Body composition and physical activity as mediators in the relationship between socioeconomic status and blood pressure in young South African women: a structural equation model analysis

Postprandial glucose variability and clusters of sex hormones, liver enzymes, and cardiometabolic factors in a South African cohort of African ancestry

INTRODUCTION

This study aimed to, first, determine the clusters of sex hormones, liver enzymes, and cardiometabolic factors associated with postprandial glucose (PPG) and, second to evaluate the variation these clusters account for jointly and independently with polygenic risk scores (PRSs) in South Africans of African ancestry men and women.

RESEARCH DESIGN AND METHODS

PPG was calculated as the integrated area under the curve for glucose during the oral glucose tolerance test (OGTT) using the trapezoidal rule in 794 participants from the Middle-aged Soweto Cohort. Principal component analysis was used to cluster sex hormones, liver enzymes, and cardiometabolic factors, stratified by sex. Multivariable linear regression was used to assess the proportion of variance in PPG accounted for by principal components (PCs) and type 2 diabetes (T2D) PRS while adjusting for selected covariates in men and women.

RESULTS

The T2D PRS did not contribute to the PPG variability in both men and women. In men, the PCs' cluster of sex hormones, liver enzymes, and cardiometabolic explained 10.6% of the variance in PPG, with PC1 (peripheral fat), PC2 (liver enzymes and steroid hormones), and PC3 (lipids and peripheral fat) contributing significantly to PPG. In women, PC factors of sex hormones, cardiometabolic factors, and liver enzymes explained a similar amount of the variance in PPG (10.8%), with PC1 (central fat) and PC2 (lipids and liver enzymes) contributing significantly to PPG.

CONCLUSIONS

We demonstrated that inter-individual differences in PPG responses to an OGTT may be differentially explained by body fat distribution, serum lipids, liver enzymes, and steroid hormones in men and women.

Risk factors associated with the incidence and prevalence of hypertension in Ghana: an integrated review (2016-2021)

This study synthesized the risk factors associated with hypertension among adolescents and adults in Ghana. A systematic search in four databases between 2016 and 2021 retrieved 119 Ghanaian studies, and through screening, 16 were selected. Thematic analysis was used to synthesize extracted data. Prevalence of hypertension ranged from 13.0% to 28.4% and prehypertension 22.0% to 51.4%. The non-modifiable hypertension risk factors were age, marital status, gender, and residence. The modifiable risk factors were consuming additional table salt, salted meat, alcohol, canned meats, smoking, and psychological factors, including stress and anxiety. Also, higher education and socio-economic status, employment, access to medical insurance, overweight, and higher BMI were risk factors. Targeted, sustained, systematic, and effective intervention programs should target high-risk people.

Migrant status disparities in blood pressure: a multiple mediation analysis of modifiable factors

BACKGROUND

We examined differences in blood pressure (BP) levels between first-generation immigrants and natives in adult residents in Northeast Italy, and investigated the role of lifestyle behaviors, body mass index (BMI), and education as potentially modifiable mediating factors.

METHODS

We included 20-69-year-old participants from the Health Surveillance Program of the Veneto Region (n = 37,710). Immigrants born in a high migratory pressure country (HMPC) were further grouped into geographical macro-areas. The outcomes were systolic BP (SBP) and hypertension. Multiple mediation analyses were performed to determine the contribution of each mediator of the SBP/migrant status association.

RESULTS

Of the 37,380 subjects included, 8.7% were born in an HMPC. BMI, education, alcohol, sweets and meat consumption were included as potential mediators. A small advantage in SBP was seen for immigrants compared to natives (β =  - 0.71,95%CI  - 1.30;  - 0.10). The direct effect (net of the covariates) of immigrant status on SBP was a reduction of 1.62 mmHg (95%CI  - 2.25;  - 0.98). BMI played the highest suppressive role (β = 1.14,95%CI 0.99; 1.35), followed by education. Alcohol consumption amplified the health advantage of immigrants. The suppressing effect of BMI was particularly evident among women and North Africans compared to natives. Similar results were seen for hypertension rates.

CONCLUSIONS

Although causation cannot be proven given the cross-sectional design, our findings identify BMI as the most effective target to preserve the health advantage of immigrants with respect to BP levels.

Physical Activity Behaviors of a Middle-Age South African Cohort as Determined by Integrated Hip and Thigh Accelerometry

PURPOSE

Descriptive studies of objectively measured physical activity behaviors in African populations are rare. We developed a method of combining hip and thigh accelerometery signals to quantify and describe physical behaviors in middle-age South African men and women.

