Is it time to say goodbye to traditional BMI?

1800s Belgian mathematician Adolphe Quetelet After being involved in a number of census studies, he attempted to define the characteristics of a “normal man.” He devised the Quetelet index, a formula that calculates the ratio of his squares of weight (kg) and height (meters). This simple calculation later became known as the “body mass index,” a measure of determining “healthy” weight.

BMI is used throughout the medical community to determine disease risk and inform public health and insurance policies. BMI is Above the surgeon’s “cutoff” point. today, “Normal” BMI is between 18.5 and 24.9. People with a BMI of less than 18.5 are classified as “underweight” and those with a BMI of 30 or more are classified as “obese”.

Despite its widespread use, there are problems associated with the traditional BMI formula. led to the criticism And we need new approaches to measure metabolic health.

the current, Institute of Systems Biology (ISB) Produced biological BMI published in journal natural medicineA team led by Senior Researcher Dr. Noah Rappaportimplementation multi-omics Profiling blood samples from 1,000 people enrolled in the now closed Arivale wellness program. Using machine learning models, researchers generated molecular BMI scores, such as BMI based on metabolomics or proteomics.

Rappaport and colleagues found that biological BMI calculations were more responsive than conventional BMI when individuals made ‘positive’ lifestyle changes. Among other insights, the team found that people with a high biological BMI and a normal conventional BMI, although not overall healthy, could easily lose weight with lifestyle interventions. Did.

technology network interview with Rapaport Dr. Kengo Watanabea molecular and systems biologist at ISB and the first author of the paper, to understand how biological BMI is calculated and why it is useful as a surrogate measure of health.

Molly Campbell (MC): Can you explain why the traditional BMI measurement is suboptimal? Why is it still used despite these problems?

Noah Rappaport (NR): Traditional BMI measurements are suboptimal because they cannot accurately classify approximately 30% of individuals. So even though some individuals appear to have a normal weight, they may have disturbed metabolic health. One reason is that his traditional BMI does not take into account health-related factors such as muscle mass, bone density, and fat distribution. Despite these problems, traditional BMI measurements have continued to be used. This is because it is a simple, cost-effective measure of obesity and correlates with many chronic diseases and all-cause mortality.

MC: What is biological BMI?

Kengo Watanabe (KW): Biological BMI is a multidimensional molecular measurement of BMI calculated from protein, metabolite, or clinical laboratory blood measurements. This is a more comprehensive and accurate measure of metabolic health compared to the traditional BMI measure of him, which only considers height and weight. Unlike traditional his BMI, biological BMI can identify misclassified individuals of normal weight but disturbed metabolic health who may not currently be monitored or treated. can.

MC: Why did you choose a multi-omics profiling approach for this study?

NR: To gather rich health information from blood analysis, the study employed a multi-omics profiling approach. With this approach, more than 1,100 blood analytes, including proteins, metabolites, and laboratory assays, were combined with genetic risk scores and gut microbiota to assess metabolic health and create a biological BMI score. It allowed us to cast a wider net when analyzing in combination with biome composition.

By analyzing a large number of blood analytes, we were able to identify a broader range of potential biomarkers that may be amenable to intervention with lifestyle changes and other interventions. It also provides a more detailed understanding of the complex relationships between different molecules and how they affect metabolic health, helping guide future research and interventions.

MC: Can you explain how a machine learning model was used to predict changes in biological BMI?

keyword: We modeled the traditional BMI score by building a signature of linear combinations of blood measurements using a machine learning algorithm. In this way, we created several types of biological BMI scores, such as his metabolomics-based BMI and his proteomics-based BMI. These scores could explain up to 78% of BMI variability. This is double what previous studies were able to achieve using different methods and different features.

MC: You find that biological BMI is a more ‘responsive measure’ than traditional BMI when people make positive lifestyle changes. Can you describe these lifestyle changes and the results obtained?

NR: Metabolomics-based BMI was found to be a more responsive measure than traditional BMI when people made positive lifestyle changes, such as lifestyle coaching, regardless of weight loss. An individual classified as metabolically unhealthy by metabolomics (although his BMI was conventionally normal) had improved metabolic status more than those classified as metabolically healthy. Participants in the (now closed) Arivale program will develop multi-omics data-driven clinical studies with the goal of optimizing health by targeting cardiovascular health, nutrition, gut microbiome health, and more. I received individual coaching from a nutritionist.

MC: Could the approach used in this study be widely adopted as an alternative BMI measure?

keyword: We see in-depth molecular profiling as the future of precision medicine, as technological advances continue to make it more cost-effective and useful for capturing the complex nature of health. Although we believe that our approach to developing a biological BMI score has the potential to be widely adopted as an alternative to traditional BMI measurement, we have not fully validated its efficacy and tested it in different populations. More research is needed to ensure its applicability to

MC: Are there any restrictions on the research you want to highlight?

NR: Yes, for example, our study only looked at a limited number of biomolecules in blood, limiting our findings to this set. It is difficult to say how effective the style intervention was.

MC: What are your next steps in this area of ​​research?

keyword: Going forward, our study aims to expand the predictive power of biological BMI for long-term health outcomes. This presents challenges due to the limited availability of datasets in this emerging field, but we hope to collaborate with other researchers and institutions to address this issue. We plan to investigate ways to further personalize interventions based on a person’s biological age in order to optimize their impact on. Overall, we believe our study is an important step towards developing more effective strategies to prevent and manage chronic diseases associated with obesity and metabolic health.

Dr. Noa Rappaport and Dr. Kengo Watanabe were talking with Molly Campbell, senior science writer at Technology Networks.

reference: Watanabe K, Wilmanski T, Diener C, et al. Multi-omics signatures of body mass index identify heterogeneous health phenotypes and responses to lifestyle interventions. Nightwiz2023. Doi:10.1038/s41591-023-02248-0.