the genetic variations in the apolipoprotein A2 gene (APOA2) that predisposes to obesity via altered energy intake (28). Individuals of Chinese and Asian-Indian origin with a specific variant in APOA2 are at increased risk of developing obesity when saturated fat intake is high, but not when saturated fat intake is low (28). Similar studies were replicated in a Mediterranean population from the southeast of Spain (29).
Additionally, polymorphisms in genes linked with the metabolism of vitamin C, D, B12 and iron, have been shown to predispose to risk of suboptimal or deficient blood levels of these nutrients leading to several abnormalities and diseases (30-33).
Despite the fact that these studies and many other similar studies provide strong evidence of benefits from personalised nutrition further research is required to deepen our knowledge of genetic association with dietary intake and physical activities in various ethnic groups (34,35). One of the ultimate objectives of nutrigenetics and nutrigenomics in this scenario is the development of genetic tests based on genetic markers. Research has shown that DNA-based personalised nutrition advice is taken more seriously and more willingly by the people in terms of alteration in behaviour and adapting to healthy lifestyle pattern (34,35).
Nutrigenetics-based personalized nutrition
Given the complexity of the matter, there is not a single definition for personalized nutrition, also called precision or tailored nutrition (36). Its aim is to give dietary advice in order to improve dietary habits (37). Personalized nutrition practices may involve direct face to face consultations or web based computer-generated nutrition advice (38). In this context, genetic-based personalized nutrition is a healthy tailored dietary recommendation based on genetic information collected by genetic testing (39). These data are coupled with lifestyle data such as age, gender, weight, height and clinical history for underlying diseases, food allergies, dietary habits and physical activity. Some centers also take into account gut microbiota (40). In fact, it can alter genetic expression in response to dietary intake and physical activity (40). Hence, personalized nutrition is a complex field (41).
Scientific validity and evidence for genotype-based dietary advice
In principle, to avoid misuse of the genetic information and protect the basic human rights of safety, privacy and well-being there should be clear guidelines to follow in nutrigenetics. Furthermore, these genetic findings should be translated carefully keeping in view the available scientific evidence. A draft framework for assessing the significance and practical application of the nutrigenetic knowledge was presented by Grimaldi et al (42). They proposed that based on this framework, transparent and scientifically valid guidelines might be developed for nutrigenetic testing for public.
They further added that genetic testing is mainly an unregulated market in the sense that they provide insufficient descriptions of results and draw inferences which lack of scientific backing (43-48). For instance, scientific studies addressing gene-diet interactions are mainly based on observations made for a group of individuals of a specific ethnic group or with mixed ethnicity. The results presented by a study are specific for that particular study group and cannot be directly applied to individuals with different ethnicity, unless these results are replicated in different ethnic groups with people having different life styles and dietary habits. This is due to the fact that the overall effect of confounding factors on an individual phenotype is far greater than a trend observed in a group of people. Besides that, diets prescribed on the basis of a single gene variant may also lack scientific backing. In fact, the overall contribution of a single gene in phenotypic expression is minimal as compared to multiple genetic variants and their interactions with each other (42). Therefore, the best personalized nutritional advice can be given only on the basis of a polygenic profile, phenotype characterization, health status, food choices, lifestyle, environmental, cultural and economic factors (43). To overcome these differences, dietary reference values have been developed to set safe limits for nutrients based on age, gender and physiological state (49-51). They are designed to meet the requirement of 97.5% of the population. This implicates that although these reference values cover the dietary requirements of most of the people some individuals may remain uncovered. In this context, EU developed a strategy to harmonize the nutrient recommendations throughout EU member states taking into account vulnerable groups and consumer satisfaction (52).
Interactions between diet and genetic variants
Wrong dietary habits such as high intake of sugars, fats, alcohol and reduced intake of vegetables and fruits may predispose to the acquisition of non-communicable diseases such as CVD, hypertension, diabetes, cancer and obesity (53).
