|
 
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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 12
| Issue : 1 | Page : 28-40 |
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Pre-obesity/obesity in relation to blood pressure among tertiary healthcare workers in an African setting
Obiageli U Onyemelukwe1, Yazid S Kaoje1, Bansi K Isa1, Ahydiru A Mamza2, Mathew A Iyanda1, Beatrice Bello-Ovosi3, Ademola O Adeleye1, Habibu Balarabe1, Maryam Ahmed1, Lilian O Okonkwo1, Innocent O Okpe1, Emediong N Idung1, Fatima Bello1, Adamu G Bakari1
1 Department of Medicine, Ahmadu Bello University (ABU) Teaching Hospital, Zaria, Nigeria
2 Department of Medicine, University of Maiduguri Teaching Hospital, Borno, Nigeria
3 Department of Medicine, Barau Dikko Teaching Hospital, Kaduna, Nigeria
| Date of Submission | 09-Jun-2022 |
| Date of Decision | 24-Jul-2022 |
| Date of Acceptance | 25-Jul-2022 |
| Date of Web Publication | 02-Sep-2022 |
Correspondence Address:
Dr. Obiageli U Onyemelukwe
Prince Sultan Cardiac Centre, King Fahad Specialist Hospital, Buraidah, Al-Qassim
Nigeria
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/ajem.ajem_12_22
Background: The study aimed to determine the predictive power of pre-obesity/obesity indices in pre-hypertension/hypertension among Northern Nigerian tertiary healthcare workers (HCWs). Materials and Methods: A cross-sectional analytical study was done on 348 HCWs. Blood pressure (BP) was defined via the 7th Joint National Committee and the American Heart Association/American College of Cardiology 2017 guidelines. Obesity was defined by body mass index (BMI)/waist circumference (WC). Pearson’s correlation, one-way analysis of variance, and binary logistic regression determined relationships. MedCalc explored the area under the curve (AUC) of obesity indices in hypertension/prehypertension prediction. Results: There were 129 (37.1%) hypertensives out of 348 HCWs. A total of 156 (44.8%) and 114 (32.8%) had systolic and diastolic pre-hypertension, respectively, whereas 241 (60.9%) were pre-obese/obese, of which nurses constituted the majority (82.8%). The anthropometric indices increased from normotension through pre-hypertension to hypertension in females. Pre-obesity [odds ratio (OR): 2.2 (95% confidence interval (CI): 1.1–4.2; P = 0.02)] and generalized obesity [OR: 2.2 (95% CI: 1.2–4.2; P = 0.02)] were associated with hypertension. Central obesity by the International Diabetes Federation (OR: 3.3; 95% CI: 1–11.2; P = 0.005) and World Health Organization (OR: 4.3; 95% CI: 1.1–16.3; P = 0.03) criteria was related to systolic hypertension in males and both systolic/diastolic in females. The optimal WC cut-off values were 87 cm [men: AUC: 0.70 (95% CI: 0.62–0.78); sensitivity (SS): 75%; specificity (SP): 63%; Youden index (YI): 0.36; P = 0.0001] and 101 cm [women: AUC: 0.65 (95% CI: 0.58–0.71); P = 0.0005] for hypertension prediction. The BMI cut-off values for hypertension were 28 kg/m2 [men: AUC: 0.72 (95% CI: 0.64–0.79); YI: 0.36; P = 0.0001] and 31 kg/m2 [women: AUC: 0.63 (95% CI: 0.56–0.69); YI: 0.21; P = 0.0014]. The WC and BMI cut-off values for pre-hypertension prediction were 86 cm (men: AUC: 0.65; SS: 65%; SP: 61%; YI: 0.26; P = 0.0013) and 82 cm (women: AUC: 0.65; SS: 86%; SP: 39%; YI: 0.25; P = 0.0001) and 23 kg/m2(men: AUC: 0.65; P = 0.0001) and 24 kg/m2 (women: AUC: 0.63; P <0.0001). Conclusion: BMI and WC can predict pre-hypertension, with WC being a more reliable predictor of hypertension in Nigerian-African male HCWs. Anthropometric indicators of overall obesity and central obesity can, therefore, be used for screening hypertension/pre-hypertension among Northern-Nigerian HCWs. Keywords: Anthropometric index, healthcare workers, hypertension, obesity, pre-hypertension, pre-obesity
How to cite this article:
Onyemelukwe OU, Kaoje YS, Isa BK, Mamza AA, Iyanda MA, Bello-Ovosi B, Adeleye AO, Balarabe H, Ahmed M, Okonkwo LO, Okpe IO, Idung EN, Bello F, Bakari AG. Pre-obesity/obesity in relation to blood pressure among tertiary healthcare workers in an African setting. Afr J Endocrinol Metab 2022;12:28-40 |
How to cite this URL:
