Your Fat Intake Master Trait
Monday, August 24, 2020. Author FitnessGenes
Monday, August 24, 2020. Author FitnessGenes
This ‘master trait’ integrates your results from multiple traits that look at your response to fats in the diet. It then advises you how to optimize your dietary fat intake.
Feed two individuals exactly the same diet, and the chances are that both will respond differently. For example, as we discovered in the APOA2 and saturated fat blog, when following a diet that is high in saturated fat, some people stand to gain more weight.
One reason for this difference in nutritional response is due to variation in our genes. Scattered throughout our genome are small single-letter changes in the DNA sequence called Single Nucleotide Polymorphisms (SNPs). Some SNPs may alter our genes: units of DNA that code for proteins. Many of these proteins, which include molecules such as enzymes, transport proteins and hormones, play a role in the absorption, transport and metabolism of nutrients in our diet. Therefore, by creating genetic variants that affect nutrition-related proteins, SNPs can influence our response to diet.
There are three main ways (described below) by which SNPs can alter our traits - measurable biological characteristics, which range from simple observable features such as eye colour to complex traits such as our response to nutrients in our diet.
1. Non-synonymous SNPs
In certain cases, a SNP may occur within the coding region of a gene and thereby change the amino acid sequence of the protein produced from that gene. This can then alter the structure, function and activity of the protein. A good example of this is the ACTN3 gene, whereby a single-letter change (C-->T) in the DNA code causes the production of a non-functional alpha actinin-3 protein, which may impair high velocity muscle contractions.
2. Synonymous SNPs
In other cases, a SNP may occur in a non-coding region of DNA. Rather than directly affecting the protein structure, SNPs in non-coding regions may instead affect other processes, such as how genes are switched on and how genetic instructions are converted into a protein (a process called gene expression).
For example, in your Fat Metabolism (beta-oxidation) trait, we encountered a SNP of the ACSL5 (acyl-CoA synthetase long chain 5) gene. This single-letter change (C-->T) occurs in something called the ‘promoter region’ of the gene, which regulates how the gene is ‘switched on’ and made into its protein product (i.e. gene expression). Individuals who have this SNP, and therefore have the letter T in the promoter region of the ACSL5 gene (in other words, they carry the ‘T’ allele), demonstrate higher gene expression and produce elevated levels of the ACSL5 enzyme. This allows them to burn more fat in response to a low-calorie diet.
3. Non-functional SNPs
Sometimes, SNPs may not appear to directly affect a gene or protein, but merely serve as a reliable genetic marker of some trait. These non-functional SNPs are typically found through studies called genome-wide association studies (GWAS).
In these types of studies, SNPs are compared between people with and without some trait or disease. For example, people may be split into those with and without Type 2 diabetes, and their DNA compared. Some SNPs / gene variants may occur significantly more frequently in the group with Type 2 diabetes and may therefore serve as a marker of disease risk.
When it comes to our traits, genes and SNPs are only one part of the picture. What we eat, how we exercise and how we live our lives, in short, environmental factors, all shape our biological characteristics. The same environmental factors, however, can affect us differently depending on what gene variants we have. Similarly, the effect of our gene variants on a trait can differ according to what environmental factors we’re exposed to. A good example of this is your APOA2 and saturated fat response trait. Individuals with the CC genotype are at an increased risk of weight gain, but only when exposed to an environment whereby dietary intake of saturated fat is high.
These interactions between genetic and environmental factors are termed “gene – environment interactions.” When the environmental factor in question specifically refers to aspects of diet (e.g. saturated fat intake, exposure to lactose), we call it a gene-diet interaction.
Two related terms you may have encountered are nutrigenetics and nutrigenomics. Nutrigenetics is the study of how gene variants affect dietary response. Nutrigenomics focuses specifically on how different nutrients in our diet alter gene expression.
Your latest master trait uses research from both these fields to guide you on the best fat intake for your gene variants.
If you go through truefeed, you’ll notice that individual traits tend to focus on one narrow aspect of your physiology. For example, your Betaine Synthesis and Metabolism Trait looks specifically at your production and usage of the micronutrient betaine. Single traits are useful because they allow us to refine the science and answer concise questions such as, “How much betaine should I consume through diet and supplements?”
The human body, however, is a complex biological system, with several traits interacting with one another. In order to answer a more complicated question such as, “What should my fat intake look like?” we need to take a more holistic approach and compile several different traits. This is exactly what we do in our master traits.
