Relationship of Anthropometric Indices with Rate Pressure Product, Pulse Pressure and Mean Arterial Pressure Among Secondary Adolescents of 12-17 Years

DOI: https://doi.org/10.21203/rs.3.rs-117233/v1

Abstract

Objectives: To determine the correlation between anthropometric indices and the selected hemodynamic parameters among secondary adolescents aged 12-17 years.

Results: Our findings showed weak positive correlation between generally body surface area, neck circumference and conicity index with the hemodynamic parameters (systolic blood pressure, diastolic blood pressure, resting pulse rate, mean arterial pressure, rate pressure product and pulse pressure). However, the ponderosity index, body mass index and waist hip ratio showed negative weak correlations with the hemodynamic parameters. There was a significant difference in pulse pressure among the BMI categories. All parameters showed significant (p<0.05) differences across the categories of neck circumference and waist hip ratio. Generally, in multivariate regression analysis, anthropometric indices showed significant prediction of the hemodynamic parameters.

Introduction

The role of anthropometric measurements in estimation of adiposity is widely used in both research and clinical settings. The indices, such as; bod mas index (BMI), and the waist hip ratio (WHR) have been used commonly to define overweight and obesity status, across various age categories. Recently, more indices such as the conicity index (CI); ponderosity index (PI); body surface are (BSA), and neck circumference (NC), attracted great attention. Overweight and obesity is highly associated with a number of non-communicable diseases including cardiovascular disease, diabetes mellitus, cancers, arthritis, ovarian dysfunction and so forth. Globally, based on the world health organization (WHO), the prevalence of obesity has tripled since 1975, as in 2016 only more than 1.9 billion adults of age 18 years and greater were overweight, of which 650 million were obese [1]. Obesity plays a very vital role in the development of cardiovascular disease (CVD) [2–4]. Several simple anthropometric parameters have been greatly associated with cardiovascular risk factors [5] and thus their relationship with hemodynamic parameters needs to be explored. The rate pressure product (RPP) also known as the cardiovascular product, is the measure of stress put on the cardiac muscle, based on the number or times it needs to contract per minute and the arterial blood pressure that is pumping against it [6]. It is a direct indication of the energy demand of the heart and thus good measure of energy consumption by the heart. Anthropometric indices such as WHR, waist circumference (WC), and BMI were found to be significantly correlated with RPP among health young adults [7, 8], and an important predictor of cardiovascular events [9]. The mean arterial pressure (MAP) is physiologically the average pressure in the arteries during a single cardiac cycle [10] and it is useful indicator of the pressure necessary for the adequate perfusion to vital organs. Additionally, the pulse pressure (PP) is an indirect marker of arterial stiffness and distensibility [11]. Both MAP and PP are important indicators of the pressure inside arteries and the extent of vasoconstriction or dilation. These have been found to be associated with anthropometric indices in elderly populations [12] and young adults elsewhere [13]. Further exploration of this relationship is needed for secondary school adolescents in Ugandan settings for a reliable comparison and better understanding of the cardiovascular related pathophysiology.

methods

Study design and data

Data included in this study was part of a larger cross-sectional study which included up to 616 [14] adolescents aged 12–19 years in Mbarara municipality, southwestern Uganda. In this paper, only data of 485 participants aged 12–17 years was analyzed.

Anthropometric measurements

The methods were already described in detail by Katamba et al [15]. Briefly, height (Ht) was measured using a wall mount height board without shoes in centimetres (cm) [2]. Weight (Wt) was measured to the nearest 0.5 kg using a standard weighing scale (Seca 762, GmbH & Co. KG, Hamburg, Germany) and participants were encouraged to put on light clothing with no items in the pockets and shoeless [3]. Waist circumference (WC) was measured by an inelastic flexible measuring tape with the participant standing while hip circumference (HC) at the level of the greater trochanter to the nearest 0.1 cm using an inelastic flexible measuring tape with the participant standing. Neck circumference (NC) was used as a surrogate measure for upper body adipose tissue distribution, measured at the level of the laryngeal prominence using an inelastic flexible measuring tape.

