ArticleStatistics in medicine2025
Integrative Multi-Omics and Multivariate Longitudinal Data Analysis for Dynamic Risk Estimation in Alzheimer's Disease.
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Longitudinal Sparse Single-Omics Factor Analysis for High-Dimensional Blood Biomarkers in Alzheimer's Disease.Statistics in medicine · 2026Article
- Multi-omics decodes host-specific and environmental microbiome interactions in sepsis.Frontiers in microbiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Alzheimer's disease (AD) is a complex and progressive neurodegenerative disorder, characterized by diverse cognitive and functional impairments that manifest heterogeneously across individuals, domains, and time. The accurate assessment of AD's severity and progression requires integrating a variety of data modalities, including multivariate longitudinal neuropsychological tests and multi-omics datasets such as metabolomics and lipidomics. These data sources provide valuable insights into risk factors associated with dementia onset. However, effectively utilizing omics data in dynamic risk estimation for AD progression is challenging due to issues including high dimensionality, heterogeneity, and complex intercorrelations. To address these challenges, we develop a novel joint-modeling framework that effectively combines multi-omics factor analysis (MOFA) for dimension reduction and feature extraction with a multivariate functional mixed model (MFMM) for modeling longitudinal outcomes. This integrative joint modeling approach enables dynamic evaluation of dementia risk by leveraging both omics and longitudinal data. We validate the efficacy of our integrative model through extensive simulation studies and its practical application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.