Multiomic blood-based profiles paint a more accurate picture of obesity and metabolic disease than height-and-weight-based BMI, according to researchers from the Institute for Systems Biology, USA.
“For years, BMI has been the go-to measure for doctors to classify individuals based on their height and weight in comparison to an average person. However, this average person doesn’t truly exist,” said Noa Rappaport, ISB senior research scientist and corresponding author of the paper, in a press release (2). “We now have the capability to 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.”
Over 1,000 individuals enrolled in the wellness program, which involved analyzing cross-sectional and longitudinal changes in blood analytes. Using ultra-high performance liquid chromatography-MS/MS (UHPLC-MS/MS), the researchers measured over 1,000 blood markers – ranging from proteins to small molecule metabolites – as well as participants’ polygenic risk scores and gut microbiome profiles from ribosomal RNA (1). Using machine learning technology, the team created “biological BMI” prediction scores.
The results indicated that participants with a high biological BMI and normal traditional BMI were less healthy, but able to lose weight easier following a lifestyle intervention. The team also found that biological BMI was more responsive to positive lifestyle changes, dropping earlier than traditional BMI.
Kengo Watanabe, lead author of the study, said: “This work has the potential to significantly improve the development of predictive and preventive clinical approaches for treating metabolic disturbances.”
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References
- K Watanabe et al., Nat Med (2023). DOI: 10.1038/s41591-023-02248-0.
- Institute for Systems Biology (2023). Available at: https://bwnews.pr/3M3qr6L.