Metabolic dysfunction-associated steatotic liver disease (MASLD) is a steatotic liver disorder closely linked to metabolic dysregulation. Insulin resistance (IR) plays a key role in its pathophysiology and is considered a significant component of cardiometabolic risk.

To investigate the association between different surrogate indicators of IR and the occurrence of major adverse cardiovascular events (MACEs) after percutaneous coronary intervention (PCI) in patients with MASLD and coronary heart disease (CHD), and to identify simple predictive indicators suitable for this population.

A total of 958 patients diagnosed with MASLD and CHD who underwent PCI at Northern Jiangsu People's Hospital from January 1, 2019 to October 31, 2023, were retrospectively enrolled. Baseline data were collected, cardiac function was assessed by echocardiography, and liver status was evaluated by ultrasound. IR surrogate indicators were calculated based on laboratory results. Patients were followed up to collect the occurrence of postoperative MACEs. Cox proportional hazards regression models were used to explore prognostic factors. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive efficacy of IR surrogate indicators. Kaplan-Meier survival curves were drawn to compare cumulative survival rates, with Log-rank tests for group comparisons. Restricted cubic spline (RCS) curves were used to explore the nonlinear trends between IR indicators and prognosis.

Based on follow-up results, patients were divided into the non-MACEs group (n=768) and the MACEs group (n=190). Significant differences were observed between the two groups in white blood cell count, neutrophil count, monocyte count, fasting plasma glucose (FPG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A, apolipoprotein B, albumin, aspartate aminotransferase, TyHGB, TyG, TG/HDL-C, and METS-IR (P<0.05). Cox regression analysis showed that elevated TyG (HR=1.820, 95%CI=1.223-2.707) and METS-IR (HR=7.309, 95%CI=3.770-14.170) were risk factors for postoperative MACEs (P<0.05). Group analysis indicated that TyG Q2-Q4, TG/HDL-C Q4, TyHGB Q3-Q4, and METS-IR Q3-Q4 were risk factors for MACEs (P<0.05). Kaplan-Meier curves showed significant differences in MACEs risk across quartiles of TyG (χ²=17.428), TG/HDL-C (χ²=41.682), TyHGB (χ²=47.345), and METS-IR (χ²=104.233) (P<0.05). RCS curves indicated a nonlinear correlation of TG/HDL-C, TyHGB, and METS-IR with MACEs (P_nonlinear<0.001), while TyG showed a linear correlation (P_nonlinear=0.643). ROC analysis showed the AUC for TyHGB, TyG, TG/HDL-C, and METS-IR were 0.653(95%CI=0.611-0.696), 0.599(95%CI=0.553-0.644), 0.652(95%CI=0.608-0.695), and 0.741, respectively.

TyHGB, TyG, TG/HDL-C, and METS-IR are positively associated with the occurrence of MACEs after PCI in patients with MASLD and CHD and can serve as potential epidemiological predictive indicators, with METS-IR demonstrating the best predictive performance.

Sleep fragmentation (SF) is one of the hallmarks of sleep disorders, and the mechanism in cardiac remodeling is still unclear.

To observe the effect of SF on cardiac remodeling in mice with high-fat diet.

The research period was from August 2023 to June 2024. Twenty-one 8-week-old male C57BL/6J mice were randomly divided into the high-fat sleep fragmentation group (SF group, n=11) and the high-fat control group (Ctrl group, n=10). They were placed in the sleep deprivation apparatus and the normal cage respectively. Both groups were given 45% high-fat feed, and the sleep fragmentation intervention lasted for 5 months. The degree of cardiac hypertrophy was evaluated by heart weight/body weight (HW/BW) and heart weight/tibia length (HW/TL). The changes in cardiac structure were evaluated by echocardiography. Myocardial HE staining, Masson staining, and WGA staining were used to observe the size of myocardial cells and collagen content. The heart tissues of mice were sequenced for transcriptomics and metabolomics and the mechanism in cardiac remodeling was analyzed.

Compared with the Ctrl group, the heart weight of the SF group increased (P<0.05), HW/BW and HW/TL rose (P<0.05), left ventricular internal dimension diastole, left ventricular internal dimension in systole, left ventricular end-diastolic volume, left ventricular end-systolic volumes all increased (P<0.05), and the left ventricular posterior wall thickness during diastole decreased (P<0.05). HE staining showed that compared with the Ctrl group, the heart of the SF group was enlarged and the cardiomyocytes were enlarged; the statistical graph of Masson staining showed that compared with the Ctrl group, the myocardial fibrosis of the SF group increased (P<0.01); the statistical graph of WGA staining showed that compared with the Ctrl group, the cardiomyocytes of the SF group were enlarged (P<0.001). The transcriptomics results suggested that the TGF-β signaling pathway in the SF group was upregulated. Metabolomics suggested that the increase in cardiac remodeling in the SF group was related to the downregulation of glycerophospholipid metabolism pathways.

Sleep fragmentation can lead to cardiac enlargement, ventricular dilation, ventricular wall thinning, increased collagen content, and cardiac remodeling in mice with a high-fat diet. The mechanism may be related to up-regulation of TGF-β signaling pathway and down-regulation of glycerophospholipid metabolism.

Acute myocardial infarction (AMI) poses a significant threat to the health and quality of life of the elderly. Previous studies have found that higher low-density lipoprotein cholesterol (LDL-C) is an independent risk factor for AMI, and increased uric acid and decreased high-density lipoprotein cholesterol are also associated with AMI.

To investigate the predictive value of cumulative LDL-C exposure, uric acid to high-density lipoprotein cholesterol ratio (UHR), and their combination on the occurrence of AMI in the elderly.

A retrospective study was conducted on 737 elderly patients diagnosed with AMI at Northern Jiangsu People's Hospital from January 2019 to December 2023 (experimental group) and 260 elderly patients admitted during the same period who were excluded from the AMI diagnosis (control group). Patient demographics and laboratory test results were collected, and cumulative LDL-C exposure and UHR values were calculated. Univariate and multivariate Logistic regression analyses were performed to identify independent risk factors for AMI in the elderly. ROC curves were plotted for cumulative LDL-C exposure, UHR, and their combined prediction of AMI in the elderly, and the area under the ROC curve (AUC) was calculated to assess the predictive efficacy of cumulative LDL-C exposure, UHR, and their combination for AMI in the elderly.

The characteristics of male gender, smoking history, alcohol abuse history, history of hypertension, history of type 2 diabetes, BMI, glycated hemoglobin A_1c, white blood cell count, neutrophil count, monocyte count, triglycerides, total cholesterol, HDL-C, LDL-C, lipoprotein a, uric acid, cumulative LDL-C exposure, UHR, apolipoprotein A1, and albumin of the experimental group were compared with the control group, results showed statistically significant differences (P<0.05). Results of multivariate Logistic regression analysis showed that male gender (OR=3.026, 95%CI=1.769-5.178, P<0.001), history of type 2 diabetes (OR=4.834, 95%CI=2.882-8.109, P<0.001), and high level of white blood cell count (OR=1.580, 95%CI=1.368-1.825, P<0.001), LDL-C (OR=3.801, 95%CI=2.712-5.327, P<0.001), cumulative LDL-C exposure (OR=1.898, 95%CI=1.042-3.457, P=0.036), UHR (OR=10.658, 95%CI=5.147-22.069, P<0.001) were independent risk factors for AMI in the elderly, while high level of HDL-C (OR=0.426, 95%CI=0.184-0.986, P=0.046) and albumin (OR=0.989, 95%CI=0.979-0.999, P=0.031) were independent protective factors. ROC curve results showed that the AUC for predicting occurrence of AMI in the elderly based on cumulative LDL-C exposure and UHR was 0.726 (95%CI=0.691-0.761) and 0.700 (95%CI=0.663-0.737), respectively, with sensitivities of 0.696 and 0.742, specificities of 0.650 and 0.607, and cutoff values of 214.86 and 317.06, respectively. The combined AUC was 0.813 (95%CI=0.784-0.842), with a sensitivity of 0.723 and a specificity of 0.730.

History of type 2 diabetes mellitus and high level of leukocytes counts, LDL-C, cumulative LDL-C, UHR are independent risk factors for AMI in the elderly, high level of HDL-C and albumin are independent protective factors. Cumulative LDL-C exposure, UHR and the combination of the two can be used as early biomarkers to effectively identify the occurrence of AMI in the elderly.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and can lead to sudden cardiac death and various complications, imposing a significant social burden. Electrocardiography is the gold standard for diagnosis, but missed and misdiagnoses of atrial fibrillation frequently occur due to variations in physicians' experience. Therefore, it is necessary to develop artificial intelligence models capable of accurately identifying AF in real-world clinically complex electrocardiogram (ECG) data.

To establish a clinical ECG database with multiple positive categories, and based on deep learning technology, utilize static ECG signal data to train a convolutional neural network (CNN) model for AF detection, aiming to improve the accuracy of automated AF diagnosis.

A total of 10 000 patients who met the inclusion and exclusion criteria and visited the Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University) in 2023 were selected. Among them, 1 462 cases were diagnosed with AF, and 8 538 cases were non-AF. General patient information (ages and genders) and static ECG data were collected to establish a clinical ECG database with multiple positive categories. The overall dataset was divided into training set (n=8 000), validation set (n=1 000), and test set (n=1 000) at 8∶1∶1 ratio. An advanced convolutional neural network model, AF Networks (AFNet), was developed using the training set for AF detection. The model's performance was evaluated using the validation and test sets, with metrics including sensitivity, positive predictive value, F1 score, accuracy, and the area under the ROC curve (AUC). These metrics were used to assess the model's performance in ECG diagnostic tasks and to analyze its strengths and limitations.

No statistically significant differences were observed in the comparisons across the three datasets for gender (χ²=1.32, P=0.517), age (F=0.87, P=0.419), and ECG distribution (χ²=2.666, P=0.264). In the test set, the AFNet model demonstrated a sensitivity of 98.00%, specificity of 99.29%, positive predictive value of 96.08%, negative predictive value of 99.65%, accuracy of 99.10%, an F1-score of 0.97, and AUC of 0.99 for diagnosing atrial fibrillation. In the validation set, the AFNet model also achieved high performance, with sensitivity, positive predictive value, accuracy, and F1-score reaching 96.81%, 95.80%, 99.67%, and 0.96, respectively.

The AFNet neural network model demonstrated efficient feature extraction for AF detection in a clinical ECG database with multiple positive categories. This is of great clinical value for AF screening.

The multi-target effects of semaglutide make it a breakthrough therapy for managing diabetes and obesity, offering comprehensive benefits particularly for patients with cardiovascular disease. However, its clinical applications for patients with heart failure (HF) are still under active investigation.

To systematically review the efficacy and safety of subcutaneous semaglutide in the treatment of HF regardless of the presence of obesity or type 2 diabetes (T2DM).

We searched Cochrane Library, PubMed, Embase, CNKI, Wanfang Data and VIP database from inception to November 2, 2024 for randomized controlled trials about subcutaneous semaglutide in the treatment of HF, where the experimental group received subcutaneous semaglutide and the control group received placebo. Data on HF hospitalization rate, cardiovascular death rate, all-cause death rate, serious adverse events, Kansas City Cardiomyopathy Questionnaire Clinical Summary Score (KCCQ-CSS) and 6-minute walk distance (6-MWD) were collected and analyzed by two investigators who independently screened the literature, extracted the data, and evaluated the risk of bias of the included studies. Subgroup analyses were performed based on comorbidities and different dosages, and data were statistically analyzed using Review Manager 5.3 software.

A total of 4 randomized controlled studies, with a total of 6 109 patients (3 070 in the experimental group and 3 039 in the control group). Meta-analysis results showed that compared with placebo, subcutaneous semaglutide reduced the risk of cardiovascular death (RR=0.75, 95%CI=0.61-0.92, P=0.005), all-cause death (RR=0.81, 95%CI=0.67-0.98, P=0.03) and serious adverse events (RR=0.53, 95%CI=0.41-0.68, P<0.000 01). Subgroup analysis found that subcutaneous semaglutide could increase KCCQ-CSS (MD=7.58, 95%CI=4.40-10.77, P<0.000 01) and 6-MWD (MD=16.91, 95%CI=8.98-24.83, P<0.000 1) and reduced the risk of HF hospitalization (RR=0.41, 95%CI=0.26-0.65, P=0.000 1) of patients in HF with preserved ejection fraction (HFpEF) with obesity. In patients without T2DM, semaglutide was superior to placebo in reducing the risk of HF hospitalization (RR=0.16, 95%CI=0.04-0.68, P=0.01) and cardiovascular death (RR=0.76, 95%CI=0.60-0.97, P=0.03); similarly, in patients with a weekly dose of 2.4 mg, semaglutide reduced the risk of HF hospitalization (RR=0.29, 95%CI=0.14-0.58, P=0.000 5) and cardiovascular death (RR=0.75, 95%CI=0.59-0.95, P=0.02) compared with placebo, but the efficacy of the weekly dose of 1.0 mg was not significant compared with placebo.

