Association and Interaction Analysis of Body Mass Index and Triglycerides Level with Blood Pressure in Elderly Individuals in China

Objectives To assess the extent of interaction between body mass index (BMI) and triglyceride (TG) level and its effects on blood pressure (BP) in elderly individuals in China. Design Cross-sectional study. Setting Data were taken from a cross-sectional study called the China Health and Retirement Longitudinal Study. Participants The analytic sample included 3629 subjects aged 45 to 96 years. Main Outcome Measurements Data were obtained from the China Health and Retirement Longitudinal Study, which is a cross-sectional study. Age-adjusted partial Pearson's correlation test was used to compare various characteristics and BP. Adjusted associations were first used as linear regression models, as appropriate. Then, general linear models adjusted for related potential confounders were used to examine the synergistic effects of BMI and TG level on BP. Finally, a binary logistic regression model adjusted for confounding factors was used to examine the association between BMI or TG level and hypertension. Results Age-adjusted partial Pearson's correlation coefficient showed that the TG level was positively correlated with both systolic blood pressure (SBP) and diastolic blood pressure (DBP) in both men and women with BMI < 24.0 kg/m2; however, TG level was positively correlated with DBP in women with BMI ≥ 24.0 kg/m2 but not with DBP in men with BMI ≥ 24.0 kg/m2. Multiple linear regression analysis showed that BMI level was significantly and positively associated with both SBP and DBP in men and women with BMI < 24.0 kg/m2, and TG level was significantly and positively associated with SBP in women with BMI < 24.0 kg/m2, independent of other confounding factors. A general linear model analysis with adjustment for confounding factors (age, educational level, marital status, current residence, smoking, eating habits, taking activities, antidiabetic medication, antihypertensive therapy, fasting plasma glucose [FPG], low-density lipoprotein cholesterol [LDL-C], estimated glomerular filtration rate [eGFR], and serum uric acid [SUA]) showed no interaction between BMI and TG level and SBP (men, β = 0.572, P = 0.845; women, β = 0.122, P = 0.923) and DBP (men, β = -0.373, P = 0.810; women, β = 0.272, P = 0.828). A binary logistic regression model analysis with adjustment for confounding factors (age, educational level, marital status, current residence, smoking, drinking, eating habits, taking activities, major accidental injury, physical activity, history of cardiovascular disease, history of liver disease, antilipidemic medication, antidiabetic medication, antihypertensive therapy, FPG, LDL-C, high-density lipoprotein cholesterol [HDL-C], eGFR, and SUA) showed that overweight and obese men and women were more likely to have hypertension (men: odds ratio [OR] = 1.781, 95% confidence interval [CI] = 1.393–2.277; women: OR = 1.653, 95% CI = 1.330–2.055) and women with high TG were more likely to have hypertension (OR = 1.558, 95% CI = 1.219–1.992). Conclusion An interactive effect of BMI and TG level on BP was not observed in either men or women; however, independent effects of BMI on BP were observed in both men and women, and an association between TG level and hypertension was observed in women.


Introduction
The prevalence of hypertension has dramatically increased in the past few years in China [1,2]; furthermore, the rate of hypertension is disproportionately high among elderly individuals in this country [3][4][5]. Hypertension is defined as a diastolic blood pressure (DBP) of ≥ 90 mmHg and/or systolic blood pressure (SBP) of ≥ 140 mmHg based on the evidence of modestly increasing risk [6][7][8] and incidence of cardiovascular disease (CVD) [9][10][11].
