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APOE ε4 gene dose effect on imaging and blood biomarkers of neuroinflammation and beta-amyloid in cognitively unimpaired elderly

Alzheimer's Research & Therapy volume 15, Article number: 71 (2023) Cite this article

Abstract

Background

Neuroinflammation, characterized by increased reactivity of microglia and astrocytes in the brain, is known to be present at various stages of the Alzheimer’s disease (AD) continuum. However, its presence and relationship with amyloid pathology in cognitively normal at-risk individuals is less clear. Here, we used positron emission tomography (PET) and blood biomarker measurements to examine differences in neuroinflammation and beta-amyloid (Aβ) and their association in cognitively unimpaired homozygotes, heterozygotes, or non-carriers of the APOE ε4 allele, the strongest genetic risk for sporadic AD.

Methods

Sixty 60–75-year-old APOE ε4 homozygotes (n = 19), heterozygotes (n = 21), and non-carriers (n = 20) were recruited in collaboration with the local Auria biobank. The participants underwent 11C-PK11195 PET (targeting 18-kDa translocator protein, TSPO), 11C-PiB PET (targeting Aβ), brain MRI, and neuropsychological testing including a preclinical cognitive composite (APCC). 11C-PK11195 distribution volume ratios and 11C-PiB standardized uptake value ratios (SUVRs) were calculated for regions typical for early Aβ accumulation in AD. Blood samples were drawn for measuring plasma glial fibrillary acidic protein (GFAP) and plasma Aβ1-42/1.40.

Results

In our cognitively unimpaired sample, cortical 11C-PiB-binding increased according to APOE ε4 gene dose (median composite SUVR 1.47 (range 1.38–1.66) in non-carriers, 1.55 (1.43–2.02) in heterozygotes, and 2.13 (1.61–2.83) in homozygotes, P = 0.002). In contrast, cortical composite 11C-PK11195-binding did not differ between the APOE ε4 gene doses (P = 0.27) or between Aβ-positive and Aβ-negative individuals (P = 0.81) and associated with higher Aβ burden only in APOE ε4 homozygotes (Rho = 0.47, P = 0.043). Plasma GFAP concentration correlated with cortical 11C-PiB (Rho = 0.35, P = 0.040), but not 11C-PK11195-binding (Rho = 0.13, P = 0.47) in Aβ-positive individuals. In the total cognitively unimpaired population, both higher composite 11C-PK11195-binding and plasma GFAP were associated with lower hippocampal volume, whereas elevated 11C-PiB-binding was associated with lower APCC scores.

Conclusions

Only Aβ burden measured by PET, but not markers of neuroinflammation, differed among cognitively unimpaired elderly with different APOE ε4 gene dose. However, APOE ε4 gene dose seemed to modulate the association between neuroinflammation and Aβ.

Background

The number of persons affected by Alzheimer’s disease (AD) across its pathological continuum was recently estimated to be as high as 416 million [1]. From this global estimate, 3/4 of individuals were classified as preclinical AD, characterized by the presence of beta-amyloid (Aβ) plaques but absence of clinical symptoms [1]. In addition to the hallmark pathologies, i.e., Aβ plaques and neurofibrillary tangles, inflammation in the central nervous system (CNS) is recognized to have an important, partly independent, role in Alzheimer’s continuum [2]. In the brain, inflammation is mainly mediated by microglia and astrocytes [3], and in AD, compiling evidence suggests that increased microglial and astrocytic reactivity could be present already during early, possibly protective processes [4,5,6,7].

Apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor of sporadic AD; it increases the risk of disease and decreases the age of onset when compared with the most common APOE ε3 or the protective APOE ε2 alleles [8]. APOE ε4 gene dose-related increase in brain Aβ load is present already in cognitively normal individuals [9,10,11], and it has been suggested to be caused by impaired degradation and clearance of Aβ, a task which is performed by glial cells and affected by APOE isoforms [12, 13]. In neuropathological studies, APOE ε4 has been seen to associate with increased microglial number in the brains of individuals with AD [14] and higher microglial cell reactivity around Aβ plaques in a mouse model of Aβ deposition and human APOE alleles [15]. Still, clinical investigations concerning the relationship between early ongoing neuroinflammatory processes and Aβ in cognitively unimpaired APOE ε4 carriers are scarce.

Investigation of regional neuroinflammation in AD in vivo has been enabled by PET imaging and specific ligands such as 11C-PK11195 that target 18-kDa translocator protein (TSPO) as a proxy for microglial reactivity. TSPO is present in the outer mitochondrial membranes of microglia and elevated in the brain in relation to injuries or pathology [16]. In humans, increased TSPO ligand-binding has recently been suggested to represent changes in cell density rather than protein overexpression [17], and to be mostly covered by microglia, and to a lesser extent astrocytes and endothelial cells [18, 19]. Previous studies using TSPO PET imaging have shown increased regional ligand-binding in patients with AD [20,21,22,23], mild cognitive impairment [4, 24, 25], and some also in Aβ-positive compared with Aβ-negative controls [7, 26]. However, results are partly inconclusive since also minor or no differences between diagnostic groups have been reported [27,28,29].

