Sometimes the things that everyone seems to accept as gospel don’t always hold true when researchers actually look into them, especially in regards to nutrition.
Take the glycemic index for example: Around since the 1980s, accepted dogma holds that white bread will very quickly raise everyone’s blood glucose levels with a GI score of 73, while a banana gives a slower increase in glucose with a GI score of 48.
Turns out that when scientist decided to look at a large group of individuals’ glycemic responses to foods, people’s responses vary a lot from what the glycemic index shows.
Individualized Glycemic Response
There have been a couple of good studies over the last couple of years that have shown the huge variation in blood glucose levels in response to carbohydrates. For example, some people may have blood sugar spikes from eating a banana, while others may have their blood sugar drop from eating bread. The individual variation found in the studies is striking.
Researchers summarized the findings on the individual postprandial glycemic responses (PPGR): “we demonstrate on 800 individuals that the PPGR of different people to the same food can greatly vary. The most compelling evidence for this observation is the controlled setting of standardized meals, provided to all participants in replicates. This high interpersonal variability suggests that at least with regard to PPGRs, approaches those grade dietary ingredients as universally “good” or “bad” based on their average PPGR in the population may have limited utility for an individual.”[ref]
Another study looked at the glycemic response of 20 individuals to white bread and to whole wheat sourdough bread. Turns out that for about half the people in the study, white bread caused less of a blood glucose spike, while for the other half, the whole wheat bread was better.[ref]
One reason for the individual variation is differences in the gut microbes of each individual. The types of microbes in your gut depend on a lot of factors, including early childhood experiences, types of food that you currently eat, antibiotic exposure, and also your genes.
Genetic variations also play a role in how people react to carbohydrates in the diet.
Why is this important? Diet advice is everywhere — from the doctor on TV to your own doctor telling you to drop a few pounds. But generic recommendations may do nothing for you, and, if your goal is to prevent type 2 diabetes, the recommendations may actually work against you.
Below are a few genes that affect insulin or glucose levels based on carbohydrate consumption.
Carbohydrate Response: Genotype Report
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Variants in UCP3 (uncoupling protein 3) have been linked in many studies to obesity and insulin resistance. One study found that people with one variant had a better response (lower glucose levels, more weight loss) on a diet higher in protein and lower in carbs. Those without the variant lost a little weight on the diet, but it had no impact on their blood glucose levels.
Check your genetic data for rs1800849 (23andMe v4; AncestryDNA):
- A/A: less weight loss, no decrease in glucose or insulin levels on high protein/low carb diet [ref]
- A/G: less weight loss, no decrease in glucose or insulin levels on high protein/low carb diet
- G/G: lower glucose levels, better weight loss on high protein/low carb diet
Members: Your genotype for rs1800849 is —.
Another study on the same variant looked a the response to a reduced-calorie, but higher in monounsaturated fat, diet. Both those with the G/G genotype and those with an A allele lost weight, but the G/G genotype lost quite a bit more weight (over 10lbs more on average). Additionally, those with the G/G genotype had improved cholesterol levels and other cardiovascular parameters.
The insulin receptor substrate 1 (IRS1) plays a role in insulin signaling. Variants of the gene have been tied to the risk of type 2 diabetes.
A large study of over 25,000 people without diabetes looked at the role of an IRS1 variant in conjunction with diet. The results found that for women with the T allele who ate fewer carbs had a lower risk of diabetes; while for men, it was a low-fat diet that, along with the T allele, resulted in a lower risk of diabetes.
Check your genetic data for rs2943641 (23andMe v4, v5; AncestryDNA):
- T/T: women had lower diabetes risk with low-carb diet, but men had lower a lower diabetes risk with lower fat diet[ref]
- C/T: women had lower diabetes risk with low-carb diet, but men had lower a lower diabetes risk with lower fat diet
- C/C: no differences due to diet
Members: Your genotype for rs2943641 is —.
