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Background: Identification of the unknown pathogenic factor driving atherosclerosis not only enhances the development of disease biomarkers but also facilitates the discovery of new therapeutic targets, thus contributing to the improved management of coronary artery disease (CAD). We aimed to identify causative protein biomarkers in CAD etiology based on proteomics and 2-sample Mendelian randomization (MR) design. Methods: Serum samples from 33 first-onset CAD patients and 31 non-CAD controls were collected and detected using protein array. Differentially expressed analyses were used to identify candidate proteins for causal inference. We used 2-sample MR to detect the causal associations between the candidate proteins and CAD. Network MR was performed to explore whether metabolic risk factors for CAD mediated the risk of identified protein. Vascular expression of candidate protein in situ was also detected. Results: Among the differentially expressed proteins identified utilizing proteomics, we found that circulating Golgi protein 73 (GP73) was causally associated with incident CAD and other atherosclerotic events sharing similar etiology. Network MR approach showed low-density lipoprotein cholesterol and glycated hemoglobin serve as mediators in the causal pathway, transmitting 42.1% and 8.7% effects from GP73 to CAD, respectively. Apart from the circulating form of GP73, both mouse model and human specimens imply that vascular GP73 expression was also upregulated in atherosclerotic lesions and concomitant with markers of macrophage and phenotypic switching of vascular smooth muscle cells (VSMCs). Conclusions: Our study supported GP73 as a biomarker and causative for CAD. GP73 may involve in CAD pathogenesis mainly via dyslipidemia and hyperglycemia, which may enrich the etiological information and suggest future research direction on CAD.

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BACKGROUND: Atherosclerosis is a chronic inflammatory disease with its molecular basis incompletely understood. Here, we determined whether the Golgi phosphoprotein 73 (GP73), a novel protein highly related to inflammation and disrupted lipid metabolism, was involved in the development of atherosclerosis. METHODS: Public microarray databases of human vascular samples were analyzed for expression patterns. Apolipoprotein-E-gene-deficient (ApoE-/-) mice (8-week-old) were randomly assigned to either a chow diet group or a high-fat diet group. The levels of serum GP73, lipid profiles and key inflammatory cytokines were determined by ELISA. The aortic root plaque was isolated and used for by Oil Red O staining. PMA-differentiated THP-1 macrophages were transfected with GP73 small interfering RNA (siRNA) or infected with adenovirus expressing GP73, and then stimulated with oxidized low density lipoprotein (ox-LDL). The expressions of pro-inflammatory cytokines and signal pathway key targets were determined by ELISA kit and Western blot respectively. In addition, ichloro-dihydro-fluorescein diacetate (DCFH-DA) was used to measure the intracellular ROS levels. RESULTS: The expressions of GP73 and NLRP3 were substantially upregulated in human atherosclerotic lesions. There were significant linear correlations between GP73 and inflammatory cytokines expressions. High-fat diet-induced atherosclerosis and increased levels of plasma inflammatory mediators (IL-1ß, IL-18, and TNF-α) were observed in ApoE-/- mice. Besides, the expressions of GP73 in the aorta and serum were significantly upregulated and positively correlated with the NLRP3 expression. In the THP-1 derived macrophages, ox-LDL treatment upregulated the expressions of GP73 and NLRP3 proteins and activated the inflammatory responses in a concentration-dependent and time-dependent manner. Silencing of GP73 attenuated the inflammatory response and rescued the decreased migration induced by ox-LDL, inhibiting the NLRP3 inflammasome signaling and the ROS and p-NF-κB activation. CONCLUSIONS: We demonstrated that GP73 promoted the ox-LDL-induced inflammation in macrophages by affecting the NF-κB/NLRP3 inflammasome signaling, and may play a role in atherosclerosis.

