ArticleLipids in health and disease2018
Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database.
Article in Lipids in health and disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Temporal Validation Study of Diagnostic Prediction Models for the Screening of Elevated Low-Density and Non-High-Density Lipoprotein Cholesterol.Journal of clinical medicine · 2025Article
- Diagnostic prediction model for screening of elevated low-density and non-high-density lipoproteins in young Thai adults between 20 and 40 years of age.BMJ health & care informatics · 2025Article
- The Association between Hypertriglyceridemia and Colorectal Cancer: A Long-Term Community Cohort Study in Taiwan.International journal of environmental research and public health · 2022Article
- Prediction model for the onset risk of impaired fasting glucose: a 10-year longitudinal retrospective cohort health check-up study.BMC endocrine disorders · 2021Article
- A cohort study on risk factors of high-density lipoprotein cholesterol hypolipidemia among urban Chinese adults.Lipids in health and disease · 2021Article
- Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study.Lipids in health and disease · 2021Article
- Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers.Frontiers in bioengineering and biotechnology · 2020Article
- Metabolic-related markers and inflammatory factors as predictors of dyslipidemia among urban Han Chinese adults.Lipids in health and disease · 2019Article
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Authors and funding
7 authors.
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Abstract
objectiveThis study aimed to provide an epidemiological model to evaluate the risk of developing dyslipidaemia within 5 years in the Taiwanese population.
methodsA cohort of 11,345 subjects aged 35-74 years and was non-dyslipidaemia in the initial year 1996 and followed in 1997-2006 to derive a risk score that could predict the occurrence of dyslipidaemia. Multivariate logistic regression was used to derive the risk functions using the check-up centre of the overall cohort. Rules based on these risk functions were evaluated in the remaining three centres as the testing cohort. We evaluated the predictability of the model using the area under the receiver operating characteristic (ROC) curve (AUC) to confirm its diagnostic property on the testing sample. We also established the degrees of risk based on the cut-off points of these probabilities after transforming them into a normal distribution by log transformation.
resultsThe incidence of dyslipidaemia over the 5-year period was 19.1%. The final multivariable logistic regression model includes the following six risk factors: gender, history of diabetes, triglyceride level, HDL-C (high-density lipoprotein cholesterol), LDL-C (low-density lipoprotein cholesterol) and BMI (body mass index). The ROC AUC was 0.709 (95% CI: 0.693-0.725), which could predict the development of dyslipidaemia within 5 years.
conclusionThis model can help individuals assess the risk of dyslipidaemia and guide group surveillance in the community.
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