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Ning Zhang

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Open access Aug 2026

Plasma proteomic profiling reveals potential biomarkers for sinus node dysfunction in persistent atrial fibrillation

Aims: In patients with persistent atrial fibrillation (PerAF), detecting sinus node dysfunction (SND) before rhythm control therapy is challenging, which may increase the risk of post-cardioversion bradyarrhythmia and complicate clinical management. This study aimed to identify plasma proteomic signatures associated with prolonged sinus node recovery time (SNRT) and to explore potential predictors of SND in PerAF patients. Methods: A total of 226 consecutive patients with PerAF undergoing catheter ablation were screened, and 177 eligible patients were included. SNRT was measured after cardioversion, with 1,500 ms used as the cutoff to define prolonged SNRT. Propensity score matching was applied to select matched controls. Plasma samples were analyzed using proteomic techniques to compare protein expression profiles between the prolonged-SNRT and normal-SNRT groups. A weighted gene co-expression network analysis (WGCNA) was conducted to determine the most significant module and hub proteins associated with SNRT. An enzyme-linked immunosorbent assay was used for further assessment of candidate biomarkers. Results: Proteomic analysis identified 435 differentially expressed proteins, including 138 upregulated and 297 downregulated proteins in the prolonged-SNRT group. Integrated data-independent acquisition-based proteomics and WGCNA revealed a distinct plasma proteomic signature associated with prolonged SNRT in patients with PerAF, characterized by enrichment of extracellular matrix remodeling and metabolic reprogramming pathways. Acid phosphatase 5 (ACP5) was selected as a candidate biomarker, which was significantly elevated in patients complicated by SND in the validation set. Conclusions: This study revealed distinct plasma proteomic features in PerAF patients with prolonged SNRT. ACP5 may serve as a potential biomarker to assess sinoatrial node function in PerAF patients prior to rhythm control therapy.

Tian-Yi Lin, Taojie Zhou, Yongying Lan et al. · Vessel Plus · 0 citations

Open access Aug 2026

Temporal trends and decomposition analysis of cardiovascular disease burden in China and worldwide from 1990 to 2021, with projections to 2036

Cardiovascular disease (CVD) is a major public health concern, and its burden and trends require further clarification. This study aimed to compare the CVD burden between China and the global population and provide projections. Data on CVD metrics from the Global Burden of Disease (GBD) 2021. The Joinpoint regression model was applied to analyze long-term trends in age-standardized incidence (ASIR), prevalence (ASPR), mortality (ASMR), and age-standardized disability-adjusted life year (ASDR) rates. Decomposition analysis quantified drivers of change, and autoregressive integrated moving average models projected future trends. From 1990 to 2021, ASMR and ASDR showed significant downward trends in China and globally. However, the ASIR for CVD in China increased from 783.897 to 811.813 per 100,000, while globally it declined from 878.787 to 787.04 per 100,000, with average annual percent change of 0.105% (95% CI: 0.067%, 0.144%) and –0.353% (95% CI: –0.404%, –0.301%), respectively. The upward trend of ASPR in China exceeded the global level. The crude rates of CVD metrics were low among individuals younger than 40 years and then increased with advancing age; sex differences were also observed. Decomposition analysis highlighted population aging as an important driver of increased CVD incidence in China (total population: 41.88%, male: 24.17%, female: 47.81%). Projections suggested that ASIR in China and globally will continue to rise to 825.94 and 807.96 per 100,000, respectively, by 2036, whereas ASMR and ASDR will continue to decline. This study revealed a complex CVD epidemiological landscape in China and globally, with increasing ASPR alongside declining ASMR and ASDR. The ASIR of CVD in China remains significant and is projected to continue increasing.

Yue Zhang, Ning Zhang, Chang-Jian Lin · Archives of Medical Science · 0 citations