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4 "Dyslipidemia"
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Research Paper
Incidence and Risk Factors of Dyslipidemia after Menopause
Jeong, Ihn Sook , Yun, Hae Sun , Kim, Myo Sung , Hwang, Youn Sun
J Korean Acad Nurs 2022;52(2):214-227.   Published online April 30, 2022
DOI: https://doi.org/10.4040/jkan.21188
AbstractAbstract PDF
Purpose
This study was aimed at investigating the incidence and risk factors of dyslipidemia in menopausal women using a Korean community-based longitudinal study.
Methods
The subjects were 245 postmenopausal women without dyslipidemia who had participated in the Ansan-Ansung cohort study from 2001~2002 (baseline) to 2015~2016 (seventh follow-up visit). The dyslipidemia incidence was measured as incidence proportion (%) and incidence rate per 100 person-years. The predictors of developing dyslipidemia were analyzed with Cox’s proportional hazard model.
Results
The incidence of new dyslipidemia during the follow-up period was 78.4% (192 patients), and 11.9 per 100 person-years. Mean duration from menopause to developing dyslipidemia was 5.3 years in new dyslipidemia cases. The triglyceride/high density lipoprotein (TG/HDL-C) ratio at baseline (hazard ratio = 2.20; 95% confidence interval = 1.39~3.48) was independently associated with developing dyslipidemia.
Conclusion
Dyslipidemia occurs frequently in postmenopausal women, principally within five years after menopause. Therefore, steps must be taken to prevent dyslipidemia immediately after menopause, particularly in women with a high TG/HDL-C ratio at the start of menopause.
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Original Articles
Identifying Latent Classes of Risk Factors for Coronary Artery Disease
Eunsil Ju, JiSun Choi
J Korean Acad Nurs 2017;47(6):817-827.   Published online January 15, 2017
DOI: https://doi.org/10.4040/jkan.2017.47.6.817
AbstractAbstract PDF
Abstract Purpose

This study aimed to identify latent classes based on major modifiable risk factors for coronary artery disease.

Methods

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.

Results

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.

Conclusion

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.

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Quality of Sleep and Serum Lipid Profile in Patients with Restless Legs Syndrome
Yeon-Gyung Bak, Hyoung-Sook Park
J Korean Acad Nurs 2011;41(3):344-353.   Published online June 13, 2011
DOI: https://doi.org/10.4040/jkan.2011.41.3.344
AbstractAbstract PDF
Purpose

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).

Methods

The data were obtained from 116 patients with RLS through questionnaires and blood sampling.

Results

The results of this study showed correlations between lower quality of sleep and serum lipid profile (LDL Cholesterol) in patients with RLS (r=.19, p=.040). There were correlations for scores of quality of sleep from the, Pittsburgh Sleep Quality Index (PSQI) sub-region between lower subjective sleep quality and serum lipid profile (LDL Cholesterol) (r=.20, p=.026), between fewer hours of sleep duration and serum lipid profile (Total Cholesterol) (r=-.21, p=.024), and, between higher daytime dysfunction and serum lipid profile (LDL Cholesterol) (r=.42, p<.001) of patients with RLS.

Conclusion

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.

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Comparison of Cardiovascular Health Status and Health Behaviors in Korean Women based on Household Income
Young-Joo Park, Nah-Mee Shin, Ji-Won Yoon, Jiwon Choi, Sook-Ja Lee
J Korean Acad Nurs 2010;40(6):831-843.   Published online December 31, 2010
DOI: https://doi.org/10.4040/jkan.2010.40.6.831
AbstractAbstract PDF
Purpose

In this study cardiovascular health status and health behavior of Korean women based on their household income were explored.

Methods

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.

Results

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.

Conclusion

Effective community programs for vulnerable women at risk of cardiovascular disease should be based on strategies targeting unhealthy behaviors and modifiable risk factors.

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