Independent Risk Factor For Coronary Heart Disease Research Articles

Screening significance of systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) in coronary heart disease of symptomatic youth.

The incidence of coronary heart disease (CHD) in youth is rapidly increasing but difficultly recognized in the early stage. In this retrospective study, 194 CHD patients under the age of 45 who previously experienced chest pain symptoms and 170 non-CHD patients were included and demographic data were collected. Systemic inflammation index (SII) and systemic inflammation response index (SIRI) were increased in young CHD patients (p < 001). Spearman's correlation analysis showed that both SII and SIRI were negatively correlated with HDL and positively correlated with hypertension, Gensini score, and hsTnI. Logistic regression analysis indicated that SII and SIRI were independently associated with the presence of CHD in youth with chest pain symptoms. The area under the ROC curve (AUC) of the SII model for young CHD patients was 0.805 (0.728-0.869), and the sensitivity and specificity were 0.65 and 0.823, respectively. Meanwhile, the AUC for the SIRI model was 0.812 (0.739-0.872), and the sensitivity and specificity were 0.673 and 0.8022. The calibration curves of both SII and SIRI models are in good agreement with the actual curves. And the decision curves of both models indicated their clinical practicality. SII and SIRI are independent risk factors for CHD in young adults, which can quickly and effectively identify CHD patients among young adults who have previously experienced chest pain symptoms.

Clinical Value of the Diagonal Earlobe Crease in Patients with Chest Pain for Diagnosing Coronary Heart Disease.

To investigate the clinical application value of diagonal earlobe crease (DELC) in patients with chest pain for the diagnosis of coronary heart disease (CHD) and to construct a risk model by multivariate logistic regression. Our trial enrolled prospectively and consecutively 706 chest pain patients with suspected CHD between January 2021 to June 2023 from Chengde Central Hospital. According to coronary angiography results, they were categorized into the CHD (n=457) and non-CHD groups (n=249). The trial demonstrated a significant positive relationship between DELC and CHD. Independent risk factors were sex, age, hypertension, diabetes mellitus, LP (a), Cys C, and DELC, whilst HDL-C was a protective factor, for CHD. Patients with-DELC were older than those in the without-DELC arm (P<0.001) and had a higher proportion of males than females (61.6% vs 50.0%, P=0.026). After multifactorial correction, independent risk factors for CHD included DELC (OR=1.660, 95% CI:1.153 to 2.388, P=0.006), age (OR=1.024, 95% CI:1.002 to 1.045, P=0.030), gender (OR=1.702, 95% CI:1.141 to 2.539, P=0.009), hypertension (OR=1.744, 95% CI:1.226 to 2.482, P=0.002), diabetes mellitus (OR=2.113, 95% CI:1.404 to 3.179, P<0.001), LP(a) (OR=1.010, 95% CI:1.003 to 1.017, P=0.005), Cys C (OR=3.549, 95% CI:1.605 to 7.846, P=0.002). The Hosmer and Lemeshow (H-L) test (P=0.818) suggests a high goodness of fit, and the area under the ROC curve was calculated to be 0.721 (95% CI:0.682 to 0.760, P<0.001), which demonstrates that the model has a superior diagnostic value for CHD. DELC is an independent risk factor for CHD after adjusting for sex, age, hypertension, diabetes mellitus, smoking index, LP (a), Cys C, and HDL-C. Our model can be used clinically for assessing the risk of CHD.

Depression and coronary heart disease: mechanisms, interventions, and treatments.

Coronary heart disease (CHD), a cardiovascular condition that poses a significant threat to human health and life, has imposed a substantial economic burden on the world. However, in contrast to conventional risk factors, depression emerges as a novel and independent risk factor for CHD. This condition impacts the onset and progression of CHD and elevates the risk of adverse cardiovascular prognostic events in those already affected by CHD. As a result, depression has garnered increasing global attention. Despite this growing awareness, the specific mechanisms through which depression contributes to the development of CHD remain unclear. Existing research suggests that depression primarily influences the inflammatory response, Hypothalamic-pituitary-adrenocortical axis (HPA) and Autonomic Nervous System (ANS) dysfunction, platelet activation, endothelial dysfunction, lipid metabolism disorders, and genetics, all of which play pivotal roles in CHD development. Furthermore, the effectiveness and safety of antidepressant treatment in CHD patients with comorbid depression and its potential impact on the prognosis of CHD patients have become subjects of controversy. Further investigation is warranted to address these unresolved questions.