Several epidemiological studies have consistently shown that high intake of sugar sweetened beverages (54-56), fried food (57) and sedentary life style are particular risk factor for obesity among adults and children due to their complex interaction with genetic variants associated with obesity (58-61).
Several genome wide association studies have assessed the association of genetic polymorphisms with metabolic pathways (62). For instance, clinically significant interactions have been observed among the APOA2 2265T>C variant and saturated fats intake and BMI (42); MTHFR gene variants and homocyteine levels (26,61-66); CYP1A2 genetic variants and hypertensive response to caffeine consumption.
Genome wide association and linkage studies have added more than 600 genetic variants and chromosomal regions to the repertoire of genetic factors associated with obesity, body weight, body mass index, body fat composition and distribution, energy expenditure and fuel oxidation and various other phenotypic features linked with nutrition (67-83). One of the first genetic variants associated with obesity was a single nucleotide polymorphism in the FTO gene. This variant affects body weight and body composition and carriers having FTO rs9939609 AA genotype are more likely to be obese as compared to non-carriers (71). Although this variant is the strongest risk factor associated with polygenic obesity, its effect can be modified by either physical activity or reduction in energy intake, providing an excellent example of cross talk between genetic variants and life style interventions (84). In addition to that another study revealed that patients carrying variant alleles of the FTO rs9939609 and MC4R rs17782313 are at a higher risk of type 2 diabetes mellitus (73). This implicates that the genetic variants can either affect the phenotype alone or in association with other genetic variants present in other loci or by complex interactions between genetic variants and environmental factors.
Genes and their polymorphs associated with obesity
One of the best method to achieve healthy weight loss is to create a calorie deficit by burning more calories than what we intake through diet. This method involves extensive and vigorous exercise or physical activity. On the other hand, intake of low calories with a more satiating diet may also result in weight loss in association with physical activity (67,81,83). Several genes have been reported to be linked with weight loss in response to hypocaloric diet coupled with physical activity. These genes mainly encodes important enzymes regulating lipid metabolism, adipogenesis, carbohydrate metabolism, circadian clock, energy intake and expenditure, appetite control, thermogenesis and cell differentiation (67-76). Furthermore, genetic variants in taste-, olfactory- and texture-related genes can influence sensitivity and preferences for certain food, thereby affecting the susceptibility towards nutrition-induced obesity (71). The key genetic variants that influence metabolic processes involved in increasing the risk of obesity and obesity-related diseases are FTO, ADIPOQ, LEP, LEPR, INSIG2, MC4R, PCSK1, PPARG, ADBR2, ADBR3, GHRL, PPAR, FABP2, APOA5, APOA1, LIPC, CETP, MTNR1B, NPY, GIPR, IRS1, TCF7L2, PCSK1 (84-120). The encoded proteins and their metabolic functions are presented in .
Table1.
