Prognostic significance of metabolomic biomarkers in patients with diabetes mellitus and coronary artery disease

The relationship between ABO blood types and clopidogrel-related low on-treatment platelet reactivity in patients with coronary artery diseases and type 2 diabetes mellitus: a secondary analysis of a prospective cohort study

BACKGROUND

The risk of bleeding associated with antiplatelet therapy in patients with coronary artery disease (CAD) has received a lot of attention. The aim of this study was to investigate the relationship between ABO blood system and low on-treatment platelet reactivity (LTPR) in patients with CAD and type 2 diabetes mellitus (T2DM).

METHODS

This study examined 10,724 consecutive patients who received percutaneous coronary intervention in China between January and December in 2013 and applied logistic regression to assess the association between ABO blood types and LTPR. These patients who were diagnosed with T2DM, had thromboelastogram (TEG) results and were administered clopidogrel were ultimately enrolled. LTPR is defined by a platelet maximum amplitude of < 31 mm on TEG, induced by adenosine diphosphate.

RESULTS

Among 3,039 patients (mean age, 59.35 ± 9.89; male, 74.60%), 1,089 (35.83%) presented with LTPR. Multivariate logistic regression revealed that blood type O was independently related to higher odds of LTPR (OR O vs. Non-O: 1.298, 95% CI 1.099-1.534) and that blood type A was independently related to lower odds of LTPR (OR A vs. Non-A: 0.804, 95% CI 0.674-0.958). For further analysis, multivariate logistic regression revealed that, compared to blood type A, type O was independently related to higher odds of LTPR (OR O vs. A: 1.409, 95% CI 1.147-1.729).

CONCLUSIONS

This study reported that in patients with CAD and T2DM, blood type O was independently associated with higher odds of LTPR, indicating a greater likelihood of bleeding, while blood type A was independently related to lower odds of LTPR, suggesting a reduced likelihood of bleeding.

Plasma metabolomics differentiating and predicting prognosis of coronary artery disease patients with distinct nutritional status

This study investigated the metabolic mechanisms underlying the association between malnutrition and poor prognosis in coronary artery disease (CAD). We hypothesized that specific metabolites associated with nutritional status impact all-cause mortality and Major Adverse Cardiovascular Events in CAD patients. To test this hypothesis, we evaluated the nutritional status of 5182 CAD patients from multiple centers using three nutritional risk screening tools and analyzed the impact on CAD outcomes with restricted cubic splines and Cox regression. Poor nutritional status was found to be linked to increased adverse outcomes. Further analysis using multiple linear regression and mediation analysis identified elevated concentrations of β-pseudouridine and dulcitol, and decreased concentrations of l-tryptophan and LPC (18:2/0:0), among other metabolites, as mediators of this association. Employing Least Absolute Shrinkage and Selection Operator for variable selection, we integrated these metabolites with clinical variables, which significantly improved the predictive accuracy for adverse outcomes. Our results highlight significant metabolic disparities in CAD patients based on nutritional status and provide novel insights into the role of nutrition-associated metabolites in CAD prognosis. These findings suggest that customized nutritional interventions targeting these metabolites could positively influence the progression of CAD.

Advancements in Biomarkers for Early Detection and Risk Stratification of Cardiovascular Diseases-A Literature Review

Introduction

CVDs is a leading cause of morbidity, mortality, and healthcare expenditure worldwide. Identifying individuals at risk or in the incipient stages of disease is instrumental in enabling timely interventions, preventive measures, and tailored treatment regimens. The landscape of CVDs is complicated by their heterogeneity, encompassing a spectrum of conditions such as coronary artery disease, heart failure, arrhythmias, and valvular disorders. In recent years, the integration of biomarkers into cardiovascular medicine has emerged as a paradigm-shifting approach with the potential to revolutionize early detection and risk stratification. By synthesizing a multitude of studies, we aim to provide a comprehensive resource that illuminates the transformative potential of biomarkers in ushering in a new era of precision cardiovascular medicine.

Aim

To identify the biomarkers for the detection and diagnosis of CVDs.

Materials and Methods

This review examines key studies from 2015 to the present that investigate the impact of cardiac biomarkers on cardiovascular outcomes. Data were gathered from PubMed, Cochrane Library, and Embase to ensure a comprehensive analysis. The review focuses on various cardiac biomarkers, assessing their levels and changes in relation to cardiovascular health, with special emphasis on advanced biomarkers such as proteomic and metabolomic markers in cardiovascular disease (CVD) diagnosis. Peer-reviewed studies published in English that evaluated the diagnostic, prognostic, or therapeutic role of cardiac biomarkers were included, with priority given to clinical trials, cohort studies, systematic reviews, and meta-analyses providing quantitative biomarker data. Studies unrelated to cardiac biomarkers, case reports, editorials, conference abstracts, and those with small sample sizes or insufficient methodological rigor were excluded. The review also accounts for potential confounding factors and research limitations, ensuring a balanced assessment of the literature. By synthesizing data from academic papers, clinical reports, and research articles, this study provides a comprehensive evaluation of the evolving role of cardiac biomarkers in CVD diagnosis and risk stratification.

Results

Biomarkers play a pivotal role in cardiovascular disease risk prediction, diagnosis, and treatment by providing dynamic biological insights. High-sensitivity cardiac troponins (hs-cTn) enhance myocardial injury detection, while circulating microRNAs (miR-208, miR-499) serve as early indicators of myocardial infarction and heart failure. Lipoprotein(a) [Lp(a)] predicts long-term cardiovascular risk, and inflammatory biomarkers such as C-reactive protein (CRP) and interleukin-6 (IL-6) are linked to adverse outcomes. Multi-biomarker panels, such as hs-cTn with B-type natriuretic peptide (BNP), improve heart failure prognosis, while metabolomic profiling enables precision medicine. Additionally, biomarkers like BNP and NT-proBNP facilitate real-time therapeutic monitoring. These findings underscore the critical role of biomarkers in refining risk stratification, improving diagnostic accuracy, and enabling personalized treatment strategies in cardiovascular medicine.

