The CCL2 rs4586 SNP Is Associated with Slower Amyloid-β Deposition and Faster Tau Accumulation of Alzheimer’s Disease

Abstract

Background:

CC-chemokine ligand 2 (CCL2), the key immunomodulatory chemokine for microglial activation, has been implicated in the pathogenesis of Alzheimer’s disease (AD). Whether the association of CCL2 single nucleotide polymorphisms (SNPs) and the risk of AD is still controversial.

Objective:

We aimed to investigate whether CCL2 rs4586 SNP is associated with the pathological changes and cognitive decline of AD.

Methods:

A total of 486 participants with longitudinal cerebrospinal fluid (CSF) amyloid-β (Aβ) and phospho-tau (P-tau) biomarkers, ¹⁸F-Florbetapir and ¹⁸F-flortaucipir-positron emission tomography (PET), and cognitive assessments from the Alzheimer’s disease Neuroimaging Initiative were included in the study. The effects of CCL2 rs4586 SNP on the pathological changes and cognitive decline of AD were assessed with linear mixed-effects models and evaluated according to the Aβ-status so as to identify whether the effects were independent of Aβ status.

Results:

CCL2 rs4586-CC carriers exhibited a slower global Aβ-PET accumulation, particularly within stage I and stage II. However, they exhibited a faster accumulation of CSF P-tau and global tau-PET standard uptake value ratios, especially in Braak I and Braak III/IV and the inferior temporal gyrus. The congruent effects of CCL2 rs4586 on tau accumulation existed only in the Aβ–group, as is shown in global tau-PET and Braak I. However, CCL2 rs4586 was not associated with the cognitive decline.

Conclusion:

Our findings showed that the CCL2 rs4586-CC (versus TT/TC) genotype was associated with slower Aβ deposition and faster tau accumulation, and the latter of which was independent of Aβ status.

Keywords

Alzheimer’s disease Alzheimer’s Disease Neuroimaging Initiative amyloid-β CCL2 tau

INTRODUCTION

Alzheimer’s disease (AD) is the most common type of dementia characterized by the deterioration of cognitive function and typical pathological changes of amyloid-β (Aβ) plaques and tau protein hyperphosphorylation in the brain [1]. Various hypotheses regarding the pathogenesis of AD have been proposed [2], and among them neuroinflammatory response of microglia is increasingly recognized as a pivotal causative factor [3 –6].

The CC-chemokine ligand 2 (CCL2, also known as monocyte chemoattractant protein-1, MCP-1), one of the most potent immunomodulatory chemokines for microglial and macrophage recruitment [7, 8], has been found to be upregulated in the cerebrospinal fluid (CSF) of patients with mild cognitive impairment (MCI) and AD [9, 10]. Microglia activated by Aβ are able to produce CCL2, which attracts microglial and peripheral immune cells to the sites of inflammation [11]. The deficiency of CCL2 signaling leads to impaired microglial accumulation and accelerated Aβ deposition in the mouse model of AD [12, 13]. Conversely, overexpression of CCL2 accelerates tau pathology [14]. Consistently, clinical findings showed that CCL2 significantly increased in the CSF and serum of AD patients [15, 16], together with a decline in cognition [17], indicating that CCL2 might play a key role in AD pathogenesis.

It has been found that the expression of CCL2 exhibits considerable inter-individual variability, which might be due to the modification of single nucleotide polymorphisms (SNPs) locus [18, 19]. So far, only a few studies have explored the association of CCL2 genotype and the risk of AD with controversial conclusions [20 –22]. The CCL2 A-2518 G promoter region polymorphism has been reported to slightly influence MCI conversion to AD [22]. However, a meta-analysis suggested that CCL2 A-2518 G polymorphism was not associated with genetic susceptibility of AD in general population [20]. Recent findings have revealed that synonymous variants are able to alter protein function through a wide variety of mechanisms [23]. CCL2 rs4586 SNP (Cys35Cys), a synonymous variant of CCL2 gene, has been extensively studied in a spectrum of diseases associated with inflammatory process such as gastric cancer, macular degeneration, and chronic obstructive pulmonary disease [24 –26]. CCL2 rs4586 SNP may affect the expression of CCL2 protein and the biological activity of its receptor (CCR2), which subsequently affects the signaling pathways of CCL2 [26, 27]. Furthermore, CCL2 rs4586 SNP was significantly associated with increased mRNA levels of CCL13, another chemokine that plays a key role in the biological course of inflammatory responses [24]. Therefore, in the present study, we aim to explore whether CCL2 rs4586 SNP has impacts on the cognitive decline, longitudinal changes of CSF biomarkers, or neuroimage of AD using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.