Characteristics of metabolic inflammatory syndrome among inpatients with type 2 diabetes: A cross-sectional study in China.

As meta-inflammation is a common feature for obesity, type 2 diabetes (T2D), nonalcoholic fatty liver disease and atherosclerosis, we have proposed a new concept, metabolic inflammatory syndrome (MIS), to cluster such diseases. We aimed to characterize MIS and explore its association with coronary heart disease (CHD) among T2D inpatients in China. A total number of 8344 T2D participants were enrolled. Each component of MIS and metabolic syndrome (MS) was analyzed. Their association with the risk of CHD was assessed using a binary logistic analysis. Among the T2D inpatients, the detection rate of MIS was much higher than that of MS (93.6 % vs. 53.2 %). Among all the components of MIS and MS, carotid atherosclerosis (71.9 %) was most commonly detected, which increased with aging in subgroups. Surprisingly, the most common combination of MIS was with all 4 components in T2D patients, with a constituent ratio of 30.9 %. According to the odds ratios (ORs), MIS was a better predictor of CHD than MS, especially after adjustment for age, sex, smoking, and alcohol consumption (adjusted OR for MIS: 3.083; for MS: 1.515). The presence of more components of MIS was associated with a higher detection rate of CHD (P<0.001). Among all the components of MIS and MS, carotid atherosclerosis best predicted the risk of CHD (adjusted OR: 1.787). MIS is an independent risk factor for CHD, with a bigger OR value than MS. Carotid atherosclerosis, with the highest detection rate, was the best individual predictor of CHD and thus a critical component of MIS. The concept of MIS represents the understanding of metabolic diseases from the perspective of holistic integrative medicine.

A combined analysis of TyG index, SII index, and SIRI index: positive association with CHD risk and coronary atherosclerosis severity in patients with NAFLD.

Insulin resistance(IR) and inflammation have been regarded as common potential mechanisms in coronary heart disease (CHD) and non-alcoholic fatty liver disease (NAFLD). Triglyceride-glucose (TyG) index is a novel biomarker of insulin resistance, System immune-inflammation index(SII) and Systemic inflammation response index(SIRI) are novel biomarkers of inflammation, these biomarkers have not been studied in CHD with NAFLD patients. This study investigated the correlation between the TyG index, SII index, and SIRI index and CHD risk among NAFLD patients. This cross-sectional study included 407 patients with NAFLD in the Department of Cardiology, The Second Hospital of Shanxi Medical University. Of these, 250 patients with CHD were enrolled in the NAFLD+CHD group and 157 patients without CHD were enrolled as NAFLD control. To balance covariates between groups, 144 patients were selected from each group in a 1:1 ratio based on propensity score matching (PSM). Potential influences were screened using Lasso regression analysis. Univariate and multivariate logistic regression analyses and the Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to assess independent risk and protective factors for CHD. Construction of nomogram using independent risk factors screened by machine learning. The receiver operating characteristic(ROC) curve was used to assess the ability of these independent risk factors to predict coronary heart disease. The relationship between the Gensini score and independent risk factors was reflected using the Sankey diagram. The LASSO logistic regression analysis and Logistic regression analyses suggest that TyG index (OR, 2.193; 95% CI, 1.242-3.873; P = 0.007), SII index (OR, 1.002; 95% CI, 1.001-29 1.003; P <0.001), and SIRI index (OR,1.483;95%CI,1.058-2.079,P=0.022) are independent risk factors for CHD. At the same time, Neutrophils, TG, and LDL-C were also found to be independent risk factors in patients, HDL-C was a protective factor for CHD in patients with NAFLD. Further analysis using three machine learning algorithms found these independent risk factors to have good predictive value for disease diagnosis, SII index shows the highest predictive value. ROC curve analysis demonstrated that combining the SII index, SIRI index, and TyG index can improve the diagnostic ability of non-alcoholic liver cirrhosis patients with CHD.ROC curve analysis showed that the combined analysis of these independent risk factors improved the predictive value of CHD(AUC: 0.751; 95% CI: 0.704-0.798; P <0.001). TyG index, SII index, and SIRI index are all independent risk factors for CHD in patients with NAFLD and are strongly associated with prediction and the severity of CHD.

