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. 2020 Jul 17;6(7):e04432. doi: 10.1016/j.heliyon.2020.e04432

Comparative evaluation of anthropometric measurements and prevalence of hypertension: community based cross-sectional study in rural male and female Cambodians

Yuki Shimotake a, Etongola P Mbelambela a,∗, Sifa MJ Muchanga b, Antonio F Villanueva c, Sok Seng Yan d, Marina Minami a, Rie Shimomoto e, Ambis Joelle Lumaya b, Narufumi Suganuma a
PMCID: PMC7371755  PMID: 32715126

Abstract

Introduction

Hypertension is a major and preventable risk factor that can lead to cardiovascular disease. The extent that obesity impacts hypertension differs when using body mass index (BMI), waist circumference (WC) or waist to height ratio (WHtR). This study aimed to determine the prevalence of hypertension and to compare several anthropometric measurements in the prediction of hypertension between males and females from Chet Borey district of Kratie province, in Cambodia.

Methods

A cross-sectional study was conducted among 276 healthy adults aged 18 years or older, including 94 males and 182 females who visited the local health post of Kaoh chraeng in Kratie province from November 21 to 27, 2015. Systolic and diastolic blood pressures were measured. Anthropometric measures: Body mass index (BMI), as well as waist circumference (WC) and waist to height ratio (WHtR) were assessed to analyze adiposity indices. Multivariate analysis was performed to evaluate the association between anthropometric measures and hypertension among males and females after adjustment for confounders.

Results

Hypertension was more prevalent in males (38.3%) compared to females (26.4%). When considering adiposity indices, WC was higher in females than males (35.7% vs 10.6% females vs males), the same for WHtR (55.0% vs 30.9% females vs males). In the multivariate analysis, for males, in addition to high BMI [aOR 4.37 (1.01–18.81)], high WC [aOR 7.55 (1.42–39.99)] was associated with the risk of developing hypertension. Whereas for females, only WC [aOR 3.24 (1.54–6.83)] was associated with the concerned risk.

Conclusion

Prediction of hypertension using anthropometric measurements differs by sex and by the index used. In our population, BMI and WC appeared more appropriate for men while only WC was applicable to women. These results afford alternatives to hypertensive screening that may be useful tools for the majority of rural Cambodians since accessibility to health facilities is limited.

Keywords: Health sciences, Public health, Anthropometric measures, Blood pressure, Obesity, Cambodia

Health Sciences; Public health; anthropometric measures; blood pressure; Obesity; Cambodia

1. Introduction

Hypertension is one of the most prominent and preventable medical conditions that can lead to cardiovascular disease (CVD) and death, thus it is regarded as an important public health issue. The prevalence of hypertension has been increasing in most areas worldwide, especially in developing countries (Wolf-Maier et al., 2003). Obesity is associated with several health problems including: hypertension, insulin resistance, and dyslipidemia, thereby increasing the risk for cardiovascular disease as well (Bouchi et al., 2016). Evidence has shown the accuracy of central obesity indices such as waist circumference (WC) and waist-to-hip ratio (WHR) as more specific predictors of CVD and metabolic syndrome (Ashwell et al., 2012; Lo et al., 2016). However, the prevalence that overweightness increases health risks differed when using body mass index (BMI), WC or waist to height ratio (WHtR) (Staub et al., 2018). A Brazilian study has reported that the WHtR presented a greater magnitude of association with cardiometabolic risk factors than other indices, besides having an association with Diastolic blood pressure (DBP) and high density lipoproteins (HDL) in women (Milagres et al., 2019). Cambodia is not spared by the epidemic of non-communicable diseases (NCDs) observed in the Southeast Asia region (Dans et al., 2011). A nationwide study in 2010 reported 11.2% prevalence of hypertension among the population and 43% of adult aged 55–64 years have never had their blood pressure measured (Isaakidis et al., 2011). A national STEP survey of risk factors for NCDs conducted by Department of Preventive Medicine, Ministry of Health, Cambodia in 2010 found that 10.5% of men and 16.3% of women were classified as overweight; and 11.8% of men and 16.9% of women were classified as having central obesity (Oum et al., 2010). Given its high prevalence in both urban and rural settings, the Cambodian government then designated hypertension control as a priority in the national health strategic plan. Therefore building evidence-based data for its prevention and control. The aim of this study was to first determine the prevalence of hypertension; and second to evaluate and compare several anthropometric measures in the prediction of hypertension among male and female subjects of Chet Borey rural district, province of Kratie, in Cambodia.

2. Methods

2.1. Study design and setting

The present community-based cross-sectional study collected data from the local health post of Kaoh chraeng, in Chet Borey rural district, Kratie province in the Kingdom of Cambodia.

Cambodia is a developing country located in the southern portion of the Indochinese peninsula in Southeast Asia. It is 181,035 square kilometers in area, bordered by Thailand to the northwest, Laos to the northeast, Vietnam to the east and the Gulf of Thailand to the southwest. The country is characterized by a relatively low gross domestic product (GDP), and high dependence on environmental resources, and a high poverty rate (Park, 2000; Travers et al., 2015). The agricultural sector accounts for about 35% of GDP and over 80% of the population live in rural areas (Benjamin et al., 2014). Kratie is one of the poorest provinces located in the northeast of Cambodia. It borders on Stung Treng to the north, Mondulkiri to the east, Kampong Thom and Kampong Cham to the west, and Tbong Khmum and Vietnam to the south. Kaoh Chraeng Island, in Kratie Province, Cambodia, is located in the middle of the Mekong River and has no medical centers. Five villages of the Island were the focus of study: Phum Wat with a population of 886, Phum Khalkoh with 691, Phum Kandal with 387, Phum Prek with 374, and Phum Roka Khro with 359, totaling a population of about 3000 (Figure 1a). All the five villages depend on Kaoh chraeng, health post. Access to medical care is limited because a raft is needed to leave the Island (Figure 1b) and the nearest hospital is located at 17 km away. Figure 1 describes the geographic localization of the study setting.

