The 2024 Levels guide to genetics and metabolic health
Genetics is an important determinant of metabolic health and Type 2 diabetes risk, but weight and habits are also also a large influence.
As with many aspects of wellbeing, both nature and nurture play a role in determining our metabolic health. Nurture includes things like diet and exercise, and it has a massive influence on metabolic health and risk of Type 2 diabetes. But a portion of this risk also comes from nature: our genes.
Studies over the past two decades show scores of genetic variants that influence metabolic health, for example, by affecting insulin secretion. Some of these put us at greater risk of Type 2 diabetes while others are protective. One theory is that genes that today increase the risk of Type 2 diabetes and obesity may have been beneficial in our ancestors because they help save energy as fat, which could tide them over during times of scarce food supply, but that theory is far from proven.
There are two main goals of genetics research in Type 2 diabetes. First is pinpointing genetic variants and what they do so researchers can make therapies to target these pathways and improve metabolic health. Second is to calculate genetic risk scores that can identify people at the highest risk of Type 2 diabetes so they can work on prevention.
However, this field of research is still in its early days, and while scientists have found several genes related to metabolic health, these practical applications are still likely years away. For most people, maintaining optimal metabolic health is still mainly about your habits; there's little actionable information in your genetics.
Here's a deep dive into what we know today, and where the field might be going.
How does genetics affect Type 2 diabetes risk?
A person inherits one set of genes, made up of DNA, from each of their parents. Genes lay the blueprint for how a person will look and function, and differences between people are largely due to differences caused by mutations in a person's genome. These naturally occurring mutations are known as variants. However, genes often don't determine traits entirely on their own because the environment (i.e., nurture) also plays a large role. The combination of variants and environmental effects can lead to different traits, like how tall a person grows to be. Variants that increase risk of dysfunction or disease are known as risk variants.
After scientists set out to map out all of the DNA in humans with the launch of the Human Genome Project in the 1990s, some researchers began studying genes they suspected were related to metabolic dysfunction in so-called candidate gene studies. In 1998, they confirmed one for the first time: variants of the PPARG gene affect body mass index (BMI), insulin concentration and sensitivity, and Type 2 diabetes risk in some ethnic groups. But candidate gene studies turned out to produce conflicting results that were difficult to replicate.
By 2007, technology advanced to allow for genome-wide association studies (GWAS), which could test hundreds of thousands of variants across many people's genes to find variants that are more common in people with conditions such as Type 2 diabetes. Through August 2022, GWAS have identified more than 700 novel gene locations that influence Type 2 diabetes risk.
As data sharing increased, sample sizes in GWAS grew, and scientists invented new techniques, they began identifying rare variants, including some found only in certain ethnic groups. For example, the Pro50Thr variant of AKT2 --- linked to a 39% decrease in whole-body glucose uptake and a 56% increase in the body's rate of glucose production --- is found almost exclusively in Finnish people, and then only a tiny fraction of the population.
In theory, rare variants should be more likely to contribute to Type 2 diabetes risk because natural selection weeds out most harmful variants before they become common, so many of the variants that notably boost disease risk are rare. How much of the heritability of Type 2 diabetes is due to rare variants versus common variants is still unclear.
Many risk variants are still unknown, partly because studies haven't had enough participants to detect rare variants or variants with small effects on risk. Better statistical methods may be needed. And many studies haven't accounted for interactions between multiple genes, or between genes and the environment.
Gene-gene and gene-environment effects
Gene-gene interactions occur when a person has variants that work together to raise or lower diabetes risk more than each could individually.
Gene-environment interactions occur when variants interact with environmental factors, such as alcohol, diet, and exercise habits, to raise or lower diabetes more than either could on its own. In other words, if two people drink the same amount of alcohol or eat the same meal, the one with a particular gene may see different effects.
Many studies on gene-environment effects have focused on one gene with a strong effect on Type 2 diabetes: TCF7L2. A 2019 review, for example, found interactions between variants in TCF7L2 and environmental factors such as fiber and whole grain consumption and coffee intake. However, most of the evidence for these associations is weak as many of the studies that found them haven't been replicated and only investigated Europeans.
