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Record W4413004884 · doi:10.3389/fnagi.2025.1630022

Neuroinflammation-mediated YKL-40 correlates with tau pathology and predicts longitudinal cognitive impairment and brain atrophy in Alzheimer’s disease, with hypertensive dependency

2025· article· en· W4413004884 on OpenAlexfundno aff
Yayu Wang, Man Zhang, Shu-Jian Chen, Wei Miao, Zhixin Wang, Yajun Zhou, Siqi Yu, Zhongwu Sun, Xia Zhou, Xianfeng Yu, Xiaoqun Zhu

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicStudies on Chitinases and Chitosanases
Canadian institutionsnot available
FundersNatural Science Foundation of Anhui ProvinceCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierEisaiNorthern California Institute for Research and EducationPfizerNovartis Pharmaceuticals CorporationUniversity of Southern CaliforniaBiogenEli Lilly and CompanyBristol-Myers SquibbBioClinicaU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsAlzheimer's Association
KeywordsNeuroinflammationPsychologyAlzheimer's Disease Neuroimaging InitiativeInternal medicineNeuroimagingEntorhinal cortexAtrophyCognitionCognitive declineDementiaNeuroscienceMedicineOncologyDiseaseHippocampus

Abstract

fetched live from OpenAlex

Background: Neuroinflammation and hypertension are involved in Alzheimer's disease (AD). However, their independent and additive impacts on astrocytes and AD-related pathologies have not been fully explored. Hence, this study investigated whether the associations between astrocyte reactivity, measured by cerebrospinal fluid (CSF), Chitinase 3-like protein 1 (CHI3L1/YKL-40), and AD-related pathologies were mediated by neuroinflammation and whether these associations were modified by hypertension. We also investigated the influence of hypertension on the relationship between baseline levels of CSF YKL-40 and longitudinal changes in cognitive function and brain structures. Methods: This study analyzed 288 participants from the AD Neuroimaging Initiative (ADNI) database. Multivariate linear regression, interaction, and subgroup analyses were conducted to explore the interrelationship between CSF YKL-40, AD biomarkers, neuroinflammation, cognitive function, and brain structures. Causal mediation analyses with 10,000 bootstrapped iterations were performed, using CSF YKL-40 as the independent variable and AD-related pathologies as the dependent variables, to explore mediation effects of neuroinflammation. Linear mixed-effects models were employed to study the associations between CSF YKL-40 and longitudinal changes in cognitive function and brain structures. Results: Higher baseline CSF YKL-40 levels were correlated with higher p-tau, t-tau, and neuroinflammatory biomarkers (ICAM1, VCAM1, sTNFR1, and sTNFR2), but with lower entorhinal cortex volume. Interaction showed that hypertension had a moderating influence on the associations between CSF YKL-40 and p-tau and t-tau. The significant associations of CSF YKL-40 with p-tau and t-tau were partially mediated by neuroinflammatory biomarkers (ICAM1, VCAM1, sTNFR1, and sTNFR2) in the whole sample (proportions: 13.0%∼78.8%). Similarly, the partial mediation effects of VCAM1, sTNFR1, and sTNFR2 on the aforementioned associations also existed in hypertensive subgroup (proportions: 17.9%∼50.3%). Additionally, higher baseline levels of CSF YKL-40 predicted faster decline in cognitive performance and brain atrophy (volumes of whole brain, hippocampus, entorhinal cortex, and middle temporal lobe) in the whole sample. Notably, subgroup analyses showed that the associations between higher CSF YKL-40 and faster brain atrophy were pronounced in hypertensive individuals. Conclusion: These findings suggest that neuroinflammation may mediate the relationship between astrocyte reactivity, measured by CSF YKL-40, and AD-related pathologies, with significant hypertensive dependency. Furthermore, elevated baseline CSF YKL-40 levels accelerated cognitive decline and brain atrophy, particularly in hypertensive individuals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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 teacher head, 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

Citations8
Published2025
Admission routes1
Has abstractyes

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