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Diet, Metabolism, and Disease / Nutrition, Genetics, and Disease · Journal article
Frontiers in Endocrinology · September 11, 2026
Encouraging direction, but not yet definitive.
A six-metabolite signature (HDL_size, S_HDL_CE, XL_HDL_TG, GlycA, M_VLDL_C, Omega_3) discriminates metabolically unhealthy from metabolically healthy obesity with AUC 0.78 and identifies hidden cardiometabolic risk within the MHO phenotype through lipoprotein and inflammatory pathways. This is a discovery-phase metabolomic characterization requiring prospective validation and clinical outcome linkage before translation to risk stratification or intervention.
Cross-sectional metabolomic analysis with machine learning. UK Biobank adults with obesity with available clinical biomarker and NMR-based metabolomic data; stratified by conventional metabolic health criteria (triglycerides, HDL cholesterol, hypertension, fasting glucose, type 2 diabetes status, lipid-lowering medication use).. n = 13,215. UK Biobank.
Six-metabolite signature identified and selected via LASSO regression; discriminates MHO from MUHO. Combined clinical-metabolomic model AUC 0.78 in test set, versus 0.69 for clinical variables alone. Within conventionally defined MHO, higher metabolomic score associated with higher triglycerides, HbA1c, waist-to-hip ratio, lower HDL cholesterol, and greater comorbidity burden.
Cross-sectional design; no prospective follow-up or hard clinical outcome data (MI, stroke, mortality) reported.
The metabolomic signature may refine conventional obesity phenotyping and identify individuals with MHO at hidden cardiometabolic risk, potentially enabling earlier intervention. However, clinical adoption requires prospective validation linking metabolomic score to hard outcomes and demonstration of actionability.
A metabolomic signature study with sound observational design, clear discriminatory performance (AUC 0.78), and mechanistic pathway findings, but based on cross-sectional association in a single population requiring validation before clinical use.
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Quoted from the source exactly as published.
The metabolomic signature may refine conventional obesity phenotyping and identify individuals with MHO at hidden cardiometabolic risk, potentially enabling earlier intervention. However, clinical adoption requires prospective validation linking metabolomic score to hard outcomes and demonstration of actionability.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Background/objectives Metabolically healthy obesity (MHO) is commonly defined by the absence of metabolic syndrome-related abnormalities despite obesity. However, conventional clinical definitions may overlook substantial metabolic heterogeneity and hidden cardiometabolic risk. We aimed to identify metabolomic signatures distinguishing MHO from metabolically unhealthy obesity (MUHO), evaluate their discriminatory performance, and determine whether metabolomic profiling could further characterize heterogeneity within conventionally defined MHO. Methods We analyzed 13215 UK Biobank adults with obesity and available clinical biomarker and NMR-based metabolomic data. Metabolic health was defined using triglycerides, HDL cholesterol, hypertension, fasting glucose, type 2 diabetes, and lipid-lowering medication use. Univariable logistic regression and LASSO regression were used for metabolite selection. Logistic regression and XGBoost models were developed using clinical variables, metabolomic markers, and their combination. A weighted metabolic signature score was applied within the MHO group to characterize cross-sectional metabolic and clinical heterogeneity, and proteomic analyses were performed in approximately 1408 participants. Results A six-metabolite signature comprising HDL_size, S_HDL_CE, XL_HDL_TG, GlycA, M_VLDL_C, and Omega_3 was selected. The combined clinical-metabolomic model showed better discrimination than clinical variables alone in the test set, with AUCs of 0.78 and 0.69, respectively. Within MHO, higher metabolomic score was associated with higher triglycerides, HbA1c, waist-to-hip ratio, lower HDL cholesterol, and greater metabolic and cardiovascular comorbidity burden. Proteomic analyses identified 10 metabolite-associated core proteins implicating lipoprotein remodeling, adipokine signaling, inflammation, and vascular-related pathways. Conclusions A six-metabolite signature distinguished MHO from MUHO and revealed hidden metabolic risk within conventionally defined MHO, provides a metabolomic framework for refining obesity phenotyping and warrants further validation before clinical translation.
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