Since that study, a 2022 review on interactions between *TCF7L2 *and lifestyle factors found that its variants impacted how fatty acid and fiber consumption related to insulin resistance in people without diabetes. Additionally, people who didn't have diabetes but had risk variants of *TCF7L2 *had a higher glucose concentration and impaired insulin sensitivity after meals than those without risk variants of the gene. The review also found that some studies reported weight loss, physical activity, and smoking may interact with TCF7L2 variants, but evidence is mixed.
A 2023 study found that interactions between genetics and socioeconomic factors like income and education increase Type 2 diabetes risk than either could on their own. And a 2019 study found that some genetic variants increase the risk that drinking alcohol will contribute to Type 2 diabetes development, especially in men.
The role of ethnicity
Many studies of genetics in Type 2 diabetes have only or primarily included participants with European ancestry. However, in recent years some have investigated different ethnic groups and found that they have unique risk variants. For example, one of the largest GWAS published yet included 428,452 people with Type 2 diabetes and 2,107,149 without diabetes. Nearly 40% of participants were not of European ancestry. The study identified 145 novel areas of the genome linked with the risk of Type 2 diabetes. Across the three ancestry groups, there were different links to distinct subtypes of Type 2 diabetes based on the mechanisms by which their variants affect disease risk. One group was more likely to have reduced insulin secretion and lower insulin resistance, while another was more likely to have greater insulin resistance --- underscoring the need for more research on non-European ancestral groups.
Another reason more diversity is needed in these studies is that different ethnic groups have different rates of Type 2 diabetes. The difference can be dramatic; as many as half of all Pima indigenous people in Arizona have diabetes, for example. Although this is partly due to environmental factors, genetics almost certainly also plays a role.
Unique variants also play a role in distinctions between how Type 2 diabetes emerges in different ethnicities. For example, Europeans typically only develop Type 2 diabetes after obesity, but East Asians have Type 2 diabetes at a lower weight due to genetic differences in insulin secretion.
The mechanisms by which variants influence Type 2 diabetes risk
Research has revealed important information about the processes by which genetic variants affect diabetes risk and development. Importantly, variants are more likely to affect insulin secretion than insulin sensitivity. Insulin is the hormone that clears glucose from the bloodstream, and is secreted by the pancreas. Sensitivity refers to how responsive cells are to insulin, which triggers them to absorb glucose. Higher sensitivity is better for metabolic health.
Some genetic variants, including TCF7L2, increase risk by causing dysfunction in beta cells, or pancreatic cells that make insulin. Beta cell dysfunction can include: a) problems with cell division or death that results in fewer beta cells, b) a difficulty converting the insulin precursor proinsulin into insulin, or 3) issues with transporting molecules in and around beta cells.
Specifically, TCF7L2 is responsible for producing a molecule that plays an important role in the Wnt signaling pathway, which is important for development and growth. The gene also helps regulate fat storage cells and metabolism, particularly by regulating beta cells and insulin production and processing. Evidence suggests that some variants of TCF7L2 reduce insulin secretion by causing problems with exporting insulin out of beta cells and lowering responsiveness of beta cells to incretins (proteins that stimulate beta cells to release insulin).
But TCF7L2 is an exception --- for most variants that affect Type 2 diabetes risk, scientists have not yet figured out the mechanisms involved.
Some variants have a sex-dependent effect on diabetes risk, such as a variant of the gene ALDH2, which codes for an important enzyme in alcohol metabolism. In East Asians, this risk variant is associated with better alcohol tolerance. It also increases Type 2 diabetes risk in males, but not females. The difference in how the variant affects men and women may be due to differences in drinking habits, or the effects of alcohol on BMI or insulin sensitivity.
Genetics and Type 2 diabetes subtypes
Type 2 diabetes is thought of as a single disease. However, it's clear that there are categories of people with different symptoms, disease courses, susceptibility to complications, and responses to treatment. Especially in the past decade, experts have begun proposing Type 2 diabetes subtypes --- and have, to some degree, found differing genetic underpinnings for these subtypes, or used genetic variants to help classify them.
