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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Aging Neurosci.</journal-id>
<journal-title>Frontiers in Aging Neuroscience</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Aging Neurosci.</abbrev-journal-title>
<issn pub-type="epub">1663-4365</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnagi.2025.1630022</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Neuroscience</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Neuroinflammation-mediated YKL-40 correlates with tau pathology and predicts longitudinal cognitive impairment and brain atrophy in Alzheimer&#x2019;s disease, with hypertensive dependency</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Wang</surname> <given-names>Ya-Yu</given-names></name>
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<contrib contrib-type="author">
<name><surname>Zhang</surname> <given-names>Man</given-names></name>
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</contrib>
<contrib contrib-type="author">
<name><surname>Chen</surname> <given-names>Shu-Jian</given-names></name>
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<contrib contrib-type="author">
<name><surname>Miao</surname> <given-names>Wei</given-names></name>
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<contrib contrib-type="author">
<name><surname>Wang</surname> <given-names>Zhi-Xin</given-names></name>
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<contrib contrib-type="author">
<name><surname>Zhou</surname> <given-names>Ya-Jun</given-names></name>
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<contrib contrib-type="author">
<name><surname>Yu</surname> <given-names>Si-Qi</given-names></name>
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<contrib contrib-type="author">
<name><surname>Sun</surname> <given-names>Zhong-Wu</given-names></name>
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<contrib contrib-type="author">
<name><surname>Zhou</surname> <given-names>Xia</given-names></name>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Yu</surname> <given-names>Xian-Feng</given-names></name>
<xref ref-type="corresp" rid="c002"><sup>&#x002A;</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Zhu</surname> <given-names>Xiao-Qun</given-names></name>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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</contrib>
<on-behalf-of>the Alzheimer&#x2019;s Disease Neuroimaging Initiative</on-behalf-of>
</contrib-group>
<aff><institution>Department of Neurology, The First Affiliated Hospital of Anhui Medical University</institution>, <addr-line>Hefei</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Boon-Seng Wong, Singapore Institute of Technology, Singapore</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Enshe Jiang, Henan University, China</p><p>Yoon A. Kim, The Rockefeller University, United States</p><p>Muge Akinci, Columbia University, United States</p></fn>
<corresp id="c001">&#x002A;Correspondence: Xiao-Qun Zhu, <email>zxq_ayfy@163.com</email></corresp>
<corresp id="c002">Xian-Feng Yu, <email>Dr.YXF@outlook.com</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>06</day>
<month>08</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>17</volume>
<elocation-id>1630022</elocation-id>
<history>
<date date-type="received">
<day>16</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>17</day>
<month>07</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 Wang, Zhang, Chen, Miao, Wang, Zhou, Yu, Sun, Zhou, Yu and Zhu.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Wang, Zhang, Chen, Miao, Wang, Zhou, Yu, Sun, Zhou, Yu and Zhu</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Neuroinflammation and hypertension are involved in Alzheimer&#x2019;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.</p>
</sec>
<sec>
<title>Methods</title>
<p>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.</p>
</sec>
<sec>
<title>Results</title>
<p>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%&#x223C;78.8%). Similarly, the partial mediation effects of VCAM1, sTNFR1, and sTNFR2 on the aforementioned associations also existed in hypertensive subgroup (proportions: 17.9%&#x223C;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.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>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.</p>
</sec>
</abstract>
<kwd-group>
<kwd>Alzheimer&#x2019;s disease</kwd>
<kwd>astrocyte reactivity</kwd>
<kwd>YKL-40</kwd>
<kwd>neuroinflammation</kwd>
<kwd>hypertension</kwd>
</kwd-group>
<contract-sponsor id="cn001">Natural Science Foundation of Anhui Province<named-content content-type="fundref-id">https://doi.org/10.13039/501100003995</named-content></contract-sponsor>
<counts>
<fig-count count="5"/>
<table-count count="1"/>
<equation-count count="0"/>
<ref-count count="63"/>
<page-count count="13"/>
<word-count count="9763"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Alzheimer&#x2019;s Disease and Related Dementias</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="S1" sec-type="intro">
<title>Introduction</title>
<p>Alzheimer&#x2019;s disease is pathologically characterized by the extracellular accumulation of amyloid beta (A&#x03B2;) plaques and intracellular tau neurofibrillary tangles. Neuroinflammation has been considered as the third core pathological feature of AD (<xref ref-type="bibr" rid="B31">Kinney et al., 2018</xref>; <xref ref-type="bibr" rid="B41">McGeer and Rogers, 1992</xref>). Neuroinflammation, triggered by microglial and astrocytic activation, occurs decades before the clinical manifestation of AD and is strongly linked to the misfolded and aggregated proteins (<xref ref-type="bibr" rid="B55">Steardo et al., 2015</xref>). Evidence from clinical studies demonstrated that inflammatory biomarkers were elevated in AD patients (<xref ref-type="bibr" rid="B51">Shen et al., 2019</xref>), and utilizing anti-inflammatory drugs might reduce the risk of AD (<xref ref-type="bibr" rid="B58">Wang et al., 2015</xref>). Astrocytes, as a key component of the neurovascular unit (<xref ref-type="bibr" rid="B26">Iadecola, 2017</xref>), are crucial for maintaining the integrity of the blood-brain barrier (BBB) (<xref ref-type="bibr" rid="B38">Luissint et al., 2012</xref>). Notably, astrocytes are important regulators of central nervous system inflammatory response and actively contribute to neuroinflammatory cascades in AD pathogenesis (<xref ref-type="bibr" rid="B19">Ferrari-Souza et al., 2022</xref>). Elucidating the astrocyte-neuroinflammation interplay may therefore provide potential clinical value for diagnosis and treatments for AD.</p>
<p>Chitinase 3-like protein 1 (CHI3L1/YKL-40) is almost exclusively expressed by astrocytes throughout neurodevelopment in human brain tissue (<xref ref-type="bibr" rid="B12">Connolly et al., 2023</xref>), and strongly colocalizes with the astrocyte marker Glial fibrillary acidic protein (GFAP) (<xref ref-type="bibr" rid="B37">Llorens et al., 2017</xref>; <xref ref-type="bibr" rid="B47">Querol-Vilaseca et al., 2017</xref>). It is being increasingly considered as a biomarker of reactive astrocytes in AD (<xref ref-type="bibr" rid="B9">Carter et al., 2019</xref>; <xref ref-type="bibr" rid="B46">Pelkmans et al., 2024</xref>). Previous research has shown that high levels of CSF YKL-40 are significantly associated with the accumulation of AD pathologies and increase the risk of AD dementia (<xref ref-type="bibr" rid="B19">Ferrari-Souza et al., 2022</xref>; <xref ref-type="bibr" rid="B29">Janelidze et al., 2018</xref>; <xref ref-type="bibr" rid="B46">Pelkmans et al., 2024</xref>; <xref ref-type="bibr" rid="B56">Vergallo et al., 2020</xref>; <xref ref-type="bibr" rid="B59">Warmenhoven et al., 2024</xref>). However, there are studies reporting somewhat contradictory findings that higher CSF YKL-40 predicts slower cognitive decline in AD dementia (<xref ref-type="bibr" rid="B30">Kester et al., 2015</xref>). Together, the contribution of astrocytic activity, as reflected by the elevated levels of CSF YKL-40, to the pathogenesis of AD and its value in monitoring the progression of AD may be complex and require further elucidation.</p>
<p>Additionally, numerous studies have pointed out that vascular risk factors, especially hypertension, were strongly associated with AD (<xref ref-type="bibr" rid="B32">Kivipelto et al., 2001</xref>; <xref ref-type="bibr" rid="B42">McGrath et al., 2017</xref>). In vitro and in vivo studies indicate that hypertension exacerbates amyloid and tau pathology, activates neuroinflammation, and accelerates cognitive decline (<xref ref-type="bibr" rid="B2">Bajwa and Klegeris, 2022</xref>; <xref ref-type="bibr" rid="B17">Denver et al., 2019</xref>). In a mouse APP/PS1 model of AD, decreases in Chi3l1/YKL-40 promote astrocytic A&#x03B2; phagocytosis, and mitigate the formation of misfolded proteins (<xref ref-type="bibr" rid="B35">Lananna et al., 2020</xref>). It is noteworthy that YKL-40 levels are elevated in patients with hypertension (<xref ref-type="bibr" rid="B61">Xu et al., 2016</xref>), and other chronic inflammatory conditions, such as diabetes, atherosclerosis (<xref ref-type="bibr" rid="B48">Rathcke and Vestergaard, 2006</xref>). Hypertension has the potential to cause damage to the cerebral microcirculation, decrease cerebral blood flow, and subsequently lead to ischemia, initiating a neuroinflammatory cascade (<xref ref-type="bibr" rid="B53">Snyder et al., 2017</xref>) that involves the activation of astrocytes and microglia, which release YKL-40 (<xref ref-type="bibr" rid="B5">Bonneh-Barkay et al., 2010</xref>) and sTREM2 (<xref ref-type="bibr" rid="B7">Brendel et al., 2017</xref>) as part of the tissue repair process. Taken together, the complex interrelationships between hypertension, neuroinflammation, and YKL-40, and their implications in the progression of AD, remain to be thoroughly explored.</p>
