Metabolomic signatures of data-driven type 2 diabetes subtypes and their associations with dementia and stroke risk
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Background
Data-driven type 2 diabetes (T2D) subtypes differ in their risks of dementia and stroke. We examined whether their metabolomic profiles also differed and whether subtype-related metabolic patterns were associated with dementia, stroke, and all-cause mortality.
Methods
We analyzed NMR-based metabolomic profiles across previously defined T2D subtypes in the UK Biobank. Subtype-related metabolites were summarized using principal component analysis (PCA), and their associations with incident dementia, stroke, and all-cause mortality were examined using Cox models. Attenuation analyses and two-sample Mendelian randomization further assessed subtype-outcome relationships and the potential causal relevance of outcome-associated metabolites.
Results
Among 7,671 individuals (mean age 59.85 years; 37% female), the first five PCs explained 76.7% of variance in subtype-related metabolites and mainly reflected lipid and lipoprotein signatures. After adjustment for T2D subtype and confounders, the HDL-remodeling PC increased risks of all-cause dementia (HR 1.17, 95% CI 1.08-1.27), VaD (HR 1.18, 95% CI 1.05-1.32), and all-cause mortality (HR 1.16, 95% CI 1.13-1.19). Lower scores on the LDL cholesterol-enriched axis increase risks of all-cause dementia (HR 0.75, 95% CI 0.62-0.91) and mortality (HR 0.76, 95% CI 0.69-0.83). The VLDL/LDL-enriched PC was inversely associated with mortality (HR 0.93, 95% CI 0.88-0.98). No significant stroke results were observed. Adjustment for the PCA-derived metabolomic patterns generally attenuated subtype-outcome associations, MR analyses identified 197 metabolite-outcome associations that remained significant after FDR correction.
Conclusions
Metabolomic profiling showed that the metabolic signatures differed across data-driven T2D subtypes and highlighted lipid and lipoprotein remodeling as a major metabolic feature associated with dementia, stroke, and all-cause mortality.
Research in context
What is this study about?
This study examined metabolomic signatures across previously defined data-driven type 2 diabetes (T2D) subtypes and their associations with dementia, stroke, and all-cause mortality. Using NMR-based metabolomics in the UK Biobank, we identified distinct metabolic patterns that differed across T2D subtypes and were related to long-term outcomes.
What is novel about this study?
This study integrates comprehensive NMR metabolomics with data-driven T2D subtypes to identify metabolic patterns associated with dementia, stroke, and mortality. Rather than focusing on individual metabolites, we identified biologically interpretable metabolomic patterns and showed that differences in lipid and lipoprotein composition were prominent features of subtype heterogeneity and long-term outcome risk.
What is the main take-home message?
Data-driven T2D subtypes exhibit distinct metabolomic profiles, largely driven by lipid and lipoprotein metabolism. Metabolomic signatures reflecting high-density lipoprotein (HDL) remodeling and lipoprotein composition were consistently associated with dementia, stroke, and all-cause mortality.
What is the clinical implication?
Metabolomic profiling may complement conventional T2D classification by improving biological characterization of metabolic heterogeneity and identifying candidate biomarkers for future risk stratification and precision prevention.
Highlights
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NMR metabolomics distinguished data-driven T2D subtypes in the UK Biobank.
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Subtype differences were largely driven by lipid and lipoprotein remodeling.
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HDL patterns were associated with dementia, stroke, and all-cause mortality.
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Metabolomics may improve T2D phenotyping and future risk stratification.
