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American Journal of  Physical Education 
and Health Science (AJPEHS)

Correlations between Anthropometric Indices and Hypertension in Adults: Emerging 
Trends in a Contemporary Urban Setting

Norbert Sunday Chukwu1, Aloysius Obinna Ikwuka2*, Francis Chigozie Udeh2

Volume 2 Issue 2, Year 2024
ISSN: 2992-9679 (Online) 

DOI: https://doi.org/10.54536/ajpehs.v2i2.3616
https://journals.e-palli.com/home/index.php/ajpehs

Article Information ABSTRACT

Received: August 01, 2024

Accepted: September 12, 2024

Published: October 21, 2024

Anthropometric indices are infl uenced by nutrition, genetics, lifestyle, age, and ethnicity 
thus assessing these indices is a cheap tool to predict risk factors that affect blood pressure. 
There is no consensus on the anthropometric index that best predicts adults’ blood pressure. 
This research aimed to determine the relationships between anthropometric indices (weight, 
height, waist circumference (WC), hip circumference (HP), neck circumference (NC), chest 
circumference (CC), subscapular fold (SSF),  triceps fold (TF), body mass index (BMI), 
waist-hip ratio (WHR), waist-height ratio (WHtR), subscapular-triceps ratio (STR), and 
blood pressure; and to determine the best predictor(s) of  hypertension among these indices. 
This cross-sectional study was carried out in Enugu City, South-East Nigeria, and involved 
355 adults, aged between 20-75 years. The comparison of  the indices was done using the 
Student’s t-test. Pearson correlation coeffi cient (r) was used to relate the indices with blood 
pressure, and Receiver Operating Characteristic (ROC) was used to determine the cut-off  
points of  the indices to identify the best predictor(s) of  hypertension. The prevalence of  
hypertension was 38.0%. All the anthropometric indices except height, NC, and STR were 
signifi cantly higher in hypertensive participants than in the non-hypertensive participants. 
Pearson correlation coeffi cient (r) showed that all the indices except height, NC, and STR 
have weak linear positive relationships with blood pressure (r=0.1127-0.375) at p<0.001. 
The ROCs showed that WC was the best predictor with an area under curve (AUC) of  0.692, 
a cut-off  point of  96.50cm, and a PPV of  57%. However, with an AUC <0.7, all the indices 
were weak predictors of  hypertension. Hypertension correlated positively with increased age. 
Although anthropometric indices are weak predictors of  hypertension, WC is the best index 
in predicting hypertension. Due to the inconsistent effects of  WC on sensitivity, specifi city, 
and PPV, WC is still a weak predictor of  hypertension. However, the predictive power of  
WC could be augmented by age if  the participants were above 45 years. At age >45 years, 
the participants had 5 times the chance of  developing hypertension regardless of  gender.

Keywords
Adults, Anthropometric Indices,  
Blood Pressure, Correlations, 
Emerging Trends, Hypertension, 
Obesity, Risk Factors, Urban 
Setting

INTRODUCTION
Anthropometry is a highly effective method for assessing 
the nutritional and health status of  an individual or a 
population (Hamieda, 2002). Anthropometry is universally 
applicable, serving as a valuable technique in evaluating 
body size and proportions, and offers cheap, non-invasive, 
ease of  use and high precision advantages (Bates, 2017). 
Various indices of  anthropometric measurements are 
infl uenced by nutrition, genetics, lifestyle, and ethnicity, 
thus making anthropometric assessment a cheap tool to 
predict risk factors such as hypertension and obesity.
Standard anthropometric measurements include 
weight, height/length, mid-upper arm circumference 
(MUAC), triceps skinfold thickness (TSF), subscapular 
skinfold, head circumference, chest circumference, waist 
circumference, and hip circumference. Additionally, 
various indices such as body mass index (BMI), weight-
for-height, weight-for-age, subscapular-triceps ratio, 
waist-hip ratio, and waist-height ratio are used to assist in 
assessing health and nutritional status (Hamieda, 2002).
It has been established that anthropometric parameters 
are related to different metabolic disorders. Metabolic 
disorders e.g. Hypertension, Adiposity, Diabetes mellitus 

and Dyslipidemia collectively known as Metabolic 
Syndrome Diseases (MSDs) are diseases related to one 
another and have very high morbidity and mortality rates 
(Ikwuka, 2015; Ikwuka, 2017a; Ikwuka, 2017c; Ikwuka, 
2023c; Ikwuka, 2023f; Virstyuk, 2016). Results obtained 
from different researches have shown that hypertension, 
diabetes mellitus, adiposity and dyslipidemia, 
asymptomatic hyperuricemia, activation of  systemic 
immune infl ammatory processes, and fi brogenesis 
can lead to kidney damage (Ikwuka, 2017d; Ikwuka, 
2017e; Ikwuka, 2018c; Ikwuka, 2018d; Ikwuka, 2019a; 
Ikwuka, 2019c; Ikwuka, 2022; Ikwuka, 2023d; Virstyuk, 
2017a; Virstyuk, 2018a; Virstyuk, 2019; Virstyuk, 2021a; 
Virstyuk, 2021b). It has also been reported that chronic 
metabolic disorders have the ability to compromise 
immunity (Iorhemba, 2024).
High blood pressure (HBP) or hypertension is a 
medical condition in which the blood exerts a force on 
the arterial wall which exceeds normal, 120/80 mmHg 
(World Health Organization, 2023). The main modifi able 
causes of  HBP are diet (high salt intake), lack of  exercise, 
obesity, and excessive alcohol consumption (Le, 2017). 
Moreover, HBP, smoking, diabetes, and hyperlipidemia 

