Abstract
Background and Objectives
Higher YKL-40 levels in the CSF are a known biomarker of brain inflammation. We explored the utility of plasma YKL-40 as a biomarker for accelerated brain aging and dementia risk.
Methods
We performed cross-sectional and prospective analyses of 4 community-based cohorts in the United States or Europe: the Age, Gene/Environment Susceptibility-Reykjavik Study, Atherosclerosis Risk in the Communities study, Coronary Artery Risk Development in Young Adults study, and Framingham Heart Study (FHS). YKL-40 was measured from stored plasma by a single laboratory using Mesoscale Discovery with levels log transformed and standardized within each cohort. Outcomes included MRI total brain volume, hippocampal volume, and white matter hyperintensity volume (WMHV) as a percentage of intracranial volume, a general cognitive composite derived from neuropsychological testing (SD units [SDU]), and the risk of incident dementia. We sought to replicate associations with dementia in the clinic-based ACE csf cohort, which also had YKL-40 measured from the CSF.
Results
Meta-analyses of MRI outcomes included 6,558 dementia-free participants, and for analysis of cognition, 6,670. The blood draw preceded MRI/cognitive assessment by up to 10.6 years across cohorts. The mean ages ranged from 50 to 76 years, with 39%–48% male individuals. In random-effects meta-analysis of study estimates, each SDU increase in log-transformed YKL-40 levels was associated with smaller total brain volume (β = −0.33; 95% CI −0.45 to −0.22; p < 0.0001) and poorer cognition (β = −0.04; 95% CI −0.07 to −0.02; p < 0.01), following adjustments for demographic variables. YKL-40 levels did not associate with hippocampal volume or WMHV. In the FHS, each SDU increase in log YKL-40 levels was associated with a 64% increase in incident dementia risk over a median of 5.8 years of follow-up, following adjustments for demographic variables (hazard ratio 1.64; 95% CI 1.25–2.16; p < 0.001). In the ACE csf cohort, plasma and CSF YKL-40 were correlated (r = 0.31), and both were associated with conversion from mild cognitive impairment to dementia, independent of amyloid, tau, and neurodegeneration status.
Discussion
Higher plasma YKL-40 levels were associated with lower brain volume, poorer cognition, and incident dementia. Plasma YKL-40 may be useful for studying the association of inflammation and its treatment on dementia risk.
Introduction
Tremendous progress has been made in developing and validating plasma biomarkers for cognitive impairment. Because Alzheimer disease (AD) is the most common form of dementia, biomarkers that map onto the National Institute on Aging and Alzheimer's Association (NIA-AA) Research Framework (amyloid, pathologic tau, and neurodegeneration) have received considerable attention.1 However, in parallel to the development of such biomarkers, a need exists to study biomarkers associated with other pathways leading to dementia.
Inflammation in AD, mediated by microglial and astrocytic activation, predates clinical onset by decades and correlates strongly with the accumulation of misfolded proteins.2,3 Other common causes of dementia, such as cerebrovascular disease4 and Lewy bodies, also trigger a neuroinflammatory response.5,6 The 2018 NIA-AA framework acknowledges that immune system activation is an important AD process and raises the possibility of incorporating biomarkers of astrocytosis and microgliosis into the current AD framework when biomarkers become available.1 There is a need to expedite biomarker work in this area because anti-inflammatory disease-modifying therapies (DMTs) for AD have already begun. A review of 2021 AD drug development pipelines revealed 18.8% of DMT phase 2 trials and 11.8% of DMT phase 3 trials involve anti-inflammatory interventions.7
YKL-40 is a glycoprotein secreted by numerous cells, including macrophages, chondrocytes, and vascular smooth muscle.8 YKL-40 expression is upregulated in multiple neurologic conditions, including stroke, amyotrophic lateral sclerosis, multiple sclerosis, and AD.9-11 Expression is most abundant in reactive astrocytes.10 CSF levels of YKL-40 are higher in AD and mild cognitive impairment (MCI), than in controls, and YKL-40 expression is found near β-amyloid (Aβ) plaques and tau neurofibrillary tangles.11-13 YKL-40 is also expressed throughout the body.14
Although blood is much more feasible to obtain than the CSF, the utility of plasma YKL-40 as a biomarker for cognitive impairment, brain injury, and dementia risk remains unclear. We hypothesized that plasma YKL-40 may have utility as a dementia biomarker not only because CSF and plasma YKL-40 levels are moderately correlated (r = 0.24–0.30),15,16 but because systemic inflammation can also induce CNS inflammation.17 Moreover, targeting peripheral inflammation with lifestyle interventions in a primary prevention setting was recently suggested as a means to prevent dementia on the back of evidence showing that diets with a low inflammatory index were associated with a lower risk of dementia.18 Therefore, the validation of low-cost and minimally invasive biomarkers of inflammation may help to risk stratify for inclusion in intervention studies, quantify target engagement, and monitor therapeutic effects.