Figure 1.

Figure 1

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The location of study area, a) The distance from Kratie to Kaoh Chraeng Island is 17 km. Kaoh Chraeng Island is located in the middle of the Mekong River. Kaoh Chraeng Island has five villages. Lozenge indicates the location of the village.: 1. Phum Wat, 2. Phum Kbal Koh, 3. Phum Kandal, 4. Phum Prek, 5. Phum Roka Khnol. b) There is only one raft that leaves and arrives at the island, and is the only means of transportation for the residents. The raft serves as transportation for people, motorcycles and cars. About 40 people can get on at a time, and it runs twice a day in the morning and the evening, and other times as needed. c) The Health Post in Kaoh Chraeng Island where a physical examination was conducted.

2.2. Data collection

From November 21 to 27, 2015, residents aged 18 or older gathered at the Health Post for medical screening, the first of its kind in Kaoh Chraeng Island (Figure 1c) and organized in collaboration with an outside organization. Demographic data collection, anthropometric measurements, blood pressure measurements and urinalysis were performed. The training session for organization of the screening and standardization of data collection was conducted according to standard operating procedure (Muntner et al., 2019). A total of 278 people was screened. Two participants who were younger than 18 years were excluded from the analysis. The final sample size was 276 subjects comprised of 94 males and 182 females.

2.3. Sample size calculation

We derived our sample size on the basis of a previously reported 20.0% prevalence central obesity for both sex among Cambodians aged from 25 to 64 years olds (An et al., 2013).

The sample size formula was:

n= Z1−α/22x p (1−p) /e2

n = sample size

Z = standard normal variate (at 5% type 1 error it is 1.96)

p = expected proportion in population (based on previous and pilot studies)

e = absolute error or precision

The required minimum sample size was 245 participants.

2.4. Blood pressure (BP)

The BP of each participant, after sitting at rest for 10 min was measured twice by nurses. Measurements were taken by auscultation using a manual sphygmomanometer. The appearance of Korotkoff sounds (phase 1) was the criterion for measurement of the systolic blood pressure (SBP), and the disappearance of the Korotkoff sounds (phase 5) was the criterion for the measurement of diastolic blood pressure (DBP). SBP and DBP values were recorded as the average of two different measurements, separated by 5 min. These values were then used to assess hypertension adopting systolic and diastolic BP cutoffs of 140 mmHg and 90 mmHg, respectively, or the use of antihypertensive medication or previously diagnosed hypertension according to the JNC7 guidelines (Chobanian et al., 2003).

2.5. Anthropometric measures

Anthropometric measures were assessed by trained staff following standard operating procedures. Weight was determined with individuals wearing light clothes and no shoes on a calibrated scale. Height was also measured while individuals were barefoot using a wall-mounted meter. Body mass index (BMI), as well as waist circumference (WC) and waist to height ratio (WHtR) were measured to analyze adiposity indices. WC was assessed at the level of the umbilicus, and BMI was calculated as weight in kilograms divided by height in meters squared (kg/m2) (Wang et al., 2015). To define central obesity based on WC, we used the recommendation of the International Diabetes Federation for South Asians as WC ≥ 90 cm in women and ≥85 cm in men (Clasey et al., 1999). Likewise, according to the World Health Organization definition, definition, in relation to the BMI values, participants were classified for BMI <18.5 kg/m2 as being underweight, for BMI 18.5 < 25 kg/m2 as having normal weight, for BMI 25 < 30 kg/m2 as overweight and for a BMI ≥30 kg/m2 obese (Consultation, 2000; Organization, 2000).

The waist-height ratio (WHtR) was computed by dividing WC (cm) by height (cm). WHtR of ≥0.5 was used to define obesity dividing the population into healthy and obese (Browning et al., 2010).

2.6. Compliance with ethical standards

This study was conducted in accordance with the ethical standards established by the Helsinki declaration. The study protocol was approved by the ethical committee of Japan Health Sciences University/Kochi Medical School (approval number: 2706-1/31-185). The permission was obtained from the district Health Post of Chet Borey in Kratie province before conducting this study. To protect the patients’ privacy, all data were anonymized.

2.7. Statistical analysis

Chi-square and independent t-test were performed for categorical and numerical variables respectively. Continuous variables were represented by mean and standards deviation (mean; SD) and categorical variables by proportions (%).

In order to evaluate the association between different anthropometric measures and blood pressure, logistic regression was performed.

For analyses purposes, obesity indices were grouped as follows: for BMI, underweight, normal weight, overweight/obese; for WC, normal fat and central fat; and for WHtR, healthy, and obese.

Multivariate logistic regression analysis was performed to evaluate the association between anthropometric measures and blood pressure, after adjusting for age and income. Results are presented as adjusted odds ratios (aOR) with 95% confidence intervals (CI). Statistical significance was set at p < 0.05. All analyses were performed using STATA Software version 13.0 for Windows (StataCorp, College Station, TX).