Anthropometric indices computations

Ponderosity index (PI) = [16]

Body surface area (BSA) = in m2 based on the Mosteller formula [17]

Conicity index (CI) = in kgm−3 [18]

Blood pressure and heart rate

Blood pressure was measured using a digital blood pressure machine (Scian SP-582 Digital BP Monitor, Honsun, Jiangsu, China (Mainland) as already described by Katamba et al.,[15]. Each participant was allowed to rest quietly for 5 minutes, sitting on a chair with the back supported and feet on the floor [14]. The participant was asked to remain calm and quiet as the machine begun to measure automatically after pressing the start button. When the reading was complete, the monitor displayed the BP and the resting pulse rate on the digital panel [19]. Three readings were recorded per participant at 5 minutes’ interval as recommend by the WHO steps surveillance guidelines for non-communicable diseases [20]. The average of the 2nd and 3rd respective BP measurements was used as the subject’s BP respectively

Statistical analysis

The subjects were classified into different groups using anthropometric indices such as BMI, NC and WHR. On the basis of BMI, subjects were classified into 3 groups, i.e., normal (BMI = 18.5–24.99 kg/m2), overweight (BMI = 25–29.99 kg/m2), and obese (BMI ≥ 30 kg/m2) [1]. Based on WHR, the subjects were classified into two groups, i.e., WHR < 0.90 and WHR ≥ 0.90 (WHO cutoff points). The NC cut-offs for boys and girls were 30.75 cm and 29.75 cm respectively [21]. To compare two samples of a continuous variable, an unpaired t-test was used whereas for those with three categories, one‑way ANOVA test was implemented. The Pearson moment correlation coefficient was used to determine the correlations between anthropometric indices and the hemodynamic variables parameters followed by linear regression analysis. A p value < 0.05 was considered to be statistically significant. The analysis of data was done by Stata software version 13.0 (College Station, Texas, USA).

Results

Physical characteristics of study participants by sex

The mean age of the participants was 14.9 ± 1.6 years, the SBP and DBP of the participants were 111.9 ± 8.9 mmHg which differed significantly by sex and 65.0 ± 7.4 mmHg respectively. The mean RPR was 75.0 ± 8.1 bpm. The mean RPP was 84.1 ± 12.8 mmHgbpm, while the mean PP was 46.9 ± 7.9 mmHg as showed in Table 1.

Table 1

showing physical characteristics of the participants by compared by sex

Variable

Male (n = 173)

Female (n = 312)

p-value

Total (n = 485)

Mean ± SD

Mean ± SD

Mean ± SD

Age (years)

Ht (cm)

Wt (kg)

WC (cm)

NC (cm)

HC (cm)

BMI (kgm− 2)

WHR

PI (kgm− 3)

BSA (m2)

CI (kgm− 3)

RPR (bpm)

SBP (mmHg)

DBP (mmHg)

RPP (mmHgbpm)

MAP (mmHg)

PP (mmHg)

14.5 ± 1.5

157.2 ± 7.8

55.0 ± 7.3

67.6 ± 6.3

29.7 ± 2.5

79.8 ± 8.4

22.3 ± 2.9

0.85 ± 0.06

14.2 ± 2.2

1.55 ± 0.12

1.05 ± 0.09

73.6 ± 7.5

109.6 ± 10.9

64.3 ± 6.9

80.8 ± 11.9

79.4 ± 7.5

45.3 ± 8.6

15.1 ± 1.5

156.9 ± 7.4

60.2 ± 7.6

69.9 ± 6.9

29.8 ± 1.6

93.3 ± 11.1

24.5 ± 2.3

0.75 ± 0.06

15.6 ± 1.8

1.6 ± 0.13

1.04 ± 0.08

75.8 ± 8.4

113.2 ± 7.3

65.4 ± 7.6

86.0 ± 12.9

81.3 ± 6.6

47.8 ± 7.4

< 0.01

0.620

< 0.01

< 0.01

0.368

< 0.01

< 0.01

< 0.01

< 0.01

< 0.01

0.072

0.005

< 0.01

0.123

< 0.01

< 0.01

< 0.01

14.9 ± 1.6

156.9 ± 7.6

69.1 ± 6.8

69.1 ± 6.8

29.8 ± 1.9

88.5 ± 12.1

23.7 ± 2.8

0.79 ± 0.08

15.1 ± 2.1

1.6 ± 0.13

1.04 ± 0.08

75.0 ± 8.1

111.9 ± 8.9

65.0 ± 7.4

84.1 ± 12.8

80.6 ± 6.9

46.9 ± 7.9
SD: standard deviation,

 

Comparison of hemodynamic variables based on BMI categories

Table 2 shows the comparison hemodynamic parameters across the BMI categories. Data analysis was done by one‑way ANOVA. There was a significant difference in PP and systolic BP between the different BMI categories of the subjects. However, there was no significant difference between the RPP and MAP for the respective BMI categories.