Genes and their respective polymorphisms related to obesity and fat metabolism
| Gene (OMIM ID) | Full name | SNP ID | Association | Reference |
| FTO | Alpha-ketoglutarate dependent dioxygenase | rs9939609rs1558902rs8050136 | Stimulation of food consumption | 84-89 |
| ADIPOQ | Adipocyte-, C1q-, and collagen domain-containing | rs1501299 | Energy expenditure | 90 |
| LEP | Leptin | 164160 | Appetite regulation | 91 |
| LEPR | Leptin receptor | rs1805094 | 92 | |
| INSIG2 | Insulin-induced gene 2 | rs7566605 | Regulation of cholesterol and fatty acid synthesis | 93,94 |
| MC4R | Melanocortin 4 receptor | rs17782313rs17066866rs1943226rs11875096rs1943224rs7235242rs11872992rs8093815rs17066856rs17066836rs1943227rs1943218rs17066829rs9966412rs17066859rs9965495rs12970134rs17700633rs11873305rs8091237rs7240064 | Energy homeostasis, appetite regulation | 95,96 |
| PCSK1 | Proprotein convertase subtilisin/kexin type 1 | rs236918 | Insulin resistance | 97 |
| PPARG | Peroxisome proliferator-activated receptor gamma | rs1801282 | Increased BMI | 98,99 |
| ADBR2 | Adeno receptor beta2 | rs1042714rs1042713 | Adaptive thermogenesis | 100, 101 |
| ADBR3 | Adeno receptor beta 3 | rs4994 | 102,103,104 | |
| GHRL | Ghrelin | rs696217 | Appetite regulation | 105 |
| FABP2 | Fatty acid binding protein 2 | rs1799883 | Fatty acid uptake | 107 |
| APOA5 | Apolipoprotein A5 | rs964184rs662799 | Lipoprotein metabolism | 108,109 |
| APOA1 | Apolipoprotein A1 | rs670 | 110 | |
| LIPC | Lipase C hepatic type | rs2070895 | 111 | |
| CETP | Cholesteryl ester transfer protein | rs3764261 | 112 | |
| MTNR1B | Melatonin receptor 1B | rs10830963 | Appetite regulation | 113,114 |
| NPY | Neuropeptide Y | rs16147 | 115 | |
| GIPR | Gastric inhibitory polypeptide receptor | rs2287019 | Insulin signaling | 116 |
| IRS1 | Insulin receptor substrate 1 | rs1522813rs2943641 | 117,118 | |
| TCF7L2 | Transcrption factor 7 like 2 | rs12255372rs7903146 | Blood glucose homeostasis | 119 |
| PCSK1 | Proprotein convertase subtilisin/Kexin type 1 | rs6232rs6234 | Energy metabolism | 120,121 |
Genes and physical activity
Physical activity levels and intensity play an important role in moderating the risk of weight gain, in both weight loss program and in prevention of weight regain (121). Physical activity is a modifiable life style and is associated with cardio-metabolic outcomes, including obesity, type 2 diabetes, and cardiovascular diseases (122,123). A recent study by Goryakin et al., reported that more than half of the Italian population does not meet the least moderate activity threshold set by world health organization despite the fact that the government has passed several health policies for the promotion of active transport, mass media campaigns, physical activity in primary care, school-based interventions and mobile apps in order to promote exercise and physical activity (124). Another study in 2015 has highlighted that only 20.8% of the US population meets the US national guidelines for physical activity while 50.8% of all adults do not meet those guidelines (125). In this context, approximately 35% of the mortality due to coronary heart disease has been attributed to insufficient physical activity (126), leading to the estimated $117 billion health care expenditures annually in the US (127).
Given the importance of physical activity many research reports based on twin studies (128), heritage analysis (129-134), linkage studies (135-139), genome wide association studies have been dedicated to unravel the genetic variants associated with physical activity (140-143). Genes that have been consistently associated with physical activity are FTO, ANKRD6, IL15R, PPARD, LEPR, CASR, PAPSS2, DRD2, GABRG3, ACE, MC4R (137, 144-151). The names, functions and notable variants of these genes have been listed in .
Table2.
