If obesity were tied only to too much food or too little physical activity, the cure would be a simple matter of counting calories or keeping track of steps with a pedometer. Unfortunately, obesity is much more complex. Many other factors come into play, including so-called hunger genes that make a person more susceptible to obesity, infection with certain pathogens linked to weight gain, hormones that dampen or stimulate appetite, sleep disruption that can trigger overeating, and social networks that can promote obesity.
With studies continually uncovering more and more genetic, hormonal, behavioral, cultural, physiological, societal, bacteriological, and pathogenic pathways that potentially—and differentially—have an impact on an individual’s propensity for weight gain, obesity might seem an inescapable global health issue. Researchers, however, have taken up the gauntlet and redoubled their efforts to improve the understanding of obesity and, they hope, make headway toward treating and preventing it.
Setting Up Models
Because so many risk factors influence obesity, researchers such as information scientist Keisuke Ejima and statistician David Allison of the University of Alabama at Birmingham (UAB) are turning to mathematical models as ways to estimate the effects of risk factors on the prevalence of obesity and to predict future prevalence. Ejima is a postdoctoral fellow in UAB’s Office of Energetics and Nutrition Obesity Research Center (NORC), and Allison is the director of NORC and a distinguished professor of biostatistics.
Ejima began obesity work as an offshoot of his doctoral project to develop a mathematical model of the spread of an infectious disease in a population. In the midst of that project, he came across a research paper showing that obesity can also spread, but through social networks [1]. Put another way, people emulate many characteristics of those in their own social circles and that includes their weight. This led Ejima to view obesity as a “social contagion” to which he could apply his modeling expertise.
For the model [2], Ejima focused on three of the multiple factors involved in obesity: spontaneous weight gain risk, social-contagion risk factors, and genetic and nongenetic transmission from mother to offspring. (The latter refers to evidence showing that, irrespective of genetics, children of an obese mother have a higher risk for obesity themselves.) The model uses a series of interrelated ordinary differential equations to make nonlinear projections.
Allison is quick to point out that this model in its current form is not meant to be an accurate representation of the actual causal effects on obesity. Rather, it is designed to show that it’s possible to build a working model that can draw in many key component parts and have them work together. “The next step is to make all the parts more accurate representations of what they need to be,” he says.
Ejima adds, “Our model is set up so we can incorporate other risk factors or structures, so we are very open to advances in the understanding of obesity.” For now, however, he finds it quite interesting that the handful of research groups working on different obesity models are all coming to one consistent conclusion: the upward rise of obesity will eventually plateau at about 35–40% of the population, so the entire human race is not destined to tip the scales into the red.
Models like Ejima’s are vital in the fight against obesity for several reasons, according to Allison. “One,” he says, “is that it allows science to feed back on itself by figuring out where its predictions are good and where they’re not, and thereby not only helping us to tweak the model, but also enhancing our understanding of the factors that affect obesity.” A second reason is pure applied empiricism or making predictions of how large the average human will be in the years ahead. This, he explains, has all sorts of applications. “If you’re an insurance underwriter or a demographer, you want to know lifespans in the future, and obesity will affect that,” he says. On the engineering front, he sees implications for a variety of design issues, ranging from the width of airplane seats and the weight capacity of elevators to the diameter of MRI tunnels and the length of intramuscular-injection needles that must extend through subcutaneous fat.
Another benefit of models is that they provide a simulated control group—one that predicts obesity rates at certain points in time based on fluctuations in risk factors. This can then be used to evaluate obesity-intervention programs. For example, if a city implements a prevention program for ten years, the simulated control group can offer an accurate appraisal of its impact. “Otherwise, there’s no real control group to compare against, because just looking at the rate before and after doesn’t take into account what would have occurred in that population over the ten years had there been no external intervention,” Allison says. This helps evaluators avoid falsely attributing rate hikes or declines to programs when, in reality, those changes might have occurred anyway.
Faulty inferences, assumptions, and myths are all too common to the field of obesity, he adds. “We all have lots of everyday familiarity with obesity—we can know our own body weight, we can see how big others are—and that familiarity is often mistaken for scientific objective knowledge and expertise,” he asserts, noting that many well-intentioned but scientifically unproven intervention efforts often fail as a result. “That’s why this kind of modeling work, as well as good research about obesity in general, is important to try to figure out what trends may be, what factors may be involved, and how they may play out.”
