医学
脂类学
疾病
内科学
生物标志物
集合(抽象数据类型)
阿尔茨海默病
诊断生物标志物
生物信息学
肿瘤科
诊断准确性
生物
遗传学
计算机科学
程序设计语言
作者
Jie Kawakami,Stephen Piccolo,John S. K. Kauwe,Steven W. Graves
标识
DOI:10.2217/bmm-2022-0462
摘要
Background: Alzheimer's disease (AD) cannot currently be diagnosed by a blood test. One reason may be gender differences. Another may be the statistical methods used. The authors evaluate these possibilities. Objective: The authors applied serum lipidomics to find AD biomarkers in men and women. They hypothesized that AD biomarkers would differ between genders and that machine-learning algorithms would improve diagnostic performance. Methods: Serum lipids were analyzed by mass spectrometry for a training set of AD cases and controls and in a blinded test set. Statistical analyses considered gender differences. Results: Lipids best classifying AD subjects differed significantly between men and women. Robust statistical algorithms did not improve diagnostic performance. Conclusion: Poor performance of AD biomarkers appears to be due primarily to inherent variability in AD patients.
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