New Study Redefines Types of Obesity

Obesity is a condition known to contribute to a wide range of health problems. However, a precise definition of obesity and a proper framework for diagnosing it has largely eluded us. No two bodies are the same, and what is a healthy weight and size for one may be entirely unhealthy for another.

To help clarify our understanding of what obesity is and how it should be diagnosed, researchers at Van Andel Institute conducted a study on obesity that led to the discovery of two new obesity subtypes. Now, the discovery of these two subtypes is poised to redefine the clinical definition of obesity.

How is Obesity Currently Defined?

Modern medicine has come a long way, but there are still a few instances where physicians continue to rely on largely outdated methods and understandings. Such is the case with using body mass index (BMI) to diagnose obesity – a practice that has been standard since the 1830s when BMI was first invented by Belgian mathematician Lambert Adolphe Jacques Quetelet.

BMI takes into account a person’s weight and height in order to classify them as either underweight, normal, overweight, or obese, with a BMI of 30 or higher being considered obese. However, there is one glaring issue with using BMI to define and diagnose obesity, and that’s the fact that it does nothing to take into account a person’s actual body composition. For example, someone who is particularly muscular may weigh more due to their high muscle mass. This extra weight could cause their BMI to fall under the overweight or obese categories even if the person has a low amount of body fat.

The Search for a Better Way to Define and Diagnose Obesity

To fix the shortcomings associated with our current method of defining and diagnosing obesity, researchers at Van Andel Institute set out to explore the genetic factors that contribute to body type and composition. Using a combination of laboratory studies in mouse models and deep analysis of data from identical twins, they were able to identify four metabolic subtypes that influence individual body types. Two of these subtypes that they discovered were associated with lean body types, while the other two were associated with obesity. By identifying these metabolic subtypes associated with obesity, researchers hope to form a better understanding of what obesity is, what causes it, and how it should be diagnosed and treated.

“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 a co-author of the study. “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.”

One of the newly discovered obesity subtypes was characterized by greater fat mass while the other was characterized by greater fat mass and greater muscle mass. Somewhat surprisingly, the study revealed that the obesity subtype characterized by both greater fat and muscle mass was more associated with increased inflammation. This would suggest that this type of obesity is the one that presents the greatest health risks such as cancer and heart disease.

“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.

While there is still much work to be done before physicians are provided with a better framework for diagnosing obesity, understanding the condition’s genetic triggers is an important first step. Pospisilik and his team of researchers are hopeful that their discoveries will lead to new precision medicine strategies and a better way of understanding individual patient health.

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