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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
Background
Hypertension (HTN), defined as diastolic blood pressure (DBP) ≥ 90 mmHg and/or systolic blood pressure (SBP) ≥ 140 mmHg, is one of the most common chronic diseases worldwide (Citation1). Many factors are reported to associate with HTN, such as smoking (Citation2), consuming alcohol (Citation3), obesity (Citation4), and stress (Citation5). With the aging of the population, lacking physical activity, and high sodium intake, the prevalence of HTN is increasing significantly (Citation6). In China, 23.2% of the people were estimated to be hypertensive, and 41.3% were pre-hypertensive (Citation7). HTN causes 7.6 million premature deaths (about 13.5% of the global total), and it also leads to about 54% of stroke and 47% of ischemic heart disease globally (Citation8). Despite accessibility to safe and effective antihypertensive medications for many years, control of blood pressure remains to be poor, especially for postmenopausal women (Citation9).
Epidemiological evidence has demonstrated that the prevalence of HTN has increased to 75% in postmenopausal women aged 65–74 years and 85% in those aged over 75 years, markedly higher than 19% in premenopausal women and 44% in perimenopausal women (Citation10). In women after surgical or natural menopause, estrogen levels suddenly drop or gradually decline. This menopausal status is conductive to elevate the risk of cardiovascular disease (CVD) and metabolic disturbances (Citation11). There is also a study suggesting that postmenopausal women had significantly increased SBP and DBP compared with reproductive women (Citation12). In recent years, evidence has supported that long-term blood pressure (e.g. blood pressure trajectory) provides incremental prognostic value over the present blood pressure (Citation13). Blood pressure trajectory reflects changes in an individual’s blood pressure levels over time, taking into consideration many aspects of life patterns, such as starting levels, slope, and cumulative exposure (Citation14). However, little is known about the factors affecting blood pressure variability trajectory. Zhang et al. have found that physical activity, waist circumferences, and daily diet are correlated with blood pressure variability trajectories in Chinese population (Citation15). To the best of our knowledge, factors influencing long-term blood pressure variability in postmenopausal women have not been reported yet.
In the present study, we analyzed the blood pressure variability trajectories and the factors influencing blood pressure variability in postmenopausal women based on the China Health and Nutrition Survey (CHNS) between 1993 and 2015, with the aim of early monitoring and controlling blood pressure.
Materials and methods
Data source
The CHNS, a multipurpose longitudinal survey, had completed 10 rounds of surveys, respectively, conducted in 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011 and 2015. With three additional metropolises involved in 2011 and 3 more provinces comprised in 2015, the CHNS was widely carried out in 15 provinces and municipal cities through a multistage and random-cluster process, and over 30000 individuals (approximately 7200 households) participated in the survey (Citation16). It contained important public health outcomes and risk factors, as well as in-depth demographic, economic, and social factors at levels of individuals, households, and communities, and was designed to identify the impact of the health, nutrition, and family policies implemented by local and national governments and to observe how the personal health status was influenced by the social and economic transformation (Citation17). The CHNS instruments, protocols, and the process of achieving informed consent were approved by the institutional review boards of the University of North Carolina at Chapel Hill and the National Institute for Nutrition and Health at the Chinese Center for Disease Control and Prevention, and written informed consent was provided by all the participants before surveys (Citation17,Citation18).
Study population
This study included women with menopause before and in 1993, with 8 followed-up visits between 1993 and 2015 (1993, 1997, 2000, 2004, 2006, 2009, 2011, and 2005), in which, women with missing values of blood pressure during the follow-up period were excluded. The questions defined menopause were only recorded in the 1993 survey; therefore, except blood pressure that can be obtained in the above-mentioned 8 years, the other variables used in subsequent analysis of influencing factors were all from the 1993 survey. Our data were from the CHNS, which was administered in Liaoning, Jiangsu, Shandong, Guangxi (four provinces in the Eastern region), Hunan, Hubei, Henan (three provinces in the Middle region), and Guizhou (one province in the Western region). The present study did not obtain approval from the institutional review board of the Tianjin First Center Hospital because the data used were extracted from the CHNS, an ongoing open cohort.
