By Sean Zucker –
Traditionally, obesity has been defined as simply being overweight by a significant margin. In fact, the World Health Organization (WHO) states that obesity and overweight are essentially interchangeable terms and are understood equally as, “abnormal or excessive fat accumulation that presents a risk to health.” But what has been seemingly more concerning to the organization isn’t the terminology used but rather how increasingly prevalent obesity is. However, scientists are now altering and expanding the definition of obesity and believe doing so may help combat both issues.
Regardless of the definition, more people are carrying more fat than ever. WHO, in fact, reports obesity rates have continued to grow in both adults and children over the last few decades. The organization notes that from 1975 to 2016, the prevalence of overweight or obese children and teens aged 5 to 19 years increased from 4 percent to 18 percent globally. The increase represents more than a four-fold boost, which is having massive impacts on human health. Globally, the two leading causes of death, heart disease and stroke, are common byproducts of being overweight. Diabetes is also a major risk.
Currently, obesity is diagnosed using body mass index (BMI), an index correlated to body fat that is generated by comparing weight in relation to height. Unfortunately, these classifications can be vague and ignore biological differences. This can make treating and preventing resulting conditions much more difficult, as one expert explains.
“Nearly two billion people worldwide are considered overweight and there are more than 600 million people with obesity, yet we have no framework for stratifying individuals according to their more precise disease etiologies,” said Dr. J. Andrew Pospisilik, chair of Van Andel Institute’s Department of Epigenetics.
The institute’s researchers sought to rectify this issue by reexamining how the scientific community talks about obesity. Their findings, which were recently published in Nature Metabolism, identified two distinct types of obesity with physiological and molecular differences.
The researchers attacked the study in two stages. First, they implemented a deep analysis of human data from TwinsUK, a pioneering research resource and study cohort developed in the United Kingdom, of weight and what factors into it. The work focused heavily on twins in the data, as people who are genetically the most similar would show the largest impact of outside factors. They looked at data from over 150 pairs of human twins.
The effort unveiled four metabolic categories that influence individual body types — two prone to leanness and two prone to obesity. But for each pair, one subtype was seemingly influenced by biology and one by the environment. After the subtypes were identified in the human data, the researchers verified the results in mouse models by comparing genetically identical mice that were raised in the same environment and fed the same amounts of food.
“Our findings in the lab almost carbon-copied the human twin data. We again saw two distinct subtypes of obesity, one of which appeared to be epigenetically ‘triggerable,’ and was marked by higher lean mass and higher fat, high inflammatory signals, high insulin levels, and a strong epigenetic signature,” Pospisilik explained.
While the classic question of ‘Nature vs. Nurture’ will probably go on forever, the study suggests it’s likely close to an even split. The institute’s scientists state that anywhere between 30 and 50 percent of human traits are linked to either genetics or environmental influence. Ultimately, the cause of obesity will differ from person to person. Researchers, therefore, believe a more expansive view and definition of obesity that reflects these variances will ultimately provide better medical care.
“Using a purely data-driven approach, we see for the first time that there are at least two different metabolic subtypes of obesity, each with their own physiological and molecular features that influence health. Translating these findings into a clinically usable test could help doctors provide more precise care for patients,” Pospisilik concluded.
