Digital Biomarkers for Alzheimer’s Disease

阿尔茨海默病的数字生物标志物

• 批准号: 10768533
• 负责人: IHAB M HAJJAR
• 金额: $ 77.89万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10768533
• 关键词: Digital; Biomarkers; Alzheimer; Disease

Alzheimer’s disease (AD) is marked by progressive neuropathological changes that begin decades before cognitive and functional symptoms, and thus efforts have been focused on developing innovative tools and biomarkers for early identification of pre-dementia stages. To date, clinical ability to identify those with pre-dementia stages of AD has been limited and requires expensive (amyloid PET) or invasive (lumbar puncture) testing. However, subtle changes in connected speech may be detectable years before overt disease symptoms present. Our team has developed an approach that uses machine learning and natural language processing combined with advanced acoustic phonetic and lexical-semantic analyses. Preliminary data show promise in identifying AD biomarker status and predicting 2-year cognitive progression. In the proposed study, we leverage our success in collecting cerebrospinal fluid (CSF) biomarkers, neuroimaging and detailed cognitive phenotyping combined with audio recordings of participants in the Brain Stress, Hypertension and Aging Research Program cohort. This cohort, now in its third year of follow-up, consists of 400 individuals 50 years or older with normal cognition or mild cognitive impairment. We plan to extend this cohort of 400 participants for 3 more years to collect additional waves of voice recordings, cognitive assessments, follow-up CSF biomarkers and neuroimaging. Our overarching hypothesis is that the derived novel features reflecting poor lexical- semantic connectedness or acoustic perturbations are significantly different between biomarker-positive and -negative participants, have better diagnostic performance with regards to the ATN framework than traditional cognitive tests and can track disease progression. The Specific Aims are: 1) Determine the accuracy of the derived digital biomarkers in detecting in-vivo AD pathology and ATN classification in the B- SHARP cohort; 2) Investigate the association of the derived digital biomarkers with disease progression and cognitive decline; and 3) Investigate the ability of repeated measurement of the digital biomarkers to track disease progression. This project will provide needed insight into the use of non-invasive digital biomarkers to improve the ability to detect and track longitudinal changes in cognitive and functional status in AD and will set the foundation for a future larger pivotal study.

阿尔茨海默氏病（AD）以几十年前开始的进行性神经病理学变化为特征 认知和功能性症状，因此努力集中于开发创新工具 和生物标志物，用于早期识别止动阶段。迄今为止，识别患者的临床能力 AD的静脉前阶段受到限制，需要昂贵（淀粉样蛋白宠物）或侵入性（腰部） 穿刺）测试。但是，在公开之前可能会发现连接的语音的细微变化 疾病症状存在。我们的团队开发了一种使用机器学习和自然的方法 语言处理结合了高级声音和词汇语义分析。 初步数据显示了识别AD生物标志物状态并预测2年认知的希望 进展。在拟议的研究中，我们利用了我们在收集脑脊液（CSF）方面的成功。 生物标志物，神经影像学和详细的认知表型结合了录音 参加大脑压力，高血压和衰老研究计划队列的参与者。这个队列，现在 随访的第三年，由400个50岁以上的人组成，具有正常认知或中期 认知障碍。我们计划将这一组合的400名参与者延长3年以收集 其他语音录音，认知评估，随访CSF生物标志物和 神经影像学。我们的总体假设是，衍生的新特征反映了词汇差的。 生物标志物阳性之间的语义连接性或声学扰动显着差异 和 - 负参与者，在ATN框架方面具有更好的诊断性能 传统的认知测试可以跟踪疾病的进展。具体目的是：1）确定 衍生的数字生物标志物在检测B-中检测体内病理学和ATN分类方面的准确性 尖锐的队列； 2）研究衍生的数字生物标志物与疾病进展的关联 和认知能力下降； 3）研究数字生物标志物重复测量的能力 跟踪疾病进展。该项目将为使用非侵入性数字提供所需的见解 生物标志物提高检测和跟踪认知和功能状态的纵向变化的能力 在AD中，将为将来的更大的关键研究奠定基础。