Identifying Risk Factors For Late-Life Dementia Based On Job Characteristics During The Working Life

根据工作期间的工作特征识别晚年痴呆症的风险因素

基本信息

批准号：
10213393
负责人：
Peter Hudomiet
金额：
$ 52.87万
依托单位：
RAND CORPORATION
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10213393
关键词：
Adult Affect Age Alzheimer&apos s Disease Alzheimer&apos s disease related dementia American Brain Brain Diseases Characteristics Cognition Cognitive Collection Complement Complex Critical Thinking Data Databases Dementia Descriptor Deterioration Dictionary Dimensions Disease Disease Progression Education Educational Background Elderly Face Future Gender Genetic Risk Health and Retirement Study Impaired cognition Individual Information Networks Intervention Life Link Measurement Measures Medical Methodological Studies Methodology Methods Modeling Occupational Occupations Outcome Participant Physical activity Population Probability Process Race Records Reproducibility Research Retirement Risk Risk Factors Science Policy Social Sciences Statistical Methods Techniques Testing Time Validation Work base brain health career cognitive ability cognitive function cognitive reserve daily functioning dementia risk effective therapy genetic risk factor genome wide association study human old age (65+)interest longitudinal dataset mathematical theory modifiable risk multidimensional data novel physical conditioning skills social social science research theories

项目摘要

Title: Identifying risk factors for late-life dementia based on job characteristics during the working life Project participants: Péter Hudomiet (RAND, PI), Irineo Cabreros (RAND), Michael D. Hurd (RAND), and Susann Rohwedder (RAND) Revised Abstract 01/05/2021 To date, no treatment is available for Alzheimer’s disease and most types of Alzheimer’s disease-related dementias (AD/ADRD), even though this devastating condition affects many older adults. However, there are several known modifiable risk factors for AD/ADRD, which offer hope of finding interventions that may delay its onset. Investigating how job characteristics are related to the risk of AD/ADRD is a promising way of identifying such modifiable risk factors, because individuals spend a substantial portion of their lives working. Differences in job characteristics and work activities, for example, may explain (at least partially) why highly educated individuals face a substantially lower risk of developing dementia than lower educated individuals do. We propose to investigate the relationship between a large number of job characteristics and AD/ADRD using novel estimation methods, and a systematic and reproducible approach. We will use hundreds of occupational job characteristics from the Occupational Information Network (O*NET) database, which can be linked to the nationally representative Health and Retirement Study (HRS). The O*NET is promising for dementia research because it includes many measures describing cognitive activities, such as whether a job requires memorization, critical thinking, mathematical reasoning, or social orientation. The O*NET has hundreds of measures that are often strongly correlated. We will develop statistical methods to optimally process the items into easy-to-use low dimensional measures; and study their explanatory power for dementia. This study has four specific aims. First, we will develop and implement a methodology which we call Occupation-Wide Association Study (OWAS), to derive occupational risk factors of AD/ADRD. OWAS is inspired by the statistical approach employed in Genome-Wide Association Studies, which is used to identify genetic risk factors of various medical conditions. As such, the OWAS methodology will leverage established statistical techniques to handle the complex O*NET data with highly correlated items, and to correct for the number of false positives in multiple hypotheses testing. To complement the data-driven OWAS methodology, our second aim is to construct more detailed job characteristic measures guided by prior medical and social science research, such as quantitative skills, executive cognitive functions, job control, and social orientation. Third, we will examine the explanatory power of the developed scores for various cognitive outcomes in the HRS, such as age-adjusted probabilities of dementia and longitudinal change in cognition. Fourth, we will test how much of the correlation between basic demographic covariates and dementia can be explained by the developed job measures. We are particularly interested in testing how much of the explanatory power of education for AD/ADRD shrinks after controlling for the new job measures. We will also explore effects of other variables such as gender, race, and the physical health of individuals.

标题：根据工作期间的工作特征识别晚年痴呆症的风险因素项目参与者：Péter Hudomiet（兰德，PI）、Irineo Cabreros（兰德）、Michael D. Hurd（兰德）和 Susann Rohwedder（兰德）修订摘要 01/05/2021 迄今为止，阿尔茨海默病和大多数类型的阿尔茨海默病相关痴呆症 (AD/ADRD) 尚无治疗方法，尽管这种毁灭性的疾病影响着许多老年人，但是，有几种已知的 AD/ADRD 风险因素，这些因素是可以改变的。研究工作特征与 AD/ADRD 风险之间的关系是识别此类可改变风险因素的一种有希望的方法，因为每个人一生中很大一部分时间都在工作。和工作例如，活动可以（至少部分地）解释为什么受过高等教育的人患痴呆症的风险比受教育程度较低的人要低得多。我们将使用职业信息网络 (O*NET) 数据库中的数百个职业工作特征，该数据库可以链接到具有全国代表性的健康与退休研究 (HRS)。O*NET 对痴呆症研究很有前景，因为它包含许多措施。描述认知活动，例如工作是否需要记忆、批判性思维、数学推理或社会取向。 O*NET 有数百种通常密切相关的测量方法，我们将开发统计方法，将这些项目优化处理为易于使用的低维度测量方法，并研究它们对痴呆症的解释力。这项研究有四个具体目标。首先，我们将开发并实施一种称为全职业关联研究 (OWAS) 的方法，以得出 AD/ADRD 的职业风险因素。 OWAS 的灵感来自于全基因组中采用的统计方法。关联研究用于识别各种医疗状况的遗传风险因素，因此，OWAS 方法将利用现有的统计技术来处理具有高度相关项目的复杂 O*NET 数据，并纠正误报的数量。多重假设检验。为了补充数据驱动的 OWAS 方法，我们的第二个目标是在先前的医学和社会科学研究的指导下构建更详细的工作特征测量，例如定量技能、执行认知功能、工作控制和社会取向。第三，我们将检查 HRS 中各种认知结果的开发分数的解释力，例如痴呆的年龄调整概率和认知的纵向变化。第四，我们将测试基本人口统计协变量与痴呆之间的相关性。我们特别感兴趣的是，在控制了新的工作措施后，教育对 AD/ADRD 的解释力会下降多少。我们还将探讨其他变量的影响，例如性别、种族、和身体上的个人的健康。