1 Faculty of  Postgraduate Studies, Texila American University, Georgetown, Guyana
2 College of  Medicine and Health Sciences, American International University West Africa, Banjul, The Gambia
* Corresponding author’s e-mail: aloysiussweet@yahoo.com



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Am. J. Phys. Educ. Health Sci. 2(2) 40-53, 2024

are documented risk factors for cardiovascular diseases 
like coronary artery disease (CAD), atherosclerosis, 
stroke and heart failure (Le, 2017).
According to the WHO, hypertension is a growing 
world health problem as the prevalence is on a steady 
rise. The global prevalence of  HBP is approximately 
1.28 billion adults between ages 30–79 years, of  which a 
majority  (2⁄3) of  them reside in low- and middle-income 
countries (WHO, 2023). The WHO African Region has 
a 27% prevalence of  hypertension (WHO, 2023), and in 
Nigeria, approximately 38.1% of  adults are hypertensive 
according to a national survey (Odili, 2020). In addition, 
hypertension is termed “a silent killer” because it may 
express no symptoms, a reason 46% of  hypertensive 
individuals are unaware they have it (WHO, 2023). To 
make this situation worse, HBP previously known as a 
disease for adults is now becoming common in teenagers 
and children (Katamba, 2020).
Anthropometry has been enormously studied in children 
(Sebati, 2020; WHO, 2017), in adolescents (Katamba, 
2020; WHO, 2017), in youth and adults (Ejheisheh, 2022). 
Fat deposit in the body is logically the link associating 
anthropometry to blood pressure. Anatomically, fat is 
stored in the hypodermis and yellow bone marrow, and 
pathologically in the stroma of  organs and within blood 
vessels. Accumulation of  fat in the hypodermis causes 
robustness in the physique and if  uncontrolled can lead 
to obesity. Similarly, the accumulation of  cholesterol 
(fat) plaques in the elastic arteries causes atherosclerosis 
which occludes the lumen of  elastic arteries (Le, 2017). 
Just as hypertension is a risk factor for atherosclerosis, 
atherosclerosis on its own can be asymptomatic and 
could be fatal when complications set in (Le, 2017). With 
possible complications and a set relationship between 
atherosclerosis, hypertension, and excess fat accumulation, 
it is therefore crucial to assess anthropometric indices 
among adults in order to boost the awareness of  their 
health status and to determine the best predictor(s) for 
high blood pressure in such adults.  

MATERIALS AND METHODS
Study Area
Enugu State University Teaching Hospital (ESUTH), 
Parklane is in Enugu City, South-East Nigeria. With a 
total land area of  556 km2, the city is made up of  three 
local government areas (Enugu North, Enugu South, and 
Enugu East), and is historically known for coal mining. 

Study Design
This research employed a descriptive, cross-sectional 
approach because the data was meant to be collected 
quickly. Data collection in this study spanned over 3 
months (September to November 2023), at the Enugu 
State University Teaching Hospital (ESUTH), Parklane, 
Enugu City.

Study Population
Enugu City has a population of  approximately 875,552 

(World Population Review, 2024), but the adult population 
was not delineated. However, with this fi gure, the adult 
population is estimated to be over 100,000. 

Sample Size
With an estimated adult population of  over 100,000 in 
Enugu City, the Cochran equation for descriptive, cross-
sectional studies with a study population >10,000 was 
used to determine the sample size (Udeh, 2023a). 
n0, sample size for large population >10,000 = (Z2 PQ)/d2

Where: 
Z is the abscissa of  the normal curve that cuts off  an area 
α at the tail set at a 95% confi dence interval (1.96); 
P is the proportion of  participants with hypertension 
which is 35.3% (0.353) as derived from the studies of  
(Ekwunife, 2011) and (Akinlua, 2015); 
Q is (1 - P) = (1 – 0.353) = 0.647; and
d is the precision set at 5% (p<0.05).
The prevalence of  hypertension among adults in Enugu 
City is unknown but (Ekwunife, 2011) and (Akinlua, 2015) 
estimated the prevalence of  hypertension in Nigeria to be 
12.4-34.5% and 47.2% respectively. The prevalence of  
35.3% was derived from the mean of  the two studies and 
used to calculate the minimum sample size (n0).
n0 = (1.962×0.353×0.647)/ 0.052 = 350.95 ~ 351
The minimum sample size (n) of  351 participants was 
generated. Meanwhile, after adding 10% attrition rate, 
386 was generated as the sample size.

Inclusion and Exclusion Criteria
To be included in this study, participants must reside in 
Enugu City, consent to participate with no motive of  
fi nancial compensation, must be between ages 20-75 
years, and must possess basic education. Excluded in this 
study were pregnant women, individuals residing outside 
Enugu City, the physically impaired whose physique 
has a great chance to alter the results of  this study, 
patients using psychotropic drugs, and those with renal 
disease confi rmed by either self-reporting or medical 
confi rmation.

