-Written by Sai Lavanya Patnala, Intern, Apollo Institute of Medical Sciences and Research


Obesity may not the mean the same as it used to anymore. 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. Scientists at the Department of Epigenetics at Van Andel Institute are claiming that it (BMI) is an imperfect measure, because it doesn’t account for underlying biological differences and can misrepresent an individual’s health status[1]. Some people who fit the “obese” category, according to BMI, may never receive a disease diagnosis, while others in the “normal” BMI range could have a genetic predisposition to heart disease and other illnesses, no matter their weight[3].


“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 J. Andrew Pospisilik, Ph.D., chair of Van Andel Institute’s Department of Epigenetics and corresponding author of the study[2].

Using a combination of laboratory studies in mouse models and deep analysis of data from Twins UK, a pioneering research resource and study cohort developed in the United Kingdom, Pospisilik and his collaborators discovered four metabolic subtypes that influence individual body types: two prone to leanness and two prone to obesity.

One obesity subtype is characterized by greater fat mass while the other was characterized by both greater fat mass and lean muscle mass. They also found that the second obesity type also was associated with increased inflammation, which can elevate the risk of certain cancers and other diseases. Both subtypes were observed across multiple study cohorts, including in children. 

After the subtypes were identified in the human data, the team verified the results in mouse models. This approach allowed the scientists to compare individual mice that are genetically identical, raised in the same environment and fed the same amounts of food. The study revealed that the inflammatory subtype appears to result from epigenetic changes triggered by pure chance. A similar pattern was seen in data from more than 150 human twin pairs, each of whom were virtually the same genetically.

“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 said.

Depending on the calculation and traits in question, only 30%–50% of human trait outcomes can be linked to genetics or environmental influences. The remaining is explained, or rather unexplained by a phenomenon called unexplained phenotypic variation (UPV) 

The study indicates that the roots of UPV likely lie in epigenetics, the processes that govern when and to what extent the instructions in DNA are used. Epigenetic mechanisms are the reason that individuals with the same genetic instruction manual, such as twins, may grow to have different traits, such as eye color and hair color. Epigenetics also offer tantalizing targets for precision treatment.

“Accounting for UPV doesn’t exist in precision medicine right now, but it looks like it could be half the puzzle. Today’s findings underscore the power of recognizing these subtle differences between people to guide more precise ways to treat disease.”, said Pospisilik[1].


Until now, scientists placed people in one of three metabolic types: endomorph (stores fat easily), mesomorph (easily gains muscle), and ectomorph (thin, struggles to gain fat or muscle). These recent findings divides people into four metabolic subtypes (two prone to leanness and two prone to obesity)[3].

These insights are an important step toward understanding how these different types impact disease risk and treatment response[1] and may one day help doctors provide more precise care for patients and inform more precise ways to diagnose and treat obesity and associated metabolic disorders, Pospisilik explained[3].



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