Outcome measures and data extraction
The menopause in the present study was defined through the following questions: Have you stopped menstruating? Why have you stopped menstruating? At what age did you have menopause? As the variables involved in these questions were only recorded in the 1993 survey, all the other variables used in subsequent analysis of influencing factors were from the 1993 survey except blood pressure. Extracted variables included age, body height, body weight, hip circumference, waist circumference, educational levels, living regions and types, marital status, SBP, and DBP, as well as presence or absence of smoking, alcohol consumption, and antihypertensive agents.
Body mass index (BMI) was calculated according to the formula: body weight in kilograms divided by body height in meters squared. Underweight, normal weight, and overweight were defined as BMI <18.5 kg/m2, 18.5 kg/m2 ≤ BMI <24.0 kg/m2, and BMI ≥24.0 kg/m2, respectively (Citation19). Standard mercury sphygmomanometers were used to measure blood pressure 3 times on the right arm by experienced physicians after 10 minutes of seated rest, with 30-second intervals between the cuff inflation, and the average of three blood pressure measures was employed in the analysis. These measuring physicians finished relevant training for 7 days and passed a comprehensive examination in relation to the dependability of blood pressure measurement. SBP ≥140 mmHg or DBP ≥90 mmHg was defined as HTN (Citation20).
Group-based trajectory modeling (GBTM)
We extracted all follow-up data on blood pressure from the database in 8 years (1993, 1997, 2000, 2004, 2006, 2009, 2011, and 2015). The mean processing method was used for the blood pressure measured three times in the same year. Then, we adopted the GBTM approach, which is an application of finite-mixture modeling that allows the identification of population subgroups (classes) characterized by statistically distinct trajectories for one or more outcomes of interest (Citation21). The model was developed based on the blood pressure trajectories in the 8 years identified by the TRAJ procedure in SAS software. During the modeling process, Bayesian Information Criterion (BIC) was used to determine the optimal number of groups. BIC was a model selection benchmark and could balance model fit and complexity. The smaller BIC values indicated the better fit (Citation21). For different group numbers and BIC of the model, we drew elbow diagrams to choose the group number according to the number corresponding to the inflection point. Then, the trajectory trend of the groups was separately visualized for the assignment of a descriptive label.
Statistical analysis
In this study, normally distributed measurement data were compared by the t test, with the mean ± standard deviation (±s) as manifestations. Abnormally distributed measurement data were compared using the Mann–Whitney U rank-sum test, described as the median and interquartile ranges [M (Q1, Q3)]. The χ2 test or Fisher’s exact test was used to compare the enumeration data expressed as cases and percentages [n (%)]. Group-based trajectory modeling was first used to identify the development track of blood pressure changes, and then the trajectory group with the highest probability was selected as the best fitting model through the goodness-of-fit tests of Bayesian information criterion (BIC). According to the sample size and scree plots of blood pressure, it was considered that two groups were relatively appropriate. Based on this, the subjects were divided into SBP group and DBP group, and univariate difference analysis was performed. Finally, the variables with pronounced differences in the univariate analysis and those influencing blood pressure reported in previous studies were included into the multivariate logistic regression analysis for investigating the influencing factors of blood pressure variability. All statistical tests were two-sided. The differences were considered significant at P < .05. Statistical analyses were conducted using SAS software (SAS Institute Inc., Cary, NC, USA; version 9.4).
Results
Baseline characteristics of study population
There were 500 women with menopause occurring in and before 1993. When 154 cases with missing values of blood pressure were excluded, 346 women were finally eligible for the study, with the mean age of 43.21 ± 13.57 years. The baseline characteristics of the included population are listed in .
Table 1. Baseline characteristics of women included in different SBP and DBP groups, n (%).
shows that the inflection point corresponds to number 2, indicating that the SBP and DBP was appropriate to be divided into two types of trajectories according to the BIC value. As displayed in , one group of SBP trajectory maintained at a low level and increased slowly, called a low-level, slowly developed type (group 1, n = 267). Another group of SBP trajectory sustained at a high level, initially increased faster than group 1 and then tended to be stable, which was called a high-level, rapidly developed, stable type (group 2, n = 79). As demonstrated in , for DBP, one group remained at a low level and developed slowly, called a low-level, slowly developed type (group 1, n = 265), and another group remained at a high level and increased slowly, named a high-level, slowly developed type (group 2, n = 81).