Machine learning aided non-invasive diagnosis of coronary heart disease based on tongue features fusion.

Coronary heart disease (CHD) is the first cause of death globally. Hypertension is considered to be the most important independent risk factor for CHD. Early and accurate diagnosis of CHD in patients with hypertension can plays a significant role in reducing the risk and harm of hypertension combined with CHD. To propose a non-invasive method for early diagnosis of coronary heart disease according to tongue image features with the help of machine learning techniques. We collected standard tongue images and extract features by Diagnosis Analysis System (TDAS) and ResNet-50. On the basis of these tongue features, a common machine learning method is used to customize the non-invasive CHD diagnosis algorithm based on tongue image. Based on feature fusion, our algorithm has good performance. The results showed that the XGBoost model with fused features had the best performance with accuracy of 0.869, the AUC of 0.957, the AUPR of 0.961, the precision of 0.926, the recall of 0.806, and the F1-score of 0.862. We provide a feasible, convenient, and non-invasive method for the diagnosis and large-scale screening of CHD. Tongue image information is a possible effective marker for the diagnosis of CHD.

Association between serum PCSK9 and coronary heart disease in patients with type 2 diabetes mellitus

Background and aimsProprotein convertase subtilisin/kexin type 9 (PCSK9) is considered a new biomarker for atherosclerosis, but its ability to predict cardiovascular outcomes has been controversial. This study aimed to address the lack of data on PCSK9, coronary heart disease (CHD) severity, and major cardiovascular events (MACEs) in patients with type 2 diabetes mellitus (T2DM).MethodsA total of 2984 T2DM patients underwent selective coronary angiography, and their serum PCSK9 levels were measured using enzyme-linked immunosorbent assay. Correlation and logistic regression analyses were performed to investigate the association between PCSK9 expression and CHD severity. This study used Cox regression analysis to assess the association between circulating PCSK9 levels and the risk of MACEs.ResultsCirculating PCSK9 levels were significantly higher in the CHD group than in the non-CHD group [554.62 (265.11) ng/mL vs. 496.86 (129.05) ng/mL, p < 0.001]. Circulating PCSK9 levels positively correlated with CHD severity (diseased vessels: r = 0.35, p < 0.001; Gensini score: r = 0.46, p < 0.001). Elevated PCSK9 levels are an independent risk factor for CHD risk and severity (CHD group vs. non–CHD group: OR = 2.829, 95% CI: 1.771–4.520, p < 0.001; three vessel disease group vs. one vessel disease group: OR = 4.800, 95% CI: 2.387–9.652, p < 0.001; high GS group vs. low GS group: OR = 5.534, 95% CI: 2.733–11.208, p < 0.001). Through a six-year follow-up and multivariate Cox regression analysis, elevated circulating PCSK9 levels were found to be independently associated with MACEs in all participants (HR: 3.416, 5% CI: 2.485–4.697, p < 0.001; adjusted HR: 2.780, 95% CI: 1.930–4.004, p < 0.001).ConclusionsSerum PCSK9 levels were positively correlated with multi-vessel CHD and Gensini score. Elevated circulating PCSK9 levels are an independent risk factor for CHD and increased incidence of MACEs in T2DM.

Comparison of the value of various complex indexes of blood cell types and lipid levels in coronary heart disease.