3. Results

Baseline characteristics of the study population are shown in Table 1.

Table 1.

General Characteristics of the study population.

Male
 Female
 Total
n = 94 n = 182 n = 276
Hypertension#, ∗ 36 (38.3) 48 (26.4) 84 (30.4)
Age Mean (SD) 49.3 (16.0) 49.7 (15.4) 49.6 (15.6)
Interval of age n (%)#
 18-36 25 (26.6) 38 (20.9) 63 (22.8)
 37-55 28 (29.8) 78 (42.9) 106 (38.4)
 56-74 38 (40.4) 54 (29.7) 92 (33.3)
 ≥75 3 (3.2) 12 (6.6) 15 (5.4)
Education level n (%)#,†
 Never 16 (17.2) 43 (24.2) 59 (21.8)
 Primary school 25 (26.9) 58 (32.6) 83 (30.6)
 Junior high school 38 (40.9) 62 (34.8) 100 (36.9)
 High school 8 (8.6) 12 (6.7) 20 (7.4)
 University 6 (6.5) 3 (1.7) 9 (3.3)
Income $/year n (%)#,†, ∗
 ≤500 45 (48.4) 111 (62.4) 156 (57.6)
 501-1000 21 (22.6) 42 (23.6) 63 (23.3)
 1001-1500 12 (12.9) 10 (5.6) 22 (8.1)
 ≥1501 15 (16.1) 15 (8.4) 30 (11.1)
BMI n (%)#
 Underweight 20 (21.3) 30 (16.5) 50 (18.1)
 Normal 64 (68.1) 118 (64.8) 182 (65.9)
 Overweight/Obesity 10 (10.6) 34 (18.7) 44 (15.9)
WC n (%)#, ∗∗
 Normal fat 84 (89.4) 117 (64.3) 201 (72.8)
 Moderate/High central fat 10 (10.6) 65 (35.7) 75 (27.2)
WHtR n (%)#, ∗∗
 Healthy 65 (69.2) 82 (45.1) 147 (53.3)
 Obese 29 (30.9) 100 (55.0) 129 (46.7)
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BMI, body mass index, WC, waist circumference, WHtR, waist to height ratio.

∗p < 0.05, ∗∗p < 0.001.

#

n (%) Chi-square test.

†

Total 5 missing values (with 1 for male and 4 for female).

Among the 276 participants, 94 (34%) were males and 182 (66%) were females.

In total, 30.4% of the study population had hypertension with males (38.3%) having more hypertensive disorders than females (26.4%), p < 0.05. The mean age was globally at 49.6 (15.6) years, with no statistical difference by gender. Men had higher income compared to women, p < 0.05. When we consider anthropometric measures, women had higher WC (35.7% vs 10.6%) and higher WHtR (55.0% vs 30.9%) than men with p < 0.01.

Table 2 shows characteristics of the study population stratified by gender and hypertensive status. When we divided the study population according to the hypertensive status and gender, for male participants’ low education (38.9%), and low income (50%) were more frequent in the hypertensive group compared to their non-hypertensive counterparts. Central fat (58.3%) and overweight/obesity assessed by BMI (29.2%) and by WHtR (70.8%) were more prevalent among hypertensive women as compared to non-hypertensive ones.

Table 2.

Characteristics of the study population stratified by gender and hypertensive state.

Male
 p value Female
 p value
Hypertension
 Hypertension
Yes
 No
 Yes
 No
n = 36 n = 58 n = 48 n = 134
Age Mean (SD) 52.9 (14.4) 47.1 (16.7) 57.1 (13.0) 47.0 (15.4) <0.001
Interval of age# <0.001
 18-36 7 (19.4) 18 (31.0) 2 (4.2) 36 (26.9)
 37-55 11 (30.6) 17 (29.3) 18 (37.5) 60 44.8)
 56-74 17 (47.2) 21 (36.2) 23 (47.9) 31 (23.1)
 ≥75 1 (2.8) 2 (3.5) 5 (10.4) 7 (5.2)
Education level#,† <0.05
 Never 3 (8.3) 13 (22.8) 14 (30.4) 29 (22.0)
 Primary school 14 (38.9) 11 (19.3) 14 (30.4) 44 (33.3)
 Junior high school 11 (30.6) 27 (47.4) 16 (34.8) 46 (34.9)
 High school 5 (13.9) 3 (5.3) 2 (4.4) 10 (7.6)
 University 3 (8.3) 3 (5.3) 0 (0.0) 3 (2.3)
Income $/year#,†
 ≤500 18 (50.0) 27 (47.4) 26 (56.5) 85 (64.4)
 501-1000 8 (22.2) 13 (22.8) 10 (21.7) 32 (24.2)
 1001-1500 5 (13.9) 7 (12.3) 2 (4.4) 8 (6.1)
 ≥1501 5 (13.9) 10 (17.5) 8 (17.4) 7 (5.3)
BMI# <0.05
 Underweight 8 (22.2) 12 (20.7) 4 (8.3) 26 (19.4)
 Normal 21 (58.3) 43 (74.1) 30 (62.5) 88 (65.7)
 Overweight/Obesity 7 (19.4) 3 (5.2) 14 (29.2) 20 (14.9)
WC# <0.01 <0.001
 Normal fat 28 (77.8) 56 (96.6) 20 (41.7) 97 (72.4)
 Moderate/High central fat 8 (22.2) 2 (3.5) 28 (58.3) 37 (27.6)
WHtR# <0.05
 Healthy 22 (61.1) 43 (74.1) 14 (29.2) 68 (50.8)
 Obese 14 (38.9) 15 (25.9) 34 (70.8) 66 (49.3)
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BMI, body mass index, WC, waist circumference, WHtR, waist to height ratio.