<p>The present study utilized the cross-sectional and longitudinal data with the aims to (1) ascertain the relationships of CSF levels of YKL-40 with neuroinflammation, AD core biomarkers (A&#x03B2;<sub>42</sub> and p-tau [phosphorylated tau]), neurodegeneration (t-tau [total tau], MRI brain structures) and cognitive function; (2) explore whether AD modifiable risk factors (vascular risk factors, particularly hypertension) or non-modifiable risk factors (age, sex, apolipoprotein <italic>E4</italic> [<italic>APOE</italic>&#x03B5;<italic>4</italic>] status) moderate these associations; (3) determine whether neuroinflammation serves as a mediating factor in the correlation between CSF levels of YKL-40 and AD-related pathologies; and (4) evaluate the predictive value of CSF level of YKL-40 for longitudinal changes in cognitive function and brain structures from the AD Neuroimaging Initiative (ADNI) database.</p>
</sec>
<sec id="S2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="S2.SS1">
<title>Participants</title>
<p>The data utilized in this study were derived from the ADNI database (<xref ref-type="bibr" rid="B60">Weiner et al., 2013</xref>). The detailed inclusion and exclusion criteria in ADNI are publicly available at <ext-link ext-link-type="uri" xlink:href="https://adni.loni.usc.edu/">https://adni.loni.usc.edu/</ext-link>. A total of 288 participants with defined basic clinical features, CSF parameters, imaging analysis, and cognitive evaluation were enrolled, with an average follow-up time of 4.28 years (standard deviation [SD] = 3.75 years). Specifically, the participants were classified into 84 cognitively normal persons (CN, Mini-Mental State Examination [MMSE] &#x003E; 24, Clinical Dementia Rating Sum of Boxes [CDR] = 0), 127 persons with mild cognitive impairment (MCI; MMSE &#x003E; 24, CDR = 0.5) and 77 patients with AD dementia according to predefined criteria (<xref ref-type="bibr" rid="B1">Aisen et al., 2010</xref>). Individuals were defined as hypertensive if they reported a history of hypertension or using antihypertensive medication. Prior to enrollment, written informed consent was obtained from each participant or their legally authorized representative.</p>
</sec>
<sec id="S2.SS2">
<title>Measurements of CSF biomarkers</title>
<p>CSF YKL-40 was quantified at the University of Washington using the MicroVue YKL-40 Enzyme-Linked Immunosorbent Assay (ELISA) (Quidel Corp.) (<xref ref-type="bibr" rid="B14">Craig-Schapiro et al., 2010</xref>). Concentrations of CSF A&#x03B2;<sub>42</sub>, p-tau, and t-tau were determined using multiple xMAP technology platforms (Luminex Corporation) and INNOBIA AlzBio3 kit (Ghent, Belgium) at the University of Pennsylvania (<xref ref-type="bibr" rid="B50">Shaw et al., 2009</xref>). Neuroinflammatory biomarkers in CSF, comprising of four anti-inflammatory molecules (soluble tumor necrosis factor receptor 1 [sTNFR1], sTNFR2, transforming growth factor-beta 1 [TGF-&#x03B2;1], interleukin-10 [IL-10]) and four pro-inflammatory molecules (intercellular cell adhesion molecule-1 [ICAM1], vascular cell adhesion molecule-1 [VCAM1], IL-6, and IL-7) were quantified using commercially available ELISA kits (Millipore Sigma, Burlington, MA) (<xref ref-type="bibr" rid="B13">Craig-Schapiro et al., 2011</xref>). According to the A/T scheme, participants were categorized as A + (CSF A&#x03B2;<sub>42</sub> &#x003C; 976.6 pg/mL) or T + (CSF p-tau &#x003E; 21.8 pg/mL) based on established biomarker cutoffs for subsequent analyses (<xref ref-type="bibr" rid="B23">Hansson et al., 2018</xref>). The measurements of all CSF biomarkers were duplicated, and the mean values were calculated. Coefficients of variation for CSF analytes, both within and between lots, were less than 15%. The original data and detailed assay protocols are available in the ADNI database.</p>
</sec>
<sec id="S2.SS3">
<title>Cognitive assessment</title>
<p>Cognitive function was evaluated through a range of scales. The MMSE and the AD Assessment Scale-Cognitive Subscale 13 (ADAS-13) were used to assess global cognitive function. Functional Assessment Questionnaire (FAQ) were used to evaluate the life quality. Domain-specific cognitive functions were assessed using neuropsychological test batteries, with composite scores reflecting episodic memory (MEM), language (LAN), executive function (EXF) and visuospatial function (VP) (<xref ref-type="bibr" rid="B15">Crane et al., 2012</xref>; <xref ref-type="bibr" rid="B20">Gibbons et al., 2012</xref>). Detailed information can be found in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>. All evaluations were performed at baseline and at follow-up visits.</p>
</sec>
<sec id="S2.SS4">
<title>Structural magnetic resonance imaging (MRI) measures</title>
<p>The protocols for MRI acquisition have been detailed in prior publications (<xref ref-type="bibr" rid="B28">Jack et al., 2008</xref>). Structural MRIs were acquired using the Siemens Verio 1.5 T or 3 T MRI Scanner. Cortical reconstruction and volumetric segmentation were automated using FreeSurfer software based on T1-weighted images to estimate the volumes of regional brain regions. We focus on the volumes of the whole brain, hippocampus, entorhinal cortex, and middle temporal lobe as the primary AD signature regions of interest (ROI). All MRI measures were averaged for the left and right hemispheres.</p>
</sec>
<sec id="S2.SS5">
<title>Statistical analyses</title>
<p>Baseline characteristics were compared between different hypertensive status (hypertension versus normotension) using Mann-Whitney U tests for continuous and &#x03C7;<sup>2</sup>-tests for categorical variables. Data of the CSF biomarkers exhibited a non-normal distribution and therefore log<sub>10</sub>-transformation was conducted for statistical analysis.</p>
<p>The first aim of this study was to assess the direct correlations between CSF levels of YKL-40 (independent variable) and CSF levels of AD biomarkers, neuroinflammatory biomarkers, brain structural MRI data and cognitive scores (dependent variables) at baseline. To achieve this, we conducted multivariate linear regression (MLR) analyses. Sensitivity analyses were performed by constructing various MLR models with adjustment of more potential confounding factors, including the level of CSF A&#x03B2;<sub>42</sub>, cognitive status and vascular risk factors (history of hypertension, diabetes, hyperlipidemia, stroke and smoking). Stratified analyses among different diagnostic (CN, MCI, and AD) and biological (A- versus A+, T- versus T+) and combined (A-CN, A-MCI, A + CN, A + MCI, etc.) subgroups were also conducted as the exploratory analyses. Moreover, interaction terms between baseline CSF levels of YKL-40 and A&#x03B2; status, sex, <italic>APOE</italic>&#x03B5;<italic>4</italic> status, age, or vascular risk factors (hypertension and hyperlipidemia) were added to MLR models to test their influences on the aforementioned associations.</p>
<p>Next, mediation analyses, proposed by <xref ref-type="bibr" rid="B3">Baron and Kenny (1986)</xref>, were used to assess whether CSF levels of neuroinflammatory biomarkers could not, or could partially or entirely mediate any significant association between CSF YKL-40 levels and AD-related pathologies. For each mediation model, we used the CSF level of YKL-40 as an independent variable and AD-related pathologies as the dependent variables. The Sobel test (<xref ref-type="bibr" rid="B27">Imai et al., 2010</xref>) was employed to estimate the direct and indirect effects, as well as the proportion mediated, with the significance of these effects established through 10,000 bootstrap replications.</p>
<p>Finally, linear mixed effects (LME) models were conducted to identify whether the baseline CSF levels of YKL-40 could predict longitudinal changes in cognitive function and MRI brain structures. To facilitate the analysis, CSF YKL-40 was categorized as a dichotomous variable (low versus high) determined by the median concentration with a threshold of 390 ng/mL. These LME models were created using cognitive functions and MRI brain structures as dependent variables. Time since baseline (years), CSF YKL-40 (continuous variable), and their interaction were included as fixed effects. Sex, age, education, <italic>APOE</italic>&#x03B5;<italic>4</italic> carrier status, intracranial volume ([ICV], when appropriate), and their interactions with time were used as covariates with subject-specific intercepts and slopes.</p>