In addition to imaging, more easily accessible biomarkers for AD pathology measured in blood have become available recently thanks to the development of more sensitive methods [30]. Unfortunately, since proteins expressed by microglia in the CNS are also present in peripheral macrophages, the development of blood-based assays targeting microgliosis is demanding, and interpretation of measurements from blood is complicated [31]. However, one interesting fluid biomarker for glial reactivity, glial fibrillary acidic protein (GFAP, a marker of reactive astrocytosis), is measurable from blood using the single molecule array (Simoa) technology and has been recently shown to be associated with Aβ deposition and increased already in early stages of AD [32,33,34].

Based on previous literature suggesting an early involvement of neuroinflammation during the AD continuum, we hypothesized that in vivo TSPO binding and plasma GFAP concentrations would be elevated in cognitively normal APOE ε4 homozygotes or APOE ε4 heterozygotes, representing different genetically increased risk for Aβ accumulation and sporadic AD, compared to age-matched non-carriers. To test this hypothesis, we evaluated differences in (i) regional Aβ and TSPO PET and (ii) plasma Aβ42/40 and GFAP concentrations and their associations primarily among cognitively normal APOE ε4 homozygotes, heterozygotes, and non-carriers. In addition, we performed secondary analyses between Aβ-positive (representing Alzheimer’s pathological change or preclinical AD [35]) and Aβ-negative individuals and investigated the association between imaging and fluid biomarkers of neuroinflammation and Aβ deposition and markers of disease progression (cognitive performance and volumetric brain changes) in our cohort comprised by cognitively unimpaired participants enriched with APOE ε4 carriers.

Methods

Study design and participants

The study design is illustrated in Fig. 1 and detailed study protocol including power calculations has been previously published [36]. Briefly, participants in this cross-sectional, observational study were recruited in collaboration with the local Auria biobank (Turku, Finland). Set inclusion criteria were 60–75 years of age and CERAD total score > 62 points at screening. Main exclusion criteria were dementia or cognitive impairment; other severe neurological or psychiatric disease; diabetes; chronic inflammatory condition; and contraindication for MRI or PET. In total, sixty-three cognitively normal individuals were recruited. However, one individual from the APOE ε4 heterozygotes and of the non-carriers discontinued the study (two experienced claustrophobia inside the scanner; one had an unexpected and excluding finding in MRI); thus, sixty participants from three groups were included in the analysis (APOE ε4 homozygotes: n = 19, median MMSE = 28 (interquartile range (IQR) 27-29); APOE ε4 heterozygotes: n = 21, median MMSE = 29 (IQR 28-30), non-carriers: n = 20,  median MMSE = 29 (IQR 27-30)).

Fig. 1
figure 1

Study flowchart. Altogether 60 individuals were recruited based on their APOE ε4 gene dose (APOE ε4/ε4, n = 19, APOE ε4/ε3, n = 21, APOE ε3/ε3 n = 21). All underwent positron emission tomography (PET) imaging targeting Aβ using 11C-PiB, 18-kDa translocator protein (TSPO) as a proxy for glial reactivity using.11C-PK11195, magnetic resonance imaging (MRI), and cognitive testing. A blood sample was drawn for laboratory measurements, including plasma markers of Aβ pathology (Aβ1-40 and Aβ1-42) and reactive astrocytosis (glial fibrillary acidic protein, GFAP)

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Brain imaging measurements

Structural T1-weighted brain MRI scan was performed on either a Philips Ingenuity 3.0 T TF PET/MRI (n = 38; Philips Healthcare, Amsterdam, the Netherlands) or a Philips Ingenia 3.0 T (n = 22; Philips Healthcare, Amsterdam, the Netherlands). PET scans were acquired on an ECAT high-resolution research tomograph (HRRT, Siemens Medical Solutions, Knoxville, TN). For amyloid imaging, 11C-PiB scans (n = 60) were acquired 40 to 90 min post injection (mean injected dose 497 (standard deviation (SD) 30) MBq), and for TSPO imaging, dynamic 11C-PK11195 scans (n = 57) were acquired for 60 min post injection (mean injected dose 494 (SD 21) MBq). All images were reconstructed with 3D ordinary Poisson ordered subset expectation maximization algorithm (OP-OSEM3D), and list mode data was histogrammed into 8 (6 × 5 + 2 × 10 min, 11C-PiB) and 17 (2 × 15; 3 × 30; 3 × 60; 7 × 300; 2 × 600 s, 11C-PK11195) time frames.

