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Related Concept Videos

Alzheimer's Disease: Overview01:26

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Integrative Multi-Omics and Multivariate Longitudinal Data Analysis for Dynamic Risk Estimation in Alzheimer's

Yuanyuan Guo1, Haotian Zou1, Mohammad Samsul Alam1

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.

Statistics in Medicine
|May 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework integrating multi-omics and longitudinal data for Alzheimer's disease (AD) risk assessment. The approach enhances dynamic risk evaluation for neurodegenerative disorders.

Keywords:
high‐dimension datajoint modelingmultivariate functional mixed modelmulti‐omics factor analysis

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Genomics

Background:

  • Alzheimer's disease (AD) presents heterogeneous cognitive and functional impairments.
  • Accurate AD progression assessment requires integrating diverse data, including neuropsychological tests and multi-omics (metabolomics, lipidomics).
  • Challenges exist in utilizing high-dimensional, heterogeneous omics data for dynamic dementia risk estimation.

Purpose of the Study:

  • To develop a novel joint-modeling framework for integrating multi-omics and longitudinal data.
  • To enable dynamic risk evaluation for Alzheimer's disease progression.
  • To address challenges in omics data utilization for dementia risk.

Main Methods:

  • Combined multi-omics factor analysis (MOFA) for dimension reduction and feature extraction.
  • Employed a multivariate functional mixed model (MFMM) for longitudinal outcome modeling.
  • Integrated MOFA and MFMM into a joint-modeling framework.

Main Results:

  • The proposed integrative joint modeling framework effectively combines multi-omics and longitudinal data.
  • Demonstrated the framework's efficacy through extensive simulation studies.
  • Successfully applied the model to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

Conclusions:

  • The novel joint-modeling framework facilitates dynamic evaluation of dementia risk.
  • This approach leverages both omics and longitudinal data for improved AD progression assessment.
  • The method shows practical utility in real-world datasets like ADNI.