<p>All regression analyses were adjusted for covariates, including sex, age, education, and <italic>APOE</italic>&#x03B5;<italic>4</italic> status. Additionally, ICV was also included as a covariate in the analyses of the structural MRI data. A two-sided <italic>p</italic>-value &#x003C; 0.05 was considered statistically significant. False discovery rate (FDR) was used for multiple corrections where specifically noted (<xref ref-type="bibr" rid="B4">Benjamini and Hochberg, 1995</xref>). R version 4.4.1 (packages including &#x201C;lm,&#x201D; &#x201C;ggplot2,&#x201D; &#x201C;car,&#x201D; &#x201C;mediate,&#x201D; and &#x201C;nlme&#x201D;) and IBM SPSS Statistics version 27.0.1 were used for statistical analyses and figure preparation.</p>
</sec>
</sec>
<sec id="S3" sec-type="results">
<title>Results</title>
<sec id="S3.SS1">
<title>Baseline participants characteristics</title>
<p>The demographic features, CSF biomarker levels, cognitive scores, and MRI brain structures data stratified by hypertensive status are presented in <xref ref-type="table" rid="T1">Table 1</xref>. A total of 288 participants were included in this study, of whom 131 (46.2%) had hypertension. The whole sample had a male proportion of 41.0%, and an <italic>APOE</italic> &#x03B5;<italic>4</italic> carrier proportion of 50.0%. The mean (&#x00B1;SD) values of the age, years of education, and CSF level of YKL-40 (ng/ml) were 74.97 (&#x00B1;7.36), 15.56 (&#x00B1;2.97), and 406.57 (&#x00B1;133.31), respectively. There were higher CSF levels of sTNFR1 and sTNFR2 in the hypertensive subgroup (HTN+) than in the normotensive subgroup (HTN-) (<italic>p</italic> &#x003C; 0.05). However, we found no difference in demographic features, other biomarkers or cognitive function between the two subgroups at baseline.</p>
<table-wrap position="float" id="T1">
<label>TABLE 1</label>
<caption><p>Demographic and clinical characteristics of participants in the study.</p></caption>
<table cellspacing="5" cellpadding="5" frame="box" rules="all">
<thead>
<tr>
<td valign="top" align="left" style="color:#ffffff;background-color: #7f8080;">Variables</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Total (<italic>n</italic> = 288)</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Hypertension (<italic>n</italic> = 131)</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;">Normotension (<italic>n</italic> = 157)</td>
<td valign="top" align="center" style="color:#ffffff;background-color: #7f8080;"><italic>P</italic>-value</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Age, mean (SD), y</td>
<td valign="top" align="center">74.97 (7.36)</td>
<td valign="top" align="center">75.92 (7.05)</td>
<td valign="top" align="center">74.18 (7.52)</td>
<td valign="top" align="center">0.069</td>
</tr>
<tr>
<td valign="top" align="left">Male, n (%)</td>
<td valign="top" align="center">118 (40.97)</td>
<td valign="top" align="center">54 (41.22)</td>
<td valign="top" align="center">64 (40.76)</td>
<td valign="top" align="center">0.937</td>
</tr>
<tr>
<td valign="top" align="left">Education, mean (SD), y</td>
<td valign="top" align="center">15.56 (2.97)</td>
<td valign="top" align="center">15.63 (3.05)</td>
<td valign="top" align="center">15.49 (2.90)</td>
<td valign="top" align="center">0.619</td>
</tr>
<tr>
<td valign="top" align="left"><italic>APOE &#x03B5;4</italic> carriers (%)</td>
<td valign="top" align="center">144 (50.00)</td>
<td valign="top" align="center">63 (48.09)</td>
<td valign="top" align="center">81 (51.59)</td>
<td valign="top" align="center">0.554</td>
</tr>
<tr>
<td valign="top" align="left">YKL-40, mean (SD), ng/ml</td>
<td valign="top" align="center">406.57 (133.31)</td>
<td valign="top" align="center">424.24 (136.45)</td>
<td valign="top" align="center">388.18 (124.68)</td>
<td valign="top" align="center">0.162</td>
</tr>
<tr>
<td valign="top" align="left" colspan="5" style="background-color: #dcdcdc;"><bold>CSF AD biomarkers, mean (SD), pg/ml</bold></td>
</tr>
<tr>
<td valign="top" align="left">A&#x03B2;<sub>42</sub></td>
<td valign="top" align="center">961.22 (574.23)</td>
<td valign="top" align="center">987.45 (564.03)</td>
<td valign="top" align="center">939.34 (581.70)</td>
<td valign="top" align="center">0.371</td>
</tr>
<tr>
<td valign="top" align="left">p-tau</td>
<td valign="top" align="center">29.27 (13.86)</td>
<td valign="top" align="center">30.00 (15.31)</td>
<td valign="top" align="center">28.67 (12.48)</td>
<td valign="top" align="center">0.812</td>
</tr>
<tr>
<td valign="top" align="left">T-tau</td>
<td valign="top" align="center">299.54 (119.19)</td>
<td valign="top" align="center">306.03 (130.67)</td>
<td valign="top" align="center">294.12 (108.40)</td>
<td valign="top" align="center">0.759</td>
</tr>
<tr>
<td valign="top" align="left" colspan="5" style="background-color: #dcdcdc;"><bold>CSF neuroinflammatory biomarkers, mean (SD), pg/ml</bold></td>
</tr>
<tr>
<td valign="top" align="left">sTNFR1</td>
<td valign="top" align="center">874.82 (230.71)</td>
<td valign="top" align="center">900.52 (231.46)</td>
<td valign="top" align="center">853.37 (227.87)</td>
<td valign="top" align="center"><bold>0.042</bold></td>
</tr>
<tr>
<td valign="top" align="left">sTNFR2</td>
<td valign="top" align="center">1059.48 (387.00)</td>
<td valign="top" align="center">1109.88 (493.05)</td>
<td valign="top" align="center">1017.42 (260.80)</td>
<td valign="top" align="center"><bold>0.047</bold></td>
</tr>
<tr>
<td valign="top" align="left">TGF-&#x03B2;1</td>
<td valign="top" align="center">106.58 (39.72)</td>
<td valign="top" align="center">106.51 (38.10)</td>
<td valign="top" align="center">106.64 (41.03)</td>
<td valign="top" align="center">0.949</td>
</tr>
<tr>
<td valign="top" align="left">IL-10</td>
<td valign="top" align="center">5.75 (2.68)</td>
<td valign="top" align="center">5.78 (2.96)</td>
<td valign="top" align="center">5.73 (2.43)</td>
<td valign="top" align="center">0.384</td>
</tr>
<tr>
<td valign="top" align="left">ICAM1</td>
<td valign="top" align="center">374.79 (191.05)</td>
<td valign="top" align="center">378.76 (193.73)</td>
<td valign="top" align="center">371.49 (188.72)</td>
<td valign="top" align="center">0.893</td>
</tr>
<tr>
<td valign="top" align="left">VCAM1</td>
<td valign="top" align="center">43047.53 (23033.19)</td>
<td valign="top" align="center">43816.10 (22429.97)</td>
<td valign="top" align="center">42406.32 (23505.47)</td>
<td valign="top" align="center">0.440</td>
</tr>
<tr>
<td valign="top" align="left">IL-6</td>
<td valign="top" align="center">5.14 (4.79)</td>
<td valign="top" align="center">5.55 (5.17)</td>
<td valign="top" align="center">4.79 (4.42)</td>
<td valign="top" align="center">0.450</td>
</tr>
<tr>
<td valign="top" align="left">IL-7</td>
<td valign="top" align="center">1.29 (1.76)</td>
<td valign="top" align="center">1.20 (1.34)</td>
<td valign="top" align="center">1.37 (2.14)</td>
<td valign="top" align="center">0.318</td>
</tr>
<tr>
<td valign="top" align="left" colspan="5" style="background-color: #dcdcdc;"><bold>MRI measures (mm<sup>3</sup>)</bold></td>
</tr>
<tr>
<td valign="top" align="left">Hippocampus</td>
<td valign="top" align="center">6488.55 (1169.38)</td>
<td valign="top" align="center">6444.97 (1140.61)</td>
<td valign="top" align="center">6524.87 (1191.60)</td>
<td valign="top" align="center">0.602</td>
</tr>
<tr>
<td valign="top" align="left">Entorhinal cortex</td>
<td valign="top" align="center">3337.46 (840.30)</td>
<td valign="top" align="center">3307.00 (858.15)</td>
<td valign="top" align="center">3362.85 (824.28)</td>
<td valign="top" align="center">0.488</td>
</tr>
<tr>
<td valign="top" align="left">Mid temporal</td>
<td valign="top" align="center">18683.23 (3293.88)</td>
<td valign="top" align="center">18340.28 (3116.37)</td>
<td valign="top" align="center">18969.03 (3408.54)</td>
<td valign="top" align="center">0.146</td>
</tr>
<tr>
<td valign="top" align="left">Whole brain</td>
<td valign="top" align="center">983889.73 (105778.04)</td>
<td valign="top" align="center">979612.18 (95668.20)</td>
<td valign="top" align="center">987341.26 (113161.98)</td>
<td valign="top" align="center">0.644</td>
</tr>
<tr>
<td valign="top" align="left">ICV</td>
<td valign="top" align="center">1557361.91 (171809.40)</td>
<td valign="top" align="center">1556777.63 (16058.20)</td>
<td valign="top" align="center">1557849.43 (180644.01)</td>
<td valign="top" align="center">0.967</td>
</tr>
<tr>
<td valign="top" align="left" colspan="5" style="background-color: #dcdcdc;"><bold>Cognitive scores</bold></td>
</tr>
<tr>
<td valign="top" align="left">MMSE</td>
<td valign="top" align="center">26.63 (2.60)</td>
<td valign="top" align="center">26.68 (2.70)</td>
<td valign="top" align="center">26.58 (2.52)</td>
<td valign="top" align="center">0.583</td>
</tr>
<tr>
<td valign="top" align="left">MEM</td>
<td valign="top" align="center">&#x2013;0.05 (0.69)</td>
<td valign="top" align="center">&#x2013;0.04 (0.69)</td>