Current evidence shows that subcutaneous semaglutide can reduce the risk of cardiovascular death, all-cause death and serious adverse events of heart failure patients while improving quality of life and activity tolerance and reducing risk of HF hospitalization in patients with HFpEF and obesity. Due to limited quantity and populations of the included studies, more high-quality studies are needed to verify the above conclusion.

Cardiovascular diseases are prevalent in China, with cardiometabolic multimorbidity (CMM) being a common comorbidity pattern. Postmenopausal women represent a high-risk group for cardiovascular diseases, yet there is a lack of predictive models for CMM risk specifically in this population.

To develop an interpretable machine learning (ML) model to predict the risk of CMM among Chinese postmenopausal women, based on data from the China Health and Retirement Longitudinal Study (CHARLS).

The study included postmenopausal women aged≥45 years from the CHARLS cohort in 2011 who were free of CMM at baseline. Data on demographic characteristics, family background, health status, and laboratory indicators were collected at baseline and during follow-up in 2013, 2015, 2018, and 2020 to observe CMM incidence. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm. Seven ML algorithms were constructed for risk prediction. The optimal model was further optimized on the test set using a combined strategy of "class_weight='balanced' dynamic weighting+optimal threshold selection" and visually interpreted using Shapley Additive Explanations (SHAP). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F1-score.

A total of 5 575 participants completed the 4 rounds of follow-up and were included, comprising 4 363 in the non-CMM group and 1 212 in the CMM group. Over a median follow-up of 9 years, the cumulative incidence of CMM was 21.74%. LASSO regression identified 22 key features as significant predictors of CMM: self-rated health, mental disorders, arthritis, dyslipidemia, kidney disease, retirement status, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean pulse rate, waist circumference, BMI, headache, lower back pain, serum creatinine (Scr), triglycerides (TG), C-reactive protein (CRP), glycated hemoglobin (HbA_1c), uric acid (UA), age, Center for Epidemiologic Studies Depression Scale (CES-D) score, smoking status, and geographic region. Among the models, the Logistic regression (LR) model demonstrated the best predictive performance (test set AUC=0.758, accuracy =79.2%). The SHAP mean bar plot revealed core predictors: SBP, HbA_1c, geographic region, waist circumference, CES-D score, BMI, DBP, and age. The SHAP summary plot indicated that higher values of SBP, HbA_1c, waist circumference, and others were associated with increased predicted CMM risk.

This study develops a clinically interpretable prediction model for CMM in Chinese postmenopausal women, with the LR algorithm showing favorable performance. Key risk factors include SBP, HbA_1c, and waist circumference. The model provides an evidence-based tool for screening high-risk individuals and guiding personalized interventions.

Metabolic associated fatty liver disease (MAFLD) and hypertension are prevalent chronic conditions in older adults, with accumulating evidence linking both to metabolic dysfunction. The cardiometabolic index (CMI) is a composite parameter that evaluates visceral adipose distribution and metabolic status, yet its association with incident hypertension among elderly MAFLD patients remains to be elucidated.

To investigate the association of CMI with incident hypertension in elderly patients with MAFLD, and to evaluate the predictive value of CMI for hypertension development in this population.

Elderly non-hypertensive patients diagnosed with MAFLD during health check-ups at Yingzhong and Zijin Community Health Centers affiliated with Changzhi People's Hospital between January and December 2021 were enrolled. Follow-up investigations were conducted from January 2022 to December 2024, and 426 elderly MAFLD patients who completed follow-up were ultimately included in this study. Baseline characteristics and relevant clinical data were collected, and CMI was calculated. Participants were stratified into three tertile groups based on CMI: T1 (CMI<0.467, n=141), T2 (0.467≤CMI<0.758, n=142), and T3 (CMI≥0.758, n=143). They were further categorized into non-hypertension (n=355) and hypertension (n=71) groups based on incident hypertension during follow-up. Kaplan-Meier survival analysis was performed to estimate the cumulative incidence of hypertension across CMI groups, with between-group differences assessed using the Log-rank test. Multivariate Cox proportional hazards regression and restricted cubic spline (RCS) analysis were used to evaluate the association between CMI and incident hypertension. Sex-stratified analysis and sensitivity analysis were performed to examine the robustness of this association.Furthermore, time-dependent receiver operating characteristic (ROC) curves were constructed to assess the predictive performance of CMI for hypertension development in elderly MAFLD patients.

During a median follow-up period of 36.00 (27.75, 38.00) months, 71 patients (16.7%) developed incident hypertension. There were statistically significant differences between the non-hypertension and hypertension groups in age, waist circumference, BMI, CMI, TC, TG, LDL-C, and HDL-C (P<0.05). Log-rank test demonstrated that the cumulative incidence of hypertension increased significantly across T1 to T3 groups with elevated CMI levels (χ²=26.468, P<0.001). Multivariate Cox proportional hazards regression analysis revealed that when analyzed as a continuous variable, CMI was significantly and positively associated with incident hypertension in elderly MAFLD patients after adjusting for relevant confounders (HR=1.927, 95%CI=1.381-2.689, P<0.001). When CMI was analyzed as a categorical variable with the T1 group as the reference, the risk of developing hypertension was significantly higher in the T3 group after adjusting for confounders (HR=5.453, 95%CI=2.268-13.109, P<0.001). Interaction analysis showed no statistically significant interaction between CMI and gender (P_interaction=0.557). Sensitivity analysis demonstrated that the association between CMI and hypertension remained significant after excluding participants with MAFLD remission. RCS analysis showed a non-linear dose-response relationship between CMI and the risk of hypertension in elderly MAFLD patients (P_{non-linearity}=0.005). The risk of hypertension exhibited a continuous upward trend with increasing CMI levels, which plateaued when CMI exceeded 1.185. Time-dependent ROC curve analysis showed that CMI demonstrated optimal predictive performance for hypertension at 24 months of follow-up, with an AUC of 0.722 (95%CI=0.618-0.827). Over time, the AUC decreased to 0.648 (95%CI=0.537-0.759) and 0.652 (95%CI=0.542-0.763) at 30 and 36 months, respectively.

CMI is significantly positively associated with incident hypertension in elderly MAFLD patients, and its measurement can be used to assess the short-term (24-month) risk of hypertension development in this population.

Acute myocardial infarction (AMI) is a highly critical and acute condition, and early electrocardiographic monitoring is vital for prognosis. Traditional monitoring has limitations such as poor real-time performance and insufficient capture of sudden arrhythmias. Digital-intelligent technology, which integrates digital, information, and intelligent technologies, offers a novel pathway for AMI monitoring.

To systematically summarize the current applications, clinical effects, and existing challenges of digital-intelligent technologies in AMI electrocardiographic monitoring, thereby providing a reference for its standardized application and further research.

Following the Joanna Briggs Institute (JBI) scoping review guidelines, databases including PubMed, Cochrane Library, CINAHL, Embase, Scopus, Web of Science, CNKI, CBM, VIP, and Wanfang Data were searched from inception to February 2, 2025. Relevant studies were selected for comprehensive analysis.

A total of 21 studies from 5 countries were included, comprising randomized controlled trials and prospective studies. Digital-intelligent technologies included remote ECG monitoring systems, wearable devices, AI-assisted diagnosis systems, and mobile devices, which were applied across the full spectrum of patient care, from pre-hospital emergency and in-hospital monitoring to home management. These technologies were shown to improve the detection rate of abnormal ECG, shorten critical treatment time windows, reduce medical costs, and improve prognosis. However, several challenges persist, including the need for data accuracy validation, inadequate privacy protection, fragmented device functionality, and poor user adherence among special populations.

Digital-intelligent technologies are feasible and effective in AMI monitoring, enhancing diagnosis and therapeutic efficiency. Future efforts should focus on technical standardization, data security and privacy protection, functional integration, and age-friendly design to promote clinical translation and application, aligning with the "Healthy China 2030" initiative.

Primary health institutions serve as the frontline defense against hypertension and diabetes. Their capabilities are critical to China's ability to effectively prevent and control these two chronic conditions.

To analyze the current situation of health management and treatment services provided by community hospitals in China for patients with hypertension and diabetes to identify problems and make suggestions.

The "Quality Service Grassroots Activities Application System" collected information on hypertension and diabetes prevention and treatment capacity and service provision in 3 718 community hospitals. Descriptive statistical analysis and multiple linear regression analysis were carried out based on Stata15.0.

There were statistically significant differences in the allocation of electrocardiogram machines and peripheral blood glucose meters among primary health institutions across different regions (P<0.001). Similarly, significant regional disparities were observed in the availability of essential antihypertensive and hypoglycemic medications (P<0.001). The annual number of hypertension and diabetes diagnoses and treatments per institution also varied significantly by region (P<0.001). Additionally, significant differences were found in the renewal rates of hypertensive and diabetic patients across regions(P<0.001). Furthermore, significant variations were observed among regions in the standardized management rates of hypertensive and diabetic patients, as well as in blood pressure and blood glucose control rates (P<0.001). Multiple linear regression analysis revealed that factors such as region, institution type, the number of essential antihypertensive drugs available, the number of registered general practitioners, the proportion of medical income to total income, the proportion of medical insurance income to medical income, and the contract renewal rate significantly influenced the annual number of diagnosed and treated hypertensive patients (P<0.05). Similarly, region, institution type, the number of practicing (assistant) physicians, the proportion of medical income to total income, and the renewal rate were found to affect the standardized management rate of hypertensive patients (P<0.05). Moreover, region, institution type, the number of electrocardiogram machines, the number of practicing (assistant) physicians, and the proportion of medical income to total income had statistically significant effects on blood pressure control.

The hardware conditions of community hospitals in the western region are better, but the medical service capacity is not as good as that in the east, and the soft power still needs to be improved. The ECG machine is the best, but the peripheral blood glucose meter, drug equipment, diagnosis and treatment times and other indicators that reflect the ability of medical services are not as good as those in the east. The integration of medical prevention of hypertension and diabetes still needs to be implemented, and public health indicators such as standardized management rate and blood pressure and blood glucose control rate are "decoupled" from the medical service capacity of community hospitals, and the indicators related to medical services and public health services are "inverted", with the former being high in the east and the latter in the west, and the quality and service connotation of public health data need to be improved.

Ischemic heart disease (IHD) is the second leading cause of death in China, and second-hand smoke is a major risk factor for IHD deaths.

To analyze the disease burden and its change trends of IHD attributable to second-hand smoke in China and five social demographic index (SDI) regions from 1990 to 2021, providing scientific reference for reducing the risk of death from IHD attributable to second-hand smoke.

The data was collected from the Global Burden of Disease (GBD) 2021. Population attribution fraction (PAF) was used to evaluate the impact of second-hand smoke on IHD deaths; The annual average percentage change (AAPC) of mortality and disability adjusted life years (DALYs) were calculated by Joinpoint regression among the population aged 25-94 years during 1990-2021. The age-period-cohort model was performed to analyze the age, period, and cohort effects of mortality and DALYs rates.

Second-hand smoke was a risk factor for IHD in China, and the PAF for deaths and DALYs were higher compared to the five SDI regions, with a decrease of 0.66% and 0.67% from 1990-2021, respectively. The age-standardized mortality rate (1990: 12.27/100 000; 2021: 11.62/100 000) and age-standardized DALYs rate (1990: 284.58/100 000; 2021: 239.26/100 000) of IHD attributable to second-hand smoke presented a slight downward trend, with the AAPC values of -0.20% (95%CI=-0.65% to 0.24%) and -0.58% (95%CI=-0.98% to -0.19%), respectively. The age effects of IHD death and DALYs attributable to second-hand smoke increased with age in China, with a significant rise after the age of 75 years. The period effects showed a downward trend, while the cohort effects increased first and then decreased. However, both the period and cohort effects of IHD deaths and DALYs were on a rise trend among males.

The disease burden of IHD attributable to second-hand smoke is relatively heavy in China, and the overall downward trend may be explained by the decline in the disease burden of IHD among women. More attention should be paid to the control of second-hand smoke in men and the status of disease in the elderly.

Atrial fibrillation (AF) severely impairs patients' quality of life, leads to high morbidity and mortality, and increases healthcare costs. Current classification methods for atrial fibrillation primarily rely on clinical symptoms and examination results, but they struggle to quantify the underlying pathophysiological burden, often leading to treatment strategies that do not match patients' actual risks.

To investigate the factors associated with persistent AF and to develop a classification model based on these factors.

Patients diagnosed with paroxysmal and persistent AF at the First Affiliated Hospital of Xinjiang Medical University between April 2012 and September 2023 were enrolled in this study. Clinical data, including demographic characteristics, biochemical parameters, renal function indices, and cardiac function-related metrics, were collected for analysis. Initially, univariate Logistic regression analysis was performed to screen for variables associated with the type of AF. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied for further feature selection to reduce model complexity and prevent overfitting. A multivariate Logistic regression model was then constructed to identify factors independently associated with persistent AF. Utilizing the bootstrap resampling method, the significant variables were incorporated into six machine learning algorithms—Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGB)—to establish classification models. The discriminative performance of these models was evaluated using receiver operating characteristic (ROC) curves. Finally, the SHapley Additive exPlanations (SHAP) method was employed to evaluate the contribution of each variable to the classification models.