Genes with their SNPs that influence metabolism of specific food and nutrients with a major impact on health status
| Gene (OMIM ID) | Full name | SNP ID (risk allele) | SNP-associated phenotype | Diet intervention | Reference |
| MCM6 (*601806) | Minichromosome maintenance complex component 6 | rs4988235 C>T (C)rs182549 G>A (G)rs145946881 G>C (G) | Lactose intolerance, adult type (#223100) | Avoid milk and its derivatives with high content of lactose | OMIM |
| HLA-DQA1 (*146880) | Major histocompatibility complex, class II, DQ alpha 1 | rs2187668 C>T (C)rs2395182 G>T (G)rs4639334 G>A (G)rs4713586 A>G (G)rs7454108 T>C (C)rs7775228 T>C (C) | Susceptibility to celiac disease 1 (#212750) | Avoid foods containing gluten | |
| HLA-DQB1 (*604305) | Major histocompatibility complex, class II, DQ beta 1 | Avoid foods containing gluten | |||
| HJV (*608374) | Hemojuvelin BMP co-receptor | rs74315323 C>Ars74315324 G>Ars74315325 A>Trs74315326 A>Grs28940586 A>C,Grs74315327 A>Grs121434374 G>C,Trs786205063 (GA)3G>GAGrs121434375 T>A | Hemochromatosis, type 2A (#602390) | Avoid iron-rich foods | |
| SLC40A1 (OMIM *604653) | Solute carrier family 40 member 1 | rs104893662 T>A,Grs28939076 G>Trs878854984 (CAA)4>(CAA)3,(CAA)5rs104893663 T>A,Crs104893670 C>A,Trs104893671 C>Ars104893672 T>Ars104893673 C>Ars104893664 C>T | Hemochromatosis, type 4 | ||
| HFE (*613609) | Homeostatic iron regulator | rs1800562 G>Ars1799945 C>G,Trs1800730 A>Trs1800758 G>Ars28934889 G>Ars111033557 G>Ars28934595 A>Crs111033558 G>C,Trs28934596 T>Crs28934597 G>Crs111033563 A>C | Hemochromatosis | ||
| TFR2 | Transferrin receptor 2 | rs80338880 G>Crs80338877 (G)5>(G)6rs80338879 A>Trs41303501 C>Trs80338889 T>C,G | Hemochromatosis, type 3 | OMIM | |
| FTH1 | Ferritin heavy chain 1 | rs387906549 T>A | Hemochromatosis, type 5 | ||
| HAMP (*606464) | Hepcidin antimicrobial peptide | Hemochromatosis, type 2B | |||
| ADH1B (*103720) | Alcohol dehydrogenase 1B | rs1229984 A>Grs2066702 C>T | G/C, higher alcohol consumption.A/T, accumulation of acetaldehyde | Reduce the alcohol consumption | |
| ADH1C (*103730) | Alcohol dehydrogenase 1C | rs1693482 C>T (T)rs698 T>A,C (C) | Type II alcoholism | ||
| ALDH2 (*100650) | Aldehyde dehydrogenase 2 family member | rs671 G>A (A) | Acute alcohol sensitivity (#610251) | ||
| CYP1A2 (+124060) | Cytochrome P450 family 1 subfamily A member 2 | rs762551 C>A (C) | Higher risk of nonfatal myocardial infarction | Reduce caffeine consumption | doi:10.1007/s00213-010-1900-1 |
| ADORA2A (*102776) | Adenosine A2a receptor | rs5751876 T>C (C) | Greater caffeine sensitivity, sleep impairment, increased beta activity during non-REM sleep | ||
| rs35320474 delT (T) | Greater caffeine-induced anxiety | ||||
| DRD2 | Dopamine receptor D2 | rs1110976 T>G (G) | Greater caffeine-induced anxiety | ||
| COMT | Catechol-O-methyltransferase | rs4680 G>A (A) | Higher risk of acute myocardial infarction | ||
| ALDOB (*612724) | Fructose-bisphosphate B aldolase | rs1800546 C>G (G)rs76917243 G>T (T)rs118204425 AAGdel (del) | Fructose intolerance (#229600) | Reduce consumption of fruit and vegetables | OMIM |
| UGT1A1 (*191740) | UDP glucuronosyltransferase family 1 member A1 | rs6742078 G>T (T) | Bilirubin serum level (#601816) | Reduce the consumption of proteins | |
| G6PD (*305900) | Glucose-6-phosphate dehydrogenase | rs1050829 T>A,C (A) rs1050828 C>T (T) | Nonspherocytic hemolytic anemia (#300908) | Avoid the consumption of broad beans |
Table3.
