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Article

Utility of Obesity Indicators for Predicting Hypertension among Older Persons in Limpopo Province, South Africa

by
Perpetua Modjadji
1,*,
Mulalo Caroline Salane
1,
Kebogile Elizabeth Mokwena
1,
Tshimangadzo Selina Mudau
2 and
Peter Modupi Mphekgwana
3
1
Department of Public Health, School of Health Care Sciences, Sefako Makgatho Health Sciences University, 1 Molotlegi Street, Ga-Rankuwa 0208, South Africa
2
Department of Nursing Science, School of Health Care Sciences, Sefako Makgatho Health Sciences University, 1 Molotlegi Street, Ga-Rankuwa 0208, South Africa
3
Research Administration and Development, University of Limpopo, Polokwane 0700, South Africa
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(9), 4697; https://doi.org/10.3390/app12094697
Submission received: 1 February 2022 / Revised: 2 March 2022 / Accepted: 28 April 2022 / Published: 7 May 2022
(This article belongs to the Special Issue Prevention and Treatments of Cardiovascular Diseases)
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Abstract

:
In view of the epidemic proportions of obesity in South Africa and its relationship to cardiometabolic diseases, such as hypertension, a cross sectional study was conducted to investigate the utility of obesity indicators for predicting hypertension among older persons (≥60 years, n = 350) in the Limpopo Province of South Africa. The WHO STEPwise approach was used to collect data on demographic and lifestyle factors. Anthropometrics and blood pressure were measured according to the standard procedures. Receiver operating characteristic curves (ROC) were used to investigate and compare the ability of obesity indicators to predict overall hypertension and either increased systolic (SBP) or increased diastolic (DBP) blood pressure. The area under the ROC curve (AUC) was used to assess a certain indicator’s potential to predict overall hypertension and either increased SBP or increased DBP. Multivariate logistic regression analysis was used to determine the relationship of hypertension with obesity indicators. The mean age of the participants was 69 years (±SD = 7), and hypertension (46%), general obesity (36%) and abdominal obesity (57%) were prevalent among older persons. The obesity indicator body mass index (BMI) (AUC = 0.603 (0.52; 0.69)) was the best predictor of hypertension in older men. Waist circumference (WC) (AUC = 0.640 (0.56; 0.72)) and waist-to-height ratio (WHtR) (AUC = 0.605 (0.52; 0.69)) were better predictors of hypertension than BMI and waist-to-hip ratio (WHR) in older women. After adjustment for risk factors, only WC (AOR = 1.22 (1.16; 1.79)) was significantly associated with hypertension in older women, proposing WC as a screening tool for the prediction of hypertension in South African older women.

1. Introduction

Population aging occurs globally, and older persons will reach two billion by 2050 [1]. During aging, there is a decline in metabolism and health, which predispose to diseases like hypertension [2,3]. Hypertension is common among older persons, causing cardiovascular diseases, mostly heart attacks and strokes [4]. Furthermore, aging predisposes to obesity, one of the predictors of hypertension [5,6,7]. The presence of obesity might lead to life threatening health issues, such as hypertension, type 2 diabetes mellitus, etc. [8]. The burden of hypertension, has been emphasized as a public health issue [9,10,11,12,13], occurring simultaneously with communicable diseases [14,15,16]. Formerly, hypertension, among other non-communicable risk factors, has been reported to be dominant in wealthy people, although lately, it occurs among households that are poverty stricken and residing in rural settings, mainly because of the epidemiologic shift [17,18].
South Africa has recorded 4.1 million older persons in 2011, with the number expected to increase to over 15 million by 2030 [19]. Hypertension alone is the most common diagnosed vascular disease in the country, leading to attending primary care [20]. Additionally, the country is affected by obesity across the entire population and all age groups [21,22,23,24]. Rates of hypertension and obesity among older persons in South Africa are high [25,26]. The Human Sciences Research Council (HSRC) has reported a 70.6% age-standardized prevalence of hypertension among older persons [27]. Additionally, the high prevalence of obesity (49.8%) has been reported in older person. Using waist circumference (WC), abdominal obesity has been estimated at 29.6% among older men versus 66.6% among older women, as well as by waist-hip ratio (WHR; 29.6% vs. 71.2%), respectively. A high waist-to-height ratio (WHtR; 62.6%), reported in South Africa, predisposes to cardio metabolic risk and hypertension [21,26]. The link of obesity to hypertension is suggested through the renin-angiotensin system, in addition to other mechanisms such as the sympathetic nervous system. the amount of intra-abdominal and intra-vascular fat, etc. [8,28].
The relation between obesity and hypertension is apparent [7,26,28]; nonetheless, the degree of the relationship is still unclear [29]. The literature documents obesity indicators (i.e., BMI, WC, WHR and WHtR) as good predictors of hypertension [30,31], but discrepancies in terms of settings and race have been reported in addition to the definitions of obesity [32]. Peltzer et al. [26] have reported a high prevalence of hypertension (77.3%), associated with being overweight/obese (by BMI; 72.4%), among older persons (≥50 years) from the locality types (urban and rural) and population groups (including Blacks, Coloureds, Indians or Asians and Whites) in South Africa. However, despite older persons being at a high risk of hypertension [33], the data are limited on the relationship among older persons in the rural settings of South Africa.
In view of the epidemic proportions of obesity in South Africa and its association with the cardio-metabolic diseases of hypertension, diabetes and dyslipidaemia, it is important to accurately identify obese individuals at increased risk for these conditions [21]. Therefore, the study investigated the utility of obesity indicators for predicting hypertension among older persons in a rural community, the Vhembe district of the Limpopo Province, South Africa. This study highlights the importance of community-based screening and more health research on hypertension among older persons in rural communities. This is because older persons are extremely affected by hypertension, and they have different health needs as they age [34].

