The Impact of Marital Status on Hypertension: SEM Analysis of a Chinese General Population Cohort

doi:10.21203/rs.3.rs-4340785/v1

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The Impact of Marital Status on Hypertension: SEM Analysis of a Chinese General Population Cohort

https://doi.org/10.21203/rs.3.rs-4340785/v1

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Background

Hypertension, a chronic non-communicable disease, has multifaceted and complex etiologies influenced by various risk factors. This study examines the role of marital status among the factors influencing blood pressure levels and assesses its potential indirect effects through other variables.

Methods

This study involved 1,852 individuals from the Baoding area in the Beijing-Tianjin-Hebei region, China. Participants were categorized by gender and marital status. Descriptive statistical methods and Structural Equation Modeling (SEM) were utilized for comprehensive data analysis in this study.

Results

Women in the marital turmoil group (divorced or widowed) had higher mean systolic blood pressure compared to those in the married group. A significant age difference between the two groups (p=0.024) may introduce bias into the results. Gender-stratified SEM results show that marital status neither directly nor indirectly affects blood pressure in men. In women, marital status indirectly affects blood pressure through two pathways: "Marital Status (standardized path coefficient, β = 0.15, p < 0.001) - Economic Status (β = -0.33, p < 0.001) - Dietary Habits (β = 0.12, p < 0.05) - Blood Pressure" and "Marital Status (β = 0.15, p < 0.001) - Economic Status (β = -0.33, p < 0.001) - Dietary Habits (β = 0.02, p < 0.01) - Obesity (β = 0.24, p < 0.001) - Blood Pressure."

Conclusion

Results confirm a close association between marital status and hypertension in women, with factors like age and economic status potentially influencing this relationship. This underscores the need for further research to explore the intricate connections between marriage and blood pressure. The study advocates for medical institutions and communities to offer psychological health support to those undergoing marital difficulties and stresses the significance of health education in addressing hypertension risk factors.

Marital Status

Hypertension

Structural Equation Modeling

Cardiovascular Diseases (CVDs) have emerged as the foremost cause of death and disability worldwide [1]. Hypertension stands as a pivotal risk factor for CVDs and global premature mortality, drawing significant research focus [2][3]. The 2010 global epidemiology on hypertension highlighted that around 1.39 billion adults globally are afflicted, with heightened prevalence in middle-income regions [4]. A 2014 study noted an ascending trend in hypertension prevalence within China over recent decades [5], marking it as an escalating health concern both domestically and globally.

Hypertension, a chronic non-communicable disease, is influenced by multifaceted etiologies. Research underscores the strong association of demographic factors (age, gender, marital status, educational level, family medical history, economic status), social background, lifestyle choices (smoking, alcohol intake, diet, physical activity), and psychological conditions with hypertension[6][7][8]. Marital status, in particular, significantly influences hypertension's development and progression. Stable marital relationships are known to confer positive health impacts, with married individuals often leading healthier lifestyles than their unmarried peers—highlighting marriage's positive causal link to health [9][10]. Conversely, separated, divorced, widowed, or unmarried individuals face increased all-cause mortality risks, especially from CVDs, influenced by marital quality, psychological stress, and socio-economic factors [11]. This underscores marital status's profound effect on both mental and physical health, correlating with hypertension incidence.

The biopsychosocial medical model's evolution has shifted focus towards latent variables like social and psychological factors, examining their specific impact on blood pressure. However, measuring these latent variables may introduce errors, and traditional analysis methods might not accurately assess their outcome impacts. In contrast, Structural Equation Modeling (SEM) adeptly mitigates these challenges.

Despite existing research on marital status and blood pressure, scant studies have applied SEM to dissect the relationship's underlying influencers. Thus, this study delves into health data from a natural population cohort in Baoding, Beijing-Tianjin-Hebei, employing SEM to investigate marital status's effect on blood pressure. This approach offers fresh insights and elucidates both direct and indirect pathways through which marital status influences blood pressure across genders.

Conceptual Framework

Given the limited research on the marriage-blood pressure nexus both domestically and globally, a definitive framework has yet to be established. This study draws upon recent insights into marital status, blood pressure influencers, and the biopsychosocial model, to initially set up a conceptual framework (Fig. 1), leveraging survey data to explore both direct and indirect connections between marriage and blood pressure. Subsequent analysis involves the formulation of latent variables from identified risk factors and survey content, validated through exploratory factor analysis (EFA) and confirmatory factor analysis, culminating in a comprehensive hypothesis model that synthesizes the interplay among various factors.