A total of 6 938 patients were enrolled, including 5 085 with paroxysmal AF and 1 853 with persistent AF. Univariate Logistic regression analysis initially identified 19 statistically significant independent variables. Following LASSO regression selection, 13 key variables were retained for multivariate Logistic regression analysis. The results indicated that the following were independently associated with persistent AF (all P<0.05): gender (OR=1.248, 95%CI=1.086-1.435), surgical history (OR=0.809, 95%CI=0.706-0.926), BMI (OR=1.028, 95%CI=1.012-1.045), mean platelet volume (MPV) (OR=1.121, 95%CI=1.059-1.186), serum magnesium (Mg_plus2 ) (OR=0.098, 95%CI=0.046-0.208), cardiac output (CO) (OR=1.115, 95%CI=1.009-1.233), left ventricular posterior wall thickness (LVPW) (OR=0.777, 95%CI=0.665-0.909), left atrial diameter (LAD) (OR=1.144, 95%CI=1.123-1.166), left ventricular ejection fraction (LVEF) (OR=0.955, 95%CI=0.938-0.972), right atrial diameter (RAD) (OR=1.031, 95%CI=1.005-1.057), triglycerides (TG) (OR=0.821, 95%CI=0.751-0.898), uric acid (UA) (OR=1.003, 95%CI=1.002-1.003), and left ventricular end-diastolic diameter (LVEDD) (OR=0.903, 95%CI=0.879-0.927). ROC curve analysis demonstrated that the XGB model achieved the best performance (mean AUC=0.823), followed by the SVM model (mean AUC=0.820) and the RF model (mean AUC=0.814). SHAP analysis of the XGB model revealed that LAD and RAD had the highest SHAP values, suggesting that atrial structural parameters exert the greatest influence on model classification.

Increased BMI, male sex, elevated MPV, higher UA, decreased TG levels, decreased Mg_plus2, reduced LVEF, decreased LVEDD, no surgical history, and enlarged RAD and LAD were all closely associated with the occurrence of persistent AF. These clinical parameters can be readily obtained through routine examinations, most of which are non-invasive, and may serve as important clinical indicators for identifying patients with persistent AF, thereby supporting early clinical risk stratification and the development of targeted intervention strategies.

Early detection of atrial fibrillation is important for timely initiation of anticoagulation. Hand-held single-lead electrocardiogram devices are recommended for atrial fibrillation screening, but limited evidence is available for its use in Chinese population.

To investigate the diagnostic performance of a hand-held single-lead ECG device (MyDiagnostick) in atrial fibrillation screening and its potential in the opportunistic screening of atrial fibrillation in the community.

Diagnostic performance of the MyDiagnostick was examined among 296 hospitalized patients with atrial fibrillation, using 12-lead electrocardiograms as the gold standard. Sensitivity, specificity, and kappa value were calculated. A single-time point opportunistic screening for atrial fibrillation was conducted using the MyDiagnostick in 1 577 community dwellings. Detection rate of atrial fibrillation was calculated.

The sensitivity and specificity of the MyDiagnostick for diagnosing atrial fibrillation were 0.945 and 0.947, respectively. The Kappa value was 0.892. Among 1 577 community dwellings, 60 cases of atrial fibrillation were detected (3.80%), among which 33 cases were new-onset atrial fibrillation (2.13%). Male gender (OR=2.553, 95%CI=1.504-4.333) and a history of stroke/transient ischemic attack/thrombosis (OR=2.840, 95%CI=1.330-6.064) were risk factors for atrial fibrillation in the community population.

The handheld single-lead electrocardiogram device of MyDiagnostick has a high accuracy in atrial fibrillation diagnosis and shows the potential to be used in atrial fibrillation screening in the community.

Obstructive sleep apnea (OSA) is associated with cardiac structural remodeling and functional impairment. Most studies have focused on the overall OSA population, with limited attention given to the impact of different distributions of hypopnea and apnea events on cardiac structure and function.

To compare the impact of different hypopnea/ apnea ratio (HAR) classifications on cardiac structure and function in patients with OSA.

A retrospective analysis was conducted on 193 hospitalized patients with OSA and 44 control subjects at the Department of General Practice, Daping Hospital, Army Medical University from January 2023 to February 2025. All participants underwent comprehensive echocardiography during the same period. Based on the HAR, OSA patients were classified into apnea-dominant (n=66) and hypopnea-dominant (n=127) groups. They were also divided into mild (n=68), moderate (n=73), and severe (n=52) OSA groups according to the apnea hypopnea index (AHI). General demographics, comorbidities, sleep monitor parameters, and echocardiography data were collected to analyze the impact of different HAR classifications on cardiac structure and function in patients with OSA.

Left atrial systolic diameter (LADs), left ventricular diastolic diameter (LVDd), right atrial systolic diameter (RADs), aortic sinus diameter (AOAs), main pulmonary artery systolic diameter (MPAs), interventricular septum diastolic thickness (IVSTd), and left ventricular posterior wall diastolic thickness (LVPWd) were significantly higher in apnea-dominant OSA patients than those in hypopnea-dominant patients (P<0.05). Spearman correlation analysis revealed that in patients with OSA, LADs, LVDd, RADs, AOAs, MPAs, IVSTd, and LVPWd were positively correlated with AHI, and oxygen desaturation index (ODI), while negatively correlated with HAR. The lowest SpO₂ (LSpO₂) was negatively correlated with MPAs. The percentage of total sleep time with SpO₂ <90% (T₉₀) was positively correlated with LADs, LVDd, RADs, AOAs, and MPAs (P<0.05). Multiple linear regression analysis revealed that after adjusting for the effects of gender, age, BMI, and AHI, there were no significant differences in cardiac structural indicators between the two types of OSA. Gender, age, and BMI were significant predictors of multiple cardiac structural parameters (P<0.05), while AHI was identified as an independent predictor of MPAs (P<0.05).

The distribution patterns of hypopnea and apnea events show no difference in their impact on cardiac structure and function in OSA patients. Gender, age, BMI, and AHI may have more significant impacts on cardiac remodeling.

Hypertension is an important risk factor for cardiovascular disease (CVD). Traditional body mass index (BMI) has limitations in identifying high-risk patients, and the predictive value of new obesity indicators in the hypertensive population remains to be clarified.

To explore the relationship between novel obesity indicators, such as the Chinese visceral adiposity index (CVAI), body roundness index (BRI), relative fat mass index (RFM), weight-adjusted waist index (WWI), triglyceride-glucose index (TyG), and its related indices TyG-BMI, TyG-waist circumference (TyG-WC), TyG-waist-to-height ratio (TyG-WHtR) and the risk of CVD in patients with hypertension, and to compare their predictive performance, thereby providing a basis for the prevention and management of cardiovascular disease in the hypertensive population.

A retrospective cohort study design was adopted, including 69 627 hypertensive individuals in the public health surveillance database in Urumqi, Xinjiang Uygur Autonomous Region from 2016 to 2022 as the study population. Patient baseline characteristics, general physical examination indicators and laboratory test indicators were collected, and CVAI, BRI, RFM, WWI, TyG, TyG-BMI, TyG-WC, and TyG-WHtR were calculated accordingly.The follow-up endpoint was defined as the first diagnosis of CVD in patients or the end of the study period. Cox proportional hazards regression models and restricted cubic spline models were constructed to assess the risk of cardiovascular disease associated with different indicators. Time-dependent C indices and the area under the ROC curve (AUC) were used to compare the predictive performance of different indicators.

A total of 2 466 (3.54%) individuals developed CVD by the end of follow-up. Multivariate Cox regression analysis showed that after adjusting for confounding factors, the following indicators were associated with an increased risk of cardiovascular disease: CVAI (HR=1.122, 95%CI=1.072-1.175), BRI (HR=1.104, 95%CI=1.061-1.149), RFM (HR=1.236, 95%CI=1.141-1.338), WWI (HR=1.073, 95%CI=1.029-1.118), TyG-BMI (HR=1.099, 95%CI=1.054-1.146), TyG-WC (HR=1.105, 95%CI=1.058-1.154), TyG-WHtR (HR=1.113, 95%CI=1.066-1.161)(P<0.05). Restricted cubic spline analysis indicated a significant nonlinear dose-response relationship between RFM and CVD risk (P_nonlinear<0.05). Time-dependent C-index analysis showed that the discriminative ability of each indicator remained generally stable over the follow-up period. ROC curve analysis showed that the AUCs of TyG, CVAI, BRI, WWI, TyG-BMI, TyG-WC and TyG-WHtR in predicting the risk of CVD were 0.512, 0.568, 0.558, 0.566, 0.518, 0.531 and 0.553, respectively. Subgroup analysis showed that BRI, RFM, WWI and TyG-WHtR showed obvious sex interaction (P_interaction<0.05).

Among hypertensive patients, novel obesity indices such as CVAI, BRI and WWI were associated with the risk of incident CVD and had certain reference value for CVD risk assessment, but their independent predictive performance was limited.

In recent years, the prevalence of sleep disturbances during pregnancy has significantly increased due to rising social pressures and lifestyle changes. Studies suggest that circadian rhythm disruption may contribute to blood pressure dysregulation through mechanisms such as hypothalamic-pituitary-adrenal (HPA) axis activation and inflammatory responses. However, the interaction between environmental factors and genetic susceptibility remains unclear. Melatonin receptor 1B (MTNR1B), a key regulator of melatonin signaling, not only modulates circadian rhythms but also plays a critical role in maintaining placental vascular endothelial function. While MTNR1B polymorphisms are strongly associated with type 2 diabetes and insulin resistance, their role in gestational hypertension (GH) susceptibility remains undetermined.

To investigate potential synergistic effects between maternal sleep quality during pregnancy and peripheral blood MTNR1B polymorphisms on GH development.

This study enrolled 235 mid-pregnancy women receiving prenatal care at a provincial hospital in Gansu from March to December 2021, with 235 age-matched healthy pregnant women as controls. Assessments included the Pittsburgh Sleep Quality Index (PSQI), Hospital Anxiety and Depression Scale (HADS), and a pregnancy-specific sleep health questionnaire. Peripheral venous blood was collected before delivery, and improved multiplex ligation detection reaction (im-LDR) technology was employed for genotyping three MTNR1B single-nucleotide polymorphisms (rs3781638, rs10830963, rs3781637). Logistic regression analyzed associations between late-pregnancy sleep parameters, MTNR1B polymorphisms, and GH risk, with multiplicative interaction models evaluating sleep-genotype interactions.

The genotype, allele type, dominant, and over-dominant genotypes comparison of the MTNR1B gene rs3781638 locus in the case group and the control group showed statistically significant differences (P<0.05); there was no statistically significant difference in the comparison of the MTNR1B gene rs10830963 and rs3781637 loci between the two groups (P>0.05). The results of the multivariate Logistics regression analysis showed that carrying the genotype GT (OR=1.88, 95%CI=1.24-2.84), allele type T (OR=1.28, 95%CI=1.02-1.71), dominant genotype GG+GT (OR=1.93, 95%CI=1.29-2.89), and over-dominant genotype GT (OR=1.84, 95%CI=1.22-2.72) were independent risk factors for the occurrence of GH; the interaction analysis results showed that carrying the TT genotype and coughing/snoring 1~2 times/week during nighttime sleep (OR=2.82, 95%CI=1.36-5.84) and coughing/snoring ≥3 times/week (OR=2.21, 95%CI=1.09-4.48) had a significantly higher risk of GH compared to carrying the TT genotype and no coughing/snoring during nighttime sleep (P<0.05); compared to carrying the TT genotype and no coughing/snoring during nighttime sleep, carrying the GT+GG genotype, regardless of coughing/snoring during nighttime sleep, increased the risk of GH, with the highest risk of GH occurring when coughing/snoring ≥3 times/week (OR=4.90, 95%CI=2.24-10.75).

The MTNR1B rs3781638 (G>T) polymorphism may confer GH susceptibility and demonstrate synergistic effects with nocturnal snoring on GH pathogenesis.

The Neutrophil percentage to albumin ratio (NPAR) is considered a novel inflammatory marker. Previous studies have confirmed that admission NPAR is an independent predictor of clinical outcomes in various diseases such as sepsis, acute kidney injury, cardiogenic shock, chronic obstructive pulmonary disease, and cerebral hemorrhage.

The aim of this study is to investigate the potential role of Neutrophil Percentage to Albumin Ratio (NPAR) in predicting the in-hospital adverse events among patients with acute myocardial infarction (AMI).