2. Materials and Methods

2.1. Study Design and Setting

A cross-sectional study was conducted in the Vhembe District of the Limpopo Province, South Africa. The Vhembe District municipality is one of five districts in the Limpopo Province, the northern part of the country, and shares its border with Zimbabwe [35]. In terms of population size, in 2019, the Vhembe District had a total of 1,402,779 people, made up of four local municipalities; Thulamela, Makhado, Collins Chabane and Musina. Ninety-nine percent (99%) of the population are Black Africans [36].

2.2. Sample Size, Sampling Technique and Population

The study population consisted of older black persons aged 60 years and above. Purposive sampling was used to select the Thulamela local municipality because it is the most populated municipality (±497,237) in the Vhembe District, made up of 40 wards (i.e., geopolitical subdivisions of municipalities used for electoral purposes) with clustered areas [36]. According to Cooperative Governance and Traditional Affairs (COGTA) 2020 [36], older persons (≥60 years) in Thulamela made up 6% of the total population, which was estimated at ~29,800. Rao software calculator [37] was used to calculate a sample size, taking into consideration a 5% margin of error, 50% response distribution and between 90% CI and 95% CI. The sample size was estimated between 269 and 380.
A random sampling was used to select a significant number of main areas (six, in this case) in the Thulamela local municipality, followed by a systematic sampling used to select older persons by households and recruited through house visits. During house visits, every fifth house was considered in the selected sections of the communities. Initially, 501 households were selected, and, from each house, one elderly person participated in the study. However, at the end of data collection, 360 participated in the study, making the response rate in the study 87%, which is considered good [38]. The 501 households that were initially selected did not include the houses that did not have elderly people and the houses that had elderly people who were critically ill. Some houses were empty, as is the case in most rural areas, due to most of the children and grandchildren of elderly people taking them to their houses in the cities, as was explained by the neighbors during recruitment. Most of the older Black South Africans continue to live in extended household structures with children, grandchildren and other kin [39]. Rural areas are sparsely populated because many people leave rural areas and settle in the urban areas for more facilities. This society has homogeneity in terms of culture and socioeconomic status [40]. A sample of 360 was obtained; however, during data analysis, 10 questionnaires had missing information of over 10%, which included important variables such as SBP, DBP and weight values.