Based on the hypothetical model diagram, the following hypotheses are proposed:

H1: Marital status affects blood pressure levels;

H2: Marital status affects socioeconomic status;

H3: Marital status affects dietary habits;

H4: Socioeconomic status affects dietary habits;

H5: Dietary habits affect blood lipid levels;

H6: Dietary habits affect obesity;

H7: Dietary habits affect blood pressure levels;

H8: Blood lipid levels affect blood pressure levels;

H9: Obesity affects blood pressure levels.

Data Source

This investigation draws from a general population health cohort survey executed across the Beijing-Tianjin-Hebei region between 2020 and 2023[12]. It specifically targeted the urban residents of Baoding's Jingxiu District, encompassing 1,853 participants. Eligibility criteria mandated a minimum one-year residence in the community, being 20 years of age or older, and a readiness to engage in the study's subsequent follow-up activities.

Data Collection Tools and Techniques

Questionnaire

A meticulously crafted questionnaire was administered via face-to-face interviews by uniformly trained investigators, capturing a broad spectrum of data, including gender, age, ethnicity, residence, educational level, personal monthly income, smoking and drinking habits, engagement in physical labor and exercise, dietary patterns (detailing the intake frequency of eggs, fruits, milk, and dairy products), along with the menstrual conditions of women [13]. Furthermore, comprehensive physical health assessments were conducted on all participants, measuring height, weight, blood pressure, body composition, and blood biochemistry.

For variable categorization, marital status was segmented into four groups: unmarried, married, divorced, and widowed, with corresponding codes of 1 = unmarried; 2 = married; 3 = divorced; 4 = widowed. Monthly income, reported in CNY, was organized into twelve brackets ranging from less than 1,000 to 15,000 CNY or more, to accurately reflect financial standing. Educational attainment was classified from illiteracy to graduate education, coded as: 1 = illiterate; 2 = primary school; 3 = junior high school; 4 = high school; 5 = college and undergraduate; 6 = graduate student. The consumption frequency of eggs, dairy products, and fruits was delineated into five levels, from daily (5–7 days per week) to never, offering detailed insight into dietary habits.

Blood Pressure

Participants were instructed to refrain from consuming alcohol or coffee, smoking, or eating prior to blood pressure assessment. Measurements were conducted with the participant seated, following a 5-minute rest period. Blood pressure readings were recorded thrice consecutively, at 1-minute intervals, utilizing an Omron electronic blood pressure monitor (model: HEM-907) on the right brachial artery of the participants[14].

Physical examination

Height was assessed using a stadiometer, ensuring precision to 0.1 cm, while weight was determined through a body composition analyzer (TANITA BC-420), achieving accuracy to 0.1 kg. These measurements were performed by uniformly trained health examination staff, and the body mass index (BMI) was subsequently calculated as the weight in kilograms divided by the square of the height in meters (m^2) [15].

Blood Biochemistry

Participants were mandated to fast for a minimum of 8 hours prior to the collection of blood samples. A total of 9 ml of peripheral venous blood was drawn, allowed to stand for 30 minutes, then centrifuged on-site, with serum aliquoted within 4 hours. The samples were subsequently stored in a -20°C freezer[16]. Serum biochemistry analyses were conducted at the Chinese People's Liberation Army General Hospital.

Data Processing

Preprocessing

During the data processing phase, R software version 4.3.1 was employed for data preprocessing, encompassing outlier identification and remediation, missing value management, and the selection and definition of pertinent variables.

The unmarried cohort consisted of 72 individuals, split into 26 males and 46 females, with an age range of 20 to 64 years—predominantly clustered between 20 and 30 years. Given the anticipated discrepancies in economic status and dietary habits between the unmarried and those married, divorced, or widowed, incorporating the unmarried group into the analysis alongside spouseless participants was considered unsuitable. Consequently, the unmarried group was omitted from further analysis.

To streamline the interpretability and practicality of the findings, marital status was consolidated into two distinct categories: normal marriage (married) and marital turmoil (divorced and widowed), with normal marriage designated as 0 and marital turmoil as 1.

This refinement led to the inclusion of 1,498 participants in the final analysis, comprising 459 males—440 categorized under normal marriage and 19 under marital turmoil, and 1,039 females—903 within the normal marriage category and 136 classified under marital turmoil.

Descriptive Statistics

In the descriptive statistics phase, continuous variables were summarized as mean values along with their 95% confidence intervals, whereas categorical data were depicted through frequencies and proportions. For assessing group differences, continuous variables underwent t-test evaluations, and chi-square tests were applied to categorical data, establishing a statistical significance threshold at p = 0.05.

