School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen, Beijing, 100069, China.
Beijing Municipal Key Laboratory of Clinical Epidemiology, 10 Xitoutiao, Youanmen, Beijing, 100069, China.
Lipids Health Dis. 2018 Nov 17;17(1):259. doi: 10.1186/s12944-018-0906-2.
This study aimed to provide an epidemiological model to evaluate the risk of developing dyslipidaemia within 5 years in the Taiwanese population.
A 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.
The 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.
This model can help individuals assess the risk of dyslipidaemia and guide group surveillance in the community.
本研究旨在提供一种流行病学模型，以评估台湾人群在 5 年内发生血脂异常的风险。
在 1996 年初始年份，对 11345 名年龄在 35-74 岁之间且无血脂异常的受试者进行了队列研究，并在 1997-2006 年进行随访，以得出一个风险评分，该评分可预测血脂异常的发生。使用整个队列的体检中心，多变量逻辑回归用于从风险函数中得出风险函数。基于这些风险函数的规则在其余三个中心作为测试队列进行评估。我们使用接收者操作特征曲线（ROC）下面积（AUC）评估模型的可预测性，以确认其在测试样本上的诊断性能。我们还根据这些概率的截断点建立了风险程度，这些概率通过对数转换转化为正态分布。
在 5 年内，血脂异常的发生率为 19.1%。最终的多变量逻辑回归模型包括以下六个危险因素：性别、糖尿病史、甘油三酯水平、高密度脂蛋白胆固醇（HDL-C）、低密度脂蛋白胆固醇（LDL-C）和体重指数（BMI）。ROC AUC 为 0.709（95%CI：0.693-0.725），可预测 5 年内血脂异常的发生。
该模型可以帮助个体评估血脂异常的风险，并指导社区中的群体监测。