Brain image analysis

PET and MR image preprocessing and analysis was performed using an automated pipeline at Turku PET Centre [37] which executed the PET data frame by frame realignment, PET-MRI co-registration, FreeSurfer ROI parcellation, and PET data kinetic modeling. Regional and voxel level 11C-PiB-binding was quantified as standardized uptake value ratios (SUVR) calculated for 60 to 90 min post injection, using the cerebellar cortex as reference region. Regional 11C-PK11195-binding was quantified as distribution volume ratios (DVR) within 20–60 min post injection using a reference tissue input Logan’s method with pseudo-reference region extracted using supervised clustering algorithm [38, 39]. Voxel-level kinetic modeling for 11C-PK11195 was carried out using basis function implementation of simplified reference tissue model with respect to the aforementioned clustered pseudo-reference region and with 300 basis functions calculated within the Θ3 parameter limits 0.06 ≤ Θ3 ≤ 0.6 [40]. Partial volume effect (PVE)-corrected data was used for all 11C-PK11195 analysis in order to minimize the effect TSPO uptake in sinuses to cortical regions. PVE correction was carried out using PETPVE12 toolbox [41] in both region-of-interest (ROI, geometric transfer matrix method) and voxel-level (Muller-Gartner method) data. ROI-level analysis for both 11C-PiB and 11C-PK11195 data was performed in a priori defined regions known for early Aβ deposition (prefrontal cortex, parietal cortex, anterior cingulum, posterior cingulum, precuneus, lateral temporal cortex, and a volume weighted composite containing all the regions).41 For 11C-PK11195, additional volume-weighted ROIs for transentorhinal (Braak I–II) and limbic composite (Braak III–IV) regions [42] were analyzed to investigate TSPO-binding in regions associated with early tau deposition. Details of the combined FreeSurfer regions are previously published [36]. Spatially normalized parametric SUVR and BPND images in MNI152 space were smoothed using Gaussian 8 mm FWHM filter and used for all voxel-wise statistical analysis. For all figures, BPND were transformed to DVRs for clarity, using the formula: DVR = BPND + 1. Aβ positivity was defined as cortical composite 11C-PiB SUVR > 1.5 [43, 44].

Total hippocampal volume and total entorhinal area volume (left + right, ml) normalized for intracranial volume, age, and sex were obtained from the T1-weighted MR images using an automatic cNeuro image analysis tool (Combinostics Oy, Tampere, Finland) [45, 46]. Since two different instruments were used for acquiring MRI images the used scanner was added as a covariate in all analyses including hippocampal or entorhinal volumes.

Cognitive testing

All participants completed CERAD cognitive test battery at screening, as well as more extensive neuropsychological testing during one of the study visits as previously described [36]. CERAD total score, mini-mental state examination (MMSE) score, and Alzheimer’s Prevention Initiative Preclinical Composite Cognitive test (APCC) score were used to investigate the association between both imaging and blood biomarkers and cognitive performance.

Blood biomarker measurements

All plasma biomarker measurements were performed in the Clinical Neurochemistry Laboratory, Mölndal, Sweden. Plasma Aβ1-40 and Aβ1-42 concentrations were measured using an in-house immunoprecipitation mass spectrometry method (IP-MS) described in detail elsewhere [47, 48]. Briefly, Aβ peptides were immunoprecipitated from 250 μl of sample using 4G8 and 6E10 anti-Aβ antibodies (BioLegend) coupled to Dynabeads™ M-280 Sheep Anti-Mouse IgG magnetic beads and a KingFisher Flex instrument (Thermo Fisher Scientific) and further analyzed by liquid chromatography-tandem mass spectrometry (LC–MS/MS). Recombinant Aβ1-40 and Aβ1-42 peptides were used as calibrators, and heavy labeled peptides were added to both samples and calibrators for internal standards.

Plasma GFAP concentration was measured using the Single molecule array (Simoa) platform, a HD-X analyzer (Quanterix, Billerica, MA), and a commercial GFAP discovery kit (Quanterix, #102,336) following the instructions provided by the manufacturer. Two internal quality control (QC) samples with mean concentrations of 100 pg/ml and 608 pg/ml were measured in the beginning and after samples in both plates. Calibrators and QC samples were measured as duplicates and samples as singlicates. The intra-assay precision (variation within run, CVr (%)) and inter-assay precision (variation between runs, CVrw (%)) were < 5% and < 15%, respectively.

Statistical analysis

All data following normal distribution are presented as mean (SD), otherwise as median (IQR). Normality of the data was established visually and from the residuals. Missing data points for each variable are presented in eTable 1 in Additional file 1. For continuous variables, differences in group demographics and in regional 11C-PiB and 11C-PK11195-binding between the three APOE ε4 gene doses were tested using one-way ANOVA with Tukey’s honest significance test or Kruskal–Wallis test with Dunn's method for multiple comparisons depending on the distribution of data. χ2 test was used for testing categorical variables. Associations between regional PET data and fluid biomarker concentrations were evaluated using Spearman’s rank correlation. Differences in 11C-PK11195-binding between Aβ-positive and Aβ-negative individuals were first tested with Student's t-test. We also wanted to see if regional 11C-PK11195-binding differed between Aβ-positive and Aβ-negative individuals accounting for APOE ε4 status, so we additionally tested the effect of APOE ε4 gene dose, amyloid positivity, and their interaction (ε4 gene dose ˟ amyloid positivity) on 11C-PK11195 in a priori defined ROIs with linear regression models. If an interaction term with P < 0.1 was found, a post hoc comparison of all groups was performed to explore the nature of the interaction.