Conclusion

The advancement of cardiovascular biomarkers has significantly enhanced early detection, risk stratification, and personalized treatment. Emerging biomarkers, including genetic variants, metabolomics, microRNAs, and imaging-based markers, provide deeper insights into disease mechanisms. Integrating multi-omic approaches with artificial intelligence may further refine predictive accuracy and therapeutic decision-making. However, clinical translation requires rigorous validation through large-scale, multicenter studies to ensure reliability and applicability across diverse populations. Standardization, cost-effectiveness assessments, and the development of biomarker panels are essential for clinical adoption. Future research should focus on bridging discovery and implementation, advancing precision medicine to improve cardiovascular outcomes.

Coronary imaging characteristics and risk factors in patients with type 2 diabetes mellitus with coronary heart disease complication

BACKGROUND

Coronary heart disease (CHD) is a prevalent type 2 diabetes mellitus (T2DM) complication. Further, the risk stratification before angiography may help diagnose T2DM with CHD early. However, few studies have investigated the coronary imaging characteristics and risk factors of patients with T2DM complicated with CHD.

AIM

To compare the differences in coronary imaging between patients with T2DM with and without CHD, determine the risk factors of T2DM complicated with CHD, and establish a predictive tool for diagnosing CHD in T2DM.

METHODS

This study retrospectively analyzed 103 patients with T2DM from January 2022 to May 2024. They are categorized based on CHD occurrence into: (1) The control group, consisting of patients with T2DM without CHD; and (2) The observation group, which includes patients with T2MD with CHD. Age, sex, smoking and drinking history, CHD family history, metformin (MET) treatment pre-admission, body mass index, fasting blood glucose (FBG), triglyceride (TG), total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), serum creatinine, blood urea nitrogen (BUN), alanine aminotransferase, aspartate aminotransferase, glycosylated hemoglobin (HbA1c), and coronary imaging data of both groups were collected from the medical record system. Logistic risk analysis was conducted to screen risk factors. The prediction model's prediction efficiency was evaluated with receiver operating characteristic curves.

RESULTS

The control and observation groups consisted of 48 and 55 cases, respectively. The two groups were statistically different in terms of age (t = 2.006, P = 0.048), FBG (t = 6.038, P = 0.000), TG (t = 2.015, P = 0.047), LDL-C (t = 2.017, P = 0.046), and BUN (t = 2.035, P = 0.044). The observation group demonstrated lower proportions of patients receiving MET (χ 2 = 5.073, P = 0.024) and higher proportions of patients with HbA1c of > 7.0% (χ 2 = 6.980, P = 0.008) than the control group. The observation group consisted of 15, 17, and 23 cases of moderate stenosis, severe stenosis, and occlusion, respectively, with a greater number of coronary artery occlusion cases than the control group (χ 2 = 6.399, P = 0.041). The observation group consisted significantly higher number of diffuse lesion cases at 35 compared with the control group (χ 2 = 15.420, P = 0.000). The observation group demonstrated a higher right coronary artery (RCA) stenosis index (t = 6.730, P = 0.000), circumflex coronary artery (LCX) stenosis index (t = 5.738, P = 0.000), and total stenosis index (t = 7.049, P = 0.000) than the control group. FBG [odds ratio (OR) = 1.472; 95% confidence interval (CI): 1.234-1.755; P = 0.000] and HbA1c (OR = 3.197; 95%CI: 1.149-8.896; P = 0.026) were independent risk factors for T2DM complicated with CHD, whereas MET (OR = 0.350; 95%CI: 0.129-0.952; P = 0.040) was considered a protective factor for CHD in T2DM.

CONCLUSION

Coronary artery occlusion is a prevalent complication in patients with T2DM. Patients with T2MD with CHD demonstrated a higher degree of RCA and LCX stenosis than those with T2DM without CHD. FBG, HbA1c, and MET treatment history are risk factors for T2DM complicated with CHD.

Pregnancy as a Susceptible Period to Ambient Air Pollution Exposure on the Maternal Postpartum Metabolome

Pregnancy is a potential critical window to air pollution exposure for long-term maternal metabolic effects. However, little is known about potential early metabolic mechanisms linking air pollution to maternal metabolic health. We included 544 pregnant Mexican women with both ambient PM2.5 levels during pregnancy and untargeted serum metabolomics to examine associations between pregnancy PM2.5 exposure (overall and monthly) and postpartum metabolites, implementing FDR-adjusted robust linear regression controlling for covariates. Pathway enrichment analyses (in Reactome and MetaboAnalyst) and effect modification by fetal sex and folic acid supplementation were also evaluated. Higher PM2.5 exposure levels throughout pregnancy were associated with higher bile acids and amino acids, dysregulated glycerophospholipids, or lower fatty acyl levels (FDR < 0.05), among other metabolites. Potential critical windows of susceptibility to monthly PM2.5 on metabolites were observed in early to midpregnancy (FDR < 0.005). Main findings were consistent by strata of fetal sex and folic acid supplementation. Metabolic pathways corresponding to positive PM2.5-metabolite associations indicated enriched bile acid, dietary lipid, and transmembrane transport metabolism, whereas for negative PM2.5-metabolite associations, we identified altered pathways involving adipogenesis, incretin peptide hormone, GLP-1, PPAR-alpha, and fatty acid receptors (FDR < 0.05). PM2.5 exposures during pregnancy, especially in early gestation, altered maternal postpartum lipids as well as amino acid metabolism.