Figure 1. Establishment of two different trajectories of SBP and DBP according to the Bayesian information criterion. Figures are created by R (v3.6.3, Institute for Statistics and Mathematics, Vienna, Austria).
Figure 2. The trajectory grouping of SBP and DBP. Figures are created by R (v3.6.3, Institute for Statistics and Mathematics, Vienna, Austria).
Baseline characteristics of women included in different SBP and DBP groups
Regarding SBP grouping, the women in group 2 had older age (p < .001), higher hip (p < .001), and waist circumferences (p < .001), as well as more proportions of overweight (p < .001) and antihypertensive use (p < .001), but lower education levels (p < .001) than those in group 1. The difference was also pronounced between groups 1 and 2 in the living regions (p = .034) (). Compared with DBP-based group 1, the women in group 2 had older age (p < .001), higher hip (p < .001), and waist circumferences (p < .001), as well as higher proportions of overweight (p = 0001), smoking (p = .014), alcohol consumption (p < .001), and antihypertensive use (p < .001), while lower education levels (p = .018). A significant difference was also present in the living regions between these two groups (p = .039) ().
Factors influencing blood pressure variability
The variables with significant differences in the univariate analysis and marital status reported in a previous study (Citation22) were included into the multivariate logistic regression analysis for screening (backward method). The multivariate analysis of SBP exhibited that the risk of women being assigned to group 2 would increase by 0.118 folds with each age increase of 1 year (OR = 1.118, 95%CI: 1.082–1.156, P < .001). Compared with women with normal weight and those without antihypertensive agents, the overweight and those taking antihypertensive agents had a 1.239- and 6.293-fold increase in the risk of being allocated to group 2, respectively (OR = 2.239, 95%CI: 1.010–4.964, P = .047; OR = 7.293, 95%CI: 2.191–24.275, P = .001). The risk of women being assigned to group 2 would increase by 0.069 times with each elevation of 1 cm in hip circumference (OR = 1.069, 95%CI: 1.014–1.128, P = .013). The risk of unmarried/divorced women assigned to group 2 was 3.103 times than the married women (OR = 3.103, 95%CI: 1.028–9.361, P = .044) ().
Figure 3. Three-line forest plots of the factors influencing SBP. Figures are created by R (v3.6.3, Institute for Statistics and Mathematics, Vienna, Austria).
In the multivariate analysis of DBP, it was observed that the risk of women being allocated to group 2 would increase by 0.067 times with each age increase of 1 year (OR = 1.067, 95%CI: 1.039–1.096, P < .001). The risks of women with alcohol consumption and antihypertensive agents allocated to group 2 were 2.741 and 4.577 times than those without, respectively (OR = 2.741, 95%CI: 1.169–6.429, P = .020; OR = 4.577, 95%CI: 1.553–13.492, P = .006). With each increase of 1 cm in hip circumference, the risk of women allocated to group 2 would increase by 0.093 times (OR = 1.093, 95%CI: 1.049–1.138, P < .001). The risk of unmarried/divorced women had a 2.615-fold increase in the risk of being assigned to group 2 (OR = 3.615, 95%CI: 1.228–10.644, P = .020) ().
Figure 4. Three-line forest plots of the factors influencing DBP. Figures are created by R (v3.6.3, Institute for Statistics and Mathematics, Vienna, Austria).
Discussion
In the present study, 346 out of 500 postmenopausal women from the CHNS were eligible for the analysis. Through group-based trajectory modeling, it was found that there were two different trajectories of blood pressure. SBP could be divided into low-level, slowly developed type and high-level, rapidly developed, stable type. DBP was composed of low-level, slowly developed type and high-level, slowly developed type. Multivariate analysis showed age, antihypertensive agents, hip circumference, and marital status as the significant factors influencing both SBP and DBP in postmenopausal women. Moreover, BMI was associated with SBP variability, while alcohol consumption was related to DBP changes.