2.3. Data Collection and Tool

The data were collected by trained research assistants between August 2019 and December 2019, using a modified WHO STEPwise questionnaire, which included demographic information (personal and household), lifestyle factors (tobacco and alcohol use) and physical measurements (anthropometry and blood pressure) [41] (see supplementary file). For quality assurance, the questionnaire was validated through content and face validity, independent translators, a pilot study and the training of research assistants [42]. Three readings were conducted for both systolic (SBP) and diastolic (DBP) blood pressure using an Elite 1219 Blood Pressure Monitor (Shanghai International Holding Corp. GmbH (Hamburg, Germany), adhering to standard procedure [43]. The average of the last two readings was used as a participant’s blood pressure [44], hypertension was defined at SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg for hypertension [18], high SBP was ≥140 mmHg and high SBP was ≥90 mmHg. Weight, height, and waist and hip circumferences were measured according to the WHO recommendations [45] using a smart D-quip electronic scale, stadiometer and non-stretchable tape measure, respectively, and to the nearest 0.1 kg, 0.1 cm and 0.1 cm, respectively. BMI was calculated by dividing an individual’s weight in kilograms (kg) by height in meters squared, and overweight was defined as BMI ≥ 25–29.9 kg/m2 and obesity as BMI ≥ 30 kg/m2, while abdominal obesity was indicated by WHtR of ≥0.5 [46], WC ≥ 90 cm and WHR > 0.90.

2.4. Data Analysis

STATA (Intercooled Stata® Version 14; StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX, USA) was used to analyze descriptive and inferential data. We used complete case analysis to identify the participants with missing data during analysis. Descriptive statistics were computed and the results are presented as means ± standard deviation (SD). A chi-squared test was used to compare variables by gender and blood pressure status, and the results are presented as frequency (n) and percentage (%). To investigate and compare the ability of obesity indicators to predict overall hypertension and either increased SBP or increased DBP, we used receiver operating characteristic curves (ROC). The area under the ROC curve (AUC) was used to assess a certain indicator’s potential to predict overall hypertension and either increased SBP or increased DBP. The closer the AUC is to 1, the better the model, and the closer it is to 0.5, the poorer the model. Multivariate logistic regression analysis used dichotomous data to determine the relationship of hypertension with overweight/obesity indicators by gender and adjusted odds ratios (AOR) for age, smoking, alcohol, marital status, education, old age grant and family history of hypertension. The study adjusted for confounders by entering them as covariates in the model. A probability of p < 0.05 was considered significant.

2.5. Ethical Considerations

The study was approved by the Sefako Makgatho Health Sciences University Research and Ethics Committee [SMUREC/H/25/2019:PG] and adhered to the guidelines laid down in the Declaration of Helsinki [47]. Participants gave written consent prior to participating in the study.

3. Results

3.1. The Demographic Characteristics of Participants

Table 1 shows a comparison of the characteristics of participants by gender. Out of 350 participants, 165 (46%) were men and 185 (53%) were women. The mean age of the participants was 69 years (±SD = 7). Most participants were or had been married (97%), while 69% had a low literacy level. The majority of participants lived in brick houses (85%), had electricity (97%) and access to municipal water (100%) but used pit toilets (87%). Significant differences in proportions were observed for level of education (p = 0.002), household headship (p ≤ 0.0001), household income (p = 0.015), alcohol use (p = 0.001) and family history of hypertension (p = 0.024) between men and women.

