Evidence map›Paper›PMID 41441216›Full record

ReviewNeurology international2025

Systematic Review: Proteomics-Driven Multi-Omics Integration for Alzheimer's Disease Pathology and Precision Medicine.

Jonathan Mingsong Dong, Huan Zhong

Abstract readReview
In one paragraph

Review in Neurology international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jonathan Mingsong DongLower Canada College, Montreal, QC H4A 2L1, Canada.ORCID 0009-0000-1711-6235
Huan ZhongMichael Smith Laboratories, Department of Biochemistry and Molecular Biology, Life Sciences Institute, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.ORCID 0000-0002-7294-2254

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNeurodegenerative diseases remain a central topic in biomedical research, with Alzheimer's disease (AD) being the most extensively studied. Recent advances in multi-omics integration, particularly proteomics-based approaches, have enabled a deeper understanding of AD-related molecular pathways and their interconnections. However, challenges such as data heterogeneity and the complexity of large-scale datasets continue to hinder comprehensive integration and model interpretation.

methodsA total of 792 publications were retrieved from PubMed, among which, 27 peer-reviewed studies from 2024 and 2025 focusing on proteomics-anchored multi-omics integration for AD were selected for detailed analysis. These papers were categorized based on their integration strategies, omics combinations, and analytical methodologies. Additionally, statistical analysis of 218 studies published in 2024-2025 was performed to identify dominant omics layers and common integration trends.

resultsProteomics emerged as the most frequently studied omics layer and was most often integrated with transcriptomics in AD multi-omics studies. The analysis also revealed recurrent machine learning methods used for feature extraction and integration, along with key biological pathways implicated in AD pathogenesis, including amyloid metabolism, synaptic function, and neuroinflammation.

conclusionsThis review provides a systematic overview of recent trends in proteomics-based multi-omics integration for AD research. It highlights both the scientific advances and methodological limitations in current approaches, serving as a valuable reference for researchers seeking to refine analytical frameworks and design more interpretable, data-driven studies in neurodegenerative disease research.

Indexed as

Alzheimer’s diseasemachine learningmulti-omicsproteomics

Identifiers

PMID41441216
PMCPMC12736289

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.