BACKGROUND: Pulmonary arterial hypertension (PAH) presents a significant health burden in Asia and remains a critical challenge. This study aims to delineate the PAH burden in Asia from 1990 to 2021. METHODS: Using the latest data from the Global Burden of Disease 2021, we evaluated and analyzed the distributions and patterns of PAH disease burden among various age groups, sexes, regions, and countries in Asia. Additionally, we examined the associations between PAH disease burden and key health system indicators, including the socio-demographic index (SDI) and the universal health coverage (UHC) index. RESULTS: In 2021, there were 25,989 new PAH cases, 103,382 existing cases, 13,909 PAH-associated deaths, and 385,755 DALYs attributed to PAH in Asia, which accounted for approximately 60% of global PAH cases. The age-standardized rates (ASRs) for prevalence and deaths were 2.05 (95% uncertainty interval [UI]: 1.66-2.52) per 100,000 population and 0.31 (95% UI: 0.23-0.38) per 100,000 population, respectively. From 1990 to 2021, Asia reported the lowest ASRs for PAH prevalence but the highest ASRs for deaths compared to other continents. While the ASRs for prevalence increased slightly, ASRs for mortality and DALYs decreased over time. This increasing burden of PAH was primarily driven by population growth and aging. The burden was especially pronounced among individuals aged ≥60 years and <9 years, who collectively accounted for the majority of deaths and DALYs. Moreover, higher SDI and UHC levels were linked to reduced incidence, but higher prevalence rates. CONCLUSIONS Although progress has been made in reducing PAH-related mortality and DALYs, the disease continues to impose a substantial burden in Asia, particularly among older adults and young children. Region-specific health policies should focus on improving early diagnosis, expanding access to treatment, and effectively addressing the growing PAH burden in the region.
Humans ; Global Burden of Disease ; Male ; Female ; Middle Aged ; Adult ; Asia/epidemiology* ; Prevalence ; Aged ; Pulmonary Arterial Hypertension/mortality* ; Adolescent ; Young Adult ; Child ; Child, Preschool ; Infant ; Hypertension, Pulmonary/epidemiology*

BACKGROUND AND OBJECTIVE: Hypertension (HT) and atrial fibrillation (AF) are highly prevalent cardiovascular conditions that frequently coexist. Coronary artery disease (CAD) is a major global cause of mortality. The co-occurrence of HT, AF, and CAD presents significant management challenges. This study aims to explore the clinical characteristics and risk factors associated with CAD in patients with HT and persistent AF (HT-AF). METHODS: In this retrospective cross-sectional study, data were collected from 384 hospitalized HT-AF patients at the People's Liberation Army General Hospital between January 2010 and December 2019. CAD diagnosis was confirmed by coronary angiography or computed tomography angiography. Clinical characteristics and comorbidities were compared between patients with and without CAD. Multivariate logistic regression analyses were performed to identify independent risk factors associated with CAD development. RESULTS: The prevalence of CAD among HT-AF patients was 66.41% (255/384). Cardiovascular complications, particularly heart failure (44.7% vs 25.6%, P < 0.05), were significantly more prevalent in the CAD group than in the non-CAD group. Only age was identified as an independent risk factor for CAD (adjusted OR: 1.047; 95% CI: 1.022-1.073; P = 0.000). Of all HT-AF patients, 54.7% had a CHA₂DS₂-VASc score of ≥4, indicating high stroke risk. There was a slightly higher anticoagulant usage rate in the CAD group than those without CAD (8.6% vs 4.7%, P = 0.157), and the overall anticoagulant usage remained low. CONCLUSION: There is a high prevalence of CAD among hospitalized HT-AF patients, among whom age is the sole independent risk factor for CAD. Despite a high stroke risk, the utilization of oral anticoagulants is alarmingly low.
Humans ; Atrial Fibrillation/epidemiology* ; Coronary Artery Disease/etiology* ; Hypertension/epidemiology* ; Male ; Female ; Risk Factors ; Middle Aged ; Retrospective Studies ; Cross-Sectional Studies ; Aged ; Prevalence

