Institute for Systems Biology (ISB) researchers have constructed biological body mass index (BMI) measures that offer a more accurate representation of metabolic health and are more varied, informative, and actionable than the traditional, long-used BMI equation. The work will be published today (March 20) in the journal Nature Medicine. For decades, clinicians have relied on BMI as a crude tool to classify individuals as underweight, normal weight, overweight or obese. BMI scores are calculated by dividing a person’s weight in kilograms by height in meters squared. About 30 percent of the population is misclassified by this approach. Despite its limitations, BMI continues to be insightful and widely accepted in the clinic, as it is a major risk factor for a number of chronic diseases, including diabetes, cardiovascular diseases, and cancer. Rappaport and colleagues studied 1,000 individuals who enrolled in a wellness program by performing multi-omic profiling, examining more than 1,100 blood analytes such as proteins and metabolites, as well as genetic risk scores and gut microbiome composition collected at various time points. The researchers then generated machine learning models that led to more accurate predictive variations of a biological BMI than traditional measures of BMI alone. The team made several important findings, including:
Those with a high biological BMI and normal traditional BMI were less healthy, but able to lose weight easier following a lifestyle intervention.Those classified as obese with traditional BMI but with a normal biological BMI were more biologically healthy, and found it harder to lose weight.When people made positive lifestyle changes, biological BMI was more responsive and dropped earlier than traditional BMI.
With positive lifestyle changes, the findings suggest that even if someone is not losing weight, they may be getting healthier biologically. Added Rappaport: “We have demonstrated the value of multi-omic profiling to reveal important insights into the complex relationships between obesity, metabolic health and chronic disease, and emphasized the need to consider a range of factors beyond traditional measures of BMI in understanding and addressing these issues.” Reference: “Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention” 20 March 2023, Nature Medicine.DOI: 10.1038/s41591-023-02248-0