Onyemelukwe OU, Kaoje YS, Isa BK, Mamza AA, Iyanda MA, Bello-Ovosi B, Adeleye AO, Balarabe H, Ahmed M, Okonkwo LO, Okpe IO, Idung EN, Bello F, Bakari AG. Pre-obesity/obesity in relation to blood pressure among tertiary healthcare workers in an African setting. Afr J Endocrinol Metab [serial online] 2022 [cited 2023 Jun 10];12:28-40. Available from: http://www.ajemjournal.org/text.asp?2022/12/1/28/355331 |
| Introduction | |  |
Pre-hypertension was first defined by the 2003 7th Joint National Committee (JNC-7) guidelines for the Prevention, Treatment, and Control of Hypertension to include adults with systolic blood pressure (SBP) ≥120–139 mmHg and or diastolic BP (DBP) ≥80–89 mmHg.[1] The epidemiological observations related to pre-hypertension include its higher prevalence than hypertension in the European countries[2] and USA[2],[3] as well as in sub-Saharan Africa[4]; its association with pre-obesity/obesity and other cardiovascular risks[2],[5]; its accelerative potential to hypertension with some studies documenting rates of 19–40% over 4 and 2 years, respectively,[2],[6] as well as its increased risk for cardiovascular diseases (CVDs) such as coronary artery disease or stroke in comparison to normalcy.[2] Lifestyle modifications are therefore advocated to prevent or slow its progression to hypertension.[1]
The current American Heart Association/American College of Cardiology (AHA/ACC) 2017 definition of hypertension has set BP targets at lower levels with re-definition of elevated BP as 120–129/<80 mmHg and hypertension ≥130/80 mmHg, thereby increasing the hypertension prevalence from 32% to almost 46% in the USA.[7] Rates of 40% have been reported in sub-Saharan Africa[8] with values of 28.9% documented in Nigeria from meta-analysis in 2015[9] and 27.5% from the recent population-based urban study in Lagos-Nigeria.[10] A meta-analysis done among West-African healthcare workers (HCWs) showed the prevalence rate ranging from 17.5% to 37.5% with crude prevalence progressively increasing from 12.9% in the 1980s to 34.4% from 2010 to 2014[11]: a tripling effect associated with cardiovascular complications more common, severe, and rampant at younger ages.[11]
With increasing urbanization, globalization, epidemiologic, and nutritional transition, hypertension is likely to increase further,[8] with the occupational health service systems being unable to match this rapid rise in most West-African countries.[11] Among formal HCWs, hypertension awareness rates are low, with consequent poor control rates and increased target organ damage,[12] despite supposed access to pre-employment and periodic medical screening.[11] More so, hypertension is linked with other CVDs such as obesity and pre-obesity among others, and obesity is a major risk factor for hypertension and CVDs.[13],[14]
The World Health Organization (WHO) 2019 has reported a global tripling in burden of obesity since the past 4.5 decades involving 2.1 billion adults (30%) of the world’s population, with higher burden of pre-obesity.[15],[16] The trends in Africa are rising[17],[18],[19] with a population-based study cutting across four countries; Nigerian nurse HCWs, South African and Tanzanian school teachers; peri-urban and rural residents of Uganda, respectively, documenting a combined burden of pre-obesity and obesity of 46%, 48%, 68%, 75%, and 85%, respectively.[19] Several studies across the geopolitical zones of Nigeria in the recent past have documented high burden of obesity/overweight in urban/rural populations.[20],[21],[22],[23],[24],[25] A systematic review in Nigeria reported pre-obesity and obesity rates of 20.3–35.1% and 8.1–22.2%, respectively,[26] with an upward trend in the recent past.[18],[23] A similar trend has been documented among HCWs in sub-Saharan Africa, Nigeria inclusively.[11],[13],[17],[24],[27],[28],[29],[30] Furthermore, there are conflicting findings regarding obesity indices among healthcare professionals: perceived as role models and mentors to the society but ailing, with some study in Canada documenting lower rates,[31] whereas some other in England and America reporting higher rates.[16],[32]
