TyG-Biomarker discordance profiling identifies metabolically heterogeneous phenotypes with divergent cardiovascular and diabetes risk: metabolomic characterization from the UK biobank
- 1General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, Sichuan China
- 2Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, Sichuan China
Abstract
Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
Graphical Abstract:
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12944-026-03027-9.
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BibTeX
@article{yang2026tygbiomarker,
title = {{TyG-Biomarker discordance profiling identifies metabolically heterogeneous phenotypes with divergent cardiovascular and diabetes risk: metabolomic characterization from the UK biobank}},
author = {Yuhao Yang and Yaxi Liu and Xi Li and Zhenmei An and Shuangqing Li and Qingguo Lyu},
journal = {Lipids in Health and Disease},
year = {2026},
month = aug,
doi = {10.1186/s12944-026-03027-9},
url = {https://doi.org/10.1186/s12944-026-03027-9},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - TyG-Biomarker discordance profiling identifies metabolically heterogeneous phenotypes with divergent cardiovascular and diabetes risk: metabolomic characterization from the UK biobank
AU - Yuhao Yang
AU - Yaxi Liu
AU - Xi Li
AU - Zhenmei An
AU - Shuangqing Li
AU - Qingguo Lyu
PY - 2026
DA - 2026/08/08
JO - Lipids in Health and Disease
DO - 10.1186/s12944-026-03027-9
UR - https://doi.org/10.1186/s12944-026-03027-9
AB - Background: 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. Methods: 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. Results: 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. Conclusions: 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. Graphical Abstract: Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12944-026-03027-9.
SN - 1476-511X
ER -