Approx reading time 8 mins – Fact checked ✔️
The pathogenesis of chronic disease appears to be a complex, multi-factorial and individual interplay between genetics, environmental, lifestyle and dietary interactions (1), influencing epigenetic gene expression (2). Darwin proposed that natural selection depended on variation, genetic heritability and the adaptation premise, with the ability to adapt dependent on evolutionary fitness and reproductive success, with the view to pass on genetic information to successive generations (3), while evolutionary biology identified three major causes of disease; genetic, environmental and infectious (4).
Diseases of genetic origin may confer a reproductive edge involving resistance to infection (5), however, moving out of a particular environment and failing to adapt may result in the loss of that advantage leading to a particular gene or trait being removed from a population forming the basis of natural selection (6). Darwin stated that animals went through a series adaptations to changing environments and a gradual phasing out of particular parts of the genome due to mismatches between the body and the current environment (7). Gluckman (8) stated that evolutionary biology does not cause disease nor is it predetermined by the human genome, but rather that the modern-day environment in which humans live is novel and exposures to which may confer disease susceptibility to some individuals, but not to others.
Genome-wide association studies (GWAS) have sought to understand how variations in the human genome could be used to predict susceptibility to chronic diseases (9). Variations may occur via mutations, either heritable changes in DNA sequence or via somatic mutations which may occur during a person’s lifespan (10), whilst non-genetic factors such as food behaviours and physical activity could also influence the genome, conferring individual risk for developing disease. DNA methylation may alter gene expression and the resulting phenotype via the addition of a methyl group on cytosine-phosphate-guanine (CpG) at promoter regions by DNA methyltransferases, resulting in transcriptional silencing and linked to positive health (11). However, hypermethylation of CpG may lead to functional loss of tumour suppressor genes and may result in colorectal cancer (12). Conversely, global hypomethylation may also induce various forms of cancer (11). Robertson and Wolffe (11) stated that hypomethylation was also observed in healthy tissue beside tumour cells, suggesting a role in the initiation of disease, while demethylation resulted in increased transcription which could result in carcinogenesis and other diseases.
Modulation of gene expression may also occur with nutrients. Vitamin D response elements bind to retinol-dependent receptors producing dimers, influencing gene transcription via interaction with histone acetyltransferases (13). Histone acetylation of lysine residues has been associated to the pathogenesis of neurodegenerative diseases such as Alzheimer’s activating transcription, while deacetylation results in transcription repression (14).
Single nucleotide polymorphisms (SNPs) consist of different versions of a DNA sequence known as alleles (15), and may increase susceptibility or protection from certain diseases on different individuals (16). Human genome studies have calculated the frequency by which particularly genes occur in a population which determine whether particular SNPs have a cause-effect relationship to disease progression, but there appears to be substantial variations within populations and healthy individuals (15). Homozygous and heterozygous SNPs of the methylenetetrahydrofolate reductase (MTHFR) enzyme involved in the one-carbon folic acid cycle may affect the bioavailability and production of 5-methyl-tetrahydrofolate (5-MTF) by 70% and 30% respectively (17). Reduced MTHFR activity may lead to hypomethylation, elevating homocysteine levels (18), increasing the risk of CVD by disrupting arterial endothelial integrity and inflammation (19).
However, SNP are not always disease-causing as some genes control for more than one phenotypic trait (20). For example, although SNPs such as the APOE E4 allele may be associated with a significantly higher risk CVD, Alzheimer’s disease and cognitive impairment in later life, it is also associated with improved cognition (21) and intelligence (22) in early life, and an example of antagonistic pleiotropy (20). Melzer et al., 2020 (9) stated that antagonistic pleiotropy may promote early-life survival by inhibiting neoplastic cells, but eventually limiting longevity as dysfunctional senescent cells accumulate, increasing expression of neoplastic cell mutations (23). Senescent fibroblasts secrete matrix metalloproteinases, epithelial growth factors, and inflammatory cytokines predisposing somatic mutations, oncogenes, tumorigenesis, inhibition of apoptosis and increasing functional decline, characteristic of age-related diseases (24).
Case-controlled (CC) population GWAS (25) have evidenced the cumulative risk of on increased BMI, weight and Waist circumstance from several obesity-susceptible genes, including the fat-associated obesity gene (FTO). In contrast, other CC GWAS of obesity-susceptible genes (26), showed conflicting results (25), even when stratified for BMI, body fat and waist circumference, possibly due to heterogeneity and bias within the included observational studies. However, some studies (27) showed that SNPs such as the FTO gene, increased the risk of obesity in all populations, whilst others (28) showed that polygenic predisposition was deemed mandatory but not enough to trigger obesity, highlighting that dietary and lifestyle interventions could prevent obesity risk in genetically predisposed children.
Disease management in western medicine tends to treat symptoms and not the root cause, for that reason, some qualified nutrition practitioners prefer a functional medicine systems-based clinical reasoning model to identify the root cause of the disease (29). The functional medicine model obtains cues from patients signs and symptoms, with consideration for antecedents, triggers and mediators (ATM) of disease pathophysiology across a lifespan (30), which then translates in to dietary and lifestyle interventions. However, nutrigenetics may determine how an individual’s genetic make-up may predispose for dietary susceptibility, while nutrigenomics may determine how nutrition influences the expression of the genome, highlighting the importance of personalised nutrition recommendations (31).
Personalised interventions translate in to the delivery of relevant information to a client based on their unique ATMs, which may help improve health outcomes (32), but is dependent on adherence (33). Phenotyping by genotyping using direct to consumer genetic testing (DTCGT) (34), could provide anadditional layer of personalisation to interventions (32), by considering a cluster of client-specific SNPs to help stratify individuals by risk of developing a particular disease trait (35). However, genetic risk estimates of DTCGT often have low to moderate predictive value to determine disease susceptibility, with considerable heterogeneity in the predictive algorithms used by different DTCGT companies, posibly due to insufficient power, pleiotropy or bias within the included studies (34), or a result of small effect sizes of most disease risk variants (35). Additionally, issues regarding clinical validity and ethics regarding data protection, handling and disclosures of DTCGT is a recent concern (36) and general data protection regulations should be considered. Lack of accountability with regards ownership and usage of data, and informed consent, were highlighted by The American College of Medical Genetics and Genomics (37), whilst exaggerated and misleading claims have also been noted in DTCGT companies (38), leading to an imbalance between advertised benefits and stated risks.
Thus, the evidence appears to suggest that DTCGT could provide clinicians with a valuable prophylactic tool to help identify the risk of development or progression of chronic diseases in susceptible individuals when used in conjunction with other anthropometric and functional biomarkers, but never in isolation, which may help to further personalise nutrition interventions, while ensuring that client’s rights and safety is preserved.
Check out our future SERVICES section as from Jan 2022 onwards, to see how genetic testing may help you create a bespoke dietary pattern based on your genes, which could help you achieve your optimum health.
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