Construction of SEM

To affirm the analysis results' reliability, variables underwent initial tests for normal distribution. Skewness and kurtosis tests determined normality; data were deemed approximately normal if skewness's absolute value was < 2 and kurtosis's absolute value (minus 3) was < 7. For assessing multivariate normal distribution, the chi-square and Mahalanobis distance plot method was employed. Variables were considered to follow a multivariate normal distribution if plot points largely aligned along a straight line, fulfilling SEM analysis prerequisites.

In constructing latent variables, exploratory factor analysis (EFA) was performed on selected variables, with suitability confirmed by a KMO value > 0.6 and a significant Bartlett's Test of Sphericity. Principal component analysis facilitated factor extraction, with the scree plot's inflection point guiding the number of factors extracted. The varimax rotation method was applied, selecting factors with an absolute loading > 0.5.

This process determined the final latent variables, their designations, and constituent manifest variables. Marital status was included as a manifest variable, and maximum likelihood estimation applied for SEM fitting. Notably, the significant male and female sample size disparity necessitated separate structural equation models to mitigate gender-based confounding bias. The hypothetical model was informed by prior literature and findings.The 'lavaan' package in R language facilitated SEM's construction and validation. Statistically significant differences or effects were acknowledged at p-values less than 0.05.

Descriptive Analysis

Appendix 1 presents the baseline characteristics of the study participants. Gender differences reached statistical significance across all the listed characteristics, with the exception of egg consumption. Specifically, males exhibited higher values than

Furthermore, among males, there were no significant differences between the group experiencing marital turmoil and the group with marital stability in any aspect. However, within the female participants, the group experiencing marital turmoil exhibited significant differences in age, educational level, metabolic age, systolic blood pressure, and total cholesterol when compared to the group with marital stability. Except for educational level, which was lower, the female group facing marital turmoil had higher values in age, metabolic age, systolic blood pressure, and total cholesterol compared to the group with marital stability.

SEM Analysis

As shown in Appendix 2, all variables conform to a univariate normal distribution.

From Fig. 2, a chi-square and Mahalanobis distance plot was created for all continuous variables in this study. The plot shows that the points generally form a straight line, indicating that the above variables follow a multivariate normal distribution, meeting the requirements for subsequent SEM (Structural Equation Modeling) analysis.

The Kaiser-Meyer-Olkin (KMO) test result is 0.629 > 0.6, and the Bartlett's test of sphericity is significant (X^2/df = 119.057, P < 0.001), indicating that the data is suitable for exploratory factor analysis. According to the scree plot (Fig. 3), there is an inflection point at the fifth factor, and the line graph shows that there are four common factors with eigenvalues greater than 1.0.

Principal component analysis was employed for extraction, with four factors accounting for a cumulative variance explanation rate of 97.01%, meaning that 97.01% of the original data information can be explained by these four common factors. Five factors were set for extraction. The varimax method was used for rotation, and factors with loadings of absolute value > 0.5 were selected for division. The divided factor loading matrix is shown in Appendix 3.

From Appendix 3, it is observed that Component 1 has the highest factor loadings for fat mass, body mass index, and metabolic age factors, which, combined with theoretical knowledge and related literature, leads to Component 1 being named as Obesity. Following this logic, Component 2 is named Blood Pressure Level, including two variables: systolic and diastolic pressures; Component 3 is named Socioeconomic Status, comprising two variables: educational level and monthly income (log-transformed); Component 4 is named Dietary Habits, including three variables: milk and dairy products, eggs, and fruits. In subsequent SEM (Structural Equation Modeling) analysis, the variable of total cholesterol is removed, and the latent variable of obesity is also removed from the hypothesis model.

The R 4.3.1 software and the lavaan package were used to establish and fit the hypothesis model for data from both male and female populations separately, choosing the maximum likelihood estimation method for fitting. The fitting results are in Appendix 4.

