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Abstract Background The 2018/2023 ESC/ESH Guidelines underlined a gap how baseline cardiovascular disease (CVD) risk predicted blood pressure (BP) lowering benefits. Further, 2017 ACC/AHA Guideline and 2021 WHO Guideline recommended implementation studies about intensive BP control. Now, to bridge these guideline gaps, we conducted a post hoc analysis to validate whether the baseline CVD risk influences the effectiveness of the intensive BP control strategy, which was designed by China Rural Hypertension Control Project (CRHCP). Methods This is a post hoc analysis of CRHCP, among which participants were enrolled except those having CVD history, over 80 years old, or missing data. Subjects were stratified into quartiles by baseline estimated CVD risk and then grouped into intervention and usual care group according to original assignment in CRHCP. Participants in the intervention group received an integrated, multi-faceted treatment strategy, executed by trained non-physician community health-care providers, aiming to achieve a BP target of

Abstract Background Most adult patients with depression complain about sleep symptoms, including insufficient and excessive sleep. However, previous studies investigating the impact of sleep duration on depression have yielded conflicting results. Therefore, this study aimed to analyse the link between depression and sleep duration, daytime napping, and snoring among rural Chinese adults. Methods A cross-sectional study was conducted with 9104 individuals. Interviews were conducted with the participants regarding their sleep patterns and their daytime napping routines. The individuals were then assessed for depression using the Patient Health Questionnaire-9. The risk of depression was assessed using a multifactor binary logistic regression analysis. A generalized additive model was used to evaluate the nonlinear relationship between depression and sleep duration/nap time. Additionally, subgroup analysis was conducted to investigate the correlation between sleep duration, daytime napping, snoring, and depression. Results Less than 6 h or more than 8 h of nighttime sleep, daytime napping for more than 1 h, and snoring were all significantly associated with an increased risk of depression. A U-shaped relationship was found between the duration of nighttime sleep and depression. In addition, we found that the nighttime duration of sleep, daytime naps, and snoring had a significant combined effect on the risk of depression. The subgroup analysis further revealed that lack of sleep at night significantly increased the risk of depression in all subgroups. However, snoring and excessive nighttime sleep and napping were only associated with the risk of depression in some subgroups. Conclusions Lack of nighttime sleep (short sleep duration), excessive sleep, and napping for more than one hour during the day were associated with a high risk of depression and had a combined effect with snoring.

Abstract Background Accurately predicting the risk of atherosclerotic cardiovascular disease (ASCVD) is crucial for implementing individualized prevention strategies and improving patient outcomes. Our objective is to develop machine learning (ML)-based models for predicting ASCVD risk in a prospective Chinese population and compare their performance with conventional regression models. Methods A hybrid dataset consisting of 551 features was used, including 98 demographic, behavioral, and psychological features, 444 Electrocardiograph (ECG) features, and 9 Echocardiography (Echo) features. Seven machine learning (ML)-based models were trained, validated, and tested after selecting the 30 most informative features. We compared the discrimination, calibration, net benefit, and net reclassification improvement (NRI) of the ML models with those of conventional ASCVD risk calculators, such as the Pooled Cohort Equations (PCE) and Prediction for ASCVD Risk in China (China-PAR). Results The study included 9,609 participants (mean age 53.4 ± 10.4 years, 53.7% female), and during a median follow-up of 4.7 years, 431 (4.5%) participants developed ASCVD. In the testing set, the final ML-based ANN model outperformed PCE, China-PAR, recalibrated PCE, and recalibrated China-PAR in predicting ASCVD. This was demonstrated by the model's higher area under the curve (AUC) of 0.800, compared to 0.777, 0.780, 0.779, and 0.779 for the other models, respectively. Additionally, the model had a lower Hosmer–Lemeshow χ2 of 9.1, compared to 37.3, 67.6, 126.6, and 18.6 for the other models. The net benefit at a threshold of 5% was also higher for the ML-based ANN model at 0.017, compared to 0.016, 0.013, 0.017, and 0.016 for the other models, respectively. Furthermore, the NRI was 0.089 for the ML-based ANN model, while it was 0.355, 0.098, and 0.088 for PCE, China-PAR, and recalibrated PCE, respectively. Conclusions Compared to conventional regression ASCVD risk calculators, such as PCE and China-PAR, the ANN prediction model may help optimize identification of individuals at heightened cardiovascular risk by flexibly incorporating a wider range of potential predictors. The findings may help guide clinical decision-making and ultimately contribute to ASCVD prevention and management.

Abstract We aimed to compare the relationship between hypertension and obesity-related anthropometric indices (waist circumference [WC], waist-height ratio, waist-hip ratio [WHR], and body mass index; unconventional: new body shape index [ABSI] and body roundness index [BRI]) to identify best predictors of new-onset hypertension. The study included 4123 adult participants (2377 women). Hazard ratios (HRs) and 95% confidence intervals (CIs) were determined using a Cox regression model to estimate the risk of new-onset hypertension with respect to each obesity index. In addition, we assessed the predictive value of each obesity index for new-onset hypertension using area under the receiver operating characteristic curve (AUC) after adjusting for common risk factors. During the median follow-up of 2.59 years, 818 (19.8%) new hypertension cases were diagnosed. The non-traditional obesity indices BRI and ABSI had predictive value for new-onset hypertension; however, they were not better than the traditional indexes. WHR was the best predictor of new-onset hypertension in women aged ≤ 60 and > 60 years, with HRs of 2.38 and 2.51 and AUCs of 0.793 and 0.716. However, WHR (HR 2.28, AUC = 0.759) and WC (HR 3.24, AUC = 0.788) were the best indexes for predicting new-onset hypertension in men aged ≤ 60 and > 60 years, respectively.

