Xi Li

Background: Despite the growing amount of patients who underwent coronary artery bypass grafting (CABG) in low- and middle-income countries like China, their glucose control was suboptimal, likely due to poor adherence to healthy lifestyles and preventive medications. Mobile health tools facilitating secondary prevention seem promising, but evidence focusing on this high-risk population is scarce. Objective: This study aimed to evaluate the significance of mobile health tools in long-term glycemic management for post-CABG patients with comorbid diabetes mellitus. Methods: GUIDEME (glycemic control using mini program–based intervention in patients with diabetes undergoing coronary artery bypass to promote self-management) is a multicenter, open-label, closed-user group, randomized controlled trial, in which 1066 patients with diabetes who had recently undergone CABG were enrolled and allocated into 2 groups. Patients in the control group received conventional health education before discharge, whereas those in the intervention group additionally received automatic delivery of bite-sized health education and medication reminders through a smartphone app during the 6 months after discharge. The primary end point was a change in glycosylated hemoglobin (HbA _1c ) from baseline to 6 months. Results: Among the 1066 eligible participants enrolled, a total of 1038 (97.4%) had completed the follow-up, while 1000 (93.8%) had 6-month HbA _1c results available. Although only 79 (14.9%) patients in the intervention group were defined as active users, a greater reduction of HbA _1c in the intervention group was observed (adjusted between-group mean difference −0.13, 95% CI −0.25 to −0.01; P =.04). The intervention group also had a high proportion of good medication adherence (96.1% vs 93.2%, P =.04). There was no difference between the 2 groups regarding the secondary end points. Conclusions: Health education and medication reminders based on smartphone app achieved a statistically significant but modest between-group difference in HbA _1c , the clinical relevance of which remains uncertain.

Objectives: Carotid plaque is an early manifestation of atherosclerosis and is closely associated with the risk of myocardial ischaemia, ischaemic stroke and other atherosclerotic cardiovascular diseases (ASCVDs). To identify new protein biomarkers associated with carotid plaque, which will enhance early warning of ASCVDs Design: Nested case–control study within a prospective cohort, with external assessment of transportability in an independent population-based cohort. Participants and data sources: The development cohort is from China, which is a nationwide, community-based cohort and the external validation cohort is from the UK Biobank in the UK. In the development cohort, 292 participants without carotid plaque at baseline were included, comprising 145 incident cases and 147 matched controls selected based on age, sex, low-density lipoprotein cholesterol, systolic blood pressure, body mass index, smoking status and diabetes history. In the validation dataset, 30 800 participants without baseline cardiovascular disease were included from a large population-based cohort. Results: A total of 11 biomarkers, including thrombomodulin, intercellular cell adhesion molecule-3, P-selectin, growth differentiation factor-15, adiponectin, monocyte chemoattractant protein-1, IL-10, placental growth factor, tyrosine kinase receptor-2, vascular endothelial growth factor-D and vascular cell adhesion molecule-1, were selected by least absolute shrinkage and selection operator regression and used to construct a prediction model for the carotid plaque. The area under the receiver operator characteristic curve (AUC) of the eventual model is 0.761, and it showed good calibration capability graphically with a Brier score of 0.192. In the UK Biobank cohort, when these biomarkers were added to a traditional predictive model, a better predictive power was generated, with an AUC improvement of 0.021 (p<0.001, Delong test), Brier score of 0.093, a continuous net reclassification improvement of 0.259 (0.223–0.294, p<0.001) and an integrated discrimination improvement of 0.017 (0.015–0.019, p<0.001) relative to the traditional model. Conclusions: The identified protein biomarkers were used to predict carotid plaque and may provide modest incremental value for risk stratification beyond traditional risk factors. However, the findings should be interpreted with caution and further studies are needed to evaluate their clinical utility.

While the U-shaped association between high-density lipoprotein cholesterol (HDL-C) levels and the risk of all-cause and cardiovascular mortality is well-e

Aims: Effective control of cardiovascular risk factors reduces mortality among patients with type 2 diabetes (T2D), but most evidence is based on baseline measurements. This study investigated the associations between changes and cumulative control of cardiovascular risk factors over the follow-up period and mortality. Methods and results: We analysed 138 193 participants with T2D aged 35–75 years from the ChinaHEART project. Risk factor control included no current smoking, healthy diet, physical activity, and normal levels of blood pressure, cholesterol, glucose, and body mass index. Cumulative control was assessed using time-weighted average, and Cox models estimated hazard ratios (HRs). Changes in control were evaluated by comparing the number of risk factors controlled at baseline and the second follow-up. Over a median follow-up period of 5.6 years, 9698 deaths occurred [4475 from cardiovascular disease (CVD)]. Compared with participants with 0–2 risk factors controlled, those with 3 and 6–7 factors controlled had lower risks of all-cause mortality [HRs: 0.77 (95% confidence interval: 0.73–0.81) and 0.43 (0.38–0.48), respectively]. Improved control was associated with reduced mortality [HR: 0.86 (0.77–0.96)] compared to stable low control, whereas degraded control increased mortality risk by 23% compared to stable high control. Participants in the highest quartile of time-weighted average risk factor control had 47% lower mortality [HR: 0.53 (0.47–0.60)] than those in the lowest quartile. Similar associations were observed for CVD mortality. Conclusion: Sustained and cumulative cardiovascular risk factor control substantially lowers mortality among patients with T2D, underscoring the critical importance of continuous risk factor management.