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AIMS: We aimed to explore the heterogeneous treatment effects (HTEs) for spironolactone treatment in patients with Heart failure with preserved ejection fraction (HFpEF) and examine the efficacy and safety of spironolactone medication, ensuring a better individualized therapy. METHODS AND RESULTS: We used the causal forest algorithm to discover the heterogeneous treatment effects (HTEs) from patients in the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial. Cox regressions were performed to assess the hazard ratios (HRs) of spironolactone medication for cardiovascular death and drug discontinuation in each group. The causal forest model revealed three representative covariates and participants were partitioned into four subgroups which were Group 1 (baseline BMI ≤ 31.71 kg/m2 and baseline ALP ≤ 80 U/L, n = 759); Group 2 (BMI ≤ 31.71 kg/m2 and ALP > 80 U/L, n = 1088); Group 3 (BMI > 31.71 kg/m2 , and WBC ≤ 6.6 cells/µL, n = 633); Group 4 (BMI > 31.71 kg/m2 and WBC > 6.6 cells/µL, n = 832), respectively. In the four subgroups, spironolactone therapy reduced the risk of cardiovascular death in high-risk group (Group 4) with both high BMI and WBC count (HR: 0.76; 95% CI 0.58 to 0.99; P = 0.045) but increased the risk in low-risk group (Group 1) with both low BMI and ALP (HR: 1.45; 95% CI 1.02 to 2.07; P = 0.041; P for interaction = 0.020) but showed similar risk of drug discontinuation (P for interaction = 0.498). CONCLUSION: Our study manifested the HTEs of spironolactone in patients with HFpEF. Spironolactone treatment in HFpEF patients is feasible and effective in patients with high BMI and WBC while harmful in patients with low BMI and ALP. Machine learning model could be meaningful for improved categorization of patients with HFpEF, ensuring a better individualized therapy in the clinical setting.

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BACKGROUND: Prediction of cardiovascular disease (CVD) is important in clinical practice. Machine learning (ML) may offer an improved alternative to current CVD risk stratification in individual patients. We aim to identify important predictors and compare ML models with traditional models according to their prediction performance in a large long-term follow-up cohort. METHODS: The Atherosclerosis Risk in Communities (ARIC) study was designed to study the progression of subclinical disease to cardiovascular events over a 25-year follow-up period. All phenotypic variables at visit 1 were obtained. All-cause death, CVD, and coronary heart disease were the outcomes for analysis. The ML framework involved variable selection using the random survival forest (RSF) method, model building, and 5-fold cross-validation. Model performance was evaluated by discrimination using the Harrell concordance index (C-index), accuracy using the Brier score (BS), and interpretability using the number of variables in the model. RESULTS: Of the 14,842 participants in ARIC, the average age was 54.2 years, with 45.2% male and 26.2% Black participants. Thirty-eight unique variables were selected in the RSF top 20 importance ranking of all 6 outcomes. Aging, hypertension, glucose metabolism, renal function, coagulation, adiposity, and sodium retention dominated the predictions of all outcomes. The ML models outperformed the regression models and established risk scores with a higher C-index, lower BS, and varied interpretability. CONCLUSIONS: The ML framework is useful for identifying important predictors of CVD and for developing models with robust performance compared with existing risk models.

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Introduction: Hypertension (HT) and atrial fibrillation (AF) often coexist. However, the causality between these two conditions remains to be determined. Methods: We used individual participant data from the Atherosclerosis Risk in Communities (ARIC) prospective cohort with 9,474 participants. HT was ascertained at visit 1 (1987-1989), and incident AF was identified by ECGs conducted during study examinations at each visit, hospital discharge codes, and death certificates. We used the Kaplan-Meier estimate to compute the cumulative incidence of AF by the HT subgroup. Then we used Cox hazard regression model to assess the association between HT and incident AF. The causality between genetically determined HT and AF was analyzed by the two-sample Mendelian randomization (MR) based on publicly summarized genome-wide association studies (GWASs) data. Results: A total of 1,414 cases (14.9%) of AF were identified during the follow-up period (median 24.1 years). After adjusting for all covariates, the hazard ratio between the participants with HT and incident AF was 1.50 [95% confidence interval (CI) 1.29-1.73]. In the HT → AF MR analysis, we detected a causal correlation between HT and AF (OR: 1.90, 95% CI 1.18-3.04, P = 0.01) with no evidence of heterogeneity from single-nucleotide polymorphisms. Besides, the genetically determined SBP and DBP (10 mmHg) were consistently associated with a higher risk of AF. Conclusions: In the ARIC study, the incident AF increased by 50% in patients with HT. In the MR analysis, our results supported causal inference between HT and AF.

