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.

有关阿尔茨海默氏病和相关痴呆症的补充要求的摘要/项目摘要 [NOT-AG-22-025]（父项目：R21DC02056501） 阿尔茨海默氏病及其相关痴呆症（ADRD）在全球范围内遭受了约5000万人的困扰。虽然测试 数十年来研究了诊断和区分不同形式痴呆形式的方法，唯一 确认ADRD诊断的明确方法是尸检。诊断很慢，通常基于排除标准 以及多种形式的昂贵测试结果。但是，如果ADRD更容易获得的化学标记 可以根据对存在（或不存在）的评估来识别，快速而准确的诊断。 相关化合物。这样的成就将在方法和成本方面彻底改变ADRD诊断，并且可以 甚至揭示了疾病发病机理和进展的其他维度，可能会揭示疾病的病因，并领导 进行替代，更有效的治疗。 在这里假设，基于研究结果，该发现表明ADRD部分在脂质变化方面表现出来 剖面，富含脂质的陶艺基质的化学轮廓可以作为ADRD存在的记者，并且 羊毛素谱可能是痴呆症类型的函数。可以将这些差异概况的知识借给 通过将机器学习算法应用于化学物质，准确，迅速地揭示了ADRD的存在 数据。该假设将通过追求以下特定目的进行调查： 特定目的I：确定健康捐赠者的Cerumen质谱衍生化学特征， 阿尔茨海默氏病（AD）患者和被诊断为其他痴呆症的患者。 特定目的II：开发机器学习预测模型，能够准确确定存在 基于所有类型的痴呆症的特征，但与cerumen不同，阿尔茨海默氏病和/或其他痴呆症 来自健康的捐助者。 特定目标III：开发机器学习预测模型，以区分阿尔茨海默氏病样本与 其他类型的痴呆症使用Cerumen化学轮廓，并确定化合物的子集 每种痴呆症。 特定目的IV：由机器学习预测模型揭示的生物标志物的结构表征 在特定的目标II和III中。 这项工作的结果将揭示Cerumen的脂质分布与存在之间是否存在相关性 阿尔茨海默氏病和相关痴呆症。结构信息将在负责的分子上获取 对于健康和痴呆症患者的差异。揭示的信息将为未来提供机会 开发潜在的非侵入性方法，以快速诊断阿尔茨海默氏病和相关痴呆症。