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pmid:41567547

MALT1 in cerebrospinal fluid: a prognostic biomarker and potential therapeutic target in Alzheimer's disease.

Siyi Jiang · Ping Qi · Jia Tian · Huaizheng Liu · Chuanzheng Sun

Frontiers in neurology · 2025 doi:10.3389/fneur.2025.1732729

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complete

Experiment

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Data

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Analysis

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title
MALT1 in cerebrospinal fluid: a prognostic biomarker and potential therapeutic target in Alzheimer's disease.
doi
10.3389/fneur.2025.1732729
pmid
41567547
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Siyi Jiang
[1]
Ping Qi
[2]
Jia Tian
[3]
Huaizheng Liu
[4]
Chuanzheng Sun
journal
Frontiers in neurology
year
2025
abstract
Background: Alzheimer's disease (AD) is a devastating neurodegenerative disorder, and early intervention remains the only reliable strategy to slow its progression. Notably, cerebrospinal fluid (CSF) metabolites play a crucial role in the early diagnosis of AD, making their investigation highly significant. Methods: We integrated two-sample Mendelian randomization (MR), transcriptomic, and machine learning (ML) analyses to identify causal CSF metabolites and their downstream molecular mediators in AD. MR assessed the causal effects of 338 CSF metabolites on AD risk, while integrated GEO datasets (GSE4757, GSE48350, and GSE122063) were analyzed to identify 50 differentially expressed associated genes (DEAGs). Immune infiltration and correlation analyses were performed to characterize immune infiltration. Predictive ML models, including Random Forest (RF), Support Vector Machine (SVM), Generalized Linear Model (GLM), and Extreme Gradient Boosting (XGBoost), were used to screen biomarkers, construct a diagnostic nomogram, and validate findings in vivo. Results: The MR analysis identified 15 potential CSF metabolites associated with AD. Elevated creatine levels (OR = 0.610, 95% CI: 0.441-0.845, p = 0.003) were protective against AD, whereas increased leucine levels (OR = 1.548, 95% CI: 1.210-1.981, p < 0.001) were associated with higher AD risk. Transcriptomic analysis revealed 50 DEAGs enriched in lipid metabolism, inflammation, and neural signaling pathways. Immune infiltration analysis demonstrated altered adaptive and innate immune populations, linking risk genes to immune activation. ML analysis identified five robust predictors (PLXDC2, DTNB, ALOX5, MALT1, and F13A1), with the SVM model showing optimal performance (AUC = 0.895) and an independently validated nomogram (AUC = 0.933). MR and GEO datasets (GSE138260) further confirmed MALT1 as a potential risk biomarker, and in vivo characterization supported its association with adverse prognosis in AD. Conclusion: This study demonstrates a causal association between CSF metabolites and AD risk, highlighting MALT1 as a promising biomarker and potential therapeutic target for AD.
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We conducted two-sample MR to assess potential causal associations between differential CSF metabolites and AD risk in the forest plot (IVW as primary, complemented by weighted median, MR-Egger, and mode-based estimators) (Supplementary Table S1).
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We conducted two-sample MR to assess potential causal associations between differential CSF metabolites and AD risk in the forest plot (IVW as primary, complemented by weighted median, MR-Egger, and mode-based estimators) (Supplementary Table S1).
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The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
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MR analysis identified 15 potential CSF metabolites associated with AD.
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253
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null
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570
taxon
Homo sapiens
title
Alzheimer's Disease Dataset
resolution_checked_at
2026-11-01T15:39:54Z
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[5]
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[7]
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[8]
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[9]
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We conducted two-sample MR to assess potential causal associations between differential CSF metabolites and AD risk in the forest plot (IVW as primary, complemented by weighted median, MR-Egger, and mode-based estimators) (Supplementary Table S1).
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evidence object · 3 fields
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complete
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mr_estimate
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basis
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tier
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[0]
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GSE48350