Usefulness of the Córdoba Equation for Estimating Body Fat When Determining the Level of Risk of Developing Diabetes Type 2 or Prediabetes

Background and Objectives: Type 2 diabetes (T2D) and prediabetes represent major global health concerns, with obesity being a key risk factor. However, recent evidence suggests that the adipose tissue composition and distribution play a more critical role in metabolic dysfunction than the total body weight or body mass index (BMI). This study evaluates the predictive capacity of the Córdoba Equation for Estimating Body Fat (ECORE-BF) for identifying individuals at high risk of developing T2D and prediabetes. Materials and Methods: A cross-sectional study was carried out involving 418,343 Spanish workers. Body fat percentage was estimated using the ECORE-BF equation, and diabetes risk was assessed using validated predictive models, including the Finnish Diabetes Risk Score (FINDRISC), QDiabetes score (QD-score), and others. The discriminatory power of ECORE-BF in predicting T2D and prediabetes was assessed using receiver operating characteristic (ROC) curve analysis. Results: ECORE-BF showed a strong correlation with high-risk classifications across all diabetes risk scales. The area under the ROC curve (AUC) exceeded 0.95 for both men and women, demonstrating high predictive accuracy. Conclusions: Adipose tissue distribution, particularly visceral adiposity, is a central factor in metabolic dysfunction. ECORE-BF provides a cost-effective alternative for large-scale T2D and prediabetes risk assessment. Future research should explore the impact of visceral adipose tissue reduction on diabetes prevention and the integration of estimation scales into clinical and public health strategies.

Ceramide in Coronary Artery Disease: Troublesome or Helpful Future Tools in the Assessment of Risk Prediction and Therapy Effectiveness?

Lipids are a complex entity of different molecules, among which ceramides (Cers), ubiquitous sphingolipids with remarkable biological activity, can represent a potential additive biomarker that can be used to better understand the underlying mechanisms which drive the onset and development of atherosclerotic damage and plaque vulnerability and facilitate coronary disease management, as possible risk/prognostic biomarkers and targets for therapeutic intervention. Accordingly, this review aims to discuss the available results on the role Cersplay in contributing to atherosclerosis development and acute coronary event precipitation, their impact on complications and adverse prognosis, as well as the impact of treatment options in modulating Cerlevels.

A global perspective on the status of clinical metabolomics in laboratory medicine - a survey by the IFCC metabolomics working group

OBJECTIVES

Metabolomics aims for comprehensive characterization and measurement of small molecule metabolites (<1700 Da) in complex biological matrices. This study sought to assess the current understanding and usage of metabolomics in laboratory medicine globally and evaluate the perception of its promise and future implementation.

METHODS

A survey was conducted by the IFCC metabolomics working group that queried 400 professionals from 79 countries. Participants provided insights into their experience levels, knowledge, and usage of metabolomics approaches, along with detailing the applications and methodologies employed.

RESULTS

Findings revealed a varying level of experience among respondents, with varying degrees of familiarity and utilization of metabolomics techniques. Targeted approaches dominated the field, particularly liquid chromatography coupled to a triple quadrupole mass spectrometer, with untargeted methods also receiving significant usage. Applications spanned clinical research, epidemiological studies, clinical diagnostics, patient monitoring, and prognostics across various medical domains, including metabolic diseases, endocrinology, oncology, cardiometabolic risk, neurodegeneration and clinical toxicology.

CONCLUSIONS

Despite optimism for the future of clinical metabolomics, challenges such as technical complexity, standardization issues, and financial constraints remain significant hurdles. The study underscores the promising yet intricate landscape of metabolomics in clinical practice, emphasizing the need for continued efforts to overcome barriers and realize its full potential in patient care and precision medicine.

Impact of risk factors on intervened and non-intervened coronary lesions

INTRODUCTION

In-stent restenosis (ISR) and aggravated non-intervened coronary lesions (ANL) are two pivotal aspects of disease progression in patients with coronary artery disease (CAD). Established risk factors for both include hyperlipidemia, hypertension, diabetes, chronic kidney disease, and smoking. However, there is limited research on the comparative risk factors for the progression of these two aspects of progression. The aim of this study was to analyze and compare the different impacts of identical risk factors on ISR and ANL.

METHODS

This study enrolled a total of 510 patients with multiple coronary artery lesions who underwent repeated coronary angiography (CAG). All patients had previously undergone percutaneous coronary intervention (PCI) and presented non-intervened coronary lesions in addition to the previously intervened vessels.

RESULTS

After data analysis, it was determined that HbA1c (OR 1.229, 95% CI 1.022-1.477, P=0.028) and UA (OR 1.003, 95% CI 1.000-1.005, P=0.024) were identified as independent risk factors for ISR. Furthermore, HbA1c (OR 1.215, 95% CI 1.010-1.460, P=0.039), Scr (OR 1.007, 95% CI 1.003-1.017, P=0.009), and ApoB (OR 1.017, 95% CI 1.006-1.029, P=0.004) were identified as independent risk factors for ANL. The distribution of multiple blood lipid levels differed between the ANL only group and the ISR only group. Non-HDL-C (2.17 mmol/L vs. 2.44 mmol/L, P=0.007) and ApoB (63.5 mg/dL vs. 71.0 mg/dL, P=0.011) exhibited significantly higher values in the ANL only group compared to the ISR only group.

CONCLUSIONS

Blood glucose levels and chronic kidney disease were identified as independent risk factors for both ISR and ANL, while elevated lipid levels were only significantly associated with ANL. In patients with non-intervened coronary lesions following PCI, it is crucial to assess the concentration of non-HDL-C and ApoB as they serve as significant risk factors.

Biomarker Profiling with Targeted Metabolomic Analysis of Plasma and Urine Samples in Patients with Type 2 Diabetes Mellitus and Early Diabetic Kidney Disease

Background: Over the years, it was noticed that patients with diabetes have reached an alarming number worldwide. Diabetes presents many complications, including diabetic kidney disease (DKD), which can be considered the leading cause of end-stage renal disease. Current biomarkers such as serum creatinine and albuminuria have limitations for early detection of DKD. Methods: In our study, we used UHPLC-QTOF-ESI+-MS techniques to quantify previously analyzed metabolites. Based on one-way ANOVA and Fisher's LSD, untargeted analysis allowed the discrimination of six metabolites between subgroups P1 versus P2 and P3: tryptophan, kynurenic acid, taurine, l-acetylcarnitine, glycine, and tiglylglycine. Results: Our results showed several metabolites that exhibited significant differences among the patient groups and can be considered putative biomarkers in early DKD, including glycine and kynurenic acid in serum (p < 0.001) and tryptophan and tiglylglycine (p < 0.001) in urine. Conclusions: Although we identified metabolites as potential biomarkers in the present study, additional studies are needed to validate these results.

