“The more a law lays claim to universal validity, the less it does justice to the individual facts.”Carl Jung
For generations, Americans have ping ponged back and forth between different fad diets. Each new wave of research brings with it a new way of eating. We’re told fat is dangerous, eat more whole grains. Now we’re in the midst of another movement towards fat, with a greater emphasis on the dangers of sugar. Some advocate for the exclusion of all plant foods in favor of a carnivore diet. Others tout the benefits of beans and whole plant foods as enhancing longevity. Book after book is published telling all Americans how to eat.
While the nutrition wars rage on, a new and growing body of science is teaching us that there is no one size fits all when it comes to diet.
Randomized controlled trials are rare in the nutrition world. After all, how can we expect to control the diets of large groups of people over long periods of time?
However, in 2018 a study appeared in the Journal Atherosclerosis that did just that. Called the Retterstøl study, researchers in Oslo measured changes in biomarkers of heart health for 30 healthy individuals placed on a high fat, low carbohydrate diet. The results were fascinating. Although the average “bad cholesterol” numbers went up by 44% on the high fat diet, variability between individuals was astonishing. Some in the high fat group only saw an increase in bad cholesterol of 5%. Other saw increases as high as 107%! Still others had to drop out after becoming very ill eating a high fat diet. Retterstøl teaches us that people respond very differently to dietary inputs based on genetics.
Our mission at Gene Food is to anticipate an individual’s response to a given diet and then assign them to a diet type that will work for them.
Gene Food’s resident geneticist, Dr. Aaron Gardner, created a scoring system that places people into one of twenty dietary categories based on how well they metabolize fats, carbohydrates, sugars, histamine, dairy and more. Individuals that are more likely to fall into the 107% increase in bad cholesterol group we saw in the Retterstøl study are assigned to a diet type that is more plant based. By contrast, individuals estimated to have greater ability to deal with a higher saturated fat diet are placed in a diet type that allows for a greater percentage of calories to come from fat. In addition to the broad diet categories, Gene Food’s system scores people for micronutrient balance, sterol absorption, LDL, genes, methylation strength and more. Each gene we report on is assigned a Science Grade, which gives our readers and customers the opportunity to see what the state of the research is for a given gene. Gene with a higher Science Grade are backed by more research, lower Science Grades represent genes that are just starting to emerge. While we do not believe genetics are the only factor in evaluating dietary choices, we see them as a foundational tool to help people get started sifting through the mountain of marketing material aimed at funneling them to one of the popular dietary camps.
The days of one size fits all nutrition are over.
To look, feel, and perform at our highest level, we must learn the science of our own bodies. At Gene Food, we hope to play a small role in this awakening towards a greater recognition of bio-individuality. To that end, we research and publish content to our blog with the goal of arming the public with the information they need to begin the process of creating a personal health regimen. We have also developed a groundbreaking genetics product that helps people establish a foundation for eating that we hope will last a lifetime.
John O’Connor – Founder
John O’Connor is the Founder of Gene Food and host of the Gene Food Podcast. Like many who make their way into the wellness world, John was inspired to create a platform that could empower people to find personalized health solutions after he realized his health and dietary needs didn’t fit the template offered by conventional nutrition and medicine.
Dr. Aaron Gardner – Head of Research
Dr. Aaron Gardner holds a BSc in Genetics, a Masters of Research in Medical and Molecular Biosciences as well as a doctorate in immunology. He has worked in a variety of medical research laboratories including those at Durham University and Newcastle University in the UK and Columbia University in New York. His work has concentrated on understanding the genetic modulation of various fibrotic disorders. Through his clinical experience, Aaron became fascinated with the potential for targeted personalized medicine to stem the tide of disease. His work at Gene Food is an extension of that passion. At Gene Food, Aaron created the scoring system used for Custom Nutrition Plans as well as the Guide to Nutrigenomics.