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BACKGROUND: Cystatin C (CysC) is a cysteine protease inhibitor involved in proteins catabolism and plays an essential role in human vascular pathophysiology. CysC may also increase the risk of aortic stenosis (AS), but limited studies have reported on this association. This study aimed to investigate if elevated serum CysC levels are associated with hemodynamically significant AS. METHODS: Serum CysC levels were estimated in 4,791 participants, samples were collected in 1990-1992. The study population was divided into quintile groups. Follow-up continued in 2011-2013 when participants returned for echocardiography examination. Incidence of aortic valve disease (AVD) was ascertained by Doppler echocardiography through the end of 2013. AVD defined in hemodynamic progression was assessed and classified as aortic sclerosis, mild stenosis, and moderate-to-severe stenosis. RESULTS: Overall, a total of 4,791 participants (mean age: 54.8 ± 5.0 years, females: 57.6%, blacks: 8.2%) were included in this study. During a follow-up of 21 years, we identified 736 cases (15.4%) of aortic sclerosis, 194 cases (4.0%) of mild stenosis, and 42 cases (0.7%) of moderate-to-severe stenosis. Compared with serum CysC levels within individual quintile groups, the odds ratio (OR) was per standard deviation associated with an increased incidence of AVD (OR = 1.15, 95% CI: 1.05-1.26,P = 0.002). CONCLUSIONS: In this large population-based study, an increased serum CysC levels is independently associated with the incidence of hemodynamically significant AS. However, this association appears not to extend to patients with extremely high serum CysC levels and necessitate further investigation.

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BACKGROUND: In most situations, many patients undergoing coronary artery bypass graft (CABG) are on dual antiplatelet therapy (DAPT), which is also required after CABG. The adjustment of antiplatelet strategy remains controversial. In this study, we systematically review current guidelines, seeking consensus and controversies to facilitate clinical practice. METHODS AND RESULTS: Guidelines are searched in PubMed, Embase, ECRI Guidelines Trust and websites of guidelines organizations and professional society. Guidelines with recommendations of DAPT for patients undergo CABG are included. Two reviewers appraised guidelines with the Appraisal of Guidelines for Research and Evaluation II (AGREE II). Relevant recommendations are extracted and summarized. A total of 14 guidelines meeting inclusion criteria are selected, with average AGREE II scores from 44% to 86%. Most guidelines score high in domains other than 'applicability'. Many guidelines are not detailed enough in reporting considerations behind recommendations. Current guidelines are consistent on the management of antiplatelet strategy before elective CABG and using DAPT after surgery for preventing graft vessel occlusion. Evidence is still lacking in urgent CABG and resumption of the previous DAPT after surgery. CONCLUSIONS: Current guidelines on DAPT in CABG are generally satisfying. Suspending P2Y12 inhibitors while aspirin continued before elective CABG is recommended, as well as 12 months of DAPT following CABG. More evidence is needed to guide antiplatelet therapy in urgent CABG and to prove the benefits of resuming previous DAPT.