Study Instruments
The following instruments were used to obtain data for 
this study:
A well-structured questionnaire to collect socio-
demographic characteristics, past medical history, past 
family history, and record the anthropometric indices and 
blood pressure.
For anthropometric indices: an electronic weighing 
scale was used to measure the weight of  the study 
participants to the nearest 0.1 kg; a stadiometer was used 
for height measurement to the nearest 0.1 cm; fi bre-glass 
in elastic tape was used for the measurement of  waist 
circumference (WC), head circumference (HC), neck 
circumference (NC), and chest circumference (CC) to 
the nearest 0.1 cm; Holtian skinfold calliper was used to 
measure subscapular skinfold (SSF) and triceps fold (TF) 
to the nearest 0.1mm.



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Accoson® Desktop mercury sphygmomanometer was 
used to measure blood pressure (BP).

Data Collection
Anthropometric measurements: Eight anthropometric 
indices were measured directly from the participants, 
others were calculated from measured indices: 
Body Mass Index (BMI =  (weight (kg))/( height2 (m2))); 
Waist-Hip Ratio (WHR = (waist circumference (cm))/
(hip circumference (cm))); 
Waist-Height Ratio (WHtR =(waist circumference (cm))/
(height (cm))); 
Subscapular-Triceps ratio (STR = (subscapular fold 
(cm))/(triceps fold (cm))).

Height
Participants were asked to remove their wig (if  any), 
jewelry, and shoes. They stood on the stadiometer 
platform with the back straight against the backboard 
and the head aligned in Frankfort’s horizontal plane. The 
headpiece was lowered to compress the hair as they took 
and held a deep breath.

Weight
With wig (if  any), jewelry, shoes, and extra clothing 
removed except underwear, the participants stood on an 
electronic scale placed on a solid-fl at ground. The arms 
were by the sides and face forward, then the weight was 
recorded.
WC was measured as the study participants stood erect 
and raised their clothes to reveal their waists. The tape 
was wound around from the left/right anterior superior 
iliac spine to the other.
HC was measured at the level of  the widest buttocks with 
the tape not loosened or too tight.
CC was measured by encircling the upper part of  the 
chest with the tape.
NC was measured at the level of  cervical vertebra 4 (C4) 
or below the laryngeal prominence (in men) with the tape 
while the participants stood and looked straight ahead 
with the shoulders relaxed.

Skinfolds
The study participants stood at ease while measuring the 
subscapular and triceps folds. A thickness of  skinfold was 

pinched at the inferior angle of  the right scapular and the 
midpoint of  the posterior arm respectively, with the left 
thumb and index fi nger. The caliper’s jaws were applied 
2cm above the two fi ngers and 2cm laterally (for the 
subscapular fold). Readings were taken after three seconds.

BP
The study participants sat comfortably for 5-10 minutes. 
Three consecutive measurements of  4-5 minutes apart 
were made on the dominant arm by tying an adult cuff  2-3 
cm above the cubital fossa. The radial artery was palpated 
as the cuff  was infl ated until the pulse disappeared. The 
bell of  the stethoscope was placed over the brachial artery 
in the cubital fossa as the cuff  was gradually defl ated. 
The fi rst sound (1st Korotkoff) denotes the systolic 
blood pressure and the disappearance of  the sound (5th 
Korotkoff) is the diastolic blood pressure. The average of  
the last two blood pressure measurements was recorded 
to the nearest 2 mmHg.

Data Analysis
Data collected was analyzed using Statistical Package 
for Social Sciences (SPSS) version 22.0. The socio-
demographic characteristics, blood pressure, and 
anthropometric indices were presented as mean and 
standard deviation. Mean was compared using a Student’s 
t-test. Pearson correlation coeffi cient (r) was used to 
measure the relationship between anthropometric 
indices and blood pressure. The association between 
anthropometric indices and hypertension was done using 
logistic regression. Receiver Operating Characteristic 
(ROC) was used to determine the cut-off  values of  
anthropometric indices used to identify the predictors of  
hypertension. Sensitivity and specifi city were calculated 
and the point having the highest sum was taken as the 
cut-off  of  the indicator. All tests of  signifi cance were 
two-tailed and the level of  signifi cance was set at p<0.05.

Ethical Consideration
Ethical clearance and permission to conduct the research 
were obtained from the Ethics Committee of  Enugu 
State University Teaching Hospital (ESUTH), Parklane, 
Enugu City.

RESULTS AND DISCUSSION

Table 1: Socio-demographic characteristics of  the study participants
Parameters Frequency Percentage
Sex
Male 154 43.4
Female 201 56.6
Age group
20 – 24 32 9.0
25 – 29 38 10.7
30 – 34 54 15.2
35 – 39 53 14.9



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A total of  355 study participants were involved in this 
study, males and females accounted for 43.4% and 56.6% 
respectively. 9.0% of  the participants were between ages 
20-24 years, 10.7% were between 24-29 years, 15.2% were 
between 30-34 years, 14.9% were between 35-39 years, 
11.3% were 40-44 years old, 11.3% were 45-49 years old, 
and 27.6% were ≥50 years old.
Only 26.2% of  the participants were single, while a 
greater percentage (65.1%) were married. 35.5% of  the 
participants were administrative employees, 27.6% were 
skilled workers, 1.1% were unskilled workers, 5.6% were 
unemployed, 4.2% were retired, and 25.9% were in 
various jobs classifi ed as others.
Most (68.5%) of  the participants had tertiary education, 
11.3% had only primary school education, and 20.3% had 
only secondary school education. The level of  physical 
activity shows that 67.3% of  the participants trek, 2.8% 
ride bicycles, 1.4% were generally involved in an exercise, 
and 28.5% drive always irrespective of  the distance.