Voxel-level differences in 11C-PIB and 11C-PK11195-binding between APOE ε4 gene doses were evaluated using one-way ANOVA, followed by post hoc pairwise comparisons in Statistical Parametric Mapping (SPM12 v12; Wellcome Trust Centre for Neuroimaging, London, UK) running on MATLAB, whereas voxel-level 11C-PIB SUVRs and 11C-PK11195 BPND Spearman’s rank correlation coefficients were calculated using built-in MATLAB functions. False discovery rate-corrected cluster level threshold was set at P < 0.05. Differences in blood biomarker concentrations between APOE ε4 gene doses were analyzed using Kruskal–Wallis test with Dunn's method for multiple comparisons.

Finally, we used multivariable linear regression models adjusted for age, sex, and education (and MRI scanner for models explaining hippocampal or cortical volumes) to test how well PET and fluid biomarkers of Aβ and glial reactivity could explain different cognitive and structural variables that could be interpreted as markers of disease progression. For comparison, standardized βs were calculated and presented in figures.

All statistical analyses were performed using SAS JMP Pro v.15.1.0 (SAS institute, Cary, NC) and visualizations using GraphPad Prism version 9.0.1 (GraphPad, San Diego, California, USA). A P-value < 0.05 (2-tailed) was considered statistically significant in all analysis, except for interaction effects, where stratified analysis was run already if P (interaction) < 0.1.

Results

Demographics

Demographics and descriptive data for the APOE ε4 gene dose groups are presented in Table 1. No statistically significant differences in age, sex, education, body mass index (BMI), or CERAD total score were present between the APOE ε4 gene dose groups (P > 0.37 for all). APOE ε4 heterozygotes had significantly higher MMSE than homozygotes (P = 0.036). Age had positive correlation with 11C-PiB cortical composite SUVRs in APOE ε4 homozygotes (Rho = 0.63, P = 0.0039), but not in heterozygotes, non-carriers, or the whole cohort (P > 0.19 for all). There was no correlation between age and composite cortical 11C-PK11195 DVRs (P > 0.42 for all), plasma GFAP (P > 0.17 for all), or plasma Aβ1-42/1–40 (P = 0.22 for all). Using a cut-off value of cortical composite 11C-PiB SUVR > 1.5, 84% (n = 16) of the APOE ε4 homozygotes, 48% (n = 10) of the heterozygotes, and 40.0% (n = 8) of non-carriers in our cohort were classified as amyloid positive.

Table 1 Demographics and descriptive data for cognitively unimpaired APOE ε4 homozygotes, heterozygotes, and non-carriers included in the study
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For secondary analyses, we also stratified the cohort based on Aβ-positivity (composite 11C-PiB SUVR > 1.5). Demographics are presented in eTable 2 in Additional file 1. Significant differences between Aβ-positive and Aβ-negative individuals were found in education level (P = 0.046), CERAD total score (P = 0.0034), and MMSE score (P = 0.0074).

Fibrillar Aβ deposition distinguishes cognitively unimpaired APOE ε4 homozygotes from heterozygotes and non-carriers

APOE ε4 gene dose-related differences in fibrillar amyloid load were visually detectable from mean 11C-PiB distribution maps in regions typical for early amyloid deposition (Fig. 2A). ROI-level analysis verified the findings, revealing significant differences in 11C-PiB-binding between gene doses in all evaluated regions (P < 0.016 for all regions, Kruskal–Wallis test). After post hoc comparison of all groups, 11C-PiB-binding was significantly higher in APOE ε4 homozygotes compared with heterozygotes in the prefrontal cortex (P = 0.023) and in all evaluated regions when compared with non-carriers (P < 0.017 for all regions) (Fig. 2B, Table 2).

Fig. 2
figure 2

Mean 11C-PiB and 11C-PK11195 distribution maps and regional ligand-binding in cognitively unimpaired volunteers stratified by APOE ε4 gene dose. A Mean 11C-PiB standardized uptake value ratio (SUVR) distribution maps and B region-of-interest analysis showed significantly higher uptake in APOE ε4 homozygotes compared with non-carriers in all evaluated regions and compared with heterozygotes in anterior cingulate and prefrontal cortex (Kruskal–Wallis test with Dunn's method for multiple comparisons). C Mean11C-PK11195 standardized distribution volume ratio (DVR) maps showed regional differences in tracer-binding, D but no significant differences between the APOE ε4 gene dose groups (one-way ANOVA with Tukey’s honest significance test for multiple comparisons). ACIN, anterior cingulate; HIP, hippocampus; LTC, lateral temporal cortex; PFC, prefrontal cortex, PARC, parietal cortex; PCIN, posterior cingulate cortex; PREC, precuneus. * P < 0.05; ** P < 0.01; *** P < 0.001

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Table 2 Regional 11C-PiB SUVR values for APOE ε4 homozygotes, heterozygotes, and non-carriers
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Table 3 Regional 11C-PK11195 DVR values for APOE ε4 homozygotes, heterozygotes, and non-carriers
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Voxel-level comparisons verified the findings showing significantly higher 11C-PiB-binding in the prefrontal cortex, precuneus, and lateral temporal cortex of the APOE ε4 homozygotes compared with non-carriers (eFigure 1A in Additional file 1). Weaker effects with similar spatial distribution were seen in APOE ε4 homozygotes compared with heterozygotes (eFigure 1B in Additional file 1). No significant clusters were found when comparing heterozygotes and non-carriers.

