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Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches

使用多模式机器学习方法评估阿尔茨海默病风险和异质性

基本信息

批准号：
10698063
负责人：
ANITA L DESTEFANO
金额：
$ 63.6万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
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：
2021-09-15 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10698063
关键词：
Acceleration Accounting Alzheimer&apos s Disease Alzheimer&apos s disease risk Alzheimer’s disease biomarker Autopsy Biological Markers Blood Brain Cerebrospinal Fluid Characteristics Clinical Cognitive Collection Communities Complex Data Data Storage and Retrieval Dementia Development Diagnosis Disease Elderly Ethnic Population European ancestry Future Gene Expression Regulation Genes Genetic Genetic Predisposition to Disease Heterogeneity Human Image Impaired cognition Individual Intervention Machine Learning Measures Medicine Methods Modality Modeling Molecular Multiomic Data National Institute on Aging Nature Nerve Degeneration Non-linear Models Ontology Participant Pathogenesis Pattern Persons Plasma Population Heterogeneity Prevention strategy Regulator Genes Resources Sampling Statistical Models Symptoms Therapeutic Therapeutic Clinical Trial Tissues Weight clinical risk cognitive function combinatorial deep learning disease heterogeneity disease phenotype disorder subtype effective therapy endophenotype ethnic diversity feature selection follow-up gene interaction gene regulatory network genetic risk factor high risk innovation insight learning network machine learning algorithm machine learning framework machine learning method machine learning model model building multimodal data multimodality neuroimaging phenotypic data risk stratification supervised learning therapeutic target unsupervised learning

项目摘要

PROJECT SUMMARY/ABSTRACT Alzheimer's disease (AD) is the most common form of dementia characterized by progressive loss of cognitive function. Unfortunately, currently there is no effective treatment for AD and clinical interventions of AD have largely failed despite enormous efforts. For the current application, we seek to develop multimodal machine learning models by leveraging the rich collection of AD-related omics data and phenotypical data recently generated from large-scale collaborative projects such as Alzheimer Disease Neuroimaging Initiative (ADNI), Accelerating Medicines Partnership-AD (AMP-AD) and the Alzheimer's Disease Sequencing Project (ADSP). Three aims will be pursued in the current application. Aim 1. We will build an expandable multimodal unsupervised machine learning framework to investigate AD heterogeneity. Given the multifactorial nature of AD, we will perform AD subtyping by harnessing the rich information across multiple spectrum of data. Aim 2. We will build an expandable multimodal supervised machine learning framework to quantify AD risk from longitudinal follow up of cognitively normal elders. The models will be built from genetic susceptibility and gene regulatory information as well as endophenotypes measured when participants were cognitive normal. Aim 3. We will build AD-related gene interaction networks in post-mortem human brain samples. We will examine the association of multiple omics data with AD in brain samples, and build tissue-specific interaction networks to understand potential molecular mechanisms underlying AD pathogenesis. The present application represents an innovative approach to identify individuals at high risk of AD from both clinical and genetic risk factors in ethnically diverse populations. The outlined strategy will provide new insights into the risk stratification and prevention strategies for AD. We also commit to share our methods through GitHub or CRAN for free access across the scientific community.

项目总结/文摘

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Association of lifestyle with deep learning predicted electrocardiographic age.

DOI：
10.3389/fcvm.2023.1160091
发表时间：
2023
期刊：
FRONTIERS IN CARDIOVASCULAR MEDICINE
影响因子：
3.6
作者：
Zhang, Cuili;Miao, Xiao;Wang, Biqi;Thomas, Robert J. J.;Ribeiro, Antonio H.;Brant, Luisa C. C.;Ribeiro, Antonio L. P.;Lin, Honghuang
通讯作者：
Lin, Honghuang

Integrated omics analysis of coronary artery calcifications and myocardial infarction: the Framingham Heart Study.

DOI：
10.1038/s41598-023-48848-1
发表时间：
2023-12-07
期刊：
Scientific reports
影响因子：
4.6
作者：
通讯作者：

No evidence of association between habitual physical activity and ECG traits: Insights from the electronic Framingham Heart Study.

DOI：
10.1016/j.cvdhj.2021.11.004
发表时间：
2022-03
期刊：
Cardiovascular digital health journal
影响因子：
0
作者：
Kornej J;Murabito JM;Zhang Y;Liu C;Trinquart L;Sardana M;Manders ES;Hammond MM;Spartano NL;Pathiravasan CH;Wang X;Borrelli B;McManus DD;Benjamin EJ;Lin H
通讯作者：
Lin H

Factors associated with long-term use of digital devices in the electronic Framingham Heart Study.

与长期使用数字设备在电子弗雷明汉心脏研究中的因素。

DOI：
10.1038/s41746-022-00735-1
发表时间：
2022-12-27
期刊：
NPJ digital medicine
影响因子：
15.2
作者：
通讯作者：

Association Between Acoustic Features and Neuropsychological Test Performance in the Framingham Heart Study: Observational Study.

DOI：
10.2196/42886
发表时间：
2022-12-22
期刊：
JOURNAL OF MEDICAL INTERNET RESEARCH
影响因子：
7.4
作者：
Ding, Huitong;Mandapati, Amiya;Karjadi, Cody;Ang, Ting Fang Alvin;Lu, Sophia;Miao, Xiao;Glass, James;Au, Rhoda;Lin, Honghuang
通讯作者：
Lin, Honghuang