Danielle Moore – Nutritionist and Recipe Developer
Danielle Moore holds a bachelor’s degree in biochemistry and a graduate degree in Nutrition. Danielle has seen firsthand the healing power of personalized nutrition in her successful journey to overcome an autoimmune condition. At Gene Food, Danielle is responsible for developing and testing our database of recipes.
Leigh Matthews – Features Writer, Nutritional Therapist, Researcher
Leigh Matthews holds a degree in Nutritional Therapy from the University of West London in the UK. She has been working as a medical writer since 2010 and in the natural health industry since 2006 and is the author of Eat to Beat Acne: how a plant-based diet can help heal your skin, as well as four novels. At Gene Food, Leigh is a features writer and indispensable member of the research team.
Ned – Official Mascot
Ned O’Connor is an Australian Labradoodle and the official Gene Food mascot. Ned’s nutritional awakening began upon realizing he couldn’t tolerate poultry or beef based dog food. Making the switch to kibble made with fish as well as adding venison and sweet potato resolved Ned’s digestive issues and made him a believer in the personalized health revolution. When he’s not at work, Ned enjoys counter surfing, playing with tennis balls and napping on cool tile floors.
How We Rate Genes
To give you an idea of our methods here we’ll run through an example of how we assess and grade one of our polymorphisms on the site. Below we’ll run through an example for perhaps the most well known polymorphism, rs1801133, commonly known as C677T, in the MTHFR gene.
To start, we extract the most up to date information about a polymorphism of interest from the dbSNP website, a freely available resource provided by the National Center for Biotechnology Information in the US, which is updated and maintained by researchers from around the globe.
The above screenshot from the site provides us with some key bits of information. First, it describes the possible alleles in this case G and A, and also gives information about which is the most common of the two. Here we can see that the A allele occurs in about 30% of individuals making it the minor allele. In the same image we can also see that it has a reported Clinical Significance and that a large number of publications are reported to have investigated it.
Based on this we would be able to grade this SNP as C-.
Further down the page we can extract some further information. Here we can see that the reference polymorphisms are coming from the minus strand of the DNA molecule. To interface with 23andme and other consumer kits we need to switch this to the positive orientation. To do this we simply switch G > C and A > T.
We can also see here that this particular polymorphisms causes an amino acid change in the MTHFR protein. Together all this information points to this polymorphism being of increased interest.
Based on this and significant research and clinical interest we would increase therefore increase the grade up to a C or C+.
For several SNPs we leave the grade here as there is no extra information available for us to base further assessment on, or there are significantly conflicting results about the impact of the polymorphism on health.
But for C677T we keep moving onwards, and switch over to dbSNPs sister site (and possibly one of the most amazing resources on the web) PubMed. PubMed is an indexes a huge range of life-science journal articles providing a really important search function. dbSNP provides a handy link at the bottom of the page to take you there directly but you can freely search for other polymorphisms, or anything else of interest.
In the image above you can see what an example search result for all papers referencing C667T looks like. 664 papers is a bit too much for anyone to read so luckily we have a few filters we can apply to the left.
I’ve listed five key types of article that we use to assess the importance of a polymorphism and also its function.
For lay people a good starting point is to select “Review” and then also “Free full text”. This will filter any articles that are behind paywalls, and show review articles which as the name suggests review the field of research. They’re a great way to get started in a field, but we’re most interested in primary research.
Our first step is to identify if the genetic change described is associated with a change in the actual function of the protein, and identify which of the alleles, C or T, is associated with any potential poor health outcomes.
Searching the PubMed index above we can find numerous articles which reference a mechanism. By reading the above article, and others, we can see that the T allele of C677T is associated with a more heat sensitive MTHFR enzyme, which impairs its function.
Next step, is to see if there are any associations with clinical outcomes. A good filter to use here is “Case Reports” which are small papers based on single individuals. Using these and other articles we can rapidly determine what health effects a particular polymorphism may have. For C677T and MTHFR there is a long list, which we cover in our blogs, for other polymorphisms however they may be much more targeted.
With a mechanism of action and clear health impacts we can upgrade this SNP to B grade.
