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

关于阿尔茨海默病和相关痴呆的补充请求摘要/项目摘要 [NOT-AG-22-025](父项目：R21DC02056501) 阿尔茨海默病及其相关痴呆症(ADRD)困扰着全球约5000万人。虽然测试 几十年来，人们一直在研究诊断和区分不同形式痴呆症的方法，这是唯一的 确认ADRD诊断的最终手段是进行尸检。诊断很慢，而且通常是基于排除标准 以及多种形式的昂贵测试的结果。然而，如果更容易获得ADRD的化学标记物 可以被识别，可以基于对存在(或不存在)的评估来实现快速和准确的诊断 相关化合物。这样的成就将在方法和成本方面彻底改变ADRD的诊断，并可能 甚至揭示了疾病发生和发展的其他方面，这可能有助于阐明疾病的病因，并导致 其他更有效的治疗方法。 这里假设，基于研究发现，ADRD的部分表现为血脂的变化 对于ADRD，富含脂质的脑脊液基质的化学成分可以作为ADRD存在的报告 根据痴呆症类型的不同，脑脊液的特征也可能不同。可以利用这些差异配置文件的知识来 通过将机器学习算法应用于化学品，准确、快速地揭示ADRD的存在 数据。这一假设将通过追求以下具体目标来进行研究： 具体目标一：确定来自健康捐赠者的鹿茸的质谱学化学特征， 阿尔茨海默病(AD)患者，以及被诊断为其他痴呆的患者。 具体目标二：开发机器学习预测模型，使之能够准确地确定是否存在 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，但不同于脑干 来自健康的捐赠者。 具体目标三：开发机器学习预测模型，以区分阿尔茨海默病样本和 其他类型的痴呆症使用Cerumen化学图谱，并确定独特的化合物子集 每种类型的痴呆症。 具体目标四：S建立的机器学习预测模型揭示的生物标志物的结构特征 具体目标二和目标三。 这项工作的结果将揭示脑脊液的脂类分布是否与脑脊液中 阿尔茨海默病和相关痴呆症。将获得有关负责的分子的结构信息 对于健康人和痴呆症患者的差异。披露的信息将为未来提供机会 开发一种潜在的非侵入性快速诊断阿尔茨海默病和相关痴呆的方法。

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.