Hypertension is a complex disease, and patients with the condition suffer from an economic, psychosocial, and physical burden. Recently, hypertension has become an important global public health challenge [12]. Therefore, an effective strategy to prevent hypertension and determine its associated risks should be carefully implemented. Numerous studies [13][14][15][16][17][18][19][20] have been conducted to determine the risk factors for hypertension, such as aging, overweight, central obesity, lifestyle, family history of hypertension, history of diabetes and dyslipidemia, lack of physical activity, smoking, alcohol consumption, low-density lipoprotein cholesterol (LDL-C) level, triglycerides (TG) level, and high-density lipoprotein cholesterol (HDL-C) level. Lipid abnormalities have been shown to increase the risk factors for hypertension [21]. For example, Teng [22] found an additive effect of TG on DBP. Brennan [23] conducted a study to investigate the effects of body weight on TG and SBP in individuals aged 20 to 49 years and found that, in men, significant correlations with BMI were found for all variables, while in women younger than 40, only the correlation with BP was significant. TG level has been associated with BP since TG level itself can lead to endothelial dysfunction [24,25], arterial stiffness [26], and loss of vasomotor reactivity [27]. However, it is also important to understand the interrelationships among various risk factors for hypertension. In particular, the association between TG level and risk of hypertension and the effects of obesity on this association are of considerable interest, and an interactive effect between body mass index (BMI) and TG level on BP may also be considered.
No consistent recognition of the association and interaction analysis between BMI and TG level and BP in elderly individuals exists. Thus, the present study particularly aimed to determine the prevalence of normotension and hypertension and their association with BMI, TG level, and other confounding factors based on sex, using cross-sectional data from community-dwelling individuals aged ≥ 60 years in China.

Methods
. . Study Design and Setting. Data for this cross-sectional study were taken from the China Health and Retirement Longitudinal Study (CHARLS), a biennial and nationally representative longitudinal survey conducted by the China Centre for Economic Research at Peking University [28]. The baseline survey had a four-stage, stratified, cluster probability sampling design.
In the first stage, all counties in China were stratified by region, rural/urban status, and gross domestic product per capita. A random sample of 150 counties was selected to represent the socioeconomic and geographic pattern of all the counties. In the second stage, three primary sampling units (PSUs) were selected in each county with the probability of inclusion of each county in the sample proportional to their population size. In the third stage, all households in each selected PSU were mapped, and a random sample of 24 households was selected among all the households with residents aged ≥ 45 years within each PSU. Finally, for each selected household, one resident aged ≥ 45 years was randomly selected as a participant in the survey. From the 2011 CHARLS Wave1, we included a total of 3629 individuals in our study.
. . Information on Demographic Characteristics and Self-Reported Risk Factors. Data including age, education, marital status, current residence, smoking, drinking, eating habits, taking activities, accidental injury, physical exercise, history of cardiovascular disease, history of liver disease, antilipidemic medication, antidiabetic medication, and antihypertensive therapy were obtained using a self-reported questionnaire. (1) Median age was 68 years, and age was categorized as < 68 years and ≥ 68 years. (2) Educational levels were classified into illiterate, less than elementary school, high school, and above vocational school. (3) Marital status was classified into married and single. (4) Current residence was classified into rural and urban. (5) Smoking status was never smoked, ex-smoker, and current smoker. (6) Alcohol consumption was classified into more than non-drinker, less than once a month, and once