We aimed to examine plasma YKL-40 as a biomarker for dementia and related endophenotypes, including cognitive function and MRI-based markers of accelerated brain aging and injury. As the failure to replicate is a barrier to the translation of research findings, we explored associations in 4 independent community-based cohorts with results pooled in meta-analyses. Where data were available, we also examined the association between YKL-40 levels and the risk of incident dementia. In a fifth cohort, we examined the correspondence between CSF and plasma YKL-40 and compared their associations with risk of incident dementia.
Methods
We studied participants from 4 community-based cohort studies: the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES-RS), Atherosclerosis Risk in the Communities (ARIC) Study, Coronary Artery Risk Development in Young Adults (CARDIA) study, and Framingham Heart Study (FHS). A supplemental analysis was also performed in the clinic-based ACE csf cohort (ACE). Figure 1 presents an overview of the study design.
Figure 1. Overview of the Study Design.
Levels of YKL-40 were related to brain MRI and cognitive outcomes in all 4 cohorts and the risk of incident dementia in the FHS. Brain MRI and cognition were performed the same day as the YKL-40 blood draw in the AGES-RS and CARDIA study. Brain MRI and cognition were performed after a mean of 1.7 (SD = 1) years after the blood draw in the FHS. In the ARIC study, brain MRI and cognition were performed after a mean of 10.3 (SD = 0.8) and 10.6 (SD = 0.9) years, respectively. In the FHS, follow-up for risk of dementia commenced from the time of the blood draw for a maximum of 7.7 years (median = 5.8). The blood draw for YKL-40 was from 2002 to 2006 in the AGES-RS, from 1993 to 1995 in the ARIC study, from 2010 to 2011 in the CARDIA study, and from 2011 to 2014 in the FHS. AGES-RS = Age, Gene/Environment Susceptibility-Reykjavik Study; ARIC = Atherosclerosis Risk in the Communities; CARDIA = Coronary Artery Risk Development in Young Adults; FHS = Framingham Heart Study.
The AGES-RS was initiated in 2002 to examine risk factors of conditions of aging in an already established prospectively followed up Icelandic cohort of men and women born during 1907–1935.19 The ARIC study is a biracial community-based prospective cohort study established to examine the etiology of atherosclerosis and related clinical conditions.20 A total of 15,792 participants aged 45–64 years were recruited from 4 US communities, including Forsyth County, NC, Jackson, MS, Minneapolis, MN, and Washington County, MD. The CARDIA study aims to examine factors in young adulthood that predispose to the development of clinical and subclinical cardiovascular disease and cardiovascular disease risk factors.21 A total of 5,116 adults were recruited from Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA. The FHS is an ongoing community-based prospective cohort study conducted in Framingham, MA. This study uses data from the second-generation cohort, who attended examination cycle 9 between 2011 and 2014 and provided a blood sample for YKL-40 levels. Further information about each cohort is provided in the eMethods (links.lww.com/WNL/D373). eTable 1 outlines the details of sample selection. We included participants from the 4 cohorts who provided a blood sample, had a valid YKL-40 measurement, and completed brain MRI and/or cognitive testing.
Standard Protocol Approvals, Registrations, and Patient Consents
Informed consent was obtained from all study participants, and all studies received local Institutional Review Board approval.
Measurement of YKL-40
Blood samples were obtained from each cohort following an overnight fast. Samples were immediately centrifuged, aliquoted, and stored at −70°C for the ARIC study and CARDIA study and −80°C in the AGES-RS and FHS. In 2019–2020, samples were sent frozen to the laboratory of RT at the University of Vermont for analysis. Assays were performed by a certified laboratory technician blinded to demographic and clinical data. YKL-40 was measured using the Mesoscale Discovery multiarray assay system from these stored plasma samples. The assay has a lower limit of detection of 0.49 pg/mL. The mean interassay coefficient of variation was 6.06%.
Measurement of Cognition
To facilitate cross-study comparisons, we derived a general cognitive factor from several neuropsychological tests capturing multiple domains using cohort-specific principal component analysis with a forced single factor solution. This approach is consistent with our multicohort collaborations in the CHARGE consortium.22,23 Although the specific cognitive tests vary across studies, we have assessed the correlation between tests in any particular domain, enabling us to pool cognitive domain-specific data across these cohorts.22,24-26 Higher scores indicate superior general cognitive function, expressed in SD units. eTable 2 (links.lww.com/WNL/D373) summarizes the cognitive tests contributing to the general cognitive factor in each cohort.
Brain MRI Protocols
MRI-derived outcomes of total brain volume, hippocampal volume, and white matter hyperintensity volume (WMHV) were quantified and expressed as a percentage of total intracranial volume (ICV). All scans were completed using a 1.5 or 3T machine, and all scans were read blind to participant identifying and clinical information. Details for each cohort are described in eMethods (links.lww.com/WNL/D373).