Blood pressure usually increases in women after menopause (Citation23). Compared with aging men, the risk of developing HTN in postmenopausal women significantly increased (Citation24). There is a study showing that aging is accompanied by elevated muscle sympathetic nerve activity, which may partially explain why blood pressure raises with age, particularly in older women (Citation25). Additionally, a decline in cardiac vagal modulation was also observed in women after surgical menopause (Citation26). These changes may be associated with the level of estrogen affecting central sympathetic outflow in humans and enhancing sympathetic baroreflex sensitivity (Citation27,Citation28). During the mid-luteal phase of the menstrual cycle with relatively high estrogen levels, sympathetic baroreflex sensitivity was found to be greater than that during the follicular phase with relatively low estrogen levels (Citation28). This significant reduction in estrogen levels after menopause may affect blood pressure autonomic control, and loss of vasodilator effects of estrogen and/or β-adrenergic mediated vasodilation seems to augment the vascular resistance (Citation29,Citation30).
Weight gain accompanied by an elevated risk of central fat distribution is prevalent in aging women, particularly during the menopausal transition. Central obesity can result in several unfavorable metabolic consequences, such as HTN, dyslipidemia, and dysglycemia (Citation31). A previous study has revealed BMI as the strongest predictor for both SBP and DBP in women around menopause (Citation32). However, in the present study, BMI was identified to be a predominant predictor for SBP variability in postmenopausal women, but not DBP variability, which was consistent with the results in the study by of Khitan et al. (Citation33). This close association between BMI and systolic HTN may be attributed to abnormal adipose tissue expansion and central obesity.
Alcohol consumption is positively correlated with an elevated HTN risk regardless of age and sex (Citation34). Heavy alcohol consumption has been demonstrated to be associated with an increased HTN risk in women (Citation35). The guidelines for clinical management have proposed that all the patients undergoing HTN assessment or treatment should accept periodic lifestyle advice involving confirmation of alcohol consumption levels and encouragement of intake reduction (Citation36). Our results showed the correlation between alcohol consumption and DBP variability instead of SBP variability, which might be associated with less sample size and less alcohol consumption in postmenopausal women.
It is well-known that systemic HTN is an important indication for use of antihypertensive drugs (Citation37). In our study, antihypertensive agents were confirmed to be a predominant predictor for SBP and DBP changes in postmenopausal women. There are studies suggesting that antihypertensive agents can effectively control the blood pressure in most patients when complemented by lifestyle modifications (Citation38,Citation39). The guidelines also propose that the initial antihypertensive drug should be given at the lowest dose and then increased gradually to the maximum tolerated dose according to blood pressure responses (Citation23). Additionally, unmarried/divorced status was also found to be linked with a higher risk of blood pressure variability in postmenopausal women. Many cross-sectional studies have revealed an independent association between marital status and HTN (Citation40–42). Compared with their married counterparts, never married/divorced women had an increased risk of developing HTN (Citation43). The mechanisms underlying the effect of marital status on HTN may be partially explained by psychopathological factors, health behaviors (diet, physical activity, and adherence), biological mediators, neuroendocrine, and immune pathways (Citation22,Citation44).
Previous studies have suggested that defining long-term blood pressure trajectories can provide predictive value for CVD risk (Citation45–47). In a study based on The Atherosclerosis Risk in Communities Study data, six distinct SBP trajectories were identified, and the incidence rate of stroke, heart failure, and coronary heart disease could be estimated across the SBP trajectories (Citation45). In our study, we found two types of trajectories of SBP (low-level, slowly developed type; the high-level, rapidly developed, stable type) and of DBP (low-level, slowly developed type; the high-level, slowly developed type) in postmenopausal women. Similar SBP trajectories were found in the study by Tielemans et al., which indicated that the risk of CVD death was three times higher in the highest-trajectory group than in the lowest-trajectory group (Citation47).
One of the major strengths in the present study was that 8-year follow-up data from the CHNS were extracted to analyze the trajectory of blood pressure in postmenopausal women with time, and two different trajectories were identified in SBP and DBP, respectively. Nevertheless, the present study also had several limitations that should be noted. First, the measurement of menopause was self-reported. Second, some important information associated with HTN occurrence was missing in the CHNS, such as family history of HTN. Third, health behavior factors like physical activity and diet might exert an effect on blood pressure during the follow-up, but these factors were not taken into consideration. Finally, the sample was small in our study, which may not be representative of China. In the future, large-scale, prospective studies with longer follow-up duration need to be conducted to further validate our findings.