In 2015, researchers attempted to divide Type 2 diabetes into subtypes based on genetics and electronic medical records. They divided cases into three subtypes based on their complications: 1) retinopathy and diabetic nephropathy, 2) cardiovascular diseases and cancer, and 3) neurological diseases, cardiovascular diseases, and allergies. The researchers found that several risk variants are specific to one of the subtypes, but they didn't look into how they could affect the development of diabetes.
In another study, researchers primarily considered 94 variants and their effects on 47 diabetic traits to derive five subtypes. Two subtypes had beta cell dysfunction, and the other three had insulin resistance mediated by obesity, fat distribution, and fatty acid metabolism in the liver.
Another team constructed a five-subtype system in 2018 based on age at diagnosis, BMI, HbA1c (a three-month average of blood glucose levels), GAD antibodies (common in Type 1 diabetes), HOMA2-B (a measure of insulin secretion), and HOMA-IR (a measure of insulin resistance). A follow-up study in 2021 found that a mutation near the *LRMDA *gene --- which codes for a protein thought to play a role in the differentiation of skin cells that make melanin --- is associated with a subtype that develops obesity at a young age but without insulin resistance. It also found that various subtypes are linked to risk variants with different diabetes-influencing mechanisms. For instance, only one subtype is related to variants that contribute to fasting insulin.
Still, there is not yet consensus on which subtypes are meaningful and should be used in managing Type 2 diabetes. But having these defined subtypes could eventually allow for improvements in precision medicine, perhaps by targeting the pathways linked to that subtype's risk variants.
Genetic similarities and differences to other diabetes types
Other types of diabetes have genetic similarities and differences to Type 2 diabetes --- another reason why some experts theorize that the disease exists more along a continuum than is currently recognized. These include the following:
Type 1 diabetes
In this subtype, the body attacks and destroys beta cells. The genetic underpinnings of Type 1 diabetes are far more understood than those of Type 2. This is because researchers discovered that the HLA region --- a genetic region consisting of hundreds of genes, many of which are related to immune function --- contributes to up to 50% of genetic risk for Type 1 diabetes.
Recently, scientists have revealed some genetic overlap between the two types. A 2021 study found five genomic locations associated with both types. However, the effect of four of the variants was opposite in direction on the effect of Type 1 and Type 2 diabetes; in other words, a mutation that increased the risk of one was protective against the other. Larger sample sizes, particularly of GWAS in Type 1 diabetes, will be needed to uncover more overlap between variants common to both diseases.
Another 2021 study found that having a genetic profile that increases risk of Type 1 diabetes alters biological pathways in a manner that also increases Type 2 diabetes risk. However, the opposite is not true; having genetic risk for Type 2 diabetes does not contribute to an increased risk of Type 1.
Gestational diabetes
A person with gestational diabetes has high blood sugar during pregnancy, likely due to beta cell dysfunction causing insulin resistance. Gestational diabetes can contribute to a higher risk of Type 2 diabetes---half the people with gestational diabetes develop Type 2 diabetes within 10 years after pregnancy. One reason may be that gestational diabetes has many risk variants that overlap with Type 2.
It's worth noting that studies have found overlap between genes that affect glycemic traits in non-pregnant and pregnant people, but they have also discovered genes with a unique association to glycemic traits in pregnancy.
Monogenic diabetes
About 1 to 4% of diabetes cases in the U.S. are monogenic, or caused by a single gene. These include maturity-onset diabetes of the young, infantile onset diabetes, and neonatal diabetes. Mutations in more than 20 genes have been identified as a cause of monogenic diabetes, most of which impair insulin production. Some variants that cause monogenic diabetes are still unknown, and some that cause monogenic diabetes are implicated in Type 1 and Type 2 diabetes too.