Going to the Gut
Evidence points to the microbiome as a significant contributor to obesity risk. Among the researchers hoping to alter gut bacteria as a way to derail the runaway obesity epidemic is Sean Davies, associate professor in the Department of Pharmacology at Vanderbilt University in Nashville, Tennessee. He explains, “The reason that we think an engineered microbiota could be useful is that the bacteria in your gut stick around for a long time—unless you’re using antibiotics—so they could continue to produce beneficial compounds to reduce the risk of obesity and would be a useful sustainable strategy to prevent and to treat obesity.”
Davies and his colleagues at Vanderbilt set their sights on engineering the genes of certain probiotic bacteria to make N-acyl-phosphatidylethanolamines (NAPEs), which are phospholipids produced in the small intestine following a meal. NAPEs, in turn, are almost immediately converted into highly potent appetite-suppressing lipids called N-acyl-ethanolamines [3]. Through this route, the researchers hoped to make the subject feel less hungry and consequently eat less.
Once they successfully engineered the genes, they tested their ultimate effect in a mouse model. To introduce the bacteria into the mouse gut, they added a bit of gelatin to drinking water and mixed in the bacteria. The gelatin provided sufficient protection for the bacteria to survive the transit through the digestive system and to colonize the small intestine.
To test how well the altered genes worked, they put all the experimental mice on a high-fat diet and divided them into four groups, of which only one received the drinking water with gelatin and NAPE-producing bacteria [4]. The other three were control groups: one group received standard drinking water, one received drinking water with gelatin, and one received drinking water with gelatin and nonengineered bacteria. The three control groups all gained weight at the same high rate and had unhealthy inflammatory markers, such as insulin resistance, high blood glucose levels, and accumulations of triglycerides in the liver. In contrast, the animals that received NAPE-expressing bacteria not only ate less and consequently had lower body fat and lower body weight, but they also scored much lower for inflammatory markers that signify other health problems. “The markers are important because, in the end, we’re not really worried about the weight itself; we’re worried about all the conditions that are linked to obesity, such as diabetes and cardiovascular disease,” Davies says.
Davies and his research group are now looking at how the NAPE-producing bacteria specifically affect certain diseases. “We are looking at animals that are susceptible to atherosclerosis and to nonalcoholic fatty liver disease,” he explains. “We are still preparing our results for peer review, but we think that the results so far are quite interesting and support the potential use of NAPE-producing bacteria for treatment of these diseases.”
Davies believes these modified bacteria would work well in people too, but human trials are still far off. “One of the big things that we’re working on is finding a way to limit the transmission of these living bacteria between individuals,” he says. So far, the engineered bacteria appear to have absolutely no adverse effects on healthy mice, but testing is continuing. “Nonetheless, what we’d ideally like to do is to minimize the opportunity for the bacteria to be spread, so we don’t wind up treating people who didn’t plan on being treated,” he says. “We still need to work that out, but we’re hopeful that we will be able to do that.”
In the meantime, his group is continuing to look into other gut compounds that are lacking in obese individuals and working on either developing multiple bacteria that each produce separate compounds or one bacterium that is able to make multiple compounds.
In looking at engineered bacteria overall, Davies comments, “I think it offers something that can be helpful for obesity, that can be sustained over a long period of time and that can provide a not especially challenging treatment regime for people to stay on. Of course, there’s still a lot of things that will have to be worked out, but I expect that ten years from now, engineered microbiota will have a big impact on obesity.”
A Panoramic View
Another approach to the obesity epidemic is to look at the big picture, and that is what Frank Hu, M.D., Ph.D., is trying to do. Hu is codirector of the Obesity Program at the Harvard T.H. Chan School of Public Health and director of the Boston Nutrition Obesity Research Center’s Epidemiology and Genetics Core. He is blending omics technologies, epidemiological studies, and bioinformatics to decipher biomarkers for obesity, the interplay between genes and diet, the impact of blood metabolites, and how these can potentially affect intervention efforts.