This retrospective study included AMI patients (n=6 768) admitted to the People's Hospital of Xinjiang Uygur Autonomous Region from August 1, 2011, to January 10, 2022. Baseline data and laboratory results were collected, and NPAR was calculated. Endpoint events were identified from discharge diagnoses in the electronic medical record system. Patients were divided into quartiles based on NPAR: Q1 (NPAR<1.67, n=1 753), Q2 (1.67≤NPAR≤2.02, n=1 694), Q3 (2.03≤NPAR≤2.34, n=1 624), and Q4 (NPAR>2.34, n=1 697). Multivariate Logistic regression was used to analyze the association between admission NPAR and endpoint events. Restricted cubic spline regression was employed to examine the dose-response relationship between NPAR and endpoint events.

A total of 6 768 patients were included. There were 765 cases (11.3%) of all-cause mortality, 709 cases (10.5%) of cardiogenic shock, 380 cases (5.6%) of ventricular tachycardia/ventricular fibrillation (VT/VF), and 119 cases (1.8%) of new-onset stroke. Multivariate logistic regression analysis with admission NPAR as a continuous variable and all-cause mortality as the dependent variable showed that for each standard deviation increase in NPAR, the risk of all-cause mortality increased by 18% (OR=1.18, 95%CI=1.08-1.29, P<0.001). When NPAR was treated as a categorical variable, the Q4 group (OR=1.48, 95%CI=1.11-1.97, P=0.008) was identified as a risk factor for all-cause mortality, with increasing risk across higher NPAR quartiles (P_trend=0.007). Restricted cubic spline regression revealed a linear relationship between NPAR and all-cause mortality risk (P_nonlinearity=0.171). Multivariate Logistic regression analysis with cardiogenic shock, VT/VF, atrioventricular block, and new-onset stroke as dependent variables showed that for each standard deviation increase in NPAR, the risk of cardiogenic shock increased by 20% (OR=1.20, 95%CI=1.09-1.32, P<0.001). When NPAR was treated as a categorical variable, the Q2 (OR=1.41, 95%CI=1.01-1.97, P=0.044), Q3 (OR=1.85, 95%CI=1.36-2.54, P<0.001), and Q4 (OR=2.09, 95%CI=1.53-2.89, P<0.001) groups were risk factors for cardiogenic shock, with a trend of increasing risk across higher quartiles (P_trend<0.001). Restricted cubic spline regression indicated a nonlinear relationship between NPAR and cardiogenic shock risk (P_nonlinearity=0.026). NPAR as a continuous variable was not a risk factor for VT/VF, atrioventricular block (P>0.05). When treated as a categorical variable, Q3 was a risk factor for VT/VF (OR=1.43, 95%CI=1.01-2.03, P=0.045) and atrioventricular block (OR=1.85, 95%CI=1.11-3.15, P=0.020), while Q4 was a risk factor for VT/VF (OR=1.56, 95%CI=1.09-2.26, P=0.017), atrioventricular block (OR=1.87, 95%CI=1.08-3.31, P=0.028), and new-onset stroke (OR=2.26, 95%CI=1.16-4.58, P=0.019). The risks of VT/VF, atrioventricular block, and new-onset stroke increased with higher NPAR quartiles (P_trend=0.009, 0.005, and 0.017, respectively). Stratified and interaction analyses showed that age, sex, hypertension, type 2 diabetes, smoking, and AMI type did not alter the association between NPAR and in-hospital adverse outcomes (P>0.05).

This study confirms that elevated NPAR is associated with an increased risk of in-hospital all-cause mortality and cardiogenic shock in AMI patients, exhibiting a dose-response relationship. These findings suggest that NPAR, as a simple and accessible composite marker of inflammation and nutritional status, may help identify high-risk patients early during admission and provide valuable reference for clinical risk stratification and prognostic assessment.

Artificial intelligence (AI) technologies have shortcomings like vulnerability to adversarial attacks and overfitting, which make AI far less perfect in practical applications than experimental data. Considering factors such as the cost and timeliness of remote electrocardiography (ECG) consultation, primary medical staff will directly use AI diagnosis results, which may pose medical risks.

To analyze the accuracy and the influence factor of AI technology in ECG diagnosis based on the 3-year consultation data of Huangshan Regional Remote ECG Diagnostic Center.

A retrospective collection of 18 164 ECGs from primary care institutions was conducted at the Huangshan City Regional Remote ECG Diagnosis Center between September 2020 and September 2023. Both AI and physicians categorized the ECG diagnostic conclusions into four types: normal, positive, critical, and poor acquisition. Patient identity information was linked to the inpatient electronic medical record system of Huangshan City People's Hospital to extract discharge diagnosis information from tertiary hospitals. Patients were classified into two groups based on discharge diagnosis: cardiovascular disease (CVD) hospitalization and non-CVD hospitalization. A paired-design McNemar χ² test was used to compare the classification differences between the AI and physician groups. A Pearson χ² test was used to compare the differences between the classification results of both groups and CVD hospitalization status. After excluding cases with poor acquisition, a univariate logistic regression analysis was performed to analyze the correlation between different classifications and CVD hospitalization, using the normal category as a reference. Furthermore, 17 ECG indicators from the physician group (excluding poor acquisition cases) were converted into binary variables. Using the consistency of classification between the AI and physician groups as the dependent variable, receiver operating characteristic (ROC) curves were plotted for stratified analysis of the physician group's critical and positive categories to evaluate the impact of each ECG indicator on the inconsistency between the two groups.

A total of 18 164 remote routine ECGs were included in the study. The median patient age was 69 (65, 74) years, with 8 731 males and 9 433 females. The physician group classified ECGs as normal in 5 873 cases (32.3%), positive in 11 678 (64.3%), critical in 393 (2.2%), and poor acquisition in 220 (1.2%). The corresponding figures for the AI group were 4 723 (26.0%), 12 861 (70.8%), 390 (2.1%), and 190 (1.0%), respectively. During the study period, 553 related patients were transferred to tertiary hospitals for inpatient care, of which 457 (82.6%) were for CVD. Univariate logistic regression analysis showed that, with normal ECG as a reference, the risk of CVD hospitalization for the physician group's positive and critical categories was 1.84 times (OR=1.84, 95%CI=1.11-3.04) and 2.80 times (OR=2.80, 95%CI=1.08-7.21) that of the normal category, respectively, with statistically significant differences (P<0.05). The AI group's positive (OR=1.54, 95%CI=0.88-2.67) and critical (OR=2.46, 95%CI=0.92-6.55) categories showed no statistically significant association with CVD hospitalization (P>0.05). The diagnostic classification was consistent between the AI and physician groups in 16 018 cases (88.2%) and inconsistent in 2 146 cases (11.8%), with a statistically significant difference between the two groups (χ²=680.931, P<0.001). Using the physician group as the standard, the AI group had a misdiagnosis rate of 27.7% and a missed diagnosis rate of 3.9%. ROC curve results indicated that for the physician group's critical ECGs, sinus rhythm, ST-segment abnormalities, and acute myocardial ischemia had discriminative value for the inconsistency between the two groups, with areas under the curve (AUC) of 0.74 (95%CI=0.65-0.82), 0.69 (95%CI=0.58-0.80), and 0.97 (95%CI=0.96-0.99), respectively. For the physician group's positive ECG, low voltage and T-wave abnormalities had discriminative value, with AUC of 0.58 (95%CI=0.55-0.61) and 0.61 (95%CI=0.58-0.63), respectively. For the physician group's normal ECG, bradycardia had discriminative value, with an AUC of 0.58 (95%CI=0.56-0.60).

The accuracy of current AI algorithm in ECG diagnosis is inferior to physician group, which still needs to be reviewed and confirmed by experienced physicians. We propose that AI technology applied in clinic should undergo extensive robustness verification.

This review summarizes the effects of intermittent fasting on cardiometabolic diseases in humans and examines various fasting regimens-including alternate-day fasting, the 5∶2 diet, time-restricted eating, and the 16∶8 diet. These approaches may confer benefits for cardiometabolic health by reducing blood pressure, insulin resistance, and oxidative stress. Although large-scale randomized controlled trials investigating the relationship between intermittent fasting and cardiovascular outcomes are lacking, and some studies suggest a potential increase in cardiovascular risk, the majority of existing clinical evidence indicates that this dietary pattern may reduce the risk of cardiovascular disease by improving weight control, hypertension, dyslipidemia, and diabetes. Intermittent fasting is thought to exert its effects through multiple mechanisms, including reduced oxidative stress, optimized circadian rhythms, and ketogenesis. It is generally safe and is not associated with disruptions in energy levels or increased disordered eating behaviors, while also offering additional health benefits. In conclusion, intermittent fasting represents a relatively safe dietary intervention whose potential cardiovascular implications warrant further investigation.

The incidence of type 2 diabetes mellitus (T2DM) has risen steadily in recent years. Cardiovascular disease is a common complication of T2DM, with left ventricular diastolic dysfunction (LVDD) often occurring at an early stage. Central (visceral) obesity is closely linked to cardiovascular risk; however, the performance of visceral-fat-focused indices in identifying LVDD among patients with T2DM remains under-studied.

To evaluate the association between the Chinese visceral adiposity index (CVAI) and LVDD in patients with T2DM and to assess CVAI's diagnostic utility.

This retrospective study enrolled 1 028 T2DM patients who attended the Second Affiliated Hospital of Kunming Medical University (Metabolic Management Center) from January 2019 to August 2024 (647 males, 381 females). Patients were classified as a T2DM group (n=257) or an LVDD group (n=771) based on the presence of LVDD. We assessed correlations between CVAI and other visceral-type obesity measures and echocardiographic structural and functional parameters. Multivariable Logistic regression evaluated the independent association of CVAI with LVDD. Diagnostic performance was assessed by receiver operating characteristic (ROC) curves. Subgroup analyses were conducted by sex, age, and BMI.

Compared with the T2DM group, the LVDD group had higher BMI, neck circumference, waist circumference (WC), hip circumference, visceral fat area (VFA), and CVAI (P<0.05 for all). When stratified by CVAI quartiles, LVDD prevalence increased across quartiles: Q1 64.2%, Q2 71.2%, Q3 79.4%, Q4 85.2% (χ²_trend=34.715, P<0.05). Correlation analyses demonstrated that WC, BMI, VFA, and CVAI were positively correlated with left atrial diameter (LAD), interventricular septal thickness (IVST), left ventricular posterior wall thickness (LVPWT), and left ventricular end-diastolic diameter (LVDd), and negatively correlated with left ventricular ejection fraction (LVEF) (P<0.05). After adjustment for confounders, patients in the CVAI Q4 group had a 2.361-fold increased risk of LVDD compared with Q1 (95%CI=1.349-4.133, P=0.003). ROC analysis yielded an area under the curve (AUC) of 0.621 for CVAI in diagnosing LVDD, outperforming VFA (0.557), BMI (0.589), and WC (0.599). A combined predictive model achieved an AUC of 0.727 (95%CI=0.692-0.763, P<0.001), with sensitivity 0.726 and specificity 0.638. Subgroup analyses indicated that CVAI Q4 was a significant risk factor for LVDD in both male and female subgroups (OR=1.948 and 8.617, respectively; P<0.05). In participants aged<60 years, CVAI Q3 and Q4 were associated with increased LVDD risk (OR=2.387 and 4.371, respectively; P<0.05). In the normal-BMI subgroup, CVAI Q3 was associated with higher LVDD risk (OR=3.997, P<0.05).

CVAI is an independent risk factor for LVDD among patients with T2DM and demonstrates superior discriminative ability compared with conventional obesity indices. Its predictive value is particularly notable in women and in individuals under 60 years of age.

Hypertension, diabetes and dyslipidemia exhibit high prevalence rates in the population and frequently coexist as comorbidity within individuals. The weight-adjusted waist index (WWI) represents a novel obesity assessment metric; however, its association with the comorbidity of the three diseases remains inadequately studied.

To investigate the association and predictive role of WWI for the comorbidity of hypertension, diabetes and dyslipidemia among community-dwelling adults.

Based on the Chronic Disease and Risk Factor Surveillance, a cross-sectional survey was conducted using multi-stage random sampling method among permanent residents aged ≥18 years in Bao'an District from October to December 2023. Data were collected through questionnaires, physical examinations, and laboratory biochemical tests, based on which the WWI was calculated, and the comorbidity of hypertension, diabetes and dyslipidemia was documented. Logistic regression was employed to analyze the association between WWI and the comorbidity of the three diseases. Restricted cubic spline (RCS) analysis was applied to explore the dose-response relationship between WWI and the comorbidity. Stratified analyses were conducted by sex, age and BMI, and interactions were explored. Receiver operating characteristic (ROC) curves were used to evaluate and compare the predictive performance of WWI, BMI and waist circumference (WC) for these diseases comorbidities, utilizing DeLong's test comparing differences in area under the curve (AUC).