Table 2

Comparison of hemodynamic variables between normal, overweight and obese BMI subjects

Variables

Normal (n = 320)

Overweight (n = 161)

Obese (n = 4)

p-value

DBP (mmHg)

SBP (mmHg)

RPR (mmHg)

RPP (mmHgbpm)

MAP (mmHg)

PP (mmHg)

65.1 ± 7.6

111.1 ± 7.9

75.3 ± 8.7

83.8 ± 13.2

80.4 ± 7.2

46 ± 7.9

64.8 ± 6.9

113.6 ± 7.0

74.6 ± 6.8

84.9 ± 11.3

81.1 ± 6.3

48.8 ± 6.4

68 ± 10.4

113 ± 36.7

71.5 ± 6.7

82.6 ± 31.8

83 ± 17.2

45 ± 32.1

0.689

0.010

0.456

0.604

0.444

0.001
Data expressed in mean ± SD form. Statistical test was one‑way ANOVA. n = Number of subjects, SD = Standard deviation

 

Comparison of hemodynamic variables-based on neck circumference and waist hip ratio

Table 3 depicts the effect of NC and WHR on hemodynamic parameters. Comparison analysis was done using the unpaired t‑test. The subjects of normal NC cm showed significant difference in all hemodynamic parameters with those of high NC (p < 0.01). The same significant differences were found between the WHR categories.

Table 3

Comparison of hemodynamic variables based on neck circumference and waist hip ratio categories

Variables

Neck circumference (cm)

Waist hip ratio

Normal (n = 265)

High (n = 220)

p

Normal (n = 374)

High (n = 111)

p

DBP (bpm)

SBP (bpm)

RPR (bpm)

RPP (mmHgbpm)

MAP (mmHg)

PP (mmHg)

63.4 ± 0.4

108.8 ± 0.5

74.1 ± 0.5

80.7 ± 12.0

78.5 ± 6.0

45.3 ± 7.6

66.9 ± 0.5

115.7 ± 0.6

76.1 ± 0.5

88.2 ± 12.5

83.2 ± 7.2

48.8 ± 7.9

< 0.01

< 0.01

< 0.01

< 0.01

< 0.01

< 0.01

65.6 ± 0.4

112.9 ± 0.5

75.7 ± 0.4

85.6 ± 13.1

81.4 ± 7.2

47.3 ± 8.5

62.0 ± 0.5

108 ± 0.7

72.7 ± 0.6

79.1 ± 10.0

78.1 ± 5.5

45.8 ± 5.6

< 0.01

< 0.01

< 0.01

< 0.01

< 0.01

0.041
Data expressed in mean ± SD form. Statistical test was unpaired t-test, n = Number of subjects, SD = Standard deviation

 

Correlation analysis

Correlation analysis of hemodynamic parameters with anthropometric indices was done as shown in table 4. Significant positive correlation coefficients were found between body surface area and neck circumference with RPP, MAP and PP. However, some negative coefficients were found for WHR and PI.

Step wise regression analysis

The multiple and stepwise regression analysis for of anthropometric indices in relation to hemodynamic parameters is shown in table 5. BMI alone predicted only less than 3% of the hemodynamic parameters which was only significant for SBP, RPR, MAP and PP. In step 3, combining BMI, WHR and CI significantly predicted all the hemodynamic parameters. Finally, in step 4, combining all the anthropometric indices predicted up to 99.8% of all the hemodynamic parameters.

Regression equations for different stages of analysis

Mean arterial pressure; 1MAP = 23.7-0.0005BMI,

2 MAP = 33.3-0.025BMI – 9.6WHR,

3 MAP = 34.9–0.010BMI – 3.77CI-8.3 WHR,

4 MAP = 7.47BSA-6.13-0.003BMI + 1.17PI + 0.5CI-0.22WHR.

In step 1, the regression coefficient associated with BMI was − 0.0005. thus, each one unit increase in BMI is associated with a -0.0005 unit increase in mean arterial pressure. The association was statistically significant. After adjusting for WHR, there was more than 10% increase in coefficient of association between BMI and MAP, showing that WHR affects the interaction between BMI and MAP. In step 4, it was further illustrated that part of the association between MAP and BMI was significantly explained by other indices such as WHR, BSA, PI and CI.

Pulse pressure; 1PP = 20.9 + 0.059BMI,

2 PP= 28.0 + 0.051BMI-8.55WHR,

3 PP = 31.6 + 0.056BMI – 4.63CI – 7.27WHR,

4 PP = 7.5BSA-6.3 + 1.17PI + 0.5CI-0.165WHR-0.0019BMI.

In step 1 analysis of pulse pressure with BMI, the regression coefficient associated with BMI was 0.059, implying that each unit increase in BMI was significantly associated with 0.059 times increase in pulse pressure. In steps 2 and 3, there was less than 10% change after adding WHR in predicting PP, implying that WHR doesn’t affect the association between BMI, WHR CI. In steps4, 10% change was recorded (0.059 vs-0.0019), implying that adding BSA and PI to the model, significantly affected the association between BMI and PP.