A central part of his work is making sense of large epidemiological data sets and trying to uncover the underpinnings of obesity. “One thing we have identified that I think really stands out in our data sets is that people began to gain weight at about age 18, and every year they gained on average of 1 pound so that by age 55, almost a third of the cohort had become obese, and another third had become overweight,” Hu reports [5]. This cumulative trajectory suggests that prevention should begin with young adults, a far cry from the current practice of scrutinizing weight only after it increases to the point of causing health problems, such as hypertension, type 2 diabetes, or high cholesterol.
“People need to be vigilant at every life stage, beginning with college age,” Hu asserts. “People need to monitor their weight and waist circumference, and we should also pay attention to changes in certain biochemical parameters, including even modest increases in blood pressure, blood sugar, and cholesterol, which can be signs of being on the wrong trajectory in terms of metabolic health.” In addition, an ounce of prevention is almost literally worth a pound of cure when it comes to body weight. Once people become obese, he remarks, they may be able to lose weight, but they often cannot keep it off. Part of the reason is that weight loss is accompanied by a decrease in the appetite-suppressing hormone leptin and an increase in the hunger hormone ghrelin, and when that is combined with the ready availability of food in today’s society, “it’s hard to resist the biological urge,” he explains.
In addition to these studies, Hu and his group are using high-throughput, state-of-the-art technologies to investigate biomarkers related to obesity, as well as diabetes and cardiovascular disease. They are especially interested in the interplay between genes and diet. For instance, using sophisticated statistical models that integrate genomics and dietary data, they discovered not only that individuals who carry numerous obesity-associated genes may be more prone to gain weight when they drink sugar-sweetened beverages, but also that the effects of those genes are amplified among regular drinkers of sugar-sweetened beverages. “That means that what we eat or drink can actually influence how the genes are expressed and activated, so genetic effects are not static and can be modified,” he says. That’s good news, he believes, because it suggests that people can mitigate their genetic susceptibility to obesity through a healthier diet.
In other areas of research, Hu and his team are also using metabolomics technologies combined with computational biology/bioinformatics approaches to measure and quantify the influence of thousands of metabolites, which are derived from the metabolism of carbohydrates, fats, sugars, and other micronutrients. “What we found is that blood metabolites can be used to predict the future risk for obesity, diabetes, and even cardiovascular disease,” Hu explains. “For example, we found that high concentrations of branched amino acids, such as leucine, isoleucine, and valine, in the blood predict increased risk of becoming obese or developing diabetes and heart disease later in life.”
More work is needed before blood metabolite levels can be translated into a clinical tool for predicting a person’s propensity to become obese, but Hu believes it is a promising possibility. “These are very exciting findings because they provide a link between our diet and obesity outcomes through metabolic pathways that can be targeted for prevention and intervention.”
All the work described here, in addition to Hu’s studies of obesity and metabolic diseases in China, is possible because of recent advances in omics and bioinformatics technologies that allow a panoramic view of obesity taking into account many variables. He explains, “With the new bioinformatics methods developed by us and others, we have been able to integrate genomics data and metabolomics data with traditional epidemiological data, and that has turned out to be very fruitful for looking at the whole picture of obesity.”
New Approaches
As research proceeds to untangle the dynamics of obesity, which is indispensible for designing prevention efforts, millions of people the world over are struggling with the condition today. Fortunately, the suite of treatment options is broadening.
For instance, the U.S. Food and Drug Administration (FDA) approved both the Obalon Balloon System and the AspireAssist device in 2016. The Obalon Balloon System [6] is a balloon folded inside a capsule, which the patient swallows. Once it reaches the stomach, the balloon is inflated with gas via a microcatheter and then remains there, taking up space in the stomach so that the patient eats less and loses weight. Two additional balloons are similarly added to the stomach over the next three months, and at the six-month mark, all three are removed during outpatient endoscopy. The AspireAssist device [7] is a tube, placed in the stomach through a small incision, that connects to an external port. Following a meal, the patient connects a separate tube to the port to drain a portion of the stomach contents as a means to reduce food intake and lose weight. As abdominal girth decreases, the internal tube is shortened accordingly.
