Abnormal mean blood pressure and the risk for hypertension are proportional to degree of adiposity, but whether an individual’s gender can provide protection against the pro-hypertensive effects of obesity is explored in this article. Subjects from Monash University provided, in total, 2073 sets of observations values over the course of four years. This data was compiled, and then statistical analysis was used to galvanise the data into different continuous variables. Adiposity was expressed as body mass index kg/m^2, which accounted for effects of age, which, in this study, was almost exclusively within the ages of 19-22 years of age. Overall, the following observations were made. Increased adiposity level directly correlated with increased blood pressure levels, and that blood pressure differences in this study were not attributed by gender but by differences in body mass index. In conclusion, there a clear association of the influence adiposity has on blood pressure when young adults reach high levels of adiposity, but the relationship between adiposity risks and individual gender is not fully supported by this experiment. The findings have relevance with aiding the development of future research with regards to obesity and heath risks, as well as provide data for the demographic of young adults, who are not readily researched in currently available literature.

Introduction

It has long been proposed that high adiposity level in an individual has been recognized as a risk factor for hypertension (Tu et al. 2011). Specifically, the relationship between an individual’s gender and their potential protection against the pro-hypertensive effects of adiposity will be explored within this article. High levels of adiposity, commonly known as ‘obesity,’ is often used as a potent predictor of risk factors (Taylor & Sullivan 2016), although the biological mechanisms that influence the risks of obesity on the differences between the different genders are not completely elucidated. Recent guidelines for obesity have introduced the use of a measurement known as body mass index (BMI) (Jiang et. al 2016), which is calculated by an individual’s weight and height, although it does have the limitation of not taking account of the weight carried as either muscle or fat. Adiposity levels has been connected to noteworthy metabolic irregularities, including blood pressure abnormalities and an increased frequency of hypertension (Rexrode et al 1998). This study sets out to analyse if the relationship between BMI and arterial pressure will be less in females than males, with the range of 100/140 and 60/90 mmHg for systolic and diastolic pressure, respectively, being used as the benchmark for a healthy adult.

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In this study, each participant measured and then self-reported their gender, age, level of physical activity, diet, blood pressure, heart rate, height, weight, and we included this data, as well as including additional data gathered from previous, identical experiments, for a total of 2073 sets of observations. This allowed us to perform a detailed analysis of the various variables using a variety of techniques such as unpaired t-tests, linear regression, analysis of covariance, and ordinary least products method. The relationship between obesity and hypertension is well established in children and adults, and that physical exercise plays an important role in migrating negative effects of obesity (Torrance et al 2007). What is unexplored in current available research is the degree of protection one’s gender can provide in deference of the pro-hypertensive effects of adiposity, which will be investigated in this study.

Overall, the purpose of this article is to provide more knowledge on the effects adiposity has on blood pressure health, as well as to galvanise research into the potential differences gender has on various BMI level health risks. This article also employs recently published literature describing the influence of adiposity on blood pressure, and what limited research is available on gender differences with pro-hypotension protection. Conclusively, we would like to explore the potential role of gender, in particular sex hormone differences, could have as a protective measure against the risks that adiposity carries.

Methods:

The methods and protocol have been described previously in the PHY2042 practical manual (Evans and Krause, 2019). For the statistical analysis, the first step was to divide the data according to gender only. This enabled us to partition the data into the variables that aided us in this article, which are presented in tables 1, 3, 6, 7, and figure 5. For Table 3, a t test was used, as described by Student (1908). We used two variables to determine the strength of the relationship of BMI and blood pressure, r2 and P (Table 6). The P value tests the null hypothesis that there is no linear relationship between the two variables, while the Pearson product moment correlation coefficient (r2) quantifies the variation in the y axis, the various continuous independent variables, that can be explained by the variation in the x axis, in this case, body mass index. These were used to determine how close the data can be accurately fitted into a regression line, thereby presenting the interrelation between BMI and blood pressure. These lines of best fit, as seen in figure 5, were generated by regression analysis. We conducted an analysis of covariance, first detailed by Fisher (1918), which is a multivariable method called ANCOVA. ANCOVA enabled the determination of whether the relationship between the continuous independent variable, in this case, BMI, and a continuous dependent variable (mean arterial pressure) differs according to gender. In all tables, a P value of ≤ 0.05 was considered statistically significant.

Results

Table 1: Characteristics of the sample: categorical variables

____________________________________

____________________________________  

Gender

Male

757

36.6

Female

1314

63.4

____________________________________ 

Diet

Vegetarian

174

8.4

Non-vegetarian

1898

91.6

____________________________________  

Level of Physical Activity

None

328

15.8

1-2 Times Per Week

725

35.0

3-5 Times Per Week

712

34.4

>5 times per week

306

14.7

____________________________________

Table 3:  Characteristics of participants according to gender.