MATERIALS AND METHODS

Study design and data sources

The study was designed to investigate whether SNP rs4586 is associated with cognitive function and well-validated AD biomarkers by means of longitudinal analyses.

Data used in the preparation of this article were obtained from the ADNI database (http://adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging, positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. For up -to-date information, see http://www.adni-info.org. The ADNI database includes participants recruited from almost 63 sites across the United States and Canada [28]. Institutional review boards of all participating institutions approved the ADNI, and written informed consent according to the Declaration of Helsinki was signed by all participants or their authorized representatives.

Participants

Subjects included in our study were obtained from the ADNI-1, ADNI-2, ADNI-GO, and ADNI-3 databases, which include individuals with AD, MCI due to AD, and cognitively normal (CN). Follow-up visits were carried out at six-month intervals for the first two years and then annually. At each follow-up visit, participants were recorded in the ADNI database for any changes in clinical diagnostic or biomarker data. Detailed inclusion and exclusion criteria of ADNI have been reported previously [29]. Subjects were included in our study if they: 1) had genetic material available for analysis; 2) had demographic (age, sex, education) information and clinical evaluations of apolipoprotein (APOE) ɛ4 status; 3) were available with baseline cognitive diagnosis and CSF biomarkers of Aβ₄₂ and phospho-tau (P-tau); and 4) had at least two research visits assessing cognition and CSF biomarkers for a minimum of six months apart.

Among the 486 participants resulting from the inclusion criteria, 477 underwent ¹⁸F-Florbetapir (AV45)-PET and 87 underwent ¹⁸F-Flortaucipir (AV1451)-PET at baseline and at least one visit of ADNI phase. The average follow-up time of the included population was 39.1 months (range, 11.0 to 122.8 months). The participants were divided into different groups according to the cognitive status, with MCI and AD dementia being labeled as cognitively impaired group.

Genetic data

Genotype data of CCL2 rs4586 SNP were obtained from genome-wide association study (GWAS) and whole genome sequencing (WGS) in the ADNI study. Genotyping on these samples from GWAS was performed using the Human610-Quad Bead Chip (for subjects in ADNI-1), the Human Omni Express Bead Chip (for subjects in ADNI-GO and ADNI-2), and Ilumina Infinium Global Screening Array v2 (for subjects in ADNI-3). Detailed genotyping protocols were described previously [30]. Samples from WGS underwent genotyping using the Illumina Omni2.5M BeadChip by Illumina [31]. All genotype data were quality controlled, which were made available for the ADNI LONI Web site (http://adni.loni.usc.edu) in PLINK data formats [31].

Cognitive assessment

Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to examine the global cognition of the participants. Baseline and longitudinal data for MMSE and MoCA were obtained.

CSF biomarker data

CSF biomarker data (CSF Aβ₄₂ and P-tau) were obtained from ADNI database. CSF was sampled through lumbar puncture and the concentration of CSF biomarkers were measured by means of automated Roche Elecsys and cobas e immunoassay analyzer systems [32]. Longitudinal data were analyzed and described in detail previously [33]. All CSF biomarker assays were performed in duplicate and averaged. The cutoff of CSF Aβ₄₂ has been previously set at 1098 pg/ml [34].

Neuroimaging acquisition and PET preprocessing

Neuroimage of AD, including Aβ-PET (¹⁸F-AV45) and partial volume corrected tau-PET (¹⁸F-AV1451) imaging data, were downloaded from the ADNI website in their most fully preprocessed format. Preprocessing of the PET data using the ADNI pipeline has been previously described [34]. The normalized standard uptake value ratio (SUVR) for each region of interest (ROI) was calculated by dividing tracer uptake in these regions according to the value in a predefined reference region. To obtain volume weighted composite regions SUVR, we calculated the ratio of the sum of products of the PET signal in each ROI included in the composite ROI with its volume and the sum of volumes of the ROIs.

Amyloid and tau staging

For Aβ-PET (¹⁸F-AV45), we assessed global amyloid-PET levels plus an anatomical amyloid staging system that was introduced recently. For global amyloid load, we considered tracer binding in a lateral and medial frontal, anterior, and posterior cingulate, lateral parietal, and lateral temporal regions [35]. For local amyloid levels, we used a 4-stage model following a pre-established approach [36] (Supplementary Table 5), as the earliest in vivo amyloid stages were mostly missed by conventional dichotomous classification approaches based on global florbetapir-PET signal. The reference region of Aβ-PET was a composite region comprising whole cerebellum, brainstem/pons, and subcortical white matter. This composite region has more dependable longitudinal florbetapir results in ADNI compared with using only the cerebellum as a reference region [37].