Genes with their SNPs that influence metabolism of specific nutrients with minor effects on health status
| Gene (OMIM ID) | Full name | SNP ID (risk allele) | SNP-associated phenotype | Diet intervention | Reference |
| BCO1 | Beta-carotene oxygenase 1 | rs12934922 A>T (T) | Reduced conversion of beta-carotene to retinol | Increase the intake of beta-carotene | SNPedia |
| rs7501331 C>T (T) | |||||
| GC | GC Vitamin D Binding Protein | rs2282679 T>G (G) | Lower vitamin D levels | Increase consumpion of vitamin D-containing food | |
| rs4588 G>T (T) | |||||
| rs842999 C>G (C) | |||||
| SLC23A1 | Solute carrier family 23 member 1 | rs33972313 C>T (T) | Reduction of circulating levels of vitamin C | Increase consumpion of vitamin C-containing food | doi:10.3945/ajcn.2010.29438 |
| SLC30A8 | Solute carrier family 30 member 8 | rs11558471 A>G (G) | Susceptibility to diabetes mellitus | Reduce consumption of food with high content of carbohydrates | doi:10.1186/1471-2350-11-97 |
| SLC5A6 | Solute carrier family 5 member 6 | rs1395 G>A (A) | Reduced intestinal uptake, cellular delivery and transplacental transport of pantothenate and biotin | Increase consumption of biotin- and pantothenate-containing food | doi:10.1111/jnc.13092 |
| TCN2 | Transcobalamin 2 | rs1801198 C>G (G) | Decreased serum vitamin B12, increased homocysteine | Increase consumption of B12-containing food | SNPedia |
| TTPA | Alpha tocopherol transfer protein | rs4501570 G>A (A) | Vitamin E deficiency | Increase consumption of vitam E-containing food | doi:10.3945/jn.112.160333 |
| rs4587328 T>C (C) | |||||
| rs4606052 C>T (T) | |||||
| VDR | Vitamin D receptor | rs731236 A>G (G) | Immune weakness, increased cancer risk, early bone loss, increased cognitive decline risk, mood disorders | Increase consumption of vitam D-containing food | SNPedia |
| CYP2R1 | Cytochrome P450 family 2 subfamily R member 1 | rs10741657 A>G (G) | Lower vitamin D levels | Increase consumption of food rich in vitamin D | OMIM |
| rs10766197 A>G (A) | |||||
| LPA | Lipoprotein(A) | rs10455872 A>G (G)rs3798220 C>T (C) | Coronary artery disease | Reduce trygliceride and cholesterol consumption | SNPedia |
| Cardiovascular events risk | |||||
| CDKN2B-AS1 | Cyclin dependent kinase inhibitor 2B-antisense RNA 1 | rs10757274 A>G (G)rs2383206 A>G (G)rs2383207 A>G (G) | Heart attack risk | ||
| Intergenic | / | rs10757278 A>G (G) | Heart disease risk | ||
| MC4R | Melanocortin 4 receptor | rs17782313 C>T (C) | Increased BMI | Reduce calorie intake | SNPedia |
| APOA2 | Apolipoprotein A2 | rs5082 C>T (C) | Higher total energy, fat, protein intake | ||
| PCSK1 | Proprotein convertase subtilisin/Kexin type 1 | rs6232 A>G (G) | Obesity and insulin sensitivity higher risk | ||
| APOA5 | Apolipoprotein A5 | rs662799 A>G (G) | Early heart attack higher risk; less weight gain on high fat diets | ||
| SH2B1 | SH2B adaptor protein 1 | rs7498665 A>G (G) | Obesity; type-2 diabetes | ||
| SLC2A2 | Solute carrier family 2 member 2 | rs5400 C>T (T) | Higher sugar consumption | ||
| F2 | Coagulation factor II, thrombin | rs1799963 A>G (A) | Thrombosis and cerebral stroke higher risk | Reduce tryglicerides and cholesterol consumption | |
| F5 | Coagulation factor V | rs6025 A>G (A) | Thrombosis higher risk | ||
| FUT2 | Fucosyltransferase 2 | rs602662 A>G (G) | Lower vitamin B12 levels | Increase consumption of food rich in vitamin B12 | |
| ALPL | Biomineralization associated alkaline phosphatase | rs4654748 C>T (C) | Lower Vitamin B6 blood concentration | Increase consumption of food rich in vitamin B6 | |
