A novel metabolic obesity assessment tool (metBMI) improves upon conventional BMI by capturing a wide array of metabolic factors that underlie obesity risks, including complex adiposity-related factors that can present a risk even when an individual’s body weight is normal, according to new data.
“These findings define an adipose-linked, microbiome-connected metabolic signature that outperforms BMI in stratifying cardiometabolic risk and guiding precision interventions,” reported the authors of the study published in Nature Medicine.
“The traditional BMI often misses people who have normal weight but high metabolic risk,” said lead author Fredrik Bäckhed, MD, a professor at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, in a press statement.
“MetBMI can contribute to a fairer and more accurate assessment of disease risk and thus pave the way for more personalized prevention and treatment,” he added.
The spectrum of health risks associated with obesity, ranging from type 2 diabetes (T2D) to cardiovascular diseases, are well-established, but the complexities of obesity underlying those risks are far more multifactorial than those represented simply with conventional BMI.
Up to 30% of individuals with T2D, for instance, do not have BMI-defined obesity, and many cardiovascular deaths linked to abnormal BMI in fact involve patients not considered obese, the authors explained.
“This has prompted calls to refine diagnostic criteria to prevent undertreatment of at-risk individuals not identified by BMI,” the authors said.
Developing an Obesogenic Signature Beyond BMI
To provide an obesogenic signature that captures the broad array of factors underlying obesity risks, Bäckhed and colleagues developed the metBMI, which is mainly based on metabolites in the blood that are closely linked to inflammation, fat distribution, blood sugar, lipids, and other factors.
“Circulating metabolites, shaped by host genetics, diet, and the gut microbiome, offer a systems-level readout of metabolic health beyond excess weight,” the authors wrote.
The metric was developed by phenotyping 1408 individuals (794 women) from the cross-sectional Impaired Glucose Tolerance and Microbiota Study in Sweden who were considered to be at-risk but did not have established cardiovascular disease or T2D.
Participants were aged 50-65 years and had a mean BMI of 27. Data available for analysis in the cohort included fecal samples for gut microbiome analysis, CT-based adipose tissue quantification, blood samples for metabolomic, proteomic, and clinical chemistry, genotyping for polygenic risk scores (PRSs) as well as comprehensive clinical, lifestyle, dietary, and physical activity reports.
The profiling tool was designed to capture “adipose tissue-related dysfunction across organ systems,” the authors said.
“Together, these signals give a much more realistic picture of metabolic health than weight alone,” first author Rima M. Chakaroun, MD, also of the Department of Clinical Physiology Region Västra Götaland, at Sahlgrenska University Hospital, told Medscape Medical News.
Data was also evaluated on an external cohort of 466 individuals aged 50-65 years.
The results showed that those with higher-than-expected metBMI scores had a twofold to fivefold increased odds of fatty liver disease, diabetes, severe visceral fat accumulation and attenuation, insulin resistance, hyperinsulinemia, and inflammation.
Of note, among 75 patients who underwent bariatric surgery, those with a high metBMI score achieved 30% less weight loss.
The metBMI profile was also associated with notable differences in bacterial diversity in the gut microbiome, which are associated with metabolite levels, T2D, and cardiometabolic risk.
“This obesogenic signature aligned with reduced microbiome richness, altered ecology, and functional potential,” the authors reported.
While PRSs are associated with specific measures including insulin secretion, adipose tissue distribution, circulating lipids, and ectopic fat accumulation, metBMI was not associated with any PRS. This lack of association “underscores environmental and lifestyle influences over genetic predisposition in shaping metabolic obesity,” the authors noted.
“Taken together, our findings suggest that the gut microbiome both reflects and potentially contributes to the metabolic derangements of obesity, particularly via circulating metabolites,” they said. “The metBMI signature captured a constellation of clinically relevant features, including central adiposity, insulin resistance and hypersecretion, kidney dysfunction, dietary composition, and physical activity — traits not fully captured by anthropometry or standard risk assessment tools.”
It also incorporates imaging-based quantification of adipose tissue, “both in terms of quantity and tissue quality, allowing us to better understand the biological drivers underlying this metabolic signature,” said Chakaroun.
“Importantly, this framework aligns with newer clinical concepts of obesity that emphasize central adiposity rather than body weight alone,” she added.
Not Ready for Prime Time
A key caveat is that the tool is not yet easily accessible for use in routine practice, said Chakaroun.
“The full metabolic BMI relies on advanced metabolomics analyses, which are not part of standard blood panels and are still relatively expensive,” she explained.
“That said, we showed that much of the information can be captured with a much smaller set of 66 metabolites, which is a crucial step toward clinical feasibility.”
Once validated in diverse cohorts such as age and ethnicity, the tool could be translated into a specialized blood test. In that context, the test “would help identify high-risk patients who look ‘healthy’ by BMI, guide earlier prevention strategies, and help personalize treatment decisions, rather than relying on weight thresholds alone,” said Chakaroun.
Of note, people of normal weight have increases in metabolic risk “more often than many people realize,” she added. “In this and other studies, roughly 10-20% of people who appear normal-weight show signs of metabolic dysfunction when you look beyond BMI.”
In the study, the risk profiles of people with normal BMI but high metabolic BMI were similar to those seen in obesity, she noted. “This explains why conditions like [T2D] or fatty liver disease can occur in people who don’t look overweight.”
Limitations, in addition to not yet representing a routine clinical test, include that the assessments represent a snapshot in time.
“We still need more long-term data to understand how it changes and how best to intervene,” Chakaroun noted. Ultimately, “metabolic BMI should be seen as complementary, not as a replacement for clinical judgment or existing risk assessments at this point.”
In the meantime, however, “clinicians should remain attentive to metabolic risk even in patients who appear healthy, particularly when subtle warning signs are present, such as a family history of metabolic disease, mild dyslipidemia, or early insulin resistance,” Chakaroun added.
Causation, Clinical Utility Remain Unclear
Commenting on the study, Paul W. Franks, PhD, professor of genetic epidemiology at Lund University, Lund, Sweden, and chair and professor in Translational Precision Medicine at PHURI, Queen Mary University of London, London, England, agreed that metBMI’s clinical benefits have yet to be established.
“One of the most important unanswered questions is whether metBMI helps predict future disease events, and whether, if it does, it outperforms standard clinical prediction models,” he told Medscape Medical News.
“While there is extensive discussion in the paper about omics features, these analyses are almost all correlational — and, as the saying goes ‘correlation does not always equal causation,’” Franks noted.
“I don’t want to dismiss the possibility that this work helps elucidate functional processes underpinning adiposity-associated dysmetabolism, but without formal causal inference analyses, functional studies, or experiments, I’d certainly remain very cautious,” he said.
Nevertheless, “it’s exciting work and some of the findings are very likely to extend understanding of obesogenic cardiometabolic disease,” Franks added. “However, as is almost always the case with highly innovative work in precision medicine, distinguishing signal from noise is the biggest challenge.”
Bäckhed reported being co-founder and shareholder in Implexion Pharma AB and Roxbiosens, Inc.; being on the scientific advisory board of Bactolife A/S; and receiving research funding from BioGaia AB and Novo Nordisk A/S. Chakaroun and Franks had no disclosures to report.
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