Olfactory Phenotypes as Non-Invasive Biomarkers for Alzheimer's Disease: A Machine Learning Approach

项目摘要/摘要 目前，全球有5000万人患有阿尔茨海默病(AD)。95%的人口 65岁以上的人担心他们患痴呆症的风险，80%的人对痴呆症筛查感兴趣。有一个 迫切需要可用于识别ADRD上的生物标记物的可获得且经济高效的生物标记物 连续体--包括无症状阶段--不仅在研究和专业护理中心，而且在 社区和初级保健环境也是如此。这一信息可以极大地改善转介 早期临床试验登记，专科评估的分类过程，以及综合护理计划。 所使用的方法必须适用于护理点、社区或居家部署，同时保持 准确度和预测价值。 嗅觉(嗅觉)障碍(OD)与机器学习(ML)算法相结合，是一种 ADRD有希望的非侵入性生物标志物。我们之前已经证明了经济实惠的可靠性 快速嗅觉测量阵列(AROM)客观测量OD并对嗅觉表型进行分类 (对各种气味和多种浓度的正确和不正确反应的模式)。香气用途 精油是气味分子的复杂混合物，可能更能反映“现实世界”的嗅觉。 比大多数其他测试中使用的单一化学物质更多。这是因为当在现实生活中遇到气味时， 大脑处理和识别组成每种完全气味的气味组合不同于 单个成分的化学品。我们对ADRD中香气的研究表明，香气可以 区分认知正常(CU)、轻度认知受损(MCI)和AD患者。 此外，使用机器学习检测嗅觉表型，并区分疾病 各州。我们的算法对CU和MCI/AD的正确分类具有100%的敏感性和83%的特异性。 对MCI和AD进行分类的算法具有100%的敏感性和75%的特异性。 我们建议对CU、MCI、AD受试者(n=324名男性和女性；55岁)进行为期3年的纵向测试，以 评估OD、功能状态和神经认知的变化。将包括一组神经控制措施以 确保嗅觉表型是ADRD特有的。使用传统统计和机器学习 检测香气性能与ATN生物标志物和临床标志物关系的技术 疾病(目标1)；使用香气数据定义预测模型，以预测功能和脆弱程度的变化(目标2)； 并开发一种简化的ADRD版本的香气，只使用最高的气味和浓度 影响(目标3)。我们的长期目标是获得由ML实时分析的护理点嗅觉生物标志物数据 算法，可广泛使用，为临床、研究和护理人员的决策提供有意义的信息。

PROJECT SUMMARY/ABSTRACT There are currently 50 million people suffering globally with Alzheimer’s disease (AD). 95% of the population over age 65 is concerned about their dementia risk and 80% are interested in dementia screening. There is a critical need for accessible and cost-effective biomarkers that can be used to identify those on the ADRD continuum – including the asymptomatic stages – not only in research and specialty-care centers, but in community-based and primary care settings as well. This information could dramatically improve referrals for early clinical trial enrollment, the triage process for specialty evaluation, and comprehensive care planning. The methods used must be appropriate for point-of-care, community, or at-home deployment while maintaining accuracy and predictive value. Olfactory (sense of smell) dysfunction (OD), in combination with machine learning (ML) algorithms, is a promising non-invasive biomarker for ADRD. We have previously demonstrated the reliability of the Affordable Rapid Olfactory Measurement Array (AROMA) to objectively measure OD and categorize olfactory phenotypes (patterns of correct and incorrect responses to various odorants and multiple concentrations). AROMA uses essential oils, which are complex blends of odor molecules and may be more reflective of “real world” olfaction than the single chemicals used in most other tests. This is because when scents are encountered in real life, the brain processes and recognizes the odorant combinations making up each complete scent differently from the individual component chemicals. Our research with AROMA in ADRD has shown that AROMA can distinguish cognitively unimpaired (CU), mildly cognitively impaired (MCI), and AD patients from one another. Additionally, olfactory phenotypes were detected using machine learning and differentiated between disease states. Our algorithms had 100% sensitivity, 83% specificity for correctly classifying CU versus MCI/AD. Algorithms tasked with classifying MCI versus AD had 100% sensitivity, 75% specificity. We propose longitudinal testing of CU, MCI, AD subjects (n=324 men and women > 55 years) over 3 years to assess changes in OD, functional status, and neurocognition. A group of neurologic controls will be included to ensure olfactory phenotypes are specific for ADRD. Using traditional statistics and machine learning techniques to examine the relationship of AROMA performance with ATN-biomarkers and clinical markers of disease (Aim 1); define predictive models using AROMA data to predict changes in function and frailty (Aim 2); and develop a streamlined ADRD-version of AROMA using only the scents and concentrations of highest influence (Aim 3). Our long-term goal is for point-of-care olfactory biomarker data, analyzed in real-time by ML algorithms, to be widely accessible to meaningfully inform clinical, research, and caregiver decisions.