Sleeping duration, physical activity, alcohol drinking and other risk factors as potential attributes of metabolic syndrome in adults in Ethiopia: A hospital-based cross-sectional study.

Background: Available evidence showed that metabolic syndrome in the adult population is persistently elevated due to nutrition transition, genetic predisposition, individual-related lifestyle factors, and other environmental risks. However, in developing nations, the burden and scientific evidence on the pattern, and risk exposures for the development of the metabolic syndrome were not adequately investigated. Thus, the study aimed to measure the prevalence of metabolic syndrome and to identify specific risk factors among adult populations who visited Dessie Comprehensive Specialized Hospital, Ethiopia.

Methods: A hospital-based cross-sectional study was conducted among randomly selected 419 adults attending Dessie Comprehensive Specialized Hospital from January 25 to February 29, 2020. We used the WHO STEP-wise approach for non-communicable disease surveillance to assess participants' disease condition. Metabolic syndrome was measured using the harmonized criteria recommended by the International Diabetes Federation Task Force in 2009. Data were explored for missing values, outliers and multicollinearity before presenting the summary statistics and regression results. Multivariable logistic regression was used to disentangle statistically significant predictors of metabolic syndrome expressed using an odds ratio with a 95% of uncertainty interval. All statistical tests were managed using SPSS version 26. A non-linear dose-response analysis was performed to show the relationships between metabolic syndromes with potential risk factors.

Results: The overall prevalence of metabolic syndrome among adults was 35.0% (95% CI, (30.5, 39.8)). Women were more affected than men (i.e. 40.3% vs 29.4%). After adjusting for other variables, being female [OR = 1.85; 95% CI (1.01, 3.38)], urban residence [OR = 1.94; 95% CI (1.08, 3.24)], increased age [OR = 18.23; 95% CI (6.66, 49.84)], shorter sleeping durations [OR = 4.62; 95% CI (1.02, 20.98)], sedentary behaviour [OR = 4.05; 95% CI (1.80, 9.11)], obesity [OR = 3.14; 95% CI (1.20, 8.18)] and alcohol drinking [OR = 2.85; 95% CI (1.27,6.39)] were positively associated with the adult metabolic syndrome. Whilst have no formal education [OR = 0.30; 95% CI (0.12, 0.74)] was negatively associated with metabolic syndrome.

Conclusions: The prevalence of adult metabolic syndrome is found to be high. Metabolic syndrome has linear relationships with BMI, physical activity, sleep duration, and level of education. The demographic and behavioural factors are strongly related with the risk of metabolic syndrome. Since most of the factors are modifiable, there should be urgent large-scale community intervention programs focusing on increased physical activity, healthy sleep, weight management, minimize behavioural risk factors, and healthier food interventions targeting a lifecycle approach. The existing policy should be evaluated whether due attention has given to prevention strategies of NCDs.