Likewise, different obesity indices have shown different relationships with hypertension in studies outside Nigeria with some studies among rural Chinese females reporting increased hypertension risk associated with high body mass index (BMI) and waist circumference (WC), similar to some Nigerian studies[25],[33]; yet some other showed WC being more associated with hypertension than BMI in middle aged to elderly Taiwan, Chinese, and Korean population.[34],[35],[36],[37]
Furthermore, diseases attributable to obesity/pre-obesity include type 2 diabetes; hypertension and its attendant complications viz-a-viz., stroke, kidney disease, and myocardial infarction; cancers; osteoarthritis; gall stones; fatty liver; obstructive sleep apnea; and consequently less productivity, early retirement within the healthcare sector, and shortened lifespan.[13],[24],[28],[38]
Unfortunately, there is a dearth of studies on the relationship between anthropometric indices and pre-hypertension/hypertension among Nigerian-African HCWs taking into cognisance the potential impact of pre-obesity/obesity on productivity and retention within the limited workforce in the healthcare sector.[24] The previous study on HCWs in Zaria, Nigeria (ABU WoDiDaC survey) determined cardiovascular risks among apparently healthy HCWs (unpublished data). Furthermore, data are needed in the tertiary healthcare sector of sub-Saharan Africa regarding the burden of pre-obesity/obesity in relation to hypertension to guide evidence-based decisions and advocacy for change. Therefore, this study was done to determine the relationship between anthropometric indices and pre-hypertension/hypertension among HCWs in Ahmadu Bello University Teaching Hospital, Nigeria. The predictive power of obesity in relation to hypertension/pre-hypertension was further determined.
| Materials and methods | | |
Study location and design
This was a cross-sectional epidemiological-analytical study carried out at the Ahmadu Bello University Teaching Hospital, Zaria, Nigeria, a 596-bedded tertiary healthcare facility and a major referral center in the Northern geopolitical zone of Nigeria in sub-Saharan Africa. This study is an extension of an earlier work.[39]
Ethical approval and consent participation
Ethical approval was obtained from the Ahmadu Bello University Teaching Hospital, Zaria, Nigeria, and the study was carried out according to the amended Helsinki’s Declaration with written informed consent obtained from all participants.
Inclusion and exclusion criteria
Inclusion criteria were adult HCWs ≥ 20 years with willingness to participate. Subjects with historical and clinical evidence of sickle cell disease, pregnancy or lactation, acute febrile illness, historical and clinical evidence of stroke, peripheral vascular disease, heart failure, and incomplete/missing data on anthropometry were excluded. Subjects on medications that raise BP such as steroids, non-steroidal anti-inflammatory drugs, cocaine, and oral contraceptives were also excluded.[34]
Sample size determination
This was calculated using Fisher’s statistical formula for sample size for descriptive studies,[13] viz.: N = Z2PQ/d2, where N is the minimum sample size; Z the standard deviation score at 95% = 1.96; P the prevalence of overweight and obesity based on BMI of health workers at a tertiary health center in Jos of 72% = 0.72[38]; Q the complimentary probability (1 − P) = 1 − 0.72 = 0.28; d the error margin = 5%. Substituting, N = (1.96)2 × 0.72 × 0.28/(0.05)2 = 309.8. Hence, a minimum sample size of 309 was calculated and rounded up to 348 to take care of 5–10% attrition rate.
Screening evaluation and data collection
There were 377 respondents who presented at the medical outpatient department of ABUTH, Zaria, Nigeria, in response to formal invitation to their various departments as well as poster communiqué. Of these, 29 were excluded due to incomplete anthropometric data and inability to reach the HCWs via phone contacts from “initial study date,”[39] up until January 26–29, 2020. Hence, a total of 348 participants were included in the final data analysis.