Allison applauds the increasing number of choices for currently obese patients. “We often like to lament in the field of obesity and say we’re just not doing enough, and while there’s no question we’re not exactly where we wish we were today (in terms of obesity rates), we clearly are making progress in the area of surgery,” he remarks. “It’s getting safer with fewer side effects, it is getting more effective in some cases, it is getting less invasive in some cases, and, importantly, the evidence shows we are in fact having profound impacts on improving health and increasing life span.”
The range of obesity-related pharmaceuticals is also growing, Allison says. In the last five years alone, the FDA has approved the oral drugs Phentermine/Topiramate ER (Qsymia), Lorcaserin (Belviq), and Naltrexone SR/Bupropion SR (Contrave), as well as the injectable Liraglutide 3.0 mg (Saxenda), all of which interfere with hunger signals from the brain as a way to reduce appetite and therefore caloric intake. “Clearly, we don’t want to get to point where two-thirds of the people on our planet have to take an anti-obesity drug for the rest of their lives, nor do we believe that the long-term solution for obesity is to have everybody undergo surgery. But we have to acknowledge that there are obese people today or people who struggle with their weight, and who benefit from these options.”
With a better understanding of obesity overall, notes Davies, new approaches will also become available, and none too soon. “Most of the current strategies are focused on reducing food intake and increasing exercise, and that’s effective for short-term weight loss,” he says, “but people regain the weight, so these strategies don’t provide very good long-term outcomes.” Even surgeries, such as gastric bypass, may reduce weight and associated health risks, but patients “typically remain obese—it’s just not morbidly obese—so it’s helpful to a great extent, but not completely.” He adds, “There is still a lot of unmet need in terms of people who are obese and need to lose weight to protect themselves from diabetes and cardiovascular disease. We need new and different approaches.”
Hu agrees. “A lot of people have been saying if you eat a low-calorie diet, obesity will go away, or if you cut out all the sugars, the obesity trend will be completely reversed. Some of the strategies can help, but I don’t think there’s one magic bullet that can solve obesity. And I think that’s one of the reasons why the obesity trend hasn’t slowed: most of the approaches have involved single factors and have not considered the combination of myriad factors that actually lead to obesity.”
Concluding, Hu adds, “The combination of unhealthy diet, sedentary behavior, high-stress lifestyle, [and] disrupted sleep patterns is one that is very common in modern life and may help to explain the dramatic increase in the prevalence of obesity in the U.S. population and also in other countries. So, to a large degree, obesity isn’t just a medical problem. It’s a societal problem, and one that we’re clearly recognizing now can only be dealt with through multipronged approaches. This remains a very active research area.”
References
- N. A. Christakis and J. H. Fowler, “The spread of obesity in a large social network over 32 years,” New Engl. J. of Med., vol. 357, no. 4, pp. 370–379, July 2007.
- K. Ejima. (2017, Feb. 19). Modeling social contagion of obesity. American Association for the Advancement of Science. [Online].
- M. P. Gillum, D. Zhang, X.-M. Zhang, D. M. Erion, R. A. Jamison, C. Choi, J. Dong, M. Shanabrough, H. R. Duenas, D. W. Frederick, J. J. Hsiao, T. L. Horvath, C. Min Lo, P. Tso, G. W. Cline, and G. I. Shulman, “N-acylphosphatidylethanolamine, a gut-derived circulating factor induced by fat ingestion, inhibits food intake,” Cell, vol. 135, no. 5, pp. 813–824, Nov. 2008.
- Z. Chen, L. Guo, Y. Zhang, R. L. Walzem, J. S. Pendergast, R. L. Printz, L. C. Morris, E. Matafonova, X. Stien, L. Kang, D. Coulon, O. P. McGuinness, K. D. Niswender, and S. S. Davies, “Incorporation of therapeutically modified bacteria into gut microbiota inhibits obesity,” J. Clin. Investi., vol. 124, no. 8, pp. 3391–3406, Aug. 2014.
- M. Song, F. B. Hu, K. Wu, A. Must, A. T. Chan, W. C. Willett, and E. L. Giovannucci. (2016, May). Trajectory of body shape in early and middle life and all cause and cause specific mortality: Results from two prospective US cohort studies. BMJ. [Online]. 353, p. i2195.
- Obalon. [Online].
- AspireAssist. [Online].