3.2. Anthropometric Characteristics of Older Persons

In Table 2, the blood pressure and weight statuses of the participants are compared by gender. The overall prevalence of hypertension, overweight and obesity were 45%, 46% and 36%, respectively. Significant differences in proportions for BMI (p ≤ 0.0001), WC (p ≤ 0.0001) and WHtR (p = 0.039) were observed between older men and women. Abdominal obesity by WC was observed among 57% of the participants, with women being more affected (87%, p ≤ 0.0001) compared to men (23%).
Table 3 presents the comparison of anthropometric indices by blood pressure status. Significant differences in BMI and WC proportions were observed by blood pressure status among men and women.
In Figure 1, ROC analyses for elevated SBP among men showed that the WC (AUC = 0.69, p = 0.001), WHtR (AUC = 0.66, p = 0.001) and BMI (AUC = 0.64, p = 0.002) were the largest and most significant as compared to WHR (AUC = 0.46, p = 0.363). On the other hand, no significant predictor was observed among men for elevated DBP with obesity indicators (WC, WHtR, WHR and BMI), Figure 2.
Figure 3 and Figure 4 demonstrate that the WC and WHtR were the greatest and most significant predictors of elevated SBP and DBP in women. BMI, on the other hand, was found to be a significant predictor of elevated DBP rather than SBP in women. Furthermore, it was also found that WHR was a poor predictor of SBP and DBP in older women.
Table 4 shows ROC analyses for hypertension among older men and women. It was observed that BMI had the highest significant AUC value for men. The BMI AUC differs significantly from 0.5 (p-value = 0.026), indicating that BMI was likely to be a better predictor of hypertension for men. The cut-off value in men was 27.5 for BMI. Among women, WHtR and WC were found to be significant predictors to hypertension, with AUC values of 60% and 64%, respectively. The cut-off value in women was 0.58 and 90.50 for WHtR and WC, respectively. It was observed that the AUC value of WC was greater than WHtR (Table 4). WHR was not a significant predictor of hypertension in both sexes. The WHtR and WC in men were non-significant predictors of hypertension.
Table 5 shows the analysis of multivariate logistic regression for adjusted odds ratio (AOR) for the association of obesity indicators and hypertension. The results for unadjusted Model 1 showed non-significant anthropometric indices for men in women. After adjusting for age and smoking and alcohol use (Model 2 and Model 3), non-significant anthropometric indices in both sexes were also observed. In Model 4, after adjusting for covariates (age, smoking and alcohol use, marital status, education, old age grant and family history of hypertension), only WC (AOR = 1.22 (1.16; 1.79)) in women was found to be significantly associated with hypertension.

4. Discussion

The main objective of this study was to investigate the utility of obesity indicators for predicting hypertension among older persons in the rural community of Limpopo Province, South Africa. The study determined the prevalence of general obesity, abdominal obesity and hypertension and, thereafter, the relationship between obesity indicators and hypertension. The area under the ROC curve (AUC) was used to assess a certain indicator’s potential to predict overall hypertension and either increased SBP or increased DBP. Regarding the socio-demographic status, the current study reported a high level of low literacy and dependence on old age grants among the participants in the Vhembe District, similar to other reports in South Africa [36,48,49,50].
Generally, this study showed a 46% rate of hypertension among older persons. The reported prevalence of hypertension in this study is lower than the prevalence reported in South Africa (77.9%) among older persons [26,51] and in other LMICs, such as Ghana (57.1%) and Mexico (58.2%). However, a lower prevalence of hypertension among older persons in comparison to the current study has been reported in countries like Zambia (34.8%) [52] and Ethiopia (41.9%) [17], while similar prevalence has been reported in Nepal (44.9%) [53] and in Brazil (46.7%) [54]. Researchers have suggested that the increased prevalence of hypertension among older persons might be due to endothelial function, which contributes significantly to increased arterial stiffness in patients with isolated systolic hypertension and increased age [55,56]. The present study also showed a higher prevalence of increased SBP (69%) than DBP (49%) among older persons. The literature documents that, until the age of 50 to 60 years, both SBP and DBP increase with age. Over the age of 60 years, in the majority of cases, SBP increases with age [57]. Elevated SBP is attributed to arterial stiffness as a major cause [57], and, among older persons, SBP is a superior marker of cardiovascular risk than DBP [58]. Although no significant difference in overall hypertension and increased SBP and DBP was observed between men and women in this study, the prevalence was slightly higher in men. The disproportion between genders in terms of hypertension prevalence that was observed in this study is consistent with several studies that report men to be more affected than women in developing countries [17,51,53,54].
Additionally, overweight/obesity (82%) and abdominal obesity (57%) were also high among older persons in this study. Obesity has been reported among older persons in South Africa (72.4%) [26], but the prevalence was high compared to studies in other African countries, e.g., Ghana (39.5%) [59] and Ethiopia (23.9%) [17], and lower in Malawi (21.9%) [60] and Nigeria (29.2%) [61]. Newman [62] reported that obesity among older persons is fuelled by several factors from social, environmental, genetic, etc. perspectives. There is a possibility that a diminishing energy expenditure, predominantly in older persons aged 50 to 65-years, interferes with body fat that increases with age. However, consequent fat accumulation among older persons aged ≥65 years could be attributed to hormonal changes accompanied by aging [62].
This study also showed a significant relationship between obesity indicators (WC, BMI and WHtR) and overall hypertension or either increased SBP or increased DBP in men and women, separately, and among older persons, collectively. Multivariate analysis showed that WC was significantly associated with hypertension after adjusting for marital status, education, old age grant and family history of hypertension. Generally, WC (64%) was a better predictor, followed by WHtR and BMI, with AUC around 60% in this study. Furthermore, most studies have reported a relationship between WC and hypertension among older persons [63,64], since WC may be a better indicator of total body fat rather than visceral fat [65]. WC is an indicator of abdominal obesity, linked to inflammation and contributing to the risk of CVD [66]. It is worth noting that BMI and WHtR [67] have been reported alongside WC as predictors of hypertension among both men and women in this study. Studies have reported that WHtR is more valued than BMI and WC in predicting hypertension [31,68,69]. While the risk of developing hypertension was greater with increased BMI compared to WC [70], other researchers suggest that visceral adiposity is generally a stronger predictor of hypertension than BMI-based measures [71]. Therefore, the current study showed that, with the exception of WHR, all other obesity indicators (WC, WHtR and BMI) are predictors of hypertensin in South African older persons.
Calderón-García et al. [72] have summarized that BMI does not differentiate between fat or lean mass, and it does not separate the location of central or peripheral fat; therefore, WC and WHR are the proposed indicators of abdominal obesity for their relation with fat distribution. On the other hand, WC is a limited indicator since it does not take an individual’s height and weight into account, therefore overestimating and underestimating obesity among individuals who are either tall or short, respectively [72]. WHR has no benefit over WC alone, and it is infrequently used during the routine evaluation of obesity. On the other hand, a meta-analysis has shown that BMI, WC and BMI are similarly related to hypertension [72]. Furthermore, WHtR has been recommended as a determinant of metabolic and cardiovascular abnormalities since it is suggested to reduce limitations of BMI, WC and WHR [72]. Therefore, the use of a combination of obesity indicators, especially WC, WHtR and BMI, in various population groups provides important information. This also highlights a need to revise the cut-points to identify cardiometabolic diseases in African populations.
The above substantiates our findings that WC and WHtR were significantly related to hypertension, with the AUC value of BMI approximately equivalent to that of WHtR. However, the results have not been consistent between men and women. The main contribution of our study is that it focuses on older persons, a research area that has been rarely addressed in South Africa, although the possible mechanisms of the association between measures of abdominal obesity and hypertension are consistently well established [8,28]. In the current study, we identified cut-off levels that may be applied to evaluate the risk of adverse outcomes. This is because the cut-off levels of anthropometric measures have been adapted from Caucasian or non-black African populations [73,74,75], influencing the variability of the predictive capabilities for cardiovascular risks in other populations.