| CYP2R1 | Cytochrome P450 family 2 subfamily R member 1 | rs10741657 A>G (G)rs10766197 A>G (A) | Lower vitamin D levels | Increase consumption of food rich in vitamin D, increase sun exposure | |
| GC | GC vitamin D binding protein | rs4588 G>T (T)rs842999 C>G (C) | |||
| MTHFR (*607093) | Methylenetetrahydrofolate reductase | rs1801133 G>A (A) | Homocystinuria (#236250) | Increase folic acid intake | OMIM |
| CBS (*613381) | Cystathionine beta synthase | rs121964962 C>T (T) | Homocystinuria (#236200) | ||
| FOXO3 | Forkhead box O3 | rs2802292 C>T (T) | Longer lifespan | SNPedia | |
| rs2802288 A>G (A) | |||||
| rs2802292 T>G (G) | |||||
| SIRT1 | Sirtuin 1 | rs3740051 | Higher basal energy expenditure | doi:10.1371/journal.pone.0058636 | |
| rs2236319 | |||||
| rs2272773 | |||||
| PEMT | Phosphatidylethanolamine N-methyltransferase | rs12325817 G>C (C) | Low choline | Increase choline intake | doi:10.1096/fj.06-5734com |
| CHDH | Choline dehydrogenase | rs12676 G>T (T) |
A cross talk between genes, diet and physical activity for weight management
Genetic polymorphisms linked to obesity may exert their effects by influencing physical activity and, on the other hand, physically active life style may reduce the risk of obesity. Current evidences have shown that physical activity can modulate heritability estimates for obesity-related traits (125). For instance a study revealed significantly strong interactions between FTO intron 1 and physical activity in 16 independent cross sectional and interventional studies carried out in adults and children with European, African and East African origin (19, 127). In addition to this, a recent meta-analysis in individuals of European ancestry (sample size: 111421) proved a significant physical activity-genetic risk score (GRS) interaction with respect to 12 obesity predisposing polymorphism. The study indicated that this physical activity-GRS association is more evident in subjects living in North America (60).
Another meta-analysis on 218166 adults and 19268 children revealed that greater leisure time physical activity attenuated the effects of FTO variants, however, prolonged sedentary periods, such as watching TV, enhanced genetic predisposition to elevated adiposity (152). Moreover, Rankinen et al., reported that genetic association of FTO with BMI was strengthened by prolonged sedentary periods of watching TV in both men and women (153). Similar finding were reported by in another UK Biobank study with large sample size (154). They reported a strong genetic association with sedentary life style and risk of obesity which was worsened by inadequate sleep (154). The Diabetes Prevention Program in the US reported a strong interaction between FTO and a 1-y lifestyle intervention regime of physical activity, diet, and weight loss with respect to subcutaneous fat area among 869 individuals (155). They found the minor allele of the FTO variant is associated with increased subcutaneous fat area in the control group as compared to the lifestyle intervention group (155). Similarly a recent study indicated that physical activity combined with vegetarian diet can reduce the increased BMI caused by susceptibility due to FTO rs3751812 minor allele (156). Other physical activity-associated genes that can respond to dietary intake are genes involved in muscle structure and strength. A study in 461 European American adults showed the association of ACE I/D polymorphism rs4340 with a weekly walking distance of approximately 8 Km, ultimately reducing the BMI (157).