a month. (7) Eating habits were categorized into 2 meals per day or fewer, 3 meals per day, and 4 meals per day or more. (8) Activity status was dichotomized into at least once a month versus never. (9) Major accidental injury information was obtained by asking the participant whether he/she suffered from any type of major accidental injury and received medical treatment; the answer was "yes" or "no." (10) Regular physical exercise was defined as exercising at least 3 days per week and more than 30 minutes per day, including moderate to vigorous physical activity and walking. (11) History of CVD, history of liver disease, antilipidemic medication, antidiabetic medication, and antihypertensive therapy were defined as a history of receiving treatment for the respective diseases; the answer was "yes" or "no." . . Glucose, LDL, HDL, Triglycerides, eGFR, BP, and Uric Acid Measurement. Venous blood samples were obtained at the Centers for Disease Control and Prevention (CDC) station, then immediately stored and frozen at −20 ∘ C, and transported within 2 weeks to the Chinese CDC in Beijing, where they were placed in a deep freezer and stored at −80 ∘ C until the relevant assay was performed at the China Medical University laboratory. (1) Fasting plasma glucose (FPG), LDL, HDL, and TG levels were analyzed at the Youanmen Center for Clinical Laboratory at Capital Medical University using the enzymatic colorimetric tests, and serum uric acid (SUA) levels were analyzed using the urinalysis (UA) plus method. We classified TG levels into 2 categories: ≥ 150 mg/dL and < 150 mg/dL, a categorization widely used in previous studies [29,30]. (2) BP was measured 3 times at intervals of 45 seconds with a sphygmomanometer. The value of BP was determined based on the mean of the 3 measurements. Normotension was defined as absence of antihypertensive therapy with an SBP of < 140 mmHg and DBP of < 90 mmHg, while hypertension was defined as an SBP of ≥140 mmHg and/or DBP of ≥ 90 mmHg; this categorization has been widely used in previous studies. (3) Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI creatinine-cystatin equations [31]: (1)  . . Measurement of Body Mass Index. Weight and height were measured using a weight and height measurement instrument. BMI was calculated based on the measured weight and height of the participants, who were classified into four categories: underweight (BMI, ≤ 18.5 kg/m 2 ), normal weight (18.5-24 kg/m 2 ), overweight (24-28 kg/m 2 ), and obese (> 28 kg/m 2 ) [32].
. . Statistical Analysis. Our data are represented as mean ± standard deviation (SD; continuous data) and number and percentage (categorical data). Differences between normotension and hypertension, or between normal weight individuals and those with underweight or adiposity, were evaluated using the t-test or chi-square test, followed by Bonferroni adjustment. Correlations between various characteristics and BP were compared using the age-adjusted partial Pearson's correlation test. The adjusted associations between various characteristics and BP were first compared using linear regression models, as appropriate. Then, general linear models adjusted for related potential confounders (age, educational level, marital status, current residence, smoking, drinking, eating habits, taking activities, major accidental injury, physical activity, history of CVD, history of liver disease, antilipidemic medication, antidiabetic medication, antihypertensive therapy, FPG, LDL-C, HDL-C, eGFR, and SUA) [33] were constructed to examine the synergistic effect of BMI and TG level on BP. A binary logistic regression model adjusted for related potential confounders was used to examine the association between BMI or TG and hypertension. A P-value of 0.05 was considered significant. All data were analyzed using the SPSS version 17.0 (IBM Corp., Armonk, NY, USA).
. . Patient and Public Involvement Statement. Consent from the respondents was obtained by the CHARLS. The data information obtained from the study was public, and patients were not involved.

Results
