\n<\/p>\n
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\u2019s body weight is normal, according to new data.<\/p>\n
\u201cThese findings define an adipose-linked, microbiome-connected metabolic signature that outperforms BMI in stratifying cardiometabolic risk and guiding precision interventions,\u201d reported the authors of the study published in Nature Medicine<\/em>.<\/p>\n \u201cThe traditional BMI often misses people who have normal weight but high metabolic risk,\u201d said lead author Fredrik B\u00e4ckhed, MD, a professor at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, in a press statement.<\/p>\n \u201cMetBMI can contribute to a fairer and more accurate assessment of disease risk and thus pave the way for more personalized prevention and treatment,\u201d he added.<\/p>\n 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.<\/p>\n 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.<\/p>\n \u201cThis has prompted calls to refine diagnostic criteria to prevent undertreatment of at-risk individuals not identified by BMI,\u201d the authors said.<\/p>\n To provide an obesogenic signature that captures the broad array of factors underlying obesity risks, B\u00e4ckhed 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.<\/p>\n \u201cCirculating metabolites, shaped by host genetics, diet, and the gut microbiome, offer a systems-level readout of metabolic health beyond excess weight,\u201d the authors wrote.<\/p>\n 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.<\/p>\n 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.<\/p>\n The profiling tool was designed to capture \u201cadipose tissue-related dysfunction across organ systems,\u201d the authors said.<\/p>\n \u201cTogether, these signals give a much more realistic picture of metabolic health than weight alone,\u201d first author Rima M. Chakaroun, MD, also of the Department of Clinical Physiology Region V\u00e4stra G\u00f6taland, at Sahlgrenska University Hospital, told Medscape Medical News<\/em>.<\/p>\n Data was also evaluated on an external cohort of 466 individuals aged 50-65 years.<\/p>\n 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.<\/p>\n Of note, among 75 patients who underwent bariatric surgery, those with a high metBMI score achieved 30% less weight loss.<\/p>\n 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.<\/p>\n \u201cThis obesogenic signature aligned with reduced microbiome richness, altered ecology, and functional potential,\u201d the authors reported.<\/p>\n 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 \u201cunderscores environmental and lifestyle influences over genetic predisposition in shaping metabolic obesity,\u201d the authors noted.<\/p>\n \u201cTaken together, our findings suggest that the gut microbiome both reflects and potentially contributes to the metabolic derangements of obesity, particularly via circulating metabolites,\u201d they said. \u201cThe metBMI signature captured a constellation of clinically relevant features, including central adiposity, insulin resistance and hypersecretion, kidney dysfunction, dietary composition, and physical activity \u2014 traits not fully captured by anthropometry or standard risk assessment tools.\u201d<\/p>\n It also incorporates imaging-based quantification of adipose tissue, \u201cboth in terms of quantity and tissue quality, allowing us to better understand the biological drivers underlying this metabolic signature,\u201d said Chakaroun.<\/p>\n \u201cImportantly, this framework aligns with newer clinical concepts of obesity that emphasize central adiposity rather than body weight alone,\u201d she added.<\/p>\n A key caveat is that the tool is not yet easily accessible for use in routine practice, said Chakaroun.<\/p>\n \u201cThe full metabolic BMI relies on advanced metabolomics analyses, which are not part of standard blood panels and are still relatively expensive,\u201d she explained.<\/p>\n \u201cThat 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.\u201d<\/p>\n 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 \u201cwould help identify high-risk patients who look \u2018healthy\u2019 by BMI, guide earlier prevention strategies, and help personalize treatment decisions, rather than relying on weight thresholds alone,\u201d said Chakaroun.<\/p>\n Of note, people of normal weight have increases in metabolic risk \u201cmore often than many people realize,\u201d she added. \u201cIn this and other studies, roughly 10-20% of people who appear normal-weight show signs of metabolic dysfunction when you look beyond BMI.\u201d<\/p>\n In the study, the risk profiles of people with normal BMI but high metabolic BMI were similar to those seen in obesity, she noted. \u201cThis explains why conditions like [T2D] or fatty liver disease can occur in people who don\u2019t look overweight.\u201d<\/p>\n Limitations, in addition to not yet representing a routine clinical test, include that the assessments represent a snapshot in time.<\/p>\n \u201cWe still need more long-term data to understand how it changes and how best to intervene,\u201d Chakaroun noted. Ultimately, \u201cmetabolic BMI should be seen as complementary, not as a replacement for clinical judgment or existing risk assessments at this point.\u201d<\/p>\n In the meantime, however, \u201cclinicians 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,\u201d Chakaroun added.<\/p>\n 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\u2019s clinical benefits have yet to be established.<\/p>\n \u201cOne 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,\u201d he told Medscape Medical News<\/em>.<\/p>\n \u201cWhile there is extensive discussion in the paper about omics features, these analyses are almost all correlational \u2014 and, as the saying goes \u2018correlation does not always equal causation,\u2019\u201d Franks noted.<\/p>\n \u201cI don\u2019t 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\u2019d certainly remain very cautious,\u201d he said.<\/p>\n Nevertheless, \u201cit\u2019s exciting work and some of the findings are very likely to extend understanding of obesogenic cardiometabolic disease,\u201d Franks added. \u201cHowever, as is almost always the case with highly innovative work in precision medicine, distinguishing signal from noise is the biggest challenge.\u201d<\/p>\n B\u00e4ckhed 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.<\/em><\/p>\n<\/div>\n 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\u2019s body weight is normal, according to new data. \u201cThese findings define an adipose-linked, microbiome-connected metabolic signature that outperforms BMI […]<\/p>\n","protected":false},"author":1,"featured_media":13153,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_daextam_enable_autolinks":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[2],"tags":[],"class_list":["post-13152","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-health"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/diyhaven858.wasmer.app\/wp-content\/uploads\/2026\/01\/dt-250625-obesity-word-800x450.jpg","jetpack_sharing_enabled":true,"jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/diyhaven858.wasmer.app\/index.php\/wp-json\/wp\/v2\/posts\/13152","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/diyhaven858.wasmer.app\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/diyhaven858.wasmer.app\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/diyhaven858.wasmer.app\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/diyhaven858.wasmer.app\/index.php\/wp-json\/wp\/v2\/comments?post=13152"}],"version-history":[{"count":0,"href":"https:\/\/diyhaven858.wasmer.app\/index.php\/wp-json\/wp\/v2\/posts\/13152\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/diyhaven858.wasmer.app\/index.php\/wp-json\/wp\/v2\/media\/13153"}],"wp:attachment":[{"href":"https:\/\/diyhaven858.wasmer.app\/index.php\/wp-json\/wp\/v2\/media?parent=13152"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/diyhaven858.wasmer.app\/index.php\/wp-json\/wp\/v2\/categories?post=13152"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/diyhaven858.wasmer.app\/index.php\/wp-json\/wp\/v2\/tags?post=13152"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Developing an Obesogenic Signature Beyond BMI<\/h2>\n
Not Ready for Prime Time<\/h2>\n
Causation, Clinical Utility Remain Unclear<\/h2>\n
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<<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"