Trained interviewers (eight senior medical doctors, four nurses, and three house officers) administered the pre-validated and tested non-communicable disease (NCD) risk factor survey standardized questionnaire. This has been described fully in previous reports.[39] Anthropometric measurements inclusive of WC, weight, height, and BMI as well as BPs were determined by the standard protocol approved by the WHO.[40],[41] There was removal of shoes, heavy or tightening clothing, and/or belts prior to obtaining of measurements. The WC was measured at a level parallel to the floor, mid-point between the top of the iliac crest and the lower margin of the last palpable rib in the mid-axillary line while the participants expired gently.[40] This was done with a non-stretch 1 cm wide measuring tape wrapped snugly around the subjects in the absence of any constricting effect and with the tape level and parallel to the floor at the measurement point. The subjects were standing upright with their arms relaxed at their side, feet spread apart, and body weight evenly distributed during the procedure.[40]
The weight was determined in kilogram (kg) with the subjects standing motionless on a calibrated weighing scale placed on a firm flat ground while wearing light clothing and no foot wear. Measurements were approximated to the nearest 0.5 kg, having ensured that the weighing scale was always at “zero” mark.[41],[42] The height was measured in meters approximated to the nearest 0.5 cm with the subject in erect posture against a vertical scale stadiometer without foot wear or cap and with feet placed together on a horizontal flat surface. The subjects’ occiput and heels were in contact with the stadiometer.[41],[42]
BPs were determined via an Accoson mercury sphygmomanometer, twice with an interval of 10 min and in the left arm of seated subjects previously rested for 5 min with their arms supported at the level of the heart and by standard protocol. The average of the two readings was used.[43]
Outcome measures of obesity/pre-obesity and hypertension/pre-hypertension
The BMI was determined as weight (kg)/height (m2) in kg/m2,[42] and BMI was classified as: underweight (BMI <18.5 kg/m2); normal (BMI 18.5–24.9 kg/m2); pre-obesity or overweight (BMI 25.0–29.9 kg/m2), and general obesity (BMI ≥ 30.0 kg/m2). General obesity was further graded into Class I obesity (BMI 30.0–34.9 kg/m2); Class II obesity (BMI 35.0–39.9 kg/m2); and Class III obesity (BMI ≥ 40.0 kg/m2).[42]
Central obesity was defined according to two criteria, viz-a-viz., first, the International Diabetes Federation (IDF) criteria[44] with WC >94 cm for men and >80 cm for women, which is also the threshold for sub-Saharan ethnicity recommended by the Joint Scientific Statement on Harmonizing the Metabolic Syndrome,[45] and secondly according to the WHO criteria: WC >102 cm for men and >88 cm for women.[40]
Hypertension was defined as BP ≥140/90 mmHg, current anti-hypertensive usage or history of hypertension, based on the JNC VII on prevention, detection, evaluation, and treatment of high BP guidelines.[1] Classification of hypertension by the JNC-7 classification was further determined. The new ACC/AHA 2017 hypertension guideline recommendation for re-definition of hypertension with upper limits of 130/80 mmHg was additionally applied.[7] Pre-hypertension was defined as SBP ≥ 120–139 mmHg and or DBP ≥ 80–89 mmHg by the JNC-7 definition.[1]
Data analysis
The analysis was performed with SPSS version 25 software (IBM). The Kolmogorov–Smirnov test determined the normality of distribution of continuous variables. Continuous variables were expressed as mean ± SD with difference determined by the independent Student’s t-test. Categorical variables were expressed as numbers and percentages and difference determined by the χ2 analysis. In the univariate analysis, one-way analysis of variance (ANOVA) with post hoc Bonferroni test compared hypertensive, pre-hypertensive, and non-hypertensive HCWs in relation to obesity indices. Correlations were assessed via Pearson’s correlation coefficient between different obesity indices and BPs. In the multivariate analysis, binary logistic regression was used to adjust covariates. Receiver operating characteristic (ROC) curves were generated via MedCalc for WC and BMI as predictors of hypertension/pre-hypertension. The area under the ROC curve (AUC) and the optimal cut-points for hypertension/pre-hypertension prediction were determined by the largest sum of sensitivity and specificity with the Youden index (YI) specified. The level of significance was at P ≤ 0.05 with 95% confidence interval (CI) assumed.
