Title of article :
The Risk of Lipid Profile and Obesity for Hypertension Incidence by Region (Rural, Middle Town), Age and Sex (4-year follow-up study)
Author/Authors :
H.M. Kim، نويسنده , , S.E. Joo، نويسنده , , J.W. Min، نويسنده , , H.S. Min، نويسنده , , S.J. Park، نويسنده , , L.Y. Park، نويسنده , , Y.C. Park، نويسنده , , C. Park، نويسنده ,
Abstract :
Purpose
The purpose of this study was to determine the risk of lipid profile and obesity for hypertension incidence by region, age (40’, 50’, 60’), and sex.
Methods
From the community based cohort study, started from March 2001, we classified subjects into 5 groups using the diagnostic standard of JNC-7 report, and then calculated the prevalence and incidence of hypertension (HTN). Ansung site is a rural region and the Ansan site is a middle town region in Korea. Lipid profile was categorized using the ATPIII and the degree of obesity was categorized using the Korean standard. Lipid profile and obesity were analyzed as risk factors of HTN by Chi-square tests and multiple logistic regression analyses.
Results
The prevalence and incidence of HTN was higher in Ansung than at Ansan. In Ansung, total cholesterol (p-value=0.0327), BMI (p-value=0.0139), and abdominal obesity (p-value=0.0012) were significantly related to hypertension incidence. Especially, among male subjects, BMI (p-value=0.0294) and abdominal obesity (p-value=0.0016) were significantly associated with HTN, and among female subjects, LDL (p-value=0.0182) and abdominal obesity (p-value=0.0289) were significantly associated with HTN among aged fifties. However, Ansan was not related with any risk factors among aged fifties. From logistic regression analysis, triglyceride and HDL were identified as the risk factors for HTN incidence. Triglyceride, HDL, and abdominal obesity were identified as risk factors at Ansung; wheareas, only BMI was identified as risk factor at Ansan.
Conclusion
We found that obesity was associated with HTN in Ansung than Ansan, especially among aged fifties. Further studies are needed to determine the effect of other risk factors such as education level, income level, and diet patterns