Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA.
PLoS One. 2013 Jul 17;8(7):e68741. doi: 10.1371/journal.pone.0068741. Print 2013.
While global measures of cardiovascular (CV) risk are used to guide prevention and treatment decisions, these estimates fail to account for the considerable interindividual variability in pre-clinical risk status. This study investigated heterogeneity in CV risk factor profiles and its association with demographic, genetic, and cognitive variables.
A latent profile analysis was applied to data from 727 recently postmenopausal women enrolled in the Kronos Early Estrogen Prevention Study (KEEPS). Women were cognitively healthy, within three years of their last menstrual period, and free of current or past CV disease. Education level, apolipoprotein E ε4 allele (APOE4), ethnicity, and age were modeled as predictors of latent class membership. The association between class membership, characterizing CV risk profiles, and performance on five cognitive factors was examined. A supervised random forest algorithm with a 10-fold cross-validation estimator was used to test accuracy of CV risk classification.
The best-fitting model generated two distinct phenotypic classes of CV risk 62% of women were "low-risk" and 38% "high-risk". Women classified as low-risk outperformed high-risk women on language and mental flexibility tasks (p = 0.008) and a global measure of cognition (p = 0.029). Women with a college degree or above were more likely to be in the low-risk class (OR = 1.595, p = 0.044). Older age and a Hispanic ethnicity increased the probability of being at high-risk (OR = 1.140, p = 0.002; OR = 2.622, p = 0.012; respectively). The prevalence rate of APOE-ε4 was higher in the high-risk class compared with rates in the low-risk class.
Among recently menopausal women, significant heterogeneity in CV risk is associated with education level, age, ethnicity, and genetic indicators. The model-based latent classes were also associated with cognitive function. These differences may point to phenotypes for CV disease risk. Evaluating the evolution of phenotypes could in turn clarify preclinical disease, and screening and preventive strategies. ClinicalTrials.gov NCT00154180.
虽然全球心血管 (CV) 风险评估用于指导预防和治疗决策，但这些评估并未考虑到临床前风险状况的个体间差异。本研究调查了 CV 危险因素谱的异质性及其与人口统计学、遗传和认知变量的关系。
对参加 Kronos 早期雌激素预防研究 (KEEPS) 的 727 名最近绝经后的女性的数据进行了潜在剖面分析。这些女性认知健康，处于绝经后三年内，且无当前或既往 CV 疾病。教育水平、载脂蛋白 E ε4 等位基因 (APOE4)、种族和年龄被建模为潜在类别成员的预测因子。检查了类别的归属情况，即 CV 风险谱的特征，以及与五个认知因素的表现之间的关系。使用具有 10 折交叉验证估计器的有监督随机森林算法来测试 CV 风险分类的准确性。
最佳拟合模型生成了两个不同的 CV 风险表型类别，62%的女性为“低风险”，38%为“高风险”。低风险组的女性在语言和心理灵活性任务上的表现优于高风险组（p=0.008），在整体认知测试中表现也优于高风险组（p=0.029）。受过大学或以上教育的女性更有可能属于低风险类别（OR=1.595，p=0.044）。年龄较大和西班牙裔增加了处于高风险的可能性（OR=1.140，p=0.002；OR=2.622，p=0.012；分别）。高风险组的 APOE-ε4 患病率高于低风险组。
在最近绝经的女性中，CV 风险的显著异质性与教育水平、年龄、种族和遗传指标有关。基于模型的潜在类别也与认知功能有关。这些差异可能指向 CV 疾病风险的表型。评估表型的演变反过来可以阐明临床前疾病，并明确筛查和预防策略。ClinicalTrials.gov NCT00154180。
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