| Results | | |
A total of 348 HCWs (60.3% predominantly females) were analyzed of which about a third [110 (31.6%)] had combined isolated systolic and isolated diastolic hypertension (BP≥140 and 90 mmHg) by the JNC-7 criteria, with almost a tripling effect by the ACC/AHA 2017 criteria [Table 1]. Three quarters of the HCWs had combined isolated systolic and diastolic pre-hypertension by the JNC-7 criteria with a male predominance in contrast to the female predominance with hypertension [Table 1]. The mean age was 42.8 ± 10.8 years, with females being significantly (P <0.001) older than males [Table 1]. The mean ± SD of WC and BMI was significantly (P = 0.02 and P ≤ 0.001) higher in females than in males, respectively [Table 1]. The burden of generalized obesity was 69.3% with three quarters of females and more than half of the male HCWs affected [Table 1]. The more severe forms of hypertension and generalized obesity were represented by few subjects [Table 1], whereas 37 (10.8%) had stage I hypertension and 76 (67.9%) had Class I obesity. Just above a quarter had a history of hypertension, being more (P <0.005) predominant in females than in males and almost half had a family history of hypertension. | Table 1: General characteristics and anthropometry of the tertiary healthcare workers by gender
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One-way ANOVA was used to compare the BPs and obesity/pre-obesity indices between normal, pre-hypertensive, hypertensive-controlled (history of hypertension with BP<140/90 mmHg), and hypertensive-uncontrolled HCWs (history of hypertension with BP≥140/90 mmHg and undiagnosed hypertensive). A significant (P <0.001) difference was found between the mean ages; mean SBP and DBP; mean WCs; central obesity indices by IDF criteria in females; mean BMI; and generalized obesity indices in females only [Table 2]. | Table 2: Comparison of anthropometric parameters and pre-obesity/obesity indices between normal, pre-hypertensive, hypertensive-controlled and hypertensive-uncontrolled healthcare workers at a Northern-Nigerian African Tertiary Hospital by gender
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The actual difference by the post hoc Bonferroni test was as follows: Hypertensive HCWs were in their fifth decade, whereas pre-hypertensive and normal HCWs were in their fourth decade with significantly higher mean age in the uncontrolled hypertensive than pre-hypertensive and normal HCWs [Table 2]. Both mean SBP and DBP were highest in the uncontrolled hypertensive than normal and pre-hypertensive, except controlled hypertensive HCWs [Table 2]. A similar finding was found for mean BMI, except that it did not differ between normal and pre-hypertensives. Central obesity by IDF criteria was higher among the female uncontrolled hypertensives than normal HCWs, with a similar trend for central obesity by WHO criteria though insignificant (P = 0.06) [Table 2]. Likewise, generalized obesity by BMI was significantly higher (P = 0.04) in female uncontrolled hypertensives than normal HCWs, with no significant difference among the other parameters [Table 2]. Males showed no difference (P >0.05) in mean BMI and WC among the different categories of BP [Table 2].
Importantly, the total number of hypertensive HCWs (both controlled and uncontrolled) was 129 (37.1%) [Table 2]. There were 53 (41.1%) of those controlled on anti-hypertensive therapy, of which majority 33 (62.3%) were nurses and 12 (22.6%) doctors [Table 2]. Out of the uncontrolled hypertensive 76 (58.9%), 42 (55.3%) were nurses; 10 (13.2%) were doctors; and 24 (31.6%) other HCWs. The total number of hypertensive nurse HCWs was 75 (58.6%), whereas that of hypertensive doctor HCWs was 22 (24.4%). There were 37 (48.7%) undiagnosed hypertensives, of which 14 (37.8%) were nurses, 8 (21.6%) doctors, and 15 (40.5%) other HCWs.
Additionally, there were 129 (37.1%) pre-obese and 112 (32.2%) obese HCWs [Table 2]. A total of 53 (41.1%) of the pre-obese HCWs were hypertensives and 31 (36.9%) pre-hypertensives [Table 3]. Of the obese HCWs, 59 (52.7%) were hypertensives, whereas 23 (27.4%) were pre-hypertensives [Table 3]. Out of the 128 (36.8%) nurse HCWs, 65 (50.8%) were obese and 41 (32.0%) were pre-obese, with a total of 106 (82.8%) pre-obese/obese nurse HCWs [Table 3]. The doctors were 90 (25.9%) of the total HCWs, of which 14 (15.6%) were obese and 44 (48.9%) pre-obese, totaling 58 (64.4%) pre-obese/obese doctor HCWs [Table 3].