Conclusions
In postmenopausal women, there were two different trajectories for SBP and DBP. Age, BMI, antihypertensive agents, hip circumference, and marital status were associated with SBP variability in postmenopausal women, while age, alcohol consumption, antihypertensive agents, hip circumference, and marital status were in connection with DBP variability. This study suggests that blood pressure monitoring is necessary, and postmenopausal women should keep a normal BMI and decrease alcohol consumption.
Hypertension is the main modifiable risk factor for cardiovascular disease. The prevalence of hypertension is increasing and deserves a greater focus on prevention and management. It is well known that conditions like postmenopause can be associated with hypertension. Hypertension in women warrants special attention due to the conditions unique to women throughout life. The incidence rate of hypertension in postmenopausal women is increasing. There are some special factors that need to be considered, and some comprehensive measures need to be taken to intervene, including hip circumference, smoking, drinking, marital status, etc. Because the systolic and diastolic blood pressure trajectories are different, more targeted interventions should be taken for patients with elevated SBP and DBP, which will help improve the quality of life of postmenopausal women and reduce the incidence of cardiovascular and cerebrovascular diseases to a certain extent.
Declaration of Figures Authenticity
All figures submitted have been created by the authors who confirm that the images are original with no duplication and have not been previously published as a whole or in part.
Disclosure statement
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Additional information
Funding
References
- Fuchs FD, Whelton PK. High blood pressure and cardiovascular disease. Hypertension. 2020;75:285–8. doi: 10.1161/HYPERTENSIONAHA.119.14240.
- Wang J, Zhang L, Wang F, Liu L, Wang H, et al. Prevalence, awareness, treatment, and control of hypertension in China: results from a national survey. Am J Hypertens. 2014;27(11):1355–61. doi:10.1093/ajh/hpu053.
- Alcohol’s RM. Impact on the cardiovascular system. Nutrients. 2021;13(10): 3419.
- Manus MB, Bloomfield GS, Leonard AS, Guidera LN, Samson DR, Nunn CL, et al. High prevalence of hypertension in an agricultural village in Madagascar. PloS One. 2018;13:e0201616.
- Hu B, Liu X, Yin S, Fan H, Feng F, Yuan J, et al. Effects of psychological stress on hypertension in middle-aged Chinese: a cross-sectional study. PloS One. 2015;10(6):e0129163. doi:10.1371/journal.pone.0129163.
- Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. 2020;16:223–37. doi:10.1038/s41581-019-0244-2.
- Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, Shao L, Tian Y, Dong Y, Zheng C, et al. Status of hypertension in China: results from the China hypertension survey, 2012-2015. Circulation. 2018;137:2344–56.
- Lawes CM, Vander Hoorn S, Rodgers A. Global burden of blood-pressure-related disease, 2001. Lancet. 2008;371:1513–18.doi:10.1016/S0140-6736(08)60655-8.
- Lima R, Wofford M, Reckelhoff JF. Hypertension in postmenopausal women. Curr Hypertens Rep. 2012;14:254–60.doi:10.1007/s11906-012-0260-0.
- Whelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension 2018;71:e13–115.
- Lin YY, Lee SD. Cardiovascular benefits of exercise training in postmenopausal hypertension. Int J of Mol Sci. 2018;19(9):2523.
- Begum A, Nessa A, Saki SA. Study on blood pressure changes in postmenopausal women. Mymensingh Med J. 2019;28:274–77.
- Nuotio J, Suvila K, Cheng S, Langén V, Niiranen T, et al. Longitudinal blood pressure patterns and cardiovascular disease risk. Ann Med. 2020;52:43–54.
- Lee CL, Wang JS. Systolic blood pressure trajectory and cardiovascular outcomes: an analysis using data in the Systolic Blood Pressure Intervention Trial. Int J Clin Pract. 2020;74:e13450.doi:10.1111/ijcp.13450.