Rate pressure product; 1RPP = 24.3 -0.007BMI,

2 RPP = 33.1 -0.020BMI-9.8WHR,

3 RPP= 35.1 -0.012BMI − 3.38CI-8.6WHR,

4 RPP= 7.4BSA − 6.2 + 1.2PI + 0.5CI-0.24WHR-0.0015BMI.

In step 1 analysis of rate pressure product with BMI, the regression coefficient associated with BMI was − 0.007, implying that each unit increase in BMI was significantly associated with − 0.007 times change in RPP. In steps 2, 3 and 4, there was greater than 10% change after adding WHR, CI, PI and BSA in predicting RPP, implying that adding WHR, CI, BSA and PI to the model, significantly affected the association between BMI and RPP. All these indices were very useful and the coefficient remained significant and negative.

Discussion

Our findings showed weak correlations between the anthropometric indices and the hemodynamic parameters. The correlations were all positive for NC and CI. However, the PI, BMI and WHR showed negative weak correlations with the hemodynamic parameters. In regression analysis, combination of all anthropometric indices significantly predicted the hemodynamic parameters among our adolescents with up to 99.8% of explained variability. These observations carry physiological significance, as the increase in anthropometric indices results into increased stress on the cardiovascular system, as indicated by increase in hemodynamic parameters. Rate pressure product as marker of myocardial stress through surrogate indication myocardial oxygen consumption (mVO2) increased with increase in the anthropometric indices, as was also shown by pulse pressure, mean arterial pressure and blood pressure. Our findings are in agreement with those from a study that involved 104 male young adults aged 20–25 years. The study found a positive correlation between anthropometric indices and rate pressure product [7]. Additionally, a study among primary pupils of 6–14 years found anthropometric indices such as height, weight, waist hip ratio to be significantly correlated with both blood pressure and pulse pressure [22]. The same findings were reported among secondary adolescents of 10–18 years in Gombe Nigeria [23]. Among the 397 apparently healthy men and women from Congolese in the south port city, waist circumference was found to be associated with pulse pressure and the strength of the associations varied with age, and blood pressure status [24]. In china, the simple anthropometric indices such as BMI, WC and WHR were found to be useful predictors for cardiovascular risk [25]. The same observations were reported among Brazilian adolescents [26] and in Korean women aged 40–69 years [27]. In the elderly population, it was found that higher values of anthropometric indices such as waist to height ratio and conicity index were closely related to increased diastolic BP, body fat and lipid profiles [28]. The same closely related findings were reported among the postmenopausal women in Tehran in Iran [29]. Neck circumference in children was found to show very comparable associations as those of BMI, WHR, WHtR and was closely associated cardiovascular disease risk factors among the 324 children aged 9–13 years in Greece [30].

Conclusion

A positive relationship between the anthropometric indices and hemodynamic parameters was found in general. All anthropometric indices combined predict the hemodynamic parameters better than a single index of adiposity.

Limitations

These include; (1) our results included only secondary school adolescents and hence may not be generalized across all adolescents in the general public, (2) the sample size was small to draw very valid conclusions and hence this calls for a larger national wide survey, (3) being a cross sectional study, no information on causality was obtained.

Abbreviations

ANOVA: analysis of variance

BMI: body mass index

BP: blood pressure

BSA: body surface area

CI: conicity index

DBP: diastolic blood pressure

HC: hip circumference

Ht: height

MAP: mean arterial pressure

NC: neck circumference

PI: ponderosity index

PP: pulse pressure

RPP: rate pressure product

RPR: resting pulse rate

SBP: systolic blood pressure

SD: standard deviation

SE: standard error

WC: waist circumference

WHO: world health organization

WHR: waist hip ratio

Wt: weight

Declarations

Ethical approval and consent to participate

This being part of larger cross-sectional story among secondary school adolescents of 12-19 years, the ethical issues were reported in detail by Katamba et al [15]. Briefly, approval was obtained from the Mbarara university research ethics committee ((IRB No. 18/03-18). The adolescents of 12-17 years (minors are considered anyone under 18 years according to Ugandan law) freely provided written assent, and their guardians (class teachers) provided written consent on their behalf.

Consent for publication

Not applicable

Availability of data and materials

The dataset is available on request from the corresponding author

Competing interests

The authors declare no conflict of interest.

Funding

The study received no external funding.

Author contributions

GK: Conceptualization of work & its realization, wrote the manuscript, checked the references, compiled the literature sources, data collection, statistical analysis, and interpretation of data, and wrote the manuscript and is the corresponding author. AN and FM: helped in the conceptualization of the work, helped in statistical and data analysis, support to data collection. AM and MAK: Proof read the manuscript and searched for literature. All authors read the final copy of the manuscript

Acknowledgements

We appreciate the contribution from the secondary schools and all the participants that participated in the study.

We are thankful to all the study participants

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