Genetics of Obesity and its Implications for Bariatric Surgery

| October 1, 2019 | 0 Comments

by Erik L. Bao, BS

Mr. Bao is a medical student at Harvard Medical School in Boston, Massachusetts.

Funding: No funding was provided for this article.

Disclosures: The author reports no conflicts of interest relevant to the content of this article.

Abstract: Obesity is a growing health epidemic that affects over 600 million people worldwide and contributes to many comorbidities, including Type 2 diabetes, cardiovascular disease, and cancer. While obesity arises from a complex interplay of many factors, patient genetics has long been known to play a critical role. Family and twin studies suggest that between 40 to 80 percent of variation in body mass index can be linked to genetic components. How genetics can be leveraged to improve health outcomes for patients with obesity, however, remains an evolving area of research. In this article, we review the genetic determinants of obesity and recent advances in utilizing genetic biomarkers to predict obesity and weight-related health outcomes. Finally, we look ahead to the prospects and challenges that patients and physicians may face when applying genetic data to inform medical care.

Keywords: obesity, genetics, risk, bariatrics

Column editor: Daniel B. Jones, MD, MS, FASMBS
Professor of Surgery, Harvard Medical School, Vice Chair, Beth Israel Deaconess Medical Center, Boston, Massachusetts

Bariatric Times. 2019;16(10):24.

Obesity is a complex and multifactorial disease that carries a highly heritable component. Many epidemiological studies have linked genetic factors with a substantial portion of the interindividual variability in body mass index (BMI).1 Knowing that genetic variation can impact BMI leads to some exciting questions for clinical application: What are the genes and biological pathways that influence obesity? Can they be therapeutically modulated? Can we use genetics to identify individuals with high risk of obesity and target them for earlier intervention?

Over the past 15 years, researchers have conducted large genome-wide association studies (GWAS) to identify common and rare genetic variants significantly associated with increased BMI.2 These efforts have not only identified new genes associated with obesity but also enriched tissues and molecular mechanisms driving regulation of the trait. For example, one study found that a mutation in the FTO gene alters the adipocyte thermogenesis pathway, and that genome editing of genes in the pathway led to pro-obesity phenotypes.3 These genetic studies have laid important groundwork for better understanding of how obesity is regulated.

Standing on the heels of this research, how can we translate this genetic information to help patients? One potential way is through the implementation of polygenic risk scores (PRS). A PRS uses the cumulative effect of many genetic variants to predict the risk of a given trait (i.e., BMI) for an individual. In a recent study, researchers created a PRS for BMI and were able to predict BMI in an independent population with moderately high accuracy.4 Interestingly, individuals with a high PRS not only had much higher odds of obesity compared to all other individuals, but they also had greater odds of having received bariatric surgery, risk of cardiometabolic disease, and all-cause mortality. Through a longitudinal analysis, they also found that individuals with a high genetic risk for obesity have increased weight starting in early childhood (~3 years old), and this gap increases into adulthood. This is particularly intriguing because it demonstrates that if we can identify individuals genetically predisposed to obesity, interventions might have an impact even at a young age.

The use of genetic predictors for bariatric medicine comes with many exciting clinical implications. In the preoperative assessment phase of bariatric surgery, a major component is to determine which nonsurgical weight loss approaches the patient has attempted and what their results were. If patient genetic risk scores were available, this would help in determining a patient’s genetic predisposition to obesity based on their intrinsic biology as opposed to being due to unhealthy lifestyle choices. Therefore, a patient with a high genetic score for obesity might benefit from a lower criteria threshold for receiving bariatric surgery. In addition, there is currently a wide interindividual variation in long-term outcomes of bariatric surgery that draws from many factors.5 To this end, patient genetics could help determine how well a patient will respond to bariatric surgery, which, in turn, could be more accurately conveyed to the patient during the informed consent process. Finally, genetic information could help a patient decide which bariatric procedure to undergo, such as gastric band, sleeve gastrectomy, or Roux-en-Y gastric bypass (RYGB). For example, if a patient has a high genetic risk for obesity, he or she might choose a more aggressive approach, such as RYGB, over more conservative options, such as gastric banding.6

The incorporation of genetics into clinical care also poses many potential challenges. If individualized genetic data on risk were disclosed to patients, they might feel demotivated and that they have less control over their weight loss, since they have “obesity-prone” deoxyribonucleic acid (DNA). Therefore, an essential component to a bariatric clinic utilizing patient genetics would be to include clinicians who specialize in genetics as part of the multidisciplinary team. These specialists could then counsel the patients on what their genetic data means, similar to the genetic specialist used in oncology practice.

Another challenge is the possibility of insurance companies using genetic information to raise premiums for those at higher risk of obesity. The Genetic Information Nondiscrimination Act of 2008 (GINA) is a safeguard legislation that provides protection for most patients against genetic discrimination from health insurance companies.7 However, a major caveat to GINA is that it does not extend to other types of insurance, including life, long-term care, disability, or supplemental cancer insurance. Given the high prevalence of obesity and its substantial impact on mortality and morbidity, insurance companies will be heavily incentivized to use genetic risk scores for obesity to assign risk for individuals. The full social and public health implications of utilizing genetic data in healthcare remain to be seen and will likely evolve over the next 10 years.

In summary, the genetics of obesity is an active area of research that has seen tremendous progress over the last decade, most recently with the development of BMI genetic risk scores that can predict the propensity of individuals to develop obesity. These genetic tools have the potential to play a major role in clinical decision-making for weight loss medicine and surgery. However, from the patient’s perspective, they also carry risks of being misinterpreted or being used by insurance companies against them. It will be interesting to see how these open-ended issues evolve as the genetics of obesity continues to advance and become more integrated into bariatric patient care.


  1. Elks C, den Hoed M, Zhao JH, et al. Variability in the heritability of body mass index: a systematic review and meta-regression. Front Endocrinol (Lausanne). 2012;3:29.
  2. Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206.
  3. Claussnitzer M, Dankel SN, Kim KH, et al. FTO obesity variant circuitry and adipocyte browning in humans. N Engl J Med. 2015;373(10):895–907.
  4. Khera AV, Chaffin M, Wade KH, et al. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell. 2019;177(3):587–596.e9.
  5. Courcoulas AP, Yanovski SZ, Bonds D, et al. Long-term outcomes of bariatric surgery: a National Institutes of Health symposium. JAMA Surg. 2014;149(12):1323–1329.
  6. Arterburn D, Wellman R, Emiliano A, et al. Comparative effectiveness and safety of bariatric procedures for weight loss: a PCORnet cohort study. Ann Intern Med. 2018;169:741–750.
  7. Genetic Information Nondiscrimination Act of 2008.

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Category: Medical Student Notebook, Past Articles

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