Regional 11C-PK11195 binding does not differ between APOE ε4 gene doses

Mean 11C-PK11195 DVR distribution maps for each APOE ε4 gene dose are shown in Fig. 2C. Even though amyloid imaging results showed presence of Aβ pathology in the APOE ε4 homozygote group, we did not observe hypothesized increase in TSPO-binding in the same brain regions as a response to presence of Aβ (P > 0.08 for all, one-way ANOVA, Fig. 2D, Table 3) measured by 11C-PK11195 PET. In agreement with the ROI-level analyses, no significant clusters were detected in voxel-level comparisons between the APOE ε4 gene dose groups.

For secondary analysis, we stratified the cohort based on Aβ-positivity (11C-PiB SUVR > 1.5). Again, we found no regional differences in TSPO-binding between Aβ-positive and -negative individuals (P > 0.21 for all regions, Student’s t test, eTable 3 in Additional file 1). To further evaluate the possible effects of amyloid status on TSPO-binding in different APOE ε4 gene doses, we analyzed also the interaction of Aβ-positivity × APOE ε4 gene dose for predicting regional TSPO-binding. Whereas amyloid status (accounted for APOE ε4 gene dose) did not have a significant effect on TSPO-binding (P > 0.28 for all regions), the interaction term approached statistical significance in the cortical composite (P = 0.089), lateral temporal cortex (P = 0.063), transentorhinal (Braak I–II, P = 0.052), and limbic (Braak III–IV, P = 0.019) ROIs (Table 4). Exploratory analysis in those regions showed that median TSPO-binding was higher in Aβ-positive APOE ε4 carriers than in non-carriers and, interestingly, also in Aβ-negative non-carriers compared with Aβ-positive non-carriers. However, these differences did not reach statistical significance after post hoc comparison between all six groups (eFigure 2 in Additional file 1). Since we had two highly 11C-PiB-positive non-carriers (with cortical composite SUVRs of 3.4 and 2.2) included in our cohort, we also verified that the found interactions were still present for LTC (P = 0.079), Braak I–II (P = 0.063), and Braak III–IV, (P = 0.032) when these two individuals were excluded.

Table 4 Test effects from multivariate linear regression models explaining regional 11C-PK11195 binding
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Regional 11C-PK11195-binding correlates with Aβ load measured by 11C-PiB only in cognitively unimpaired APOE ε4 carriers

No significant correlation between 11C-PiB and PVE-corrected 11C-PK11195-binding was present in any of the a priori chosen ROIs in the total study population (Rho =  − 0.11–0.13, P > 0.35 for all) or Aβ-positive individuals (Rho =  − 0.13–0.21, P > 0.23 for all). However, when stratified by APOE ε4 gene dose, higher 11C-PiB-binding in the cortical composite ROI associated with higher TSPO-binding in the cortical (Rho = 0.47, P = 0.043) and limbic (Rho = 0.49, P = 0.032) composite ROIs in APOE ε4 homozygotes (Fig. 3A), but not in APOE ε4 heterozygotes. In contrast, a negative correlation between 11C-PiB-binding in the cortical composite ROI was observed for non-carriers in the transentorhinal (Rho =  − 0.63, P = 0.0065) and limbic ROIs (Rho = -0.68, P = 0.0025).

Fig. 3
figure 3

Regional association between amyloid PET and TSPO PET in cognitively unimpaired volunteers stratified by APOE ε4 gene dose. A Scatterplots from ROI level data showed positive correlation (Spearman’s rank correlation) for APOE ε4 carriers in cortical and Braak III–IV composite regions, whereas negative associations were present for non-carriers. B Most significant voxel-wise positive correlations between 11C-PiB and 11C-PK11195-binding were present in the APOE ε4/ε3 heterozygotes (yellow scale) and in APOE ε4 homozygotes (red scale), whereas only sparse significant voxels were seen in non-carriers (green scale). Partial volume corrected 11C-PiB SUVR and.11C-PK11195 BPND images smoothed using Gaussian 8 mm FWHM filter were used for all voxel-wise analysis. False Discovery Rate corrected cluster level threshold was set at P < 0.05

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Voxel-wise analysis (not limited to specific predefined regions) did reveal clusters with significant positive correlation between 11C-PiB- and 11C-PK11195-binding in both APOE ε4 homozygotes (Fig. 3B, red scale) and heterozygotes (Fig. 3B, yellow scale), whereas only small spare clusters were found in non-carriers (Fig. 3B, green scale). However, many of the clusters were located outside our primary regions of interest (chosen based on presence of early amyloid or tau pathology), such as in the white matter and the paracentral lobule.

