Obesity and hypertension are public health problems associated with cardiovascular events worldwide. Bus drivers, whose lifestyle is primarily sedentary and characterized by poor eating habits are at increased risk. This study determined the prevalence and lifestyle-related risk factors of obesity and hypertension among Inter-Regional Metromass Bus Drivers (IRMBDs) in Ghana. This cross-sectional study recruited 527 professional drivers from Metromass Bus stations in Accra and Kumasi Metropolis, Ghana. Structured questionnaires were administered to obtain socio-demographic and lifestyle characteristics from all participants. Anthropometric measurements including body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR) and blood pressure (BP) were determined. The prevalence of unrecognized hypertension was 38.7%. The prevalence of obesity using BMI, WC, and WHR as obesity indices were 19.0%, 19.9%, and 19.4%, respectively. Use of sleep inhibitors, long-duration sitting and eating late at night were independent risk factors for obesity, regardless of the obesity index used (p < 0.05). Physical inactivity, high caloric intake and eating at stressful periods were independent risk factors for obesity based on WC and WHR measurements (p < 0.05). Ageing, smoking history, alcoholic beverage intake, sleep inhibitor drug use, high calorie intake, long-duration sitting, eating late and under stressful conditions were independent risk factors for hypertension (p < 0.05). There is a high prevalence of unrecognized hypertension and obesity among IRMBDs which were associated with individual lifestyle and behaviours. Increased awareness through educational and screening programs will trigger lifestyle modifications that will reduce cardio-metabolic disease onset and offer clues for better disease predictive, preventive and personalized medicine.

The study determined the association of wrist circumference (WrC) and waist-to-height ratio (WHtR) with cardiometabolic risk factors among diabetics in a Ghanaian population. This cross-sectional study involved 384 diabetic patients at Begoro District Hospital, Ghana. Blood pressure, anthropometrics, and biochemical indices were measured. The overall prevalence of dyslipidaemia, metabolic syndrome (MetS), and hypertension was 42.4%, 76.3%, and 39.8%, respectively. The optimum cut-off range of WrC to identify individuals at increased cardiometabolic risk was 17.5 to –17.8 cm for men and 16.0 to 16.7 cm for women while that of WHtR was 0.52 to 0.61 for men and 0.53 to 0.59 for women. WrC for women was a significant independent predictor for MetS [aOR = 3.0 (1.39–6.72), p=0.005] and systolic blood pressure [aOR = 2.08 (1.17–3.68), p=0.012]. WHtR was a significant positive predictor for triglycerides [aOR = 3.23 (0.10–3.82), p=0.001] for women. Using Framingham risk scores, 61% of the subjects had elevated 10-year risk of developing cardiovascular diseases (CVDs), with no significant difference in gender prevalence. WrC [aOR = 6.13 (0.34–111.4), p=0.107] and WHtR [aOR = 2.52 (0.42–15.02), p=0.309] were associated with statistically insignificant increased odds of moderate-to-high risk of developing CVDs in 10 years. The use of gender-specific cut-offs for WrC and WHtR may offer putative markers for early identification of CRFs.

This study determined noncompliance to antihypertensive therapy (AHT) and its associated factors in a Ghanaian population by using the health belief model (HBM). This descriptive cross-sectional study conducted at Kintampo Municipality in Ghana recruited a total of 678 hypertensive patients. The questionnaire constituted information regarding sociodemographics, a five-Likert type HBM questionnaire, and lifestyle-related factors. The rate of noncompliance to AHT in this study was 58.6%. The mean age (SD) of the participants was 43.5 (±5.2) years and median duration of hypertension was 2 years. Overall, the five HBM constructs explained 31.7% of the variance in noncompliance to AHT with a prediction accuracy of 77.5%, after adjusting for age, gender, and duration of condition. Higher levels of perceived benefits of using medicine [aOR=0.55(0.36-0.82),p=0.0001] and cue to actions [aOR=0.59(0.38-0.90),p=0.0008] were significantly associated with reduced noncompliance while perceived susceptibility [aOR=3.05(2.20-6.25), p<0.0001], perceived barrier [aOR=2.14(1.56-2.92), p<0.0001], and perceived severity [aOR=4.20(2.93-6.00),p<0.0001] were significantly associated with increased noncompliance to AHT. Participant who had completed tertiary education [aOR=0.27(0.17-0.43), p<0.0001] and had regular source of income [aOR=0.52(0.38-0.71), p<0.0001] were less likely to be noncompliant. However, being a government employee [aOR=4.16(1.93-8.96), p=0.0002)] was significantly associated increased noncompliance to AHT. Noncompliance to AHT was considerably high and HBM is generally reliable in assessing treatment noncompliance in the Ghanaian hypertensive patients. The significant predictors of noncompliance to AHT were higher level of perceived barriers, susceptibility, and severity. Intervention programmes could be guided by the association of risk factors, HBM constructs with noncompliance to AHT in clinical practice.