<td valign="top" align="center">&#x2013;0.06 (0.69)</td>
<td valign="top" align="center">0.839</td>
</tr>
<tr>
<td valign="top" align="left">LAN</td>
<td valign="top" align="center">0.27 (0.59)</td>
<td valign="top" align="center">0.26 (0.63)</td>
<td valign="top" align="center">0.28 (0.55)</td>
<td valign="top" align="center">0.615</td>
</tr>
<tr>
<td valign="top" align="left">EF</td>
<td valign="top" align="center">0.16 (0.65)</td>
<td valign="top" align="center">0.10 (0.64)</td>
<td valign="top" align="center">0.21 (0.64)</td>
<td valign="top" align="center">0.172</td>
</tr>
<tr>
<td valign="top" align="left">VP</td>
<td valign="top" align="center">&#x2013;0.05 (0.42)</td>
<td valign="top" align="center">&#x2013;0.10 (0.47)</td>
<td valign="top" align="center">0.003 (0.37)</td>
<td valign="top" align="center">0.168</td>
</tr>
<tr>
<td valign="top" align="left">FAQ</td>
<td valign="top" align="center">5.18 (6.73)</td>
<td valign="top" align="center">5.59 (7.08)</td>
<td valign="top" align="center">4.85 (6.40)</td>
<td valign="top" align="center">0.521</td>
</tr>
<tr>
<td valign="top" align="left">ADAS13</td>
<td valign="top" align="center">18.53 (9.38)</td>
<td valign="top" align="center">19.17 (9.00)</td>
<td valign="top" align="center">18.01 (9.65)</td>
<td valign="top" align="center">0.208</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn><p>Categorical variables are depicted as counts and percentages, while continuous variables are expressed as mean (SD). Significant at the level of p &#x003C; 0.05 were shown in bold. SD, standard deviation; <italic>APOE &#x03B5;4</italic>, apolipoprotein <italic>E4</italic>; YKL-40, chitinase-3-like protein 1; CSF, cerebrospinal fluid; A&#x03B2;, amyloid-&#x03B2;; p-tau, phosphorylated tau; t-tau, total tau; sTNFR, soluble tumor necrosis factor receptor; TGF, transforming growth factor; IL, interleukin; ICAM1, intercellular cell adhesion molecule-1; VCAM1, vascular cell adhesion molecule-1; MRI, Magnetic resonance imaging; ICV, Intracranial volume; MMSE, Mini-Mental State Examination; MEM, memory function; LAN, language; EF, executive function; VP, visuospatial function; FAQ, Functional Assessment Questionnaire; ADAS13, Alzheimer&#x2019;s disease Assessment Scale 13.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="S3.SS2">
<title>Interrelationship of baseline CSF levels of YKL-40 with various biomarkers, cognitive function, and brain structures</title>
<p>As shown in <xref ref-type="fig" rid="F1">Figure 1</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 1</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>, higher baseline levels of CSF YKL-40 were associated with higher levels of p-tau (&#x03B2; = 0.534, <italic>p</italic> &#x003C; 0.001) and t-tau (&#x03B2; = 0.507, <italic>p</italic> &#x003C; 0.001). Besides, we found that baseline levels of CSF YKL-40 were positively associated with sTNFR1 [&#x03B2; = 0.343, <italic>p</italic> &#x003C; 0.001], sTNFR2 [&#x03B2; = 0.349, <italic>p</italic> &#x003C; 0.001], ICAM1 [&#x03B2; = 0.319, <italic>p</italic> = 0.023], and VCAM1 [&#x03B2; = 0.493, <italic>p</italic> &#x003C; 0.001]. After FDR correction, the <italic>p</italic>-values presented for the above results are all less than 0.05. As for MRI brain structures, the negative association between baseline levels of CSF YKL-40 and entorhinal cortex volume was at the threshold of significance (&#x03B2; = &#x2212;0.128, <italic>p</italic> = 0.049), but did not remain significant following FDR correction. However, no significant associations between baseline levels of CSF YKL-40 and cognitive function were found. Consistently, the significant associations between CSF YKL-40 with p-tau, t-tau, and the four neuroinflammatory biomarkers (sTNFR1, sTNFR2, ICAM1, and VCAM1) were barely changed in sensitivity analyses using different MLR models (<xref ref-type="supplementary-material" rid="DS2">Supplementary Table 1</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). Besides, exploratory stratified analyses showed that these significant associations between CSF YKL-40 and sTNFR1, sTNFR2, and VCAM1 were more pronounced in the T + subgroup than in the T- subgroup. Similar results were obtained in the A + MCI subgroup (<xref ref-type="supplementary-material" rid="DS2">Supplementary Tables 1</xref>&#x2013;<xref ref-type="supplementary-material" rid="DS2">6</xref> in <xref ref-type="supplementary-material" rid="DS2">Supplementary Material 2</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption><p>Associations of CSF YKL-40 with AD biomarkers, neuroinflammatory markers, cognition, and MRI brain structures. <italic>P</italic>-values were obtained by multiple linear regressions models adjusted for age, sex, years of education, <italic>APOE &#x03B5;4</italic> status, and additional adjustment for ICV in the analyses of the structural MRI data. &#x201C;&#x002A;&#x201D; indicated a significant <italic>p</italic>-value after false discovery rate correction. CSF, cerebrospinal fluid; CI, confidence interval; A&#x03B2;, amyloid-&#x03B2;; p-tau, phosphorylated tau; t-tau, total tau; MMSE, Mini-Mental State Examination; MEM, memory function; LAN, language; EF, executive function; VP, visuospatial function; FAQ, Functional Assessment Questionnaire; ADAS13, Alzheimer&#x2019;s disease Assessment Scale 13; sTNFR, soluble tumor necrosis factor receptor; TGF, transforming growth factor; IL, interleukin; ICAM1, intercellular cell adhesion molecule-1; VCAM1, vascular cell adhesion molecule-1; MRI, magnetic resonance imaging.</p></caption>
<alt-text>Forest plot displaying the relationship between variables related to CSF AD biomarkers, cognitive scores, CSF neuroinflammatory markers, and MRI brain structures. Each variable is listed with its beta coefficient, confidence interval, and p-value. Significant p-values that can be corrected by FDR (lower than 0.05) are marked with an asterisk. Variables include A&#x03B2;42, p-tau, t-tau, MMSE, MEM, LAN, EF, VP, FAQ, ADAS13, sTNFR1, sTNFR2, TGF-&#x03B2;1, IL-10, ICAM1, VCAM1, IL-6, IL-7, hippocampus, entorhinal cortex, mid temporal lobe, and whole brain. Results vary in significance, with some markers showing significant associations. </alt-text>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fnagi-17-1630022-g001.tif"/>
</fig>
<p>Multivariate linear regression models were also utilized to explore the associations of CSF neuroinflammatory biomarkers (independent variables) with AD biomarkers, MRI brain structures, and cognitive function (dependent variables) (<xref ref-type="supplementary-material" rid="DS2">Supplementary Table 2</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). The results indicated that CSF neuroinflammatory biomarkers, including sTNFR1, sTNFR2, TGF-&#x03B2;1, ICAM1 and VCAM1, had positive correlations with CSF AD biomarkers (A&#x03B2;<sub>42</sub>, p-tau, and t-tau, <xref ref-type="fig" rid="F2">Figure 2</xref>), even after FDR correction (<xref ref-type="supplementary-material" rid="DS2">Supplementary Table 2</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). However, the negative correlations of CSF IL-7 level with MMSE (&#x03B2; = &#x2212;0.888, <italic>p</italic> = 0.018), MEM (&#x03B2; = &#x2212;0.229, <italic>p</italic> = 0.018) and ADAS13 (&#x03B2; = 0.072, <italic>p</italic> = 0.045), and the negative association between CSF level of ICAM1 and hippocampus (&#x03B2; = &#x2212;0.051, <italic>p</italic> = 0.046) did not survive FDR correction. Additionally, no significant correlations were observed for IL-6 and IL-10 with CSF AD biomarkers.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption><p>Associations between CSF neuroinflammatory biomarkers and AD biomarkers. The <italic>x</italic> axis represents CSF neuroinflammatory biomarkers (sTNFR1, sTNFR2, TGF-&#x03B2;1, ICAM1 and VCAM1) and the <italic>y</italic> axis represents CSF AD biomarkers (A&#x03B2;<sub>42</sub>, p-tau and t-tau). <italic>P</italic>-values were obtained by multiple linear regressions models adjusted for age, sex, years of education and <italic>APOE &#x03B5;4</italic> status. CSF, cerebrospinal fluid; A&#x03B2;, amyloid-&#x03B2;; p-tau, phosphorylated tau; t-tau, total tau; sTNFR, soluble tumor necrosis factor receptor; TGF, transforming growth factor; ICAM1, intercellular cell adhesion molecule-1; VCAM1, vascular cell adhesion molecule-1.</p></caption>
<alt-text>Scatter plots show the relationships between various neuroinflammation and cerebrospinal fluid (CSF) biomarkers: A&#x03B2;42, p-tau, and t-tau. Each column represents a different neuroinflammation: sTNFR1, sTNFR2, TGF-&#x03B2;1, ICAM1, and VCAM1. Each row corresponds to a CSF biomarker. Correlation coefficients and significance values are displayed, with shaded regions indicating confidence intervals. Each plot suggests trends and correlations specific to the neuroinflammation and biomarker on the axes.</alt-text>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fnagi-17-1630022-g002.tif"/>
</fig>
</sec>
<sec id="S3.SS3">
<title>Modifying effect of AD risk factors</title>