Inflammation and lipid infiltration play crucial roles in the development of atherosclerosis. This study aimed to investigate the association between various complex indexes of blood cell types and lipid levels with the severity of coronary artery stenosis and their predictive value in coronary heart disease (CHD). The retrospective study was conducted on 3,201 patients who underwent coronary angiography at the Department of Zhongnan Hospital of Wuhan University. The patients were divided into two groups: CHD group and non-CHD group. The CHD group was further classified into three subgroups (mild, moderate, severe) based on the tertiles of their Gensini score or SYNTAX score I. Various complex indexes of blood cell types and lipid levels were compared between the groups. It revealed a positive correlation between all complex indexes and the severity of coronary artery stenosis. The systemic inflammation-response index/high-density lipoprotein cholesterol count (SIRI/HDL) exhibited the strongest correlation with both severity scores (Gensini score: r = 0.257, P < 0.001; SYNTAX score I: r = 0.171, P < 0.001). The monocyte to high-density lipoprotein cholesterol ratio (MHR) was identified as a stronger independent risk factor for CHD. However, SIRI/HDL had higher diagnostic efficacy for CHD (sensitivity 66.7%, specificity 60.4%, area under curve 0.680, 95% CI: 0.658-0.701). Notably, the pan-immune-inflammation value multiplied by low-density lipoprotein cholesterol count (PIV × LDL) exhibited the highest sensitivity of 85.2%. All complex indexes which we investigated exhibited positive correlations with the severity of coronary artery stenosis. SIRI/HDL demonstrated higher diagnostic efficiency for CHD and a significant correlation with the severity of coronary artery stenosis.

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.

Analysis of Correlation Between Coronary Heart Disease and Genetic Polymorphism Detected by Gold Magnetic Nanoparticles Chromatography.

It aimed to explore the correlation of Glu504Lys locus mutation of aldehyde dehydrogenase-2 (ALDH2) with coronary heart disease (CHD) based on gold magnetic nanoparticles (GMNPs) chromatography and amplification refractory mutation system-PCR (ARMS-PCR). 120 CHD patients admitted to the cardiovascular Department of Wenling First People's Hospital affiliated to Wenzhou Medical University from December 2020 to December 2021 were selected as Case group and 80 non-CHD patients admitted during the same period were selected as Ctrl group. The venous blood and indexes of Total Cholesterol (TC), Triglyceride (TG), Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Fasting Blood Glucose (FBS) were collected. The ARMS-PCR GMNPs chromatography based on ARMS-PCR and immunochromatography assay was adopted to detect gene polymorphism of ALDH2. Correlation between ALDH2 gene polymorphism and risk factors of CHD was analyzed via logistic regression. In contrast to Ctrl group, the genotypes of GG, GA, and AA in Case group were evidently different (P < 0.05), and the frequency of A allelic gene was obviously increased (P < 0.05). Under the dominant model, frequency of GA + AA genotype in Case group was remarkably higher in contrast to Ctrl group (P < 0.05). Under the recessive model, there was no obvious difference in genotype frequency between two groups. In contrast to Ctrl group, TC, LDL-C, and FBS in Case group were notably increased (P < 0.05), while HDL-C was notably decreased (P < 0.05). The distribution frequency of abnormal LDL-C, HDL-C, and FBS in Case group was notably higher in contrast to Ctrl group (P < 0.05). LDL-C and FBS had no obvious effect on the genotypes and frequency distribution of alleles in CHD patients. However, the frequency distribution of genotypes of GA and AA and A allelic gene in patients with abnormal HDL-C was notably lower in contrast to those with normal HDL-C (P < 0.05). Logistic regression analysis showed that abnormal HDC-C with A allelic gene were independent risk factors for CHD (P = 0.001, OR = 1.934). The gene polymorphism of Glu504Lys locus of ALDH2 was closely related to the pathogenesis of CHD, A allelic gene may be a susceptibility gene for CHD, and patients with abnormal HDC-C and carried A allelic gene had relatively higher incidence of CHD.

Establishment and effectiveness evaluation of pre-test probability model of coronary heart disease combined with cardiopulmonary exercise test indexes

To establish a pre-test probability model of coronary heart disease (CHD) combined with cardiopulmonary exercise test (CPET) indexes and to compare the clinical effectiveness with Duke clinical score (DCS) and updated Diamond-Forrester model (UDFM), thus further explore the predictive value. 342 cases were used to establish the prediction model equation and another 80 cases were used to verify the effectiveness. The patients were divided into CHD group (n = 157) and non-CHD group (n = 185) according to coronary artery stenosis degree >50% or not. Combining DCS and UDFM as reference models with CPET indexes, a multivariate logistic regression model was established. The area under the ROC curve of the three models were calculated to compare the predictive effectiveness. There were significant differences in gender, chest pain type, myocardial infarction history, hypertension history, smoking, pathological Q wave and ST-T change between two groups (P < 0.01), as well as age, LVEF, heart rate at anaerobic domain, peak oxygen uptake in kilograms of body weight, percentage of peak oxygen uptake to the predicted value, the oxygen uptake efficiency slope and carbon dioxide ventilation equivalent slope (P < 0.05). Multivariate analysis showed gender, age, chest pain type, myocardial infarction history, hypertension history, smoking, pathological Q wave, ST-T change, and peak oxygen pulse were independent risk factors of CHD. The pre-test probability model of CHD combined with CPET indexes has good distinguish and calibrate ability, its prediction accuracy is slightly better than DCS and UDFM, which still needs to be verified externally in more samples.

