Physical Fitness as an Objective Biomarker for AD/ADRD Risk Modification

体能作为 AD/ADRD 风险缓解的客观生物标志物

• 批准号: 10055393
• 负责人: Peter F. Kokkinos
• 金额: $ 200.54万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10055393
• 关键词: Affect; African American; Age; Aging; Alzheimer' s disease related dementia; American; Artificial Intelligence; Attenuated; Biological Markers; Biological Process; Blood Glucose; Blood Pressure; Body Composition; Characteristics; Clinical; Clinical Trials; Complex; Data; Data Set; Databases; Diagnosis; Disease; Dose; Electronic Health Record; Emotional; Exercise Tolerance; Exposure to; Family; Financial Hardship; Frequencies; Future; Glycosylated hemoglobin A; Goals; Health Promotion; Heart Rate; Hispanic Americans; Individual; International Classification of Disease Codes; Intervention; Joints; Link; Longitudinal Studies; Longterm Follow-up; Machine Learning; Measurable; Measures; Medicare; Memory; Metabolic; Modeling; Modification; Natural Language Processing; Observational Study; Pathogenicity; Patient Self-Report; Patients; Pharmaceutical Preparations; Phenotype; Physical Fitness; Physical activity; Population; Prevention; Prevention Research; Prevention trial; Primary Prevention; Process; Public Health; Race; Randomized Controlled Trials; Recommendation; Records; Research; Risk; Risk Factors; Risk Reduction; Sample Size; Standardization; Subgroup; Surrogate Markers; Symptoms; Technology; Testing; United States; United States Agency for Healthcare Research and Quality; Veterans; Woman; adverse outcome; base; cardiorespiratory fitness; cost; deep learning; dementia risk; design; epidemiology study; follow-up; improved; large datasets; lifestyle intervention; molecular marker; novel; novel therapeutics; precision medicine; prevent; programs; response; risk mitigation; risk prediction model; sex; statistical learning; systematic review; unstructured data

About 6M Americans ≥65 years suffer from Alzheimer's disease and related dementias (AD/ADRD) which poses significant emotional, physical, and financial burden on patients and their families, and costs the US economy over $250 billion annually. AD/ADRD is a growing national public health crisis as the number of Americans ≥65 years is projected to double by 2050. There are no approved disease-modifying drugs for AD/ADRD, and no new symptom-modifying drug has been approved since 2003. This highlights the need for novel research approaches aimed at preventing AD/ADRD. Physical activity (PA) has been identified as the lifestyle intervention with the strongest potential to prevent AD/ADRD. However, most of the randomized controlled trials (RCTs) of PA including the 23 ongoing RCTs currently sponsored by the NIA are small with short follow-up. Larger RCTs with longer follow-up are needed to provide definitive evidence of benefit. Identification of relevant biomarkers and quantification of future risk can mitigate long duration and cost of such RCTs. Epidemiologic studies with large database and long follow-up are limited by imprecise and unreliable self-reports of PA. We propose the novel concept of using cardiorespiratory fitness (CRF) as a reliable biomarker of PA for AD/ADRD risk reduction. Like body composition, blood pressure and heart rate, CRF is considered a biomarker of PA. Just as well-controlled blood glucose leads to optimal HbA1C, PA of adequate duration, frequency and intensity also leads to optimal CRF. The current application has been designed to test the hypotheses that higher CRF is independently linked to a lower risk of incident AD/ADRD and to determine optimal levels of CRF assessed by a standardized exercise tolerance test (ETT), for minimizing AD/ADRD risk at both population and individual levels. Our Specific Aims are (1) to determine the relationship between CRF and incident AD/ADRD, taking into consideration a non-linear relationship and potential interactions of CRF with other risk factors and (2) to define incremental CRF levels that are linked to progressively lower risk of AD/ADRD, overall, and in subgroups by age, sex, and race. Our Aim 3 is to develop and validate a deep learning-based risk prediction model to determine the optimal CRF level for individuals to achieve the lowest risk of AD/ADRD. Recent advances in deep (machine) learning, a key artificial intelligence (AI) technology, allow modeling of complex non-linear relationships necessary for more precise risk prediction. These aims will be achieved by using ETT data on 911,698 Veterans free of baseline AD/ADRD (mean age, 61 years, 54,411 women, 202,000 African American, 50,107 Hispanic Americans) with up to 19 years of follow-up data. Explainable deep learning risk prediction model will be developed in the VA data, and validated with a Medicare population to develop a “precision medicine” approach that will personalize CRF recommendations for individuals to lower their AD/ADRD risk. Future studies will incorporate molecular biomarkers to validate AD diagnosis and ascertain effect of CRF and CRF modification on AD risk.

大约600万≥65岁的美国人患有阿尔茨海默病和相关痴呆（AD/ADRD）， 对患者及其家属造成重大的情感、身体和经济负担， 经济每年超过2500亿美元。AD/ADRD是一个日益严重的国家公共卫生危机， 预计到2050年，65岁以上的美国人将翻一番。没有批准的疾病修饰药物用于 AD/ADRD，自2003年以来没有新的抗抑郁药物被批准。这突出了 旨在预防AD/ADRD的新研究方法。体力活动（PA）已被确定为 生活方式干预对预防AD/ADRD的潜力最大。然而，大多数随机 PA的对照试验（RCT）包括目前由NIA申办的23项正在进行的RCT，规模较小， 短的后续行动。需要更大规模的随机对照试验和更长时间的随访来提供明确的获益证据。 相关生物标志物的鉴定和未来风险的量化可以减轻此类风险的长期持续时间和成本。 RCT。流行病学研究由于数据量大、随访时间长，存在不准确、不可靠等问题 PA的自我报告我们提出了使用心肺适能（CRF）作为可靠的 PA生物标志物用于AD/ADRD风险降低。像身体成分，血压和心率一样，CRF是 被认为是PA的生物标志物。正如良好的血糖控制可使HbA 1C达到最佳水平一样， 持续时间、频率和强度也导致最佳CRF。当前应用程序旨在测试 假设较高的CRF与较低的AD/ADRD事件风险独立相关，并确定 通过标准化运动耐量试验（ETT）评估的CRF最佳水平，以最大限度地降低AD/ADRD风险 在人口和个人层面。我们的具体目标是：（1）确定CRF之间的关系 和AD/ADRD事件，考虑到CRF的非线性关系和潜在相互作用 （2）定义与其他风险因素相关的递增CRF水平， AD/ADRD，总体和按年龄、性别和人种列出的亚组。我们的目标3是开发和验证一个深入的 基于学习的风险预测模型，以确定最佳CRF水平，使个人达到最低 AD/ADRD风险。深度（机器）学习是一项关键的人工智能（AI）技术， 允许对更精确的风险预测所需的复杂非线性关系进行建模。这些目标将 通过使用911，698名无基线AD/ADRD的退伍军人的ETT数据（平均年龄61岁，54，411 女性，202，000名非洲裔美国人，50，107名西班牙裔美国人）长达19年的随访数据。 将在VA数据中开发可解释的深度学习风险预测模型，并使用 医疗保险人群开发一种“精准医疗”方法，将个性化CRF建议 降低AD/ADRD风险。未来的研究将结合分子生物标志物来验证AD 诊断和确定CRF和CRF修改对AD风险的影响。