5. Limitations of the Study

There were some limitations in this study. First, the onset of hypertension among older persons could not be determined because of the cross-sectional designed we used, which could only report on inferences. Second, the study was limited to rural communities; hence, it cannot be generalized to older persons living in different settings, except for rural settings with similar infrastructure and culture. Third, the use of 90% CI in sample calculation adds a large variation in the study and decreases power. As a result, the results of this study need to be duplicated, at the very least, in order to be more conclusive. A sample of 350 individuals marginally reaches adequacy for analysis. By using further stratification, the results can only be seen as preliminary findings based on inferences. Fourth, we could not include biochemical markers of hypertension and obesity among older persons due to budget constraints. Fifth, the current study did not assess other comorbidities (e.g., diabetes). Future studies should consider a comprehensive assessment of behavioural factors, dietary intake, biochemical variables and comorbidities to study hypertension and obesity among older persons. However, our study sheds a light on the prevalence of obesity indicators and hypertension and their relationship among older persons in the rural Vhembe District in South Africa.

6. Conclusions

This study demonstrates predominant general overweight/obesity, abdominal obesity and hypertension among rural older persons in the Vhembe District of Limpopo Province, South Africa. These findings clearly affirm the epidemiologic shift currently experienced by poor households in rural communities, which is suggestive of routine community-based screening for hypertension and obesity among older persons in rural populations. Close monitoring of older persons with identified risk factors should be emphasized to reinforce and implement preventive strategies to change the paradigm. Furthermore, ROC analyses showed that, with the exception of waist-to-hip (WHR), all other obesity indicators (WC, BMI and WHtR) could be used to predict hypertension among older persons, although at different degrees. Mostly, WC was significantly associated with hypertension in older women. Consequently, WC is proposed as a screening tool for the prediction of hypertension in South African older women. However, we further propose the incorporation of WC, BMI and WHtR routines into clinical practice instead of the over-reliance on BMI alone. Population-specific ranges or cut-off values are still serious issues to be revised to guide the implementation of lifestyle modifications in practice. Further corroboration in the context of our population is imperative.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app12094697/s1. Questionnaire: Obesity indicators for predicting hypertension among South African older persons: A community-based study.