Differences in plasma GFAP concentrations between APOE ε4 gene doses

Absolute plasma GFAP concentrations were higher in APOE ε4 homozygotes (186 pg/ml, 124–269) compared with APOE ε4 heterozygotes (150 pg/ml, 104–170) and non-carriers (128 pg/ml, 105–147), (P = 0.077, Kruskal–Wallis test, Fig. 4A). A trend towards positive association between plasma GFAP and cortical 11C-PiB-binding was present in the whole cohort (Rho = 0.23, P = 0.085), and a significant positive correlation was observed in Aβ-positive individuals (Rho = 0.35, P = 0.040). No association between plasma GFAP and cortical TSPO-binding was present in the whole cohort (Rho = 0.069, P = 0.61) or in Aβ-positive individuals (Rho = 0.13, P = 0.47, Fig. 4A).

Fig. 4
figure 4

Plasma GFAP and plasma Aβ1-42/1–40 concentrations in cognitively unimpaired volunteers stratified by APOE ε4 gene dose. Differences in biomarker concentrations between APOE ε4 gene doses, correlations with cortical composite amyloid PET standardized uptake value ratios (SUVRs), and TSPO PET distribution volume ratios (DVRs) for A plasma glial fibrillary acidic protein (GFAP) and B plasma Aβ1-42/1–40. Differences between groups were tested with Kruskal–Wallis with Dunn's method for multiple comparisons, and correlations with Spearman’s rank correlation

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Differences in soluble Aβ concentrations estimated by plasma Aβ1-42/1–40 between APOE ε4 gene doses

Despite the clear differences in regional Aβ PET, plasma Aβ1-42/1–40 was not significantly different between APOE ε4 homozygotes (0.077, 0.059–0.098), APOE ε4 heterozygotes (0.087, 0.068–0.11)), and non-carriers (0.086, 0.076–0.10) (P = 0.50, Kruskal–Wallis test, Fig. 4B). In our cohort, plasma Aβ1-42/1–40 did not correlate with either cortical composite amyloid load measured by 11C-PiB PET (Rho = -0.18, P = 0.18), or with cortical composite TSPO-binding measured by 11C-PK11195-binding (Rho = 0.066, P = 0.64; Fig. 4B).

Amyloid and neuroinflammatory biomarker associations with markers for disease progression

Finally, we wanted to compare how the different biomarkers associate with cognitive (MMSE, CERAD total score, APCC score) and structural variables (total hippocampal and entorhinal volume) that could be seen as proxies for future disease progression (Fig. 5). In the whole cognitively unimpaired cohort, higher cortical composite 11C-PiB-binding (βstd =  − 0.29 (95% CI − 0.52 to − 0.067), P = 0.012), but not higher 11C-PK11195-binding (βstd =  − 0.041 (− 0.26 to 0.18), P = 0.70), was associated with lower APCC scores. However, higher cortical 11C-PK11195-binding was associated both with lower hippocampal volume (βstd =  − 0.35 (− 0.61 to −0.10), P = 0.0066) and entorhinal volume (βstd =  − 0.47 (− 0.72 to − 0.22), P = 0.0004). Higher plasma GFAP concentration was associated with both lower hippocampal volume (βstd =  − 0.34 (− 0.60 to − 0.084), P = 0.011), MMSE (βstd =  − 0.35 (− 0.59 to − 1.10), P = 0.0060), and APCC scores (βstd =  − 0.29 (− 0.51 to − 0.070), P = 0.011). Plasma Aβ1-42/1–40 was not associated with any of the cognitive or volumetric variables (P > 0.18 for all analysis). All models were adjusted for age, sex, and education and used MRI scanner for structural variables.

Fig. 5
figure 5

Comparison of PET and blood biomarkers of Aβ deposition and glial reactivity and their association with cognitive performance and brain structure. Higher cortical composite 11C-PiB-binding (A) but not plasma Aβ1-42/1–40 (B) was associated with lower entorhinal volumes and lower scores in the Alzheimer’s Prevention Initiatives preclinical cognitive composite (APCC) battery. C Cortical composite 11C-PK11195 PET was associated only with lower hippocampal and entorhinal volume, whereas D elevated plasma GFAP levels were associated with lower APCC and Mini Mental State Examination (MMSE) scores. The results are shown as standardized estimates (betas) derived from liner models adjusted for age, sex, and education (and used MRI scanner for structural variables). CERADTS, Consortium to Establish a Registry for Alzheimer’s Disease total score; VT, total volume

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Discussion

Microglial and recently also astrocytic reactivity has been suggested to be early events during the long AD continuum [3, 4, 6]. In this study, we used TSPO PET imaging and plasma GFAP to investigate early neuroinflammatory differences in cognitively unimpaired individuals stratified by their APOE ε4 gene dose and thus risk for AD. Despite the verified presence of Aβ pathology in APOE ε4 carriers, we found no significant regional differences in TSPO-binding between APOE ε4 gene doses or cognitively normal Aβ-positive subjects (presenting Alzheimer’s pathological change or preclinical AD) compared with Aβ-negative individuals. However, in the whole cognitively unimpaired sample, higher levels of both neuroinflammatory markers were associated with lower hippocampal volume, used as a proxy for future disease progression.