- Zhang X, Ouyang Y, Huang F, et al. Modifiable factors of 20-year blood pressure trajectories among normotensives and their associations with hypertension: a prospective study. Br J Nutr. 2022;128(2):252–262.
- Wang Y, Howard AG, Adair LS, Wang H, Avery CL, Gordon-Larsen P, et al. Waist circumference change is associated with blood pressure change independent of BMI change. Obesity (Silver Spring). 2020;28:146–53.
- Popkin BM, Du S, Zhai F, Zhang B, et al. Cohort profile: the China health and nutrition survey–monitoring and understanding socio-economic and health change in China, 1989-2011. Int J Epidemiol. 2010;39:1435–40.
- Han T, Zhang S, Duan W, Ren X, Wei C, Sun C, Li Y, et al. Eighteen-year alcohol consumption trajectories and their association with risk of type 2 diabetes and its related factors: the China health and nutrition survey. Diabetologia. 2019;62(6):970–80.doi:10.1007/s00125-019-4851-z.
- Chen C, Lu FC. The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci. 2004;17(Suppl):1–36.
- Zhang Y, Zhang W, Liu L. Comments on 2018 ESC/ESH Hypertension guidelines: Chinese perspective. Circ Res. 2019;124:978–80.doi:10.1161/CIRCRESAHA.119.314997.
- Malaba TR, Cois A, Madlala HP, Matjila M, Myer L, Newell M-L, et al. Blood pressure trajectories during pregnancy and associations with adverse birth outcomes among HIV-infected and HIV-uninfected women in South Africa: a group-based trajectory modelling approach. BMC Pregnancy Childbirth. 2020;20:742.
- Dong Y, Wang ZW, Zhang LF, et al. A study on the quality of life for hypertensive patients and related risk factors in Eastern China. Chin J Hypertens. 2016;24:541–46.
- Aronow WS, Fleg JL, Pepine CJ, Artinian NT, Bakris G, Brown AS, Ferdinand KC, Ann Forciea M, Frishman WH, Jaigobin C, et al. ACCF/AHA 2011 expert consensus document on hypertension in the elderly: a report of the American College of Cardiology Foundation Task Force on clinical expert consensus documents developed in collaboration with the American Academy of Neurology, American Geriatrics Society, American Society for Preventive Cardiology, American Society of Hypertension, American Society of Nephrology, Association of Black Cardiologists, and European Society of Hypertension. J Am Soc Hypertens. 2011;5:259–352.
- Burt VL, Whelton P, Roccella EJ, Brown C, Cutler JA, Higgins M, Horan MJ, Labarthe D, et al. Prevalence of hypertension in the US adult population. Results from the third national health and nutrition examination survey, 1988-1991. Hypertension. 1995;25(3):305–13.doi:10.1161/01.HYP.25.3.305.
- Narkiewicz K, Phillips BG, Kato M, Hering D, Bieniaszewski L, Somers VK, et al. Gender-selective interaction between aging, blood pressure, and sympathetic nerve activity. Hypertension. 2005;45(4):522–25.doi:10.1161/01.HYP.0000160318.46725.46.
- Mercuro G, Podda A, Pitzalis L, et al. Evidence of a role of endogenous estrogen in the modulation of autonomic nervous system. Am J Cardiol. 2000;85:787–89.
- Hart EC, Charkoudian N, Wallin BG, Curry TB, Eisenach J, Joyner MJ, et al. Sex and ageing differences in resting arterial pressure regulation: the role of the β-adrenergic receptors. J Physiol. 2011;589:5285–97.
- Minson CT, Halliwill JR, Young TM, Joyner MJ, et al. Influence of the menstrual cycle on sympathetic activity, baroreflex sensitivity, and vascular transduction in young women. Circulation. 2000;101:862–68.
- Harvey RE, Barnes JN, Charkoudian N, et al. Forearm vasodilator responses to a β-adrenergic receptor agonist in premenopausal and postmenopausal women. Physiol Rep. 2014. p. 2.
- Peinado AB, Harvey RE, Hart EC, Charkoudian N, Curry TB, Nicholson WT, Wallin BG, Joyner MJ, Barnes JN, et al. Neural control of blood pressure in women: differences according to age. Clin Autono Res. 2017;27:157–65.