[2]
GSE122063
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The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
basis
The paper names GSE4757, GSE48350, GSE122063, GSE138260, GSE200164 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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minimax
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minimax
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We conducted two-sample MR to assess potential causal associations between differential CSF metabolites and AD risk in the forest plot (IVW as primary, complemented by weighted median, MR-Egger, and mode-based estimators) (Supplementary Table S1).
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41567547", "kind": "science-record", "revision": 3, "content_hash": "sha256:a578d20afde2e195ab9aca0aa1ff9f532bd74bd8c6124f8f30cad9a8de5f4ab2", "created_at": "2026-11-01T20:31:31Z", "updated_at": "2026-11-01T22:15:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "MALT1 in cerebrospinal fluid: a prognostic biomarker and potential therapeutic target in Alzheimer's disease.", "doi": "10.3389/fneur.2025.1732729", "pmid": "41567547", "authors": ["Siyi Jiang", "Ping Qi", "Jia Tian", "Huaizheng Liu", "Chuanzheng Sun"], "journal": "Frontiers in neurology", "year": 2025, "abstract": "Background: Alzheimer's disease (AD) is a devastating neurodegenerative disorder, and early intervention remains the only reliable strategy to slow its progression. Notably, cerebrospinal fluid (CSF) metabolites play a crucial role in the early diagnosis of AD, making their investigation highly significant.\nMethods: We integrated two-sample Mendelian randomization (MR), transcriptomic, and machine learning (ML) analyses to identify causal CSF metabolites and their downstream molecular mediators in AD. MR assessed the causal effects of 338 CSF metabolites on AD risk, while integrated GEO datasets (GSE4757, GSE48350, and GSE122063) were analyzed to identify 50 differentially expressed associated genes (DEAGs). Immune infiltration and correlation analyses were performed to characterize immune infiltration. Predictive ML models, including Random Forest (RF), Support Vector Machine (SVM), Generalized Linear Model (GLM), and Extreme Gradient Boosting (XGBoost), were used to screen biomarkers, construct a diagnostic nomogram, and validate findings in vivo.\nResults: The MR analysis identified 15 potential CSF metabolites associated with AD. Elevated creatine levels (OR = 0.610, 95% CI: 0.441-0.845, p = 0.003) were protective against AD, whereas increased leucine levels (OR = 1.548, 95% CI: 1.210-1.981, p < 0.001) were associated with higher AD risk. Transcriptomic analysis revealed 50 DEAGs enriched in lipid metabolism, inflammation, and neural signaling pathways. Immune infiltration analysis demonstrated altered adaptive and innate immune populations, linking risk genes to immune activation. ML analysis identified five robust predictors (PLXDC2, DTNB, ALOX5, MALT1, and F13A1), with the SVM model showing optimal performance (AUC = 0.895) and an independently validated nomogram (AUC = 0.933). MR and GEO datasets (GSE138260) further confirmed MALT1 as a potential risk biomarker, and in vivo characterization supported its association with adverse prognosis in AD.\nConclusion: This study demonstrates a causal association between CSF metabolites and AD risk, highlighting MALT1 as a promising biomarker and potential therapeutic target for AD.", "claims": [{"id": "claim-method", "statement": "We conducted two-sample MR to assess potential causal associations between differential CSF metabolites and AD risk in the forest plot (IVW as primary, complemented by weighted median, MR-Egger, and mode-based estimators) (Supplementary Table S1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41567547", "locator": "sections[19] \"MR analysis between cerebrospinal fluid metabolites and Alzheimer’s disease\" paragraph 2", "quote": "We conducted two-sample MR to assess potential causal associations between differential CSF metabolites and AD risk in the forest plot (IVW as primary, complemented by weighted median, MR-Egger, and mode-based estimators) (Supplementary Table S1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41567547", "locator": "sections[33] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "MR analysis identified 15 potential CSF metabolites associated with AD.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41567547", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "MR analysis identified 15 potential CSF metabolites associated with AD."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "MR analysis identified 15 potential CSF metabolites associated with AD.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41567547"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122063"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122063"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41567547", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fneur.2025.1732729", "sha256": 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