Author Contributions

Conceptualization, P.M. and M.C.S.; Formal analysis, P.M. and P.M.M.; Investigation, M.C.S.; Methodology, P.M. and M.C.S.; Project Administration, M.C.S.; Resources, M.C.S.; Software, P.M. and P.M.M.; Supervision, P.M.; Writing—original draft, P.M. and P.M.M.; Writing—review & editing, P.M., K.E.M. and T.S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Sefako Makgatho Health Sciences University Research and Ethics Committee (SMUREC/H/25/2019:PG, approved on 7 February 2019).

Informed Consent Statement

Participation was voluntary and the participants provided written informed consent.

Data Availability Statement

The dataset for participants generated and analyzed during the original study is available from the corresponding author upon reasonable request.

Acknowledgments

The authors would like to thank the participants and research assistants for assisting with data collection.

Conflicts of Interest

The authors declare no conflict of interest.

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Applsci 12 04697 g001 550
Figure 1. ROC curves for men presenting the relationship between obesity indicators and elevated SBP (≥140 mmHg).
Figure 1. ROC curves for men presenting the relationship between obesity indicators and elevated SBP (≥140 mmHg).
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Applsci 12 04697 g002 550
Figure 2. ROC curves for men presenting the relationship between obesity indicators and elevated DBP (≥90 mmHg).
Figure 2. ROC curves for men presenting the relationship between obesity indicators and elevated DBP (≥90 mmHg).
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Applsci 12 04697 g003 550
Figure 3. ROC curves for women presenting the relationship between obesity indicators and elevated SBP (≥140 mmHg).
Figure 3. ROC curves for women presenting the relationship between obesity indicators and elevated SBP (≥140 mmHg).
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Applsci 12 04697 g004 550
Figure 4. ROC curves for women presenting the relationship between obesity indicators and elevated DBP (≥90 mmHg).
Figure 4. ROC curves for women presenting the relationship between obesity indicators and elevated DBP (≥90 mmHg).
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Table
Table 1. Comparison of the characteristics of participants by gender.
Table 1. Comparison of the characteristics of participants by gender.
VariablesAll, n = 350
n (%)		Men, n = 165
n (%)		Women, n = 185
n (%)		p-Value
Age (years) 0.017 *
0.097
Mean 69 (±SD = 7)70 (±SD = 7)68 (±SD = 7)
<69184 (53)79 (47)105 (57)
≥69166 (47)86 (52)80 (43)
Marital status 0.062
Ever married341 (97)158 (96)183 (99)
Single9 (3)7 (4)2 (1)
Level of education 0.002 *
Low literacy240 (69)100 (61)140 (76)
High literacy110 (31)65 (39)45 (24)
Receiving old age grant 0.093
No13 (4)3 (2)10 (5)
Yes337 (96)162 (98)175 (95)
Employed 0.113
No329 (94)159 (96)170 (92)
Yes21 (6)6 (4)15 (8)
Household headship ≤0.0001 *
Self224 (64) 131 (79)93 (50)
Spouse 71 (20)8 (5)63 (34)
Child of the participant51 (15)24 (15)27 (15)
Relative 4 (1)2 (1)2 (1)
Living with 0.219
Alone23 (7)8 (5)15 (8)
Family members327 (93)157 (95)170 (91)
Dwelling place 0.734
Brick-house301 (86)143 (87)158 (85)