The combination of deep learning and pseudo-MS image improves the applicability of metabolomics to congenital heart defect prenatal screening

To investigate the metabolic alterations in maternal individuals with fetal congenital heart disease (FCHD), establish the FCHD diagnostic models, and assess the performance of these models, we recruited two batches of pregnant women. By metabolomics analysis using Ultra High-performance Liquid Chromatography-Mass/Mass (UPLC-MS/MS), a total of 36 significantly altered metabolites (VIP >1.0) were identified between FCHD and non-FCHD groups. Two logistic regression models and four support vector machine (SVM) models exhibited strong performance and clinical utility in the training set (area under the curve (AUC) = 1.00). The convolutional neural network (CNN) model also demonstrated commendable performance and clinical utility (AUC = 0.89 in the training set). Notably, in the validation set, the performance of the CNN model (AUC = 0.66, precision = 0.714) exhibited better robustness than the six models above (AUC≤0.50). In conclusion, the CNN model based on pseudo-MS images holds promise for real-world and clinical applications due to its better repeatability.

Unveiling the future: precision pharmacovigilance in the era of personalized medicine

In the era of personalized medicine, pharmacovigilance faces new challenges and opportunities, demanding a shift from traditional approaches. This article delves into the evolving landscape of drug safety monitoring in the context of personalized treatments. We aim to provide a succinct reflection on the intersection of tailored therapeutic strategies and vigilant pharmacovigilance practices. We discuss the integration of pharmacogenetics in enhancing drug safety, illustrating how genetic profiling aids in predicting drug responses and adverse reactions. Emphasizing the importance of phase IV-post-marketing surveillance, we explore the limitations of pre-marketing trials and the necessity for a comprehensive approach to drug safety. The article discusses the pivotal role of pharmacogenetics in pre-exposure risk management and the redefinition of pharmacoepidemiological methods for post-exposure surveillance. We highlight the significance of integrating patient-specific genetic profiles in creating personalized medication leaflets and the use of advanced computational methods in data analysis. Additionally, we examine the ethical, privacy, and data security challenges inherent in precision medicine, emphasizing their implications for patient consent and data management.

The efficacy of minimally invasive coronary artery bypass grafting (mics cabg) for patients with coronary artery diseases and diabetes: a single center retrospective study

BACKGROUND

conventional coronary artery bypass grafting (CCABG) tends to cause severe complications in patients with comorbid Coronary Artery Diseases (CAD) and diabetes. On the other hand, the Minimally Invasive Cardiac Surgery Coronary Artery Bypass Grafting (MICS CABG) via transthoracic incision is associated with rapid recovery and reduced complications. Adding to the limited literature, this study compares CCABG and MICS CABG in terms of efficacy and safety.

METHODS

Herein, 104 CCABG and MICS CABG cases (52 cases each) were included. The patients were recruited from the Minimally Invasive Cardiac Surgery Center, Anzhen Hospital, between January 2017 and December 2021 and were selected based on the Propensity Score Matching (PSM) model. The key outcomes included All-cause Death, Myocardial Infarction (MI), Cerebrovascular Events, revascularization, Adverse Wound Healing Events and one-year patency of the graft by coronary CTA.

RESULTS

Compared to CCABG, MICS CABG had longer surgical durations [4.25 (1.50) h vs.4.00 (1.13) h, P = 0.028], but showed a reduced intraoperative blood loss [600.00 (400.00) mL vs.700.00 (300.00) mL, P  = 0.032] and a lower secondary incision debridement and suturing rate (5.8% vs.19.2%, P = 0.038). In follow up, no statistically significant differences were found between the two groups in the cumulative Major Adverse Cardiovascular and Cerebrovascular Events (MACCEs) incidence (7.7% vs. 5.9%), all-cause mortality (0 vs. 0), MI incidence (1.9% vs. 2.0%), cerebral apoplexy incidence (5.8% vs. 3.9%), and repeated revascularization incidence (0 vs. 0) (P > 0.05). Additionally, coronary CTA results revealed that the two groups' one-year graft patency (94.2% vs. 90.2%, P = 0.761) showed no statistically significant difference.

CONCLUSION

In patients with comorbid CAD and diabetes, MICS CABG and CCABG had comparable revascularization performances. Moreover, MICS CABG can effectively reduce, if not prevent, poor clinical outcomes/complications, including incision healing, sternal infection and prolonged length of stay in diabetes patients.

Plasma Metabolic Profiling and Multiclass Diagnostic Model Development for Stable Angina Pectoris and Acute Myocardial Infarction

Coronary heart disease remains a major global health challenge, with a clear need for enhanced early risk assessment. This study aimed to elucidate metabolic signatures across various stages of coronary heart disease and develop an effective multiclass diagnostic model. Using metabolomic approaches, gas chromatography-mass and liquid chromatography-tandem mass spectrometry were used to analyze plasma samples from healthy controls, patients with stable angina pectoris, and those with acute myocardial infarction. Pathway enrichment analysis was conducted on metabolites exhibiting significant differences. The key metabolites were identified using Random Forest and Recursive Feature Elimination strategies to construct a multiclass diagnostic model. The performance of the model was validated through 10-fold cross-validation and evaluated using confusion matrices, receiver operating characteristic curves, and calibration curves. Metabolomics was used to identify 1491 metabolites, with 216, 567, and 295 distinctly present among the healthy controls, patients with stable angina pectoris, and those with acute myocardial infarction, respectively. This implicated pathways such as the glucagon signaling pathway, d-amino acid metabolism, pyruvate metabolism, and amoebiasis across various stages of coronary heart disease. After selection, testosterone isobutyrate, N-acetyl-tryptophan, d-fructose, l-glutamic acid, erythritol, and gluconic acid were identified as core metabolites in the multiclass diagnostic model. Evaluating the diagnostic model demonstrated its high discriminative ability and accuracy. This study revealed metabolic pathway perturbations at different stages of coronary heart disease, and a precise multiclass diagnostic model was established based on these findings. This study provides new insights and tools for the early diagnosis and treatment of coronary heart disease.