A total of 1 882 individuals were surveyed, after excluding participants with missing key indicators such as height, body mass and WC, a final total of 1 846 participants were included in the analysis. The comorbidity rates of hypertension, diabetes and dyslipidemia was 17.06% (315/1 846). The Logistic regression analysis results indicated that, after full adjusting for covariates, each 1-unit increase in WWI was associated with a 174% increased risk of the comorbidity (OR=2.74, 95%CI=2.08-3.59, P<0.05). Compared with the Q1 group (WWI<9.69 cm/), the risk of comorbidity increased progressively in the Q2 (9.69 cm/ ≤WWI<10.19 cm/), Q3 (10.19 cm/ ≤WWI<10.66 cm/), and Q4 (WWI≥10.66 cm/) groups, with OR values of 2.62 (95%CI=1.26-5.42, P<0.05), 4.68 (95%CI=2.31-9.48, P<0.05), and 8.09 (95%CI=3.95-16.56, P<0.05), respectively. RCS analysis revealed a significant linear dose-response relationship between WWI and the risk of comorbidity (P_overall<0.001, P_nonlinear=0.079). Subgroup analysis revealed that, with the exception of the BMI<24.00 kg/m² stratum, individuals in the highest WWI quartile (Q4) exhibited a significantly increased risk of the comorbidity across the following strata: male, female, age<45 years, age≥45 years and BMI≥24.00 kg/m² (all P<0.05). Interaction analysis showed a significant interaction between WWI and age, with a stronger association between WWI and the comorbidity in the <45 years group compared to the ≥45 years group (P=0.003). The ROC analysis yielded that among males, the AUC (95%CI) for WWI, BMI and WC in predicting the risk of the comorbidity were 0.742 (95%CI=0.705-0.778), 0.705 (95%CI=0.667-0.742), and 0.738 (95%CI=0.702-0.774), respectively. Among females, the corresponding AUC (95%CI) for WWI, BMI and WC were 0.806 (95%CI=0.768-0.844), 0.717 (95%CI=0.669-0.765), and 0.804 (95%CI= 0.766-0.842). In the female population, WWI demonstrated significantly higher predictive accuracy than BMI (Z=-3.134, P=0.002).

WWI exhibits a significant positive and linear dose-response association with comorbidity of the three diseases, demonstrating favorable predictive efficacy. As a novel obesity metric, WWI offers valuable insights for the early prevention and intervention targeting this comorbidity.

Heart failure with preserved ejection fraction (HFpEF) is a universal and highly fatal global disease, accounting for nearly 50% of patients with heart failure. Innovative methods are needed to protect cardiac function and prevent the progression of HFpEF. Cardiac macrophages (CMs) have emerged as key regulators of the pathophysiology of HFpEF. CMs are a heterogeneous population consisting of subpopulations with distinct lineage origins and gene expression profiles. Several key aspects of HFpEF progression have been shown to be regulated by CMs, including the recruitment of peripheral immune cells, myocardial inflammation, and cardiac electrical conduction. In addition, CMs play a critical role in regulating cardiac fibrosis, epicardial adipose tissue dysfunction, and ventricular diastolic dysfunction. Given the multifaceted roles of CMs in the pathophysiology of HFpEF, targeted regulation of CMs represents a promising therapeutic strategy. Therefore, this article will review the research progress between CMs and the pathophysiological mechanism of HFpEF from the aspects of cardiac inflammation and fibrosis, ventricular diastolic dysfunction, epicardial adipose tissue, cardiac electrical conduction, and clinical intervention.

Hypertension has become a major health issue, impacting both health and quality of life. Due to its long course of illness, multiple and complex complications, and lack of a cure or correcting deviation, patients require lengthy and continuous support and medical management. Understanding the long-term journey and influencing factors of medical-help-seeking behaviour in hypertensive patients is crucial for developing targeted and patient-centred prevention and control strategies.

The study aimed to identify and analyze the long-term trajectories of medical-help-seeking behaviour among hypertensive patients who were managed by community health centres of Putuo District in Shanghai City. Using trajectory modeling to determine key behavioral patterns and the influencing factors, the study will inform hypertension prevention and treatment policies.

Continuous clinical records of 8 922 hypertensive patients were retrieved from Resident Electronic Health Record System of Putuo District in Shanghai from 2014 to 2021. The data include histories, encounters, diagnostic, management and follow-up information. The Group-Based Trajectory Model (GBTM) was applied to analyze the patterns of the medical-help-seeking behaviour change, simulate behavioural transitions, and identify the best fitting model. Analysis of variance and chi square test were employed to examine patient characteristics across diflerent behavioural trajectories. The persistently irregular medical-help-seeking behaviour' group served as the reference group for comparing influencing factors among medical-help-seeking behaviour trajectory groups.

A total of 444 126 outpatient records were retrieved. The GBTM analysis revealed flve distinct medicalhelp-seeking behaviour trajectories: sustained regular (39.84%), regular with a slow decline (25.36%), U-shaped (11.43%), regular with slow increase (11.86%), and persistently irregular (14.86%). Statistical diverences were observed between these groups, including gender, age, illness duration, diabetes history, transient ischemic attack (TIA) history, and family history of high blood pressure, regular exercise habits (P<0.05). Female patients and those aged 75 years or older were more likely to transition from irregular to regular medical-help-seeking behaviour. Patients with diabetes or a history of TIA were less likely to follow irregular medical-help-seeking behaviour. Longer duration of hypertensive history and a family history were associated with a less favorable shift in behaviour.

Less than 40% of hypertensive patients consistently follow a regular medical-help-seeking behaviour. However, appropriate management strategies can promote regular medical-help-seeking behaviour, particularly in females, patients aged 75 years or above, and those with diabetes or a history of TIA. Further research is suggested identifying factors that can encourage medical help-behavioral changes in other medicalhelp-seeking behaviour trajectory groups.

Currently, the landscape of cancer treatment has undergone significant transformations, with numerous cancer patients now surviving in a chronic disease paradigm over extended periods. Research indicates that a substantial number of cancer survivors succumb to non-tumor factors, with cardiovascular disease (CVD) being a prominent cause among them. Nevertheless, the potential CVD risks associated with cancer treatment are frequently overlooked, resulting in inadequate early intervention and protective measures.The estimated pulse wave velocity (ePWV) can reflect the degree of arterial stiffness and is an independent predictor of cardiovascular events. The simple calculation method provides feasibility for cardiovascular risk stratification in cancer patients.

To assess the influencing factors of ePWV on all-cause mortality and CVD mortality in a cohort of cancer patients.

A retrospective cohort design was used. The cohort included 4 632 cancer patients who attended the National Health and Nutrition Examination Survey (NHANES) database from 1999 to 2018. Baseline data were collected, including age, gender, race, BMI, chest circumference, baseline heart rate (BHR), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), diastolic blood pressure (DBP), diabetes status, history of CVD, smoking and drinking status. Follow-up to July 2023. With ePWV as the variable, the quartile method was used for grouping. All the subjects were divided into 4 groups, which were recorded as Q1, Q2, Q3 and Q4 groups. The baseline levels of the 4 groups were compared, and the Kaplan-Meier survival curves related to all-cause mortality and CVD mortality of the patients were plotted. Cox proportional hazard regression model were used to explore the relationship of ePWV and mortality in cancer patients. The receiver operating characteristic (ROC) curve of the predictive value of ePWV for death in cancer patients was drawn, and the area under the ROC curve (AUC) was calculated.

A total of 4 632 patients, with an average age of (60.7±1.0) years, were enrolled, comprising 2 426 females (52.37%) and 2 206 males (47.63%). There were 1 158 cases in Q1-Q4 groups. Significant differences were observed among the four groups in terms of age, gender, race, BMI, chest circumference, BHR, TC, HDL-C, SBP, DBP, diabetes history, CVD history, smoking status, and alcohol consumption status (P<0.05). During the follow-up period, 830 (17.92%) of 4 632 cancer patients died of all-cause and 376 (8.12%) died of CVD. There were significant differences in all-cause mortality and CVD mortality among the four groups (P<0.001). Kaplan-Meier survival analysis revealed statistically significant differences in the survival curves related to all-cause mortality and CVD mortality among 4 groups (χ²=587.11, P<0.001; χ²=322.97, P<0.001). The results of multivariate Cox regression analysis indicated that, compared with patients in Q1, those in Q2, Q3, and Q4 had an increased risk of all-cause mortality (Q2: HR=1.30, 95%CI=1.23-1.38, P=0.045; Q3: HR=1.46, 95%CI=1.01-2.13, P=0.047; Q4: HR=1.24, 95%CI=1.04-1.49, P=0.017). Additionally, patients in Q3 and Q4 exhibited an elevated risk of CVD mortality (Q3: HR=1.28, 95%CI=1.05-1.56, P=0.013; Q4: HR=2.73, 95%CI=1.67-4.48, P=0.026); ROC curve showed that the AUC values of Q1, Q2, Q3 and Q4 groups were 0.514, 0.624, 0.598 and 0.772, respectively.

It was verified that elevated ePWV was positively correlated with the risk of all-cause and CVD mortality in cancer patients. ePWV may be a predictor of the risk of death in this population.

Nocturnal hypertension (NH) is a significant contributor to multi-organ damage (cardiovascular, cerebral, and renal) and serves as a predictor of all-cause mortality. Its predictive value surpasses that of daytime blood pressure and office blood pressure. Classified as a form of masked hypertension due to its occult nature during nocturnal sleep, early screening and individualized treatment can mitigate the risk of cardiovascular, cerebral, and renal diseases.

To investigate risk factors associated with NH by leveraging routine health checkup parameters and personal health data, and to develop a clinical predictive nomogram model for NH.

A total of 406 patients who underwent 24-hour ambulatory blood pressure monitoring (ABPM) at the Affiliated Hospital of Inner Mongolia Medical University between January 1, 2021, and June 30, 2024, were included. Baseline clinical data, laboratory test results, and echocardiographic findings were collected. Patients were randomly divided into a training set (n=284) and a validation set (n=122) in a 7∶3 ratio. A risk prediction model for NH was constructed using LASSO regression analysis and multivariate Logistic regression analysis, followed by Nomogram development. The ROC curve was plotted, and the area under the ROC curve (AUC) was calculated to validate the model's accuracy. Calibration curves were generated to assess the model's predictive capability and consistency between predicted and observed risks.

Based on 24-hour ABPM results, patients were categorized into an NH group (n=254) and a non-NH group (n=152). Four predictors identified via LASSO regression "body weight, total cholesterol (TC), hypertension, and stroke "were used as independent variables in multivariate Logistic regression analysis. The results indicated that increased body weight (OR=1.029, 95%CI=1.006-1.053), elevated TC (OR=1.496, 95%CI=1.136-1.972), hypertension (OR=2.372, 95%CI=1.214-4.632), and stroke (OR=7.850, 95%CI=4.157-14.824) were all risk factors for NH (P<0.05). The Nomogram revealed that stroke history (score: 0 or 62) and hypertension (score: 0 or 26) had a more pronounced impact on diagnostic rates compared to body weight and TC (scores varied linearly with variable values). The total model score was 240, with a 95%risk of NH when the score exceeded 176. ROC curve analysis demonstrated an AUC of 0.791 (95%CI=0.739-0.843) in the training set, with a sensitivity of 0.698 and specificity of 0.786. In the validation set, the AUC was 0.820 (95%CI=0.742-0.899), with a sensitivity of 0.817 and specificity of 0.725. The Hosmer-Lemeshow calibration curve indicated good model fit, and decision curve analysis showed that the validation set achieved high net benefit within a threshold probability range of 0.2-0.6, confirming optimal clinical utility.

This study established an NH risk prediction model incorporating four clinical indicators: body weight, TC, hypertension history, and stroke history. The model demonstrates robust calibration, discrimination, and clinical applicability for screening NH risk in suspected patients.

Metabolic risk factors have become a primary driver of the increasing burden of cardiovascular diseases (CVD) in China.

To assess the temporal trends of CVD burden attributable to metabolic risk factors in China from 1990 to 2021 and project the future burden through 2035 using the Bayesian age-period-cohort (BAPC) model.

Based on data from the Global Burden of Disease Study 2021 (GBD 2021), we quantified the contributions of level 1 and major level 2 risk factors (e.g., high blood pressure, air pollution, smoking, high LDL cholesterol, and dietary risks) to CVD mortality and disability-adjusted life years (DALYs) using the population attributable fraction (PAF) method. Joinpoint regression was applied to estimate the average annual percent change (AAPC) of CVD burden attributable to metabolic factors from 1990 to 2021. A BAPC model was used to project future burden trends from 2022 to 2035.

From 1990 to 2021, the age-standardized DALYs rate of CVD attributable to metabolic factors declined, while the age-standardized mortality rate increased. In 2021, metabolic factors accounted for 68.6% of CVD-related DALYs and 70.1% of CVD deaths. Elevated systolic blood pressure remained the leading risk factor, contributing 53.3% and 55.4% to DALYs and deaths, respectively. Among the four main metabolic risks, high BMI showed the most significant increase, with the attributable age-standardized DALYs rate rising from 281.04 per 100 000 to 396.09 per 100 000 (AAPC=1.08%, P<0.000 1), and the age-standardized mortality rate increasing from 13.73 per 100 000 to 18.80 per 100 000 (AAPC=1.00%, P<0.000 1). In contrast, the burden associated with high fasting plasma glucose and high LDL cholesterol changed minimally, with most AAPC showing no statistical significance. Age-and sex-stratified analyses indicated the heaviest burden among older males, with widening gender differences at older ages. Projections based on the BAPC model suggest a continued decline in CVD burden by 2035: for males, the mortality rate is projected to decrease from 291.97 per 100 000 to 183.33 per 100 000 and the DALY rate from 5 296.99 per 100 000 to 3 274.07 per 100 000; for females, age-standardized mortality rate is projected to decline from 149.26 per 100 000 to 103.00 per 100 000 and age-standardized DALYs rate from 2 863.17 per 100 000 to 1 814.15 per 100 000. A clear downward inflection point is expected around 2030 in males, while a steady decline is projected for females. All model predictions had mean absolute percentage errors below 2%, indicating high predictive accuracy.

