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Objective: This study aims to evaluate the relationship between apolipoproteins (ApoA1, ApoB, and the ApoB/A1 ratio) and the incidence of major adverse cardiovascular events (MACE) in patients with coronary artery disease (CAD) and impaired kidney function, assessing their potential role in secondary prevention., Method: A prospective cohort of 1,640 patients with impaired kidney function who underwent percutaneous coronary intervention in China was analyzed. Patients were categorized based on the measurements of ApoA1, ApoB, and ApoB/A1 ratio. MACE, defined as a composite of all-cause mortality, cardiovascular death, nonfatal myocardial infarctions, strokes, and unplanned revascularizations, was tracked post-procedure, with statistical analyses including Kaplan-Meier survival curves and Cox regression models to identify associations with apolipoproteins. Subgroup analyses according to kidney function were conducted., Result: During a median follow-up of 3.1 years, 324 MACE events were observed. Multivariable Cox regression analyses illustrated higher levels of ApoB and the ApoB/A1 ratio were significantly associated with increased MACE incidence (adjusted HR [95%CI] 1.668[1.044-2.666]; adjusted HR [95%CI] 2.231[1.409-3.533], respectively), while lower ApoA1 levels correlated with a higher risk (adjusted HR [95%CI] 0.505[0.326-0.782]). ROC curve analyses indicated comparable predictive performances to traditional risk factors like LDL cholesterol. Subgroup analysis revealed that the above association was not statistically significant in the moderate-to-severe renal impairment CAD patients (eGFR < 45 mL/min/1.73 m 2 )., Conclusion: Our findings illustrate that apolipoproteins, specifically ApoA1 and ApoB, along with their ratio, are significant predictors of major adverse cardiovascular events in CAD patients with impaired kidney function. These results emphasize the need for incorporating apolipoprotein measurements in secondary prevention strategies for this high-risk population., Competing Interests: Declarations. Ethics approval and consent to participate: The research adhered to the ethical principles outlined in the Declaration of Helsinki and received approval from the Institutional Review Board of Fuwai Hospital (Approval Number 2016–847). All participants provided written informed consent before their inclusion in the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Background: While the survival benefits of angiotensin-converting enzyme inhibitors (ACEI) and angiotensin receptor blockers (ARB) are firmly established in the general population, their efficacy within patient undergoing dialysis with coronary artery disease (CAD) remains controversial., Methods: Between January 2015 and June 2021, 1168 patients undergoing dialysis with CAD were assessed from 30 tertiary medical centers. The primary outcome was all-cause death, and the secondary outcome was cardiovascular death. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for between-group differences., Results: Overall, ACEI or ARB were prescribed to 518 patients (44.3%) upon discharge. After a median follow-up of 22.2 months, 361 (30.9%) patients died, including 243 cardiovascular deaths. The use of ACEI or ARB was associated with a significantly lower risk of all-cause (25.3% vs 35.4%, hazard ratio [HR] 0.65, 95% confidence interval [CI] 0.52-0.82, p  < 0.001) and cardiovascular death (17.0% vs 23.8%; HR 0.64, 95% CI 0.48-0.83, p  = 0.001). These findings remained consistent across IPTW and PSM analyses. Sensitivity analyses for ACEI and ARB use separately yielded similar results., Conclusions: Our findings suggested that among patients undergoing dialysis with CAD, ACEI or ARB use was associated with a lower risk of all-cause and cardiovascular death.

Background: The triglyceride-glucose (TyG) index has been suggested as a dependable indicator for predicting major adverse cardiovascular events (MACE) in individuals with cardiovascular conditions. Nevertheless, there is insufficient data on the predictive significance of the TyG index in end-stage renal disease (ESRD) patients with coronary artery disease (CAD)., Methods: This study, conducted at multiple centers in China, included 959 patients diagnosed with dialysis and CAD from January 2015 to June 2021. Based on the TyG index, the participants were categorized into three distinct groups. The study's primary endpoint was the combination of MACE occurring within one year of follow-up, including death from any cause, non-fatal myocardial infarction, and non-fatal stroke. We assessed the association between the TyG index and MACE using Cox proportional hazard models and restricted cubic spline analysis. The TyG index value was evaluated for prediction incrementally using C-statistics, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI)., Results: The three groups showed notable variations in the risk of MACE (16.3% in tertile 1, 23.5% in tertile 2, and 27.2% in tertile 3; log-rank P = 0.003). Following complete adjustment, patients with the highest TyG index exhibited a notably elevated risk of MACE in comparison to those in the lowest tertile (hazard ratio [HR] 1.63, 95% confidence interval [CI] 1.14-2.35, P = 0.007). Likewise, each unit increase in the TyG index correlated with a 1.37-fold higher risk of MACE (HR 1.37, 95% CI 1.13-1.66, P = 0.001). Restricted cubic spline analysis revealed a connection between the TyG index and MACE (P for nonlinearity > 0.05). Furthermore, incorporating the TyG index to the Global Registry of Acute Coronary Events risk score or baseline risk model with fully adjusted factors considerably enhanced the forecast of MACE, as demonstrated by the C-statistic, continuous NRI, and IDI., Conclusions: The TyG index might serve as a valuable and dependable indicator of MACE risk in individuals with dialysis and CAD, indicating its potential significance in enhancing risk categorization in clinical settings., (© 2023. The Author(s).)