________________________________________________________________________

         Male

         Female

Variable

n

Mean ± SD

n

Mean ± SD

P

________________________________________________________________________

Age (years)

757

21.0 ± 2.2

1315

20.6 ± 1.8

<0.001

Height (cm)

756

177.0 ± 7.6

1315

163.7 ± 6.7

<0.001

Weight (kg)

751

76.2 ± 13.5

1301

61.0 ± 10.8

<0.001

BMI (kg/m2)

750

24.3 ± 3.9

1301

22.7 ± 3.5

<0.001

Systolic Pressure (mmHg)

756

122.0 ± 11.9

1308

109.1 ± 10.8

<0.001

Diastolic Pressure (mmHg)

756

70.0 ± 8.3

1308

69.5 ± 7.9

0.15

Mean Arterial Pressure (mmHg)

756

87.4 ± 8.1

1308

82.7 ± 7.9

<0.001

Heart Rate (beats/min)

749

74.7 ± 12.4

1303

77.5 ± 11.4

<0.001

Data are from a total of 2073 participants. P values are the outcomes of Student’s unpaired t-test and are shown in bold if ≤0.05. SD = standard deviation, BMI = body mass index.

Table 6: Ordinary least products regression analysis for the relationships depicted in Figure 4.

_________________________________________________________________________

Dependent Variable

r2

P

intercept

Slope

___________________________________________________________________________

Systolic Arterial Pressure

0.15

<0.001

34.8(31.0-38.7)

3.39(3.23-3.55)

Diastolic Arterial Pressure

0.06

<0.001

19.7(17.0-22.4)

2.14(2.03-2.26)

Mean Arterial Pressure

0.13

<0.001

33.0(30.5-35.6)

2.20(2.10-2.31)

Heart Rate

0.001

0.15

3.02(-1.49-7.53)

3.15(2.96-3.34)

___________________________________________________________________________

r2 = Pearson product-moment correlation coefficient. P = probability that no relationship exists between body mass index and the dependent variable. Intercept = a, slope = b for Y = a + b*X, expressed as mean and 95% confidence intervals.

Table 7: Outcomes of analysis of covariance.

___________________________________________________________________________

Dependent Variable

PGender

PBMI

PGender*BMI

___________________________________________________________________________

Systolic Arterial Pressure

0.03

<0.001

0.13

Diastolic Arterial Pressure

0.20

<0.001

0.14

Mean Arterial Pressure

0.06

<0.001

0.77

Heart Rate

0.001

0.003

0.02

Values ≤ 0.05 are bolded.

Figure 5 Relationships between body mass index (BMI) and arterial pressure and heart rate according to gender. Symbols represent data from individual males (blue, n = 757) and females (pink, n = 1314). Lines of best fit were determined by ordinary least products (model 2) regression analysis. When PGender and/or PGender*BMI was ≤ 0.05 in analysis of covariance (see Table 7), separate lines of best fit are shown for males (blue) and females (pink). Otherwise a single line of best fit is shown (black).

The data generated in this study was generated by the methods described previously. Overall, we were unable to detect significant differences in the relationships between BMI and the various measures of arterial pressure for the different genders. Table 1 is used to showcase the sample size in the experiment (2073 individuals), and, more importantly, for this experiment, the percentage of participants that identified as female. Table 7 employs the term ‘PGender,’ which tests the hypothesis that the haemodynamic variables differ by gender, independently of body mass index. Based off consideration of Figure 5, Table 7 showcases that when BMI is low, neither Pgender or P gender*BMI are statistically significant. From this, we can reject the theory that the relationship between gender and BMI is difference, hence only one-line regression line was drawn in figure 5 for mean arterial pressure.

For the systolic arterial pressure, however, there are two regression lines, with the P values for Pgender being less than significant. It is therefore concluded that differences between genders in terms of systolic blood pressure are not noteworthy, as the slope of both lines are identical, and the differences between the genders in the figure is due to the difference in x axis intercepts (body mass kg/m^2), rather than gender affecting the systolic blood pressure. Conversely, Tables 3 and 6 show different P values to the ones show in Table 7. This is explained by how there is only one independent variable and one dependent variable under consideration in Tables 3 and 6; the data does not take into consideration that individuals have different BMIs. Table 3 highlights how we were unable to detect differences in the relationships between BMI and arterial pressure from the genders.

Table 6 provides important information about the strength of the relationships between BMI and the various measures of arterial pressure by the repetition of a low P value, again illustrating no relationship exists between body mass index and the dependent variable (in this case, atrial blood pressure). Further, we were unable to detect a significant relationship between BMI and heart rate, as Pearson’s R^2 value was 0.001 (p=0.001), as well as the minimal heart rate differences between males and females (Table 3). Therefore it was unadvisable to consider the relationships between BMI, gender, and heart rate in this study for adiposity relationship with hypertension.