High BMI and high blood pressure remain the predominant metabolic risk factors for CVDs in China. The projected burden attributable to these risks is expected to continue rising, with males and older adults experiencing a higher burden. Younger adults are increasingly affected by BMI-related risks, highlighting the need for targeted, stratified prevention strategies.

Neurofilament light chain (NfL), a sensitive biomarker of neuroal injury and neuroinflammation, has attracted increasing attention. Previous studies have demonstrated its associations with hypertension and adverse cardiovascular events;however, the potential relationship between NfL and left ventricular hypertrophy (LVH) in patients with nocturnal hypertension remains remains unclear.

To examine the association between serum NfL levels and the risk of LVH in patients with nocturnal hypertension.

A total of 351 patients with nocturnal hypertension who underwent 24-hour ambulatory blood pressure monitoring (ABPM) with complete clinical data at Renmin Hospital of Wuhan University from December 2022 to December 2024 were enrolled. Patients were divided into four groups according to NfL quartiles:Q1 (NfL≤62.82, n=88), Q2 (62.82<NfL≤92.00, n=88), Q3 (92.00<NfL≤136.40, n=87), and Q4 (NfL>136.40, n=88). Baseline characteristics, 24-hour ABPM data, laboratory results, and transthoracic echocardiographic parameters were collected. Left ventricular mass (LVM) and left ventricular mass index (LVMI) were calculated using the formula recommended by the American Society of Echocardiography. Spearman's rank correlation analysis was used to assess associations between NfL and echocardiographic parameters. Generalized linear models were applied to analyze the associations between NfL quartiles and LVMI. Logistic regression analysis was used to evaluate the relationship between NfL levels and the risk of LVH. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive value of NfL and autonomic function-related indicators for the risk of LVH in patients with nocturnal hypertension, and subgroup analyses were conducted.

Significant differences were observed among the four groups in age, interventricular septal thickness in diastole (IVSd), left ventricular posterior wall thickness at end-diastole (LVPWd), LVM, and LVMI (P<0.05). Spearman correlation analysis showed that NfL levels were positively correlated with left ventricular internal diameter at end-diastole (LVIDd)(r_s=0.135, P=0.011), IVSd (r_s=0.128, P=0.016), LVPWd (r_s=0.146, P=0.006), LVM (r_s=0.162, P=0.002), and LVMI (r_s=0.277, P<0.001). After adjustment for confounding factors, the generalized linear model showed that, compared with Q1, NfL levels in Q3 (β=0.110, 95%CI=0.003-0.217, P=0.044) and Q4 (β=0.288, 95%CI=0.180-0.395, P<0.001) were significantly associated with LVMI. Logistic regression analysis showed that elevated NfL levels were an independent risk factor for LVH in patients with nocturnal hypertension (OR=1.012, 95%CI=1.007-1.016, P<0.001). Compared with Q1, NfL levels in Q3 (OR=3.328, 95%CI=1.152-9.611, P=0.026) and Q4 (OR=9.059, 95%CI=3.278-25.036, P<0.001) were associated with a significantly increased risk of LVH. ROC curve analysis showed that the areas under the ROC curve of NfL, norepinephrine (NE), acetylcholine (ACh), and the NE/ACh ratio for predicting LVH risk were 0.744, 0.618, 0.577, and 0.603, respectively, with sensitivities of 75.0%, 50.0%, 48.5%, and 48.5% and specificities of 63.3%, 69.3%, 51.5%, and 75.3%. Subgroup analyses indicated that NfL levels were positively associated with LVH risk in subgroups defined by age, male sex, BMI, and estimated glomerular filtration rate (P<0.05).

In patients with nocturnal hypertension, serum NfL levels are independently associated with the risk of LVH and may have clinical value in identifying individuals at high risk of LVH.

Early risk assessment of essential hypertension with left ventricular hypertrophy (EH-LVH) is crucial for clinical intervention, but existing predictive models often overlook Traditional Chinese Medicine (TCM) syndromes, pulse graph parameters, and other TCM clinical information. Therefore, integrating the aforementioned characteristic indicators to construct an EH-LVH risk prediction model will provide new evidence for risk stratification and clinical decision-making in integrated Traditional Chinese and Western Medicine.

To develop a nomogram model for predicting the risk of EH-LVH based on TCM syndromes and pulse graph parameters.

A total of 201 inpatients with essential hypertension admitted to Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai Integrated Traditional Chinese and Western Medicine Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, and Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from August 2018 to June 2021 were selected. Based on echocardiographic results, they were divided into the essential hypertension with left ventricular hypertrophy group (EH-LVH group) and the essential hypertension without left ventricular hypertrophy group (EH-NLVH group). General information, physicochemical indicators, and TCM inquiry data of the two groups were collected, and pulse graph parameters were detected using the SMART-Ⅰ TCM pulse analysis instrument. Multivariate Logistic regression analysis was used to explore factors independently associated with the risk of EH-LVH. Using the rms package in R software version 4.1.1, three nomogram models (Models A, B, and C) were established with pulse graph parameters, TCM syndromes, and pulse graph parameters + TCM syndromes + general information as variables, respectively. Receiver operating characteristic (ROC) curves were used to assess discrimination, calibration curves to evaluate accuracy, Hosmer-Lemeshow test to verify calibration, and decision curve analysis (DCA) to evaluate clinical utility, followed by model comparisons.

Multivariate Logistic regression analysis showed that low-density lipoprotein cholesterol (OR=1.511, 95%CI=1.709-2.115), Yin deficiency with Yang hyperactivity syndrome (OR=2.493, 95%CI=1.272-4.885), Qi stagnation and blood stasis syndrome (OR=7.866, 95%CI=2.201-28.110), T (OR=1.906, 95%CI=1.278-2.842), H3/H1 (OR=1.549, 95%CI=1.021-2.351), and W1/T (OR=2.129, 95%CI=1.369-3.310) were factors independently associated with the risk of EH-LVH (P<0.05). The areas under the ROC curves (AUC) of the nomogram models were as follows: the AUC of Model A was 0.642 (95%CI=0.571-0.713), that of Model B was 0.717 (95%CI=0.646-0.788), and that of Model C was 0.784 (95%CI=0.719-0.849). The calibration results showed that for Model A: χ²=0.133 (P>0.05), for Model B: χ²=4.316 (P>0.05), and for Model C: χ²=3.754 (P>0.05), indicating good agreement between the predicted probabilities and actual probabilities in the calibration curves of all three models. DCA curve analysis showed that when the threshold for predicting EH-LVH occurrence was between 0.05 and 0.80 (estimated value), Model C achieved optimal applicability, indicating its more significant clinical utility value.

This study successfully construct a nomogram for predicting the risk of EH-LVH, with TCM syndromes and pulse graph parameters as core predictive variables. The discrimination performance and predictive accuracy of this nomogram were validated to be favorable, demonstrating value for clinical promotion and application, and it can serve as a reference for clinical risk assessment of this disease.

Chronic obstructive pulmonary disease (COPD) is closely related to cardiovascular disease, but the mechanism of their interaction is still unclear. Hypertension is the most common comorbidity of COPD, and it is also an independent risk factor for cardiovascular diseases. Current studies believe that blood pressure variability is also a risk factor for cardiovascular disease, and blood pressure variability can better reflect the patient's blood pressure fluctuation than a single blood pressure value, so is blood pressure variability a bridge between COPD and cardiovascular disease. At present, there are no relevant studies in China.

To investigate the correlation study of pulmonary function and blood pressure variability in patients with COPD and hypertension.

A total of 341 COPD patients with hypertension who visited the respiratory and critical care medicine outpatient department of kailuan general hospital from september 2020 to september 2023. On the day of the patient's visit, data were collected on the patient's lung function indicators, height, weight, age, sex, use of antihypertensive medications, and lifestyle habits (whether they smoke or drink alcohol) . Multiple factor linear regression was used to analyze the correlation between lung function indicators (percentage of forced vital capacity to predicted value: FVC%pred; percentage of forced expiratory volume to predicted value in the first second: FEV₁%pred; the ratio of forced expiratory volume in one second to forced vital capacity: FEV₁/FVC) and blood pressure variability (standard deviation of systolic blood pressure: SDSBP, standard deviation of diastolic blood pressure: SDDBP) .

(1) Males had higher body mass index, smoking rate, and alcohol consumption rate than females, while females had a higher predicted forced vital capacity percentage (FVC%pred) than males (P<0.05) . (2) FEV₁%pred was negatively correlated with blood pressure variability (SDSBP, SDDBP) (r_s values were -0.149 and -0.114, respectively, P<0.05) . (3) FEV₁%pred was negatively linearly correlated with SDSBP and SDDBP, with B values (95% CI) of -0.566 (-1.078--0.054) and -0.427 (-0.761--0.093) , respectively; FVC%pred and FEV₁/FVC were not correlated with SDSBP and SDDBP.

FEV₁%pred is negatively linearly correlated with SDSBP and SDDBP.

Hypertension is characterized by unclear etiology, prolonged disease duration, and incurability, ranking highest in prevalence among metabolic disorders. To alleviate patient burden, hypertension health management (HHM) was incorporated into National Essential Public Health Services Program (NEPHSP). However, patient follow-up rates remain substantially below expected levels. Digital therapeutics (DTx) deliver evidence-based therapeutic interventions through high-quality software programs to prevent, manage, or treat hypertension. This approach significantly enhances clinician-patient communication frequency and optimizes healthcare resource utilization efficiency. This study examines hypertension DTx products within the Digital Therapeutics Alliance (DTA) product library and published randomized controlled trials (RCTs) to explore application prospects in China. Findings indicate that China should draw on international DTx experiences for HHM, leverage socio-environmental factors to enhance stakeholder acceptance of DTx concepts, establish regulatory frameworks aligned with product characteristics, strengthen enterprise's research and development capabilities, and accelerate DTx advancement in hypertension management.

Cardiovascular diseases (CVDs) have become one of the leading causes of death globally and in China. With changes in lifestyle and an aging population, the prevalence of CVDs continues to rise, posing significant challenges to public health. Primary healthcare plays an important role in the prevention and the management of CVDs, with risk assessment and risk communication being the core components. Grassroots general practitioners can dynamically track patient risks through long-term doctor-patient relationships by conducting comprehensive assessments of the patient's health status and utilizing effective risk assessment tools such as the China PAR model and Framingham risk score to achieve personalized risk assessment, thanks to their service characteristics of "first visit, continuity, and accessibility". This grassroots risk communication mechanism is in line with the "patient-centered" prevention strategy advocated by international guidelines such as the American Heart Association (AHA) and the European Society of Cardiology (ESC) . Through risk visualization, it helps change health behaviors and improve medication adherence. However, grassroots risk communication still faces multiple challenges. This article explores the current status, application, and challenges faced by risk assessment and communication strategies for grassroots CVDs, and proposes suggestions for improving the communication skills and implementation strategies of grassroots general practitioners. The aim is to refine risk communication strategies to enhance the prevention and control effectiveness of CVDs and ultimately improve the health management level of grassroots patients.

Cardiovascular metabolic diseases are closely associated with depression. Although the management of cardiovascular metabolic diseases at the community level has been established, psychological issues such as depression in patients have not received sufficient attention. Moreover, there is a lack of simple, accurate, and efficient screening and assessment tools for depression.

To apply single-lead wearable electrocardiographic devices to predict the risk of depression in elderly patients with cardiovascular metabolic diseases at the community level of Ning Xia Hui Autonomous Region.

A total of 3 121 elderly patients (aged over 65) with hypertension, diabetes, coronary heart disease, and other cardiovascular metabolic diseases were selected from 20 primary medical and health care institutions in Ningxia between January 2022 and June 2023. Electrocardiographic data collected via single-lead wearable electrocardiographic devices were uploaded to a cloud platform. Additionally, sociodemographic, lifestyle, and mental health data were collected from the same platform. The data were divided into a training set (2 341 cases) and a validation set (780 cases) using a simple random sampling method at a 3∶1 ratio. LASSO regression analysis and cross-validation were performed using RStudio 4.1.1 software to identify the best predictors. A multivariable Logistic regression model was then established using the predictors selected by LASSO regression. A nomogram model for predicting the risk of depression in elderly patients with cardiovascular metabolic diseases was constructed. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, calibration, and decision curve analysis.