The 2011 CHARLS Wave1 sample (N=3629) consists of 49.88% men (Age: Mean = 68.86 years; SD = 6.30; range, 60-93 years) and 50.12% women (Age: Mean = 68.55 years; SD = 6.82; range, 60-96 years). Among men, the mean SBP and DBP were 128.93 mmHg and 72.28 mmHg, respectively, and among women, the mean SBP and DBP were 133.66 mmHg and 73.00 mmHg, respectively. In men, 10.94%, 60.66%, 21.60%, and 6.80% were underweight, normal weight, overweight, and obese, respectively, whereas in women 10.34%, 49.04%, 29.08%, and 11.54% were underweight, normal weight, overweight, and obese, respectively. The mean and SD of TG level were 112.79 ± 83.55 mg/dL in men and 140.14 ± 94.12 mg/dL in women. Tables 1 and 2 present the baseline characteristics of the sample for all variables, and most variables were based on our previous research [32]. Tables 1 and 2 show the various characteristics of participants categorized on basis of BMI. The participants comprised 1,810 men aged 68.86 ± 6.30 (range: 60-93) years and 1,819 women aged 68.55 ± 6.82 (range, 60-96) years. According to the modified Chinese criteria for BMI [34], the mean BMI in men was 22.41 kg/m 2 (SD, 3.71), with 10.94% underweight (BMI, < 18.5 kg/m 2 ), 60.66% normal weight (BMI, 18.5-24 kg/m 2 ), 21.60% overweight, and 6.80% obese (BMI, ≥ 28 kg/m 2 ), whereas the mean BMI in women was 23.33 kg/m 2 (SD, 4.23), with 10.34% underweight (BMI, < 18.5 kg/m 2 ), 49.04% normal weight (BMI, 18.5-24 kg/m 2 ), 29.08% overweight, and 11.54% obese (BMI, ≥ 28 kg/m 2 ). Table 1 shows the background characteristics of male participants categorized based on BMI. Levels of FPG, LDL-C, TG, eGFR, SUA, SBP, and DBP were significantly higher in the high BMI group than in the low BMI group, whereas HDL-C level was higher in the low BMI group than in the high BMI group. However, between-group differences in the prevalence of major accidental injury, regular physical exercise, history of liver disease, and antihypertensive therapy were not observed. In women, levels of FBG, LDL-C, TG, eGFR, SUA, SBP, and DBP were significantly higher in the high BMI group than those in the low BMI group, but HDL-C level was higher in the low BMI group than in the high BMI group. However, between-group differences in categories of alcohol drinking, major accidental injury, physical activity, and history of liver disease were not observed ( Table 2). Tables 3 and 4 show the characteristics of participants categorized by BMI and BP status. First, in the hypertensive group with a BMI < 24.0 kg/m 2 in men, levels of glucose, LDL-C, BMI, SUA, SBP, and DBP were significantly higher than those in the hypertensive group, but eGFR level was significantly higher than that in the normotensive group.      Second, in the hypertensive group with a BMI ≥ 24.0 kg/m 2 in men, levels of SUA, SBP, and DBP were also significantly higher, but eGFR level was significantly lower than that in the normotensive group (shown in Table 3). Third, in the hypertensive group with a BMI < 24.0 kg/m 2 in women, levels of TG, SUA, SBP, DBP, and the prevalence of antilipidemic medication were significantly higher than those in the normotensive group, but eGFR level was significantly lower. Lastly, in the hypertensive group with a BMI ≥ 24.0 kg/m 2 in women, the prevalence of CVD, antilipidemic medication, and antidiabetic medication were significantly higher, as were levels of glucose, TG, SBP, and DBP (Table 4). Table 5 shows the various characteristics of participants categorized by age. SUA and SBP levels were significantly higher in the older age group than in the younger age group in men, whereas levels of HDL-C, eGFR, DBP, BMI, and TG were lower in the older age group than in the younger age group. However, between-group differences in categories of current residence, eating habits, taking activities, major accidental injury, regular physical exercise, history of liver disease, antidiabetic medication, antihypertensive therapy, FPG, and LDL-C were not observed. In women, SUA and SBP levels were significantly higher in the older age group than in the younger age group, but eGFR and BMI levels were lower in the older age group than in the younger age group. However, between-group differences in categories of current residence, drinking, eating habits, activity, major accidental injury, history of CVD, history of liver disease, antidiabetic medication, and antihypertensive therapy, FPG, LDL-C, HDL-C, DBP, and TG were not observed.
