This study aimed to identify latent classes based on major modifiable risk factors for coronary artery disease.
This was a secondary analysis using data from the electronic medical records of 2,022 patients, who were newly diagnosed with coronary artery disease at a university medical center, from January 2010 to December 2015. Data were analyzed using SPSS version 20.0 for descriptive analysis and Mplus version 7.4 for latent class analysis.
Four latent classes of risk factors for coronary artery disease were identified in the final model: ‘smoking-drinking’, ‘high-risk for dyslipidemia’, ‘high-risk for metabolic syndrome’, and ‘high-risk for diabetes and malnutrition’. The likelihood of these latent classes varied significantly based on socio-demographic characteristics, including age, gender, educational level, and occupation.
The results showed significant heterogeneity in the pattern of risk factors for coronary artery disease. These findings provide helpful data to develop intervention strategies for the effective prevention of coronary artery disease. Specific characteristics depending on the subpopulation should be considered during the development of interventions.
The purpose of this study was to compare the quality of sleep with the serum lipid profile in patients who have restless legs syndrome (RLS).
The data were obtained from 116 patients with RLS through questionnaires and blood sampling.
The results of this study showed correlations between lower quality of sleep and serum lipid profile (LDL Cholesterol) in patients with RLS (r=.19,
Patients with RLS have sleep disorders with lower quality of sleep and changes in the serum lipid profile for total cholesterol and LDL cholesterol. That is, patients with RLS have lower quality of sleep and dyslipidemia compared to persons without RLS. Further research is needed to monitor serum the lipid profile in early stage symptoms of midlife adult patients with RLS and especially older women.
In this study cardiovascular health status and health behavior of Korean women based on their household income were explored.
For this cross-sectional study, 91 women residing in the community were recruited to complete survey questionnaires and biophysical tests including blood pressure (BP), body mass index (BMI), body fat rate, waist circumference (WC), and blood chemistry tests.
Compared to non-low income women (NLIW), low income women (LIW) were more likely to be older, less educated, and jobless, and further more LIW were postmenopause and reported having been diagnosed with hypertension or hypercholesterolemia. Significant differences were found in systolic BP, triglyceride level, BMI, body fat rate, and WC between the groups. Two fifths of the LIW had indications for metabolic syndrome. Their 10-yr risk estimate of myocardioal infarction or coronary death demonstrated a higher probability than that of NLIW. Although these significant differences were due to age gap between the groups, advanced age is known to be one of the key characteristics of LIW as well as a non-modifiable risk factor.
Effective community programs for vulnerable women at risk of cardiovascular disease should be based on strategies targeting unhealthy behaviors and modifiable risk factors.