Objectives. Sub-Saharan Africa (SSA) is observing an accelerating prevalence rate of type 2 diabetes mellitus (T2DM) influenced by gene-environment interaction of modifiable and nonmodifiable factors. We conducted a systematic review and meta-analysis on the heritability and genetic risk of T2DM in SSA. Methods. We reviewed all published articles on T2DM in SSA between January 2000 and December 2019 and available in PubMed, Scopus, and Web of Science. Studies that reported on the genetics and/or heritability of T2DM or indicators of glycaemia were included. Data extracted included the study design, records of family history, pattern and characteristics of inheritance, genetic determinants, and effects estimates. Results. The pattern and characteristics of T2DM heritability in SSA are preference for maternal aggregation, higher among first degree compared to second-degree relatives; early age-onset (<50 years), and inherited abnormalities of beta-cell function/mass. The overall prevalence of T2DM was 28.2% for the population with a positive family history (PFH) and 11.2% for the population with negative family history (NFH). The pooled odds ratio of the impact of PFH on T2DM was 3.29 (95% CI: 2.40-4.52). Overall, 28 polymorphisms in 17 genes have been investigated in relation with T2DM in SSA. Almost all studies used the candidate gene approach with most (45.8%) of genetic studies published between 2011 and 2015. Polymorphisms in ABCC8, Haptoglobin, KCNJ11, ACDC, ENPP1, TNF-α, and TCF7L2 were found to be associated with T2DM, with overlapping effect on specific cardiometabolic traits. Genome-wide studies identified ancestry-specific signals (AGMO-rs73284431, VT11A-rs17746147, and ZRANB3) and TCF7L2-rs7903146 as the only transferable genetic risk variants to SSA population. TCF7L2-rs7903146 polymorphism was investigated in multiple studies with consistent effects and low-moderate statistical heterogeneity. Effect sizes were modestly strong [oddsratio=6.17 (95% CI: 2.03-18.81), codominant model; 2.27 (95% CI: 1.50-3.44), additive model; 1.75 (95% CI: 1.18-2.59), recessive model]. Current evidence on the heritability and genetic markers of T2DM in SSA populations is limited and largely insufficient to reliably inform the genetic architecture of T2DM across SSA regions.

To assess the variability and predictability of adiponectin, leptin, resistin and their ratios in non-obese and obese women with anovulatory polycystic ovary syndrome (aPCOS). A total of 52 ovulatory controls (mean age = 31.63 ± 4.88 years, BMI = 25.33 ± 2.68 kg/m2); 54 non-obese (mean age = 32.11 ± 4.25 years, BMI = 25.72 ± 2.95 kg/m2) and 50 obese women with aPCOS (mean age = 33.64 ± 4.14 years, BMI = 39.19 ± 2.99 kg/m2) were recruited. The aPCOS group had lower adiponectin [13.0 (10.49–16.59) vs 18.42 (15.72–19.92) µg/ml, p < 0.0001], adiponectin: leptin ratio (A:L) [0.60 (0.35–0.88) vs 1.19 (0.92–1.37), p < 0.0001], and adiponectin: resistin ratio (A:R) [0.30 (0.21–0.43) vs 0.42 (0.32–0.62), p < 0.0001] but a higher leptin [20.02 (14.54–26.80) vs 16.17 (14.51–18.36) ng/ml, p < 0.0001] and leptin: resistin ratio (L:R) [0.53 (0.37–0.82) vs 0.40 (0.27–0.48), p < 0.0001] compared to the controls. The obese aPCOS group had lower adiponectin [11.04 (5.66–13.25) vs 14.18 (11.04–18.02), p < 0.0001 and 18.42 (15.72–19.92) µg/ml, p < 0.0001], A:L [0.36 (0.27–0.44) vs 0.78 (0.61–1.16), p < 0.0001 and 1.19 (0.92–1.37), p < 0.0001], and A:R [0.24 (0.17–0.38) vs 0.40 (0.23–0.58), p < 0.0001 and 0.42 (0.32–0.62), p < 0.0001] but a higher leptin [26.80 (14.28–32.09) vs 17.95 (14.86–21.26), p < 0.05 and 16.17 (14.51–18.36) ng/ml, p < 0.0001] and L:R [0.63 (0.46–1.03) vs 0.41 (0.30–0.61), p < 0.0001 and 0.40 (0.27–0.48), p < 0.0001] compared to the non-obese aPCOS and control group, respectively. A:L showed the best discriminatory power in predicting aPCOS (AUC = 0.83), followed by adiponectin alone (AUC = 0.79), L:R and leptin alone (both AUC = 0.69). Resistin alone had the poorest discriminatory power (AUC = 0.48).