The aforementioned studies are a few examples of cross talk between genetic factors governing obesity and physical exercise. It can be concluded here that although genetic factors predispose to increased risk of obesity the environmental, behavioral, socioeconomic, psychological factors that contribute to overall life style determine the overall phenotype of an individual. Physical activity in this regard plays a vital role. Several studies have shown the importance of diet coupled with physical activity in maintaining a healthy weight. However there is no such one size fit for all diet that can meet the dietary requirements of people belonging to various ethnic groups that keeps the obesity at bay without the need of physical activity.
Commercial genetic testing for personalized diet recommendations
Two types of genetic tests are basically carried out in commercial settings i.e. laboratory developed tests and direct to consumer tests (DTC). Both of these tests are used to identify genetic factors contributing to health related issues and to provide dietary recommendations. The DTC testing industry provides individuals an easy access to their own genetic information. Many of these tests aim at informing the susceptibility to different diseases while some deliver nutrition recommendations (1). As a result, supporters of this technology assert that the information provided by these tests may result in a positive impact on health behaviors such as diet, smoking and exercise thus preventing the development of chronic diseases. However, unregulated nature of the industry is a continuous source of controversies surrounding DTC genetic testing.
The major requirement of a nutrigenetic test is that the outcomes must specify a diet-related recommendation that is advantageous for individuals. In addition to that, the results should be based on evidences that have been replicated and give consistent results. Inferences should be drawn based on realistic approaches. Ethical issues should be addressed accordingly.
Ethical concerns related to commercial genetic testing
Keeping in view the quality of genetic profiling tests and their consequent impacts on human life, these tests are prone to several ethical issues related to human welfare, sensitivity of such information and psychological effects of test outcomes. Nearly two million genetic test panels are commercially available with different companies analyzing different genetic polymorphisms (158,159).
Dietary recommendations based on genetic tests that give inconclusive or unreliable information may result in unwarranted limitations and issues (160).
In addition, the interpretation of these results is very important. Only a highly trained health professional having an in-depth knowledge of gene-diet-environment interactions should actually be allowed to interpret the results and extend recommendations . This implicates the need of education in the nutrigenetic, nutrigenomic and nutri-epigenomic disciplines and such courses should be included in curriculum (161).
Guidelines and legal regulations for genetic testing and personalised nutrition
The main aim for the guidelines and regulation for genetic test is to protect individuals from the services that can harm the individual physically, sentimentally or psychologically and to safeguard their rights. For genetic testing and personalized nutrition the regulations are mostly pertaining to analytical methods, clinical validity and clinical utility of the results. Due to increased trend of nutrigenetic research and its practical applications in commercial perspective several public, private organizations have developed guidelines for ensuring safety and well-being of human subjects that are either involved in the studies or are consumers of DTC and dietary prescriptions. European nutrigenomics organization (NuGO) established guidelines for the nutrition genomic research in 2007 (162). These guidelines were formulated based on the international laws safeguarding basic human rights. These specifically mention the importance of informed consent for participation of human subjects in any of the nutrigenetics or nutrigenomic trials or observation studies. However these guidelines are not law so every research involving human subjects should abide by the local legislations of that area. NuGO has highlighted the ethical issue related to genetic testing and consequent release of results for any studies related to nutrigenomics. According to the guidelines all possible measures should be taken in disclosing the results of genetic testing to individuals. For instance it is stated that: Genotype results attained during nutrigenomics research should preferably be communicated to the research volunteers on a study group level, and should not be disclosed individually. The reason being scarce scientific evidence linking specific genetic variant to diet sensitive pathological condition or response to any dietary intervention. Besides that phenotypic expression of a particular genotype is influenced by many confounding factors hence it is very difficult to be interpreted with respect to health risks and benefits for a particular individual. Moreover disclosing the results of epidemiological studies with large number of subjects could possibly convert the studies into mass screening which may give rise to different budgeting and counselling implications (162). In case of test result disclosure to individuals NuGO advices to convey the results through highly trained professionals who can answer any questions related to outcome of results and detailed written results should also be given in case the consultation by Physician or health care provider is required.