In addition to their direct associations, we observed the effect between BMI category and TG levels on BP in Figure 1.    Tables 6 and 7 show the relationship between various characteristics and BP status of participants categorized by BMI (< 24.0 kg/m 2 and ≥ 24.0 kg/m 2 ). Age-adjusted partial Pearson's correlation coefficient showed that TG level was positively correlated with both SBP and DBP in men with a BMI < 21.0 kg/m 2 ( Table 6). In women, the TG level was significantly correlated with SBP and DBP in subjects with a BMI < 21.0 kg/m 2 and positively correlated with DBP in those with a BMI ≥ 24.0 kg/m 2 , but there was no correlation with SBP in women with a BMI ≥ 24.0 kg/m 2 (Table 7). Tables 8 and 9 show the relationship between various characteristics and BP status of participants categorized by  Tables 10 and 11 show the multivariate-adjusted relationship between various characteristics and BP status in participants categorized by BMI (< 24.0 kg/m 2 and ≥ 24.0 kg/m 2 ). Multiple linear regression analysis showed that TG level was significantly and positively associated with SBP in women with a BMI < 24.0 kg/m 2 , independent of other confounding factors; however, TG level was not significantly associated with BP in men. Tables 12 and 13 show the multivariate-adjusted relationship between various characteristics and BP status in participants categorized by age (< 68 years and ≥ 68 years). Multiple linear regression analysis showed that TG level was significantly and positively associated with SBP in women with age of < 68 years, independent of other confounding factors. In contrast, TG level was significantly associated with SBP in men with age of ≥ 68 years.  Table 14 shows the interaction between BMI and TG level and BP status in men and women. A general linear model with the following confounding factors (age, educational level, marital status, current residence, smoking, eating habits, taking activities, antidiabetic medication, antihypertensive therapy, FPG, LDL-C, eGFR, and SUA) was used to assess the statistical significance of the synergistic relationship between BMI and SUA level. Evidence of interaction between BMI and SUA level on SBP (men, = 0.572, P = 0.845; women, = 0.122, and P = 0.923) and DBP (men, = -0.373, P = 0.810; women, = 0.272, and P = 0.828) levels was not observed. Table 15 shows the interaction between BMI and TG on BP status of participants categorized by age in men and women. A general linear model with the following confounding factors (age, educational level, marital status, current residence, smoking, eating habits, activity, physical activity, antilipidemic medication, antidiabetic medication, antihypertensive therapy, FPG, eGFR, and SUA) was used to assess the statistical significance of the synergistic relationship between BMI and SUA level. Evidence of interaction between BMI and SUA level on SBP (men with age of < 68 years, = -1.075, and P = 0.760; men with age of ≥ 68 years, = 2.138, and P = 0.654; women with age of < 68 years, = -1.345, and P = 0.718; women with age of ≥ 68 years, = -4.192, and P = 0.334) and DBP (men with age of < 68 years, = -0.095, and P = 0.964; men with age of ≥ 68 years, = -0.931, and P = 0.691; women with age of < 68 years, = 2.410, P = 0.170; women with age of ≥ 68 years = -3.386, and P = 0.439) was not observed. Table 16 shows relationships between BMI or TG level and hypertension in men and women. After adjusting for age, educational level, marital status, current residence, smoking, drinking, eating habits, activity, major accidental       [35], the prevalence of hypertension was 62.0%; the prevalence of hypertension in the Japan Gerontological Evaluation Survey [36], conducted in a population aged 65 or older, was 59.54%. The prevalence of hypertension in our study was lower than that observed in the Korean and Japanese studies. Moreover, men with hypertension had a higher prevalence of risk factors, such as age, marital status, eating habits, FPG, LDL-C, TG, eGFR, BMI, and SUA level, than those with normotension. Age, marital status, antilipidemic medication, FPG, TG, eGFR, and SUA level were significantly associated with BP among women. Furthermore, TG levels were positively associated with SBP and DBP in men with BMI < 24 kg/m 2 ; however, TG levels were positively associated with both SBP and DBP in women with a BMI < 24 kg/m 2 and also positively associated with DBP in women with a BMI ≥ 24 kg/m 2 . Studies [37,38] have explored the association and/or interaction analysis between BMI and SUA level and BP.  Lyngdoh et al. [37] reported that adiposity substantially decreased the association between SUA level and BP in young adults, and BP was independently associated with SUA level in women. Kawamoto et al. [38] concluded that BMI changes the association between SUA level and BP status among community-dwelling men. In other studies [39,40], the association of TG and SUA levels has been persistent after full adjustment in a multiple logistic model, suggesting that TG levels correlate independently with SUA level, with TG levels having the most influence on SUA. However, there were no previous studies on the interactive effect between BMI and TG level on BP. As our general linear models adjusting for 13 related potential confounders showed, interaction between BMI and TG on BP was not observed. In our investigation of the relationships between BMI or TG and hypertension, we made several observations. First, we found that overweight and obese men and women were more likely to have hypertension. We also observed that women with high TG were more likely to experience hypertension. Thirdly, no association between TG and hypertension was found in men. Lastly, the relative results in relations between BMI or TG and hypertension categorized by age in both men and women are the same as those above. Furthermore, age differences in relations between BMI or TG and hypertension were found. Specifically, individuals with age ≥ 68 years experienced lower effects of BMI or TG level on BP. Similar evidence has been found in Brazil, in a study based on 287 men and women aged between 18 and 88 years, in which Pimenta [41] found that central obesity and TG level Table 13: Multivariate-adjusted relationship between various characteristics and blood pressure status of participants categorized by age in female (N=1819).

