Clinica chimica acta; international journal of clinical chemistry · 2026
NMR-based metabolic measures of chronic stable angina and myocardial infarction in patients with diabetes mellitus.
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Gupta Ashish, Kashyap Shiridhar, Meena Khushbhu, Kumar Deepak, Gaurav Amit, Sahu Ankit Kumar, Yadav Subhash, Kumar Sudeep, Kapoor Aditya
Аннотация
BACKGROUND AND AIMS: Diabetes mellitus (DM) accelerates the onset and progression of coronary artery disease (CAD), yet metabolomic profiling in individuals with both conditions remains limited. NMR-based metabolomics offers a comprehensive assessment of metabolic alterations and may improve cardiovascular risk stratification. Hence, the objective of this study was to characterize serum metabolic signatures associated with varying CAD severity in diabetic patients and evaluate their relationship with conventional clinical measures.
METHODS: Eighty-eight diabetic patients undergoing coronary angiography were categorized into three groups: normal coronaries (DNC), chronic stable angina (DCSA), and myocardial infarction (DMI). Serum was mechanically filtered (3-kDa cutoff) and analyzed using 800 MHz 1H NMR spectroscopy. Spectral data underwent univariate ANOVA, PCA, PLS-DA, and OPLS-DA. Metabolites with VIP > 1.0 were identified. ROC and regression analyses assessed discriminative performance and clinical-metabolic associations.
RESULTS: Multivariate analyses showed clear separation among DNC, DCSA, and DMI. Seventeen metabolites distinguished the groups, with aspartate, methylguanidine, arginine, and creatinine identified as key metabolic signatures. Clinical measures-Troponin I, LDL, LDH, and total cholesterol-also demonstrated strong discriminatory ability. Combined ROC models achieved high sensitivity and specificity. Significant correlations linked myocardial injury and lipid dysregulation with nitrogen- and amino-acid-related metabolites.
CONCLUSIONS: Filtered-serum 1H NMR metabolomics reliably differentiates CAD severity in diabetic patients, revealing metabolic signatures associated with oxidative stress, amino-acid disruption, and lipid imbalance. Integrating metabolic and clinical measures offers a promising precision-medicine approach for early detection and risk stratification in DM-related CAD.
METHODS: Eighty-eight diabetic patients undergoing coronary angiography were categorized into three groups: normal coronaries (DNC), chronic stable angina (DCSA), and myocardial infarction (DMI). Serum was mechanically filtered (3-kDa cutoff) and analyzed using 800 MHz 1H NMR spectroscopy. Spectral data underwent univariate ANOVA, PCA, PLS-DA, and OPLS-DA. Metabolites with VIP > 1.0 were identified. ROC and regression analyses assessed discriminative performance and clinical-metabolic associations.
RESULTS: Multivariate analyses showed clear separation among DNC, DCSA, and DMI. Seventeen metabolites distinguished the groups, with aspartate, methylguanidine, arginine, and creatinine identified as key metabolic signatures. Clinical measures-Troponin I, LDL, LDH, and total cholesterol-also demonstrated strong discriminatory ability. Combined ROC models achieved high sensitivity and specificity. Significant correlations linked myocardial injury and lipid dysregulation with nitrogen- and amino-acid-related metabolites.
CONCLUSIONS: Filtered-serum 1H NMR metabolomics reliably differentiates CAD severity in diabetic patients, revealing metabolic signatures associated with oxidative stress, amino-acid disruption, and lipid imbalance. Integrating metabolic and clinical measures offers a promising precision-medicine approach for early detection and risk stratification in DM-related CAD.