To our knowledge, this study is the first one to compare TSPO PET findings in cognitively normal at-risk individuals grouped by their APOE ε4 gene dose. In previous human in vivo studies, most robust increases in TSPO-binding have been found in Alzheimer’s dementia in comparison to controls [20,21,22, 49], but also in Aβ-positive MCI [4, 24, 25] and Aβ-positive controls [7, 26]. Thus, we had hypothesized that early Aβ related neuroinflammatory changes should be present also in either cognitively normal APOE ε4 homozygotes or APOE ε4 heterozygotes, both representing a genetically increased risk for Aβ accumulation and sporadic AD. Here, despite clearly increased fibrillar Aβ load, we did not find associated increased TSPO binding in APOE ε4 homozygotes. In addition, our secondary analyses were not able to replicate the reported increased TSPO-binding in Aβ-positive “at-risk” individuals using 11C-PK11195 PET, even with a larger sample size compared with previous reports. Our study included approximately 20 participants in each APOE ε4 gene dose group and 34 cognitively unimpaired Aβ-positive individuals, whereas the previous studies included only six [7] or seven [26] Aβ-positive controls. However, it should be remembered that our cohort, and especially its Aβ-positive participants, was highly enriched for APOE ε4 carriers. Neuritic Aβ plaques are known to be surrounded by reactive microglia in AD; however, APOE is also directly liked to immune responses [50, 51], and APOE ε4 genotype has been suggested to attenuate the response of “amyloid related microglia” towards AD pathology [52]. Here, using TSPO imaging, we are unable to differentiate microglial phenotypes and cannot exclude a direct down modulation of microglial response in APOE ε4 carriers that could explain the lack of hypothesized increased TSPO-binding in regions with abundant Aβ pathology.

In addition to TSPO PET, we also performed 11C-PiB PET for all participants to verify the presence of Aβ pathology in our cognitively unimpaired sample. As expected, Aβ deposition in the brain increased in an APOE ε4 gene dose dependent fashion; significantly elevated cortical 11C-PiB retention was present in APOE ε4 homozygotes compared with both heterozygotes and non-carriers in all evaluated regions. These findings are in line with previous PET studies [9,10,11, 53, 54] as well as with a recent study by the Amyloid Biomarker Study Group summarizing APOE ε4 gene dose-related effects on temporal course of Aβ accumulation [55]. In our study, all APOE ε4/ε4 participants over the age of 63 were already Aβ-positive, whereas approximately 50% of the heterozygotes were still Aβ-negative, and 40% of non-carriers were classified as Aβ-positive. Activated microglia are known to be located in the proximity of Aβ plaques in AD, and using PET imaging in vivo, Aβ pathology has been shown to correlate with TSPO-binding in some, although not all studies [7, 25, 27, 56,57,58]. Our voxel level analysis showed significant positive correlations between 11C-PiB and 11C-PK11195 both in cognitively normal APOE ε4 homozygotes and heterozygotes. However, significant clusters were found also in regions outside our a priori chosen regions of interest, such as the white matter, suggesting that these effects might not all be due to higher TSPO binding associated with Aβ plaques.

Here, we also found that Aβ positivity modulated the effect of APOE ε4 gene dose on 11C-PK11195-binding in regions known for early tau deposition, where Aβ-positive APOE ε4 homozygotes seemed to have increased TSPO binding compared with Aβ-positive non-carriers. In addition to Aβ, APOE ε4 is known to accelerate tau pathology that again has been suggested to be closely associated with microglial reactivity [59], and increased tau PET signal in the entorhinal cortex has been reported for cognitively unimpaired Aβ-positive APOE ε4 homozygotes and heterozygotes compared with Aβ-positive non-carriers [53]. Since Aβ build up starts earlier in APOE ε4 carriers, we could hypothesize that increased tau deposition and related inflammatory processes in APOE ε4 carriers would be driving this interaction. Unfortunately, lack of tau PET or CSF tau measurements in our cohort prevented us from investigating the interaction with TSPO-binding and tau further in our cohort.