#

n (%) Chi-square test.

†

Total 5 missing values (with 1 for male without hypertension, 2 for female without hypertension and 2 for female with hypertension).

Table 3 shows the odds ratios for the association between anthropometric measurements and the risk of hypertension. In total population, overweight/obese based on BMI and obese based on WHtR were two times [aOR: 2.22 (1.09–4.55)] and almost two times [aOR: 1.92 (1.08–3.42)] at risk of developing hypertension, respectively. When divided by gender, Overweight/obese men and those with central fat were four times [aOR: 4.37 (1.01–18.81)] and seven times [aOR: 7.55 (1.42–39.99)] at risk of developing hypertension, respectively.

Table 3.

Association between anthropometric measures and hypertension by gender.

Foot Male
 Female
 Total
cOR (95% CI)
n = 94		 aOR (95% CI)
n = 93		 cOR (95% CI)
n = 182		 aOR (95% CI)
n = 178		 cOR (95% CI)
n = 276		 aOR (95% CI)§
n = 271
BMI
 Underweight 1.37 (0.48–3.85) 1.31 (0.46–3.78) 0.45 (0.15–1.40) 0.22 (0.06–0.89)∗ 0.81 (0.39–1.68) 0.67 (0.31–1.48)
 Normal 1.00 1.00 1.00 1.00 1.00 1.00
 Overweight/Obesity 4.78 (1.12–20.36) ∗ 4.37 (1.01–18.81)∗ 2.05 (0.92–4.56) 1.46 (0.61–3.52) 2.35 (1.20–4.60)∗ 2.22 (1.09–4.55)∗
Gender - - 1.93 (1.08–3.45)∗
Interval of age 1.25 (0.76–2.07) 2.95 (1.77–4.93)∗∗∗ 1.88 (1.34–2.65)∗∗∗
Income $/year 0.98 (0.66–1.46) 1.66 (1.13–2.44)∗∗ 1.24 (0.95–1.63)
WC
 Normal fat 1.00 1.00 1.00 1.00 1.00 1.00
 Moderate/High Central fat 8.00 (1.59–40.20)∗ 7.55 (1.42–39.99)∗ 3.67 (1.85–7.30)∗∗∗ 3.24 (1.54–6.83)∗∗ 2.94 (1.69–5.14)∗∗∗ 3.88 (2.02–7.43)∗∗∗
Gender - - 2.72 (1.44–5.17)∗∗
Interval of age 1.15 (0.68–1.92) 2.51 (1.54–4.07)∗∗∗ 1.77 (1.25–2.51)∗∗
Income $/year 1.06 (0.72–1.58) 1.58 (1.08–2.33)∗ 1.27 (0.96–1.67)
WHtR
 Healthy 1.00 1.00 1.00 1.00 1.00 1.00
 Obese 1.82 (0.75–4.45) 1.65 (0.66–4.09) 2.50 (1.23–5.08)∗ 2.11 (0.97–4.55) 1.83 (1.09–3.07)∗ 1.92 (1.08–3.42)∗
Gender - - 2.04 (1.13–3.70)∗
Interval of age 1.25 (0.76–2.06) 2.53 (1.58–4.07)∗∗∗ 1.83 (1.31–2.57)∗∗∗
Income $/year 0.97 (0.66–1.43) 1.68 (1.15–2.46)∗∗ 1.29 (0.98–1.68)
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aOR, adjusted odds ratio, cOR crude odds ratio, BMI, body mass index, WC, waist circumference, WHtR, waist to height ratio.

∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

adjusted by interval of age, income.

§

adjusted by gender, interval of age, income.

For women, those with central fat were three times [aOR: 3.24 (1.54–6.83)] at risk of developing hypertension. In addition, before adjustment, obesity as per WHtR [OR: 2.50 (1.23–5.08)] was associated with the risk of developing hypertension in women; however, the association disappeared after adjusting for age and income [aOR: 2.11 (0.97–4.55)]. In addition, being underweight revealed to be protective against the risk of hypertension [aOR 0.22 (0.06–0.89)] in women. It is worth noting that age and income are associated with hypertension in women, suggesting that they are significant predictors of hypertension in this population, particularly in women.

4. Discussion

The present study has assessed the prevalence of hypertension and the relationship between anthropometric measures and the risk of developing hypertension by sex among the Cambodian population living in rural areas. Hypertension was more prevalent in aged, low educated, poor people and mostly among men than women.

These findings are in accordance with the study conducted by Gupta et al. which used data from WHO STEPs survey, a nationwide survey in Cambodia (Gupta et al., 2013). Hypertension increases with age because of the increase in vascular resistance and arterial stiffness affected by one's culture and environment (Franklin et al., 1997). Factors such as obesity and insulin resistance, smoking, and changes in circulating sex hormone concentrations (menopausal) are associated with age-related inflammation and declines in nitric oxide (NO) production and bioavailability that can lead to hypertension (Krabbe et al., 2004; Torregrossa et al., 2011).

