Association between dietary adherence, anthropometric measurements and blood pressure in an urban black population, South Africa

Abstract

The aim was to determine participants' dietary adherence by calculating a diet adherence score based on the Dietary Approaches to Stop Hypertension (DASH)-style diet; (2) to determine if there was an association between dietary adherence score, anthropometric measurements (waist circumference, body mass index (BMI), waist-hip ratio, waist-toheight-ratio) and blood pressure (BP) in a South African urban black population. Design: Cross-sectional secondary analysis of data collected for the PURE study was undertaken. Setting: Langa, the urban PURE study site in the Western Cape province, South Africa. Subjects: The PURE study Western Cape urban cohort, 454 participants, aged 32-81 years was utilised. Outcome measures: Dietary adherence scores were calculated and the BP and anthropometric measurements, respectively, of participants in the lowest and highest tertiles of dietary adherence scores were compared. Results: Positive correlations were found between age, for both men and women, and systolic and diastolic BP. A significant positive correlation between added sugar intake and systolic blood pressure (SBP) was present only in the women. A significant positive correlation was found between BMI, diastolic BP and SBP in men only. No significant differences existed between BP of men or women in the lowest and top tertile groups according to dietary adherence score, but a significant inverse correlation between the dietary adherence score and SBP was found in women. Conclusions: BMI was positively associated with BP in men, while dietary adherence score was negatively correlated with SBP in women. Summary: Non-adherence to dietary guidelines presenting overconsumption of unhealthy foods may be associated with high blood pressure.

Multiple Points Change in the Association of Blood Pressure Subtypes with Anthropometric Indices of Adiposity among Children in a Rural Population

“…This is in agreement with the previous study by Emha et al, in which BMI was not associated with BP in hypertensive patients in Cardiology Polyclinic [18]. The reason for the lack of a significant association of BMI with SBP is thought to be the influence of potential confounders, such as diet and physical inactivity, which were not adjusted for [19,20]. Furthermore, DBP did not show any association with the adiposity indices, which affirm the results of the previous study [21], suggesting that SBP may be a better guide than DBP to evaluate cardiovascular and all mortality [22].…”

Association between food insecurity and key metabolic risk factors for diet-sensitive non-communicable diseases in sub-Saharan Africa: a systematic review and meta-analysis

“…The last subset (4 studies) used more than one tool to gauge food insecurity 39 , 46 , 53 , 54 . While studies on children ≤ 5 years used the Minimum Meal Frequency criterion 39 , the Weight-for-height criterion 43 , and the Quetelet index 35 .…”

“…Of the 20 studies demonstrating a significant association, 14 were of high quality and 6 of average quality. The association existed even for marginal food security (a less severe level of household food insecurity) (Table 3 ) 33 , 34 , 37 – 40 , 42 , 45 – 49 , 51 – 53 .…”

Association Between Food Insecurity and Key Metabolic Risk Factors for Diet-Sensitive Non-Communicable Diseases in Sub-Saharan Africa: A Systematic Review and Meta-Analysis

“…39 The remaining six studies recruited participants from both rural and urban settings. [40][41][42][43][44][45] Overall, the studies selected for inclusion (n = 22 studies) involved a total of 26 609 participants, 12 453 (46.8%) participants were males, and one study recruited only women of reproductive age living in informal settlements in Accra, Ghana 29 . The distribution of the rest of the studies by countries was as follows: 4 from Ethiopia, 25,27,40,41 ; 1 Nigeria, 24 ; 1 Madagascar, 26 ; 3 Ghana, 29,38,39 ; 1 Zambia, 37 ; 2 Tanzania, 28,35 ; 1 Malawi, 34 ; 1 Cameroon, 33 ; 2 Sudan, 30,31 ; 1 Burkina Faso, 42 ; 1 Kenya, 32 ; 1 Botswana, 45 and 3 studies from South Africa 36,43,44 .…”

“…32,37,40 Another subset (n = 9 studies) measured food insecurity at an individual level using the Minimum Adequate Diet criterion, 24,25,27,31,33,36,37,42,43 the Minimum Dietary Diversity criterion (n = 5 studies), 28,29,38,39,45 and the last subset (n = 4 studies) used more than one tool to gauge food insecurity. 30,37,44,45 While studies on children ≤ 5 years used the Minimum Meal Frequency criterion, 30 the Weight-for-height criterion, 34 and the Quetelet index 26 to ascertain food insecurity. See Table 1…”