<p>Next, we investigated whether the observed significant associations were modified by vascular risk factors, A&#x03B2; status, <italic>APOE</italic>&#x03B5;<italic>4</italic> status, sex, or age. There were significant interaction effects between CSF levels of YKL-40 and hypertension on p-tau (&#x03B2; = 0.444, <italic>p</italic> = 0.027) and t-tau (&#x03B2; = 0.347, <italic>p</italic> = 0.047) (<xref ref-type="fig" rid="F3">Figure 3A</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 3</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). In subsequent stratified analyses, we found that higher CSF levels of YKL-40 were associated with higher levels of p-tau in the HTN + subgroup (&#x03B2; = 0.708, <italic>p</italic> &#x003C; 0.001) rather than in the HTN- subgroup (&#x03B2; = 0.284, <italic>p</italic> = 0.066) (<xref ref-type="fig" rid="F3">Figure 3A</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 4</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). Nevertheless, a significant association with CSF t-tau was observed in both the HTN + (&#x03B2; = 0.632, <italic>p</italic> &#x003C; 0.001) and the HTN- subgroups (&#x03B2; = 0.323, <italic>p</italic> = 0.015) (<xref ref-type="fig" rid="F3">Figure 3A</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 4</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). Notably, the significant associations between the CSF levels of YKL-40 and p-tau, t-tau, or neuroinflammatory biomarkers were not moderated by factors such as hyperlipidemia, sex, age, <italic>APOE</italic>&#x03B5;<italic>4</italic> status, or A&#x03B2; status (<xref ref-type="supplementary-material" rid="DS2">Supplementary Tables 4</xref>, <xref ref-type="supplementary-material" rid="DS2">5</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>).</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption><p><bold>(A)</bold> Interaction effects of CSF YKL-40 and hypertensive status on CSF p-tau and t-tau. <bold>(B)</bold> Mediation effects of CSF neuroinflammatory biomarkers on the associations between CSF YKL-40 and p-tau, t-tau and entorhinal cortex volume. <bold>(A)</bold> Shows the significant interaction effects of CSF YKL-40 &#x00D7; hypertensive status on CSF p-tau and t-tau. In the stratified analyses, the significant association between CSF YKL-40 and p-tau was only observed in the hypertensive subgroup, not in the normotensive subgroup [<bold>(A)</bold>.a]. Similarly, the significant association between CSF YKL-40 and t-tau was found both in the hypertensive and the normotensive subgroups [<bold>(A)</bold>.b]. Interaction models were adjusted by age, sex, years of education and <italic>APOE &#x03B5;4</italic> status. <bold>(B)</bold> Shows mediation effects in the whole sample [<bold>(B)</bold>.a&#x2013;d], hypertensive [<bold>(B)</bold>.e&#x2013;h] and normotensive [<bold>(B)</bold>.i&#x2013;l] groups, respectively. The &#x201C;&#x00D7;&#x201D; signified the absence of a significant correlation with CSF YKL-40, and as such, no additional mediation analysis was performed. The dotted line indicated that the direct effect was not significant (<italic>P</italic> &#x2265; 0.05). Mediation analyses with 10,000 bootstrapped iterations were used to examine the mediation effects of CSF neuroinflammatory biomarkers on p-tau, t-tau and entorhinal cortex volume. Each path of the model was adjusted for age, sex, years of education, <italic>APOE &#x03B5;4</italic> status, and additional adjustment for ICV in the analyses of entorhinal cortex volume. CSF, cerebrospinal fluid; p-tau, phosphorylated tau; t-tau, total tau; ICAM1, intercellular cell adhesion molecule-1; VCAM1, vascular cell adhesion molecule-1; sTNFR, soluble tumor necrosis factor receptor.</p></caption>
<alt-text>Interaction and mediation analyses display the relationships between biomarkers in individuals with hypertension and normotension. Part A shows interaction effects of CSF YKL-40 and hypertensive status on CSF p-tau and t-tau. Part B presents mediation analyses for four biomarkers (ICAM1, VCAM1, sTNFR1, sTNFR2) in total participants, with subdivisions for hypertension and normotension, illustrating proportions, pathways to YKL-40, p-tau, t-tau, and entorhinal outcomes. Statistical significance and interactions are noted in both sections.</alt-text>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fnagi-17-1630022-g003.tif"/>
</fig>
</sec>
<sec id="S3.SS4">
<title>Causal mediation analyses</title>
<p>The primary analyses revealed the following significant associations: (1) CSF YKL-40 is positively correlated with p-tau, t-tau, and entorhinal cortex volume; (2) CSF YKL-40 exhibits significant positive associations with these neuroinflammatory biomarkers (sTNFR1, sTNFR2, ICAM1, and VCAM1); (3) these neuroinflammatory biomarkers are significantly correlated with p-tau, t-tau, and entorhinal cortex volume. Given the significant pairwise correlations among these variables, we conducted exploratory mediation analyses among the participants to assess whether these correlations between CSF YKL-40 level and p-tau, t-tau, and entorhinal cortex volume were mediated by neuroinflammatory biomarkers. As shown in <xref ref-type="fig" rid="F3">Figure 3B</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 6</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>, the mediation analyses indicated that the associations of CSF levels of YKL-40 with p-tau and t-tau were partially mediated by ICAM1 (p-tau: proportion = 13.0%, <italic>p</italic> = 0.022; t-tau: proportion = 14.3%, <italic>p</italic> = 0.020), VCAM1 (p-tau: proportion = 27.0%, <italic>p</italic> &#x003C; 0.001; t-tau: proportion = 30.6%, <italic>p</italic> &#x003C; 0.001), sTNFR1 (p-tau: proportion = 58.3%, <italic>p</italic> &#x003C; 0.001; t-tau: proportion = 60.9%, <italic>p</italic> &#x003C; 0.001), and sTNFR2 (p-tau: proportion = 31.8%, <italic>p</italic> &#x003C; 0.001; t-tau: proportion = 33.9%, <italic>p</italic> &#x003C; 0.001). Additionally, the association between CSF YKL-40 levels and entorhinal cortex volume was partially mediated by VCAM1 (proportion = 64.8%, <italic>p</italic> = 0.036), sTNFR1 (proportion = 73.4%, <italic>p</italic> = 0.030), and sTNFR2 (proportion = 78.8%, <italic>p</italic> = 0.036) before controlling for A&#x03B2;<sub>42</sub>, whereas the partial mediation effects disappeared after including A&#x03B2;<sub>42</sub> as a covariate in mediation analyses (<xref ref-type="supplementary-material" rid="DS1">Supplementary Figure 1</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>).</p>
<p>The interaction effect indicated that hypertension may be a potential modulator for the association between CSF YKL-40 levels and tau pathologies. Therefore, we further explored whether hypertension influenced the aforementioned mediation analyses by categorizing the sample into two subgroups (HTN- versus HTN+). The partially mediation of VCAM1, sTNFR1, and sTNFR2 on the associations between CSF YKL-40 levels and p-tau and t-tau were only reproduced in the HTN + subgroup (<xref ref-type="fig" rid="F3">Figure 3B</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 7</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). The mediation remained significant even after additional adjustment for A&#x03B2;<sub>42</sub> (<xref ref-type="supplementary-material" rid="DS1">Supplementary Figure 1</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). In contrast, CSF YKL-40 level was directly related to t-tau without mediation effects in the HTN- group (<xref ref-type="fig" rid="F3">Figure 3B</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 8</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). Subsequent exploratory analyses conducted among different diagnostic and biological subgroups and combined with hypertensive status showed that the mediation of sTNFR1 and sTNFR2 were particularly robust in MCI, A+, T+, A + MCI, and T + HTN + subgroups (<xref ref-type="supplementary-material" rid="DS2">Supplementary Tables 7</xref>&#x2013;<xref ref-type="supplementary-material" rid="DS2">12</xref> in <xref ref-type="supplementary-material" rid="DS2">Supplementary Material 2</xref>).</p>
</sec>
<sec id="S3.SS5">
<title>Associations of baseline CSF YKL-40 levels with longitudinal changes in cognitive function and MRI brain structures</title>