Further analysis by the independent Student’s t-test showed that the obese HCWs had significantly (P <0.001) higher mean BMI and WC than the pre-obese HCWs as well as the non-obese HCWs, respectively, by both IDF and WHO criteria. Correspondingly, mean SBP and DBP were higher (P <0.001) among the centrally obese male and female HCWs, respectively [Table 4]. SBP and DBP were shown to be higher (P = 0.03), respectively, among the generally obese male HCWs by BMI than pre-obese [Table 4]. However, the generally obese female HCWs showed no significant difference in BPs, both systolic and diastolic from their pre-obese counterpart [Table 3]. Notable was the significantly higher mean BMI (P <0.001), SBP (P = 0.03), and DBP (P = 0.002) from the lowest class of obesity to the highest class using the one-way ANOVA with the post hoc Bonferroni test [Table 4]. | Table 4: Pre-obesity/obesity indices in relation to blood pressure parameters among Northern-Nigerian tertiary healthcare workers by gender
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Pearson’s correlation analysis showed a significant (P <0.005) positive correlation of central obesity by WC as well as generalized obesity by BMI to pre-hypertension/uncontrolled hypertension combined, in both male and female HCWs, as well as all subjects combined [Table 5]. | Table 5: Correlation between pre-hypertension/hypertension (uncontrolled) and obesity indices by gender
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Additionally, multiple logistic regression analysis of hypertension-related obesity indices showed that pre-obesity and generalized obesity by BMI had significantly high odds of being associated with hypertension, both systolic and diastolic [Table 6]. Likewise, central obesity by WC using the IDF criteria was significantly (P = 0.05 and 0.03) associated with both systolic and diastolic hypertension, respectively, in females, with much higher odds and only systolic hypertension (P = 0.05) in males [Table 6]. The WHO criteria for central obesity showed that it was significantly (P = 0.03 and 0.02) associated with only systolic hypertension in males and diastolic hypertension in females, respectively [Table 6]. Following age and sex adjustment of covariates, WC by the IDF criteria was significantly (P = 0.04) an independent risk factor for systolic hypertension in the subgroup of female HCWs aged ≥ 65 years with high odds [odds ratio (OR): 9.0; 96% CI: 2.1–11.0]. | Table 6: Relationship of obesity/pre-obesity indices with hypertension among Northern-Nigerian African tertiary healthcare workers according to gender
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Finally, the ROC-AUC generated via MedCalc showed the best predictive cut-off points of WC and BMI in males and females for the prediction of hypertension and pre-hypertension [Figure 1] and [Figure 2], respectively]. WC in males >87 cm showed higher AUC, 0.70 (95% CI: 0.62–0.78; sensitivity: 75%; specificity: 63.2%; YI: 0.38; P = 0.0001) for predicting hypertension [Figure 1]. In females, a WC >101 cm showed an AUC of 0.65 (95% CI: 0.58–0.71; YI: 0.22; P = 0.0005) with a lower sensitivity and higher specificity [Figure 1]. Cut-off points for BMI prediction of hypertension were also different between males and females with males having BMI >28 cm showing an AUC of 0.72 (95% CI: 0.64–0.79; YI: 0.36; P = 0.0001) [Figure 1] and females having BMI >31 cm, associated with an AUC of 0.63 (95% CI: 0.56–0.69, YI: 0.21, P = 0.0014), both with lower sensitivity and higher specificity [Figure 1]. | Figure 1: Obesity indices as predictors of hypertension by ACC/AHA 2017 criteria according to gender. (a) WC in males (n = 138); (b) WC in females (n = 210); (c) WC in total sample (n = 348). (d) BMI in males; (e) BMI in females; (f) BMI in total sample
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 | Figure 2: Obesity indices as predictors of pre-hypertension according to gender. (a): WC in males (n = 138); (b): WC in females (n = 210); (c): WC in total sample (n = 348). (d): BMI in males; (e): BMI in females; (f): BMI in total sample. WC = waist circumference, BMI = body mass index
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With regard to prehypertension prediction, WC in males of >86 cm (sensitivity: 64.8%; specificity: 60.7%; YI: 0.26; P = 0.0013) and in females of >82 cm (sensitivity: 85.6%; specificity: 39.2%; YI: 0.25; P = 0.0001) showed similar AUC and 95% CI with higher sensitivity and lower specificity, especially in females [Figure 2]. The BMI values, in contrast, of 23 and 24 kg/m2 in males and females, respectively, showed similar AUCs with lower sensitivity and higher specificity [Figure 2].