Dementia Case Ascertainment
Risk of incident dementia was examined in the FHS. Participants in the FHS are continuously monitored for incident dementia. Full details have been previously published27 and described in eMethods (links.lww.com/WNL/D373). Dementia was adjudicated by a study dementia review committee, comprising a neurologist and neuropsychologist, who adjudicate dementia diagnosis according to the DSM-IV. A diagnosis of AD dementia was based on the criteria of the National Institute of Neurologic and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association for definite, probable, or possible AD.
Statistical Analysis
Levels of YKL-40 were natural log transformed and standardized within each cohort. WMHV were also natural log transformed to normalize their distribution. Missing data were excluded from analyses. Statistical analyses were performed using a combination of SAS version 9.4 and RStudio version 1.4.1106. Results were considered significant if p < 0.05. Analyses that were executed have been discussed further.
Primary Analyses: YKL-40, Brain MRI Measures, and Cognition
We used linear regression to examine the associations between YKL-40 levels and each cognitive and brain imaging outcome within each cohort. All models were adjusted for age, age squared (because associations between age and the outcomes were expected to be nonlinear), sex, the time interval between the blood draw for YKL-40 and outcome assessment, and years of education (for cognitive outcomes). A second statistical model included additional adjustments for systolic blood pressure, treatment for hypertension, prevalent diabetes, prevalent cardiovascular disease, total cholesterol, high-density lipoprotein cholesterol, current smoking status, body mass index, and presence of at least 1 APOE ε4 allele. In addition, all estimates from the ARIC and CARDIA cohorts included an additional adjustment for African American race (both models 1 and 2). Given the small number of studies, we used random-effects meta-analysis using the Der Simonian and Laird inverse variance method28 to combine the cohort-specific results. For all meta-analyses, the Higgins I2 test was implemented to test for heterogeneity in effect sizes.29
Sensitivity Analysis: YKL-40, Brain MRI Measures, and Cognition
We explored whether YKL-40 levels were associated with the brain MRI and cognitive outcomes independent of high sensitivity C-reactive protein (CRP) level, the most widely used systemic inflammation marker. CRP was added to the aforementioned statistical models for these analyses, and the analyses were repeated. These secondary analyses were conducted only in the AGES-RS cohort because YKL-40 and high-sensitivity CRP were measured from the same blood draw in this cohort only.
YKL-40 and the Risk of Incident Dementia in the FHS
After confirming the proportionality of hazards assumption, Cox proportional hazards regression models were conducted to examine the relationship between baseline YKL-40 levels and the risk of incident all-cause dementia and clinical AD. We excluded participants younger than 60 years and those with prevalent dementia at baseline. Surveillance for dementia commenced from the YKL-40 blood draw to the time of incident event over a maximum of 7.7 years (median = 5.8). Nonevents were censored at death or until the last date they were known to be dementia-free, also up to 7.7 years. Hazard ratios were presented accompanied by 95% CIs. Models were adjusted for age and sex. Model 2 included additional adjustments for systolic blood pressure, treatment for hypertension, prevalent diabetes, prevalent cardiovascular disease, high-density lipoprotein cholesterol, total cholesterol, prevalent atrial fibrillation, current smoking status, body mass index, and the presence of at least 1 APOE ε4 allele.
Replication and Extension in a Clinic-Based Cohort With Plasma and CSF YKL-40
To extend the validity of our findings, we sought to investigate and compare plasma and CSF YKL-40 in the independent Ace cohort, a prospective clinic-based cohort comprising 1,367 participants at baseline with either normal cognition/subjective cognitive complaints, objective MCI, or dementia and available proteomic measures. Participants were recruited from the Ace Alzheimer Center Barcelona. Further details are provided in eMethods (links.lww.com/WNL/D373). In brief, participants had both plasma and CSF drawn after an overnight fast between 2016 and 2021. YKL-40 was measured from plasma using a SOMAscan assay and from the CSF using an Olink Explore panel; values were expressed in SD units. In the ACE cohort, we first compared YKL-40 concordance between the plasma and CSF using Pearson correlations. Next, we examined the associations between YKL-40 and the CSF pTau/Aβ42 ratio (as a marker of AD pathology) using linear regression, adjusting for age, sex, and CSF biomarker technique. Last, we investigated the associations of plasma and CSF YKL-40 with incident dementia using Cox proportional hazards models, adjusting for age and sex; a further analysis included additional adjustments for AD biomarker classification. All results were considered statistically significant if p < 0.05.
Data Availability
The cohorts make phenotypic and genetic data available through online repositories such as BioLINCC and dbGap, respectively, or on reasonable request.