Low education and poor income in Cambodia due to various reasons such as, geographical disadvantage, late school entry, early marriage, low availability of schools, poor school facility have been previously reported in Cambodia (Velasco, 2001; Keng, 2003). In fact, Awareness, treatment, and control of hypertension are lower in participants with primary or no education, most likely reflecting a combination of low socioeconomic status, which may influence access to care (Chow et al., 2013).

When comparing neighboring countries, Brunei (48.3%), Indonesia (33.4%), and Malaysia (35.3%) have higher prevalence of hypertension than Cambodia (Lupat et al., 2016; Peltzer and Pengpid, 2018; Ab Majid et al., 2018), whereas a lower prevalence were reported in Vietnam (25%) (Son et al., 2012). Higher prevalence of hypertension in men than women has been also reported in Myanmar (31% for males versus 29% for females) (Bjertness et al., 2016).

Gender has an important influence on blood pressure, with premenopausal women having a lower arterial blood pressure than age-matched males. Sex hormones are responsible for the observed gender-associated differences in arterial blood pressure (Dubey et al., 2002) In contrast with our findings, Sadeghi et al. and Adeoye et al. reported no significant difference in hypertension between males and females (Sadeghi et al., 2019; Adeoye et al., 2017). This gap can be explained by the differences in population and settings. When we considered participants with hypertensive disorders, men had higher WC and women had higher BMI, WC and WHtR Circulating gonadal steroids determine the sex-specific differences in adipose tissue distribution, which can be observed even after menopause (Fuente-Martín et al., 2013). Milagres et al. while studying cardiovascular risk in the elderly reported the same trends between men and women (Milagres et al., 2019). Furthermore, the elevated WHtR in the female participants can be explained by the mean age of 49.7 years which is the period of menopausal transition when the female hormonal imbalance becomes almost similar to that of men of the same age (Su and Freeman, 2009). Comparable results were reported in the United Kingdom by Malden et al. who found while studying the body fat distribution and systolic blood pressure in 10,000 adults, that men had higher WC. The high values found among males in the mentioned study could be explained by the fact that the distribution of body fat is different between the sexes, with men having on average less subcutaneous fat in android and gynoid regions but more than twice the amount of visceral android fat (Malden et al., 2019). In opposition to our findings, studies carried out in Vietnam, Iran, Nigeria, and Brazil reported higher prevalence of overweight/obesity, WC and WHtR among females than males (Cuong et al., 2007; Sadeghi et al., 2019; Adeoye et al., 2017; Dutra et al., 2018; Milagres et al., 2019).

Overall, the present study found that anthropometric measurements were associated with the risk of hypertension. The measures for adiposity, in particular BMI and WC had a higher capacity to predict individual hypertension risk among males while only WC applied for females. In alignment with our findings, several studies reported that BMI and WC are best predictors of cardiovascular diseases (Liu et al., 2019a; Dutra et al., 2018; Ashwell et al., 2012; Milagres et al., 2019; Sebati et al., 2019; Tuan et al., 2010). Of note, adiposity indices were more sensitive to predict hypertension in men and this is probably related to the differences of fat distribution between the genders. The mechanisms by which central adiposity may cause hypertension are not fully understood. However, there are a number of hypotheses which include insulin resistance, secretion of angiotensinogen and inflammatory cytokines by adipocytes, and accumulation of visceral adipose tissue that disturbs the renal medulla and consequently increases sodium reabsorption (De Koning et al., 2007; Landsberg et al., 2013). Partially in contrast, a study by Adeoye et al. reported that WC and WHtR did not correlate with blood pressure and BMI correlated positively with DBP in both men and women (Adeoye et al., 2017). Several other studies reported that BMI and WC are weak predictors of hypertension compared to WHtR (Liu et al., 2019b; Azimi–Nezhad et al., 2009). These differences from our study can be explained by the genetic and environmental variations among the study populations. It is well documented that ethnic and racial differences affect the determination of optimal anthropometric indicators to predict cardiovascular risk factors (Harris et al., 2000).

Underweightness was a protective factor against hypertension in women. This highlights the negative effect of higher adiposity on blood pressure as described above and confirms the general consensus on the increasing incidence of CVD as the degree of BMI increases (Grundy et al., 1999; Li et al., 2006). This result, however, is in contradiction with some reports which demonstrate that underweight people are at risk of having hypertension (Kshatriya and Acharya, 2019; Salahudeen et al., 2004). The pathophysiologic mechanisms of this paradoxical phenomenon are not well elucidated yet.

4.1. Strength

This study has the merit of evaluating and comparing the association between several anthropometric measures: WC, BMI, WHtR and blood pressure among the rural Cambodian population who have limited access to health care facilities.

4.2. Limitations

Several limitations should be considered.

First, the cross-sectional design of this study could only establish the association between the study parameters and cannot infer the causality. Thus, our findings may not be generalizable to the entire population. Follow-up studies are needed to establish the evidence of causal relationship.

Second, sampling bias may be present within our study population. The proportion of middle-aged male residents was relatively low than that of female residents (29.8% vs 42.9%; male vs female), suggesting that male residents working off the island did not visit health posts for health checkups. There is a possibility that this sampling bias caused an over estimation of strength of the association.