For both men and women, the model fitting results show that the CFI (Comparative Fit Index) and GFI (Goodness of Fit Index) are both greater than 0.9, AGFI (Adjusted Goodness of Fit Index) is close to 0.9, and RMSEA (Root Mean Square Error of Approximation) is close to 0.08. Taking into account the model's statistical test results and other fitting indicators, the model fit is considered to be good. Based on the model fitting results and in conjunction with the research hypotheses and path coefficient tests, the results after excluding insignificant paths (Table 1) are as follows:

Table 1

The model fitting results
Pathway	β(95%CI)	p
Male
Dietary habits→Obesity	0.03(-0.06,0.12)	0.021
Obesity→Blood pressure levels	0.28(0.19,0.36)	0.000
Female
Marital status→Economic status	0.15(0.09, 0.21)	0.000
Economic status→Dietary habits	-0.33(-0.27,-0.38)	0.000
Dietary habits→Blood pressure levels	0.12 (0.06, 0.18)	0.017
Dietary habits→Obesity	0.02(-0.04,0.08)	0.002
Obesity→Blood pressure levels	0.24 (0.18, 0.30)	0.000

Based on the path fitting results from the table above, non-significant paths in the hypothetical model were removed, ultimately determining the structural equation model diagrams for both male and female populations separately (***p < 0.001, **p < 0.01, *p < 0.05).

The results of the Structural Equation Modeling (SEM) revealed that in men, marital status neither directly nor indirectly influences blood pressure. The only significant pathway identified was "Dietary Habits (standardized path coefficient, β = 0.03, p < 0.001) - Obesity (β = 0.28, p < 0.001) - Blood Pressure". In women, however, marital status can indirectly affect blood pressure through two pathways: "Marital Status (β = 0.15, p < 0.001) - Economic Status (β = -0.33, p < 0.001) - Dietary Habits (β = 0.12, p < 0.05) - Blood Pressure" and "Marital Status (β = 0.15, p < 0.001) - Economic Status (β = -0.33, p < 0.001) - Dietary Habits (β = 0.02, p < 0.01) - Obesity (β = 0.24, p < 0.001) - Blood Pressure".

This study delves into the intricate relationship between marital status and hypertension among adults aged 20 and above in the Beijing-Tianjin-Hebei region. The findings reveal that marital status does not exert a direct influence on blood pressure levels in the male population. However, it does have an indirect impact on blood pressure levels in the female population through various factors.

In the male population, marital status, whether assessed directly or indirectly, does not appear to affect blood pressure levels. This discovery contrasts with a previous study [17], which suggested that unmarried men are more susceptible to hypertension than their married counterparts. These results underscore the significance of gender-specific variations in the association between marriage and health, warranting further exploration in future research endeavors.

In the female population, marital status indirectly influences blood pressure by affecting economic status and dietary habits. This aligns with the stress-buffering model [18], which posits that social relationships, particularly marital ones, can mitigate health risks by improving socioeconomic status and lifestyle. Compared to women in stable marriages, women who have experienced marital disruption tend to exhibit higher systolic blood pressure levels. This finding is in line with prior research [19][20][21][22] that highlights the close link between marital stability and cardiovascular health.

Moreover, recent studies [23][24][25][26][27] have demonstrated that individual economic circumstances significantly impact the risk of hypertension, particularly among women. This conclusion further supports the results of the structural equation model analysis in this study: marital status indirectly affects women's blood pressure by influencing socioeconomic status. Additionally, the COVID-19 pandemic has introduced an additional layer of complexity to the relationship between marital status and health [28]. The data collected for this study during 2020–2023 coincided the early stages of the COVID-19 pandemic in China. The heightened social stress during this period may have amplified the advantages of marital stability and exacerbated the life stress experienced by individuals undergoing marital disruption, thereby affecting blood pressure levels.

This study carefully examines the differential effects of marital status on blood pressure between male and female populations. The SEM final path results for males and females reveal a significant difference: the male group does not include the latent variable of economic status. Previous research indicates that in China, especially in the areas selected for this study, the financial burdens associated with marriage, including expenses related to housing, dowry, and related costs, are primarily shouldered by men [29]. This inherent economic threshold in China likely subjects men to an objective economic status screening before marriage.Only when a man's overall economic status is sufficient to bear the financial pressures of forming a new family in the future can he potentially enter into marriage.For women, economic status may not be a decisive factor for marriage; parents often value the wealth, work capabilities, social status, and economic prospects of a son-in-law more than those of a daughter-in-law, with chastity being particularly valued in daughters-in-law [30]. In the cultural and historical context of China, where men are traditionally expected to provide for the family while women are responsible for household chores and child-rearing [31][32].After a change occurs in marital status, the portion of the living expenses originally borne by men is transferred to the women themselves, while men have always been responsible for their own living expenses,it is understandable why marital stability does not significantly impact blood pressure levels in men.