Apolipoprotein B - An ideal biomarker for atherosclerosis?

This review article describes the pathophysiological mechanisms linking Apolipoprotein B (Apo-B) and atherosclerosis, summarizes the existing evidence on Apo B as a predictor of atherosclerotic cardiovascular disease and recommendations of (inter)national treatment guidelines regarding Apo B in dyslipidemia management. A single Apo B molecule is present in every particle of very low-density lipoprotein, intermediate density lipoprotein, low density lipoprotein, and lipoprotein(a). This unique single Apo B per particle ratio makes plasma Apo B concentration a direct measure of the number of circulating atherogenic lipoproteins. This review of global evidence on Apo B as a biomarker for atherosclerosis confirms that Apo B is a single atherogenic lipid marker present in all lipids sub-fractions except HDL-C, and thus, Apo B integrates and extends the information from triglycerides and cholesterol, which could simplify and improve care for atherosclerotic cardiovascular disease.

Progress in Cardiac Magnetic Resonance Feature Tracking for Evaluating Myocardial Strain in Type-2 Diabetes Mellitus

The global prevalence of type-2 diabetes mellitus (T2DM) has caused harm to human health and economies. Cardiovascular disease is one main cause of T2DM mortality. Increased prevalence of diabetes and associated heart failure (HF) is common in older populations, so accurately evaluating heart-related injury and T2DM risk factors and conducting early intervention are important. Quantitative cardiovascular system imaging assessments, including functional imaging during cardiovascular disease treatment, are also important. The left-ventricular ejection fraction (LVEF) has been traditionally used to monitor cardiac function; it is often preserved or increased in early T2DM, but subclinical heart deformation and dysfunction can occur. Myocardial strains are sensitive to global and regional heart dysfunction in subclinical T2DM. Cardiac magnetic resonance feature-tracking technology (CMR-FT) can visualize and quantify strain and identify subclinical myocardial injury for early management, especially with preserved LVEF. Meanwhile, CMR-FT can be used to evaluate the multiple cardiac chambers involvement mediated by T2DM and the coexistence of complications. This review discusses CMR-FT principles, clinical applications, and research progress in the evaluation of myocardial strain in T2DM.

Inter-organ crosstalk during development and progression of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.

Metabolomics signature of cardiovascular disease in patients with diabetes, a narrative review

Objectives

The exact underlying mechanism of developing diabetes-related cardiovascular disease (CVD) among patients with type 2 diabetes (T2D) is not clear. Metabolomics can provide a platform enabling the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The aim of this review is to summarize the available evidence on the relationship between metabolomics and cardiovascular diseases in patients with diabetes.

Methods

The literature was searched to find out studies that have investigated the relationship between the alteration of specific metabolites and cardiovascular diseases in patients with diabetes.

Results

Evidence proposed that changes in the metabolism of certain amino acids, lipids, and carbohydrates, independent of traditional CVD risk factors, are associated with increased CVD risk.

Conclusions

Metabolomics can provide a platform to enable the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The association of the alteration in specific metabolites with CVD may be considered in the investigations for the development of new therapeutic targets for the prevention of CVD in patients with diabetes mellitus.

Homocysteine and Lp-PLA2 levels: Diagnostic value in coronary heart disease

Coronary heart disease (CHD) is the leading cause of mortality worldwide. Identifying effective diagnostic markers and understanding risk factors is crucial for prevention and management. This study aimed to investigate the levels of homocysteine (Hcy) and lipoprotein-associated phospholipase A2 (Lp-PLA2) in human plasma and their roles in the diagnosis and prognosis of CHD. A retrospective study was conducted on 232 patients with CHD, divided into Acute Myocardial Infarction, unstable angina pectoris, and stable angina pectoris groups, and a control group of 75 healthy adults. Blood samples were analyzed for serum Hcy and Lp-PLA2 levels using the cycling enzyme method and ELISA method, respectively. Statistical analyses were performed to evaluate the risk factors, and diagnostic efficacy was assessed using receiver operating characteristic (ROC) curves. No significant differences in age and sex were observed between the study and control groups, whereas marked disparities in risk factors such as obesity, hypertension, diabetes, and hyperlipidemia were noted. Significant differences in serum Hcy and Lp-PLA2 levels were identified among the CHD subgroups. Univariate and multivariate logistic regression analyses revealed that Hcy, Lp-PLA2, hypertension, and hyperlipidemia were significant risk factors for CHD. The combined diagnostic Area Under the Curve (AUC) for Hcy and Lp-PLA2 was found to be higher than that when using them individually. This study identified the elevation of Hcy and Lp-PLA2 levels as independent risk factors for CHD, and their conjoint analysis significantly enhanced clinical diagnostic efficacy. These findings provide valuable insights for CHD diagnosis, treatment, and prevention, highlighting the importance of these markers in CHD management.

Relationship of Acylcarnitines to Myocardial Ischemic Remodeling and Clinical Manifestations in Chronic Heart Failure

BACKGROUND

Progressive myocardial remodeling (MR) in chronic heart failure (CHF) leads to aggravation of systolic dysfunction (SD) and clinical manifestations. Identification of metabolomic markers of these processes may help in the search for new therapeutic approaches aimed at achieving reversibility of MR and improving prognosis in patients with CHF.

METHODS

To determine the relationship between plasma acylcarnitine (ACs) levels, MR parameters and clinical characteristics, in patients with CHF of ischemic etiology (n = 79) and patients with coronary heart disease CHD (n = 19) targeted analysis of 30 ACs was performed by flow injection analysis mass spectrometry.