This “master” trait compiles three major traits, two of which we have previously encountered:
- APOA2 and saturated fat
This trait analyzes your risk of weight gain when consuming a diet high in saturated fats.
- APOA5 and blood triglycerides
This trait analyzes your risk of high blood triglyceride levels, particularly in response to dietary fat and carbohydrate.
- TCF7L2 and low fat diets
This trait looks at how your body composition and blood sugar levels respond to low fat diets. More information on this new trait is included below.
Your TCF7L2 gene codes for a protein called Transcription Factor 7 like 2.
Transcription factors are molecules that switch on the expression of other genes. It’s thought that TCF7L2 activates genes in signalling pathways that are involved in insulin function and fat metabolism.
A SNP (rs12255372) within the TCFL2 gene causes a change in the DNA sequence from the letter ‘G’ to ‘T’. This creates two different gene variants or ‘alleles’ – the ‘G’ allele and the ‘T’ allele. As we inherit genes in pairs (one from each parent), this gives rise to three different genotypes: GG, GT and TT.
Studies suggest that people carrying two copies of the ‘T’ allele (i.e. the TT genotype) lose more weight when following a diet that is low in fat.
In a large study known as the ‘Pounds Lost’ trial (Preventing Overweight Using Novel Dietary Strategies trial), researchers found that, compared to GG and GT genotypes, people with the TT genotype showed significantly greater weight loss and improvements in body composition when following a low fat diet.
Furthermore, the study compared the effects of high versus low fat diets. Individuals with GG and GT genotypes did not experience any difference in weight loss between these two diets. By contrast, people with the TT genotype lost significantly more weight when following a low fat compared to a high fat diet.
The Pounds Lost trial followed 591 overweight and obese subjects across 2 years.
Half of the group adhered to a low-fat diet, whereby 20% of total energy (calories) came from fat. The other half followed a high-fat diet, whereby 40% of total energy from fat. Both diets were matched for total calories, with both designed to achieve a 750 kcal deficit per day.
The subjects were assessed every 6 months and were assessed for: weight, waist circumference, fat mass (including changes in truncal fat around the abdomen and trunk) using DXA scanning, and blood levels of glucose and insulin.
After 6 months, people with the TT genotype showed significantly greater drops in bodyweight, BMI, total fat mass and truncal fat mass in the low fat diet group compared to the high fat group.
The graph below demonstrates this difference between genotypes and between low and high fat diets (black bars refer to the low-fat diet group, grey bars = high-fat diet group).
Furthermore, these improvements in BMI and body composition were associated with favourable changes in blood glucose and insulin levels, but again, only in the low fat group. This suggests, if you have the TT genotype, low-fat diets may be particularly useful in improving blood sugar control (also known as “glycemic control”).
It’s not yet clear why TCF7L2 gene variants cause differences in weight loss and body composition in response to certain dietary fat intakes.
The rs12255372 SNP is an example of a non-synonymous SNP – it occurs in a non-coding region of the TCF7L2 gene and affects gene expression. In this respect, studies show that the T allele is associated with a 1.5 – 3 times greater expression of the TCF7L2 gene.
As mentioned earlier, the TCF7L2 protein is a transcription factor. This means it switches on the expression of other genes. Some of the genes activated by TCFL72 include those related to the production of incretin hormones.
Incretin hormones, which include GLP-1 (glucagon-like peptide-1) and GIP (Glucose-dependent insulin polypeptide), are molecules released by the gut in response to food intake. Their main effect is to increase insulin release, thereby allowing glucose to move from the bloodstream into tissues.
Incretin hormones also have other effects, including reducing appetite and increasing feelings of fullness (satiety), promoting deposition of triglycerides in adipose tissue, and delaying emptying of digestive contents from the stomach into the intestines.
It’s possible that, by altering the release and activity of incretin hormones in response to fat contained in meals, variants of the TCF7L2 modify our response to dietary fat.
X-Forward = 220.127.116.11:59600
Remote-Addr = 18.104.22.168
Your IP Address = 22.214.171.124
Override location = =>
Override pricetable = =>
Your Continent = NA - North America
Your Country = US - United States
Your State = VA - Virginia
Your City = 4744870 - Ashburn
Your Price Table = USD
Location comments =