<p>Limited by CSF AD biomarkers, LME models were only used to explore the associations between baseline levels of CSF YKL-40 and longitudinal changes in cognitive function and MRI brain structures. In the whole sample, higher baseline levels of CSF YKL-40 were associated with faster rates of decrease in MMSE (&#x03B2; = &#x2212;0.185, <italic>p</italic> = 0.006), MEM (&#x03B2; = &#x2212;0.039, <italic>p</italic> &#x003C; 0.001), LAN (&#x03B2; = &#x2212;0.025, <italic>p</italic> = 0.007), VP (&#x03B2; = &#x2212;0.068, <italic>p</italic> = 0.002), and increase in FAQ (&#x03B2; = 0.240, <italic>p</italic> = 0.042). As expected, higher baseline levels of CSF YKL-40 were also linked to more rapid atrophy in the volumes of whole brain (&#x03B2; = &#x2212;0.001, <italic>p</italic> = 0.028), hippocampus (&#x03B2; = &#x2212;0.003, <italic>p</italic> = 0.002), entorhinal cortex (&#x03B2; = &#x2212;0.0002, <italic>p</italic> = 0.002) and middle temporal lobe (&#x03B2; = &#x2212;0.002, <italic>p</italic> = 0.003) (<xref ref-type="fig" rid="F4">Figure 4</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 9</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>).</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption><p>Baseline CSF YKL-40 and longitudinal changes in cognitive function and MRI brain structures in the whole sample. The mixed-effect models were fit using continuous CSF YKL-40 values, adjusting for age, sex, years of education, <italic>APOE &#x03B5;4</italic> status and ICV (when appropriate). For better illustration, the plots displayed the trajectories for individuals with high and low CSF YKL-40 (&#x2265; 390 ng/ml VS. &#x003C; 390 ng/ml) for longitudinal cognitive function as well as for MRI brain structures. We found protective roles of low baseline levels of CSF YKL-40 in preventing decline of cognitive functions, including MMSE <bold>(A)</bold>, FAQ <bold>(B)</bold>, MEM <bold>(D)</bold>, LAN <bold>(E)</bold>, VP <bold>(G)</bold>, as well as MRI brain structures, including volumes of whole brain <bold>(H)</bold>, hippocampus <bold>(I)</bold>, Entorhinal cortex <bold>(J)</bold> and mid temporal lobe <bold>(K)</bold>, but not ADAS13 <bold>(C)</bold> or EF <bold>(F)</bold>. CSF, cerebrospinal fluid; MMSE, Mini-Mental State Examination; FAQ, Functional Assessment Questionnaire; ADAS13, Alzheimer&#x2019;s disease Assessment Scale 13; MEM, memory function; LAN, language; EF, executive function; VP, visuospatial function.</p></caption>
<alt-text>Eleven graphs depict trends over time for two groups: low YKL-40 (blue) and high YKL-40 (red). Graphs A, B, D, E, G, H, I, J, and K show significant differences between groups (p&#x003C;0.05), while graphs C and F do not (p=0.318 and p=0.408). Each graph includes individual data points, trend lines, and shaded areas representing confidence intervals. Variables include MMSE score, FAQ, ADS13, MEM, LAN, EF, VP, Whole Brain, Hippocampus, Entorhinal, and Mid Temporal, all plotted against years from baseline.</alt-text>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fnagi-17-1630022-g004.tif"/>
</fig>
<p>Intriguingly, some longitudinal associations were more pronounced in the HTN + subgroup (<xref ref-type="supplementary-material" rid="DS1">Supplementary Figure 2</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 10</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). More precisely, the associations between baseline levels of YKL-40 and longitudinal changes of FAQ (&#x03B2; = 0.631, <italic>p</italic> &#x003C; 0.001), MEM (&#x03B2; = &#x2212;0.305, <italic>p</italic> &#x003C; 0.001), and AD signature ROI volume (whole brain: &#x03B2; = &#x2212;0.001, <italic>p</italic> = 0.001; hippocampus: &#x03B2; = &#x2212;0.004, <italic>p</italic> &#x003C; 0.001; entorhinal cortex: &#x03B2; = &#x2212;0.005, <italic>p</italic> &#x003C; 0.001, and middle temporal lobe: &#x03B2; = &#x2212;0.004, <italic>p</italic> &#x003C; 0.001, respectively) were significant only in the HTN + subgroup (<xref ref-type="supplementary-material" rid="DS1">Supplementary Figure 2</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 10</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). The subgroup analyses yielded generally similar results, indicating that those significant correlations with MMSE, LAN, or VP did not appear to be affected by the hypertensive status (<xref ref-type="supplementary-material" rid="DS1">Supplementary Figure 3</xref> and <xref ref-type="supplementary-material" rid="DS2">Supplementary Table 11</xref> in <xref ref-type="supplementary-material" rid="DS1">Supplementary Material 1</xref>). Additionally, according to the clinical diagnosis and A status, we found that higher baseline levels of CSF YKL-40 were associated with faster atrophy in the volumes of whole brain, hippocampus, and mid temporal lobe in the A + CN but not in the A-CN subgroup. Combining hypertension with T status, faster atrophy in the volumes of whole brain, hippocampus, and mid temporal lobe were replicated in the T + HTN + subgroup rather than in the T- HTN-, T-HTN+, or T + HTN- subgroups (<xref ref-type="supplementary-material" rid="DS2">Supplementary Tables 13&#x2013;18</xref> in <xref ref-type="supplementary-material" rid="DS2">Supplementary Material 2</xref>).</p>
</sec>
</sec>
<sec id="S4" sec-type="discussion">
<title>Discussion</title>
<p>This study was the first to systematically explore the interrelationships of CSF YKL-40 with AD biomarkers, neuroinflammation, cognitive function and brain structures, and elucidate the additive effects of neuroinflammation and hypertension on the associations between astrocyte reactivity and AD-related pathologies. Overall, these results suggest that specific neuroinflammatory biomarkers might mediate the associations between CSF YKL-40 and p-tau and t-tau with hypertensive dependency, particularly in the preclinical and prodromal stages of AD (<xref ref-type="fig" rid="F5">Figure 5</xref>). Specifically, the key findings are that: (1) higher baseline levels of CSF YKL-40 are positively related to p-tau and t-tau; (2) hypertensive status modifies the significant associations between CSF YKL-40 and p-tau and t-tau; (3) the mediation of VCAM1, sTNFR1 and sTNFR2 on abovementioned significant associations persist in the whole sample and individuals with hypertension; (4) higher baseline levels of CSF YKL-40 interact with hypertension to promote a faster decline in cognitive function and atrophy in hippocampal volume, entorhinal volume, middle temporal lobe volume, and whole-brain volume. Our results indicate that higher levels of CSF YKL-40 may promote AD progression, and constitute a reasonable pathway to connect the effects of hypertension and neuroinflammation with astrocyte reactivity in contributing to AD pathogenesis.</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption><p>Interlinking schematic among CSF YKL-40, neuroinflammation, hypertension and AD-related pathologies. The findings in the current study suggest that hypertension and neuroinflammation interact, contributing to the associations between astrocyte, as reflected by CSF YKL-40, and AD-related pathologies, especially in the preclinical and prodromal stages of AD.</p></caption>
<alt-text>Diagram illustrating the progression from normal to Alzheimer&#x2019;s dementia. It shows the roles of hypertension, neuroinflammation, and the YKL-40 protein in astrocytes. Arrows indicate progression pathways, with A+ and T+ factors contributing to the process.</alt-text>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fnagi-17-1630022-g005.tif"/>
</fig>
<p>CSF Chitinase 3-like protein 1 (CHI3L1/YKL-40) is identified as a significant indicator of astrocyte reactivity (<xref ref-type="bibr" rid="B9">Carter et al., 2019</xref>). An increasing amount of evidence indicates that the positive correlations between CSF YKL-40 levels and tau pathologies, but not A&#x03B2; (<xref ref-type="bibr" rid="B19">Ferrari-Souza et al., 2022</xref>; <xref ref-type="bibr" rid="B29">Janelidze et al., 2018</xref>; <xref ref-type="bibr" rid="B46">Pelkmans et al., 2024</xref>). <xref ref-type="bibr" rid="B46">Pelkmans et al. (2024)</xref> reported that YKL-40 was released into the CSF slightly later in the AD cascade, specifically mediating the link between the downstream p-tau induced by A&#x03B2; and the neuronal damage induced by tau protein, whereas it did not mediate the association between soluble and insoluble A&#x03B2; aggregates. GFAP, another promising biomarker of astrocytic activation, was considered to be predominantly a response to A&#x03B2; pathology (<xref ref-type="bibr" rid="B46">Pelkmans et al., 2024</xref>). Our results were consistent with these findings that CSF YKL-40 was significantly associated with p-tau and t-tau. Similarly, the positive associations between CSF YKL-40 and p-tau and t-tau were particularly robust in the A + MCI and the T + subgroups in our exploratory analyses, which favored the hypothesis that CSF YKL-40 might contribute to tau accumulation secondary to A&#x03B2; plaques in the downstream AD cascade. In contrast, the EMIF-AD Multimodal Biomarker Discovery study revealed an association between YKL-40 and A&#x03B2; only in the non-demented population (<xref ref-type="bibr" rid="B6">Bos et al., 2019</xref>). A postmortem study found that YKL-40 was independent of tau in other neurodegenerative diseases (<xref ref-type="bibr" rid="B47">Querol-Vilaseca et al., 2017</xref>), which indicated that the astrocyte activity as measured by the CSF levels of YKL-40 might not be exclusively influenced by amyloid-tau dichotomy (<xref ref-type="bibr" rid="B46">Pelkmans et al., 2024</xref>; <xref ref-type="bibr" rid="B49">Salvad&#x00F3; et al., 2023</xref>). Besides, the marginally negative correlation between baseline CSF YKL-40 and the entorhinal cortex volume did not survive FDR correction in our results, warranting replication and validation in a larger sample. However, an animal experiment revealed that calcium-binding protein centrin-2-positive astrocytes were distributed in clusters within the entorhinal cortex and co-localized with YKL-40, providing a possible explanation for our results (<xref ref-type="bibr" rid="B16">Degl&#x2019;Innocenti et al., 2024</xref>). Nevertheless, it should be noted that the choice of brain regions in our mediation analyses was to some extent guided by statistical considerations, which may lead to insufficient exploration of the mechanisms in crucial biological regions such as the hippocampus.</p>