The outcome for both males and females combined in relation to hypertension and pre-hypertension is as reflected in [Figures 1] and [2]. The better YI for ruling out hypertension with a high sensitivity was WC in males [Figure 1], whereas BMI showed a better YI for the prediction of pre-hypertension, especially in males with similar AUC for WC in both genders [Figure 2].
| Discussion | | |
This study revealed a positive correlation between all obesity indices and pre-hypertension/hypertension. The BMI and WC were associated with hypertension via the independent Student’s t-test, correlation analysis, one-way ANOVA, and multivariate analysis. The mean values of all pre-obesity and obesity indices were higher in the pre-hypertensive and highest in the hypertensive relative to normal HCWs. This signifies that those subjects with higher obesity indices trended toward higher BP levels consistent with previous reports.[33],[34],[35] Likewise, BP levels rose significantly (P <0.001) from the lowest class of obesity to the highest class in tandem, with a corresponding rise in BMI signifying increasing severity of hypertension from moderate to morbid obesity. The central obesity index by IDF criteria remained an independent risk factor for hypertension in the subgroup of female HCWs aged ≥ 65 years, similar to some previous reports,[34] indicating the relevance of older age in association with increasing hypertension in the context of obesity.
The WC showed larger AUC associated with the highest sensitivity and YI for ruling out hypertension and pre-hypertension, especially in males than BMI. This suggests that anthropometric indices for measurement of central obesity may be better for predicting hypertension and pre-hypertension as documented previously.[34],[35],[36],[37] This, however, contrasts the findings of an Eastern-Nigerian rural–urban population-based study which did not show any significant difference between the performance of WC, BMI, and waist-to-height ratio (WHtR) in prediction of hypertension risk.[25] This may be attributable to differential target and characteristic population, sample size, and the larger number of centrally obese patients in this study. Furthermore, central obesity by both criteria showed higher ORs for association with systolic hypertension than BMI, even following adjustments, similar to the AUC-ROC results. This is also similar to previous reports indicating the stronger association of WC with the development of hypertension.[33],[34],[35],[36],[37] The BMI, on the contrary, showed a similar AUC curve to WC in predicting the risk of prehypertension.
Ultimately, the imbalance between energy intake and expenditure results in obesity. Sedentary lifestyle, poor work dietary habits, as well as work shift/night calls associated with sleep debt among HCWs, especially doctors and nurses, may play a more significant role in the amount of weight gain.[17],[46] The possible mechanisms include an increase in visceral fat for those with central obesity, leading to hyperleptinemia, insulin resistance, and hypertriglyceridemia.[14] Visceral adipocyte dysfunction leads to visceral fat cell release of adipokines such as ghrelin (hunger hormone), leptin (satiety hormone), resistin, interleukin (IL-6), tumor necrosis factor, as well as reduced adiponectin and IL-10 levels.[14],[46],[47],[48],[49],[50] Other mechanisms include activation of angiotensinogen and aldosterone-stimulating factor via the renin–angiotensin–aldosterone system stimulation as well as inappropriate sympathetic nervous system functionality.[47],[48],[51] Hyperuricemia and alteration in incretin or dipeptidyl peptidase-4 activities in obese subjects also contribute to hypertension development.[14],[34],[46],[48] All these may predispose the obese/pre-obese HCWs to endothelial dysfunction, increased arterial stiffness, pre-hypertension, and consequent hypertension.[34],[48],[49] BMI is related to an increased body fluid volume, associated peripheral resistance, and increased cardiac output.[14]
The optimal cut-off point for prediction of hypertension was approximately 28 kg/m2 and 88 cm for BMI and WC, respectively; with higher values for females than for males, which is consistent with the previous reports in Nigeria[25],[50] as opposed to the WHO and IDF criteria extrapolated from the white population.[40],[41],[44] The lower BMI values than the WHO criteria for obesity are consistent with the previous Nigerian studies[25],[32]and a Taiwanese study.[34] This may be attributed to the greater proportion of younger HCWs than older ones. It has been documented and suggested that WC values for predicting health outcomes in elderly differ from those of younger subjects, with higher WC and WHtR in elderly compared with younger age groups.[51] The higher WC cut-off value for women than for men in ruling out hypertension is similar to the Nigerian reports albeit with higher WC value.[25],[35] This further implicates central obesity which was more prominent in the female HCWs as a high risk for hypertension. Some other study outside Nigeria showed similar higher predictive values for WC.[51]