In addition to organizations like NuGO, the government organizations and the agencies of various countries have published rules regulating DTC tests which help to explain, inform and aware individuals. In USA there are three agencies regulating the genetic tests: FDA (Food and Drug Administration), CMS (Centers for medical and Medicaid services), FTC (Federal trade commission) (163). Furthermore, EU also issued a paper in 2002 based on the guidelines of genetic tests that addresses patients privileges in several European countries, mainly concerning to the right to acquire information, confidentiality, privacy and informed consent (164). The human genetics commission (HGC) in UK published a paper in 2010 on ethical guidelines for the genetic tests including the transparency, availability, ease of getting information and standardized testing procedures (165). Similarly the German governments implemented instructions associated to the authorization of laboratories, the necessity for well-versed agreement and genetic counselling (166). While there are no particular regulation for DTC tests in Belgium and Netherland yet there is a law stating that compelling the involvement of medical physician DTC tests of a medical nature in Belgium (167) and obtaining a permit from the Dutch Minister of welfare and sports in the Netherlands for some genetic tests (168). Whereas the department of health of the Australian government decreed that that testing should guarantee safety and quality and forbids the sale of DTC tests for severe diseases However the regulatory framework in Europe, Food4Me however concluded that neither EU nor its associate states have legal instruments specially dealing with personalized nutrition (169,170).
Controversies related to commercial genetic testing and personalised nutrition
Several controversies are in the air because of rapid increase in genetic testing services aimed at providing tailor made fancy diets and specifically because of some of web based companies and their fraudulent claims. Besides a research boom in the field of nutrigenetic the authenticity of genetic basis of DTCs and the dietary recommendations are still debatable. This is particularly due to the reason that where majority of nutrigenomic studies are thriving to find an evidence of association of genetic variants, polymorphisms and mutations with dietary/ metabolic phenotypes, a considerable amount of observational studies and trials based on large cohorts of human subjects have proven no association. Notable examples in this regard are the PREDICT study involving 700 twins and 400 non twins. Their preliminary results presented at the 2019 meeting of the American Society of Nutrition, confirmed that Different people respond very differently to the same dietary inputs and there is no one dietary approach thats works best for everyone (171). The differences in dietary responses in this study were attributed more to factors such as sleep habits, exercise, stress and gut microbes than to genetics. Another important study DIETFIT concluded that there were no significant differences in weight loss pattern among subjects undertaking diets matched vs unmatched with their genotypes. Moreover there was gene-diet interactions for waist circumference, BMI and body fat percentage (172). Besides these social aspects of personalised nutrition related to an increased healthism and medicalisation of diet are also continuous source of controversy (173).
Conclusion
The advancement in science and availability of technologies like, genomics, transcriptomics, proteomics, metabolomics coupled with information derived from The Human Genome Project has opened the doors for personalised interventions in diet to address metabolic issues related to diseases, disorders and behavioral patterns. Although genes have been found culprit in epidemics like obesity and sedentation, social, moral, psychological and other confounding environmental and epigenetic factors may favor or oppose a typical dietary response. Physical activity holds promise in this regard and adding moderate physical activity to daily life routines coupled with a balanced diet help achieve a desired weight loss goal. Despite of the challenges faced by the nutrigenomics and personalized nutrition in terms of scarcity of scientific evidence, technological barriers, confidentiality and validity of genetic testing, and ethical concerns related to humans both at nutrigenetic services provision and receiving end, this fields holds promise in improving the quality of human life by interventions complementing, silencing or enhancing the expression of our genes.
Conflict of interest:
Each author declares that he or she has no commercial associations (e.g. consultancies, stock ownership, equity interest, patent/licensing arrangement etc.) that might pose a conflict of interest in connection with the submitted article
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