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INTRODUCTION: We aim to characterize the nature and magnitude of the prospective association between education and incident heart failure (HF) in the Atherosclerosis Risk in Communities (ARIC) Study and investigate any causal relevance to the association between them. METHODS: The final sample size was 12,315 in this study. Baseline characteristics between education levels were compared using 1-way ANOVA test, the Kruskal-Wallis test, or the χ2 test. We used the Kaplan-Meier estimate to compute the cumulative incident of HF by education levels and the difference in estimate was compared using the log-rank test. Cox hazard regression models were used to explore the association between education levels and incident HF. Two-sample Mendelian randomization (MR) based on publicly available summary-level data from genome-wide association studies (GWASs) was used to estimate the causal influence of the education and incident HF. RESULTS: During a median follow-up of 25.1years, 2453 cases (19.9%) of incident HF occurred. After multiple adjustments in the final model, participants in the intermediate and advanced education levels were still associated with 18% and 21% decreased rate of incident HF separately. In MR analysis, we detected a protective causal association between education and HF (P=0.005). CONCLUSIONS: Participants with higher education levels were associated with a decreased rate of incident HF. There was a causal association between education and HF.

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INTRODUCTION: Long-term changes of fasting blood glucose (FBG) in relation to lower-extremity peripheral artery disease (lower-extremity PAD) in people without diabetes has barely been reported. Our study aimed to investigate the association between FBG variability and the incidence of lower-extremity PAD in people without diabetes. RESEARCH DESIGN AND METHODS: We included 7699 participants without prior lower-extremity PAD and diabetes from the Atherosclerosis Risk in Communities study in the final analysis. At least two measurements of FBG were required during follow-up. Variability of FBG was identified using SD, coefficient of variation (CV), variability independent of the mean (VIM) and average real variability. Lower-extremity PAD was defined as an ankle brachial index <0.9, or hospitalization with a lower-extremity PAD diagnosis. Cox regression model was used to calculate HR for incidence of lower-extremity PAD and FBG variability. RESULTS: During a median follow-up of 19.5 years, 504 (6.5 %) lower-extremity PAD events were observed, 54.4% (n=274) were male, and 17.5% (n=88) were African-American. FBG variability was positively associated with incident lower-extremity PAD, with a linear relationship. HRs for CV and VIM were 1.015 (95% CI: 1.001 to 1.03; p=0.023), and 1.032 (95% CI: 1.004 to 1.06; p=0.022) for lower-extremity PAD, respectively. Participants in the lowest quartile of CV were at lower lower-extremity PAD risk compared with the highest ones (HR: 1.499, 95% CI: 1.16 to 1.938; p=0.002). CONCLUSIONS: Higher FBG variability was independently associated with increased prevalence of lower-extremity PAD in people without diabetes. TRIAL REGISTRATION NUMBER: NCT00005131.

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Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. Low serum albumin level is linked to the emergence of many cardiovascular diseases, including AF. In this study, we aim to characterize the nature and magnitude of the prospective association between serum albumin and incident AF in the Atherosclerosis Risk in Communities (ARIC) Study and investigate any causal relevance to the association between them. ARIC Study is a population-based, prospective, cohort study of cardiovascular risk factors in four US communities, initially consisting of 15,792 participants, aged 45-64 years, recruited between 1987 and 1989 (visit 1). The final sample size was 12,833 in this study. Baseline (visit 1) characteristics were compared between groups using one-way ANOVA test, Chi square test, or Kruskal-Wallis test as appropriate. We used multivariable Cox' hazard regression models to assess the association between albumin and incident AF. Two-sample Mendelian randomization (MR) based on publicly available summary-level data from genome-wide association studies was used to estimate the causal influence of the serum albumin and incident AF. During a median follow-up of 25.1 years, 2259 (17.6%) participants developed incident AF. After multiple adjustment, serum albumin was inversely associated with incidence of AF [HR = 0.90, 95% CI 0.86-0.94, per SD (0.27 g/dL) increase; HR = 0.80, 95% CI 0.71-0.91, Q4 vs. Q1]. In MR analysis, we detected no evidence for a causal relation between serum albumin level and AF in inverse-variance weighted (IVW) method (odds ratio: 0.996, 95% CI 0.980-1.012, per 1 g/dL increase of albumin; P = 0.620) without evidence of heterogeneity between estimates from individual SNPs (Pheterogeneity = 0.981 [MR-Egger] and Pheterogeneity = 0.860 [IVW]) nor pleiotropy effect (Ppleiotropy = 0.193). The serum albumin level is independently inverse associated with incident AF in a linear pattern. However, MR analyses did not support a causal role of serum albumin in the etiology of AF.