Third, there was a lack of information on lifestyle (e.g., alcohol consumption, smoking status, physical activity) and nutrition intake which are important factors. Although Gupta et al. reported that the levels of exercise and nutritional metrics were not significantly related to prevalence of hypertension in Cambodia (Gupta et al., 2013), other studies focusing on physical activities and nutritional status are needed to improve greater understanding.

Fourth, our study assessed the predictive abilities of anthropometric measures on prevalence but did not directly predict the prospective risk of high blood pressure.

5. Conclusion

In conclusion, the associations between the anthropometric measures and hypertension differed by sex, with BMI and WC being the best predictor of hypertension in men while only WC was found to be useful in women. Thus, some measurements may be useful in the screening of hypertension among the rural Cambodian population. These results reveal alternatives to hypertension screening management in the general population which are useful since their accessibility to health facilities is limited. Further studies should focus on gathering more evidence on these parameters. Nevertheless, the lifestyle changes, including physical exercise and diet that can lead to reduced anthropometric measurements may have significant public health significance as well in reducing the prevalence of hypertension within this population.

Declarations

Author contribution statement

Y. Shimotake: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

E. Mbelambela, S. Muchanga and N. Suganuma: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.

A. Villanueva: Conceived and designed the experiments; Wrote the paper.

S. Yan: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

M. Minami, R. Shimomoto and A. Lumaya: Analyzed and interpreted the data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interest statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Acknowledgements

We thank the trained staff of the local Health Post established in Kratie province in Cambodia for their collaboration.