Hyperuricemia and coronary heart disease: The mediating role of blood pressure and thrombospondin 3

Background & aimsAlthough hyperuricemia is a known risk factor for coronary heart disease (CHD), little is known about the role of blood pressure in mediating this association. The purpose of this study is to investigate the role of blood pressure-related indicators and Thrombospondin 3 (THBS3) in the association between hyperuricemia and CHD. Methods and resultsOur observational epidemiology study included 593 CHD cases and 760 controls from a residential stable sample. We also chose 43 new CHD patients and 43 controls to test the expression levels of THBS3 using ELISA kits. We used logistic regression models and mediating effect analysis to investigate the relationships between hyperuricemia and CHD, as well as the mediating role of blood pressure-related indicators and THBS3. In the general population (OR: 2.001 [95% CI: 1.528–2.622]), male population (OR: 1.591 [95% CI: 1.119–2.262]), and female population (OR: 2.813 [95% CI: 1.836–4.310]), hyperuricemia is an independent risk factor for CHD. In general, average systolic blood pressure (SBP) and average pulse pressure difference (PPD) mediated 3.35% and 4.59%, respectively, of the association between hyperuricemia and CHD, and 6.60% and 6.60% in women. However, in the male population, we have not yet found that blood pressure-related indicators had a significant mediating effect. Meanwhile, we found that THBS3 mediated 19.23% of the association between hyperuricemia and CHD. ConclusionsAverage SBP, PPD, and THBS3 all play a role in the association of hyperuricemia and CHD. In the female population, similar mediating results in blood pressure-related indicators were observed.

Leukocyte telomere length, allelic variations in related genes and risk of coronary heart disease in people with long-standing type 1 diabetes

BackgroundType 1 diabetes is associated with accelerated vascular aging and advanced atherosclerosis resulting in increased rates of cardiovascular disease and premature death. We evaluated associations between Leukocyte telomere length (LTL), allelic variations (SNPs) in LTL-related genes and the incidence of coronary heart disease (CHD) in adults with long-standing type 1 diabetes.MethodsWe assessed associations of LTL, measured at baseline by RT–PCR, and of SNPs in 11 LTL-related genes with the risk of coronary heart disease (CHD: myocardial infarction or coronary revascularization) and all-cause death during follow-up in two multicenter French-Belgian prospective cohorts of people with long-standing type 1 diabetes.ResultsIn logistic and Cox analyses, the lowest tertile of LTL distribution (short telomeres) at baseline was associated with the prevalence of myocardial infarction at baseline and with increased risk of CHD (Hazard ratio 3.14 (1.39–7.70), p = 0.005, for shorter vs longer tertile of LTL) and all-cause death (Hazard ratio 1.63 (95% CI 1.04–2.55), p = 0.03, for shorter vs combined intermediate and longer tertiles of LTL) during follow-up. Allelic variations in six genes related to telomere biology (TERC, NAF1, TERT, TNKS, MEN1 and BICD1) were also associated with the incidence of CHD during follow-up. The associations were independent of sex, age, duration of diabetes, and a range of relevant confounding factors at baseline.ConclusionsOur results suggest that short LTL is an independent risk factor for CHD in people with type 1 diabetes.

Predictive Significance of High-Sensitivity C-Reactive Protein Combined with Homocysteine for Coronary Heart Disease in Patients with Anxiety Disorders.

