Characterization of vascular disease risk in postmenopausal women and its association with cognitive performance.
Characterization of vascular disease risk in postmenopausal women and its association with cognitive performance.
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DOI：
10.1371/journal.pone.0068741
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Asthana S
中科院分区：
文献类型：
--
作者：
Dowling NM;Gleason CE;Manson JE;Hodis HN;Miller VM;Brinton EA;Neal-Perry G;Santoro MN;Cedars M;Lobo R;Merriam GR;Wharton W;Naftolin F;Taylor H;Harman SM;Asthana S
虽然心血管（CV）风险的全球衡量标准用于指导预防和治疗决策，但这些估计未能解释临床前风险状态的巨大个体差异。本研究调查了心血管危险因素概况的异质性及其与人口、遗传和认知变量的关联。对参加 Kronos 早期雌激素预防研究 (KEEPS) 的 727 名最近绝经后妇女的数据进行了潜在概况分析。女性认知健康，在末次月经后三年内，并且当前或过去没有心血管疾病。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阶级成员资格的预测因素。研究了班级成员身份、心血管风险概况特征和五个认知因素表现之间的关联。使用具有 10 倍交叉验证估计器的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种不同的 CV 风险表型类别：62% 的女性为“低风险”，38% 为“高风险”。被归类为低风险的女性在语言和心理灵活性任务 (p = 0.008) 和全球认知测量 (p = 0.029) 方面表现优于高风险女性。具有大学及以上学历的女性更有可能属于低风险阶层（OR = 1.595，p = 0.044）。年龄较大和西班牙裔种族增加了处于高风险的概率（分别为 OR = 1.140，p = 0.002；OR = 2.622，p = 0.012）。与低风险组相比，高风险组中 APOE-ε4 的患病率较高。在最近绝经的女性中，心血管风险的显着异质性与教育水平、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相关。这些差异可能表明心血管疾病风险的表型。评估表型的进化反过来可以阐明临床前疾病以及筛查和预防策略。 ClinicalTrials.gov NCT00154180
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
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39.2
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