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Record W7117258740 · doi:10.1002/alz70858_098789

YKL‐40 as a Biomarker of Inflammation in Chronic Insomnia: A Potential Pathway to Alzheimer's Disease

2025· article· en· W7117258740 on OpenAlexaff
Rayan Daoudi, Marie‐Josée Quinn, Julie Otis, Caroline d'Aragon, Alex Désautels, Mélanie Vendette, Erlan Sanchez, Julie Carrier, Nadia Gosselin, REBECCA ROBILLARD, Beatriz Helena Domingos Oliveira, Nicole Lazarovici, Andrée‐Ann Baril

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsMcGill UniversityUniversity of OttawaUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsDiseaseComorbidityInflammationBiomarkerInsomniaVulnerability (computing)Mental health

Abstract

fetched live from OpenAlex

INTRODUCTION: Some sleep disturbances and disorders have been suggested as pro-inflammatory conditions acting as risk factors for Alzheimer's disease (AD). YKL-40, a protein secreted by astrocytes and microglia during neuroinflammation has been documented as a promising biomarker for neuroinflammatory processes and AD risk, and may be elevated in the context of sleep disorders, but this hasn't yet been investigated. This study aimed to characterize the relationship between sleep-defined as insomnia, its severity, and various sleep disturbances-and plasma levels of YKL-40, while also exploring the influence of potential confounders. These findings could provide insight into how sleep may act as modifiable risk factors for AD. MATERIALS AND METHODS: The study included 40 participants with clinically diagnosed insomnia (61.18 ± 8.32 years, 25W) and 34 controls (64.44 ± 5.86 years, 12W). Plasma YKL-40 concentrations were measured by ELISA. Sleep metrics were obtained through full-night polysomnographic recordings. ANCOVAs were conducted to compare YKL-40 concentrations between groups (1) insomnia vs. controls (2) non-severe insomnia vs. severe insomnia. Secondly, linear regressions were used to analyze the relationship between Insomnia Severity Index (ISI) scores and sleep characteristics with YKL-40 levels. RESULTS AND DISCUSSION: In the full sample (n = 74), no association was found between insomnia severity, insomnia status, nor any objective sleep characteristics and YKL-40 concentration. Among the participants diagnosed with insomnia, those with severe insomnia (ISI≥22, n = 12) exhibited higher YKL-40 levels compared to participants with non-severe insomnia (ISI<22, n = 28), and higher ISI scores were associated with higher YKL-40 levels. These findings remained significant after adjusting for age, sex, evidence of other sleep disorders, proinflammatory conditions and behaviors and medication usage. However, they were no longer significant when adjusting for mental health symptoms measured with the Beck Depression Inventory and Beck Anxiety Inventory. CONCLUSION: Our findings suggest that insomnia severity in individuals diagnosed with insomnia is linked to elevated YKL-40 concentrations, independently of multiple confounders. However, the comorbidity between insomnia and mental disorders may play a key role in the selective vulnerability to AD. Future studies should evaluate the interaction of insomnia severity and mental health symptomatology when predicting AD risk and related processes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.292
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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