For tau-PET (¹⁸F-AV1451), we obtained global and Braak-stage-ROI-specific tau-PET SUVR scores. For global tau, we considered tracer binding in regions of typical AD manifestation, including bilateral entorhinal, amygdala, fusiform, inferior, and middle temporal cortices as described previously [38]. For Braak-stage specific tau-PET, we applied in vivo Braak-staging that recapitulates the spatial tau-spreading pattern from early-to-late-stage tau pathology [39] (https://adni.bitbucket.io/reference/docs/UCBERKELEYAV1451/UCBERKELEY_AV1451_Methods_Dec2019.pdf). In addition, we included SUVR in the inferior temporal gyrus (IFT), a region in the temporal lobe that shows strong AD-related tau-PET signal [40]. In brief, we obtained tau-PET SUVRs for Braak-stage I, III/IV, and V/VI, and IFT composite ROIs. Braak-stage II (i.e., hippocampus) was excluded due to Flortaucipir off-target binding in this region [41]. The reference region of tau-PET was the gray matter of inferior cerebellum as previously described [40].

Statistical methods

Baseline characteristics were compared between groups (i.e., CN versus MCI+AD dementia) stratified by CCL2 rs4586 genotypes (CC versus TT/TC), using analyses of variance (ANOVAs) for continuous measures and chi-squared tests for categorical measures. Longitudinal data were log-transformed to approximate a normal distribution. We used linear mixed-effects models to determine the effects of CCL2 rs4586 genotypes (CC versus TT/TC) on longitudinal change of cognitive score, CSF biomarkers and neuroimage of AD. We used linear mixed-effects models with fixed effects of age, sex, education, APOE ɛ4, diagnosis, and a time from baseline by mutation status interaction, as well as random slope and intercept terms for each participant. The models were fitted with the lmer function in the lme4 package in R, version 4.0.2. In addition, we explored the interaction of genotypes with cognitive status (CN versus MCI+AD) on longitudinal changes of Aβ, tau, and cognition in all the participants. Further analyses stratified by cognitive status were performed once statistical significance was reached (p for interaction < 0.05). In addition, for exploration, we assessed the associations of CCL2 rs4586 (CC versus TT/TC) with longitudinal changes of tau accumulation in Aβ+and Aβ–group respectively. Statistical significance was considered to have been achieved when p was less than 0.05.

RESULTS

Sample characteristics

The demographic characteristics and clinical data of the participants were summarized in Table 1. A total of 486 participants (72.84±6.70 years, 227 women) were recruited, among whom 198 were cognitively normal individuals, 288 were cognitively impaired patients (MCI n = 272; AD n = 16). The genotype distribution of rs4586 was consistent with the Hardy-Weinberg equilibrium (p > 0.05). The frequency of T allele was 66.6% in all the participants, whereas C allele frequency was 33.4%. The frequencies did not differ significantly between the cognitively normal and impaired group (i.e., CN versus MCI+AD) (p = 0.9767). In addition, there was no interaction of cognitive status with genotypes of CCL2 rs4586 on longitudinal changes of Aβ and tau deposition, suggesting that the effects of CCL2 rs4586 on Aβ and tau deposition were independent of cognitive status (Supplementary Table 1).

