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Nutritional genomics

Nutritional genomics

An area of precision nutrition, and an extension or precision medicine, that aims to prevent, treat and manage diseases caused by a combined action of multiple genes by designing targeted therapies based on the genetic makeup and dietary intake of an individual.

Nutritional genomics is an area of precision nutrition, and an extension of precision medicine, that aims to prevent, treat and manage diseases caused by a combined action of multiple genes by designing targeted therapies based on the genetic makeup and dietary intake of an individual.

Nutritional genomics includes the study of the bidirectional relation between genes and diet. The field is concerned with how genetic variation affects the body’s nutrient response, which is termed nutrigenetics. Additionally the field seeks to understand how nutrients affect gene function, which is termed nutrigenomics. Nutritional genomics will likely integrate information from other omics information such as metabolomics, transcriptomics, epigenetic modifications and gut microbiota composition. In 2014 the Academy of Nutrition and Dietetics published a position paper and statement regarding the use of nutritional genomics, stating that nutritional genomics provides insight into how diet and genotype interact, that it is an emerging science not ready for routine dietetics practice.

Nutritional genomics has been clinically applied to diseases in the category of inborn errors of metabolism. These are monogenetic diseases caused by well-characterized genetic variants that modify critical proteins in a metabolic pathway, such as phenylketonuria, galactosemia, and maple syrup urine disease. In phenylketonuria are recommended a low-phenylalanine diet because a gene mutation in these individuals cause them not to metabolize phenylalanine effectively.

People may be predisposed to certain diseases due to the simultaneous presence of a number of genetic variants on several genes. Examples of these polygenic diseases include hypertension, coronary heart disease and diabetes. For coronary artery disease there are at least 60-100 contributing genetic loci. Around 80% of these loci reside in noncoding regions and it is not clear which biological pathways may be altered by these genetic variants. Epidemiology studies have looked at gene-lifestyle interactions for cardiometabolic diseases including obesity and type 2 diabetes. In these studies a lifestyle component or nutrient were found to either attenuate or exacerbate a genetic influence on disease. Limitations to these studies include small sample-size, imprecise measurement estimates of lifestyle exposures, specifically with respect to diet or self-reported outcomes. Results have not been well replicated in independent studies.

Adequate statistical power has been attained due to large research infrastructures and biorepositories such as the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium and the UK Biobank. The association between obesity genetic susceptibility and anthropometric traits (BMI and BMI adjusted for waist-to-hip ratio) was modified by self-reported adherence to a healthy dietary pattern in around 68,000 people of European descent from 18 cohort studies.

Post hoc analysis of well-powered and well-designed randomized clinical trials have also provided evidence of the differential impact of dietary intake on polygenic traits. For example, individuals carrying a variant in transcription factor 7-like 2 (TCF7L2) which increases the risk of type 2 diabetes showed an improved cardiovascular disease risk and a lower incidence of stroke when consuming a Mediterranean diet compared to a low-fat diet.

Direct to consumer genetic testing

Direct to consumer genetic testing (DTC-GT) companies generally provide information on the risk of monogenic disorders like intolerance and sensitivity to substance such as caffeine and lactose. They provide information about macronutrient and energy metabolism such as fatty acid oxidation, weight management, vitamins and mineral requirements. Some companies provide nutritional coaching and personalized meal deliveries. While it is often believed that understanding genetic background will lead to individuals changing their unhealthy behaviors, a large longitudinal cohort study of 2037 customer from the Scripps Genomic Health Initiative suggest this may not be the case. The study indicated that customers did not see changes in physiologic health outcomes such as anxiety symptoms, dietary intake or exercise before and after receiving the genetic information. It was found that 1042 customers using 23andMe or Pathway Genomics that received reports of elevated cancer risk did not significantly change their diet, exercise, advanced-care planning or cancer-screening behaviors.

DTC-GT for nutritional genomics appears to be beneficial for highly penetrant alleles. Penetrance is a measure of the proportion of individuals in a population that carries a particular allele and expresses the related phenotype, and the phenotype can be disease. There are significant gaps in scientific understanding of polygenic traits. Gene-lifestyle interactions are often not replicated across different ethnicities. There are specific ethnicity-specific genetic variants such as a non-sense polymorphism in the TBC1 domain family member 4 (TBC1D4) locus that increases type 2 diabetes risk 10-fold and the allele has a frequency of 17% in Inuit populations, but is almost nonexistent in other groups. The need for more accurate data on dietary intake has resulted in mobile phone applications that identify and quantify food consumption.


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