Table 2 shows that approximately one-fi fth (20.3%) of  
the participants had a family history of  diabetes mellitus, 
33.5% had a family history of  hypertension, 13.8% had a 
family history of  stroke, and 42.8% had a family history 
of  obesity, respectively.

40 – 44 40 11.3
45 – 49 40 11.3
≥50 98 27.6
Marital status
Single 93 26.2
Married 231 65.1
Divorced 3 0.8
Widow/widower 28 7.9
Occupation
Administrative 126 35.5
Skilled labor 98 27.6
Unskilled labor 4 1.1
Unemployed 20 5.6
Pensioner 15 4.2
Others 92 25.9
Level of  education
Primary 40 11.3
Secondary 72 20.3
Tertiary 243 68.5
Level of  physical activity
Trekking 239 67.3
Riding of  bicycle 10 2.8
Always using vehicle 101 28.5
Involvement in exercise 5 1.4

Table 2: Family history of  study participants
Disease condition Frequency (%)
Diabetes mellitus 72 (20.3)
Hypertension 119 (33.5)
Stroke 49 (13.8)
Fatness 152 (42.8)

Table 3: Social history of  study participants
Lifestyle Frequency (%)
Cigarette smoking
Yes 27 7.6
No 328 92.4
Use of  snuff
Yes 17 4.8
No 338 95.2
Use of  alcohol
Yes 173 48.7
No 182 51.3
Patronise fast-food vendors
Yes 213 60.0
No 142 40.0

Table 3 shows that most participants were non-tobacco 
users (92.4% do not smoke cigarettes and 95.2% do 
not use snuff). However, 7.6% of  the participants were 



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cigarette smokers whereas 4.8% used snuff. 48.7% of  
the participants drink alcohol while 60.0% patronize fast-
food vendors.

participants with transient stroke were also on medication 
(1.7%). 2.5% of  the participants had heart attack of  
which 1.7% were on medication for heart attack.

Figure 1: Pie chart showing the prevalence of  
hypertension in study participants

Table 5: Comparison of  mean anthropometric indices between hypertensive and non-hypertensive study participants
Anthropometric 
indices (scale)

Hypertension
Yes
Mean ± SD

No
Mean ± SD

t p-value

Weight (kg) 83.69 ± 18.08 76.56 ± 14.19 4.127 < 0.001
Height (m) 1.69 ± 0.11 1.69 ± 0.09 0.131 0.896
WC (cm) 99.72 ± 12.74 91.45 ± 11.20 6.404 < 0.001
HC (cm) 108.60 ± 12.66 101.67 ± 11.02 5.432 < 0.001
NC (cm) 37.48 ± 4.25 37.15 ± 9.85 0.365 0.715
CC (cm) 100.27 ± 13.11 93.52 ± 12.54 4.841 < 0.001
SSF (mm) 25.31 ± 11.24 20.19 ± 10.36 4.374 < 0.001
TF (mm) 19.81 ± 10.60 15.98 ± 8.68 3.705 < 0.001
BMI (kg/m2) 29.56 ± 6.26 27.03 ± 5.36 4.045 < 0.001
WHR 0.92 ± 0.07 0.90 ± 0.08 2.262 0.024
WHtR 59.39 ± 8.45 54.38 ± 7.54 5.795 < 0.001
STR 1.41 ± 0.47 1.51 ± 1.44 0.770 0.442

p<0.05 = statistically signifi cant relationship; p≥0.05 = no statistically signifi cant relationship

Table 6: Relationships between anthropometric indices and blood pressure
Indices Statistics Systolic BP Diastolic BP
Weight (kg) r 0.288 0.321

p-value < 0.001 < 0.001
Height (m) r 0.010 0.076

p-value 0.851 0.154
WC (cm) r 0.375 0.374

p-value < 0.001 < 0.001
HC (cm) r 0.312 0.323

p-value < 0.001 < 0.001
NC (cm) r 0.046 0.025

p-value 0.385 0.634

Table 4: Medical history of  study participants
Disease Medical history On medication

Frequency (%) Frequency (%)
Diabetes mellitus 29 (8.2) 25 (7.0)
High blood 
pressure

97 (27.3) 83 (23.4)

Stroke 6 (1.7) 6 (1.7)
Heart attack 9 (2.5) 6 (1.7)

Table 4 shows that a total of  8.2% of  the participants 
had diabetes mellitus. However, almost all of  them 
were on medication. 27.3% were hypertensive of  which 
23.4% were on medication for hypertension. All of  the 

Yes = hypertensive participants; No = non-hypertensive 
participants
38% of  the participants were hypertensive while 62% 
were non-hypertensive.

Table 5 shows that all the anthropometric indices used 
in this study were signifi cantly higher in hypertensive 
participants (p<0.001) than in non-hypertensive 
participants except for Height, NC, WHR, and STR. 

The most signifi cant of  all indices were WC (t=6.404 
at p<0.001), and HC (t=5.432 at p<0.001). The least 
signifi cant was WHR (t=2.262 at p=0.024).



