Article 3616.indd Pa ge 1 Pa ge 40 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 Pa ge 41 https://journals.e-palli.com/home/index.php/ajpehs 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. Pa ge 42 https://journals.e-palli.com/home/index.php/ajpehs Am. J. Phys. Educ. Health Sci. 2(2) 40-53, 2024 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 Pa ge 43 https://journals.e-palli.com/home/index.php/ajpehs Am. J. Phys. Educ. Health Sci. 2(2) 40-53, 2024 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 Pa ge 44 https://journals.e-palli.com/home/index.php/ajpehs Am. J. Phys. Educ. Health Sci. 2(2) 40-53, 2024 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). Pa ge 45 https://journals.e-palli.com/home/index.php/ajpehs Am. J. Phys. Educ. Health Sci. 2(2) 40-53, 2024 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) Pa ge 46 https://journals.e-palli.com/home/index.php/ajpehs Am. J. Phys. Educ. Health Sci. 2(2) 40-53, 2024 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 Pa ge 47 https://journals.e-palli.com/home/index.php/ajpehs Am. J. Phys. Educ. Health Sci. 2(2) 40-53, 2024 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 Pa ge 48 https://journals.e-palli.com/home/index.php/ajpehs Am. J. Phys. Educ. Health Sci. 2(2) 40-53, 2024 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 Pa ge 49 https://journals.e-palli.com/home/index.php/ajpehs 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). Pa ge 50 https://journals.e-palli.com/home/index.php/ajpehs Am. J. Phys. Educ. Health Sci. 2(2) 40-53, 2024 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. REFERENCES Akinlua, J. T., Meakin, R., Umar, A. M., & Freemantle, N. (2015). Current prevention pattern of hypertension in Nigeria: A systematic review. PLOS ONE, 10(10), e0140021. Ama, M. I., Ikwuka, A. O., Udeh, F. C., Ekechi, H. O., & Eteudo, A. N. (2023). 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