RESULTS

Significant differences between cohorts were found for the levels of 11 ACs. Significant positive correlations (r > 0.3) between the medium- and long-chain ACs (MCACs and LCACs) and symptoms (CHF NYHA functional class (FC); r = 0.31-0.39; p < 0.05); negative correlation (r = -0.31-0.34; p < 0.05) between C5-OH and FC was revealed. Positive correlations of MCACs and LCACs (r = 0.31-0.48; p < 0.05) with the left atrium size and volume, the right atrium volume, right ventricle, and the inferior vena cava sizes, as well as the pulmonary artery systolic pressure level were shown. A negative correlation between C18:1 and left ventricular ejection fraction (r = -0.31; p < 0.05) was found. However, a decrease in levels compared to referent values of ACs with medium and long chain lengths was 50% of the CHF-CHD cohort. Carnitine deficiency was found in 6% and acylcarnitine deficiency in 3% of all patients with chronic heart disease.

CONCLUSIONS

ACs may be used in assessing the severity of the clinical manifestations and MR. ACs are an important locus to study in terms of altered metabolic pathways in patients with CHF of ischemic etiology and SD. Further larger prospective trials are warranted and needed to determine the potential benefits to treat patients with CV diseases with aberrate AC levels.

Correlation analysis of lipid accumulation index, triglyceride-glucose index and H-type hypertension and coronary artery disease

Objective

The current research was designed to explore the relationship between the lipid accumulation index (LAP), coronary artery disease (CAD), and the triglyceride-glucose (TyG) index in patient with H-type hypertension.

Methods

From June 2021 to January 2022, our hospital's information management system collected data on 186 patients with essential hypertension. The participants were categorized into two groups (H-type hypertension (n = 113) and non-H-type hypertension (n = 73)) based on their homocysteine levels. Both groups' general condition, lipid accumulation index, triglyceride-glucose index, and Gensini score were compared to determine the factors influencing the severity of CAD in H-type hypertension patients.

Results

There were statistically significant differences (P < 0.05) in homocysteine (Hcy, GLP-1 and SAA) level, LAP, and TyG indexes, but not in body mass index (BMI), smoking, sex, age, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG), diastolic blood pressure, and systolic blood pressure. Additionally, there were substantial variations between the two groups regarding the number of lesion branches, degree of stenosis, and Gensini score (P > 0.05). patient with grade III to IV lesions had substantially higher LAP and TyG indices than those with stage I to II (P < 0.05). TyG (OR = 2.687) and TyG-LAP (OR = 4.512) were the factors determining the incidence of coronary artery disease in H-type hypertension, according to multivariate logistic regression analysis. The lesion number, stenosis degree, and Gensini score (P < 0.05) varied among both groups. LAP and TyG indexes were substantially greater in patients with double and triple vessel lesions than in those without lesions or with single vessel lesions (P < 0.05); similarly, these two indexes were considerably higher in individuals with grade III to IV lesions than in patients with grade I to II lesions (P < 0.05). As per the Pearson correlation analysis, the LAP, TyG indices and SAAlevel were adversely connected to the Gensini score (r = 0.254, 0.262, 0.299, P < 0.05), the GLP-1 level was negatively correlated to the Gensini score (r = -0.291, P < 0.05). TyG (OR = 2.687) and TyG-LAP (OR = 4.512) were the factors determining the frequency of coronary artery disease in H-type hypertension, according to multivariate logistic regression analysis.

Conclusion

In conclusion, the LAP and TyG indexes were observed to be closely related to the degree of CAD in H-type individuals with hypertension, which can better understand the pathogenesis of coronary artery disease in patients with H-type hypertension and is of great significance for guiding clinical doctors to carry out personalized treatment and management.

Prognostic Implications of Clinical, Laboratory and Echocardiographic Biomarkers in Patients with Acute Myocardial Infarction-Rationale and Design of the ''CLEAR-AMI Study''

BACKGROUND

Acute myocardial infarction (AMI) remains a major cause of death worldwide. Survivors of AMI are particularly at high risk for additional cardiovascular events. Consequently, a comprehensive approach to secondary prevention is necessary to mitigate the occurrence of downstream complications. This may be achieved through a multiparametric tailored risk stratification by incorporating clinical, laboratory and echocardiographic parameters.

METHODS

The ''CLEAR-AMI Study'' (ClinicalTrials.gov Identifier: NCT05791916) is a non-interventional, prospective study including consecutive patients with AMI without a known history of coronary artery disease. All patients satisfying these inclusion criteria are enrolled in the present study. The rationale of this study is to refine risk stratification by using clinical, laboratory and novel echocardiographic biomarkers. All the patients undergo a comprehensive transthoracic echocardiographic assessment, including strain and myocardial work analysis of the left and right heart chambers, within 48 h of admission after coronary angiography. Their laboratory profile focusing on systemic inflammation is captured during the first 24 h upon admission, and their demographic characteristics, past medical history, and therapeutic management are recorded. The angioplasty details are documented, the non-culprit coronary lesions are archived, and the SYNTAX score is employed to evaluate the complexity of coronary artery disease. A 24-month follow-up period will be recorded for all patients recruited.

CONCLUSION

The ''CLEAR-AMI" study is an ongoing prospective registry endeavoring to refine risk assessment in patients with AMI without a known history of coronary artery disease, by incorporating echocardiographic parameters, biochemical indices, and clinical and coronary characteristics in the acute phase of AMI.

Cystatin C to Left Ventricular Ejection Fraction Ratio as a Novel Predictor of Adverse Outcomes in Patients with Coronary Artery Disease: A Prospective Cohort Study

Background

While both cystatin C and left ventricular ejection fraction (LVEF) revealed established prognostic efficacy in coronary artery disease (CAD), the relationship between cystatin C/left ventricular ejection fraction ratio (CLR) and adverse clinical outcomes among patients with CAD following percutaneous coronary intervention (PCI) remains obscure, to date. Therefore, we sought to assess the predictive efficacy of CLR among CAD patients who underwent PCI in current study.