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Table 6 shows statistically signifi cant relationships 
between anthropometric indices and BP (systolic and 
diastolic), except for Height, NC, WHR, and STR with 
p≥0.05. Using Pearson correlation coeffi cient (r), the 
anthropometric indices had a weak positive linear 
correlation with BP, except for STR. STR had a weak 
negative linear correlation. The index that showed the 

most signifi cant relationship with BP was WC (r=0.375 
systolic and 0.374 diastolic, at p<0.001) and HC 
(r=0.312 systolic and 0.323 diastolic, at p<0.001). The 
index with the least signifi cant correlation was WHR 
(r=0.147 systolic and 0.127 diastolic, at p=0.005 and 
0.017 respectively).

CC (cm) r 0.278 0.382
p-value < 0.001 < 0.001

SSF (mm) r 0.285 0.277
p-value < 0.001 < 0.001

TF (mm) r 0.228 0.185
p-value < 0.001 < 0.001

BMI (kg/m2) r 0.268 0.266
p-value < 0.001 < 0.001

WHR r 0.147 0.127
p-value 0.005 0.017

WHtR r 0.332 0.307
p-value < 0.001 < 0.001

STR r -0.006 0.007
p-value 0.917 0.898

BP = Blood pressure; r = Pearson correlation coeffi cient (-1 = negative linear correlation, 0 = no linear correlation, +1 = positive linear 
correlation); p<0.05 = statistically signifi cant relationship; p≥0.05 = no statistically signifi cant relationship

Table 7: Association between BMI, WC, and hypertension 
Indices Hypertension

Yes 
n (%)

No 
n (%)

p-value OR 95% C.I. for OR

BMI (kg/m2)
Normal 37 (32.5) 77 (67.5)
Underweight 2 (18.2) 9 (81.8) 0.339 0.462 0.095 – 2.249
Overweight 34 (31.2) 75 (68.8) 0.840 0.943 0.537 – 1.658
Class 1 Obesity 36 (45.0) 44 (55.0) 0.077 1.703 0.944 – 3.071
Class 2 Obesity 20 (62.5) 12 (37.5) 0.003 3.468 1.534 – 7.844
Class 3 Obesity 6 (66.7) 3 (33.3) 0.022 4.162 0.986 – 17.572
Waist circumference (cm)
Obese 107 (44.8) 132 (55.2) <0.001 2.548 1.552 – 4.183
Normal 28 (24.1) 88 (75.9)

Table 7 shows that participants with Class 2 obesity 
were 3.5 times more likely to have hypertension when 
compared to normal participants (p=0.003, OR=3.468, 
95% C.I.=1.534–7.844). Class 3 obesity participants were 
4 times more likely to have hypertension when compared 

to normal participants (p=0.022, OR=4.162, 95% 
C.I.=0.986–17.572). Obese participants based on WC 
were 2.5 times more likely to have hypertension when 
compared to normal participants (p<0.001, OR=2.548, 
95% C.I.=1.552–4.183).

Table 8: Association between age, sex, and hypertension
Indices Hypertension

Yes 
n (%)

No 
n (%)

p-value OR 95% C.I. for OR

Age group (years)
>45 79 (61.7) 49 (38.3) < 0.001 4.923 3.086 – 7.853
≤ 45 56 (24.7) 171 (75.3)



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negative actual state group.
a. Under the non-parametric assumption
b. Null hypothesis: true area = 0.5 

In Table 9, WC had the best AUC (0.692; p=0.000). 
Next is WHtR with a good AUC (0.679; p=0.000), 

followed by HC (0.656; p=0.000). All the anthropometric 
indices had signifi cant positive AUC except for Height 
(0.492; p=0.792), and STR (0.527; p=0.396). All AUCs 
were <70% (0.70) signifying poor predictive power for 
hypertension.

Table 9: Receiver Operating Characteristics (ROCs) for anthropometric indices predicting hypertension with their 
areas under curves
Area Under Curve (AUC)
Test Result Indices AUC Std. Errora Asymptotic Sig.b Asymptotic 95% Confi dence Interval

Lower Bound Upper Bound
Weight (kg) .615 .031 .000 .554 .677
Height (m) .492 .033 .792 .427 .556
WC (cm) .692 .029 .000 .635 .749
HC (cm) .656 .031 .000 .595 .716
NC (cm) .620 .031 .000 .559 .680
CC (cm) .664 .030 .000 .605 .722
SSF (mm) .639 .031 .000 .579 .699
TF (mm) .603 .031 .001 .541 .664
BMI (kg/m2) .621 .031 .000 .559 .682
WHR .609 .031 .001 .547 .671
WHtR .679 .030 .000 .621 .738
STR .527 .032 .396 .465 .589

Table 10: Data showing the sensitivity and specifi city at various cut-off  points of  the anthropometric indices to 
predict hypertension
Indices Hypertensive

 (n=135)
Normal
(n=220)

Se
ns

iti
vi

ty

Sp
ec

ifi 
ci

ty

1-
sp

ec
ifi 

ci
ty

Positive 
predictive 
value         
(PPV %)

Negative 
predictive 
value   
(NPV %)

True 
positive 
(TP)

False 
negative 
(FN)

False 
positive 
(FP)

True 
negative 
(TN)

Weight (kg)
77.500 82 53 98 102 0.607 0.509 0.491 45  66
78.500 79 56 91 109 0.585 0.545 0.455 46  66
79.500 77 58 83 117 0.570 0.586 0.414 48  67
80.500 72 63 73 127 0.533 0.636 0.364 50  67
81.500 66 69 68 132 0.489 0.659 0.341 49  66
Height (m)
1.6850 67 68 111 89 0.496 0.445 0.555 38  57
1.6950 64 71 107 93 0.474 0.464 0.536 37  57
1.7050 58 77 100 100 0.430 0.500 0.500 37  57
1.7110 55 80 87 113 0.407 0.564 0.436 39  58
1.7160 55 80 86 114 0.407 0.568 0.432 39  59

Sex
Male 62 (40.3) 92 (59.7) 0.449 1.182 0.767 – 1.820
Female 73 (36.3) 128 (63.7)

Table 8 shows that the likelihood of  developing 
hypertension is increased in participants above 45 years 
of  age at 95% C.I. irrespective of  gender.

