Olfactory Phenotypes Differentiate Cognitively Unimpaired Seniors from Alzheimer's Disease and Mild Cognitive Impairment: A Combined Machine Learning and Traditional Statistical Approach
Olfactory Phenotypes Differentiate Cognitively Unimpaired Seniors from Alzheimer's Disease and Mild Cognitive Impairment: A Combined Machine Learning and Traditional Statistical Approach
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DOI：
10.3233/jad-210175
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发表时间：
2021-01-01
影响因子：
4
通讯作者：
Villwock, Jennifer A.
中科院分区：
文献类型：
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作者：
Li, Jennifer;Bur, Andres M.;Villwock, Jennifer A.
背景：嗅觉功能障碍(OD)是阿尔茨海默病(AD)的早期症状。目的：研究客观OD作为一种非侵入性生物标志物，准确地将受试者分为认知正常(CU)、轻度认知障碍(MCI)和AD。方法：MCI(n=24)和AD(n=24)患者和CU(n=33)对照组完成了两项客观嗅觉测试(负担得起、快速嗅觉测量阵列-气味；Sniffin‘Sticks Screen 12 Test-SST12)。还获得了人口统计学和主观鼻腔及嗅觉症状信息。分析利用传统统计学和机器学习来确定嗅觉变量和变量组合，这些变量对于区分正常和疾病状态非常重要。结果：检测后无法正确识别气味是MCI/AD的一个特征。在区分MCI和AD方面，香气优于SST12。在丁香气味上的表现在所有三组之间都有显著差异。香气回归模型得到6种香气，其ROC值为0.890(p<；0.001)。考虑气味嗅觉数据的随机森林模型机器学习算法成功地预测了MCI与AD疾病状态。仅考虑香气数据，机器学习算法的准确率为87.5%(95%CI为0.4735，0.9968)。灵敏度为100%，特异度为75%，ROC为0.875。当考虑气味、受试者人口学和主观数据时，ROC的AUC增加到0.9375。结论：OD能区分MCI和AD，并能准确地预测MCI和AD。利用OD数据可能会有意义地指导管理和研究决策。
Background: Olfactory dysfunction (OD) is an early symptom of Alzheimer's disease (AD). However, olfactory testing is not commonly performed to test OD in the setting of AD.Objective: This work investigates objective OD as a non-invasive biomarker for accurately classifying subjects as cognitively unimpaired (CU), mild cognitive impairment (MCI), and AD.Methods: Patients with MCI (n = 24) and AD (n = 24), and CU (n = 33) controls completed two objective tests of olfaction (Affordable, Rapid, Olfactory Measurement Array -AROMA; Sniffin' Sticks Screening 12 Test -SST12). Demographic and subjective sinonasal and olfaction symptom information was also obtained. Analyses utilized traditional statistics and machine learning to determine olfactory variables, and combinations of variables, of importance for differentiating normal and disease states.Results: Inability to correctly identify a scent after detection was a hallmark of MCI/AD. AROMA was superior to SST12 for differentiating MCI from AD. Performance on the clove scent was significantly different between all three groups. AROMA regression modeling yielded six scents with AUC of the ROC of 0.890 (p < 0.001). Random forest model machine learning algorithms considering AROMA olfactory data successfully predicted MCI versus AD disease state. Considering only AROMA data, machine learning algorithms were 87.5%accurate (95%CI 0.4735, 0.9968). Sensitivity and specificity were 100%and 75%, respectively with ROC of 0.875. When considering AROMA and subject demographic and subjective data, the AUC of the ROC increased to 0.9375.Conclusion: OD differentiates CUs from those with MCI and AD and can accurately predict MCI versus AD. Leveraging OD data may meaningfully guide management and research decisions.