This is where most of our polymorphisms reside as further studies are either lacking or there are inconsistent results relating to effects. So what does it take to get into the A grades?
To get an A- grade the first thing we want to see are well controlled “Clinical Trials” If we filter our previous search for clinical trials we get 30 results (again significantly larger than most other polymorphisms). This is where we start doing a deep dive into the data of the paper and determine the relative impact of a polymorphism on health. Clinical trials often report useful metrics like relative risk, that allow us to determine the likelihood of a particular polymorphism impacting on health.
Great, for MTHFR C677T we can see plenty of evidence, now what about those really high grades?
Here we’re interested in our final two article types, “Meta Analyses” and “Systematic Reviews”. The approach of both these study types is to synthesize previous studies and attempt to produce an overall picture. When dealing with genetics it isn’t uncommon to see studies reporting significantly different effects, this is because there are a whole host of other factors that come into play such as other polymorphisms, an individuals lifestyle and even environment. One way to account for this is to perform a single large study, but this is often unfeasible or prohibitively expensive.
This is where a meta analysis becomes very useful, and such studies often contain figures like the above.The solid vertical line represents the equivalent of no effect on the outcome (in this figure the effect of the T allele of C677T on total cholesterol). Each study is represented by a point on the graph, those to the right show a positive effect, and those to the left a negative association. The authors then determine the average effect across all of these studies and represent it as the red dashed line. In the above image we can therefore see that the T allele is associated with increased total cholesterol.
So for MTHFR C677T we have a described mechanism of action at the protein level, a defined impact on health which has been well studied and validated by gold standard studies.
It is therefore given a grade of A+.
How We Use Genes in our Custom Nutrition Plan
Our custom nutrition plan product is an attempt to synthesize a wide range of SNPs which we have rated as having a high impact on nutritional health and wellbeing.
While we are strong believers in the profound effect that individual polymorphisms within genes can have on an individual’s general health and wellbeing, polymorphisms, genes, proteins and the complex systems of our body do not exist in isolation and so we shouldn’t assess them or make changes to our lifestyle based on a single polymorphism. In other words, we do not hyper focus on any one gene.
Understanding how all these polymorphisms, genes and systems interact is incredibly complex, and it becomes ever more complex as new and exciting discoveries are made.
To understand how it all works the best place to start is at the beginning, the raw data file that you provided to us or that we receive when you purchase one of our test kits. This file contains information about hundreds of thousands of polymorphisms scattered throughout your genome. We extract the information we require from this file and use this to build your report.
For each polymorphism we have identified the risk and non-risk allele, and also assigned a Science Grade based on a thorough review of the literature as described above. We then assign each polymorphism to relevant categories ranging from macronutrients such as fat, carbohydrate or protein, down to micronutrients such as individual B vitamins. Then using a proprietary algorithm, we weigh all the individual SNPs, and any associated haplotypes in a particular category and assign you to a specific class. So, for example you may be recommended to limit your B6 intake, or avoid saturated fat.
We then build all of this information up into our main focus which is the overall dietary plan. Here, by incorporating all your polymorphisms we assign you to a particular diet type with associated recipes and strategies you can use to maximize your well-being.
We believe that this approach of giving you a top level overview, but also allowing you to delve further into specific categories, genes and even SNPs is the best way to provide you with the specific information to inform your dietary and supplementary choices.
Farmer’s markets and fat.
Everything but dairy.
Channel your inner Bourdain.
Chris Kresser podcast.
No grains, but a little feta never hurt anyone.
Valter Longo would be proud.
Shoutout to Dr. Mark.
Your bread and butter is no bread and butter.
Lots of plants, lil’ bit of everything else.
Eat food, not too much, know your LDL-P.
Olive oil for everything, except sunscreen.
Don’t even look at pancakes.
Rice and beans, minus the rice.
You’ll look great in a bathing suit.
Rich Roll podcast.
Can I put natto on my purple potatoes?
Watch What the Health, then watch it again.
Butter in your coffee is a bad idea.
Your Yoda is in Austin, visit him you must.
Eat like you live west of Lincoln.