Furthermore, a high burden of pre-obesity and obesity was observed, consistent with the findings in HCWs across the geopolitical zones of the country locally.[13],[27],[28] This is consistent with the findings among the general population of Nigeria and sub-Saharan Africa at large,[17],[18],[19],[20],[21],[22],[23],[24],[25],[26],[29] as well as the East-Asian countries,[34],[37] Europe,[32] and the USA in the recent past.[16] Interestingly, females are more implicated as previously reported.[13],[20],[30],[52] Disrupted sleep pattern and chronic stress have been implicated as causative factors of pre-obesity and obesity among HCWs, consequent on sub-normal hypothalamic–pituitary–adrenal axis.[28],[50] Disrupted circadian rhythm causes hormonal disturbances inclusive of melatonin and cortisol (stress hormone), consequently resulting in risk of metabolic syndrome, obesity, hypertension, type 2 diabetes mellitus, and CVDs.[14],[28],[53]
Noteworthy were the significantly (P <0.05) higher BPs, WC, and BMI among the hypertensives than pre-hypertensives and normal HCWs. All these indicate and confirm previous documented evidence of pre-hypertension being a high risk for CVD[2],[3],[4],[5],[6]; hence, it should be given high priority for intervention via promotion of lifestyle modification to prevent progression to hypertension and its attendant complications.[1]
Important to note was the large burden of hypertensive HCWs with uncontrolled BP as well as high obesity rate of which nurses formed the majority, consistent with previous reports.[13],[30],[32],[39] Additionally, there was a large proportion of undiagnosed hypertensives, of which nurses and doctors formed two-thirds as well as the high burden of isolated systolic/diastolic pre-hypertension which was a cause for concern. These may be attributed to the high burden of pre-obesity and obesity among the hypertensive HCWs, which was also shown to be significantly associated with hypertension as well as has a high ROC predictive value. Other reasons may be the higher age among the hypertensives and pre-hypertensives than normal HCWs. Hypertension is common among the elderly and associated with increased risks of CVD, stroke, and kidney failure, consequent on increased arterial stiffness, pulse pressure, declining renal function, decreased renal salt excretion, autonomic neuropathy, and altered endothelial function.[4],[5],[6],[7],[8],[34],[54] Familial factors may also be implicated, as almost half of the HCWs had a positive family history of hypertension confirming previous documented evidence for genetic predisposition to hypertension and CVD.[1],[4],[8]
| Conclusion | | |
There is a high burden of pre-obesity/obesity and pre-hypertension/hypertension among HCWs. Pre-obesity/obesity is associated with pre-hypertension/hypertension. Both BMI and WC can predict pre-hypertension, with WC being a reliable predictor of hypertension especially in men. Anthropometric indicators of overall obesity and central obesity can, therefore, be used for screening hypertension/pre-hypertension among Northern-Nigerian HCWs. Central obesity by IDF criteria is an independent predictor of systolic hypertension in elderly female HCWs. These findings will alert the ailing healthcare system, government, and policy-makers on the urgent need to curb this trend.
Recommendations
It is therefore glaring that the mentor HCWs are culprits with regard to optimal health care and lifestyle; hence, there is an urgent need for measures to be taken to curb this growing epidemic in the healthcare system, at all tiers of government. Health education, increased work exercise, healthy work diets, adequate sleep/rest, and employment of more physician and nurse HCWs to reduce the burden and consequence of shift work/excessive night calls when there are limited staff are thereby advocated.
Limitations
The causal relationship between pre-obesity/obesity indices and pre-hypertension/hypertension may not be determined by a cross-sectional study of this nature; hence, further longitudinal studies are required. Newer standard measures for determination of obesity and body composition such as magnetic resonance imaging, computed tomography, and dual-energy X-ray absorptiometry[36] were not used in this study due to exorbitance and lack of convenience for large screening exercises; however BMI and WC have been validated as common standards for determining obesity worldwide.
Acknowledgments
Many thanks to Mr B. Egaji of Global Data Analyst Centre, Nigeria for MedCalc data analysis; Emzor Pharmaceuticals Industries Ltd.; and Micro Nova Pharmaceuticals Nigerian Ltd for material support.
Financial support and sponsorship
No specific grant was received; however, Emzor Pharmaceuticals Industries Limited Nigeria and MicroNova Pharmaceuticals Nigerian Limited supported materially.
Conflicts of interest
There are no conflicts of interest.
Authors’ contributions
OUO wrote the paper. All authors contributed to conception and design of the study, performed the data collection and analysis, revised, and approved the final version of the manuscript.
Data availability statement
All relevant data are contained within the article.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]
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