Results
Demographic Characteristics
Cohort demographics for the MRI analysis samples are summarized in Table 1. The mean age of the cohorts ranged from 50 years in the CARDIA study to 76 years in the AGES-RS. The percentage of male individuals ranged from 39% in the ARIC study to 48% in the CARDIA study. African Americans were well represented in the ARIC and CARDIA studies, whereas the AGES-RS and FHS samples comprised mostly White participants. On average, YKL-40 levels were lowest in the CARDIA study and highest in the FHS. The time interval between the blood draw and outcome assessment varied between the cohorts; both were performed on the same day in the CARDIA study and AGES-RS, whereas the blood draw preceded the brain MRI by a mean of 2 years in the FHS and 10 years in the ARIC study (Figure 1). Demographic characteristics were similar in the cognitive outcome analysis sample (eTable 3, links.lww.com/WNL/D373).
Table 1.
Characteristics of the MRI Study Samples
| AGES-RS | ARIC | CARDIA | FHS | |
| Age, y | 76.3 (6) | 72.3 (4.4) | 50.3 (3.5) | 70.0 (7.9) |
| Male, n (%) | 1,708 (42) | 396 (38.8) | 341 (48.2) | 505 (44.9) |
| African American, n (%) | 0 | 520 (51.0) | 288 (40.7) | 0 |
| Education, n (%) | ||||
| No high school degree | 862 (22) | 228 (22.4) | 27 (3.8) | 21 (1.9) |
| High school degree | 1,950 (50) | 255 (25.0) | 129 (18.2) | 262 (23.3) |
| Some college | 648 (17) | 76 (7.5) | 207 (29.2) | 327 (29.0) |
| College degree | 452 (11) | 460 (45.1) | 345 (48.7) | 516 (45.8) |
| Total cholesterol, mg/dL | 218 (45) | 209.6 (38.2) | 193.52 (35.56) | 185.1 (36.2) |
| HDL cholesterol, mg/dL | 62 (17) | 56.0 (18.9) | 58.13 (17.78) | 62.8 (18.6) |
| BMI, kg/m2, median (Q1, Q3) | 27 (24, 30) | 28.0 (25.1, 31.5) | 28.0 (24.5,32.3) | 27.7 (24.7, 30.9) |
| Smoking, n (%) | 477 (12) | 75 (7.5) | 119 (17.0) | 48 (4.3) |
| Systolic blood pressure, mm Hg | 143 (21) | 133 (18) | 117 (14) | 126 (16) |
| Treatment for high BP, n (%) | 2,109 (47) | 615 (62.2) | 160 (22.6) | 590 (52.4) |
| Prevalent diabetes, n (%) | 486 (12) | 149 (14.6) | 75 (10.6) | 159 (14.5) |
| Prevalent CVD, n (%) | 826 (20) | 35 (3.5) | 12 (1.7) | 158 (14.0) |
| Positive for an APOE ε4 allele, n (%) | 1,040 (26) | 276 (28.1) | 175 (27.2) | 262 (24.0) |
| Time interval between blood draw and MRI, y | 0.0 (0.0) | 10.3 (0.8) | 0 | 1.7 (1.0) |
| YKL-40 (original units) | 52,326 (56,512) | 48,735 (55,400) | 38,694 (64,003) | 57,977 (68,643) |
| Total brain volume, % of ICV | 72.19 (3.89) | 73.04 (3.96) | 85.19 (2.84) | 75.12 (2.46) |
| Hippocampal volume, % of ICV | 0.37 (0.04) | 0.36 (0.05) | 0.56 (0.05) | 0.53 (0.05) |
| WMHV, % of ICV | 0.09 (0.09) | 0.88 (0.82) | 0.07 (0.10) | 0.44 (0.70) |
Abbreviations: AGES-RS = Age, Gene/Environment Susceptibility-Reykjavik Study; ARIC = Atherosclerosis Risk in the Communities; BMI = body mass index; BP = blood pressure; CARDIA = Coronary Artery Risk Development in Young Adults; CVD = cardiovascular disease; FHS = Framingham Heart Study; HDL = high-density lipoprotein; ICV = intracranial volume; MRI = magnetic resonance imaging; WMHV = white matter hyperintensity volume.
Values are mean (SD) unless specified otherwise.
Associations of YKL-40 With MRI and the Cognitive Markers
Across cohorts, 6,558 and 6,670 participants contributed to the MRI and cognitive outcome analyses, respectively. The AGES-RS contributed the most participants (57% and 56% of the MRI and cognitive outcome analyses samples, respectively).
Results based on models 1 and 2 were comparable with results displayed in Figure 2 and eFigure 1 (links.lww.com/WNL/D373), respectively. Across the 4 cohorts and within the meta-analysis, higher levels of YKL-40 were associated with lower total brain volume, as a percentage of ICV. This effect was strongest in the AGES-RS. Similarly, in the meta-analysis of all 4 cohorts, higher levels of YKL-40 were also associated with poorer cognitive function (expressed in SD units) across both statistical models. This effect was strongest in the AGES-RS (the cohort with the highest mean age) and weakest in the CARDIA study (the cohort with the lowest mean age).
