Evidence map›Paper›PMID 40671800›Full record

ArticleResearch square2025

Deciphering the Glycoproteomic Landscape of Mood Disorders: Unveiling Molecular Association Between CDG and Depression Resilience.

Tamas Kozicz, Rohit Budhraja, Graeme Preston, Neha Joshi, Irena Muffels, Gustavo Turecki, Mark Frye, Marin Veldic, Eva Morava, Akhilesh Pandey

Abstract readPreprint
In one paragraph

Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Tamas KoziczIcahn School of Medicine at Mount Sinai.
Rohit BudhrajaMayo Clinic.
Graeme PrestonIcahn School of Medicine at Mount Sinai.ORCID 0000-0001-6810-9326
Neha JoshiMayo Clinic.
Irena MuffelsIcahn School of Medicine at Mount Sinai.
Gustavo TureckiMcGill University.ORCID 0000-0003-4075-2736
Mark FryeMayo Clinic.ORCID 0000-0001-6997-4215
Marin VeldicMayo Clinic.
Eva MoravaIcahn School of Medicine at Mount Sinai.
Akhilesh PandeyMayo Clinic.ORCID 0000-0001-9943-6127

Funding

Women's Cancer ProgramP30CA015083 · NCI · MAYO CLINIC ROCHESTER · PI Lila J. Rutten · 1985 to 2026
$151.3M
NCI NIH HHS P30 CA015083
6 · The paper itself

Abstract

The lack of a molecular understanding of mood disorders has impeded progress in diagnosis and treatment. Glycosylation may provide insights into the complex mechanisms underlying these conditions. We conducted N-glycoproteomic analysis on dorsolateral prefrontal cortex samples from individuals with major depressive disorder (MDD) and bipolar disorder (BD), in depressive or manic states at death. Additionally, we examined depression prevalence in congenital disorders of glycosylation (CDG) through a literature review and assessment of 110 CDG patients. Glycoproteomic analysis revealed a significant increase in protein glycosylation in individuals with MDD relative to both controls and individuals with BD. Depression prevalence was lower in our pediatric and adult cohort of individuals with CDG. These results suggest brain glycosylation changes may play a role in mood disorder pathology and highlight the distinct biology of unipolar and bipolar depression. Our findings propose that impaired glycosylation may confer resilience to depression, offering potential therapeutic insights.

Indexed as

bipolar disorderCongenital disorders of glycosylationdepressed mood statedorsolateral prefrontal cortexglycoproteomicsLC-MS/MSmajor depressionmanic mood statepostmortem brainprotein N-glycosylation

Identifiers

PMID40671800
PMCPMC12265165

What Socratic holds

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LicenceCC BY
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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 Socratic graph.