2 research outputs found

    Anthropometric, socio-demographic and biochemical risk factors of hypertension in Lagos, Nigeria

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    Background: Hypertension is a major modifiable risk factor for cardiovascular diseases and allcause death globally and in Africa. The prevalence of hypertension in Nigeria is 28.9%. In Nigeria, analytical studies to determine risk factors and potential biomarkers of hypertension are lacking. This study was conducted to determine lifestyle, anthropometric, sociodemographic, and biochemical risk factors associated with hypertension in Lagos, Nigeria.Method: This case–control study was conducted among 410 participants, aged 18–65 years. A well-structured questionnaire was used to collect data from cases and controls. Anthropometric and blood pressure measurements were taken. Blood samples were also collected for biochemical analysis. Logistic regression analysis was used to determine risk factors associated with hypertension. Data obtained were analyzed using SPSS version 25.0. P-value less than 0.05 was considered statistically significant.Result: In total, 205 hypertensive cases and 205 normotensive controls were recruited. The mean ± SD age of the participants was 39.25 ± 11.49 years. Overall, 180 (44%) of participants were female. Logistic regression analysis indicated that obesity (OR = 3.324, 95% CI = 1.693–6.527, P= 0.000), family history (OR = 2.861, 95% CI = 1.731–4.729, P= 0.000), hypercholesterolemia (OR = 2.940, 95% CI = 1.577–5.480, P= 0.001), insufficient fruits and vegetables intake (OR = 0.152, 95% CI = 0.085–0.273, P= 0.000), frequent intake of dietary salt (OR = 0.400, 95% CI = 0.198–0.810, P= 0.011), and smoking status (OR = 3.709, 95% CI = 1.061–12.964, P= 0.040) were significantly associated with hypertension.Conclusion: Population-based approaches to reduce exposure to hypertension risk factors are required for effective prevention and control of hypertension and cardiovascular diseases in Lagos, Nigeria

    COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

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