Methods

A total of 14,733 participants, including 8622 patients with acute coronary syndrome (ACS) and 6111 patients with stable coronary artery disease (SCAD), were enrolled from a prospective cohort of 15,250 CAD patients who underwent PCI and were admitted to the First Affiliated Hospital of Xinjiang Medical University from 2016 to 2021. The primary outcome of this study was mortality, including all-cause mortality (ACM) and cardiac mortality (CM). The secondary outcomes were major adverse cardiovascular events (MACEs), major adverse cardiac and cerebrovascular events (MACCEs) and nonfatal myocardial infarction (NFMI). For CLR, the optimal cut-off value was determined by utilizing receiver operating characteristic curve analysis (ROC). Subsequently, patients were assigned into two groups: a high-CLR group (CLR ≥ 0.019, n = 3877) and a low-CLR group (CLR < 0.019, n = 10,856), based on optimal cut-off value of 0.019. Lastly, the incidence of outcomes between the two groups was compared.

Results

The high-CLR group had a higher incidence of ACM (8.8% vs. 0.9%), CM (6.7% vs. 0.6%), MACEs (12.7% vs. 5.9%), MACCEs (13.3% vs. 6.7%), and NFMIs (3.3% vs. 0.9%). After adjusting for confounders, multivariate Cox regression analyses revealed that patients with high-CLR had an 8.163-fold increased risk of ACM (HR = 10.643, 95% CI: 5.525~20.501, p < 0.001), a 10.643-fold increased risk of CM (HR = 10.643, 95% CI: 5.525~20.501, p < 0.001), a 2.352-fold increased risk of MACE (HR = 2.352, 95% CI: 1.754~3.154, p < 0.001), a 2.137-fold increased risk of MACCEs (HR = 2.137, 95% CI: 1.611~2.834, p < 0.001), and a 1.580-fold increased risk of NFMI (HR = 1.580, 95% CI: 1.273~1.960, p < 0.001) compared to patients with low-CLR.

Conclusions

The current study indicated that a high CLR is a novel and powerful predictor of adverse long-term outcomes in CAD patients who underwent PCI, and that, it is a better predictor for patients wtih SCAD and ACS.

Clinical Trial Registration

NCT05174143, http://Clinicaltrials.gov.

Biomarkers to monitor the prognosis, disease severity, and treatment efficacy in coronary artery disease

INTRODUCTION

Coronary Artery Disease (CAD) is a prevalent condition characterized by the presence of atherosclerotic plaques in the coronary arteries of the heart. The global burden of CAD has increased significantly over the years, resulting in millions of deaths annually and making it the leading health-care expenditure and cause of mortality in developed countries. The lack of cost-effective strategies for monitoring the prognosis of CAD warrants a pressing need for accurate and efficient markers to assess disease severity and progression for both reducing health-care costs and improving patient outcomes.

AREA COVERED

To effectively monitor CAD, prognostic biomarkers and imaging techniques play a vital role in risk-stratified patients during acute treatment and over time. However, with over 1,000 potential markers of interest, it is crucial to identify the key markers with substantial utility in monitoring CAD progression and evaluating therapeutic interventions. This review focuses on identifying and highlighting the most relevant markers for monitoring CAD prognosis and disease severity. We searched for relevant literature using PubMed and Google Scholar.

EXPERT OPINION

By utilizing the markers discussed, health-care providers can improve patient care, optimize treatment plans, and ultimately reduce health-care costs associated with CAD management.

Biomarkers for Biodosimetry and Their Role in Predicting Radiation Injury

Radiation-related normal tissue injury sustained during cancer radiotherapy or in a radiological or mass casualty nuclear incident is a major health concern. Reducing the risk and mitigating consequences of radiation injury could have a broad impact on cancer patients and citizens. Efforts to discover biomarkers that can determine radiation dose, predict tissue damage, and aid medical triage are underway. Exposure to ionizing radiation causes changes in gene, protein, and metabolite expression that needs to be understood to provide a holistic picture for treating acute and chronic radiation-induced toxicities. We present evidence that both RNA (mRNA, microRNA, long noncoding RNA) and metabolomic assays may provide useful biomarkers of radiation injury. RNA markers may provide information on early pathway alterations after radiation injury that can predict damage and implicate downstream targets for mitigation. In contrast, metabolomics is impacted by changes in epigenetics, genetics, and proteomics and can be considered a downstream marker that incorporates all these changes to provide an assessment of what is currently happening within an organ. We highlight research from the past 10 years to understand how biomarkers may be used to improve personalized medicine in cancer therapy and medical decision-making in mass casualty scenarios.

The value of lipid metabolites 9,10-DOA and 11,12-EET in prenatal diagnosis of fetal heart defects

AIMS

To explore the maternal metabolic changes of fetal congenital heart disease (FCHD), and screen metabolic markers to establish a practical diagnostic model.

METHODS

Maternal peripheral serum from 17 FCHD and 63 non-FCHD pregnant were analyzed by Ultra High-performance Liquid Chromatography-Mass/Mass (UPLC-MS/MS).

RESULTS

In the FCHD and the non-FCHD, 132 metabolites were identified, including 35 differential metabolites enriched in the purine, caffeine, primary bile acid, and arachidonic acid metabolism pathway. Finally, the screened (+/-)9,10-dihydroxy-12Z-octadecenoic acid (AUC = 0.888) and 11,12-epoxy-(5Z,8Z,11Z)-icosatrienoic acid (AUC = 0.995) were incorporated into the logistic regression model. The AUC value of the two-metabolite model was 1.0, superior to proline (AUC = 0.867), uric acid (AUC = 0.789), glutamine (AUC = 0.705), and taurine (AUC = 0.923) previously reported. The clinical decision curve analysis (DCA) showed the highest clinical net benefit of the model, and internal validation by bootstrap shows the robustness of the model (Brier Score = 0.005).

CONCLUSION

For the prenatal diagnosis of CHD, our findings are of great clinical significance. As an additional screening procedure, the identification model might be used to detect.

Association of clinical, laboratory and imaging biomarkers with the occurrence of acute myocardial infarction in patients without standard modifiable risk factors - rationale and design of the "Beyond-SMuRFs Study"

BACKGROUND

Acute myocardial infarction (AMI) remains the leading cause of mortality worldwide. The majority of patients who suffer an AMI have a history of at least one of the standard modifiable risk factors (SMuRFs): smoking, hypertension, dyslipidemia, and diabetes mellitus. However, emerging scientific evidence recognizes a clinically significant and increasing proportion of patients presenting with AMI without any SMuRF (SMuRF-less patients). To date, there are no adequate data to define specific risk factors or biomarkers associated with the development of AMIs in these patients.