References

  1. Azimi Nezhad M., Ghayour–Mobarhan M., Safarian M., Esmailee H., Parizadeh S.M.R., Rajabi-Moghadam M. Anthropometric indices of obesity and the prediction of cardiovascular risk factors in an Iranian population. Sci. World J. 2009;9:424–430. doi: 10.1100/tsw.2009.58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ab Majid N.L., Omar M.A., Khoo Y.Y., Naidu B.M., Yn J.L.M., Hasani W.S.R. Prevalence, awareness, treatment and control of hypertension in the Malaysian population: findings from the national health and morbidity survey 2006–2015. J. Hum. Hypertens. 2018;32(8):617–624. doi: 10.1038/s41371-018-0082-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Adeoye A., Adebayo O., Nwosu M., Adebiyi A., Owolabi M., Tayo B. Assessment of measures of adiposity that correlate with blood pressure among hypertensive Africans. Ann. Ib. Postgrad. Med. 2017;15(2):82–87. [PMC free article] [PubMed] [Google Scholar]
  4. An Y., Yi S., Fitzpatrick A., Gupta V., Prak P.R., Oum S. Appropriate body mass index and waist circumference cutoff for overweight and central obesity among adults in Cambodia. PloS One. 2013;8(10) doi: 10.1371/journal.pone.0077897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Ashwell M., Gunn P., Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes. Rev. 2012;13(3):275–286. doi: 10.1111/j.1467-789X.2011.00952.x. [DOI] [PubMed] [Google Scholar]
  6. Benjamin N., Beegle K., Recanatini F., Santini M. Informal economy and the World Bank. World Bank. 2014 [Google Scholar]
  7. Bjertness M.B., Htet A.S., Meyer H.E., Htike M.M.T., Zaw K.K., Oo W.M. Prevalence and determinants of hypertension in Myanmar-a nationwide cross-sectional study. BMC Publ. Health. 2016;16(1):590. doi: 10.1186/s12889-016-3275-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bouchi R., Asakawa M., Ohara N., Nakano Y., Takeuchi T., Murakami M. Indirect measure of visceral adiposity ‘A Body Shape Index’(ABSI) is associated with arterial stiffness in patients with type 2 diabetes. BMJ Open Diab. Res. Care. 2016;4(1) doi: 10.1136/bmjdrc-2015-000188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Browning L.M., Hsieh S.D., Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0· 5 could be a suitable global boundary value. Nutr. Res. Rev. 2010;23(2):247–269. doi: 10.1017/S0954422410000144. [DOI] [PubMed] [Google Scholar]
  10. Chobanian A.V., Bakris G.L., Black H.R., Cushman W.C., Green L.A., Izzo J.L., Jr. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. Jama. 2003;289(19):2560–2571. doi: 10.1001/jama.289.19.2560. [DOI] [PubMed] [Google Scholar]
  11. Chow C.K., Teo K.K., Rangarajan S., Islam S., Gupta R., Avezum A. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. JAMA. 2013;310(9):959–968. doi: 10.1001/jama.2013.184182. [DOI] [PubMed] [Google Scholar]
  12. Clasey J.L., Bouchard C., Teates C.D., Riblett J.E., Thorner M.O., Hartman M.L. The use of anthropometric and dual-energy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obes. Res. 1999;7(3):256–264. doi: 10.1002/j.1550-8528.1999.tb00404.x. [DOI] [PubMed] [Google Scholar]
  13. Consultation W. 2000. Obesity: Preventing and managing the global epidemic. [PubMed] [Google Scholar]
  14. Cuong T., Dibley M., Bowe S., Hanh T.T., Loan T. Obesity in adults: an emerging problem in urban areas of Ho Chi Minh City, Vietnam. Eur. J. Clin. Nutr. 2007;61(5):673. doi: 10.1038/sj.ejcn.1602563. [DOI] [PubMed] [Google Scholar]
  15. Dans A., Ng N., Varghese C., Tai E.S., Firestone R., Bonita R. The rise of chronic non-communicable diseases in southeast Asia: time for action. Lancet. 2011;377(9766):680–689. doi: 10.1016/S0140-6736(10)61506-1. [DOI] [PubMed] [Google Scholar]
  16. De Koning L., Merchant A.T., Pogue J., Anand S.S. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur. Heart J. 2007;28(7):850–856. doi: 10.1093/eurheartj/ehm026. [DOI] [PubMed] [Google Scholar]
  17. Dubey R.K., Oparil S., Imthurn B., Jackson E.K. Sex hormones and hypertension. Cardiovasc. Res. 2002;53(3):688–708. doi: 10.1016/s0008-6363(01)00527-2. [DOI] [PubMed] [Google Scholar]
  18. Dutra M.T., Reis D.B., Martins K.G., Gadelha A.B. Comparative evaluation of adiposity indices as predictors of hypertension among brazilian adults. Int. J. Hypertens. 2018;2018 doi: 10.1155/2018/8396570. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Franklin S.S., Gustin W., IV, Wong N.D., Larson M.G., Weber M.A., Kannel W.B. Hemodynamic patterns of age-related changes in blood pressure: the Framingham Heart Study. Circulation. 1997;96(1):308–315. doi: 10.1161/01.cir.96.1.308. [DOI] [PubMed] [Google Scholar]
  20. Fuente-Martín E., Argente-Arizón P., Ros P., Argente J., Chowen J.A. Sex differences in adipose tissue: it is not only a question of quantity and distribution. Adipocyte. 2013;2(3):128–134. doi: 10.4161/adip.24075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Grundy S.M., Pasternak R., Greenland P., Smith S., Fuster V. Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: a statement for healthcare professionals from the American Heart Association and the American College of Cardiology. J. Am. Coll. Cardiol. 1999;34(4):1348–1359. doi: 10.1016/s0735-1097(99)00387-3. [DOI] [PubMed] [Google Scholar]
  22. Gupta V., LoGerfo J.P., Raingsey P.P., Fitzpatrick A.L. The prevalence and associated factors for prehypertension and hypertension in Cambodia. Heart Asia. 2013;5(1):253–258. doi: 10.1136/heartasia-2013-010394. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Harris M.M., Stevens J., Thomas N., Schreiner P., Folsom A.R. Associations of fat distribution and obesity with hypertension in a bi-ethnic population: the ARIC study. Obes. Res. 2000;8(7):516–524. doi: 10.1038/oby.2000.64. [DOI] [PubMed] [Google Scholar]
  24. Isaakidis P., Raguenaud M., Say C., De Clerck H., Khim C., Pottier R. Treatment of hypertension in rural Cambodia: results from a 6-year programme. J. Hum. Hypertens. 2011;25(4):241. doi: 10.1038/jhh.2010.49. [DOI] [PubMed] [Google Scholar]
  25. Keng C. Why do girls drop out of primary schools? An evidence from rural Cambodia. J. Cambodian Stud. 2003;1(1):3–15. [Google Scholar]
  26. Krabbe K.S., Pedersen M., Bruunsgaard H. Inflammatory mediators in the elderly. Exp. Gerontol. 2004;39(5):687–699. doi: 10.1016/j.exger.2004.01.009. [DOI] [PubMed] [Google Scholar]
  27. Kshatriya G.K., Acharya S.K. Prevalence and risks of hypertension among Indian tribes and its status among the lean and underweight individuals. Diab. Metabol. Syn.: Clin. Res. Rev. 2019;13(2):1105–1115. doi: 10.1016/j.dsx.2019.01.028. [DOI] [PubMed] [Google Scholar]