Genetics of diabetes-related complications
Some Type 2 diabetes risk variants also increase the risk of diabetes-related complications, including coronary artery disease, ischemic stroke, diabetic retinopathy (an eye condition that can lead to blindness), diabetic neuropathy (nerve damage), kidney disease, lung cancer and impaired lung function, and other cancers. Evidence suggests that some diabetes-related complications share risk variants, but researchers haven't yet studied this extensively.
Genetics can also influence the risk of complications related to high blood glucose in people without diabetes. For instance, people without diabetes who have risk variants for high blood glucose can also have a higher risk of coronary artery disease without an increased risk of Type 2 diabetes.
Future research should consider that just as there may be Type 2 diabetes subtypes, there may also be subtypes of certain diabetes-related complications. Diabetes-related kidney disease, for instance, can present in many different ways, potentially with different genetic underpinnings.
How genetics affects metabolic health measures
Genetic risk variants that affect some glycemic measures don't necessarily affect others. The genes that influence HbA1c variability, for instance, are largely different from those that affect fasting glucose.
Many of the variants linked to fasting glucose in adults without diabetes are also linked to fasting glucose in children without diabetes. Combined with evidence that fasting glucose stays mostly the same throughout the lifetimes of people without diabetes, with only modest increases over time due to age-related factors, this suggests that genetics may largely determine an individual's set point for fasting blood glucose.
How risk variants influence glucose levels after eating in people without diabetes is uncertain. A 2023 review found that there is not strong evidence for a link between specific variants and glucose area under the curve (a measure of total glucose response) after an oral glucose tolerance test. However, this may be because the included studies didn't have enough data to identify small effects associated with individual variants.
Besides fasting glucose and blood glucose after eating, another way to measure glucose responses is via HbA1c, which has a heritability between 55% and 75% regardless of diabetes diagnosis. As of 2017, researchers have identified about 20 genes that contribute to HbA1c --- and not all of the genetic influences act via a glycemic pathway. For example, variants of genes such as HFE and *TMPRSS6 *are linked with HbA1c levels and red blood cell parameters, but not to Type 2 diabetes risk or glycemic traits. The discrepancy between HbA1c and other measures of glycemic control is actually a heritable trait itself.
What are potential uses for metabolic health genetics research?
There are two main ways that knowledge of individuals' genetics could support metabolic health. One is identifying genetic risk scores for Type 2 diabetes risk, which could allow for targeted lifestyle intervention of people at the highest risk. The second is identifying genetic variants that could impact the ideal treatment of people with Type 2 diabetes, so clinicians can tailor their healthcare to their genes.
Genetic risk scores for Type 2 diabetes
Each genetic variant only affects diabetes risk by a small degree. But pooling multiple variants into a polygenic risk score can help determine a person's genetic and overall risk of high blood glucose and Type 2 diabetes.
Researchers can use risk scores in multiple ways, including to determine a person's risk of Type 2 diabetes in general, certain subtypes of Type 2 diabetes, high blood sugar, or diabetes-related complications.
There are two main uses for polygenic risk scores for Type 2 diabetes: 1) identifying people who could most benefit from prevention efforts, and 2) discovering and implementing precision medicine treatments.
People with the highest risk scores may benefit more than those with the lowest scores from intensive lifestyle interventions to prevent diabetes. For example, one study found that people in the top 2.5% of polygenic risk score distribution have a 3.4-fold risk of developing Type 2 diabetes compared to people with the median score, and a 9.4- fold of developing Type 2 diabetes compared to people in the bottom 2.5%.
However, it's also possible that lifestyle interventions would be less able to impact disease risk in people with high genetic risk because even following a healthy lifestyle can't change their genetic risk. Studies that have attempted interventions for people at high genetic risk haven't had much success in preventing diabetes. A 2018 study showed that genetic counseling about how their genetic risk impacts their diabetes risk did not affect short-term weight loss or motivation for behavior change among participants with high genetic risk in a 12-week diabetes prevention program. There was no difference in the time it took to develop diabetes in those who received the counseling and those who did not. Another study found that genetic risk counseling didn't enhance motivation or adherence to a diabetes prevention program. But a 2024 study found that a three-year, group-based intervention focusing on diet and physical activity lowered the risk of men at the highest genetic risk from developing diabetes.