Background Currently, there are few studies on biomarkers for predicting coronary heart disease (CHD) with anxiety disorders. Objective To explore risk factors and investigate the predictive value of common clinical peripheral blood indicators, such as high-sensitivity C-reactive protein (hs-CRP) and homocysteine (Hcy) for CHD patients with anxiety disorders. Methods One hundred fifty-three hospitalized patients with chest pain as the main symptom and a Hamilton Anxiety Scale score > 14 were recruited from October 2020 to September 2021 in the hospital. Then, they were divided into an anxiety disorder with CHD group (observation group, n = 64) and a simple anxiety disorder group (control group, n = 89), according to coronary angiography (CAG) findings. Patients' demographic and clinical messages were collected and compared. Diabetes mellitus and hypertension, body mass index (BMI), and peripheral blood interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), homocysteine (Hcy), fibrinogen, D-dimer, cortisol, and norepinephrine expression levels were compared. Binary logistic regression analysis screened independent risk factors of CHD patients with anxiety disorders. The effectiveness of independent risk factors in predicting CHD with anxiety disorders was analyzed using receiver operating characteristic (ROC) curves. Results IL-6, hs-CRP, and Hcy levels of anxiety disorder in the CHD group were significantly higher than those in the simple anxiety disorder group. Binary multiple logistic regression analysis indicated that IL-6, hs-CRP, and Hcy were independent risk factors for CHD in patients with anxiety disorders. hs-CRP and Hcy levels were positively correlated with the Gensini score. ROC curve analysis indicated that the detection of hs-CRP or Hcy alone or the combined detection of the 2 had clinical predictive value for CHD in patients with anxiety disorders, and the area under the curve (AUC) of the combined detection of the 2 was significantly larger than that of any single factor alone (vs. hs-CRP, P = 0.045; vs. Hcy, P = 0.045). Conclusion IL-6, hs-CRP, and Hcy are related to CHD with anxiety disorders. Serum levels of the combined detection of hs-CRP and Hcy have a high clinical predictive value for CHD in patients with anxiety disorders.

Plasma Exosomal S1PR5 and CARNS1 as Potential Non-invasive Screening Biomarkers of Coronary Heart Disease.

BackgroundEarly diagnosis and treatment significantly improve the prognosis of coronary heart disease (CHD), but no convenient screening tools are available. This study aims to find potential non-invasive screening biomarkers of coronary heart disease.MethodWe performed microarray analysis to investigate the mRNA expression levels in Small extracellular vesicles (sEVs) and screen significantly differentially expressed mRNAs in CHD patients vs. non-CHD patients. We then performed quantitative real-time polymerase chain reaction (qRT-PCR) to validate the microarray results, and we calculated the correlations between expression levels and clinicopathological data. Microarray analysis identified 72 downregulated mRNAs and 31 upregulated mRNAs in CHD patients relative to non-CHD patients.ResultsFrom the study, we found that upregulated sphingosine-1-phosphate receptor 5 (S1PR5) and downregulated carnosine synthase 1 (CARNS1) had the most significant differences between the patient group and the control group. S1PR5 expression was correlated with diabetes, heart rate, triglycerides, total cholesterol, low-density lipoprotein cholesterol, apolipoprotein B, and fasting blood glucose (P < 0.05). CARNS1 level was correlated with uric acid (UA) (P < 0.05). Overexpressed S1PR5 and downregulated CARNS1 were independent risk factors for CHD. The area under the receiver operating characteristic curve (AUC) of S1PR5 was 0.838 for diagnosing CHD; the AUC of CARNS1 was 0.883 for non-CHD; and the AUC of S1PR5 plus CARNS1 was 0.921 for CHD.ConclusionsMicroarray analysis showed that upregulated S1PR5 and downregulated CARNS1 in sEVs have the potential to become non-invasive biomarkers for CHD screening.

Risk Prediction of Coronary Artery Stenosis in Patients with Coronary Heart Disease Based on Logistic Regression and Artificial Neural Network