<p>This study further proves that astrocytic reactivity with increased neuroinflammation play a prominent role in AD pathologies (<xref ref-type="bibr" rid="B54">Sofroniew and Vinters, 2010</xref>). We observe a robust correlation between CSF YKL-40 and ICAM1, VCAM1, sTNFR1, and sTNFR2. Simultaneously, these neuroinflammatory biomarkers partially mediate the effects of YKL-40 on tau pathology. These results reinforce the value of neuroinflammatory biomarkers to establish a more precise AD diagnostic framework in the clinic and provide a new perspective on understanding how astrocyte affects tau pathology. Of importance, the mediation analysis in this study is based on observational data and these CSF biomarkers (YKL-40, sTNFR1, sTNFR2, ICAM1, and VCAM1) may reflect overlapping aspects of a shared neuroinflammatory process, with the results reflecting correlation rather than causation. Further additional <italic>in vitro</italic> and <italic>in vivo</italic> studies are needed to verify the sequential and mechanistic associations between these molecules. Nevertheless, our findings suggest that interventions targeting astrocyte activation and neuroinflammation may benefit the prevention of tau pathology.</p>
<p>To the best of our knowledge, this is the first observational study to explore the interaction of hypertension and neuroinflammation on the associations between astrocyte reactivity and AD-related pathologies. We observed that hypertension reinforced the positive correlation between CSF YKL-40 and p-tau and t-tau in our interaction and subgroup analyses. Previous studies have shown that YKL-40 levels were elevated in hypertensive patients (<xref ref-type="bibr" rid="B61">Xu et al., 2016</xref>). Although our results revealed no difference in baseline levels of YKL-40 between the hypertensive and normotensive subgroups, further analyses indicated that the partial mediating effects of neuroinflammation (VCAM1, sTNFR1, and sTNFR2) on the associations between CSF YKL-40 and p-tau and t-tau were replicated in the HTN + subgroup rather than in the HTN- subgroup. An experiment investigating the additional or synergistic effects between AD and hypertension in rat models revealed that hypertension increased neuroinflammation (triggered by microglia and astrocytes) and tau pathology with minimal effect on A&#x03B2; pathology, which supported our results (<xref ref-type="bibr" rid="B17">Denver et al., 2019</xref>). Thus, these data seem to confirm this assumption that hypertension and astrocyte-driven neuroinflammation may interact to exacerbate AD pathologies (<xref ref-type="bibr" rid="B2">Bajwa and Klegeris, 2022</xref>; <xref ref-type="bibr" rid="B44">Nelson et al., 2014</xref>). However, the modification effects of other AD risk factors, such as age, sex, or <italic>APOE</italic>&#x03B5;<italic>4</italic> status, are not observed in this study. A potential explanation for the lack of interaction may be the relatively limited sample size. Further studies are warranted to validate our findings.</p>
<p>Existing clinical evidence has suggested that an elevated levels of soluble cell adhesion molecules (CAMs), hallmarks of vascular endothelial dysfunction (<xref ref-type="bibr" rid="B36">Lee and Benveniste, 1999</xref>), are correlated with numerous vascular risk factors (<xref ref-type="bibr" rid="B25">Hwang et al., 1997</xref>), dementia (<xref ref-type="bibr" rid="B18">Drake et al., 2021</xref>), and immune-mediated diseases (<xref ref-type="bibr" rid="B33">Kong et al., 2018</xref>). Nevertheless, the underlying mechanism of its involvement in the pathogenesis of AD is not fully understood. Earlier studies on which CAMs were most relevant to cognition have yielded inconsistent results (<xref ref-type="bibr" rid="B18">Drake et al., 2021</xref>; <xref ref-type="bibr" rid="B45">Nielsen et al., 2007</xref>), with our present research suggesting a preference for VCAM1. Another cohort study from ADNI observed a synergistic effect of CAMs and AD pathology on the clinical severity of AD (<xref ref-type="bibr" rid="B18">Drake et al., 2021</xref>). <xref ref-type="bibr" rid="B63">Yousef et al. (2019)</xref> found that glial activation in the hippocampus of aging mice was associated with focal upregulation of VCAM1 on the luminal side adjacent to the BBB. Additionally, a study from postmortem brain tissue from AD patients found that perivascular astrocytes in the white matter exhibit high CHI3L1 expression, further suggesting the involvement of YKL-40 in BBB disruption (<xref ref-type="bibr" rid="B43">Moreno-Rodriguez et al., 2020</xref>). A well-established fact is that endothelial dysfunction and chronic vascular inflammation are critical pathophysiological changes in hypertension. In the present study, we observed that CAMs mediated the associations between YKL-40 and p-tau and t-tau, and the mediation effects of VCAM1 were particularly robust in hypertensive patients. Altogether, these findings support the hypothesis that hypertension may influence the neuroinflammatory pathological cycle of astrocyte activation, subsequently inducing endothelial dysfunction and further aggravating tau pathology in AD.</p>
<p>This work identified sTNFRs as another potential mediating factor in the association between CSF YKL-40 and tau pathology. TNF-&#x03B1;, a biomarker of proinflammatory cytokine and neurodegenerative diseases (<xref ref-type="bibr" rid="B40">McCoy and Tansey, 2008</xref>), is secreted by activated astrocytes and microglia. TNFR could be stimulated upon binding to soluble TNF and cleaved to yield sTNFR. The measurement of sTNFRs provides a more sensitive determination of TNF-signaling activity than the direct measurement of TNF-&#x03B1; (<xref ref-type="bibr" rid="B34">Kreuzer et al., 1996</xref>). In line with our results, a prior study had demonstrated positive associations between sTNFRs and A&#x03B2;, as well as tau pathology in CSF (<xref ref-type="bibr" rid="B8">Buchhave et al., 2010</xref>). Furthermore, we and other previous studies found that sTNFRs were elevated in hypertensive patients compared with normotensive patients (<xref ref-type="bibr" rid="B57">Verma et al., 2019</xref>). These findings were supported by AD rat models showing that administration of telmisartan (an AT1 receptor blocker) can decrease TNF-&#x03B1; mRNA expression in the hippocampus and ameliorate spatial memory (<xref ref-type="bibr" rid="B52">Shindo et al., 2012</xref>). By coincidence, our results showed that the mediation effects of sTNFRs were notably stronger pronounced in the hypertensive group. The exact mechanism by which hypertension influences the mediation effects of sTNFRs was not entirely elucidated. Considering sTNFRs as an important signaling mechanism leading to inflammation and endothelial dysfunction, we speculate that microvascular injury and BBB disruption may be the key underlying mechanisms. Additionally, <xref ref-type="bibr" rid="B51">Shen et al. (2019)</xref> found that the levels of plasma TNF-&#x03B1; were increased and vascular endothelial growth factor A were decreased in MCI patients compared to cognitively healthy controls, while plasma sTNFR-1 were inversely correlated with MoCA scores in MCI patients. These findings supported neuroinflammatory and vascular hypothesis for dementia. However, no significant correlation between CSF sTNFRs and cognitive scores was found in the present study. The reason for the inconsistent results may be the heterogeneity of the study population, sample size, differences in the measurement of the biomarker or other confounding factors.</p>
<p>We also systematically reviewed the previous literature and found that previous studies frequently, but not consistently reported the relation of IL-6, IL-7, or IL-10 to AD biomarkers, neuroinflammation, cognitive performance, and brain structures (<xref ref-type="bibr" rid="B11">Cisbani et al., 2020</xref>; <xref ref-type="bibr" rid="B24">Hazen et al., 2020</xref>; <xref ref-type="bibr" rid="B29">Janelidze et al., 2018</xref>; <xref ref-type="bibr" rid="B39">Marsland et al., 2015</xref>). In line with our results, a prior study using ADNI database had also demonstrated that there was no significant associations of CSF IL-6, IL-7, or IL-10 with CSF AD biomarkers (<xref ref-type="bibr" rid="B62">Xu et al., 2021</xref>). But we observed suggestive inverse associations between CSF IL-7 and MMSE, MEM and ADAS13. The biological mechanism between these correlations is currently unknown, although it can be speculated that various physiologic, medical, environmental, or lifestyle factors could impact on inflammation and AD pathologies. The potential biological mechanism remains to be clarified in the future.</p>
