Data-Driven Discovery of Heterogeneous Treatment Effects of Statin Use on Dementia Risk

他汀类药物使用对痴呆风险的异质治疗效果的数据驱动发现

• 批准号: 10678219
• 负责人: Neal Jawadekar
• 金额: $ 4.39万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10678219
• 关键词: Affect; Age; Aging; Algorithms; Alzheimer' s disease related dementia; Alzheimer' s disease risk; American; Award; Behavioral; Biological; Biological Markers; Characteristics; Clinical; Clinical Trials; Cognitive aging; Consensus; Data; Data Set; Dementia; Diagnosis; Disease; Economics; Effectiveness; Genetic; Goals; Grant; Heterogeneity; Hyperlipidemia; Impaired cognition; Individual; Investigation; Machine Learning; Mentorship; Methods; Mission; Modification; Nature; Observational Study; Participant; Pharmaceutical Preparations; Policies; Prevalence; Prevention; Prevention Research; Prevention strategy; Probability; Prospective cohort; Psychometrics; Public Health; Qualifying; Race; Reproducibility of Results; Research; Research Methodology; Research Personnel; Research Proposals; Research Training; Resources; Risk Reduction; Sampling; Subgroup; Testing; Training; Trees; United States; advanced dementia; aged; apolipoprotein E-4; biobank; career; cognitive function; cohort; comorbidity; dementia risk; design; effective therapy; follow-up; forest; improved; innovation; interest; lipid metabolism; machine learning algorithm; machine learning method; novel; optimal treatments; patient subsets; precision medicine; prevent; recruit; regression trees; sex; skills; social; sociodemographics; study population; treatment effect

PROJECT SUMMARY (ABSTRACT) Alzheimer’s Disease and Related Dementias (ADRD) currently affects more than 4 million Americans and over 50 million individuals worldwide. The identification of prevention strategies for dementia is critical, particularly due to the lack of effective treatments. In parallel, there is growing consensus that lipid metabolism is a major contributor to ADRD and may be an important strategy for risk reduction and prevention. Antihyperlipidemic agents (i.e., statins) are widely used, yet evidence on the relationship between antihyperlipidemic agents (i.e., statins) and ADRD has been largely inconclusive. One possible explanation for the mixed findings is heterogeneity in study populations and their characteristics. For example, the effectiveness of statins is evidenced to vary by age, ApoE4 status, and pre-existing disease status.34-36 Accordingly, there is a growing need to identify the factors (i.e., effect modifiers) which influence heterogeneities in the effect of statins on dementia. The objective of this study is to triangulate evidence on the identification and estimation of heterogeneous treatment effects by using three causal machine learning methods, specifically the honest causal forest/policy tree, doubly robust adaptive LASSO, and Bayesian Adaptive Regression Trees (BART), to identify novel effect modifiers and optimal subgroups for the effect of statins on dementia. While traditional parametric regression approaches are designed to test a priori hypotheses regarding effect modification, such approaches are not suitable for yielding novel hypotheses. The causal machine learning methods described in this proposal fill this gap; not only do such approaches help identify novel effect modifiers, but they can also facilitate the subsequent identification of optimal treatment rules across those modifiers. In this study, I propose to use a cohort of 307,719 individuals from the UK Biobank data who were at least 55 when they were initially recruited from 2006 to 2010. The analytical sample will be large, allowing me to rigorously investigate heterogeneous treatment effects across different subgroups. Specifically, in Aim 1, I propose to estimate the real-world average treatment effect (ATE) of statins on ADRD across the entire sample. I will then, in Aim 2, apply three causal machine learning algorithms to identify novel effect modifiers and corresponding optimal subgroups for the effect of statin use on ADRD risk. Finally, in Aim 3, I will quantify the reduction in ADRD cases that would result from implementing each of the optimal treatment rules generated under Aim 2 and compare them to the reduction in ADRD cases observed under Aim 1. This F31 proposal application will support my dissertation research, as well as my interest in gaining training in causal machine learning, as well as substantive training in dementia, cognitive aging, and its psychometric methods. Under the guidance of my mentorship team, I look forward to advancing dementia prevention research while also pursuing my goal of becoming an independent investigator in research methods on cognitive aging.

项目摘要（摘要） 阿尔茨海默氏病和相关痴呆症（ADRD）目前影响超过400万美国人及以上 全球5000万个人。鉴定痴呆症的预防策略至关重要，特别是 由于缺乏有效的治疗。同时，人们普遍认为脂质代谢是主要的 贡献ADRD，可能是降低风险和预防风险的重要策略。抗血脂血症 药物（即汀类药物）被广泛使用，但证据证明了抗血脂药物之间的关系（即 他汀类药物）和ADRD在很大程度上尚无定论。混合发现的一种可能解释是 研究人群及其特征的异质性。例如，他汀类药物的有效性是 根据年龄，APOE4状态和现有疾病状态的证明。34-36因此，有一个增长 需要确定影响他汀类药物对的异质性的因素（即效应修饰符） 失智。这项研究的目的是对鉴定和估计的三角审判证据 通过使用三种因果机学习方法，特别是诚实的治疗效果 因果森林/政策树，双重健壮的自适应拉索和贝叶斯自适应回归树（BART） 确定汀类药物对痴呆症的影响的新型效应修饰剂和最佳亚组。而传统 参数回归方法旨在检验有关效果修改的先验假设 方法不适合产生新的假设。所描述的因果机学习方法 该建议填补了这一空白；这种方法不仅有助于识别新颖的效应修饰符，而且还可以 促进随后在这些修饰符中识别最佳治疗规则。在这项研究中，我提出了 使用来自英国生物库数据的307,719个人的同伙，最初至少55岁 从2006年到2010年招募。分析样本将很大，使我可以严格调查 不同亚组的异质治疗效应。具体而言，在AIM 1中，我建议估算 汀类药物对整个样本的ADRD的现实世界平均治疗效果（ATE）。然后，我将在AIM 2中 应用三种因果机学习算法来识别新颖的效应修饰符和相应的最佳 他汀类药物使用对ADRD风险的影响。最后，在AIM 3中，我将量化ADRD的减少 实施AIM 2和AIM 2和 将它们与AIM 1观察到的ADRD案件的减少相比。此F31提案申请将 支持我的论文研究，以及我对获得因果机器学习培训的兴趣 作为痴呆症，认知衰老及其心理测量方法的实质训练。在我的指导下 属团团队，我期待着预防痴呆症的研究，同时也追求我的目标 成为认知衰老研究方法的独立研究者。