Objective Coronary heart disease (CHD) is considered an inflammatory relative disease. This study is aimed at analyzing the health information of serum interferon in CHD based on logistic regression and artificial neural network (ANN) model. Method A total of 155 CHD patients diagnosed by coronary angiography in our department from January 2017 to March 2020 were included. All patients were randomly divided into a training set (n = 108) and a test set (n = 47). Logistic regression and ANN models were constructed using the training set data. The predictive factors of coronary artery stenosis were screened, and the predictive effect of the model was evaluated by using the test set data. All the health information of participants was collected. Expressions of serum IFN-γ, MIG, and IP-10 were detected by double antibody sandwich ELISA. Spearman linear correlation analysis determined the relationship between the interferon and degree of stenosis. The logistic regression model was used to evaluate independent risk factors of CHD. Result The Spearman correlation analysis showed that the degree of stenosis was positively correlated with serum IFN-γ, MIG, and IP-10 levels. The logistic regression analysis and ANN model showed that the MIG and IP-10 were independent predictors of Gensini score: MIG (95% CI: 0.876~0.934, P < 0.001) and IP-10 (95% CI: 1.009~1.039, P < 0.001). There was no statistically significant difference between the logistic regression and the ANN model (P > 0.05). Conclusion The logistic regression model and ANN model have similar predictive performance for coronary artery stenosis risk factors in patients with CHD. In patients with CHD, the expression levels of IFN-γ, IP-10, and MIG are positively correlated with the degree of stenosis. The IP-10 and MIG are independent risk factors for coronary artery stenosis.

Hemoglobin Levels, Sex, Race and Coronary Heart Disease Risk in the Reasons for Geographic and Racial Differences in Stroke Study

Background: Prior studies demonstrated both low and high hemoglobin concentrations are associated with increased mortality. It is unknown whether hemoglobin concentration has a similar U-shaped pattern for coronary heart disease (CHD) risk, or if the relationship differs by age, sex or race.Objective: We determined the association between hemoglobin concentration and risk of CHD in the national REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort, and addressed whether this association was affected by age, sex, or race.Methods: REGARDS participants were black and white adults age 45 years and older, enrolled in their homes across the contiguous United States in 2003-7 and followed every 6 months for health outcomes. Baseline risk factors levels were measured using standardized methods, including complete blood counts at a central laboratory, which was initiated after 8608 participants had been recruited. We included REGARDS participants free of CHD at baseline. Incident CHD outcomes were adjudicated by a committee based on medical records review. Cox proportional hazard models were used to examine the association of hemoglobin with incident CHD (both fatal and non-fatal events). Models were adjusted for demographic variables (age, sex, race, and region of residence) and CHD risk factors (hypertension, diabetes, smoking status (current, former, never), total cholesterol (mg/dL), HDL cholesterol (mg/dL), and use of lipid lowering medications). Penalized regression splines were used to graphically represent associations using the adjusted model. Analyses were also stratified by age (≤ 63 vs. > 63), sex (male vs. female), and race (black vs. white).Results: Of 30,239 REGARDS participants, 15,764 had hemoglobin measures, were CHD-free at baseline, and had all baseline variables. The mean age was 63 (IQR 56, 70), 34% were male, and 42% were black. The mean baseline hemoglobin was 13.6 g/dL (SD 1.4). During 118,529 person-years of follow up (median 7.4 years) there were 637 incident CHD events. A significant non-linear association of hemoglobin with CHD was identified after multivariate adjustment (p <0.001). The penalized regression spline graph demonstrated a U-shaped relationship with increasing risk for CHD with lower and higher hemoglobin concentrations, although the confidence intervals were wide (Figure 1). The appearance of age, sex, and race specific regression splines were similar. When hemoglobin was divided into age, sex, and race-specific quintiles, participants in the lowest (HR 1.77; 95% CI 1.38, 2.28) and highest (HR 1.32; 95% CI 1.00, 1.73) quintiles had increased risk of CHD compared to the 4th quintile. The 2nd (HR 1.14; 95% CI 0.86, 1.50) and 3rd quintile (HR 1.22; 95% CI 0.92, 1.60) were not associated with CHD risk.Conclusion: Hemoglobin concentration was an independent risk factor for CHD, with lower and higher concentrations associated with increased CHD risk. The effect of hemoglobin concentration on CHD risk was a similar shape regardless of age, sex, or race. Further research is needed to explore possible mechanisms mediating the increased CHD risk, but these data point to “optimal” hemoglobin concentrations associated with the lowest CHD risk. Whether hemoglobin concentration is causative or reflects underlying comorbid conditions not accounted for in our analysis requires further work.Figure 1: Penalized Regression Spline - Hemoglobin Concentration and Hazard Ratio for Coronary Heart Disease [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Knockdown of lncRNA ENST00000609755.1 Confers Protection Against Early oxLDL-Induced Coronary Heart Disease.