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BACKGROUND: Hypertensive patients are highly heterogeneous in cardiovascular prognosis and treatment responses. A better classification system with phenomapping of clinical features would be of greater value to identify patients at higher risk of developing cardiovascular outcomes and direct individual decision-making for antihypertensive treatment. METHODS: An unsupervised, data-driven cluster analysis was performed for all baseline variables related to cardiovascular outcomes and treatment responses in subjects from the Systolic Blood Pressure Intervention Trial (SPRINT), in order to identify distinct subgroups with maximal within-group similarities and between-group differences. Cox regression was used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for cardiovascular outcomes and compare the effect of intensive antihypertensive treatment in different clusters. RESULTS: Four replicable clusters of patients were identified: cluster 1 (index hypertensives); cluster 2 (chronic kidney disease hypertensives); cluster 3 (obese hypertensives) and cluster 4 (extra risky hypertensives). In terms of prognosis, individuals in cluster 4 had the highest risk of developing primary outcomes. In terms of treatment responses, intensive antihypertensive treatment was shown to be beneficial only in cluster 4 (HR 0.73, 95% CI 0.55-0.98) and cluster 1 (HR 0.54, 95% CI 0.37-0.79) and was associated with an increased risk of severe adverse effects in cluster 2 (HR 1.18, 95% CI 1.05-1.32). CONCLUSION: Using a data-driven approach, SPRINT subjects can be stratified into four phenotypically distinct subgroups with different profiles on cardiovascular prognoses and responses to intensive antihypertensive treatment. Of note, these results should be taken as hypothesis generating that warrant further validation in future prospective studies.

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BACKGROUND: The cardiovascular (CV) safety in terms of heart failure among different classes of treatment remains largely unknown. We sought to assess the comparative effect of these agents on heart failure outcomes. METHODS: This study was registered in the International Prospective Register of Systematic Reviews (CRD 42016042063). MEDLINE, EMBASE, and the Cochrane Library Central Register of Controlled Trials were searched. For the primary outcomes reported previously, studies between Jan 1, 1980 and June 30, 2016 were screened, and subsequently updated till Jan 24, 2019. We performed network meta-analysis to obtain estimates for the outcomes of heart failure, in particular by rankograms for ranking of heart failure risk as well as by pairwise comparisons among all classes of anti-diabetic medications. RESULTS: A total of 91 trials were included, among which were 171,253 participants and 4163 reported cases of heart failure events. As for rankograms, the surface under the cumulative ranking curves (SUCRA) of sodium-glucose co-transporters 2 and thiazolidinediones were 93.4% and 4.3%, respectively, signifying the lowest and highest risk of heart failure, respectively. As for pairwise comparisons in the network, sodium-glucose co-transporters 2 were significantly superior to insulin (OR: 0.75, 95% CI 0.62-0.91), dipeptidyl peptidase 4 inhibitors (OR: 0.68, 95% CI 0.59-0.78), glucagon-like peptide-1 receptor agonists (OR: 0.65, 95% CI 0.54-0.78), and thiazolidinediones (OR: 0.46, 95% CI 0.27-0.77) in terms of heart failure risk. Furthermore, in an exploratory analysis among subjects with underlying heart failure or at risk of heart failure, the superiority of sodium-glucose co-transporters 2 was still significant. CONCLUSIONS: In terms of heart failure risk, sodium-glucose co-transporters 2 were the most favorable option among all classes of anti-diabetic medications.

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