The Prevalence of Metabolic Syndrome and Its Components in Firefighters: A Systematic Review and Meta-Analysis.
Ashley BeckettJake Riley ScottAngel Marie ChaterLouise FerrandinoJeffrey William Frederick AldousPublished in: International journal of environmental research and public health (2023)
Previous studies consistently report a high prevalence of cardiovascular disease (CVD) risk factors among firefighters. However, the clustering of CVD risk factors, defined as metabolic syndrome (MetSyn), has received little attention by comparison. Therefore, the aim of this study was to estimate the pooled prevalence of MetSyn among firefighters. Using combinations of free text for 'firefighter' and 'metabolic syndrome', databases were searched for eligible studies. Meta-analyses calculated weighted pooled prevalence estimates with 95% confidence intervals (CI) for MetSyn, its components and overweight/obesity. Univariate meta-regression was performed to explore sources of heterogeneity. Of 1440 articles screened, 25 studies were included in the final analysis. The pooled prevalence of MetSyn in 31,309 firefighters was 22.3% (95% CI: 17.7-27.0%). The prevalences of MetSyn components were hypertension: 39.1%; abdominal obesity: 37.9%; hypertriglyceridemia: 30.2%; dyslipidemia: 30.1%; and hyperglycemia: 21.1%. Overweight and obesity prevalence rates in firefighters were 44.1% and 35.6%, respectively. Meta-regression revealed that decreased risk of bias (RoB) score and increased body mass index (BMI) were positively associated with an increase in MetSyn prevalence. Since one in five firefighters may meet the criteria for MetSyn, novel interventions should be explored to both prevent MetSyn and reduce the onset of CVD risk factors.
Keyphrases
- risk factors
- metabolic syndrome
- body mass index
- cardiovascular disease
- insulin resistance
- weight gain
- type diabetes
- systematic review
- cardiovascular risk factors
- single cell
- blood pressure
- physical activity
- uric acid
- magnetic resonance imaging
- clinical trial
- skeletal muscle
- meta analyses
- deep learning
- study protocol
- coronary artery disease
- contrast enhanced
- artificial intelligence