The test result indices: weight, height, WC, HC, NC, 
CC, SSF, TF, BMI, WHR, WHtR, and STR have at least 
one tie between the positive actual state group and the 



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Waist circumference (cm)
94.5000 92 43 82 118 0.681 0.591 0.409 53  73
95.5000 89 46 75 125 0.659 0.627 0.373 54  73
96.5000 85 50 64 136 0.630 0.682 0.318 57  73
97.5000 78 57 60 140 0.578 0.700 0.300 57  71
98.5000 74 61 56 144 0.548 0.718 0.282 57  70
Hip circumference (cm)
102.500 87 48 93 107 0.644 0.536 0.464 48  69
103.500 84 51 86 114 0.622 0.568 0.432 49  69
104.500 80 55 75 125 0.593 0.627 0.373 52  70
105.500 75 60 67 133 0.556 0.664 0.336 53  69
106.500 73 62 59 141 0.541 0.705 0.295 55  69
Neck circumference (cm)
35.5000 101 34 104 96 0.748 0.482 0.518 49  74
36.5000 80 55 83 117 0.593 0.586 0.414 49  68
37.5000 62 73 61 139 0.459 0.695 0.305 50  66
38.5000 47 88 47 153 0.348 0.764 0.236 50  63
39.5000 38 97 32 168 0.281 0.841 0.159 54  63
Chest circumference (cm)
95.5000 91 44 83 117 0.674 0.586 0.414 52 73
96.5000 87 48 75 125 0.644 0.623 0.377 54 72
97.5000 84 51 70 130 0.622 0.650 0.350 55 72
98.5000 78 57 62 138 0.578 0.691 0.309 56 71
99.5000 68 67 53 147 0.504 0.736 0.264 56 69
Subscapular fold (mm)
20.5000 87 48 89 111 0.644 0.555 0.445 49 70
21.5000 85 50 85 115 0.630 0.577 0.423 50 70
22.5000 82 53 75 125 0.607 0.627 0.373 52 70
23.5000 77 58 69 131 0.570 0.655 0.345 53 69
24.5000 74 61 65 135 0.548 0.677 0.323 53 69
Triceps fold (mm)
15.5000 80 55 87 113 0.593 0.564 0.436 48 67
16.5000 79 56 85 115 0.585 0.577 0.423 48 67
17.5000 75 60 79 121 0.556 0.605 0.395 49 67
18.5000 68 67 75 125 0.504 0.627 0.373 48 65
19.5000 68 67 71 129 0.504 0.645 0.355 49 66
BMI (kg/m2)
27.5984 77 58 87 113 0.570 0.564 0.436 47 66
27.6456 77 58 86 114 0.570 0.568 0.432 47 66
27.6912 77 58 85 115 0.570 0.573 0.427 47 66
27.7093 77 58 85 115 0.570 0.577 0.423 48 67
27.7250 77 58 84 116 0.570 0.582 0.418 48 67
WHR
0.9071 83 52 81 119 0.615 0.595 0.405 51 70
0.9083 83 52 80 120 0.615 0.600 0.400 51 70
0.9095 80 55 77 123 0.593 0.614 0.386 51 69
0.9100 78 57 76 124 0.578 0.618 0.382 51 68
0.9104 78 57 75 125 0.578 0.623 0.377 51 69



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In Table 10, the best cut-off  points and AUC of  the 
anthropometric indices for predicting hypertension are 
shown. The summary is shown in Table 11. None of  the 
anthropometric indices had signifi cant predicting power 
for hypertension. However, WC (AUC=0.692, cut-off  
points=96.5cm, sensitivity=63.0%, specifi city=68.2%, 
PPV=57%, and NPV=73%), and WHtR (AUC=0.679, cut-
off  points=56.88, sensitivity=65.9%, specifi city=65.9%, 
PPV=57%, and NPV=74%) were the best predictors for 

hypertension. The indices with the worst predictive power 
for hypertension were Height (AUC=0.492, best cut-off  
points=1.71m, sensitivity=43.0%, specifi city=50.0%, 
PPV=37%, and NPV=57%), and STR (AUC=0.527, 
best cut-off  points=1.30, sensitivity=52.6%, 
specifi city=52.7%, PPV=43%, and NPV=62%).
The sensitivity, specifi city, and PPV values of  the best 
predictors were below 70% which means they have low 
predictive powers.

