TGF Β Signaling in Diseases · Journal article
Lipids in Health and Disease · August 8, 2026
Encouraging direction, but not yet definitive.
This observational cohort study identified six reproducible TyG-biomarker discordance profiles in 496,724 UK Biobank participants, each characterized by distinct metabolic dysfunction patterns and divergent associations with cardiometabolic outcomes over ten years of follow-up. Metabolomic profiling confirmed biological distinctiveness of each profile, suggesting that individuals with equivalent TyG values exhibit substantially heterogeneous metabolic phenotypes with clinically relevant risk stratification potential, although the study does not establish clinical utility or causality.
Observational cohort study with cross-sectional and prospective follow-up. UK Biobank participants with complete data on triglyceride-glucose index and eight routine biomarkers (adiposity, haemodynamics, renal function, inflammation, lipid metabolism); stratified by sex. Intervention: No intervention; observational profiling of TyG-biomarker discordance patterns. Compared with: Baseline concordant profile; cross-sectional and incident cardiometabolic outcomes. n = 496,724. UK Biobank.
Six reproducible discordance profiles identified per sex, each with systematic deviation in distinct biological domain Discordant obesity profile was strongest positive predictor of incident type 2 diabetes in both sexes Discordant pro-atherogenic lipid profile inversely associated with type 2 diabetes but positively associated with incident major adverse cardiovascular events in males, inverse association in females
Discordant pro-atherogenic lipid profile inversely associated with type 2 diabetes but positively associated with incident major adverse cardiovascular events in males, inverse association in females
This work suggests that TyG-biomarker discordance profiling may improve cardiometabolic risk stratification beyond TyG level alone by identifying metabolically heterogeneous phenotypes. However, clinical adoption would require prospective validation of risk prediction accuracy and demonstration that profile-guided interventions improve outcomes compared to standard risk factors.
A large observational study identifying reproducible metabolic phenotypes with divergent clinical associations, supported by metabolomic validation, but lacking experimental intervention or prospective risk prediction validation.
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Quoted from the source exactly as published.
This work suggests that TyG-biomarker discordance profiling may improve cardiometabolic risk stratification beyond TyG level alone by identifying metabolically heterogeneous phenotypes. However, clinical adoption would require prospective validation of risk prediction accuracy and demonstration that profile-guided interventions improve outcomes compared to standard risk factors.
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.
The triglyceride-glucose (TyG) index is a widely used surrogate of insulin resistance, yet it captures only a narrow slice of the broader cardiometabolic dysregulation associated with metabolic dysfunction. Whether individuals with equivalent TyG values harbour meaningfully distinct multisystem metabolic phenotypes with divergent clinical implications remains poorly understood. We aimed to identify reproducible TyG-biomarker discordance profiles and characterize their associations with prevalent cardiometabolic disease and incident outcomes. Using data from 496,724 UK Biobank participants, we quantified discordance between observed and TyG-predicted values across eight routine biomarkers spanning adiposity, haemodynamics, renal function, inflammation, and lipid metabolism. Standardized residuals were projected using Uniform Manifold Approximation and Projection (UMAP) and clustered via a two-stage graph-based procedure. Soft cluster membership probabilities were derived using a Gaussian mixture model. Profile-specific associations with six prevalent cardiometabolic conditions, three medication classes, and incident major adverse cardiovascular events and type 2 diabetes over ten years of follow-up were estimated using logistic regression and Cox proportional hazards models, with additive log-ratio transformed probabilities as predictors. Plasma NMR metabolomic profiles were additionally characterized for each discordance profile using the same regression framework with Benjamini–Hochberg false discovery rate correction. Six reproducible discordance profiles were identified per sex alongside a baseline concordant profile, each characterized by systematic deviation in a distinct biological domain: discordant hypertensive, discordant renal, discordant high HDL-lipid, discordant obesity, discordant pro-atherogenic lipid, and discordant inflammatory. Profiles showed divergent associations with cardiometabolic outcomes. The discordant obesity profile was the strongest positive predictor of incident type 2 diabetes in both sexes. The discordant pro-atherogenic lipid profile was consistently inversely associated with type 2 diabetes yet positively associated with incident major adverse cardiovascular events in males, with an inverse association observed in females. Sex-specific reversals in the direction of association were observed for the discordant hypertensive profile across multiple outcomes. Metabolomic profiling corroborated the biological distinctiveness of each profile, revealing profile-specific lipoprotein subclass, inflammatory glycoprotein, and fatty acid signatures that were broadly consistent with their divergent clinical risk associations and persisted across sexes. Individuals with equivalent TyG levels harbour substantially heterogeneous multisystem metabolic phenotypes with divergent cardiometabolic risk profiles. TyG-biomarker discordance profiling provides a complementary framework for metabolic risk stratification that captures clinically meaningful heterogeneity not reflected by TyG level alone, with independent metabolomic signatures confirming the biological distinctiveness of each profile.
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