Table 1

Sample characteristics stratified by genotypes and cognitive status

Characteristics	Cognitively normal CCL2 rs4586-CCN, mean (SD)	Cognitively normal CCL2 rs4586-TT/TCN, mean (SD)	Cognitively impaired CCL2 rs4586-CCN, mean (SD)	Cognitively impaired CCL2 rs4586-TT/TCN, mean (SD)	p
Age (y)	34, 73.42 (5.50)	164, 73.87 (5.85)	27, 72.37 (8.08)	261, 72.16 (7.13)	0.072
Gender (F/M)	34, 18/16	164, 88 /76	27, 15/2	261, 106/155	0.037
Education (y)	34, 16.32 (2.76)	164, 16.51 (2.57)	27, 15.37 (2.50)	261, 16.26 (2.79)	0.235
APOE ɛ4 (0/1/2)	34, 27 /6 /1	164, 127 /34 /3	27, 15/8/4	261, 127 /100 /34	<0.001
MMSE	34, 29.18 (1.06)	164, 29.09 (1.22)	27, 27.59 (1.80)	261, 27.54 (2.16)	<0.001
MoCA	21, 25.33 (2.89)	139, 25.96 (1.22)	19, 22.58 (3.70)	200, 23.21 (3.42)	<0.001
CSF –Aβ₄₂ (pg/ml)	34, 1,395.08 (646.43)	164, 1,372.01 (676.74)	27, 1,349.34 (672.93)	261, 984.61 (540.80)	<0.001
CSF –P-tau (pg/ml)	34, 21.66 (8.07)	164, 22.25 (9.56)	27, 29.27 (12.54)	261, 28.79 (14.93)	<0.001
Aβ-PET	30, 0.80 (0.12)	141, 0.78 (0.13)	24, 0.83 (0.16)	222, 0.87 (0.15)	<0.001
Tau-PET	8, 1.74 (0.38)	38, 1.69 (0.27)	5, 1.82 (0.59)	36, 1.65 (0.28)	0.662

Data are given as mean (standard deviation) unless otherwise indicate. Aβ, amyloid-β; APOE ɛ4, apolipoprotein ɛ4; CSF, cerebrospinal fluid; F, female; M, male; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; PET, positron emission tomography.

CCL2 rs4586 and baseline variants

At baseline, CCL2 rs4586-CC carriers exhibited higher CSF Aβ₄₂ levels than those of TT/TC carriers, as shown by ANCOVAs after controlling for age, sex, education, APOE ɛ4, and diagnosis (estimate = 0.138, p = 0.027). However, there were no differences in CSF P-tau levels, Aβ-PET, tau-PET, and cognition between CCL2 rs4586-CC carriers versus TT/TC carriers (p > 0.05) (Supplementary Table 2).

CCL2 rs4586 and longitudinal changes in Aβ deposition

Association of CCL2 rs4586 (CC versus TT/TC) with longitudinal changes of Aβ deposition was explored in the study. Both CSF Aβ and global Aβ-PET were employed to represent Aβ deposition. Results showed that CCL2 rs4586-CC carriers exhibited a slower global Aβ-PET accumulation than TT/TC carriers (β=-0.025, p = 0.012), particularly within stage I (β=-0.023, p = 0.032) and stage II (β=-0.025, p = 0.011), controlling for age, sex, years of education, APOE ɛ4 status, baseline CSF Aβ₄₂, and diagnosis (Fig. 1) (Supplementary Table 3). However, when using CSF Aβ₄₂ levels to represent Aβ deposition, we did not find CCL2 rs4586 effects on Aβ deposition (β=-0.024, p = 0.095) (Fig. 1) (Supplementary Table 3).

Fig. 1

Differences in Aβ accumulation rates between CCL2 rs4586-CC carriers and TT/TC carriers for all the participants. A) No significant association between CCL2 rs4586 SNP and longitudinal changes of CSF Aβ₄₂ was found (β=–0.024, p = 0.095); B) Slower global Aβ-PET accumulation was correlated with CCL2 rs4586-CC carriers compared with TT/TC carriers (β=–0.025, p = 0.012); C, D) Slower Aβ-PET accumulation in Stage I and Stage II was correlated with CCL2 rs4586-CC carriers compared with TT/TC carriers (C: Stage I, β=–0.023, p = 0.032; D) Stage II, β=–0.025, p = 0.011); E, F) No significant association between CCL2 rs4586 SNP and Aβ-PET accumulation in Stage III and Stage IV was found (E: Stage III, β=–0.021, p = 0.076; F) Stage IV, β=–0.014, p = 0.313). p-values<0.05 indicate significance of group differences and were derived from linear mixed-effects models controlling for age, sex, years of education, APOE ɛ4 status, baseline CSF Aβ₄₂, and diagnosis. The gray dots and whiskers represent the mean plus/minus standard error. Aβ, amyloid-β; APOE ɛ4, apolipoprotein ɛ4; CSF, cerebrospinal fluid; PET, positron emission tomography; SUVR, standardized uptake value ratio.

CCL2 rs4586 and longitudinal changes in tau accumulation

Association of CCL2 rs4586 (CC versus TT/TC) with longitudinal changes of tau accumulation was explored in the study. Both CSF P-tau and global tau-PET were employed to represent tau accumulation. Results showed that CCL2 rs4586-CC carriers exhibited a faster rate of CSF P-tau accumulation as well as global tau-PET SUVRs uptake than T allele (TT, TC) carriers after controlling for age, gender, education, APOE ɛ4 status, baseline CSF Aβ₄₂, and diagnosis (Fig. 2) (Supplementary Table 3).

