Different Patterns in Ranking of Risk Factors for the Onset Age of Acute Myocardial Infarction between Urban and Rural Areas in Eastern Taiwan.
Hsiu-Ju HuangChih-Wei LeeTse-Hsi LiTsung-Cheng HsiehPublished in: International journal of environmental research and public health (2021)
This cross-sectional study aimed to investigate the difference in ranking of risk factors of onset age of acute myocardial infarction (AMI) between urban and rural areas in Eastern Taiwan. Data from 2013 initial onset of AMI patients living in the urban areas (n = 1060) and rural areas (n = 953) from January 2000 to December 2015, including onset age, and conventional risk factors including sex, smoking, diabetes, hypertension, dyslipidemia, and body mass index (BMI). The results of multiple linear regressions analysis showed smoking, obesity, and dyslipidemia were early-onset reversible risk factors of AMI in both areas. The ranking of impacts of them on the age from high to low was obesity (β = -6.7), smoking (β = -6.1), and dyslipidemia (β = -4.8) in the urban areas, while it was smoking (β = -8.5), obesity (β= -7.8), and dyslipidemia (β = -5.1) in the rural areas. Furthermore, the average onset ages for the patients who smoke, are obese, and have dyslipidemia simultaneously was significantly earlier than for patients with none of these comorbidities in both urban (13.6 years) and rural (14.9 years) areas. The findings of this study suggest that the different prevention strategies for AMI should be implemented in urban and rural areas.
Keyphrases
- acute myocardial infarction
- risk factors
- early onset
- type diabetes
- body mass index
- metabolic syndrome
- weight loss
- weight gain
- percutaneous coronary intervention
- insulin resistance
- smoking cessation
- left ventricular
- south africa
- end stage renal disease
- cardiovascular disease
- blood pressure
- ejection fraction
- newly diagnosed
- high fat diet induced
- bariatric surgery
- adipose tissue
- heart failure
- peritoneal dialysis
- chronic kidney disease
- coronary artery disease
- skeletal muscle
- big data
- atrial fibrillation
- deep learning