인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Purpose: The prevalence of metabolic syndrome (MetS) generally varies depending on its diagnostic definition, and many different definitions inevitably lead to substantial confusion and lack of comparability between studies. Despite extensive research, there is still no gold standard for the definition of MetS, which continues to be a matter of debate. In this study, we investigate whether and to what extent its individual components are related to the risk of cardiovascular disease (CVD) in Korean population. Materials and Methods: We used data from the 2005 Korea National Health and Nutrition Examination Survey, which is a nationally representative survey of the noninstitutionalized civilian population. The study sample consisted of 1,406 Korean adults (587 men, 819 women) who were diagnosed with MetS based on the revised National Cholesterol Education Program (NCEP) criteria. Central obesity is defined as a waist circumference cutoff point reported in Asia-Pacific criteria for obesity based on waist circumference by the World Health Organization. CVD was defined as presence of stroke, myocardial infarction, or angina pectoris on a medical history questionnaire. Results: The CVD prevalence among the subjects was 6.8% for men and 8.6% for women. Besides age, the components of MetS showing a significant difference in the number of CVD events were high fasting glucose (FG) in men and high blood pressure (BP) and high FG in women. After adjusting for gender and age, high FG was shown to yield a significant difference (odds ratio: unadjusted 2.08, adjusted 1.81), alone among all MetS components. However, after adjusting for only age, no significant difference was found. Conclusion: Fasting glucose level is the highest predicting factor for CVD in Korean patients with MetS based on the revised NECP definition.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.