  28. Landsberg L., Aronne L.J., Beilin L.J., Burke V., Igel L.I., Lloyd-Jones D. Obesity-related hypertension: pathogenesis, cardiovascular risk, and treatment: a position paper of the Obesity Society and the American Society of Hypertension. J. Clin. Hypertens. 2013;15(1):14–33. doi: 10.1111/jch.12049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Li T.Y., Rana J.S., Manson J.E., Willett W.C., Stampfer M.J., Colditz G.A. Obesity as compared with physical activity in predicting risk of coronary heart disease in women. Circulation. 2006;113(4):499–506. doi: 10.1161/CIRCULATIONAHA.105.574087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Liu J., Tse L.A., Liu Z., Rangarajan S., Hu B., Yin L. Predictive values of anthropometric measurements for cardiometabolic risk factors and cardiovascular diseases among 44 048 Chinese. J. Am. Heart Assoc. 2019;8(16) doi: 10.1161/JAHA.118.010870. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Liu J., Tse L.A., Liu Z., Rangarajan S., Hu B., Yin L. Predictive values of anthropometric measurements for cardiometabolic risk factors and cardiovascular diseases among 44 048 Chinese. J. Am. Heart Assoc. 2019;8(16) doi: 10.1161/JAHA.118.010870. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lo K., Wong M., Khalechelvam P., Tam W. Waist-to-height ratio, body mass index and waist circumference for screening paediatric cardio-metabolic risk factors: a meta-analysis. Obes. Rev. 2016;17(12):1258–1275. doi: 10.1111/obr.12456. [DOI] [PubMed] [Google Scholar]
  33. Lupat A., Hengelbrock J., Luissin M., Fix M., Bassa B., Craemer E.M. Brunei epidemiological stroke study: patterns of hypertension and stroke risk. J. Hypertens. 2016;34(7):1416–1422. doi: 10.1097/HJH.0000000000000945. [DOI] [PubMed] [Google Scholar]
  34. Malden D., Lacey B., Emberson J., Karpe F., Allen N., Bennett D. Body fat distribution and systolic blood pressure in 10,000 adults with whole-body imaging: UK biobank and Oxford BioBank. Obesity. 2019 doi: 10.1002/oby.22509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Milagres L.C., Martinho K.O., Milagres D.C., Franco F.S., Ribeiro A.Q., Novaes J.F.d. Waist-to-height ratio and the conicity index are associated to cardiometabolic risk factors in the elderly population. Ciência Saúde Coletiva. 2019;24(4):1451–1461. doi: 10.1590/1413-81232018244.12632017. [DOI] [PubMed] [Google Scholar]
  36. Muntner P., Shimbo D., Carey R.M., Charleston J.B., Gaillard T., Misra S. Measurement of blood pressure in humans: a scientific statement from the American Heart Association. Hypertension. 2019;73(5):e35–e66. doi: 10.1161/HYP.0000000000000087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Organization W.H. World Health Organization; Geneva: 2000. Obesity: Preventing and Managing the Global Epidemic; p. 894. p. i-xii, 1-253p. WHO Technical Report Series. [PubMed] [Google Scholar]
  38. Oum S., Prak P., Khuon E., Mey V., Aim S., Buonchan Y. Phnom Penh: University of Health Sciences and the Ministry of Health; 2010. Prevalence of Non-communicable Disease Risk Factors in Cambodia: STEPS Survey Country Report, September 2010. [Google Scholar]
  39. Park D. Intra-southeast asian income convergence. ASEAN Econ. Bull. 2000:285–292. [Google Scholar]
  40. Peltzer K., Pengpid S. The prevalence and social determinants of hypertension among adults in Indonesia: a cross-sectional population-based national survey. Int. J. Hypertens. 2018;2018 doi: 10.1155/2018/5610725. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Sadeghi M., Talaei M., Gharipour M., Oveisgharan S., Nezafati P., Dianatkhah M. Anthropometric indices predicting incident hypertension in an Iranian population: the Isfahan cohort study. Anatol. J. Cardiol. 2019;22(1):33. doi: 10.14744/AnatolJCardiol.2019.10594. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Salahudeen A.K., Fleischmann E.H., Bower J.D., Hall J.E. Underweight rather than overweight is associated with higher prevalence of hypertension: BP vs BMI in haemodialysis population. Nephrol. Dial. Transplant. 2004;19(2):427–432. doi: 10.1093/ndt/gfg523. [DOI] [PubMed] [Google Scholar]
  43. Sebati B., Monyeki K., Kemper H.C.G., Sekgala M.D., Mphekgwana P. Anthropometric indices for predicting cardiovascular risk factors: ellisras longitudinal study. Am. J. Hum. Biol. 2019 doi: 10.1002/ajhb.23293. [DOI] [PubMed] [Google Scholar]
  44. Son P., Quang N., Viet N., Khai P., Wall S., Weinehall L. Prevalence, awareness, treatment and control of hypertension in Vietnam—results from a national survey. J. Hum. Hypertens. 2012;26(4):268. doi: 10.1038/jhh.2011.18. [DOI] [PubMed] [Google Scholar]
  45. Staub K., Floris J., Koepke N., Trapp A., Nacht A., Maurer S.S. Associations between anthropometric indices, blood pressure and physical fitness performance in young Swiss men: a cross-sectional study. BMJ open. 2018;8(6) doi: 10.1136/bmjopen-2017-018664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Su H.I., Freeman E.W. Hormone changes associated with the menopausal transition. Minerva Ginecol. 2009;61(6):483. [PMC free article] [PubMed] [Google Scholar]
  47. Torregrossa A.C., Aranke M., Bryan N.S. Nitric oxide and geriatrics: Implications in diagnostics and treatment of the elderly. J. Ger. Cardiol. 2011;8(4):230. doi: 10.3724/SP.J.1263.2011.00230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Travers H., Winney K., Clements T., Evans T., Milner-Gulland E.J. A tale of two villages: an investigation of conservation-driven land tenure reform in a Cambodian Protection Forest. Land Use Policy. 2015;43:186–196. [Google Scholar]
  49. Tuan N.T., Adair L.S., Stevens J., Popkin B.M. Prediction of hypertension by different anthropometric indices in adults: the change in estimate approach. Publ. Health Nutr. 2010;13(5):639–646. doi: 10.1017/S1368980009991479. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Velasco E. 2001. Why are girls not in school? Perceptions, realities and contradictions in changing Cambodia. UNICEF/Cambodia. Phnom Penh. [Google Scholar]
  51. Wang S., Liu Y., Li F., Jia H., Liu L., Xue F. A novel quantitative body shape score for detecting association between obesity and hypertension in China. BMC Publ. Health. 2015;15(1):7. doi: 10.1186/s12889-014-1334-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Wolf-Maier K., Cooper R.S., Banegas J.R., Giampaoli S., Hense H.-W., Joffres M. Hypertension prevalence and blood pressure levels in 6 European countries, Canada, and the United States. Jama. 2003;289(18):2363–2369. doi: 10.1001/jama.289.18.2363. [DOI] [PubMed] [Google Scholar]

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