Background: This study investigated the association between long non-coding RNAs (lncRNAs) and coronary heart disease (CHD) and further elucidated the potential biological roles of lncRNAs in CHD pathogenesis.Methods: A case-control study (590 patients and 590 controls) was conducted from February 2017 and March 2019 in Fuzhou, China. Environmental factors were investigated using questionnaires and physical examinations. Five representative lncRNAs were screened using lncRNA microarray (peripheral blood in 5 cases and 5 controls) and further verified by quantitative real-time polymerase chain reaction (peripheral blood leukocyte in 100 cases and 100 controls). Oxidized low-density lipoprotein (oxLDL) was used to induce a human coronary artery endothelial cell (HCAECs) injury model, and loss of function was used to elucidate the role of lncRNA ENST00000609755.1 (lnc-MICALL2-2) in oxLDL-induced HCAECs injury.Results: A total of 320 lncRNAs were found dysregulated in CHD patients (fold change> 2, p < 0.05). The results of a discovery microarray, population verification and HCAEC experiments suggested the lnc-MICALL2-2 is upregulated in CHD subjects and in an oxLDL-induced HCAECs injury model. Conversely, lnc-MICALL2-2 inhibition in vitro attenuated the effects of oxLDL on HCAECs morphology, proliferation, and apoptosis.Conclusion: Elevated expression of lnc-MICALL2-2 is an independent risk factor for CHD, and knockdown subsequently confers protection against early pathological processes of oxLDL-induced CHD.

Correlation analysis of epicardial adipose tissue volume quantified by computed tomography images and coronary heart disease under optimized reconstruction algorithm.

Objectives:This paper was aimed to explore the adoption value of low-dose computed tomography (CT) imaging based on optimized ordered subset expectation maximization (OSEM) reconstruction algorithm in the correlation analysis between epicardial adipose tissue (EAT) volume and coronary heart disease (CHD).Methods:A total of 110 patients with CHD were selected for CT angiography (CTA) and coronary arteriography (CAG) examinations from October 2017 to October 2019. The predictive value of EAT for CHD was analyzed via receiver operating characteristic (ROC) curve.Results:The results showed that the iteration time and error of the improved OSEM reconstruction algorithm were better than that of MLEM algorithm under the same number of iterations. Age, smoking, hypertension, diabetes, and EAT in control group were obviously lower in contrast to those in CHD group (P<0.05). EAT in control group was (124.50±26.72) mL, and EAT in the CHD group was (159.41±38.51) mL. EAT (B=0.023, P=0.003) was an independent risk factor for CHD, which was suggested by Multiple linear regression analysis. Moreover, EAT was a risk factor for CHD, and was positively correlated with the degree and NSCV.Conclusion:The optimized OSEM algorithm was used to improve the reconstruction quality of low-dose CT images and used in quantitative measurement of epicardial fat volume. Results showed EAT was an independent risk factor for CHD, and was positively correlated with the number of coronary lesions and Gensini score. It was of great value for the prediction of CHD.

Predictive Value of Small Dense Low-Density Lipoprotein in Coronary Heart Disease in the Chinese Population.

Coronary heart disease (CHD) is a global public health concern, and CHD risk assessment remains a major challenge. Therefore, in this study, the ability of small dense low-density lipoprotein (sdLDL) alone to sufficiently predict CHD risk in the Chinese population was evaluated. Patients with CHD (139) and healthy controls (58) were included in this study. Serum sdLDL was measured using the peroxidase method. Other lipid parameters were also determined. The sdLDL level in the CHD group was significantly higher than that in the control group (p < 0.001). Logistic regression analysis revealed that sdLDL was an independent risk factor for CHD. Based on the receiver operating characteristic curves, the area under the curve (AUC) of sdLDL alone for CHD was 0.722. The AUC of triglycerides (TG), high-density lipoprotein (HDL), and sdLDL combined was 0.763, which was larger than that of the independent ones or combinations of any two; however, the value was not significant. sdLDL alone can predict CHD risk efficiently similar to the combination of TG, HDL, and sdLDL. This finding suggests that sdLDL can be considered as an ideal parameter for the preliminary diagnosis of CHD in Chinese people.