Development of a Machine Learning Prediction Model for the Detection of Meniere's Disease from Cerumen Chemical Profiles

开发机器学习预测模型，用于根据耵聍化学特征检测梅尼埃病

• 批准号: 10723489
• 负责人: RABI A MUSAH
• 金额: $ 23.28万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT ALBANY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10723489
• 关键词: Achievement; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Appearance; Autopsy; Biological Markers; Blood; Blood Vessels; Characteristics; Chemicals; Chronic; Data; Dementia; Detection; Development; Diagnosis; Diagnostic tests; Dimensions; Discriminant Analysis; Discrimination; Disease; Ear; Earwax; Etiology; Feeling; Future; Hand; Health; High Pressure Liquid Chromatography; Individual; Knowledge; Lewy Bodies; Lipids; Low Frequency Deafness; Machine Learning; Mass Fragmentography; Mass Spectrum Analysis; Meniere' s Disease; Methodology; Methods; Molecular; Nausea; Nuclear Magnetic Resonance; Pathogenesis; Patients; Persons; Process; Publishing; Recurrence; Reporter; Reporting; Research; Resolution; Sampling; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Statistical Data Interpretation; Symptoms; Techniques; Testing; Time; Tinnitus; Vertigo; Vomiting; Work; accurate diagnosis; cost; cost effective; effective therapy; feature selection; infrared spectroscopy; lipidome; machine learning algorithm; machine learning prediction; nonalzheimer dementia; parent project; pressure; rapid diagnosis; rapid technique

ABSTRACT/PROJECT SUMMARY for Supplement Request on Alzheimer’s Disease and Related Dementias [NOT-AG-22-025] (Parent Project: R21DC02056501) Alzheimer’s disease and its related dementias (ADRD) afflict ~50 million people worldwide. Although testing methodologies to diagnose and differentiate different forms of dementia have been investigated for decades, the only definitive means to confirm a diagnosis of ADRD is at autopsy. Diagnosis is slow and is often based on exclusionary criteria along with the results of multiple forms of costly testing. However, if more readily accessible chemical markers of ADRD can be identified, rapid and accurate diagnoses could be accomplished based on assessment of the presence (or absence) of relevant compounds. Such an achievement would revolutionize ADRD diagnosis in terms of methods and cost, and could even reveal other dimensions of disease pathogenesis and progression that might shed light on disease etiology, and lead to alternative, more effective treatments. It is hypothesized here that based on research findings that reveal that ADRD manifests in part in terms of changes in lipid profiles, the chemical profile of the lipid-rich cerumen matrix may serve as a reporter of the presence of ADRD, and that cerumen profiles may differ as a function of dementia type. Knowledge of these differential profiles can be leveraged to accurately and rapidly reveal the presence of ADRD via the application of machine learning algorithms to the chemical data. This hypothesis will be investigated through pursuit of the following specific aims: Specific Aim I: Determination of the mass spectrum-derived chemical signatures of cerumen from healthy donors, Alzheimer’s disease (AD) patients, and patients diagnosed with other dementias. Specific Aim II: Development of machine learning prediction models that enable accurate determination of the presence of Alzheimer’s disease and/or other dementias based on features common to all types of dementia but distinct from cerumen from healthy donors. Specific Aim III: Development of machine learning prediction models to distinguish Alzheimer’s disease samples from other types of dementia using cerumen chemical profiles, and determination of the subset of compounds that are unique to each type of dementia. Specific Aim IV: Structural characterization of biomarkers revealed by the machine learning prediction model(s) developed in Specific Aims II and III. The results of this work will reveal whether there is a correlation between the lipid profile of cerumen and the presence of Alzheimer’s disease and related dementias. Structural information will be acquired on the molecules that are responsible for the differences in healthy and dementia patients. The information revealed would provide the opportunity for future development of a potential non-invasive method for the rapid diagnosis of Alzheimer’s disease and related dementias.

关于阿尔茨海默病和相关痴呆症的补充请求摘要/项目摘要 [NOT-AG-22-025]（父项目：R21DC02056501） 尽管经过测试，阿尔茨海默病及其相关痴呆症 (ADRD) 影响着全球约 5000 万人。 诊断和区分不同形式痴呆症的方法已经研究了数十年，唯一的方法是 确认 ADRD 诊断的最终方法是尸检，诊断速度较慢，并且通常基于排除标准。 然而，如果 ADRD 的化学标记物更容易获得，就会得到多种形式的昂贵测试的结果。 可以识别，可以根据存在（或不存在）的评估来完成快速准确的诊断 这一成就将在方法和成本方面彻底改变 ADRD 诊断，并可能 甚至揭示疾病发病机制和进展的其他方面，可能有助于阐明疾病病因，并导致 更有效的替代疗法。 在此重申，根据研究结果表明 ADRD 部分表现为脂质变化 谱，富含脂质的细胞基质的化学谱可以作为 ADRD 存在的报告者，并且 耵聍特征可能随痴呆类型的不同而有所不同，可以利用这些差异特征的知识来确定。 通过将机器学习算法应用于化学物质，准确、快速地揭示 ADRD 的存在 该假设将通过追求以下具体目标进行研究： 具体目标 I：确定来自健康捐赠者的耵聍的质谱衍生化学特征， 阿尔茨海默病 (AD) 患者以及被诊断患有其他痴呆症的患者。 具体目标二：开发机器学习预测模型，能够准确确定是否存在 阿尔茨海默病和/或其他痴呆症基于所有类型痴呆症的共同特征，但与陶瓷不同 来自健康的捐赠者。 具体目标 III：开发机器学习预测模型来区分阿尔茨海默病样本 使用耵聍化学特征分析其他类型的痴呆症，并确定其特有的化合物子集 每种类型的痴呆症。 具体目标 IV：开发的机器学习预测模型揭示的生物标志物的结构表征 在具体目标 II 和 III 中。 这项工作的结果将揭示陶瓷的脂质谱与存在的相关性是否存在相关性。 阿尔茨海默病和相关痴呆症的结构信息将被获取。 所揭示的信息将为未来提供机会。 开发一种潜在的非侵入性方法来快速诊断阿尔茨海默病和相关痴呆症。