Discussion

Overall, we found no strong evidence that variations of arterial blood pressure and BMI to be directed related with an individual’s gender. This finding was largely formulated by the evidence that arterial pressure varies with BMI similarly in both young men and women (Figure 5), which indicates that arterial pressure is likely to increase similarly in men and women if they put on weight. Further, the variation in P values generated from the experiment showcased this, with Tables 3 and 7 highlighting the insignificance of the interrelationship between BMI against various continuous factors. The similarity of the relationship for BMI vs arterial pressure in Figure 5 for both males and females suggests the equal consequences that BMI would have on an individual, in particular an elevated mean blood pressure. This is consistent with a finding by Jiang et al (2016), where obesity, and the rise of health problems such as hypertension, are interrelated.

However, Table 3 reports that arterial pressure is less in females than males, at least for the age group studied in this experiment. A study by J Reckelhoff (2001) supports this finding, with Reckelhoff (2001) acknowledging that this is only with women pre-menopause. Post menopause, however, it was found that woman experience higher blood pressure than men. This could likely be the known role of estrogen, which as biological role is involved in development of sex characteristic, and is loss during the menopause period, and may be a factor for decreased blood pressure. This would explain the data in Table 3 suggesting protective effects of female gender on hypertension while they are young adults; however, further research is needed to fully explore the specific role estrogen has on blood pressure, as well as reperform the  experiment to include a wider range of ages, in order to figure out how impactful age is in regard to blood pressure levels (Dua et al 2014).

Figure 5 provides evidence that increased adiposity leads to higher arterial pressure. A study done by Torrance et al (2007) found evidence that regular exercise in children can lead to an overall reduction in blood pressure. From this, it can be argued that exercise would be the true protection against hypertensive effects of adiposity, rather than an individual’s gender. Currently, there is limited research done on this proposal on, specifically, young adults, indicating a knowledge gap that should be explored in future experiments. The ability of our experiment to solidify the knowledge that adiposity is definitively associated with greater arterial pressure does indicate that there was no serious flaw in the experimental design, thus this experiment could be repeated with similar results given. 

Conversely, details in the experiment could be altered to provide a more accurate experiment overall. Acknowledging that BMI uses cross sectional data, which does not show causality between BMI and Systolic Pressure, and therefore does not accurately measure differences in body fat content, muscle mass percentage, or bone density. Therefore, a cross-sectional observational study such as this provides weak empirical data, as we have measured the ‘exposure variable’ (BMI) and the ‘outcome variable’ (arterial pressure) at the same time. From this, we cannot determine whether the BMI affects arterial pressure solely, or whether arterial pressure affects BMI, or if an outside factor affect them both mutually. Future studies could investigate doing a prospective observational study, whereby an observed exposure variable is measured at a baseline value, and then the outcome variable is measured. The possibility that arterial pressure affects BMI is then avoided, but there is still potential that BMI affected arterial pressure indirectly, just less chance of it. The best form of evidence would be from a randomized clinical trial, as you can be confident your intervention is the sole variable that caused the difference in arterial pressure. This does add difficulty to the experiment by requiring lifestyle changes to the participants, such as diet changes, so may not be appropriate in all studies. A further improvement could also be conducting an experiment where there are equal proportions of individuals in each exercise category, as therefore there would be no distortion of the parametric statistical test, or by using animals which would have all similar lifestyles, limiting the variability of the results compared to humans.

Overall, the findings from the experiment should aid future research into investigating protection from risks associated with high adiposity levels between males and females.

References:

Dua S, Bhuker M, Sharma, Dhall M, Kappoor S (2014) ‘Body mass index relates to blood pressure among adults’ North American journal of medical science, 6:89-95

Evans, R & Krause, L (2019) Phy2042 body systems physiology, Monash University, Monash.

Fisher, R.A. (1918) ‘The correlation between relatives on the supposition of mendelian inheritance.’ Philosophical Transactions of the Royal Society of Edinburgh,52: 399–433.

Jiang S, Lu W, Zong X, Ruan H, Lui Y (2016) ‘Obesity and hypertension’ Experimental and therapeutic medicine, 12:2395-2399.

Reckelhoff J (2001) ‘Gender differences in the regulation of blood pressure’ Hypertension, 37: 1199-1208

Rexrode K, Carey V, Hennekens C, Walters E, Colditz G, Stampfer M, Willett W, Manson J (1998) ‘Abdominal adiposity and coronary heart disease in women’ JAMA, 280:1843-1848.

Student (1908) The probable error of a mean. Biometrika, 6: 1-25.

         Taylor L & Sullivan J (2016) ‘Sex differences in obesity-induced hypertension and vascular dysfunction: a protective role for estrogen in adipose tissue inflammation?’ American journal of physiology, 311: 714-720.

Torrance B, Mcguire K, Lewanczuk R, Mcgavock J (2007) ‘Overweight, physical activity, and high blood pressure in children: a review of literature’ Dovepress, 3: 139-149.

Tu W, Eckert G, DiMeglio L, Yu Z, Jung J & Pratt J (2011) ‘Intensified effect of adiposity on blood pressure in overweight and obese children.’ Hypertension, 58: 818-824.