Researchers at the Universities of Leipzig and Gothenburg have developed an AI-based computational model that calculates metabolic BMI (metBMI) from metabolic measurements. This model is intended to record the individual risk of diseases such as diabetes and fatty liver more precisely than the traditional body mass index (BMI).

The conventional BMI, calculated from height and weight, indicates overweight, but does not distinguish between healthy and pathological body fat. Up to 30 percent of people with a normal BMI already have metabolic changes, while some with an elevated BMI have an inconspicuous metabolism. This gap can lead to delayed detection of high-risk patients.

The research team analyzed data from two Swedish population studies with a total of around 2000 participants. In addition to health and lifestyle data, it collected extensive laboratory values from blood and intestinal microbiome. On this basis, a model was developed that predicts the metBMI. From over 1000 metabolites, a reduced panel of 66 metabolites was identified, which offers comparable informative value. These molecules primarily reflect the exchange between the body’s own metabolism and intestinal bacteria.

Individuals of normal weight but high metBMI carry up to five times higher risk of fatty liver, diabetes, visceral fat accumulation, and insulin resistance. In addition, patients with high metBMI lost 30 percent less weight after bariatric surgery. These data come from those operated on at Leipzig University Hospital.

A central finding is the connection to the gut microbiome: Higher metBMI correlates with lower bacterial diversity and reduced conversion of dietary fiber into health-promoting fatty acids such as butyric acid. Genetic factors play a smaller role in metBMI than lifestyle and environment.

In the future, the model will be improved by including dynamic markers for insulin secretion and experimental studies on the gut microbiome-metabolite axis. It could enable earlier identification of those affected, more precise therapy selection and personalized decisions.

The study was published in Nature Medicine.

Original Paper:

Multi-omic definition of metabolic obesity through adipose tissue–microbiome interactions | Nature Medicine

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