In the training set, LASSO regression combined with Logistic regression analysis identified several significant factors associated with depression in elderly patients with cardiovascular metabolic diseases: gender (OR=1.747, 95%CI=1.258-2.434) , BMI (OR=1.073, 95%CI=1.024-1.125) , urban and rural areas (OR=1.684, 95%CI=1.172-2.456) , exercise (OR=0.610, 95%CI=0.460-0.799) , anxiety (OR=3.041, 95%CI=1.597-5.484) , coronary heart disease (OR=2.743, 95%CI=1.971-3.815), premature beats (OR=4.745, 95%CI=1.681-19.977) , standard deviation of average normal-to-normal Intervals (SDANN) (OR=4.745, 95%CI=1.681-19.977) , root mean square deviation (rMSSD) (OR=0.986, 95%CI=0.972-0.999) , and sleep efficiency (OR=0.988, 95%CI=0.982-0.995) . The differences were statistically significant (P<0.05) . The Logistic regression equation Logit (P) =4.322+0.558×gender+0.071×BMI+0.521×urban and rural areas-0.494×exercise+1.112×anxiety+1.009×coronary heart disease+1.557×premature beat-0.011×SDANN-0.014×rMSSD-0.012×sleep efficiency was used to construct a column chart prediction model. The area under the curve for predicting the risk of depression in elderly chronic disease patients in the training and validation sets were 0.748 (95%CI=0.707-0.786, P<0.001) , 75.2%, 63.4% and 0.751 (95%CI=0.692-0.809) , 76.7%, 60.6%, respectively. The clinical decision curve analysis showed that when the probability threshold for depression risk was between 8% and 35% in the training set and between 8% and 37% in the validation set, the net benefit of predicting the risk of depression in elderly patients with cardiovascular metabolic diseases was higher.

Gender, BMI, urban and rural areas, exercise, anxiety, coronary heart disease, premature beats, SDANN, rMSSD, sleep efficiency are contributing factors to the risk of depression in elderly patients with cardiovascular metabolic diseases. This study successfully constructed a nomogram model for predicting the risk of depression in elderly patients with cardiovascular metabolic diseases at the community level, based on single-lead wearable electrocardiographic devices. The model demonstrated good predictive efficacy and clinical application value. It can assist primary medical and health care institutions in conducting depression screening and formulating individualized intervention measures for patients, thereby aiding in the prevention and control of cardiovascular diseases at the community level.

The standardized management of pain after open-heart surgery in children with congenital heart disease is very important, and there is no systematic best evidence for pain management after open-heart surgery in children with congenital heart disease.

To summarize the best evidence for pain management after open-heart surgery in children with congenital heart disease, and provide an evidence-based basis for clinical practice.

We systematically searched UpToDate Clinical Advisor, BMJ Best Clinical Practice, International Guideline Collaboration Network, National Guidelines Clearinghouse, National Institute for Health and Care Excellence, Yimaitong Guideline Network, Cochrane Library, PubMed, Web of Science, Embase, CINAHL, Wanfang Data, VIP Database, CNKI, Sinomed, American Heart Association, American College of Cardiology, European Society of Cardiology, American Pain Society, and Registered Nurses' Association of Ontario for clinical decisions, guidelines, expert consensus, evidence summaries, systematic reviews, and randomized controlled trials on pain management in children with congenital heart disease after open - chest surgery. The search period was from the establishment of the database to January 1, 2025. After methodological quality evaluation, the evidence was extracted and summarized according to the themes.

A total of 15 papers were included, including 1 guideline, 1 expert consensus, 3 systematic evaluations, and 10 randomized controlled trials, and 26 pieces of evidence in 4 areas of pain management principles, pain assessment, pharmacological pain management strategies, and nonpharmacological pain management strategies were finally summarized through reading, extraction, and summarization.

Forming the best evidence regarding pain management principles, pain assessment, drug-based pain relief strategies, and non-drug-based pain relief strategies for children with congenital heart disease after undergoing thoracotomy surgery, can provide clinical medical workers with evidence support and improve the quality of clinical care.

Cardiometabolic multimorbidity (CMM) represents one of the most prevalent and stable multimorbidity patterns. Relative fat mass (RFM), as a novel anthropometric indicator for assessing adiposity, has shown promise as a predictor of individual cardiometabolic diseases. However, evidence regarding its association with the risk of CMM remains limited.

To investigate the association between RFM and the risk of CMM across different genders, and to evaluate the potential role of RFM in the prevention and management of CMM.

A total of 116 321 permanent residents from 12 urban communities (including Suzhou) were selected as study participants from March 2017 to July 2021. Based on gender and CMM status, participants were stratified into CMM and non-CMM groups. Baseline characteristics were compared between these groups separately for each gender. Multivariable Logistic regression analysis was employed to examine the association between RFM and the risk of CMM stratified by sex. Restricted cubic spline (RCS) curves were applied to explore potential non-linear relationships. Subgroup analyses and interaction tests were conducted to investigate variations in the association across different populations.

A total of 116 321 participants were included in this study. Among them, 46 637 (40.1%) were male, with 11 969 cases (25.7%) in the CMM group and 34 668 cases (74.3%) in the non-CMM group. While 69 684 (59.9%) were female, with 16 668 cases (23.9%) in the CMM group and 53 016 cases (76.1%) in the non-CMM group. RFM levels were significantly higher in the CMM group than in the non-CMM group for both sexes(P<0.001). After adjusting for confounders including age,education level, smoking, alcohol consumption, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), remnant cholesterol, blood glucose, systolic blood pressure, and diastolic blood pressure, multivariable Logistic regression analysis revealed that among males, the risks of CMM in the T2 to T4 groups were 1.530, 2.086, and 2.945 times that of T1 group, respectively (P<0.001). Among females, the risks of CMM in the F2 to F4 groups were 1.205, 1.532, and 1.760 times that of F1 group, respectively (P<0.001). Furthermore, for each unit increase in RFM, the risk of CMM increased by 1.109 times in males (OR=1.109, 95%CI=1.101-1.116, P<0.001) and by 1.054 times in females (OR=1.054, 95%CI=1.049-1.060, P<0.001). RCS analysis demonstrated a nonlinear relationship between RFM and CMM risk in both sexes. For males,the inflection point of OR=1 was 25.26 (P_nonlinearity <0.001). For females, the inflection point of OR=1 was 38.41 (P_nonlinearity=0.001). Subgroup analysis showed that the risk of RFM and CMM was significantly associated with male (OR=1.108, 95%CI=1.101-1.115), age≥45 years old (OR=1.011, 95%CI=1.008-1.013), less than high school education (OR=1.013, 95%CI=1.011-1.015), current smoking (OR=1.062, 95%CI=1.054-1.069), current drinking (OR=1.021, 95%CI=1.015-1.028) and BMI<24 kg/m² (OR=1.010, 95%CI=1.007-1.014). The results of interaction analysis showed that the association between RFM and the risk of CMM was affected by the interaction between gender, age, education level, smoking, drinking and BMI (P_interaction<0.05).

Higher RFM is significantly associated with an increased risk of CMM, and this association is more pronounced in males, individuals aged≥45 years, those with a high school education or below, smokers, drinkers, and individuals with a BMI<24 kg/m².

Obesity is closely related to the occurrence and development of dilated cardiomyopathy (DCM). Differences in clinical characteristics of dilated cardiomyopathy patients with distinct weight status and the prognostic value of weight management have not been clarified.

To explore the baseline clinical characteristics of DCM patients with different weight statuses, and to analyze the impact of weight management on their prognosis.

This was a single-center prospective cohort study. A total of 322 obese patients with DCM admitted to the Affiliated Hospital of Jiangsu University from January 2022 to June 2024 were prospectively collected. DCM patients were assigned into the normal group (BMI<24 kg/m²), overweight group (24 kg/m²≤BMI<28 kg/m²) and obese group (BMI≥28 kg/m²) according to the BMI. Baseline characteristics were collected. They were followed up for 12 months on telephone or outpatient visits. The incidence of major adverse cardiovascular events (MACEs) was recorded. According to the weight change during the 12-month weight management, they were divided into weight change <5%, 5%≤weight change <10% and≥10% weight change groups. Plots of MACEs among the three groups and Kaplan-Meier survival curves were plots. Univariate and multivariate Cox regression and subgroup analyses were conducted to identify influlencing factors for MACEs in DCM patients.

DCM patients were divided into the normal group (84 cases), overweight group (132 cases) and obese group (106 cases) according to baseline BMI. There were significant differences in age, systolic blood pressure, diastolic blood pressure, left ventricular end-systolic diameter (LVSd), comorbidities (hypertension, diabetes, coronary atherosclerosis), lifestyle (smoking history), and drug use [orlistat, glucagon-like peptide 1 (GLP-1) agonists, soluble guanylate cyclase (GC) agonists] among the three groups (all P<0.05). Based on the magnitude of body weight change over 12 months, participants were categorized into three groups: weight change <5% (n=115), 5%≤weight change <10% (n=157), and ≥10% weight change (n=50) groups. There were significant differences in admission body weight, follow-up brain natriuretic peptide (BNP), follow-up left ventricular ejection fraction (LVEF), follow-up cardiac function, follow-up MACEs and GLP-1 agonist use among the weight change <5%, 5%≤weight change<10% and≥10% weight change groups (P<0.05). The range of weight change during the 12-month follow-up was linearly related to follow-up BNP (r_s=-0.158, P=0.004) and LVEF (r_s=0.229, P<0.001). The Kaplan-Meier survival curve showed a significant difference in the incidence of MACEs among the weight change <5%, 5%≤weight change <10% and≥10% weight change groups (χ²=16.83, P<0.001). Univariate Cox proportional hazards regression model analysis showed that follow-up BNP, LVEF, follow-up cardiac function, weight change, and the use of GLP-1 receptor agonists, mineralocorticoid receptor antagonist (MRA), and sodium-glucose cotransporter-2 inhibitor (SGLT2i) were independent influencing factors for MACEs in DCM patients (P<0.05). After adjusting for gender, diabetes, smoking history, drinking history, and drug use, MACEs was the dependent variable and weight change was the independent variable. Multivariate Cox proportional hazards regression model showed that weight change was independently related to the occurrence of MACEs in DCM patients (P<0.05). Subgroup analysis results showed that increased weight change was significantly associated with a reduced risk of MACEs (HR_overall=0.89, 95%CI=0.81-0.98, P=0.018). The interaction analysis showed the increase in weight change was consistent with the risk of MACEs in DCM patients stratified by gender, age, diabetes, and use of SGLT2i, MRA or GLP-1 receptor agonists (P_interaction>0.05), all showing a protective effect. The association between weight change and the risk of MACEs in DCM patients was significantly different among patients who used β-blockers or not (P_interaction =0.004).

DCM patients with a BMI≥24 kg/m² are younger and more likely to have metabolic disorders like hypertension and diabetes. After 12 months of weight management, DCM patients with a weight loss of≥10% have the most significant improvement in cardiac function, manifesting as significantly decreased BNP and increased LVEF at follow-up, and the lowest incidence of MACEs. Structured weight management with the goal of weight loss ≥10% is therefore recommended to be included in the comprehensive treatment of overweight/obese DCM patients to improve their cardiac function and clinical prognosis.

Cardiovascular disease (CVD) is a major threat to human health, and its prevention and treatment largely depend on evidence-based and rational medical decision-making. With the development of the shared decision-making (SDM) model, patient decision aids (PDAs) have increasingly been used to facilitate clinician-patient communication and enhance patient engagement in decision-making. However, the quality of cardiovascular PDAs varies considerably and lacks standardized regulation. The International Patient Decision Aid Standards (IPDAS 4.0) provide an evidence-based framework for the design and evaluation of PDAs. This study systematically evaluated PDAs in the cardiovascular field using the IPDAS 4.0 framework to provide evidence for clinical practice.

To evaluate the effectiveness of PDAs in SDM among CVD patients.

A systematic search was conducted in PubMed, Embase, Web of Science, Cochrane Library, CNKI, VIP, CBM, and Wanfang Data, covering publications up to October 31, 2023. Randomized controlled trials (RCTs) evaluating the effects of PDAs in patients with CVD were included. Two researchers independently screened the studies, extracted data, and assessed methodological quality. Intervention groups received PDAs in any format, while control groups received routine treatment or care. The quality of PDAs development was assessed using IPDAS 4.0, and meta-analysis was performed with RevMan 5.4.

A total of 16 RCTs involving 4 861 patients were included. According to IPDAS 4.0, the top three scoring domains were disclosure, information and values, while the lowest three were test, plain language, and decision support technology evaluation. Meta-analysis indicated that PDAs significantly improved patients' knowledge (SMD=0.88, 95%CI=0.52-1.24, P<0.001) and reduced decisional conflict (SMD=-0.21, 95%CI=-0.40--0.03, P<0.001). Reductions in decisional conflict were observed across the informed (SMD=-0.36, 95%CI=-0.48--0.25, P<0.001), values clarity (SMD=-0.24, 95%CI=-0.35--0.13, P<0.001), support (SMD=-0.19, 95%CI=-0.31--0.08, P<0.001), and effective decision (SMD=-0.20, 95%CI=-0.31--0.08, P<0.001) subscales.