To assess whether there are regional differences in CCL2 rs4586 on tau pathology, we quantified tau SUVRs within brain regions according to Braak stages I–VI. In line with the results of global tau-PET uptake, we found that CCL2 rs4586-CC carriers had a faster rate of change in tau accumulation than TT/TC carriers within Braak I (β=0.128, p = 0.034), Braak III/IV (β=0.084, p = 0.043) and the IFT (β=0.094, p = 0.025), but not Braak V/VI (β=0.072, p = 0.173) (Fig. 2) (Supplementary Table 3).

Fig. 2

Differences in tau accumulation rates between CCL2 rs4586-CC carriers and TT/TC carriers in all the participant. A) Faster rate of CSF P-tau accumulation was associated with CCL2 rs4586-CC carriers compared with TT/TC carriers (β=0.023, p = 0.035); B) Faster rate of global tau-PET accumulation was associated with CCL2 rs4586-CC carriers compared with TT/TC carriers (β=0.100, p = 0.014); C) Faster rate of tau-PET accumulation in IFT was associated with CCL2 rs4586-CC carriers compared with TT/TC carriers (β=0.094, p = 0.025); D, E) Faster rate of tau-PET accumulation in Braak I and Braak III/IV was associated with CCL2 rs4586-CC carriers compared with TT/TC carriers (D: Braak I, β=0.128, p = 0.034; E: Braak III/IV, β=0.084, p = 0.043); F) No significant association between longitudinal changes of tau-PET accumulation in Braak V/VI and CCL2 rs4586 SNP was found (β=0.072, p = 0.173). p-values<0.05 indicate significance of group differences and were derived from linear mixed-effects models controlling for age, sex, years of education, APOE ɛ4 status, baseline CSF Aβ₄₂, and diagnosis. The gray dots and whiskers represent the mean plus/minus standard error. Aβ, amyloid-β; APOE ɛ4, apolipoprotein ɛ4; CSF, cerebrospinal fluid; IFT, inferior temporal gyrus; PET, positron emission tomography; SUVR, standardized uptake value ratio.

When tested in Aβ+and Aβ–group, we found that the congruent effects of CCL2 rs4586 on tau accumulation existed only in Aβ–group, as is shown in global tau-PET (β=0.153, p = 0.031) and Braak I (β=0.198, p = 0.039) (Fig. 3) (Supplementary Table 4).

Fig. 3

Differences in tau accumulation rates between CCL2 rs4586-CC carriers and TT/TC carriers in Aβ- group. A) In Aβ- group, faster rate of global tau-PET accumulation was associated with CCL2 rs4586-CC carriers compared with TT/TC carriers (β=0.153, p = 0.031); B) In Aβ- group, faster rate of tau-PET accumulation in Braak I was associated with CCL2 rs4586-CC carriers compared with TT/TC carriers (β=0.198, p = 0.039). p-values<0.05 indicate significance of group differences and were derived from linear mixed-effects models controlling for age, sex, years of education, APOE ɛ4 status, baseline CSF Aβ₄₂, and diagnosis. The gray dots and whiskers represent the mean plus/minus standard error. Aβ, amyloid-β; APOE ɛ4, apolipoprotein ɛ4; CSF, cerebrospinal fluid; PET, positron emission tomography.

CCL2 rs4586 and longitudinal changes in cognition

Association of CCL2 rs4586 (CC versus TT/TC) with longitudinal changes of MMSE and MoCA was investigated in the study and there were no differences in cognitive decline over time in CCL2 rs4586-CC carriers versus TT/TC carriers after controlling for age, sex, education, APOE ɛ4 status, and baseline diagnosis (Supplementary Table 1). As there was significant interaction between CCL2 rs4586 and cognitive status in MMSE (p < 0.05), further sub-analyses stratified by cognitive status were performed, and no significant associations between CCL2 rs4586 and cognitive decline were found in either normal controls or cognitive impairment group (Supplementary Table 1).

DISCUSSION

The major findings of the current study were that CCL2 rs4586-CC was associated with higher CSF Aβ₄₂ levels at baseline and slower global and reginal Aβ-PET accumulation, especially in the early phases of Aβ deposition (stage I and stage II). In contrast, the genotype was associated with faster tau accumulation. The congruent effects of CCL2 rs4586-CC on tau accumulation remained unchanged when baseline Aβ-status was included as a covariate and existed only in Aβ–group, suggesting that the associations between CCL2 rs4586-CC and faster tau accumulation were independent of Aβ-status.