Background: The cardiovascular–kidney–metabolic (CKM) syndrome is highly prevalent globally. However, the associations between the CKM syndrome and its various components and all-cause and cause-specific mortality are not well understood. This study aims to clarify these mortality associations and provide insights for preventive strategies. Methods: The China Health Evaluation and Risk Reduction through Nationwide Teamwork was a nationwide and population-based project, which was performed in 353 counties/districts across 31 provincial-level administrative divisions in Chinese mainland. A total of 764,856 eligible residents aged ≥35 years from this cohort were included in the study. CKM was classified into stages 0–4 and further sub-grouped stages 2–4 by the presence of chronic kidney disease (CKD) and CKD risk stratification. CKM stage 2 was further divided into six groups in descending order of clinical risk priority. The main outcomes were all-cause and cause-specific mortality. Cox proportional hazards models were used to evaluate the associations of CKM stages and components with mortality, incorporating competing risks models to account for competing events. Results: We found significant positive associations between CKM stages and mortality risk, particularly for cardiovascular mortality with adjusted hazard ratios and 95% confidence intervals of 2.62 (2.10–3.27), 4.04 (3.22–5.07), and 5.64 (4.51–7.05) for stages 2, 3, and 4, respectively. In comparison of participants with CKD with those without CKD at each CKM stage, CKD was associated with an increased risk of all-cause mortality by 64.29%, 68.62%, and 74.89%, and an increased risk of cardiovascular mortality by 72.58%, 71.55%, and 71.95% for CKM stages 2, 3, and 4, respectively. Notably, even moderate-risk CKD was associated with an increased risk of mortality. Among the various components at CKM stage 2, CKD (both high- and moderate-risk) was the most significant risk factor for mortality, with high-risk CKD showing a higher risk (hazard ratio [95% confidence interval]: 2.83 [2.36–3.39]) than moderate-risk CKD (hazard ratio [95% confidence interval]: 2.16 [1.89–2.47]), followed by the coexistence of diabetes and hypertension (hazard ratio [95% confidence interval]: 1.87 [1.65–2.12]) (all P <0.05). Conclusions: The risk of mortality, primarily by cardiovascular disease, increases with advancing stages of CKM. Sub-staging CKM syndrome by stratifying patients according to the presence and severity of CKD and metabolic risk factors can improve the accuracy of evaluating the mortality risk. Consistent monitoring and early interventions aimed at maintaining renal function may considerably reduce the risk of mortality.

Aims: Heart failure (HF) is a serious cardiovascular condition resulting from abnormalities in multiple biological processes, affecting over 64 million people worldwide. We sought to expand our understanding of the genetic basis of HF and more specific NICM subtype in the East Asian populations and evaluate the biological pathways underlying subclinical left ventricular dysfunction. Methods and results: We conducted a meta-analysis of genome-wide association studies (GWAS) for all-cause HF in the East Asian populations (N cases ~ 13,385) and a more precise definition of nonischemic cardiomyopathy (NICM) subtype in multi-ancestry populations (N cases~3,603). We identified a low-frequency East-Asian enriched coding variant near MYBPC3 and a NICM specific locus. Follow up analyses demonstrated male-specific HF association at the M YBPC3 locus, and highlighted SVIL as a candidate causal gene for NICM. Moreover, we demonstrated that SVIL deficiency aggravated cardiomyocyte hypertrophy, apoptosis and impaired cell viability in phenylephrine (PE)-treated H9C2 cells. In addition, the gene expression level of B-type natriuretic peptide ( BNP ) which was deemed as a hallmark for HF was further elevated by SVIL silencing in PE-stimulated H9C2 cells. RNA-sequencing analysis of H9C2 cells revealed that the function of SVIL might be mediated through pathways relevant to regulation and differentiation of heart muscle. Conclusion: These results enhance our understanding of the genetic architecture of HF in the East Asian populations, and provide important insight into the biological pathways underlying NICM and sex-specific relevance of the MYBPC3 locus that warrants further replication in another datasets.

Biological aging is a critical risk factor of age-related diseases, but its impact on diabetic individuals remains unclear. This study aimed to examine the associations of biological aging with the incident cardiovascular diseases (CVDs) and life expectancy loss in diabetic individuals. We included 12,828 diabetic individuals in UK biobank. Biological aging was calculated by Klemera-Doubal method Biological Age (KDMAge) and phenotypic age (PhenoAge). Cox proportional hazard models were fitted to investigate the associations of biological aging with incident coronary heart disease (CHD), atrial fibrillation (AF), heart failure (HF), stroke, and degenerative valvular heart disease (VHD) in diabetic individuals. We also evaluated life expectancy loss in accelerated aging individuals, the interactions between biological aging and clonal hematopoiesis of indeterminate potential (CHIP), and performed causal mediation analysis. During a median follow-up of 13.1 years, we documented 3794 incident CVDs in diabetic individuals. PhenoAge accelerated aging was significantly associated with all CVD subtypes, with hazard ratios ranging from 1.23 to 1.62, and KDMAge showed even stronger associations. Accelerated biological aging was also associated with over 2 years of life expectancy loss. CHIP and PhenoAge accelerated aging had a significant synergistic effect on CHD, HF, and VHD. Inflammatory activation contributed significantly to accelerated aging-associated CHD and HF. Biological aging significantly increases CVD risk and reduces life expectancy in diabetic population, with effects modified by CHIP status. Targeting biological aging mechanisms may help prevent CVDs and premature mortality in diabetic population. AF, atrial fibrillation; CHD, coronary heart disease; CHIP, clonal hematopoiesis of indeterminate potential; CI, confidence interval; CVD, cardiovascular disease; HF, heart failure; HR, hazard ratio; KDMAge, Klemera-doubal method biological age; PAR, partial population attributable risk; PhenoAge, phenotypic age; VHD, valvular heart disease.