PDAs interventions are effective in improving knowledge, decisional satisfaction, and reducing decisional conflict among CVD patients, though their impact on decision regret requires further investigation. Future studies should integrate China's healthcare context to develop PDAs tailored to CVD patients based on the IPDAS 4.0 framework, thereby promoting the implementation of SDM in clinical practice.

The Chinese visceral adiposity index (CVAI) is a new obesity index that has been proven to be associated with prehypertension and hypertension. Still, there is a lack of research on the relationship between the CVAI and nocturnal hypertension (NH).

To investigate the correlation between CVAI and NH in young and middle-aged adults.

A total of 981 young and middle-aged patients with essential hypertension admitted to the Department of Hypertension of the Fifth Affiliated Hospital of Xinjiang Medical University were consecutively enrolled from February to September 2023, and the general data, biochemical indexes, and 24-hour ambulatory blood pressure monitoring results of the patients were collected and the CVAI was calculated, and the patients were divided into 95 cases of the non-nocturnal hypertension (NNH) group and 886 cases of the NH group according to the whether they were combined with NH or not. Differences in age, gender, and other indicators were compared between the two groups. Correlation analysis was performed using the Pearson or Spearman method. Multivariate Logistic regression was used to analyze the correlation between CVAI and NH in young and middle-aged people.

Compared with the NNH group, the NH group had higher CVAI, 24-hour average systolic and diastolic blood pressure, daytime average systolic and diastolic blood pressure, nocturnal average systolic and diastolic blood pressure, and maximum systolic and diastolic blood pressure, and the differences were statistically significant (all P<0.05). Pearson correlation analysis showed that CVAI was positively correlated with 24-hour average systolic and diastolic blood pressure, daytime average systolic and diastolic blood pressure, nocturnal average systolic and diastolic blood pressure, and maximum systolic and diastolic blood pressure (r=0.202, 0.183, 0.200, 0.171, 0.168, 0.174, 0.132, 0.157, all P<0.05). Multivariate Logistic regression showed that high CVAI was an independent risk factor for NH in young and middle-aged adults after adjustment for relevant confounders (OR=1.009, 95%CI=1.002-1.016, P=0.014). According to the CVAI quartiles, the patients were categorized into Q1 group (<103.524 3, n=245), Q2 group (103.524 3-129.714 0, n=246), Q3 group (129.714 0-156.270 4, n=245) and Q4 group (>156.270 4, n=245). The risk of developing nocturnal hypertension in the Q2 group, Q3 group and Q4 group was 1.779 (OR=1.779, 95%CI=1.002-3.157), 2.023 (OR=2.023, 95%CI=1.061-3.858), and 3.053 (OR=3.053, 95%CI=1.383-6.737) times greater than that of the Q1 group. Subgroup analysis showed that the association between CVAI and NH was more significant in the overweight/obese (BMI≥24 kg/m²) population (P=0.021).

CVAI was associated with the risk of developing NH in young and middle-aged adults, and the association was more significant in the overweight/obese (BMI≥24 kg/m²) population, which was a risk factor for NH in this population.

Hypertrophic cardiomyopathy (HCM) is a genetic disorder, characterized primarily by left ventricular outflow tract obstruction and asymmetric myocardial hypertrophy, which predisposes to sudden cardiac death and malignant arrhythmias. Although current pharmacological treatments can alleviate symptoms, they are not specific therapeutic approaches. With the increase in clinical studies on the novel targeted therapy of cardiac myosin inhibitors for HCM, there is currently a lack of systematic reviews evaluating the efficacy of these drugs.

To assess the efficacy and safety of cardiac myosin inhibitors in the treatment of HCM.

Systematic searches were conducted in PubMed, EmBase, the Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Database, and the China Biology Medicine disc, up to November 9, 2023, for randomized controlled trials (RCTs) of cardiac myosin inhibitors, including Mavacamten and Aficamten in HCM. Review Manager 5.4.1 software was utilized to conduct the statistical analysis.

A total of 6 RCTs were included[5 related to Mavacamten (4 original studies and 1 sub-study) and 1 related to Aficamten], involving 544 patients. The meta-analysis showed that, compared to placebo, the cardiac myosin inhibitors group exhibited significant reductions in peak gradient pressure under resting conditions in the left ventricular outflow tract (LVOT) (SMD=-1.24, 95%CI=-1.44 to -1.04, P<0.000 01), and under Valsalva maneuver (SMD=-1.37, 95%CI=-1.57 to -1.17, P<0.000 01), alongside at least a≥1 level improvement in the NYHA functional classification (NYHA-FC) (RR=2.22, 95%CI=1.77 to 2.78, P<0.000 01). Secondary endpoints showed reductions in the myocardial markers NT-proBNP (SMD=-1.28, 95%CI=-2.25 to -0.30), P=0.01] and cardiac troponin (SMD=-0.68, 95%CI=-1.32 to -0.04, P=0.04), improvement in the Kansas City Cardiomyopathy Questionnaire (KCCQ) clinical score (SMD=0.42, 95%CI=0.07 to 0.78, P=0.02), an increase in the rate of patients reaching the composite endpoint events (RR=1.92, 95%CI=1.28 to 2.88, P=0.002), and a reduction in the number of patients needing or eligible for septal reduction therapy (SRT)(RR=0.29, 95%CI=0.22 to 0.39, P<0.000 01). Echocardiographic parameters indicated that cardiac myosin inhibitors could improve the left ventricular mass index (LVMI)(SMD=-0.82, 95%CI =-1.45 to -0.18, P=0.01), decrease the left atrial volume index (LAVI)(SMD=-0.58, 95%CI=-0.90 to -0.27, P=0.000 3), but could also lead to a reduction in the left ventricular ejection fraction (LVEF)(SMD=-0.46, 95%CI =-0.65 to -0.27, P<0.000 01). In terms of safety, the incidence of at least one adverse event in the cardiac myosin inhibitor group was higher than in the placebo group (RR=1.12, 95%CI=1.02 to 1.22, P=0.02), but there was no statistically significant difference in other safety outcomes, including serious adverse events (RR=1.14, 95%CI=0.62 to 2.07, P=0.67), atrial fibrillation (RR=1.27, 95%CI=0.45 to 3.58, P=0.65), nausea (RR=1.77, 95%CI=0.52 to 6.04, P=0.36), dizziness (RR=1.88, 95%CI=0.75 to 4.71, P=0.18), and fatigue (RR=1.35, 95%CI=0.51 to 3.63, P=0.55) compared to the placebo group.

Cardiac myosin inhibitors can improve the peak gradient pressure in the LVOT, enhance NYHA functional classification, reduce myocardial markers, alter cardiac structure, and improve patients' quality of life in HCM, with relatively high safety. They offer clinical benefits to patients with HCM but may reduce LVEF.

The hierarchical and progressive objectives of standardized training for general practice residents require that third-year residents (R3) develop the ability to manage patients independently. Supervisors provide guidance on complex and critical cases to foster clinical reasoning and enhance diagnostic and management skills among R3. However, community outpatient teaching often relies on limited content and methods, falling short in cultivating systematic recognition and management of critical diseases such as atypical acute coronary syndrome (ACS).

To investigate the application of the PQRST pain assessment method combined with cardiovascular risk assessment in community outpatient teaching, with the aim of improving R3's ability to recognize, diagnose, and manage atypical ACS, thereby optimizing the quality of outpatient teaching.

Case-based learning was employed using a patient presenting with "subxiphoid discomfort for 2 hours after alcohol consumption". The R3 independently conducted the consultation and documented the medical record, while the supervisor observed, supplemented the documentation, and identified problems. The PQRST method was used to systematically collect symptom information, complemented by cardiovascular risk stratification using validated assessment tools. Electrocardiogram (ECG) and cardiac injury markers were utilized to confirm the diagnosis of ACS, followed by prehospital emergency management and referral. Mind map were incorporated into the teaching process to facilitate recording and feedback, reinforcing the R3's clinical reasoning and summarization skills.

Using the PQRST pain assessment method, the patient's symptoms were confirmed to be consistent with atypical chest pain. Cardiovascular risk assessment categorized the patient as being at"very high-risk". ECG and myocardial injury marker findings confirmed ST-segment elevation myocardial infarction (STEMI). Under the supervisor's guidance, the R3 successfully completed rapid assessment, prehospital management, and orderly referral. After the teaching session, the R3 demonstrated significantly improved ability to recognize atypical ACS, enhanced clinical logical reasoning, and greater familiarity with emergency referral procedures.

The combination of the PQRST pain assessment method and cardiovascular risk assessment improves R3's capacity for early recognition and management of atypical ACS. The integration of mind maps for feedback and summarization helps establish a systematic framework for differential diagnosis. This approach is applicable to teaching critical care management in community outpatient teaching and holds promise for broader implementation.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is closely associated with type 2 diabetes mellitus (T2DM). MASLD and its associated liver fibrosis contribute to the onset and progression of T2DM through the induction of insulin resistance and direct disruption of hepatic glycogen synthesis. Additionally, MASLD significantly elevates cardiovascular risk and mortality in patients with T2DM, mediated by abnormality of vascular endothelial factors and dyslipidemia resulting from insulin resistance, as well as hypercoagulability. In terms of pharmacological treatment, certain novel multi-target hypoglycemic agents have been proven to be efficacious in reducing intrahepatic lipid deposition and improving liver enzyme levels, while also having the potential to mitigate the progression of liver fibrosis. Altogether, this article reviews the association, mechanism and pharmacological treatment of MASLD and its associated liver fibrosis with T2DM and its cardiovascular complications, aiming to provide novel insights for the clinical diagnosis and treatment of patients with T2DM and MASLD.

The American Heart Association(AHA) first defined cardiovascular-kidney-metabolic syndrome (CKM) in 2023, elucidating the complex association between metabolic risk factors, chronic kidney disease (CKD) and cardiovascular disease (CVD), and emphasizing that early intervention in CKM stages 0-3 can reduce the risk of end-stage cardiovascular events. Triglyceride glucose index (TYG), as a novel marker of lipid metabolism, has been shown to be associated with CVD in the general population, but its association with CVD in the CKM stages 0-3 population still needs to be supported by evidence-based medical evidence.

To explore the association between TYG level and CVD risk in patients with CKM stages 0-3.

From a baseline established in 2015, 46 754 participants meeting CKM stages 0-3 criteria were identified from the Cheeloo Longitudinal Epidemiological Analysis Database (LEAD) during the investigation period (2015-01-01—2017-12-31). Participants were categorized into quartiles based on baseline TYG indices: Q1 (6.79≤TYG<8.19, n=11 979), Q2 (8.19≤TYG<8.55, n=11 493), Q3 (8.55≤TYG<8.93, n=11 647), and Q4 (8.93≤TYG≤11.7, n=11 635). Cox proportional hazards regression models were employed to systematically analyze the association between TYG and CVD risk within this cohort. Survival curves were plotted using the Kaplan-Meier method, and differences in survival rates among different TYG groups were analyzed using the Log-rank test. Dose-response curves were evaluated using restricted cubic splines (RCS) across the total CKM stages 0-3 population. Subgroup analyses by gender (male and female) and CKM stages (<stage 2 and ≥stage 2) were conducted to verify the robustness of the associations. Stratified multivariable Cox models were constructed, incorporating average TYG and cumulative TYG as continuous variables to explore their impact on CVD occurrence.

A total of 46 754 participants were included, comprising 19 884 males (42.5%) and 26 870 females (57.5%), with a median age of 69(65, 73) years. The mean baseline TYG was 8.59±0.60, and 32 837 (70.2%) participants were at CKM stage 2. Cox regression analysis revealed that per unit increase in TYG as a continuous variable was associated with a 19.4% increased risk of CVD incidence (HR=1.194, 95%CI=1.162-1.226, P<0.001). Group analysis indicated that the risk of newly developed CVD in Q4 was 28.0% higher than in Q1 (HR=1.280, 95%CI=1.222-1.341), with a significant dose-response relationship (P<0.05). Cumulative incidences of CVD across Q1-Q4 showed a gradient increase (33.7%, 35.2%, 42.0%, and 43.7%, respectively). Kaplan-Meier curves demonstrated statistically significant differences in CVD incidence rates among Q1-Q4 groups (χ²=328.853, P<0.05). RCS analysis suggested a linear positive correlation between TYG and CVD risk in the overall CKM stages 0-3 population (P<0.001, P_non-lineari=0.282); stratification by CKM stage also showed a linear positive correlation in CKM stages 0-1 (P<0.001, P_non-linear=0.616) and 2-3 (P<0.001, P_non-linear=0.180).

In the 0-3 stage of CKM, TYG is revealed as an early warning of CVD risk in this population, which provides theoretical support for the establishment of a precision prevention strategy based on metabolic-cardiorenal intervention.