- Kapoor E, Collazo-Clavell ML, Faubion SS. Weight gain in women at midlife: a concise review of the pathophysiology and strategies for management. Mayo Clin Proc. 2017;92:1552–58.doi:10.1016/j.mayocp.2017.08.004.
- Cifkova R, Pitha J, Lejskova M, Lanska V, Zecova S, et al. Blood pressure around the menopause: a population study. J Hypertens. 2008;26:1976–82.
- Khitan Z, Dial L, Santhanam P. Predictors of systolic blood pressure in post-menopausal euthyroid women: a study of the NHANES continuous survey data 2007-2012. Post Reprod Health. 2015;21:75–76.doi:10.1177/2053369115580911.
- Ikehara S, Iso H. Alcohol consumption and risks of hypertension and cardiovascular disease in Japanese men and women. Hypertens Res. 2020;43:477–81.doi:10.1038/s41440-020-0417-1.
- Roerecke M, Tobe SW, Kaczorowski J, et al. Sex-Specific associations between alcohol consumption and incidence of hypertension: a systematic review and meta-analysis of cohort studies. J AmHeart Assoc. 2018;7(13):e008202.
- Mancia G, Fagard R, Narkiewicz K, Redón J, Zanchetti A, Böhm M, Christiaens T, Cifkova R, De Backer G, Dominiczak A, et al. ESH/ESC guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). J Hypertens. 2013;31:1281–357.
- Ram CV. Antihypertensive drugs: an overview. Am J Cardiovasc Drugs. 2002;2:77–89.doi:10.2165/00129784-200202020-00002.
- Maruhashi T, Kihara Y, Higashi Y. Perspectives on the management of hypertension in Japan. Expert Opin Pharmacother. 2020;21:1179–87.doi:10.1080/14656566.2020.1724958.
- Burnier M, Egan BM. Adherence in Hypertension. Circ Res. 2019;124:1124–40.doi:10.1161/CIRCRESAHA.118.313220.
- Ramezankhani A, Azizi F, Hadaegh F, Shimosawa T. Associations of marital status with diabetes, hypertension, cardiovascular disease and all-cause mortality: a long term follow-up study. PloS One. 2019;14:e0215593.doi:10.1371/journal.pone.0215593.
- Manfredini R, De Giorgi A, Tiseo R, Boari B, Cappadona R, Salmi R, Gallerani M, Signani F, Manfredini F, Mikhailidis DP, et al. Marital status, cardiovascular diseases, and cardiovascular risk factors: a review of the evidence. J Womens Health. 2017;26:624–32.
- Causland FR, Sacks FM, Forman JP. Marital status, dipping and nocturnal blood pressure: results from the dietary approaches to stop hypertension trial. J Hypertens. 2014;32:756–61.doi:10.1097/HJH.0000000000000107.
- Tuoyire DA, Ayetey H. Gender differences in the association between marital status and hypertension in Ghana. J Biosoc Sci. 2019;51:313–34.doi:10.1017/S0021932018000147.
- Robles TF, Slatcher RB, Trombello JM, McGinn MM, et al. Marital quality and health: a meta-analytic review. Psychol Bull. 2014;140:140–87.
- Petruski-Ivleva N, Viera AJ, Shimbo D, Muntner P, Avery CL, Schneider ALC, Couper D, Kucharska-Newton A, et al. Longitudinal patterns of change in systolic blood pressure and incidence of cardiovascular disease: the atherosclerosis risk in communities study. Hypertension. 2016;67:1150–56.
- Tielemans SM, Geleijnse JM, Menotti A, Boshuizen HC, Soedamah‐Muthu SS, Jacobs DR, Blackburn H, Kromhout D, et al. Ten-year blood pressure trajectories, cardiovascular mortality, and life years lost in 2 extinction cohorts: the Minnesota Business and professional men study and the Zutphen Study. J Am Heart Assoc. 2015;4:e001378.
- Tielemans S, Geleijnse JM, Laughlin GA, Boshuizen HC, Barrett-Connor E, Kromhout D, et al. Blood pressure trajectories in relation to cardiovascular mortality: the Rancho Bernardo Study. J Hum Hypertens. 2017;31:515–19.