Figure 2. Pooled Associations Between YKL-40 and the Dementia Endophenotypes Adjusting for Model 1 Covariates.
Results are per unit increase in the standardized natural log of YKL-40. Adjusts for age, age squared, sex, education (for neuropsychological outcomes), and the time interval between the blood draw for YKL-40 and outcome assessment. Effect estimates from ARIC and CARDIA also include an additional adjustment for race. ARIC = Atherosclerosis Risk in the Communities; CARDIA = Coronary Artery Risk Development in Young Adults; Hetero = heterogeneity; ICV = intracranial volume; TBV = total brain volume; WMHV = white matter hyperintensity volume.
In both the AGES-RS and FHS, higher YKL-40 levels were associated with greater WMHV, as a percentage of ICV. Higher levels of YKL-40 were also associated with lower hippocampal volumes in the AGES-RS, as a percentage of ICV. However, in the meta-analysis of the 4 cohorts, YKL-40 levels were not significantly associated with WMHV or hippocampal volume.
Sensitivity Analysis in the AGES-RS Cohort
In the AGES-RS cohort, results were unchanged when high-sensitivity CRP was added to models 1 and 2 (eTable 4, links.lww.com/WNL/D373).
YKL-40 and the Risk of Incident Dementia in the FHS
The mean age of the sample was 72 (SD = 7) years, and 45% of the sample were male individuals (eTable 5, links.lww.com/WNL/D373). Among the 1,499 participants included in the analysis, there were 54 cases of incident dementia; 41 were clinically consistent with AD. Each SD unit increase in log YKL-40 was associated with a 68% increase in the risk of all-cause dementia and a 59% increase in the risk of AD dementia (Table 2), following full multivariable adjustments.
Table 2.
Association of YKL-40 (per SD Unit Increase) With Incident All-Cause and AD Dementia in the Framingham Heart Study
| Model | All dementia | AD dementia | ||||
| Events/N | HR (95% CI) | p Value | Events/N | HR (95% CI) | p Value | |
| 1a | 54/1,499 | 1.64 (1.25–2.16) | 0.0004 | 41/1,499 | 1.61 (1.17–2.22) | 0.004 |
| 2b | 54/1,423 | 1.68 (1.26–2.23) | 0.0004 | 41/1,423 | 1.59 (1.14–2.22) | 0.007 |
Abbreviations: AD = Alzheimer disease; HR = hazard ratio.
Adjusted for age and sex.
Includes additional adjustments for systolic blood pressure, treatment for hypertension, prevalent diabetes, prevalent cardiovascular disease, high-density lipoprotein cholesterol, total cholesterol, prevalent atrial fibrillation, current smoking status, body mass index, and positivity for an APOE ε4 allele. Results are expressed per SD unit increase in log YKL-40 levels.
Comparisons Between Plasma and CSF YKL-40 and Conversion From MCI to Dementia
Baseline characteristics of the ACE cohort are summarized in eTable 6 (links.lww.com/WNL/D373). Plasma and CSF YKL-40 levels were correlated, with a small to moderate effect (r = 0.31; N = 505; eFigure 2). Higher levels of CSF YKL-40 (β ± SE = 0.352 ± 0.055 per SD unit increase; p < 0.001; N = 496) but not plasma YKL-40 (β ± SE = −0.04 ± 0.04 per SD unit increase; p = 0.31; N = 1,367) were associated with a higher CSF pTau/Aβ42 ratio, adjusting for age, sex, and CSF biomarker technique (eTable 7).
Among 751 participants with MCI and plasma samples available at baseline (eTable 8, links.lww.com/WNL/D373), 326 developed dementia over a median of 2 years of follow-up (Q1, Q3 = 1.1, 3.3). Following adjustments for age and sex, each SD unit increase in plasma YKL-40 was associated with a 12% increase in dementia risk (Table 3). Among 435 participants with MCI and CSF samples available at baseline (eTable 8), 213 participants developed dementia. Following adjustments for age and sex, each SD unit increase in CSF YKL-40 was associated with a 34% increase in dementia risk (Table 3). After including additional adjustments for amyloid, tau, and neurodegeneration biomarker classification, each SD unit increase in plasma and CSF YKL-40 was associated with a 16% and 24% increase in dementia risk, respectively (Table 3).
Table 3.