Perhaps most importantly, some research shows that clinical markers are superior to polygenic risk scores for predicting Type 2 diabetes---and less expensive. But a 2023 study found that a polygenic risk score that included 5 variants increased the predictive power of the model compared to a model using just age and sex. Three other studies found that adding genetic risk to clinical predictors of disease only increases the accuracy of clinical predictors by 1% to 2%.
Obesity is the overall strongest predictor of Type 2 diabetes, with a strong impact on diabetes risk compared to the modest effect of genetics. And a 2020 study found that the top fifth of people with the healthiest lifestyle have a lower Type 2 diabetes risk than the bottom fifth, regardless of genetic risk.
Genetic risk scores don't add much to Type 2 diabetes prediction beyond clinical factors. A 2015 study found that weight, diet, and physical activity are more likely to impact glycemic traits than genetic risk for people with overweight or obesity in a year-long weight management program.
Genetic testing and polygenic risk scores may be most useful for people who are young and lean. Risk scores could, for example, identify those at risk of early onset Type 2 diabetes, and adding genetic risk scores to other risk variables leads to some amount of risk reclassification for people younger than age 50, but not for older people. And one study found that of 36 genomic locations, 29 were linked to a larger boost in the odds of developing diabetes for leaner compared to heavier people. In general, lean people who develop diabetes are more likely to have a higher genetic risk.
However, those who are younger and leaner are at low absolute risk of Type 2 diabetes, so trying to identify them isn't the most logical diabetes prevention approach. Universal genetic screening could become common in the future though. If it does, it may allow for genetic counseling of young, lean people with high genetic risk.
Precision medicine based on genetics
Precision medicine involves the targeted treatment of people with a certain condition to optimize the benefits of prevention and treatment, based on their genetic profile. For example, some medications work better for people with certain genetic variants, and certain variants make people metabolize drugs at a faster or slower pace. Knowledge of what variants a person has can help a provider choose the best treatments for them.
Healthcare professionals use precision medicine to treat monogenic diabetes subtypes, but it's much more difficult to use this approach with Type 2 diabetes. Identifying subtypes linked to certain risk variants and molecular pathways may allow for precision medicine treatments in the future.
To use this approach with Type 2 diabetes, researchers would need to uncover how risk variants that contribute to a subtype lead to metabolic dysfunction, then find existing or new drugs that could alter their pathways in a way that leads to a healthier metabolic profile. For example, a 2020 study found that two genes implicated in cardiometabolic conditions, SCN3A and SV2A, are altered by anti-epileptic agents, such as valproic acid, which could lower blood glucose. Gene-drug analyses could reveal other drugs that are already FDA-approved and that could be repurposed for Type 2 diabetes prevention and treatment.
Researchers have already started looking in this direction. One study found that a greater genetic risk may mean that the drug sulfonylureas is more likely to lower HbA1c, for instance, so people with higher genetic risk may be better candidates for this drug.
What this means for you
Scientists have just started to scratch the surface of understanding the full genetic picture of metabolic health risk, but we do know that there is a genetic component to the disease. And in the near future, genetics could become much more important to how we understand and manage our metabolic health. This will mainly be through polygenic risk scores that help people understand how at-risk they are genetically for developing Type 2, which could influence behavior change, and personalized medicine that can treat people with Type 2 diabetes based on the specific risk variants that contribute to disease development and progression.
Today, there's little reason to determine your polygenic risk score for Type 2 diabetes. But if you're curious, popular genetic testing company 23andMe calculates users' genetic risk for developing the disease between their current age and age 80---and compares it to the typical genetic risk range. Users can also enter their height, weight, and the number of times they exercise and eat fast food per week for a more refined risk calculation. But experts caution taking these results with a grain of salt---and talking to your doctor about your results---because their accuracy is uncertain, especially in people who aren't primarily of European descent.
No matter your genetic risk, research suggests that the best thing you can do to lower your risk of metabolic disease is to focus on the modifiable factors that have the greatest impact on your metabolic health, including your weight, diet, and exercise.
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