ObjectiveThe objectives of this study were to develop a coronary heart disease (CHD) risk model among the Korean Heart Study (KHS) population and compare it with the Framingham CHD risk score.DesignA prospective cohort study within a national insurance system.Setting18 health promotion centres nationwide between 1996 and 2001 in Korea.Participants268 315 Koreans between the ages of 30 and 74 years without CHD at baseline.Outcome measureNon-fatal or fatal CHD events between 1997 and 2011. During an 11.6-year median follow-up, 2596 CHD events (1903 non-fatal and 693 fatal) occurred in the cohort. The optimal CHD model was created by adding high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol and triglycerides to the basic CHD model, evaluating using the area under the receiver operating characteristic curve (ROC) and continuous net reclassification index (NRI).ResultsThe optimal CHD models for men and women included HDL-cholesterol (NRI=0.284) and triglycerides (NRI=0.207) from the basic CHD model, respectively. The discrimination using the CHD model in the Korean cohort was high: the areas under ROC were 0.764 (95% CI 0.752 to 0.774) for men and 0.815 (95% CI 0.795 to 0.835) for women. The Framingham risk function predicted 3–6 times as many CHD events than observed. Recalibration of the Framingham function using the mean values of risk factors and mean CHD incidence rates of the KHS cohort substantially improved the performance of the Framingham functions in the KHS cohort.ConclusionsThe present study provides the first evidence that the Framingham risk function overestimates the risk of CHD in the Korean population where CHD incidence is low. The Korean CHD risk model is well-calculated alternations which can be used to predict an individual's risk of CHD and provides a useful guide to identify the groups at high risk for CHD among Koreans.
This study was to assess the relation of thyroid dysfunction to metabolic syndrome (MetS) at an earlier stage in Korean population. Metabolic parameters such as body composition, blood pressure (BP), fasting glucose, total cholesterol, triglyceride (TG), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C), thyroid-stimulating hormone (TSH) and free thyroxine 4 (fT4) were measured. During a mean follow-up of 3 yr, 5,998 Koreans ages over 18 yr were assessed. There were 694 cases of MetS at follow-up. The mean age of the subjects was 45.6 ± 9.5 yr. Mean level of TSH was 2.02 ± 1.50 mIU/L, mean level of fT4 was 1.23 ± 0.20 ρM/L. At baseline, TSH levels and fT4 levels were associated to waist circumference, BP, glucose and lipids in the subjects. Increase in systolic blood pressure, diastolic blood pressure (DBP), total cholesterol and TG were significantly associated with changes in TSH levels after adjustment. Changes in DBP, TG, HDL-C and fasting glucose were significantly associated with changes in fT4 levels after adjustment. Increase in TSH levels even after further controlling for baseline TSH level predicted the MetS over the study period. In conclusion, there is a relationship between thyroid function and cardiovascular risk factors, such as BP, total cholesterol, TG, HDL-C and fasting glucose. Also, higher levels of TSH may predict the MetS in Korean.
Menopausal symptoms and fatigue in middle-aged Korean women improved after 8 weeks of HPE treatment, whereas risk factors for cardiovascular disease did not change during the study period.
Deficiency of minerals causes functional abnormality of enzymes, frequently resulting in metabolic disturbance. We investigated possible relationship between minerals and metabolic syndrome by analysis of hair tissue minerals. We selected 848 subjects older than 20 years of age at Ajou University Hospital from May 2004 to February 2007. We excluded the subjects who had cancers, steroid and thyroid medication, and incomplete record from the study. Finally, 343 subjects were eligible. We performed cross-sectional analysis for the relationship between minerals and metabolic syndrome. The contents of calcium, magnesium, and copper in the metabolic syndrome group were significantly lower than those of the normal group, whereas the amounts of sodium, potassium, and mercury in the metabolic syndrome group were significantly higher than those of the normal group. By dividing the subjects into quartile with the level of calcium, magnesium, and mercury concentrations, we carried out logistic regression analysis to study the subjects and found that the subjects in the third quartile of calcium and magnesium concentrations had significantly lower odds ratio (OR) of the metabolic syndrome compared with that of the lowest quartile group [OR = 0.30, confidence interval (CI) = 0.10-0.89; OR = 0.189, CI = 0.063-0.566] and that the subjects in the highest mercury quartile had significantly higher OR of the metabolic syndrome compared with that of the lowest mercury quartile group (OR = 7.35, CI = 1.73-31.1). As part of the metabolic syndrome, the optimal calcium and magnesium concentrations in hair tissue may reflect decreased risk of metabolic syndrome, whereas high mercury concentration in hair tissue may indicate increased risk of metabolic syndrome.
Data from the present sample, based on data linkage, show close agreement with South Korea-wide surveys (for risk factor prevalence) and the extant literature (for risk factor associations). These findings gives confidence in future results anticipated from this cohort study of east Asians - a group that has been traditionally under-researched.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.