Association Between YKL-40 (per SD Unit Increase) and Conversion From MCI to Dementia in the ACE Cohort
| Model adjustments | CSF YKL-40 | Plasma YKL-40 | ||||
| Events/N | HR (95% CI) | p Value | Events/N | HR | p Value | |
| Age, sex | 213/435 | 1.34 (1.15–1.57) | <0.001 | 326/751 | 1.12 (0.004–0.23) | 0.04 |
| Age, sex, ATN classificationa | 213/435 | 1.24 (1.04–1.48) | 0.02 | 326/751 | 1.16 (0.04–0.26) | 0.01 |
Abbreviations: ATN = amyloid, tau, and neurodegeneration biomarker classification; MCI = mild cognitive impairment.
Participants were followed up from the time of the LP/blood draw for a median (Q1, Q3, max) of 2.1 (1.1, 3.3, 6.4) years. CSF YKL-40 was measured with an Olink assay and plasma YKL40 was measured with a SOMAscan assay.
ATN categories based on: doi.org/10.3390/ijms23136891.
Discussion
We evaluated the association of plasma YKL-40 with risk of clinical dementia and markers of brain health in nondemented adults across several independent cohorts. Our analysis comprising more than 6,000 participants demonstrated that higher levels of plasma YKL-40 were associated with lower brain volume and poorer cognitive function. Furthermore, in the FHS, higher levels of YKL-40 were also associated with a higher risk of incident dementia over a median of 5.8 years of follow-up. These findings were replicated in the clinic-based ACE cohort whereby plasma and CSF YKL-40 levels were correlated and both were associated with conversion from MCI to dementia. Of interest, plasma YKL-40 levels were not associated with the CSF pTau/Aβ42 ratio; the association between YKL-40 levels and incident dementia was independent of amyloid, tau, and neurodegeneration biomarker status. Thus, plasma YKL-40 demonstrates early promise as a nonspecific prognostic biomarker for cognitive impairment that may relate to dementia independent of AD processes.
YKL-40 is a protein primarily expressed in astrocytes and coded by the ChI3L1 gene. The function of the protein is unknown but may be related to activation of microglia and astrocytes. Recent research has demonstrated that a common genetic variant that lowers YKL-40 levels in the CSF is associated with slower AD progression in humans.30 Moreover, deleting the Chi3L1 gene in preclinical models promotes the phagocytosis of Aβ through astrocytes and microglia.30 Thus, YKL-40 seems to influence glial activation and AD progression. Several studies show that CSF levels of YKL-40 may discriminate patients with AD dementia from controls.31,32 Because YKL-40 levels can be measured in the periphery, previous findings provided a strong rationale for investigating whether plasma levels of YKL-40 are associated with preclinical markers of dementia and the risk of clinical AD. As an important first step, we show that in adults without dementia at baseline, higher plasma YKL-40 levels are associated with markers of brain health and the risk of future dementia. Therefore, YKL-40 shows early promise as a plasma biomarker that is useful for studying the effects of inflammation on the brain, including in persons at risk of dementia, where anti-inflammatory DMTs for dementia are being tested.
Our findings extend previous smaller investigations of plasma YKL-40 that mainly investigated diagnostic performance for dementia in selected samples. In one of the largest earlier studies with 237 individuals recruited from the Washington University Alzheimer Disease Research Center, plasma YKL-40 levels (measured with ELISA) were significantly higher in persons with CDR scores of 0.5 or 1 than in those with 0. However, plasma YKL-40 levels were not associated with cognitive decline.16 Another study reported that plasma YKL-40 levels (measured with ELISA) were higher in 41 patients with mild AD dementia, when compared with those in 35 controls.33 Using a Luminex multiplex assay, Zhang and colleagues reported that plasma YKL-40 levels were higher in a small sample of persons with when compared with those without cognitive impairment based on an Mini-Mental State Examination score of ≤26.34 In comparing across a broad range of neurodegenerative diseases, Villar-Pique and colleagues reported that plasma YKL-40 (measured with ELISA) were highest in patients with Creutzfeldt-Jakob disease (n = 78), followed by Lewy body dementia (n = 34), vascular dementia (n = 22), AD (n = 50), frontotemporal dementia (n = 17), and controls with neurologic disease (n = 44), than in healthy controls (n = 70).35 Our data add to these findings conducted in patients with dementia by suggesting that elevated plasma YKL-40 levels correlate with markers of brain health in persons at risk of dementia and associate with risk of dementia in the future.
It is unclear whether plasma YKL-40 levels are associated with brain health and dementia risk because such levels indicate immune dysregulation, the brain's neuroinflammatory response, or because of associations with peripheral inflammation and shared risk factors. Two studies have reported statistically significant yet modest correlations between blood and CSF YKL-40 levels (r = 0.24–0.30)15,16; we replicated this finding and observed a very similar correlation (r = 0.31). In the ACE cohort, we demonstrated that both plasma and CSF YKL-40 were associated with the progression from MCI to dementia independent of amyloid, tau, and neurodegeneration biomarker status. Moreover, we did not observe an association between plasma YKL-40 and the CSF ptau181/Aβ42 ratio. In our subanalysis, associations between YKL-40 and cognition and brain volume were not meaningfully altered after including additional adjustments for peripheral inflammation, as measured by high-sensitivity CRP. Moreover, results were largely unchanged when adjusting for many risk factors associated with dementia and systemic inflammation (e.g., cardiovascular disease and smoking). Therefore, the mechanisms linking plasma YKL-40 to brain health require further investigation. Because plasma and CSF YKL-40 were correlated, plasma YKL-40 may provide a window to brain inflammation or immune function.