WHtR
56.7627 89 46 71 129 0.659 0.645 0.355 56 74
56.8466 89 46 69 131 0.659 0.655 0.345 56 74
56.8806 89 46 68 132 0.659 0.659 0.341 57 74
56.8914 89 46 67 133 0.659 0.664 0.336 57 74
56.9366 88 47 66 134 0.652 0.668 0.332 57 74
STR
1.2829 71 64 98 102 0.526 0.509 0.491 42 61
1.2887 71 64 96 104 0.526 0.518 0.482 42 62
1.2958 71 64 95 105 0.526 0.527 0.473 43 62
1.3022 70 65 95 105 0.519 0.527 0.473 43 62
1.3060 70 65 94 106 0.519 0.532 0.468 43 62

Table 11: Summary of  results of  anthropometric indices in predicting hypertension
Area Under Curve (AUC)
Anthropometric Indices AUC Best cut-off  points Sensitivity Specifi city PPV NPV
Weight (kg) 0.615 79.50 57% 59% 48% 67%
Height (m) 0.492 1.71 43% 50% 37% 57%
WC (cm) 0.692 96.50 63% 68% 57% 73%
HC (cm) 0.656 104.50 59% 63% 52% 70%
NC (cm) 0.620 37.50 46% 70% 50% 66%
CC (cm) 0.664 97.50 62% 65% 55% 72%
SSF (mm) 0.639 22.50 61% 63% 52% 70%
TF (mm) 0.603 17.50 56% 61% 49% 67%
BMI (kg/m2) 0.621 27.69 57% 57% 47% 66%
WHR 0.609 0.91 59% 61% 51% 69%
WHtR 0.679 56.89 66% 66% 57% 74%
STR 0.527 1.29 53% 53% 43% 62%

In Table 11, an area under the curve (AUC) of  less than 0.70 
showed that the test is weak for predicting hypertension. 
Tables 10 and 11 showed that the anthropometric indices 
were not excellent for predicting hypertension. However, 
the best predictors in the list were WC and WHtR. Their 
best cut-off  points still yielded weak results. At these cut-
off  points, the sensitivity, specifi city, and PPV of  these 
anthropometric indices were below 70%.
Anthropometry is related to high blood pressure 
(hypertension) and obesity. Obesity is a risk factor 
for hypertension. Obesity is also a risk factor for 
Non-Hodgkin lymphoma (Ikwuka, 2023e). Due to 
globalization and commercialization, diet transition 
is sweeping across the globe and Enugu City is not an 

exception. This diet transition has led to the sprouting 
of  fast food restaurants in all the nooks and crannies of  
Enugu City. Linked with the induction of  oxidative stress 
are major free radicals. Among these major free radicals, 
superoxide anion, hydroxyl radical, and hydroperoxyl 
radical are of  physiological signifi cance. Non-radical of  
physiological signifi cance is hydrogen peroxide (Ikwuka, 
2023b; Ama, 2023). Increased oxidative stress can lead 
to mutation which is an alteration in the DNA sequence 
which produces new alleles (Ikwuka, 2023a). The 
combination of  these factors and other factors such as 
reduced physical activities and sedentary life will affect 
anthropometry and blood pressure. This was in line with 
the research fi ndings that 60.0% of  the study participants 



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Am. J. Phys. Educ. Health Sci. 2(2) 40-53, 2024

patronized fast-food vendors, 48.7% drank alcohol, and 
28.5% always drove their vehicles irrespective of  the 
distance thereby reducing their physical activities.
The prevalence of  hypertension in this present study 
was 38%. This result is almost the same as the 38.1% 
prevalence reported by (Odili, 2020), and 47.2% reported 
by (Akinlua, 2015). Interestingly, 33.5% of  the participants 
in this present study reported having a positive family 
history of  hypertension.
The mean anthropometric indices measured in the study 
were signifi cantly higher in the hypertensive participants 
than in the non-hypertensive participants except for Height 
and NC which showed no difference, and STR which was 
not signifi cantly higher in non-hypertensive participants. 
These fi ndings agree with the fi ndings in a Korean study 
(Lee, 2014), in a Nigerian study (Ononamadu, 2017), in 
a study in the Netherlands (Grievink, 2014), in Greece 
(Panagiostakos, 2009), and in Japan (Sakurai, 2006). The 
marked signifi cance of  WHtR in hypertensive participants 
in this present study corresponds with the fi ndings in 
Barbados (Rodrigues, 2011), in Taiwan (Tseng, 2010), and 
in Korean men (Park, 2009). 
Obesity is due to massive body fat deposits, and it has been 
noted as a risk factor for hypertension. With the report 
that 60.0% of  the participants ate fast food with high 
calories, consumption of  these meals coupled with other 
risk factors could be implicated as the cause of  overweight 
or obesity noted in the hypertensive participants. 
This explains why the hypertensive participants had 
signifi cantly higher anthropometric indices. Obesity is also 
a major nexus between anthropometry and hypertension. 
In this present study, the mean values of  BMI, SSF, 
WC, HC, and WHR were signifi cantly higher (p<0.005) 
in the hypertensive participants compared to the non-
hypertensive participants. WC, HC, SSF, and WHR are 
measures of  central obesity while BMI is a measure of  
general obesity (Lee, 2015). Using Pearson correlation 
coeffi cient (r); WC, HC, SSF, WHtR, and BMI showed 
a statistically signifi cant but weak relationship with high 
blood pressure (r=(0.113-0.375); p<0.05). The greater 
the level of  obesity, the higher the chances of  developing 
hypertension (class 2 obesity with OR=3.47 at p=0.003; 
class 3 obesity with OR=4.16 at p=0.022, the obese band 
of  WC has an OR= 2.55 at p<0.001), all set at 95% 
confi dence interval (CI). These fi ndings in this present 
study agree with the fi ndings in a Korean anthropometric 
study (Lee, 2014).
In this present study, the Receiver Operator 
Characteristics (ROCs) for anthropometric indices 
predicting hypertension, all the areas under curves were 
less than 0.7 denoting that the anthropometric indices 
were not very strong in predicting hypertension because 
of  probable confounding factors. However, the best 
predictors of  hypertension for both genders were WC 
and WHtR, and their cut-off  points still yielded a weak 
result because sensitivity, specifi city, and PPV were below 
70%. Conversely, height was the least predictor with a 
PPV of  37%.

