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

嗅觉表型作为阿尔茨海默病的非侵入性生物标志物：一种机器学习方法

• 批准号: 10367769
• 负责人: Jennifer Villwock
• 金额: $ 77.46万
• 依托单位: UNIVERSITY OF KANSAS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367769
• 关键词: Age; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Alzheimer’s disease biomarker; Amyloid; Amyloid beta-42; Biological Markers; Blood specimen; Brain; Caregivers; Caring; Chemicals; Clinical; Clinical Markers; Clinical Research; Clove; Cognition; Cognitive; Communities; Complex; Comprehensive Health Care; Counseling; Data; Data Analyses; Dementia; Detection; Diagnostic tests; Disease; Disease Marker; Dreams; Early Intervention; Ensure; Evaluation; Family; Functional disorder; Future; Goals; Health; Hippocampus (Brain); Home; Individual; Infrastructure; Intervention; Lavandula; Life; Machine Learning; Measurement; Measures; Methods; Mind; Monitor; Nerve Degeneration; Neurocognition; Neurocognitive; Neurologic; Odors; Olfactory dysfunction; Outcome; Pathology; Patients; Pattern; Pennsylvania; Performance; Persons; Phenotype; Plasma; Population; Positron-Emission Tomography; Predictive Value; Process; Public Health; Questionnaires; Registries; Research; Risk; Sensitivity and Specificity; Services; Smell Perception; Specificity; Techniques; Testing; Time; Triage; United States; Universities; Validation; Volatile Oils; Woman; Work; aging population; base; burden of illness; clinical trial enrollment; clinically relevant; clinically significant; cognitive testing; cohort; cost effective; dementia risk; design; early phase clinical trial; early screening; exercise capacity; frailty; functional decline; functional status; human old age (65+); improved; interest; machine learning algorithm; medical specialties; men; mild cognitive impairment; novel; piriform cortex; point of care; pre-clinical; predictive marker; predictive modeling; primary care setting; ranpirnase; response; screening; statistics; tau Proteins; tau-1; underserved community

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.

项目总结/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患者的可获得且具有成本效益的生物标志物 连续体-包括无症状阶段-不仅在研究和特殊护理中心， 社区和初级保健机构也是如此。这些信息可以显着改善推荐， 早期临床试验入组、专科评估的分诊过程和综合护理计划。 所使用的方法必须适用于护理点、社区或家庭部署， 准确性和预测价值。 嗅觉（嗅觉）功能障碍（OD）与机器学习（ML）算法相结合，是一种 有希望的ADRD的非侵入性生物标志物。我们以前已经证明了经济实惠的可靠性 快速嗅觉测量阵列（AROMA），用于客观测量OD并对嗅觉表型进行分类 （对各种气味剂和多种浓度的正确和不正确反应的模式）。AROMA使用 精油是气味分子的复杂混合物，可能更能反映“真实的世界”的嗅觉 比大多数其他测试中使用的单一化学物质要多。这是因为当在真实的生活中遇到气味时， 大脑处理和识别构成每种完整气味的气味组合， 单个化学成分。我们对AROMA在ADRD中的研究表明，AROMA可以 区分认知未受损（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，并在疾病之间进行区分。 states.我们的算法有100%的灵敏度，83%的特异性正确分类CU与MCI/AD。 用于分类MCI与AD的算法具有100%的灵敏度和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， 评估OD、功能状态和神经认知的变化。将纳入一组神经系统对照， 确保嗅觉表型对ADRD具有特异性。使用传统的统计学和机器学习 研究AROMA表现与ATN生物标志物和临床标志物的关系的技术， 使用AROMA数据定义预测模型，以预测功能和虚弱的变化（目标2）; 并开发一种流线型的ADRD版本的AROMA，只使用最高的气味和浓度， 影响力（目标3）。我们的长期目标是通过ML实时分析床旁嗅觉生物标志物数据 算法，以广泛访问有意义地告知临床，研究和护理人员的决定。