The higher CSF Aβ₄₂ levels at baseline and slower Aβ deposition mediated by CCL2 rs4586-CC in the early phases of Aβ deposition (stage I and stage II) indicate that the genotype might accelerate microglia-mediated Aβ clearance and degradation in the early stages of AD. It is known that microglia are able to envelope Aβ plaques and act as a physical barrier that prevents outward plaque expansion [42]. CCL2 and its CCR2 have been implicated to innate microglial activation, and therefore play a role in Aβ degradation. A variety of cross-sectional works in vitro have demonstrated that CCL2 deficiency is associated with decreased microglial accumulation, impaired clearance of Aβ and increased Aβ oligomers levels as well as AD pathobiology [12 , 43]. Postmortem study also revealed an inverse relationship between Aβ₄₂ and CCL2 in male AD patients [44]. Thus, CCL2 rs4586-CC might play a protective role in the early stages of AD with a mechanism of microglia-mediated Aβ clearance. Although both CSF Aβ₄₂ and amyloid PET are accurate biomarkers to identify AD, CCL2 rs4586 affect Aβ-PET accumulation but not CSF Aβ₄₂ in the current study. The discordance might be due to that CSF Aβ₄₂ are more sensitive in the early stages of AD, while PET amyloid may still change dynamically during later stages of disease [45].

Conversely, faster tau accumulation in CCL2 rs4586-CC (versus TT/TC) was observed and consistently detected across a diversity of pre-defined ROIs capturing whole-brain. As Aβ pathology is the upstream of tau pathology, we used baseline Aβ levels as a covariate and found the effects were independent of baseline Aβ status. The results are in accordance with previous cross-sectional finding that overexpressing of CCL2 was associated with increased microglial activation and worsening of tau pathology in mouse model of AD [14]. In parallel, in patients of AD, the amount of P-tau was significantly correlated with CCL2, which was independent of Aβ [44]. The distinct effects of CCL2 rs4586-CC on longitudinal change of Aβ and tau might be due to that the expression of microglia population was AD pathology associated, which was either Aβ (AD1) associated or hyperphospho-tau (AD2) associated [46]. Although Aβ accumulation was recognized as an initial event leading to the subsequent pathological events in AD, the activated microglia exhibit diverse phenotypes as AD progresses, and have multifaceted interactions with Aβ and tau species [3]. In addition, CCR2 deficiency is able to stimulate the expression of CX₃CR1 transcripts [43], the lack of which has been reported to prevent amyloid plaque growth while exacerbating tau pathology [47]. In CCL2 rs4586-CC carriers, faster tau accumulation was observed in brain regions of Braak I and Braak III/IV but not Braak V/VI. The spatial propagation of microglial activation has been reported to colocalize with tau accumulation in a Braak-like pattern [48]. Besides, microglia-activated neuroinflammation spreads ventrally throughout the brain, particularly in the temporal regions [49], supporting our finding of CCL2 rs4586-associated tau-PET signaling in the IFT.

In this study, CCL2 rs4586 was not associated with either baseline cognition or longitudinal decline of cognition. Although clinical data have shown that increased CCL2 levels were associated with longitudinal decline in memory [17] and occurred in more advanced clinical stages of MCI and AD [9], in our study, CCL2 rs4586 was only associated with the pathological changes of AD in the early stage. As it is difficult to obtain CSF biomarkers of AD and cognitive assessments at the same time during follow-up, we included a disproportionately small sample size of AD patients. Therefore, the discrepancy might be due to that only a small sample size of AD was included in the study, and a large sample size with more AD patients are needed to elucidate the association between CCL2 rs4586 and cognition decline.

Our study revealed for the first-time that CCL2 rs4586-CC (versus TT/TC) genotype is associated with slower Aβ deposition and faster tau accumulation, and the latter of which was independent of Aβ status, which may offer a potential therapeutic target for AD treatment by targeting CCL2 protein. Future studies with more participants are needed to verify our findings and evaluate whether CCL2 rs4586 SNP is associated with faster cognitive decline.

Footnotes

ACKNOWLEDGMENTS

This work is supported by grants from Shandong Provincial Natural Science Foundation, China (ZR2017MH098) and the Affiliated Hospital of Qingdao University.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’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 & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & 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. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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