Background:While sex hormones are essential for normal cognitive health, those individuals with greater endocrine dyscrasia around menopause and with andropause are more likely to develop cognitive loss and Alzheimer’s disease (AD). Objective:To assess whether circulating sex hormones may provide an etiologically significant, surrogate biomarker, for cognitive decline. Methods:Plasma (n = 152) and serum (n = 107) samples from age- and gender-matched AD and control subjects from the Wisconsin Alzheimer’s Disease Research Center (ADRC) were analyzed for11 steroids and follicle-stimulating hormone. Logistic regression (LR), correlation analyses, and recursive partitioning (RP) were used to examine the interactions of hormones and hormone ratios and their association with AD. Models generated were then tested on an additional 43 ADRC samples. Results:The wide variation and substantial overlap in the concentrations of all circulating sex steroids across control and AD groups precluded their use for predicting AD. Classification tree analyses (RP) revealed interactions among single hormones and hormone ratios that associated with AD status, the most predictive including only the hormone ratios identified by LR. The strongest associations were observed between cortisol, cortisone, and androstenedione with AD, with contributions from progesterone and 17β-estradiol. Utilizing this model, we correctly predicted 81% of AD test cases and 64% of control test cases. Conclusion:We have developed a diagnostic model for AD, the Wisconsin Hormone Algorithm Test for Cognition (WHAT-Cog), that utilizes classification tree analyses of hormone ratios. Further refinement of this technology could provide a quick and cheap diagnostic method for screening those with AD.

中文翻译：

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利用循环性激素比率预测阿尔茨海默氏病的分类模型的开发。

背景：虽然性激素对于正常的认知健康必不可少，但绝经前后内分泌失调程度更大，更年期更年期的人更容易出现认知障碍和阿尔茨海默氏病（AD）。目的：评估循环性激素是否可为认知功能减退提供病因学上重要的替代生物标志物。方法：对来自威斯康星州阿尔茨海默氏病研究中心（ADRC）的年龄和性别相匹配的AD的血浆（n = 152）和血清（n = 107）样本进行了分析，分析了11种类固醇和促卵泡激素。使用逻辑回归（LR），相关分析和递归划分（RP）来检查激素和激素比例之间的相互作用以及它们与AD的关系。然后，在另外的43个ADRC样本上测试生成的模型。结果：对照组和AD组中所有循环性类固醇的浓度差异很大且存在实质性重叠，因此无法用于预测AD。分类树分析（RP）显示与AD状态相关的单一激素和激素比率之间的相互作用，最具预测性的仅包括由LR确定的激素比率。观察到皮质醇，可的松和雄烯二酮与AD之间的最强关联，其中孕酮和17β-雌二醇的贡献最大。利用该模型，我们正确地预测了81％的AD测试用例和64％的对照测试用例。结论：我们开发了一种针对AD的诊断模型，即威斯康星州认知激素算法测试（WHAT-Cog），该模型利用激素比率的分类树分析方法。