Non-brick house49 (14)22 (13)27 (15)
Household income 0.015 *
<R5000 (<$323.31)127 (36)49 (30)78 (42)
≥R5000 (≥$323.31)223 (64)116 (70)107 (58)
Access to electricity 1.000
No0 (0)00
Yes350 (100)165 (100) 185 (0)
Access to water 1.000
No0 (0)00
Yes350 (100)165 (100)185 (0)
Type of toilet 1.000
Flush8 (2)4 (2)4 (2)
Pit242 (98)161 (98)161 (98)
Smoking 0.068
No194 (55)83 (50) 111(60)
Yes156 (45)82 (50)74 (40)
Alcohol use 0.001 *
No225 (64)91 (55) 134 (72)
Yes125 (36)74 (45)51 (28)
Family history of hypertension 0.024 *
No279 (80)140 (85) 139 (75)
Yes71 (20)25 (15)46 (25)
n stands for frequency, % stands for percentage, * indicates significant differences, low literacy means participants did not attend secondary school and attended up to primary school, high literacy means participants attained secondary school education or post, non-brick houses are those made of mud and zinc.
Table
Table 2. Comparison of blood pressure and weight statuses of participants by gender.
Table 2. Comparison of blood pressure and weight statuses of participants by gender.
VariablesAll, n = 350
n (%)		Men, n = 165
n (%)		Women, n = 185
n (%)		p-Value
Blood pressure 0.652
(mmHg)
Normal189 (54)87 (53)102 (55)
High161 (46)78 (47)83 (45)
SBP (mmHg) 0.912
0.499
Mean135 (±SD = 21)135 (±SD = 21)135 (±SD = 22)
Normal108 (242)48 (29)60 (32)
High242 (69)117 (71)125 (68)
DBP (mmHg) 0.588
0.682
Mean 81 (±SD = 13)82 (±SD = 11)81 (±SD = 11)
Normal178 (51)82 (50)96 (52)
High 172 (49)83 (50)89 (48)
BMI (kg/m2) ≤0.0001 *
≤0.0001 *
Mean 29 (±SD = 5)28 (±SD = 4)31 (±SD = 6)
Underweight2 (1)2 (1)0 (0)
Normal61 (17)33 (20)28 (15)
Overweight 162 (46)94 (57)68 (37)
Obese 125 (36)36 (22)89 (48)
Waist (cm) ≤0.0001 *
≤0.0001 *
Mean 91 (±SD = 12)88 (±SD = 11)93 (±SD = 13)
Normal151 (43)127 (78)24 (13)
Abdominal obesity199 (57)38 (23)161 (87)
WHR ≤0.0001 *
0.658
Mean 0.95 (±SD = 0.01)0.97 (±SD = 0.01)0.92 (±SD = 0.07)
Normal41 (12)18 (11)23 (12)
Abdominal obesity 309 (88)147 (89)162 (88)
WHtR ≤0.0001 *
0.039 *
Mean 0.58 (±SD = 0.08)0.55 (±SD = 0.07)0.60 (±SD = 0.09)
Normal7 (2)6 (4)1 (1)
Abdominal obesity343 (98)159 (96)184 (99)
n stands for frequency, % stands for percentage, * indicates significant differences. BMI stands for body mass index: normal (19 to 24.9 kg/m2), underweight (<18.5 kg/m2), overweight (25 to 29.9 kg/m2), obesity (≥30 kg/m2). WC stands for waist circumference: normal (<88 cm), abdominal obesity (≥94 cm for men and ≥80 cm for women). WHR stands for waist-hip ratio: normal (<0.85), abdominal obesity (>0.90 for men and >0.85 for women). WHtR stands for waist-to-height ratio: normal (<0.5), abdominal obesity (≥0.5).
Table
Table 3. Comparison of anthropometric indices in men and women by blood pressure status.
Table 3. Comparison of anthropometric indices in men and women by blood pressure status.
VariablesNormotensive, n (%)Hypertensive, n (%)
All
n = 189		Male
n = 87		Female
n = 102		All
n = 161		Male
n = 78		Female
n = 83
BMI (kg/m2)
Normal37 (20) *20 (23)17 (17) *24 (15) *13 (17)11 (13) *
Overweight98 (52)53 (61)45 (44)64 (40)41 (52)23 (28)
Obesity52 (27)12 (14)40 (39)73 (45)24 (31)49 (59)
Underweight2 (1)2 (2)0000
Waist (cm)
Normal91 (48) *71 (82)20 (20) *60 (37) *56 (72)4 (5) *
