How are genetics and body mass index (BMI) connected?
Body Mass Index, or BMI, is used around the world to gauge a person’s physical makeup. It’s a fairly simple measurement—sometimes too simple—that estimates your body fat percentage based on your weight and height. We often think of BMI as something that reflects solely upon a person’s diet, but what if that’s not entirely correct? What if your BMI was also influenced by genetics?
Human genetics is a fascinating field of study. Each year we make new discoveries that carry us towards a deeper understanding of who we are—not just as a species, but as individuals. Buried within the nearly 3.2 billion base pairs of our DNA are thousands of idiosyncrasies that help to make each of us genetically unique. Most of the time, these variations in a person’s DNA sequence have no observable effect, but sometimes they do. It’s a desire to understand this, and to characterize how specific changes in a person’s DNA may affect their lives, that motivates genetic research. Such efforts have shown us that many of our traits are complex, often arising due to a vast network of interactions between different genes and life experiences. A good example of this is a person’s BMI.
What is BMI?
When it comes to measurements, BMI is pretty straightforward: It’s just a calculation that divides your weight (in kilograms) by the square of your height (measured in meters). The resulting number serves as an indirect readout of your body mass—the relative proportions of fat, muscle, and bone in your body. Oftentimes when we hear about BMI, we mistakenly think of it as a direct readout of body fat. While helpful, BMI is an imperfect measurement because it cannot differentiate between a person who is dense with muscle, and a person who weighs the same but is less muscular and carrying excess body fat. Because of this, it’s possible that a muscular body builder could be categorized as obese based on their BMI1,2. This is a reason why it’s important for BMI scores to be taken with a grain of salt.
Use of BMI as a standard measurement came into practice in the 1970s, and it didn’t take long for researchers to see that BMI may be influenced by more than a person’s diet1. Along with height and hair color, BMI scores are similar among family members. This could be explained by a shared environment (family members often have similar dietary habits), or it could point to genetics. Research into the matter has shown that its a little bit of both1-3.
Genetics and BMI
Large-scale studies in which thousands of people had their BMI recorded and their DNA sequenced have put a focus on more than 90 genes that may influence a person’s BMI1,3. One such gene is known as SEC16B. Exactly what this gene does is still being investigated, but so far it appears to play a critical role in packing and distributing fatty acids, proteins, and cholesterol throughout a cell, and possibly between other cells4-7. We don’t yet know exactly how this affects BMI, but multiple studies have found that people who inherit a change in their DNA coding for this gene—a so-called variant—tend to have a slightly higher BMI compared to people without the variant7-11. In an attempt to explain this correlation, some researchers have suggested that SEC16B may help control the distribution of appetite-regulating hormones in the hypothalamus (based on results in animal models). Other findings suggest SEC16B contributes to the inner workings of adipocytes (fat storing cells)4-7. For now, researchers continue to investigate how (and if) this change may be affecting a person’s BMI.
There are multiple other genes that have been associated with BMI, the most well studied being FTO. When determining whether a person is genetically predisposed to having a higher BMI, both FTO and SEC16B are often included in the analysis as a part of a polygenic score—a method of interpreting genetic results which accounts for many different genetic factors before coming to a result. These types of findings tell us that a person’s BMI isn’t solely determined by their lifestyle. It is true that our lifestyles affect our BMI, but lifestyle isn’t everything.
2Sperrin, Matthew et al. “Body mass index relates weight to height differently in women and older adults: serial cross-sectional surveys in England (1992-2011)” Journal of public health (Oxford, England) vol. 38,3 (2016): 607-613.
3Pagliassotti, Michael J et al. “Endoplasmic reticulum stress in obesity and obesity-related disorders: An expanded view” Metabolism: clinical and experimental vol. 65,9 (2016): 1238-46.
4Budnik, Annika et al. “Characterization of human Sec16B: indications of specialized, non-redundant functions” Scientific reports vol. 1 (2011): 77.
5Bhattacharyya, Dibyendu and Benjamin S Glick. “Two mammalian Sec16 homologues have nonredundant functions in endoplasmic reticulum (ER) export and transitional ER organization” Molecular biology of the cell vol. 18,3 (2007): 839-49.
6Schmid, Peter M et al. “Expression of fourteen novel obesity-related genes in Zucker diabetic fatty rats” Cardiovascular diabetology vol. 11 48. 13 Jul. 2012, doi:10.1186/1475-2840-11-48
7Lu, Yingchang et al. “New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk” Nature communications vol. 7 10495. 1 Feb. 2016, doi:10.1038/ncomms10495
8Li, Shengxu et al. “Physical activity attenuates the genetic predisposition to obesity in 20,000 men and women from EPIC-Norfolk prospective population study” PLoS medicine vol. 7,8 e1000332. 31 Aug. 2010, doi:10.1371/journal.pmed.1000332
9Ahmad, Shafqat et al. “Gene × physical activity interactions in obesity: combined analysis of 111,421 individuals of European ancestry” PLoS genetics vol. 9,7 (2013): e1003607.
10Sahibdeen, Venesa et al. “Genetic variants in SEC16B are associated with body composition in black South Africans” Nutrition & diabetes vol. 8,1 43. 19 Jul. 2018, doi:10.1038/s41387-018-0050-0
11Hotta, Kikuko et al. “Association between obesity and polymorphisms in SEC16B, TMEM18, GNPDA2, BDNF, FAIM2 and MC4R in a Japanese population.” Journal of Human Genetics vol. 54, pages 727–731. Oct. 2009, doi: 10.1038/jhg.2009.106.