Abstract Background Hypertension and dyslipidemia are considered reversible risk factors for cardiovascular disease. The purpose of this study was to explore the impact of traditional and nontraditional blood lipid profiles on the risk of left ventricular hypertrophy (LVH) and to explore the superposition effect of dyslipidemia combined with hypertension. Methods Data on 9134 participants (53.5 ± 10.3 years old) from the Northeast China Rural Cardiovascular Health Study (NCRCHS) were statistically analyzed. The blood lipid profile was measured by total cholesterol (TC), low‐density lipoprotein cholesterol (LDL‐C), high‐density lipoprotein cholesterol (HDL‐C), total glyceride (TG), and calculated nontraditional blood lipid indices including non‐HDL‐C, atherosclerosis index (AI), TC/HDL‐C, and residual cholesterol (RC). Results After the adjustment of age and gender, the odds ratios (ORs) of LVH in patients with hypertension, high LDL‐C, high non‐HDL‐C, high AI, and high TC/HDL‐C were 3.97 (3.31–4.76), 1.27 (1.02–1.59), 1.21 (1.04–1.39), 1.33 (1.15–1.53), and 1.42 (1.22–1.65), respectively. After full adjustment of potential confounding factors, high AI and TC/HDL‐C were associated with LVH rather than traditional blood lipid indices. The combination of hypertension and nontraditional dyslipidemia (defined by high AI and TC/HDL‐C) was associated with the highest risk of LVH, especially in participants under 45 years of age. The risk was more significant in men, 5.09‐fold and 6.24‐fold, respectively, compared with 3.66‐fold and 4.01‐fold in women. Conclusions People with dyslipidemia defined by nontraditional blood lipid indices (high AI and high TC/HDL‐C) and hypertension were more likely to develop LVH.

Abstract Background Hyperlipidemia (HLP) and hypertension (HTN) are both independent risk factors for ischemic stroke. This study aimed to assess whether HTN and HLP have a synergistic effect on the risk of ischemic stroke. Methods Between January and August 2013, 11,695 subjects in rural areas of northeastern China were enrolled. The additive and multiplicative scales were used to evaluate the interaction. Results The prevalence of ischemic stroke was 5.7%. Using the healthy group (without HTN or HLP) as the reference group, subjects with both HTN and HLP had a higher risk of ischemic stroke (odds ratio [OR]: 3.369, 95% confidence interval [CI]: 2.579–4.402), and this OR was greater than that of subjects with only HTN (OR: 1.995, 95% CI 1.526–2.610) or HLP (OR: 1.321, 95% CI 0.937–1.862) (adjusting for age, sex, race, education level, family income, current smoking and drinking status, physical activity, body mass index, diabetes, family history of stroke, and atrial fibrillation). Regarding the additive scale, the relative excess risk due to interaction (OR: 1.053, 95% CI 0.458–1.648) was positive after adjusting for confounders. Moreover, the attributable proportion was 31.3%, which means that 31.3% of the total risk of ischemic stroke was due to the synergistic interaction between HTN and HLP. Furthermore, the synergistic index (S) of ischemic stroke was 1.8 (95% CI 1.157–2.801), which also indicates a synergistic interaction between HTN and HLP. Regarding the multiplicative scale, the interaction effect was also significant after adjusting for confounders (OR: 2.163, 95% CI 1.817–2.575). Conclusion The results suggest that the synergistic effect of HTN and HLP on ischemic stroke is significantly higher than the sum of their independent effects. The quantification of the combined effect should help to promote healthy blood pressure and blood lipid levels among the general population.

Oxidative stress-associated endothelial damage is the initiation factor of cardiovascular disease, and protein posttranslational modifications play critical roles in this process. Bcl-2-associated athanogene 3 (BAG3) is a molecular chaperone regulator of the BAG family, which interacts with various proteins and influences cell survival by activating multiple pathways. BAG3 undergoes posttranslational modifications; however, research evaluating BAG3 acetylation and its regulatory mechanism is lacking. In addition, the interacting protein and regulatory mechanism of BAG3 in oxidative stress-associated endothelial damage remain unclear. Here, key molecular interactions and protein modifications of BAG3 were identified in oxidative stress-associated endothelial damage. Endothelial-specific BAG3 knockout in the mouse model starkly enhances oxidative stress-associated endothelial damage and vascular remodeling, while BAG3 overexpression in mice significantly relieves this process. Mechanistically, poly(ADP-ribose) polymerase 1 (PARP1), causing oxidative stress, was identified as a novel physiological substrate of BAG3. Indeed, BAG3 binds to PARP1's BRCT domain to promote its ubiquitination (K249 residue) by enhancing the E3 ubiquitin ligase WWP2, which leads to proteasome-induced PARP1 degradation. Furthermore, we surprisingly found that BAG3 represents a new substrate of the acetyltransferase CREB-binding protein (CBP) and the deacetylase Sirtuin 2 (SIRT2) under physiological conditions. CBP/SIRT2 interacted with BAG3 and acetylated/deacetylated BAG3's K431 residue. Finally, deacetylated BAG3 promoted the ubiquitination of PARP1. This work reveals a novel regulatory system, with deacetylation-dependent regulation of BAG3 promoting PARP1 ubiquitination and degradation via enhancing WWP2, which is one possible mechanism to decrease vulnerability of oxidative stress in endothelial cells.