The brain-derived neurotrophic factor (BDNF) is essential for neuron development and repair. A Jan 2017 study of 8,840 Korean adults looked at the interaction between diet and a BDNF variant in regards to the risk of type 2 diabetes. The study found that a high carb/low protein diet that did not have excess calories was protective against T2D for those with the rs6265 Met variant. Excess calories in the diet negated the protective effect of the variant. The study concluded: “BDNF Val/Met and Met/Met variants (rs6265) decreases the risk for glucose intolerance and type 2 diabetes. BDNF variants interacted with nutrient intake, especially energy and protein intake: Middle-aged individuals with BDNF Val/Val are prone to developing type 2 diabetes even with low energy and protein intake.”
Check your genetic data for rs6265 (23andMe v4, v5; AncestryDNA):
- T/T: (Met/Met) low-protein, high-carb (but not excessive calories) protective against T2D [study]
- C/T: (Val/Met) low-protein, high-carb (but not excessive calories) protective against T2D
- C/C: (Val/Val) no dietary protection against T2D
Members: Your genotype for rs6265 is —.
The FTO gene has been linked in a lot of studies to obesity and weight gain. A 2009 trial that investigated the outcome of a lower carb/higher fat vs higher carb/lower fat diet found that a variant of the gene was linked to a greater decrease in HOMA-IR (a measure of insulin resistance) when on a lower fat/higher carb diet.
Check your genetic data for rs9939609 (23andMe v4, v5; AncestryDNA):
- T/T: greater decrease in HOMA-IR on a lower fat/higher carb diet [ref]
- A/T: generally associated with higher BMI
- A/A: generally associated with higher BMI
Members: Your genotype for rs9939609 is —.
Variations in circadian rhythms have been shown to play a role in obesity and insulin levels. Studies have shown that the CLOCK gene variants are involved.
Switching gears from looking at carbohydrates, a 2013 study looked at two different diets – low-fat diet vs. Mediterranean (high in monounsaturated fat) diet- and the effect of a CLOCK gene variant on the outcome of each diet.
Check your genetic data for rs1801260 (23andMe v4, v5; AncestryDNA):
- A/A: lower insulin levels, lower insulin resistance on a low-fat vs Mediterranean diet [ref] (this is the most common variant)
- A/G: no differences in response to the two diet types
- G/G: no differences in response to the two diet types
Members: Your genotype for rs1801260 is —.
Individual response to a diet is not nearly as simple and easy as most diet gurus make it out to be in their numerous books and websites selling supplements.
In addition to applying what you’ve learned about your genetics, you may want to quantify your blood glucose levels by using a test kit. Blood glucose test kits are not that expensive and can give you a fairly accurate picture of how your body reacts to different meals.
Recap of your genes:
|Gene||RS ID||Risk Allele||Your Genotype||Notes About Risk Allele|
|IRS1||rs2943641||T||--||Women had lower diabetes risk with low-carb diet, but men had lower T2D risk with lower fat diet|
|BDNF||rs6265||T||--||Low-protein, high-carb (but not excessive calories) protective against T2D|
|FTO||rs9939609||T||--||Greater decrease in HOMA-IR on lower fat diet|
|CLOCK||rs1801260||A||--||lower insulin levels, lower insulin resistance on a low-fat vs Mediterranean diet (this is the most common variant)|
|UCP3||rs1800849||A||--||Less weight loss and no decrease in glucose or insulin levels on a low carb diet.|
More to read:
- Personalized Nutrition by Prediction of Glycemic Responses
- Blood glucose variations in normal individuals: A chaotic mess
Debbie Moon is the founder of Genetic Lifehacks. Fascinated by the connections between genes, diet, and health, her goal is to help you understand how to apply genetics to your diet and lifestyle decisions. Debbie has a BS in engineering and also an MSc in biological sciences from Clemson University. Debbie combines an engineering mindset with a biological systems approach to help you understand how genetic differences impact your optimal health.