<p>Furthermore, a better understanding of the predictive role of YKL-40 in AD progression will help to develop reliable prognostic and outcome indicators in clinical practice. We therefore evaluated the utility of baseline CSF YKL-40 levels in predicting longitudinal changes of cognitive function and brain atrophy. Consistent with previous studies, our longitudinal investigations show that higher CSF YKL-40 levels are associated with fast decline in cognitive performance (<xref ref-type="bibr" rid="B59">Warmenhoven et al., 2024</xref>). Other studies have also reported that higher levels of CSF YKL-40 increased risk of developing AD dementia in non-demented elderly (<xref ref-type="bibr" rid="B29">Janelidze et al., 2018</xref>). A Chinese community-based aging cohort demonstrated that higher cortical tau tangle load and lower temporal cortical thinning were more pronounced in the presence of higher levels of astrocyte activation, as measured by plasma GFAP (<xref ref-type="bibr" rid="B22">Guo et al., 2024</xref>). Likewise, our study observes significant associations between higher baseline CSF levels of YKL-40 and accelerated rates of atrophy in AD signature ROI volumes (whole brain, hippocampus, entorhinal cortex, and middle temporal lobe), which are vulnerable regions associated with dementia. Aligning with previous research that hypertension was related to global gray matter atrophy (<xref ref-type="bibr" rid="B21">Glodzik et al., 2012</xref>), our study found that hypertension had additional deleterious effects on the typical atrophy pattern linking to CSF YKL-40. These findings underscored that CSF YKL-40 and hypertension could contribute to cognitive decline and brain atrophy in an additive way. Interestingly, our longitudinal sensitivity analyses showed that higher baseline levels of CSF YKL-40 were associated with faster atrophy in the volumes of whole brain, hippocampus, and mid temporal lobe in the A + CN rather than the A-CN subgroup. As expected, combining blood pressure profile with T status, faster atrophy in the volumes of whole brain, hippocampus, and mid temporal lobe was replicated in the T + HTN + subgroup but not in other subgroups. This is theoretically plausible given that A&#x03B2; burden in the early phases of AD is strongly associated with brain atrophy, and as the disease progresses, downstream pathological events resulting from A&#x03B2; accumulation combined with hypertension may accelerate the sustained brain atrophy (<xref ref-type="bibr" rid="B10">Ch&#x00E9;telat et al., 2010</xref>). Our findings highlighted the clinical application of CSF YKL-40 in combination with hypertension for monitoring and predicting the progression of AD.</p>
<p>The study included participants with CN, MCI and AD, had long-term follow-up within repeated tests covering multiple domains of cognitive function and brain structures, and utilized multiple analysis models, which might strengthen the plausibility and robustness of our findings. However, there were some possible limitations in this study. First, the assessment of CSF YKL-40 concentration at a single time point limited our ability to ascertain the impact of changes in YKL-40 levels on the primary AD-related pathologies, highlighting a significant area for future research endeavors. Secondly, this study only employed a simple binary classification for hypertension. A more detailed differentiation of specific blood pressure parameters, including systolic, diastolic blood pressure, pulse pressure, as well as duration, could provide a better understanding of the additive impacts of hypertension. Thirdly, the scarcity of other biomarkers for astrocyte activation (i.e., GFAP) in the ADNI database constrained our capacity to perform a more comprehensive analysis. Last, despite the exploration of potential pathways through mediation analysis, the biological overlap among these inflammatory biomarkers could impact the directional inferences made.</p>
</sec>
<sec id="S5" sec-type="conclusion">
<title>Conclusion</title>
<p>In summary, we have observed that ICAM1, VCAM1, sTNFR1, and sTNFR2 mediate the associations between CSF YKL-40 and p-tau and t-tau. Baseline CSF YKL-40 levels adversely affected longitudinal changes in cognitive function and brain structures in the AD spectrum. Additionally, our findings highlight that hypertension has an additive effect on the mediation effects, as well as the longitudinal correlations. Taken together, this study provides convincing evidence for the development of therapeutic strategies targeting astrocyte reactivity and neuroinflammation while emphasizing the importance of controlling blood pressure to prevent AD progression.</p>
</sec>
</body>
<back>
<sec id="S6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in this study are included in this article/<xref ref-type="supplementary-material" rid="S13">Supplementary material</xref>, further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec id="S7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>Publicly available data used in this study was approved by ADNI. The detailed inclusion and exclusion criteria in ADNI are available at <ext-link ext-link-type="uri" xlink:href="https://adni.loni.usc.edu/">https://adni.loni.usc.edu/</ext-link>. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="S8" sec-type="author-contributions">
<title>Author contributions</title>
<p>Y-YW: Data curation, Methodology, Conceptualization, Writing &#x2013; original draft, Software, Formal Analysis. MZ: Conceptualization, Formal Analysis, Writing &#x2013; review and editing. S-JC: Conceptualization, Writing &#x2013; review and editing, Formal Analysis. WM: Formal Analysis, Conceptualization, Writing &#x2013; review and editing, Supervision. Z-XW: Validation, Supervision, Writing &#x2013; review and editing. Y-JZ: Validation, Writing &#x2013; review and editing, Investigation, Supervision. S-QY: Validation, Supervision, Writing &#x2013; review and editing, Investigation. Z-WS: Writing &#x2013; review and editing, Supervision, Investigation, Validation. XZ: Writing &#x2013; review and editing, Supervision, Investigation. X-FY: Visualization, Validation, Conceptualization, Supervision, Writing &#x2013; review and editing. X-QZ: Supervision, Funding acquisition, Writing &#x2013; review and editing, Visualization, Conceptualization.</p>
</sec>
<sec id="S9" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the Natural Science Foundation of Anhui Province (Grant number: 2108085MH274), the Scientific Research Projects of Universities in Anhui Province, Major Projects (Grant number: 2022AH040159), and the Domestic and International Visiting Scholar and Research Training Program for Outstanding Young Backbone Talents in Higher Education Institutions (Grant number: gxgwfx2020027).</p>
</sec>
<ack><p>We want to thank all participants from the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense Award number W81XWH-12-2-0012). ADNI is supported by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering, and makes possible by the generous contributions from the following: AbbVie, Alzheimer&#x2019;s Association; Alzheimer&#x2019;s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research &#x0026; Development, (<ext-link ext-link-type="uri" xlink:href="https://fnih.org">https://fnih.org</ext-link>). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer&#x2019;s Therapeutic Research Institute at the University of Southern California. ADNI data are announced by the Laboratory for Neuro Imaging at the University of Southern California. LLC.; Johnson &#x0026; Johnson Pharmaceutical Research &#x0026; Development LLC.; Lumosity; Lundbeck; Merck &#x0026; Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. The Foundation for the National Institutes of Health facilitates the donation process for the private sector. Data used in this study were sourced from the Alzheimer&#x2019;s Disease Neuroimaging Initiative (ADNI) (<ext-link ext-link-type="uri" xlink:href="https://adni.loni.usc.edu/">https://adni.loni.usc.edu/</ext-link>). Investigators affiliated with ADNI were involved in the design and implementation of ADNI and/or provided data but they were not engaged in analysis or writing of this report. A complete listing of ADNI investigators can be found at <ext-link ext-link-type="uri" xlink:href="http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf">http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf</ext-link>.</p>
</ack>
<sec id="S10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="S11" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The authors declare that no Generative AI was used in the creation of this manuscript.</p>
</sec>
<sec id="S12" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="S13" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fnagi.2025.1630022/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fnagi.2025.1630022/full#supplementary-material</ext-link></p>
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