Title

Development of classification models for the prediction of Alzheimer's disease utilizing circulating sex hormone ratios

Document Type

Journal Article

Publication Title

Journal of Alzheimer's disease

Volume

76

Issue

3

First Page

1029

Last Page

1046

PubMed ID

32623397

Publisher

IOS Press

School

School of Medical and Health Sciences

Comments

Hayashi, K., Gonzales, T. K., Kapoor, A., Ziegler, T. E., Meethal, S. V., & Atwood, C. S. (2020). Development of Classification Models for the Prediction of Alzheimer’s Disease Utilizing Circulating Sex Hormone Ratios. Journal of Alzheimer's Disease, 76(3), 1029-1046. https://doi.org/10.3233/JAD-200418

Abstract

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 for 11 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.

DOI

10.3233/JAD-200418

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