Variables
Age <68 years (n=887) Age≥68 years (n=932) Systolic blood pressure (P-value) were independent risk factors for hypertension according to multivariate analysis. However, several studies have reported divergent findings regarding the association between obesity and hypertension. Some studies have suggested an increased risk of hypertension with higher BMI, waist circumference (WC), and waist-to-hip ratio (WHR) [42,43], whereas other studies found that the association was not significant [44][45][46]. Zhou [47] conducted the first meta-analysis of cohort studies to quantify the relationship between obesity and the incidence of hypertension and found a positive association between the risk of hypertension and BMI, WC, and WHR.
Arabshahi [48] conducted the first meta-analysis of crosssectional studies to investigate the relationship between BMI or WC and hypertension, and concluded that the risk of hypertension was associated with adiposity. Jayedi [49] conducted a meta-analysis of prospective cohort studies to report the risk estimates of hypertension for abdominal adiposity (BMI, WC, and WHR) and found that the risk of hypertension increased with a somewhat steeper trend with increasing BMI, in comparison with WC and WHR. Such discrepancies between our findings and the null studies may be a result of methodological differences in design,  measurement of obesity, and populations. Moreover, TG level has been associated with BP since TG level itself can cause endothelial dysfunction [24,25], arterial stiffness [26], and the loss of vasomotor reactivity [27]. Such pathophysiology induced by increased TG and FPG levels and low HDL-C level may be greater than that of SUA. To explore the extent of the effects of TG level on blood pressure, we controlled for SUA and the related confounders, such as LDL-C and HDL-C [38]. The findings showed that TG level and hypertension were observed in women, but no independent effect was observed in men. Sanchez-Inigo [50] conducted a cohort study to identify the association of TG with the incidence of hypertension in Spain and found that the incidence of hypertension was associated with TG level in both men and women independent of adiposity. Tohidi [51] found that high TG independently predicted incidence of hypertension in Middle Eastern women. The mechanisms that lead to hypertension in participants with high BMI or TG levels have not been completely understood. Current studies may provide insight into the pathogenic mechanisms of BMI/TG that induce hypertension. The present study suggests that TG level may play an important role in hypertension in women. We speculate that sex-specific factors may also play an important role. TG levels are higher in women than in men, which partially explains the underlying mechanism that accounts for sex difference based on hormone levels. Additionally, body fat, sex steroids, and their interaction in elderly participants may also be associated with hypertension. As an important sex hormone, estrogen may also affect BP. Elderly women have lower concentrations of estrogen, which may affect the level of TG and result in a smaller protective effect.

Strengths and Limitations of the Study
There are several limitations of our study. First, the association and interaction of BMI and serum TG on blood pressure become seriously more complex; we only consider the confounders as possible as we can, but there are some unknown factors. Secondly, the relationship should be studied prospectively. Our study investigated BP in the elderly participant through a cross-sectional study. Follow-up study was relatively short to comprehensively observe changes in the next step. Last, more research is needed to observe the result. Several strengths could be found in our study. Firstly, the study was based on a nationwide survey. Secondly, we conducted the analyses according to gender.

Conclusions
An interactive effect of BMI and TG level on BP was not observed in either men or women; however, independent 22 BioMed Research International