OBJECTIVES: To analyze the multimorbidity patterns and core diseases among hospitalized patients in different age groups and to explore the impacts of multimorbidity patterns on hospitalization costs. METHODS: Electronic medical records of adult inpatients (aged ≥18 years) from Ningbo Medical Center Lihuili Hospital between January 1, 2018, and June 30, 2023 were collected. The multimorbidity status involving 53 specific diseases was analyzed across different age groups. Association rule mining was used to identify common multimorbidity patterns. Complex network analysis was used to identify core diseases within the multimorbidity networks. Generalized estimating equations (GEE) were used to analyze the impact of different multimorbidity patterns on hospitalization costs. RESULTS: The prevalence of multimorbidity among the 359 402 adult inpatients was 38.51%, with higher rates observed in males (43.60%) and elderly patients (58.29%). Association rule mining identified 15 common multimorbidity patterns, which exhibited differences across age groups. The most prevalent multimorbidity pattern overall was "diabetes→hypertension" (support=7.04%, confidence=62.17%, lift=2.17). In the young adult group, the most prevalent pattern was "dyslipidemia→chronic liver disease" (support=1.19%, confidence=53.17%, lift=6.04). In the middle-aged group, it was "diabetes→hypertension" (support=4.84%, confidence=50.28%, lift=2.15). In the elderly group, it was "coronary heart disease, diabetes→hypertension" (support=2.38%, confidence=77.43%, lift=1.63). Complex network analysis revealed that the core diseases within multimorbidity networks differed across age groups. The core disease identified in the young adult group was chronic liver disease (degree centrality=50, betweenness centrality=0.055, closeness centrality=0.963). Core diseases in the middle-aged group included hypertension, chronic liver disease, and diabetes (all with degree centrality=52, betweenness centrality=0.022, closeness centrality=1.000). Core diseases in the elderly group comprised hypertension, diabetes, malignant tumors, chronic liver disease, thyroid disease, anemia, and arrhythmia (all with degree centrality=52, betweenness centrality=0.009, closeness centrality=1.000). Generalized estimating equations analysis indicated that, most multimorbidity patterns were significantly associated with increased hospitalization costs. However, the magnitude of cost increase varied across different multimorbidity patterns. Specifically, hospitalization costs for patients with patterns such as "heart failure→hypertension", "stroke→hypertension", "malignant tumor, diabetes→hypertension", "stroke, diabetes→hypertension", and "diabetes, heart failure→hypertension" were more than double those of patients without any target diseases. CONCLUSIONS Multimorbidity patterns and core diseases among hospitalized patients differ significantly across age groups, and different patterns exert varying impacts on hospitalization costs. These findings underscore the necessity for age-stratified and multimorbidity pattern specific management strategies.
Humans ; Multimorbidity ; Male ; Hospitalization/economics* ; Female ; Aged ; Middle Aged ; Adult ; Age Factors ; Young Adult ; Adolescent ; Diabetes Mellitus/epidemiology* ; Electronic Health Records ; Aged, 80 and over ; Hospital Costs ; China/epidemiology* ; Hypertension/economics* ; Liver Diseases/epidemiology*

BACKGROUND: Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as the heart, brain, and kidneys. Our goal is to predict the risk of new onset hypertension using machine learning algorithms and identify the characteristics of patients with new onset hypertension. METHODS: We analyzed data from the 2011 China Health and Nutrition Survey cohort of individuals who were not hypertensive at baseline and had follow-up results available for prediction by 2015. We tested and evaluated the performance of four traditional machine learning algorithms commonly used in epidemiological studies: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, and two deep learning algorithms: TabNet and AMFormer model. We modeled using 16 and 29 features, respectively. SHAP values were applied to select key features associated with new onset hypertension. RESULTS: A total of 4,982 participants were included in the analysis, of whom 1,017 developed hypertension during the 4-year follow-up. Among the 16-feature models, Logistic Regression had the highest AUC of 0.784(0.775∼0.806). In the 29-feature prediction models, AMFormer performed the best with an AUC of 0.802(0.795∼0.820), and also scored the highest in MCC (0.417, 95%CI: 0.400∼0.434) and F1 (0.503, 95%CI: 0.484∼0.505) metrics, demonstrating superior overall performance compared to the other models. Additionally, key features selected based on the AMFormer, such as age, province, waist circumference, urban or rural location, education level, employment status, weight, WHR, and BMI, played significant roles. CONCLUSION We used the AMFormer model for the first time in predicting new onset hypertension and achieved the best results among the six algorithms tested. Key features associated with new onset hypertension can be determined through this algorithm. The practice of machine learning algorithms can further enhance the predictive efficacy of diseases and identify risk factors for diseases.
Humans ; China/epidemiology* ; Hypertension/diagnosis* ; Machine Learning ; Male ; Female ; Middle Aged ; Adult ; Nutrition Surveys ; Algorithms ; Aged ; Risk Factors

BACKGROUND: Studies have shown that married couples often share similar lifestyles, as well as lifestyle-associated conditions such as diabetes, hypertension, and hyperlipidemia. This study aims to prospectively investigate the association between an individual's development of a non-communicable disease and the subsequent development of the same condition in their spouse. METHODS: This population-based cohort study utilized 12 waves of annual prospective surveys from 2005 onwards in Japan, with a discrete-time design. A total of 9,417 middle-aged couples (18,834 participants; discrete-time observations = 118,876) were included. Each participant whose spouse had developed one of six conditions was propensity score-matched with five controls whose spouses had not been diagnosed with the condition: diabetes [n = 1374 vs n = 6870], hypertension [n = 2657 vs n = 13285], hypercholesterolemia [n = 3321 vs n = 16605], stroke [n = 567 vs n = 2835], coronary heart disease (CHD) [n = 1093 vs n = 5465] or cancer [n = 923 vs n = 4615]. Using conditional logistic regression, we assessed participants' development of the same condition within three years following their spouse's diagnosis. RESULTS: Participants whose spouses had developed diabetes, hypertension, hypercholesterolemia, or CHD were more likely to develop the same condition within three years. The odds ratios (ORs) and 95% confidence intervals (CIs) were: 1.96 (1.53-2.50), 1.20 (1.06-1.36), 1.63 (1.47-1.81) and 1.43 (1.05-1.95), respectively. No significant associations were observed in stroke [1.69 (0.80-3.58)] or cancer [1.08 (0.75-1.54)]. CONCLUSION Spouses of individuals recently diagnosed with certain metabolic conditions are at a higher risk of developing those conditions themselves. These findings may provide valuable guidance for targeting and personalizing chronic disease screening and prevention efforts.
Humans ; Spouses/statistics & numerical data* ; Male ; Female ; Middle Aged ; Aged ; Japan/epidemiology* ; Longitudinal Studies ; Noncommunicable Diseases/epidemiology* ; Prospective Studies ; Neoplasms/epidemiology* ; Diabetes Mellitus/epidemiology* ; Risk Factors ; Aged, 80 and over ; Adult ; Hypertension/epidemiology*

BACKGROUND: High visceral fat mass (FM) is associated with a high risk of cardiometabolic morbidity. Meanwhile, loss of skeletal muscle (lean mass, LM) has been suggested to contribute to metabolic diseases. METHODS: We investigated associations between cardiometabolic diseases and dual energy X-ray absorptiometry (DXA)-measured body composition indices, including the FM index (FM/height²), percent body fat, trunk-to-appendicular fat ratio (TAR), trunk-to-leg fat ratio (TLR), LM index (LM/height²) and FM-to-LM ratio in 595 community-dwelling elderly Japanese men (mean age, 74 years; standard deviation, 6; range, 65 to 94). Hypertension was identified as high blood pressure and/or the use of antihypertensive drugs. Diabetes was identified as high hemoglobin A1c and/or the use of antidiabetic drugs. The ability of DXA-based indices to discriminate between the presence and absence of cardiometabolic diseases was evaluated using area under the curve (AUC) calculated by receiver operating characteristic curve analysis. RESULTS: Body mass index, FM index, percent body fat, TAR, TLR and FM-to-LM ratio were significantly associated with hypertension (P < 0.05). TAR and TLR, but not body mass index, FM index, percent body fat, LM index and FM-to-LM ratio, showed significant positive associations with diabetes. The AUC for the LM index was significantly lower than those for the FM index, percent body fat and FM-to-LM ratio. No associations were observed between the LM index and hypertension, dyslipidemia and diabetes. CONCLUSION The association between cardiometabolic function and LM, which includes skeletal muscle, may not be as pronounced or stronger than associations between cardiometabolic function and FM. Further detailed studies are needed to clarify how skeletal muscle contributes to cardiometabolic disease.
Humans ; Male ; Absorptiometry, Photon ; Aged ; Japan/epidemiology* ; Body Composition ; Aged, 80 and over ; Hypertension/epidemiology* ; Cardiovascular Diseases/epidemiology* ; Body Mass Index ; Muscle, Skeletal ; Osteoporosis/epidemiology* ; Diabetes Mellitus/epidemiology* ; Risk Factors ; East Asian People

OBJECTIVES: Non-alcoholic fatty liver disease (NAFLD) and hypertension are common metabolic disorders, both closely associated with insulin resistance (IR), suggesting potential shared pathological mechanisms. This study aims to investigate the mediating role of IR in the relationship between hypertension and NAFLD, and to evaluate the applicability and modeling value of various IR surrogate indices in predicting NAFLD risk. METHODS: A total of 280 976 individuals who underwent health examinations at the Health Management Center of the Third Xiangya Hospital of Central South University between August 2017 and December 2021 were included. NAFLD was diagnosed based on abdominal ultrasound findings, and hypertension was defined according to the criteria of the Chinese Guidelines for the Management of Hypertension. Demographic information, anthropometric indices, and biochemical parameters were collected, and multiple IR surrogate indices were constructed, including the triglyceride-glucose index (TyG) and its derivatives, as well as the metabolic score for insulin resistance (METS-IR). Group comparisons were performed between hypertensive and non-hypertensive participants, as well as between NAFLD and non-NAFLD participants. Pearson correlation analysis was applied to assess the associations of metabolic parameters and IR indices with NAFLD. Furthermore, mediation models were constructed to explore the mediating role of IR in the "hypertension-NAFLD" relationship. Finally, parametric models and machine learning algorithms were compared to evaluate their predictive performance and value in assessing NAFLD risk in this population. RESULTS: The prevalence of NAFLD was significantly higher in hypertensive individuals than in non-hypertensive participants (63.61% vs 33.79%, P<0.001), accompanied by elevated IR levels and adverse metabolic features. Correlation analysis and variable importance rankings across multiple models consistently identified TyG-waist circumference (TyG-WC) and METS-IR as the IR indices most strongly associated with NAFLD. In mediation analysis, the TyG-WC pathway explained 32.03% of the total effect, and the METS-IR pathway explained 17.02%. Interaction analysis showed that hypertension status may attenuate the mediating effect of IR (all interaction estimates were negative). In prediction model comparisons, the simplified model incorporating sex, age, WC, TyG-WC, and METS-IR demonstrated good performance in the test set. Logistic regression and its regularized form (LASSO regression) achieved an accuracy of 0.83, receiver operating characteristic (ROC)-area under the curve (AUC) of 0.91, and a Brier score of 0.12, comparable to ensemble models (random forest and XGBoost), with consistently stable performance across different algorithms. CONCLUSIONS IR plays a significant mediating role in the association between hypertension and NAFLD, with TyG-WC identified as a key indicator showing strong mechanistic relevance and predictive value. Risk prediction models based on IR surrogate indices demonstrate advantages in simplicity and interpretability, providing empirical support for the early screening and individualized prevention of NAFLD in the general population.
Humans ; Non-alcoholic Fatty Liver Disease/complications* ; Insulin Resistance ; Hypertension/epidemiology* ; Male ; Female ; Middle Aged ; Risk Factors ; Adult ; Machine Learning ; Triglycerides/blood*

OBJECTIVE: To investigate hypertension (HTN) trends, key risk factors, and gender disparities in rural China, and to propose targeted strategies for improving HTN control in resource-limited settings. METHODS: This longitudinal study used data from the Henan Rural Cohort Study, including baseline (2015-2017; n = 39,224) and follow-up (2018-2022; n = 28,621) participants. HTN was defined as systolic/diastolic blood pressure ≥ 140/90 mmHg, self-reported diagnosis, or use of antihypertensive medication. Severity was classified using a 7-tier blood pressure (BP) staging system (optimal, normal, high normal, and HTN stages 1-4). A generalized linear mixed-effects model (GLMM) identified associated risk factors. RESULTS: HTN prevalence increased modestly from 32.7% (95% CI: 32.2-33.2) to 33.9% (95% CI: 33.3%-34.4%). Awareness and treatment improved from 20.1% to 25.3%, and from 18.8% to 24.4%, respectively, but control rates remained low (6.2% to 12.3%). After adjustment, women had a 1.53-fold higher HTN risk than men ( OR = 1.53, 95% CI: 1.43-1.63), revealing gender-specific trends. Key risk factors included alcohol use ( OR = 1.37, 95% CI: 1.27-1.47) and overweight status ( OR = 1.76, 95% CI: 1.66-1.86). BP staging showed an increase in optimal BP (42.3% to 45.8%), but stagnant management of advanced HTN stages. CONCLUSION Hypertension in rural China is shaped by behavioral risk factors and healthcare access gaps. Gender-sensitive, community-based interventions, including task-shifting models, are necessary to mitigate the growing burden of hypertension.
Humans ; Hypertension/etiology* ; China/epidemiology* ; Female ; Male ; Rural Population/statistics & numerical data* ; Prevalence ; Risk Factors ; Middle Aged ; Adult ; Aged ; Longitudinal Studies ; Sex Factors ; Cohort Studies ; East Asian People

Humans ; Air Pollutants/adverse effects* ; Genotype ; China/epidemiology* ; Hypertension/epidemiology*

Objective:This study aimed to construct a risk prediction model for obstructive sleep apnea（OSA） related hypertension based on the nomogram, and to explore the independent risk factors for OSA-related hypertension, so as to provide reference for clinical treatment decision-making. Methods:The clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 were collected retrospectively and randomly divided into training sets and validation sets. A total of 1 493 OSA patients with 27 variables were included. The least absolute shrinkage and selection operator（Lasso） logistic regression model was used to select potentially relevant features and establish a nomogram for OSA-related hypertension.The performance and clinical benefits of this nomogram were verified in terms of discrimination, calibration ability and clinical net benefit. Results:Multivariate logistic regression showed that body mass index（BMI）, family history of hypertension, lowest oxygen saturation（LSaO2）, age and cumulative percentage of total sleep time with oxygen saturation below 90% were independent risk factors for OSA-related hypertension. Lasso logistic regression identified BMI, family history of hypertension, LSaO2 and age as predictive factors for inclusion in the nomogram. The nomogram provided a favorable discrimination, with a C-indexes of 0.835（95% confidence interval[CI ]0.806-0.863） 0.865（95%CI 0.829-0.900） for the training and validation cohort, respectively, and well calibrated. The clinical decision curve analysis displayed that the nomogram was clinically useful. Conclusion:Compared with cumulative percentage of total sleep time with blood oxygen saturation below 90%, LSaO2 may have a greater impact on the incidence of OSA-related hypertension, and the effects of different times and degrees of hypoxia on OSA-related hypertension should be further explored in the future. Apnea hypopnea index involvement is weak in predicting OSA-related hypertension, and the blood oxygen index may be a better predictor variable. Furthermore, we established a risk prediction model for OSA-related hypertension patients using nomogram, and demonstrated that this prediction model was helpful to identify high-risk OSA-related hypertension patients. This model can provide early and individualized diagnosis and treatment plans, protect patients from the serious.
Humans ; Sleep Apnea, Obstructive/complications* ; Nomograms ; Hypertension/epidemiology* ; Male ; Female ; Risk Factors ; Middle Aged ; Retrospective Studies ; Polysomnography ; Logistic Models ; Body Mass Index ; Adult