For the male study participants, the best predictors 
for hypertension were WC (best cut-off  points=92.5 
cm; PPV=61%), WHtR (best cut-off  points=53.92; 
PPV=57%), and CC (best cut-off  points=93.5 cm; 
PPV=54%). For the female study participants, the 
best predictors for hypertension were WC (best cut-
off  points=97.5 cm; PPV=53%), HC (best cut-off  
points=108.5 cm; PPV=55%), and WHtR (best cut-off  
points=58.98 cm; PPV=54%). These fi ndings agree 
with the WHO recommendation cut-off  point for 
cardiometabolic risk: WC ≥94 cm for WC in males and 
WHR ≥0.90 cm for both men and women (WHO, 2008). 
Meanwhile, the fi ndings were at variance with the WHO 
cut-off  recommendation for WC which is (>80 cm) for 
cardiometabolic risk for women (WHO, 2008). Moreover, 
the WC cut-off  point (WC >120 cm) for males in an 
American study was higher compared to this present 
study, just as the WC cut-off  point (88 cm) for females 
differs with the fi ndings of  this present study (Expert 
Panel on Detection, Evaluation, and Treatment of  High 
Blood Cholesterol in Adults, 2001). The regionalization, 
sometimes intra-regionalization, nutrition in childhood, 
and the current nutritional status accounted for the 
difference in anthropometric indices cut-off  points 
(WHO, 2008; IDF, 2006).
The prevalence of  hypertension in this study was 38%. 
Hypertension reported in this present study might not 
be attributed to obesity even as 60% of  the participants 
patronize fast-food vendors because 71.5% of  the 
participants were involved in at least a form of  physical 
activity. However, there is a link connecting hypertension 
to genetics in this present study because 33.5% of  the 
participants had a positive family history of  hypertension. 
In addition, 20.3% had a positive family history of  diabetes 
mellitus, and 8.2% of  the participants were diabetics. 
Other factors that could be associated with hypertension 
in this present study were cigarette smoking (7.6%), use 
of  snuff  (4.8%), alcohol use (48.7%), patronizing fast-
food vendors (60.0%) and age. Tobacco has a teratogenic 
effect on fetuses and causes hepatorenal injury during 
pregnancy (Udeh, 2023b; Udeh, 2023c). At above 45 
years of  age, the likelihood of  becoming hypertensive 
signifi cantly increases. The consequences of  hypertension 
were also present among the study participants – heart 
attack (2.5%), and stroke (1.7%).
Nevertheless, there is also need for new and effective 
treatment options in patients with Metabolic Syndrome 
Diseases. Sodium-Glucose Linked Transporter 2 (SGLT-
2) inhibitors e.g. Dapaglifl ozin and Glucagon-like Peptide 
1 Receptor Agonists (GLP-1 RAs) e.g. Liraglutide have 
been found to improve the effi cacy of  treatment and 
clinical course of  type 2 diabetes mellitus and hypertension 
in patients with such comorbidities (Ikwuka, 2017b; 
Ikwuka, 2018a; Ikwuka, 2018b; Ikwuka, 2019b; Ikwuka, 
2021; Ikwuka, 2024; Virstyuk, 2017b; Virstyuk, 2018b; 
Virstyuk, 2018c). It has also been documented that 
coconut water has hepatorenal protective functions in 
alloxan-induced type 1 diabetes mellitus (Ekechi, 2023).



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However, it is pertinent to point out some of  the 
limitations of  this present study because other causes 
of  hypertension e.g. renal disease, aortic and renal 
artery stenosis, and pheochromocytoma were not 
investigated. Bias in this present study could be from 
selection, information, and measurements even though 
extreme case sampling, well structured self-administered 
questionnaire, participants with basic education, and 
standardized equipments were used. The AUCs of  
ROCs were not adjusted in this study. Therefore, the 
relationship between each anthropometric index and 
hypertension might be confounded by the infl uence of  
age, thereby underestimating the actual predictive value 
of  the anthropometric index during ROC determination.

CONCLUSION
Anthropometry is related to hypertension and obesity is 
a risk factor for hypertension. Hypertension is a global 
health challenge which also affects Enugu City in South-
East Nigeria. The prevalence of  hypertension in this 
present study was 38.0%. Apart from obesity, other factors 
such as nutrition or diet transition, genetics, medical 
conditions, and age are also associated with hypertension. 
All the mean values of  anthropometric indices were 
higher in the hypertensive participants than in the non-
hypertensive participants except height, NC and STR. 
WC best correlated with systolic blood pressure (SBP) 
and diastolic blood pressure (DBP) and thus WC was the 
best predictor of  hypertension, followed by WHtR. The 
sensitivity, specifi city, and PPV for all anthropometric 
indices were below 70%.

Acknowledgements
Special thanks to the management and staff  of  Enugu 
State University Teaching Hospital (ESUTH), Parklane, 
Enugu City; and to all the men and women who 
participated in this study for their cooperation and 
support in carrying out this research.

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