Background: The triglyceride-glucose (TyG) index is validated as a reliable biomarker of insulin resistance and an independent predictor of cardiovascular prognosis. However, the prognostic value of the TyG index in patients on dialysis with coronary artery disease (CAD) remained unexplored. This study aimed to determine the association between the TyG index and CAD severity and mortality in these patients., Methods: A total of 1061 dialysis patients with CAD were enrolled in this multi-center cohort study from January 2015 to June 2021. The extent and severity of CAD were evaluated using the multivessel disease and Gensini score (GS). Patients were followed up for all-cause death and cardiovascular death., Results: The multivariable logistic regression model indicated that the TyG index was significantly associated with multivessel disease (odds ratio [OR] 1.51, 95% confidence interval [CI] 1.18-1.94, P = 0.001), and high GS (OR 1.33, 95% CI 1.10-1.61, P = 0.003). After adjusting for baseline risk factors, the hazards of all-cause death and cardiovascular death were 1.23 (95% CI 1.06-1.43, P = 0.007), and 1.33 (95% CI 1.11-1.59, P = 0.002), independent of CAD severity. Restricted cubic spline analysis identified a dose-response association between the TyG index and both CAD severity and mortality (all P for nonlinearity > 0.05). When modeling the TyG index as a categorical variable, these independent associations remained. Subgroup analyses did not substantially modify the results. Furthermore, incorporating the TyG index into the existing risk prediction model improved the predictive accuracy for all-cause death and cardiovascular death, as evaluated by C-statistic, continuous net reclassification improvement, and integrated discrimination improvement., Conclusions: In patients on dialysis with CAD, the TyG index was significantly associated with more severe CAD as well as mortality. These results highlight the clinical importance of the TyG index for assessing CAD severity and risk stratification in patients on dialysis with CAD., (© 2023. BioMed Central Ltd., part of Springer Nature.)

After cardiac ischemia, there is often insufficient myocardial perfusion, even if flow has been successfully and completely restored in an upstream artery. This phenomenon, known as the "no-reflow phenomenon," is attributed to coronary microvascular dysfunction and has been associated with poor clinical outcomes. In clinical practice, a reduction in coronary flow reserve (CFR) is frequently used as an indicator of coronary artery disease. CFR is defined as the ratio of the peak flow velocity induced by pharmacologic or metabolic factors to the resting flow velocity. This protocol focused on assessing the dynamic changes in CFR before and after ischemia-reperfusion (IR) using pulse wave Doppler measurements. In this study, normal mice exhibited the ability to increase the peak velocity of coronary blood flow up to two times higher than the resting values under isoflurane stimulation. However, after ischemia-reperfusion, the CFR at 1 h significantly decreased compared to the pre-operation baseline. Over time, the CFR showed gradual recovery, but it remained below the normal level. Despite the preservation of systolic function, early detection of microvascular dysfunction is crucial, and establishing a practical guide could aid doctors in this task, while also facilitating the study of cardiovascular disease progression over time.

Objective: This study aimed to explore the association between the triglyceride glucose index (TyG) and the risk of in-hospital and one-year mortality in patients with chronic kidney disease (CKD) and cardiovascular disease (CAD) admitted to the intensive care unit (ICU)., Methods: The data for the study were taken from the Medical Information Mart for Intensive Care-IV database which contained over 50,000 ICU admissions from 2008 to 2019. The Boruta algorithm was used for feature selection. The study used univariable and multivariable logistic regression analysis, Cox regression analysis, and 3-knotted multivariate restricted cubic spline regression to evaluate the association between the TyG index and mortality risk., Results: After applying inclusion and exclusion criteria, 639 CKD patients with CAD were included in the study with a median TyG index of 9.1 [8.6,9.5]. The TyG index was nonlinearly associated with in-hospital and one-year mortality risk in populations within the specified range., Conclusion: This study shows that TyG is a predictor of one-year mortality and in-hospital mortality in ICU patients with CAD and CKD and inform the development of new interventions to improve outcomes. In the high-risk group, TyG might be a valuable tool for risk categorization and management. Further research is required to confirm these results and identify the mechanisms behind the link between TyG and mortality in CAD and CKD patients., (© 2023. The Author(s).)

Objective: Chronic kidney disease (CKD) patients with coronary artery disease (CAD) in the intensive care unit (ICU) have higher in-hospital mortality and poorer prognosis than patients with either single condition. The objective of this study is to develop a novel model that can predict the in-hospital mortality of that kind of patient in the ICU using machine learning methods., Methods: Data of CKD patients with CAD were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Boruta algorithm was conducted for the feature selection process. Eight machine learning algorithms, such as logistic regression (LR), random forest (RF), Decision Tree, K-nearest neighbors (KNN), Gradient Boosting Decision Tree Machine (GBDT), Support Vector Machine (SVM), Neural Network (NN), and Extreme Gradient Boosting (XGBoost), were conducted to construct the predictive model for in-hospital mortality and performance was evaluated by average precision (AP) and area under the receiver operating characteristic curve (AUC). Shapley Additive Explanations (SHAP) algorithm was applied to explain the model visually. Moreover, data from the Telehealth Intensive Care Unit Collaborative Research Database (eICU-CRD) were acquired as an external validation set., Results: 3590 and 1657 CKD patients with CAD were acquired from MIMIC-IV and eICU-CRD databases, respectively. A total of 78 variables were selected for the machine learning model development process. Comparatively, GBDT had the highest predictive performance according to the results of AUC (0.946) and AP (0.778). The SHAP method reveals the top 20 factors based on the importance ranking. In addition, GBDT had good predictive value and a certain degree of clinical value in the external validation according to the AUC (0.865), AP (0.672), decision curve analysis, and calibration curve., Conclusion: Machine learning algorithms, especially GBDT, can be reliable tools for accurately predicting the in-hospital mortality risk for CKD patients with CAD in the ICU. This contributed to providing optimal resource allocation and reducing in-hospital mortality by tailoring precise management and implementation of early interventions., (© 2023. The Author(s).)