Regarding the finding that economic status does not have a significant impact on dietary habits among male groups compared to female groups, on one hand, changes in marital status only have a significant impact on the economic conditions of women, thereby affecting their dietary habits.On the other hand ,Alysha L. Deslippe's psychological analysis suggests that during adolescence, females may be more influenced by external factors in their dietary habits than males [33].In other words, the dietary habits of the female group may be influenced by events such as changes in marital status. Liu H also noted that women may be more susceptible to the psychological and physical impacts of marital stress than men [34].These findings also provide some clues for the significance of the path between the latent variable of dietary habits among women in this study and their blood pressure status.

Finally, it is important to consider the complex interplay between sexual orientation and marital status [35] when discussing how marriage influences health. After accounting for socioeconomic status and health behaviors, the disparities in the relationship between marital status and health among individuals of different sexual orientations and genders tend to diminish.

However, it's important to acknowledge certain limitations of this study. Due to its cross-sectional design, we cannot establish a causal relationship between latent variables and blood pressure. Future prospective cohort studies are necessary to validate our findings. Additionally, the small number of men in marital turmoil group (19) and the significant age distribution differences between the normal marital and marital turmoil group among women could potentially introduce bias into the study's results. Furthermore, the study focused on permanent residents of the urban area of Baoding City, Hebei Province, China, and collected data during the early stages of the COVID-19 pandemic in China. Therefore, the conclusions drawn from this study possess specific temporal and spatial specificity and may not be universally applicable.

In summary, this study underscores the importance of considering marital status in the development of preventive and intervention strategies for hypertension. Particularly for women who have experienced marital disruption, additional social support and targeted health interventions may be warranted. These findings also suggest that strategies for hypertension prevention may need to be tailored to different gender groups.

In women, marital status indeed has a close relationship with hypertension, but factors such as age may influence this relationship. Further research is necessary to unveil the multiple connections between marriage and blood pressure. This study suggests that medical institutions and communities provide psychological health support for individuals experiencing marital strife and emphasizes the importance of health education, particularly regarding risk factors for hypertension.

SEM：Structural Equation Modeling

CVDs：Cardiovascular Diseases

EFA：Exploratory factor analysis

BMI：Body mass index

CFI:Comparative Fit Index

GFI:Goodness of Fit Index

AGFI:Adjusted Goodness of Fit Index

RMSEA :Root Mean Square Error of Approximation

Availability of data and materials

The datasets analyzed during the current study are available from the corresponding authors on reasonable request.

Acknowledgements

The authors express their sincere appreciation to all the staff for performing the field work. They are also grateful to all the study participants.

Funding

This work was supported by CAMS Innovation Fund for Medical Sciences (CIFMS) (2020-I2M-2-009), Medical research and incubation project of Hebei University（NO. 2021B07) and Natural Science Foundation of Hebei Province (No. C2023201049).

Author information

Yajiao Fan and Huijing He contributed equally to this work and shared first authors.

Authors and Affiliations

School of Public Health, Hebei University,Baoding, Hebei Province, China

Yajiao Fan, Haojun Li, Chang Liu, Lei Ma, Zehua Xu, Jiayi Zhao & Yuhan Wu

School of Basic Medical Sciences, Hebei University, Baoding, Hebei Province, China

Zeqi Liu

Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, 5 Dong Dan San Tiao, Dong Cheng District, Beijing, 100005, China

Guangliang Shan, Huijing He & Zhili Chen

State Key Laboratory of Common Mechanism Research for Major Diseases, Beijing, China

Guangliang Shan&Huijing He

Contributions

Study concepts: YJ, HJ and GL; Study design: YJ, HJ and GL; Data acquisition: YJ, HJ, HJ L, ZH,YH, ZQ and GL; Quality control of data and algorithms: HJ L, ZL, JY, YH, ML, ZQ and ZH; Data analysis and interpretation: HJ L, JY, LM, YH and ZL; Manuscript preparation: YJ, HJ, HJ L and GL; Manuscript editing: YJ, HJ, HJ L and GL; Manuscript review: GL. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Guangliang Shan

Ethics declarations

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by bio Ethical Committee of Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences（NO.055-2020）. The patients/participants provided their written informed consent to participate in this study. All methods and experiments were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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Editorial decision: Revision requested
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The Impact of Marital Status on Hypertension: SEM Analysis of a Chinese General Population Cohort

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Abstract

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Introduction

Methods

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Results

Appendix 1 presents the baseline characteristics of the study participants. Gender differences reached statistical significance across all the listed characteristics, with the exception of egg consumption. Specifically, males exhibited higher values than

Discussion

Conclusion

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