Regardless of its source, plasma YKL-40 provided information on dementia risk additional to amyloid and tau and thus may improve the risk stratification of future dementia beyond established dementia biomarkers. Research is needed to ascertain whether plasma YKL-40 levels improve the prediction of incident dementia in conjunction with established and emerging plasma biomarkers such as neurofilament light chain, glial fibrillary acidic protein, and p-tau 217. For incident all-cause dementia, a multiple biomarker approach may best contribute for assessing risk due to multiple pathophysiologic mechanisms.
The strengths of this study include the inclusion of multiple independent population-based cohorts contributing more than 6,000 participants. Moreover, we included both markers of subclinical brain aging and injury (endophenotypes of dementia) and incident dementia. However, our study was not without limitations. Because YKL-40 was measured at a single time point, we could not determine whether changes in YKL-40 levels track with the progression of preclinical or clinical disease, which is an essential aim for future research. Furthermore, the time interval between the blood draw for YKL-40 measurement and outcome assessment varied substantially between studies (same day in the CARDIA and AGES-RS and approximately 10 years in the ARIC study). Although this variability does not invalidate our findings, it may have attenuated some of the relationships observed between YKL-40 and the outcomes. Moreover, our significant findings despite heterogeneity in these assessment intervals speaks to the potential of YKL-40 as a biomarker for predicting cognitive impairment.
Although inflammation is a hallmark of AD and other dementias, biomarkers of inflammation or immune function have not been sufficiently validated for widespread use. In this study, plasma YKL-40 showed promise as a minimally invasive biomarker for studying inflammation and immune function in the context of brain injury leading to dementia. Of importance, we demonstrate the association between YKL-40 and markers of brain health and dementia risk in a community setting. In a clinic-based sample, we showed that higher YKL-40 levels were associated with conversion from MCI to dementia, independent of amyloid, tau, and neurodegeneration status and regardless of whether YKL-40 was measured from the CSF or plasma. Therefore, plasma YKL-40 may have prognostic value in preclinical disease stages, where DMTs for dementia are being evaluated. To test this hypothesis further, research is needed to examine whether longitudinal changes in YKL-40 track with the progression of neurodegenerative disease leading to dementia. Also important will be to determine how plasma YKL-40 levels respond to anti-inflammatory interventions.
Glossary
- Aβ
β-amyloid
- ACE
ACE csf cohort
- AD
Alzheimer disease
- AGES-RS
Age, Gene/Environment Susceptibility-Reykjavik Study
- ARIC
Atherosclerosis Risk in the Communities
- CARDIA
Coronary Artery Risk Development in Young Adults
- CRP
C-reactive protein
- DMT
disease-modifying therapy
- DSM-IV
Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition
- FHS
Framingham Heart Study
- ICV
intracranial volume
- MCI
mild cognitive impairment
- NIA-AA
National Institute on Aging and Alzheimer's Association
- WMHV
white matter hyperintensity volume
Appendix. Authors
| Name | Location | Contribution |
| Matthew P. Pase, PhD | School of Psychological Sciences and the Turner Institute for Brain and Mental Health, Monash University; Turner Institute for Brain and Mental Health, Monash University, Australia | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data; and additional contributions: obtained study funding |
| Jayandra J. Himali, PhD | Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Raquel Puerta, MSc | ACE Alzheimer Center, Barcelona, Spain | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Alexa S. Beiser, PhD | Boston University School of Public Health, MA | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Mitzi M. Gonzales, PhD | Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Claudia L. Satizabal, PhD | University of Texas Health Sciences Center, San Antonio | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Qiong Yang, PhD | Department of Neurology, Boston University School of Medicine, MA | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Hugo J. Aparicio, MD, MPH | Department of Neurology, Boston University School of Medicine, MA | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Daniel J. Kojis, BA | Boston University School of Public Health, MA | Major role in the acquisition of data |
| Charles S. Decarli, MD | Department of Neurology, School of Medicine & Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California at Davis | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Oscar L. Lopez, MD | Department of Neurology, School of Medicine, University of Pittsburgh, PA | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Will Longstreth | University of Washington, Seattle | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Vilmundur Gudnason | Faculty of Medicine, University of Iceland, Reykjavík | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Thomas H. Mosley, PhD | University of Mississippi Medical Center, The MIND Center, Jackson | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Joshua C. Bis, PhD | Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data; and additional contributions: obtained study funding |