METHODS

The ''Beyond-SMuRFs Study'' is a prospective, non-interventional cohort trial designed to enroll patients with AMI and no previous coronary intervention history, who undergo coronary angiography in two academic hospitals in Thessaloniki, Greece. The rationale of the study is to investigate potential relations between SMuRF-less AMIs and the clinical, laboratory and imaging profile of patients, by comparing parameters between patients with and without SMuRFs. Complete demographic and comprehensive clinical data will be recorded, Venous blood samples will be collected before coronary angiography and the following parameters will be measured: total blood count, standard biochemistry parameters, coagulation tests, hormone levels, glycosylated hemoglobin, N- terminal pro-B-type natriuretic peptide and high-sensitivity troponin T levels- as well as serum levels of novel atherosclerosis indicators and pro-inflammatory biomarkers. Furthermore, all participants will undergo a complete and comprehensive transthoracic echocardiographic assessment according to a pre-specified protocol within 24 h from admission. Among others, 2D-speckle-tracking echocardiographic analysis of cardiac chambers and non-invasive calculation of myocardial work indices for the left ventricle will be performed. Moreover, all patients will be assessed for angiographic parameters and the complexity of coronary artery disease using the SYNTAX score. Multivariable linear and logistic regression models will be used to phenotypically characterize SMuRF-less patients and investigate independent clinical, laboratory, echocardiographic and angiographic biomarkers-predictors of SMuRF-less status in AMI.The first patient was enrolled in March 2022 and completion of enrollment is expected until December 2023.

DISCUSSION

The ''Beyond-SmuRFs'' study is an ongoing prospective trial aiming to investigate potential clinical, laboratory and imaging biomarkers associated with the occurrence of AMIs in SMuRF-less patients. The configuration of these patients' profiles could lead to the development of personalized risk-stratification models predicting the occurrence of cardiovascular events in SΜuRF-less individuals.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05535582 / September 10, 2022.

Significance of Circulating Remnant Lipoprotein Cholesterol Levels Measured by Homogeneous Assay in Patients with Type 2 Diabetes

Remnant lipoproteins (RLs), which are typically present at high concentrations in patients with type 2 diabetes mellitus (T2DM), are associated with cardiovascular disease (CVD). Although an RL cholesterol homogeneous assay (RemL-C) is available for the measurement of RL concentrations, there have been no studies of the relationship between RemL-C and clinical parameters in T2DM. Therefore, we evaluated the relationships between RemL-C and CVD-related parameters in patients with T2DM. We performed a cross-sectional study of 169 patients with T2DM who were hospitalized at Kumamoto University Hospital. Compared with those with low RemL-C, those with higher RemL-C had higher fasting plasma glucose, homeostasis model assessment for insulin resistance (HOMA-R), total cholesterol, triglyceride, small dense low-density lipoprotein cholesterol (sdLDL-C), and urinary albumin-creatinine ratio; and lower high-density lipoprotein cholesterol, adiponectin, and ankle brachial pressure index (ABI). Multivariate logistic regression analysis showed that sdLDL-C and ABI were significantly and independently associated with high RemL-C. Although LDL-C was lower in participants with CVD, there was no difference in RemL-C between participants with or without CVD. Thus, RemL-C may represent a useful index of lipid and glucose metabolism, and that may be a marker of peripheral atherosclerotic disease (PAD) in male patients with T2DM.

Emerging Biomarkers for Predicting Clinical Outcomes in Patients with Heart Disease

Cardiovascular disease is most frequently caused by the development and progression of atherosclerosis. When coronary arteries are afflicted, and the stenoses caused by atherosclerotic plaques are severe enough, the metabolic supply-and-offer balance is disturbed, leading to myocardial ischemia. If atherosclerotic plaques become unstable and local thrombosis develops, a myocardial infarction occurs. Sometimes, myocardial ischemia and infarction may result in significant and irreversible heart failure. To prevent severe complications, such as acute coronary syndromes and ischemia-related heart failure, extensive efforts have been made for developing biomarkers that would help identify patients at increased risk for cardiovascular events. In this two-part study, we attempted to provide a review of existing knowledge of blood biomarkers that may be used in this setting. The first part of this work was dedicated to conventional biomarkers, which are already used in clinical practice. In the second part, here presented, we discuss emerging biomarkers which have not yet become mainstream.

Association of ApoE gene polymorphisms with serum lipid levels and the risk of type 2 diabetes mellitus in the Chinese Han population of central China

Background

Apolipoprotein E (ApoE) is involved in lipid transformation and metabolism. Although some studies have examined the association between ApoE polymorphisms and the risk of type 2 diabetes mellitus (T2DM), the findings differ depending on the location and population.

Methods

A total of 1,738 participants, including 743 patients with T2DM and 995 controls without T2DM, were enrolled from central China, and ApoE polymorphisms, 388T > C (rs429358) and 526C > T (rs7412), were genotyped. The association between ApoE alleles and T2DM and blood lipid levels was analyzed. Logistic regression analysis was performed to evaluate the interactions between ApoE polymorphisms and various factors, such as age, sex, and prevalence of hypertension in patients with T2DM.

Results

The genotype ɛ3/ɛ4 and ɛ4 alleles of ApoE were associated with T2DM risk in the Chinese Han population in central China. Moreover, in patients with T2DM, participants in the E4 (ɛ3/ɛ4, ɛ4/ɛ4) group had significantly higher lipid profiles than those in the E3 (ɛ3/ɛ3) group, whereas participants in the E2 group (ɛ2/ɛ2, ɛ2/ɛ3) showed lower total cholesterol, low-density lipoprotein cholesterol, and ApoE-A1 levels than those in the E3 (ɛ3/ɛ3) group. The results from the current study may help in understanding ApoE polymorphisms and lipid profiles in the Chinese Han population.

Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial

Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691−0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD.