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Zhang Y, Sun Q, Yu C, Sun D, Pang Y, Pei P, Du H, Yang L, Chen Y, Yang X, Chen X, Chen J, Chen Z, Li L, Lv J. Associations of traditional cardiovascular risk factors with 15-year blood pressure change and trajectories in Chinese adults: a prospective cohort study. J Hypertens 2024; 42:1340-1349. [PMID: 38525868 PMCID: PMC7616121 DOI: 10.1097/hjh.0000000000003717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
OBJECTIVE How traditional cardiovascular disease (CVD) risk factors are related to long-term blood pressure change (BPC) or trajectories remain unclear. We aimed to examine the independent associations of these factors with 15-year BPC and trajectories in Chinese adults. METHODS We included 15 985 participants who had attended three surveys, including 2004-2008 baseline survey, and 2013-2014 and 2020-2021 resurveys, over 15 years in the China Kadoorie Biobank (CKB). We measured systolic and diastolic blood pressure (SBP and DBP), height, weight, and waist circumference (WC). We asked about the sociodemographic characteristics and lifestyle factors, including smoking, alcohol drinking, intake of fresh vegetables, fruits, and red meat, and physical activity, using a structured questionnaire. We calculated standard deviation (SD), cumulative blood pressure (cumBP), coefficient of variation (CV), and average real variability (ARV) as long-term BPC proxies. We identified blood pressure trajectories using the latent class growth model. RESULTS Most baseline sociodemographic and lifestyle characteristics were associated with cumBP. After adjusting for other characteristics, the cumSBP (mmHg × year) increased by 116.9 [95% confidence interval (CI): 111.0, 122.7] for every 10 years of age. The differences of cumSBP in heavy drinkers of ≥60 g pure alcohol per day and former drinkers were 86.7 (60.7, 112.6) and 48.9 (23.1, 74.8) compared with less than weekly drinkers. The cumSBP in participants who ate red meat less than weekly was 29.4 (12.0, 46.8) higher than those who ate red meat daily. The corresponding differences of cumSBP were 127.8 (120.7, 134.9) and 70.2 (65.0, 75.3) for BMI per 5 kg/m 2 and WC per 10 cm. Most of the findings of other BPC measures by baseline characteristics were similar to the cumBP, but the differences between groups were somewhat weaker. Alcohol drinking was associated with several high-risk trajectories of SBP and DBP. Both BMI and WC were independently associated with all high-risk blood pressure trajectories. CONCLUSIONS Several traditional CVD risk factors were associated with unfavorable long-term BPC or blood pressure trajectories in Chinese adults.
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Affiliation(s)
- Yiqian Zhang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Qiufen Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Xiaoming Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Xiaofang Chen
- Chengdu Medical College, Chengdu, Sichuan 610500, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University
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Tao Z, Qu Q, Li J, Li X. Factors influencing blood pressure variability in postmenopausal women: evidence from the China Health and Nutrition Survey. Clin Exp Hypertens 2023; 45:2181356. [PMID: 36842972 DOI: 10.1080/10641963.2023.2181356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2023]
Abstract
BACKGROUND The aim is to identify the factors influencing blood pressure variability in postmenopausal women based on the China Health and Nutrition Survey (CHNS). MATERIAL AND METHODS The data on postmenopausal women between 1993 and 2015 were extracted from the CHNS. Group-based trajectory modeling was used to analyze the development track of blood pressure changes, based on which the subjects were separately divided into two groups for systolic blood pressure (SBP) and diastolic blood pressure (DBP). Univariate and multivariate analyzes were performed to analyze the factors influencing SBP and DBP. RESULTS A total of 346 women were eligible for the study. Group-based trajectory modeling showed two different trajectories of blood pressure, including the low-level, slowly developed type and the high-level, rapidly developed, stable type of SBP, as well as the low-level, slowly developed type and the high-level, slowly developed type of DBP. In multivariate analysis, age (odds ratio [OR]: 1.118, 95% confidence interval [CI]: 1.082-1.156), body mass index (BMI) (OR: 2.239, 95%CI: 1.010-4.964), antihypertensive agents (OR: 7.293, 95%CI: 2.191-24.275), hip circumference (OR: 1.069, 95%CI: 1.014-1.128) and marital status (OR: 3.103, 95%CI: 1.028-9.361) were found to be the significant factors influencing SBP; age (OR: 1.067, 95%CI: 1.039-1.096), alcohol consumption (OR: 2.741, 95%CI: 1.169-6.429), antihypertensive agents (OR: 4.577, 95%CI: 1.553-13.492), hip circumference (OR: 1.093, 95%CI: 1.049-1.138), and marital status (OR: 3.615, 95%CI: 1.228-10.644) were the predominant factors influencing DBP. CONCLUSIONS In postmenopausal women, age, BMI, antihypertensive agents, hip circumference, and marital status are associated with SBP changes, while age, alcohol consumption, antihypertensive agents, hip circumference, and marital status with DBP variability. MESH KEYWORDS postmenopausal women, blood pressure, development track, influencing factors, CHNS.
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Affiliation(s)
- Zhonge Tao
- Department of Gynecology, Tianjin First Center Hospital, Tianjin, P.R. China
| | - Quanxin Qu
- Department of Gynecology, Tianjin First Center Hospital, Tianjin, P.R. China
| | - Jing Li
- Department of Gynecology, Tianjin First Center Hospital, Tianjin, P.R. China
| | - Xiaolin Li
- Department of Gynecology, Tianjin First Center Hospital, Tianjin, P.R. China
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Chu C, Liao YY, He MJ, Ma Q, Zheng WL, Yan Y, Hu JW, Xu XJ, Fan YN, Yang RH, Mu JJ. Blood Pressure Trajectories From Childhood to Youth and Arterial Stiffness in Adulthood: A 30-Year Longitudinal Follow-Up Study. Front Cardiovasc Med 2022; 9:894426. [PMID: 35845038 PMCID: PMC9278647 DOI: 10.3389/fcvm.2022.894426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 06/06/2022] [Indexed: 11/24/2022] Open
Abstract
Background This study aimed to identify the subgroups of individuals sharing similar blood pressure (BP) trajectories from childhood to youth and explore the associations of these trajectories with arterial stiffness in adulthood. Methods A group-based trajectory model was used to identify BP trajectories among 2,082 individuals in the Hanzhong adolescent hypertension cohort by using BP values repeatedly measured at four visits from childhood (6–15 years) to youth (14–23 years). The brachial–ankle pulse wave velocity (baPWV) was examined 30 years after the baseline survey. Mixed linear regression models were used to examine the associations of these trajectories with adult baPWV. Results Among the 2,082 individuals, three trajectory groups of systolic BP were identified as follows: the low-level group (n = 889), medium-level group (n = 1,021), and high-level group (n = 172). The baPWV in adulthood was higher in medium-level and high-level groups compared with the low-level group (1271.4 ± 224.7 cm/s, 1366.1 ± 249.8 cm/s vs. 1190.1 ± 220.3 cm/s, all p < 0.001). After adjustment for potential confounding factors, the association between baPWV and systolic BP trajectories was statistically significant (adjusted β = 49.4 cm/s; p < 0.001 for the medium-level group and β = 107.6 cm/s; p < 0.001 for the high-level group compared with the low-level group). Similar results were obtained for the association of baPWV with the trajectories of diastolic BP and mean arterial pressure (MAP), except for pulse pressure. Conclusion Our investigation demonstrates different BP trajectories from childhood to youth and shows the trajectories of systolic BP, diastolic BP, and MAP are significant predictors of arterial stiffness in adulthood.
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Affiliation(s)
- Chao Chu
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi’an, China
| | - Yue-yuan Liao
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi’an, China
| | - Ming-jun He
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi’an, China
| | - Qiong Ma
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi’an, China
| | - Wen-ling Zheng
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi’an, China
| | - Yu Yan
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi’an, China
| | - Jia-wen Hu
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi’an, China
| | - Xian-jing Xu
- Department of Cardiovascular Medicine, Henan Province People’s Hospital, Zhengzhou, China
| | - Ya-ning Fan
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi’an, China
| | - Rui-hai Yang
- Institute of Cardiovascular Sciences, Hanzhong People’s Hospital, Hanzhong, China
| | - Jian-jun Mu
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi’an, China
- *Correspondence: Jian-jun Mu,
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