ISB researchers have developed a biological body mass index that provides a more precise picture of metabolic health. In addition, the biological body mass index features measurements that are more diverse, informative, and actionable than those of the traditional BMI.
Researchers at the Institute for Systems Biology (ISB) have developed biological body mass index (BMI) measures that provide a more realistic depiction of metabolic health. These new biological BMI measures are also more varied, informative, and actionable than the classic BMI equation that has been used for a long time. This work is going to be published in the journal Nature Medicine today (March 20), so stay tuned for that!
Clinicians have depended on the primitive instrument that is the body mass index (BMI) for decades to classify individuals as either underweight, normal weight, overweight, or obese. The body mass index (BMI) is determined by dividing a person's weight in kilograms by the square of their height in meters. This method results in an incorrect classification for approximately thirty percent of the total population. Despite its shortcomings, the body mass index (BMI) continues to be insightful and is generally recognized in the clinical setting. This is because it is a key risk factor for several chronic diseases, such as diabetes, cardiovascular diseases, and cancer.
“The body mass index (BMI) has been the standard for many years as the method that medical professionals use to categorize patients based on how their height and weight compared to that of the average person. On the other hand, there is no such thing as a typical human. According to Noa Rappaport, Ph.D., a senior research scientist at the ISB and corresponding author of the paper, “We now can use advanced molecular measurements as a more comprehensive representation of a person's metabolic health, which can be used to make more accurate clinical recommendations for individuals.”
Rappaport and his colleagues examined the genetic risk scores and gut microbiota composition of one thousand people who participated in a wellness program. They did this by undertaking multi-omic profiling, which involved looking at more than 1,100 blood analytes, such as proteins and metabolites, as well as collecting the data at several different periods. Following this step, the researchers developed machine learning models, which resulted in predicting variations of a biological BMI that were more accurate than traditional assessments of BMI alone.
The group came to several significant conclusions, including the following:
Those who had a normal traditional BMI but a high biological BMI were less healthy overall, but they were able to shed weight more easily after participating in a lifestyle intervention.
Those who were considered obese according to the standard BMI but had a normal biological BMI were healthier from a biological standpoint but had a more difficult time losing weight.
When individuals made healthy adjustments to their lifestyle, the biological BMI responded more quickly and dropped earlier than the standard BMI did.
According to the findings, a person's biological health may be improving even if they do not experience weight reduction when they make healthy changes to their lifestyle.
According to the study's lead author and K. Carole Ellison Fellow in Bioinformatics, Kengo Watanabe, Ph.D., “This work is a valuable asset for comprehending the molecular changes associated with obesity and metabolic health, and it has the potential to significantly improve the development of predictive and preventive clinical approaches for treating metabolic disturbances.” “This work has the potential to significantly improve the development of predictive and preventive clinical approaches for treating metabolic disturbances,” Watanabe said.
Rappaport went on to say that they had “demonstrated the value of multi-omic profiling to reveal important insights into the complex relationships between obesity, metabolic health, and chronic disease,” and that they had “emphasized the need to consider a range of factors beyond traditional measures of BMI in understanding and addressing these issues.”
