Metabolism: clinical and experimental · 2026
Pathophysiology-based subtypes of individuals at high risk of type 2 diabetes: multi-omics profiles and lifestyle differences across subtypes.
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Deng Keyong, Hameete Annoeska, Schrauwen Patrick, Wagner Robert, van Hylckama Vlieg Astrid, Rosendaal Frits R, Mook-Kanamori Dennis O, le Cessie Saskia, van Dijk Ko Willems, de Mutsert Renée, Li-Gao Ruifang
Аннотация
BACKGROUND: Previous studies identified subtypes among individuals at elevated risk of type 2 diabetes mellitus (T2DM), yet the molecular signatures distinguishing these subtypes remain poorly characterized. We aimed to identify and characterize T2DM risk subtypes using routine and non-routine clinical variable lists, and to compare their metabolomic, proteomic, and lifestyle profiles.
METHODS: In the Netherlands Epidemiology of Obesity study (median age 56 years; median BMI 29 kg/m2), we applied partitioning around medoids (PAM) clustering using two variable lists (routine, N = 5235; non-routine, N = 1510), both including variables derived from a liquid mixed meal challenge. Cox proportional hazards models estimated associations between subtypes and T2DM incidence. Random forest models identified discriminative metabolites and proteins across subtypes.
RESULTS: Each variable list yielded four subtypes ranging from an insulin-sensitive and lean profile (subtype 1) to an obese profile with ectopic fat accumulation and insulin resistance (subtype 4), with a graded increase in T2DM risk (hazard ratios ranging from 1.9 [95% confidence interval (CI): 0.4-9.8] to 19.5 [95% CI: 9.1-41.6]). Both subtyping schemes captured metabolic heterogeneity beyond conventional weight-by-glycemia categories, e.g., redistributing overweight or obese but normoglycemic individuals across subtypes with divergent metabolic profiles and T2DM risks. While multi-omics profiling revealed shared metabolic and proteomic markers across higher-risk subtypes (e.g., glycoprotein acetyls, glucose, hepatocyte growth factor), subtype assignment was predominantly driven by fasting glucose and lipoprotein levels (e.g., very-low-density lipoprotein [VLDL]), with additional subtype-specific molecular signatures (e.g., branched-chain amino acids). Individuals in the higher-risk subtypes also exhibited less healthy lifestyle characteristics, including poorer dietary quality.
CONCLUSIONS: Four metabolic subtypes with graded T2DM risk were identified in a predominantly overweight or obese, middle-aged population, revealing metabolic heterogeneity among individuals who appear homogeneous under conventional weight-by-glycemia categories. Although subtype differentiation was largely driven by fasting glucose and lipoproteins, multi-omics profiling uncovered additional molecular signatures, suggesting that data-driven subtyping may complement conventional risk markers and inform targeted prevention.
METHODS: In the Netherlands Epidemiology of Obesity study (median age 56 years; median BMI 29 kg/m2), we applied partitioning around medoids (PAM) clustering using two variable lists (routine, N = 5235; non-routine, N = 1510), both including variables derived from a liquid mixed meal challenge. Cox proportional hazards models estimated associations between subtypes and T2DM incidence. Random forest models identified discriminative metabolites and proteins across subtypes.
RESULTS: Each variable list yielded four subtypes ranging from an insulin-sensitive and lean profile (subtype 1) to an obese profile with ectopic fat accumulation and insulin resistance (subtype 4), with a graded increase in T2DM risk (hazard ratios ranging from 1.9 [95% confidence interval (CI): 0.4-9.8] to 19.5 [95% CI: 9.1-41.6]). Both subtyping schemes captured metabolic heterogeneity beyond conventional weight-by-glycemia categories, e.g., redistributing overweight or obese but normoglycemic individuals across subtypes with divergent metabolic profiles and T2DM risks. While multi-omics profiling revealed shared metabolic and proteomic markers across higher-risk subtypes (e.g., glycoprotein acetyls, glucose, hepatocyte growth factor), subtype assignment was predominantly driven by fasting glucose and lipoprotein levels (e.g., very-low-density lipoprotein [VLDL]), with additional subtype-specific molecular signatures (e.g., branched-chain amino acids). Individuals in the higher-risk subtypes also exhibited less healthy lifestyle characteristics, including poorer dietary quality.
CONCLUSIONS: Four metabolic subtypes with graded T2DM risk were identified in a predominantly overweight or obese, middle-aged population, revealing metabolic heterogeneity among individuals who appear homogeneous under conventional weight-by-glycemia categories. Although subtype differentiation was largely driven by fasting glucose and lipoproteins, multi-omics profiling uncovered additional molecular signatures, suggesting that data-driven subtyping may complement conventional risk markers and inform targeted prevention.