Abdominal obesity98 (52)16 (18)82 (80)101 (63)22 (28)79 (95)
WHR
Normal19 (10)6 (10)13 (13)22 (14)12 (15)10 (12)
Abdominal obesity170 (90)81 (90)89 (87)139 (86)66 (85)73 (88)
WHtR
Normal5 (3)4 (5)1 (1)2 (1)2 (3)0 (0)
Abdominal obesity184 (97)83 (95)101 (99)159 (99)76 (97)83 (100)
n stands for frequency, % stands for percentage, * indicates significant difference (p < 0.05) between normotensive and hypertensive groups.
Table
Table 4. The area under the ROC curve (AUC) to predict hypertension among men and women.
Table 4. The area under the ROC curve (AUC) to predict hypertension among men and women.
Anthropometric IndexAUC (95% CI)Cut-Off PointSensitivity (%)Specificity (%)p-Value
Men
BMI0.603 (0.52; 0.69) *27.5061570.026
WHR0.505 (0.41; 0.60)0.9655470.914
WHtR0.589 (0.50; 0.68)0.5555610.053
WC0.582 (0.49; 0.67)87.5062550.077
Women
BMI0.554 (0.47; 0.64)29.0462530.208
WHR0.497 (0.41; 0.58)0.9050430.954
WHtR0.605 (0.52; 0.69) *0.5861550.014
WC0.640 (0.56; 0.72) *90.5067570.001
* Significant difference (p < 0.05).
Table
Table 5. Association of obesity indicators with hypertension.
Table 5. Association of obesity indicators with hypertension.
VariableModel 1
AOR (95% CI)		Model 2
AOR (95% CI)		Model 3
AOR (95% CI)		Model 4
AOR (95% CI)
Men
BMI (≥25 kg/m2)1.84 (0.78; 4.62)1.77 (0.74; 4.51)1.78 (0.74; 4.55)1.57 (0.61; 4.30)
WHR (≥0.90)0.51 (0.18; 1.42)0.49 (0.16; 1.40)0.49 (0.16; 1.42)0.43 (0.13; 1.38)
WHtR (≥0.50)1.58 (0.73; 3.42)1.62 (0.74; 3.53)1.62 (0.74; 3.53)1.20 (0.49; 2.88)
WC (≥90 cm)0.86 (0.35; 2.10)0.85 (0.34; 2.10)0.84 (0.34; 2.09)0.58 (0.19; 1.66)
Women
BMI (≥25 kg/m2)0.99 (0.42; 2.40)1.04 (0.43; 2.57)1.01 (0.41; 2.50)1.17 (0.42; 3.37)
WHR (≥0.90)0.90 (0.36; 2.27)0.79 (0.31; 2.04)0.78 (0.30; 2.01)1.01 (0.33; 3.28)
WHtR (≥0.50)1.11 (0.51; 2.45)1.11 (0.50; 2.46)1.11 (0.51; 2.48)0.83 (0.34; 2.03)
WC (≥90 cm)2.99 (0.94; 10.75)2.93 (0.92; 10.55)2.86 (0.89; 10.34)1.22 (1.16; 1.79) *
* Significant difference (p < 0.05). Model 1: BMI, WHR, WHtR, WC; Model 2: BMI, WHR, WHtR, WC, age; Model 3: BMI, WHR, WHtR, WC, age, smoking, alcohol; Model 4: BMI, WHR, WHtR, WC, age, smoking and alcohol use, marital status, education, old age grant, family history of hypertension.
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Modjadji, P.; Salane, M.C.; Mokwena, K.E.; Mudau, T.S.; Mphekgwana, P.M. Utility of Obesity Indicators for Predicting Hypertension among Older Persons in Limpopo Province, South Africa. Appl. Sci. 2022, 12, 4697. https://doi.org/10.3390/app12094697

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Modjadji P, Salane MC, Mokwena KE, Mudau TS, Mphekgwana PM. Utility of Obesity Indicators for Predicting Hypertension among Older Persons in Limpopo Province, South Africa. Applied Sciences. 2022; 12(9):4697. https://doi.org/10.3390/app12094697

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Modjadji, Perpetua, Mulalo Caroline Salane, Kebogile Elizabeth Mokwena, Tshimangadzo Selina Mudau, and Peter Modupi Mphekgwana. 2022. "Utility of Obesity Indicators for Predicting Hypertension among Older Persons in Limpopo Province, South Africa" Applied Sciences 12, no. 9: 4697. https://doi.org/10.3390/app12094697

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