| Alison Fohner, PhD | Department of Epidemiology, University of Washington, Seattle | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Bruce M. Psaty, MD, PhD | University of Washington, Seattle | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Mercè Boada, MD, PhD | ACE Alzheimer Center, Barcelona, Spain | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Pablo García-González, MS | ACE Alzheimer Center, Barcelona, Spain | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Sergi Valero, PhD | ACE Alzheimer Center, Barcelona, Spain | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Marta Marquié, MD, PhD | ACE Alzheimer Center, Barcelona, Spain | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; and analysis or interpretation of data |
| Russell Tracy, PhD | University of Vermont, Burlington | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data; and additional contributions: obtained study funding |
| Lenore J. Launer, PhD | Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Bethesda, MD | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data; and additional contributions: obtained study funding |
| Agustín Ruiz, MD, PhD | ACE Alzheimer Center, Barcelona, Spain | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; analysis or interpretation of data; and additional contributions: obtained study funding |
| Myriam Fornage, PhD | University of Texas Health Science Center, Houston | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data; and additional contributions: obtained study funding |
| Sudha Seshadri, MD | University of Texas Health Sciences Center, San Antonio | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data; and additional contributions: obtained study funding |
Footnotes
Editorial, page e209145
Study Funding
Funding for this work was provided by a MarkVCID grant from the National Institute of Neurological Disorders and Stroke (1UH2/UH3 NS100605 and UF1NS125513), by grants from the NIA to the Cross Cohorts Consortium (CCC) (AG059421), and by the Cohorts for Age and Aging Research in Genomic Epidemiology (CHARGE) infrastructure grant from the NHLBI (HL105756). AGES-RS: The Age, Gene/Environment Susceptibility-Reykjavik Study was supported by NIH contracts N01-AG-1-2100 and HHSN27120120022C, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). ARIC: The Atherosclerosis Risk in Communities Study is performed as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, 75N92022D00005). The ARIC Neurocognitive Study is supported by U01HL096812, U01HL096814, U01HL096899, U01HL096902, and U01HL096917 from the NIH (NHLBI, National Institute of Neurological Disorders and Stroke, NIA and NIDCD). The authors thank the staff and participants of the ARIC study for their important contributions. CARDIA: The Coronary Artery Risk Development in Young Adults Study (CARDIA) is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the University of Alabama at Birmingham (HHSN268201800005I & HHSN268201800007I), Northwestern University (HHSN268201800003I), University of Minnesota (HHSN268201800006I), and Kaiser Foundation Research Institute (HHSN268201800004I). This manuscript has been reviewed by CARDIA study for scientific content. The FHS: This work was made possible by grants from the Alzheimer's Drug Discovery Foundation (GDAPB-202010-2020940), NIH (N01-HC-25195, HHSN268201500001I, 75N92019D00031), and the National Institute on Aging (AG059421, AG054076, AG049607, AG033090, AG066524, NS017950, P30AG066546, and UF1NS125513). M.P. Pase is funded by a National Health and Medical Research Council of Australia Emerging Leader Fellowship (GTN2009264). C. Decarli directs the UC Davis Alzheimer's Disease Center with funding from the NIH (P30 AG010182). H.J. Aparicio is supported by an American Academy of Neurology Career Development Award, the Alzheimer's Association (AARGD-20-685362), and the NIH (L30 NS093634). A.E. Fohner is supported by NIH/NIA K01AG071689. Additional support for the investigators at UTHSA was provided by the NIA P30 AG066546 to the South Texas ADRC and by philanthropic support from the JMR Barker Foundation. ACE csf cohort: M. Boada, A. Ruiz, and M. Marquié are supported by AES national grants PI13/02434, PI16/01861, PI17/01474, PI19/01240, PI19/01301, PI19/00335, BA19/00020, and PI22/011403, A. Ruiz is also supported by CIBERNED grant 2019/08 and by ISCIII national grant PMP22/00022, funded by the European Union (NextGenerationEU). Acción Estratégica en Salud is integrated into the Spanish National R + D + I Plan and funded by ISCIII (Instituto de Salud Carlos III)-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER “Una manera de Hacer Europa”), by JPco-fuND-2 “Multinational research projects on Personalised Medicine for Neurodegenerative Diseases,” PREADAPT project (ISCIII grant: AC19/00097), and by EURONANOMED III Joint Transnational call for proposals (2017) for European Innovative Research & Technological Development Projects in Nanomedicine (ISCIII grant: AC17/00100). AR is the local PI (Spain) of Harpone project funded by Flanders Innovation and Entrepreneurship (VLAIO) and Janssen.
Disclosure
The authors report no relevant disclosures. Go to Neurology.org/N for full disclosures.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The cohorts make phenotypic and genetic data available through online repositories such as BioLINCC and dbGap, respectively, or on reasonable request.