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Background <p>Type 2 diabetes mellitus (T2DM) is a major driver of coronary artery disease (CAD). Prior studies often conflate T2DM- and CAD-specific metabolic alterations, limiting insights into CAD pathogenesis in T2DM. This study aimed to distinguish CAD-unique signatures from T2DM-specific dysmetabolism, and to identify potential metabolic biomarkers for CAD risk escalation in T2DM patients.</p> Methods <p>We performed an untargeted plasma metabolomic study with 123 healthy controls (HCs), 50 T2DM patients without CAD, and 155 T2DM patients with CAD. T2DM_CAD was defined as T2DM diagnosed at least 5&#xa0;years prior to CAD, with coronary angiography-confirmed stenosis (&gt; 30%) in major coronary arteries. Differential metabolites were identified via intergroup comparisons, with T2DM-specific and CAD-specific signatures distinguished based on unique expression patterns. Machine learning models were developed to evaluate the discriminatory performance of these metabolites for CAD.</p> Results <p>Plasma metabolomic profiling identified distinct metabolic patterns across the three cohorts. Metabolites specific to T2DM were enriched in carbohydrates and certain lipid species, reflecting disturbances in glucose and lipid metabolism. CAD-specific metabolites were predominantly lipids and organic acids, with notable involvement in amino acid and fatty acid metabolic pathways. Several metabolites changed progressively from HCs through T2DM to T2DM_CAD, reflecting advancing metabolic dysregulation, whereas others showed opposing trends, suggesting compensatory or protective adaptations. Integration of key metabolites with clinical parameters in machine learning models effectively distinguished between study groups, demonstrating promising performance for CAD risk assessment in T2DM patients.</p> Conclusions <p>These findings disentangle T2DM- and CAD-specific metabolic disturbances and identify escalation/de-escalation features of CAD risk in diabetic patients, which are potential candidates for future risk stratification pending validation.</p> Graphical abstract <p></p>

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Plasma metabolomics disentangles T2DM- and CAD-specific dysmetabolism and identifies potential biomarkers for CAD risk escalation in diabetic patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Ting Hu,
  • Wen Zhang,
  • Xian Ding,
  • Feifei Han,
  • Zhuoling An

摘要

Background

Type 2 diabetes mellitus (T2DM) is a major driver of coronary artery disease (CAD). Prior studies often conflate T2DM- and CAD-specific metabolic alterations, limiting insights into CAD pathogenesis in T2DM. This study aimed to distinguish CAD-unique signatures from T2DM-specific dysmetabolism, and to identify potential metabolic biomarkers for CAD risk escalation in T2DM patients.

Methods

We performed an untargeted plasma metabolomic study with 123 healthy controls (HCs), 50 T2DM patients without CAD, and 155 T2DM patients with CAD. T2DM_CAD was defined as T2DM diagnosed at least 5 years prior to CAD, with coronary angiography-confirmed stenosis (> 30%) in major coronary arteries. Differential metabolites were identified via intergroup comparisons, with T2DM-specific and CAD-specific signatures distinguished based on unique expression patterns. Machine learning models were developed to evaluate the discriminatory performance of these metabolites for CAD.

Results

Plasma metabolomic profiling identified distinct metabolic patterns across the three cohorts. Metabolites specific to T2DM were enriched in carbohydrates and certain lipid species, reflecting disturbances in glucose and lipid metabolism. CAD-specific metabolites were predominantly lipids and organic acids, with notable involvement in amino acid and fatty acid metabolic pathways. Several metabolites changed progressively from HCs through T2DM to T2DM_CAD, reflecting advancing metabolic dysregulation, whereas others showed opposing trends, suggesting compensatory or protective adaptations. Integration of key metabolites with clinical parameters in machine learning models effectively distinguished between study groups, demonstrating promising performance for CAD risk assessment in T2DM patients.

Conclusions

These findings disentangle T2DM- and CAD-specific metabolic disturbances and identify escalation/de-escalation features of CAD risk in diabetic patients, which are potential candidates for future risk stratification pending validation.

Graphical abstract