METHODS

We integrated signals from two triaxial accelerometers worn simultaneously during free-living, in a subsample of the Middle-age Soweto Cohort ( n = 794; mean (SD) age, 53.7 (6.3) yr). Acceleration time series from the accelerometers were combined and movement-related acceleration was derived using Euclidean Norm Minus One (in milligrams), to determine total movement volume (mean Euclidean Norm Minus One) and nonmovement time (<28 m g ), light-intensity physical activity (LPA; 28-85 m g ), and moderate- to vigorous-intensity physical activity (MVPA; >85 m g ); thigh pitch angle and a sleep diary were used to divide nonmovement time (in minutes per day) into sleep, awake sitting/lying, and standing. Sociodemographic factors were self-reported, and weight and height were measured.

RESULTS

Mean (SD) wear time was 128 (48) h. Movement volume was 15.0 (6.5) m g for men and 12.2 (3.4) m g for women. Men spent more time in MVPA and sitting/lying, whereas women spent more time standing. Age was inversely associated with movement volume, MVPA, and LPA. When compared with their normal-weight counterparts, men who were overweight or obese spent less time in MVPA, whereas women who were overweight or obese spent less time in LPA and more time sitting/lying. Socioeconomic status was inversely associated with total movement volume, MVPA, and time spent sleeping, and positively associated with time spent sitting/lying, in both men and women.

CONCLUSIONS

Integrating signals from hip and thigh accelerometers enables characterization of physical behaviors that can be applied in an African population.

Socioeconomic and lifestyle determinants of the prevalence of hypertension among elderly individuals in rural southwest China: a structural equation modelling approach

Background

This study examines the association between socioeconomic and lifestyle factors and the prevalence of hypertension among elderly individuals in rural Southwest China.

Methods

A cross-sectional survey of 4833 consenting adults aged ≥ 60 years in rural regions of Yunnan Province, China, was conducted in 2017. Data on individual socioeconomic status, sleep quality, physical activity level, and family history of hypertension were collected with a standardized questionnaire. Blood pressure, fasting blood glucose, height, weight, and waist circumference were also measured. An individual socioeconomic position (SEP) index was constructed using principal component analysis. Structural equation modelling (SEM) was applied to analyse the association between socioeconomic and lifestyle factors and the prevalence of hypertension.

Results

The overall prevalence of hypertension was 50.6% in the study population. Body fat distribution, including measures of obesity and central obesity, had the greatest total effect on hypertension (0.21), followed by family history of hypertension (0.14), biological sex (0.08), sleep quality (− 0.07), SEP (− 0.06), physical inactivity (0.06), and diabetes (0.06). Body fat distribution, SEP, and family history of hypertension had both direct and indirect effects on hypertension, whereas physical inactivity, diabetes, and sleep quality were directly associated with the prevalence of hypertension. Biological sex was indirectly associated with the prevalence of hypertension.

Conclusions

SEP, body fat distribution, physical inactivity, diabetes, and sleep quality critically influence the prevalence of hypertension. Future interventions to prevent and control hypertension should give increased attention to individuals with low SEP and should focus on controlling diabetes and obesity, increasing physical activity levels, and improving quality of sleep among older adults aged ≥ 60 years in rural Southwest China.

Social and Psychological Predictors of Body Mass Index among South Africans 15 Years and Older: SANHANES-1

This study investigated how psychological distress and the proxies for social position combine to influence the risk of both underweight and overweight in South Africans aged 15 years and older. This was a cross-sectional study that included 2254 men and 4170 women participating in the first South African National Health and Nutrition Examination Survey (SANHANES-1). An analysis exploring the associations of social and mental health characteristics with body mass index (BMI) was conducted using binary and multinomial logistic regressions. Results suggested that, overall, women had a higher risk of overweight/obesity compared to men (age-adjusted odds ratio [AOR] 4.65; 95% confidence intervals [CI] 3.94–5.50). The gender effect on BMI was smaller in non-African participants (AOR 3.02; 95% CI 2.41–3.79; p-value for interaction = 0.004). Being employed and having a higher level of education were associated with higher risks of overweight and obesity and a lower risk of underweight. Being single or without a spouse and poor mental health were found to increase the odds of being underweight, especially in men. To conclude, there are strong social gradients and important gender and ethnic differences in how BMI is distributed in the South African population.