During recent years, significant efforts have been made to measure various biomarkers of AD pathology in plasma that would provide a less invasive and more easily accessible alternative to brain imaging and lumbar puncture [30]. Plasma GFAP has been recently reported to be an early marker of astrocytosis in response to AD pathology that strongly correlates with Aβ pathology [34, 60] but not with tau when accounting for Aβ [32]. In our cohort, plasma GFAP levels showed elevated concentrations in the most Aβ positive individuals and correlated with composite amyloid PET SUVRs. Interestingly, plasma GFAP was the only biomarker showing significant associations with both cognitive performance and hippocampal volume that could be considered as markers for progression in the Alzheimer’s continuum. On the contrary, plasma GFAP concentration did not correlate with composite TSPO-binding (Fig. 4). This is not surprising, considering that the two inflammatory markers present different targets; plasma GFAP is expected to reflect more astrocytic reactivity associated with Aβ pathology [34], whereas TSPO PET is thought to reflect microglial density [17]. Our results with GFAP support the previous findings suggesting that reactive astrocytosis is present already in cognitively normal individuals and related to Aβ pathology [32,33,34]. Despite clear differences in fibrillar Aβ levels measured by PET, we did not see significant differences between the APOE ε4 gene doses in plasma Aβ1-42/1–40 measured by previously described IP-MS method [47]. Plasma Aβ1-42/1–40 was previously reported to correlate with global cortical Aβ PET signal in another study including cognitively normal individuals using the same IP-MS method [48]. We could not replicate this finding in our cohort, comprised of slightly older and highly APOE ε4-enriched cognitively normal participants, although a trend towards a negative association could be seen in the whole cohort.

Last, we also wanted to compare all the biomarkers and their associations with cognitive and structural variables that could serve as proxies for disease progression in our “at-risk” cohort. We found a negative association between composite cortical TSPO-binding and hippocampal and entorhinal volumes, suggesting that more global elevation in TSPO-binding, and thus microglial density, could be present in individuals with subtle neurodegeneration. Interestingly, higher plasma GFAP associated with both lower cognitive performance and lower hippocampal volume in our cognitively normal cohort. Previously, Hamelin and colleagues reported a positive correlation with both hippocampal volume and MMSE score, suggesting that higher glial reactivity associated with higher hippocampal volume would likely be protective [7]. However, our study population is composed of only cognitively unimpaired individuals highly enriched for APOE ε4 carriers, and all having MMSE scores > 25, thus likely presenting more subtle structural brain changes compared with the population of the previous study. In addition, we did not find any association with TSPO-binding and MMSE, CERAD total score, or the preclinical cognitive composite, in line with other studies performed with 11C-PK11195 [25]. Based on our results, increased TSPO-binding in the preclinical phase, at least in APOE ε4 carriers, could be more related to a later preclinical phase when subtle neurodegeneration already starts to be present.

Strengths and limitations

The strength of this study is our well characterized and balanced cohort of cognitively unimpaired participants stratified by their APOE ε4 gene dose and related risk for sporadic AD and a relatively large number of rare homozygotic carriers of the APOE ε4 allele. In most studies, group comparisons are done between APOE ε4 carriers against non-carriers and especially cognitively unimpaired homozygotes are rarely studied. In addition, we have measured both imaging and more easily available fluid biomarkers for amyloid deposition and neuroinflammatory processes from the same individuals and were able to combine them with additional information concerning brain structure and cognitive data. However, this study does not go without limitations. First, it would have been optimal to compare TSPO PET findings with CSF biomarkers specific to microglial responses, but unfortunately, we did not have CSF samples available from the whole cohort. Thus, plasma GFAP analysis were included. However, we are fully aware that TSPO PET and plasma GFAP reflect different neuroinflammatory processes. Second, we were not able to include tau PET or CSF tau measurements to further investigate association of glial biomarkers and early tau pathology in our cohort. The lack of tau biomarkers prevented us also from evaluating possible early tau pathology as a confounding factor that could have contributed to the reported variation in 11C-PK11195 retention seen in this study. However, since the cohort was comprised of cognitively unimpaired individuals, we did not expect wide-spread tau pathology. Third, even though 11C-PK11195 has shown robust changes in primary inflammatory conditions such as multiple sclerosis, it has been suggested that its sensitivity is limited and outperformed by the second generation TSPO ligands, such as 11C-PBR28. However, affinity of the second generation TSPO ligands is affected by a single nucleotide polymorphism rs6971 in the TSPO gene, leading to division of people into high, mixed, and low affinity binders. Due to the difficulty of recruiting rare homozygotic APOE ε4 carriers, we wanted to avoid the unfortunate scenario of having multiple homozygotic participants excluded due to low-binding TSPO genotype.

Conclusion

Our study on cognitively unimpaired “at-risk” individuals carrying either one or two copies of the APOE ε4 gene showed clear differences in fibrillar Aβ load in the brain, but the changes were not accompanied by higher glial reactivity as measured with TSPO PET either in APOE ε4 carriers, or in Aβ-positive individuals, representing preclinical AD. However, cortical TSPO binding was associated with lower hippocampal and entorhinal volumes in the whole cognitively unimpaired sample. These findings suggest that in our cognitively unimpaired cohort enriched by APOE ε4 carriers, neuroinflammatory processes measured by TSPO PET are related to a more advanced preclinical phase of AD where Aβ accumulation is accompanied by subtle structural changes.

Availability of data and materials

Metadata of the project will be made public using national Finnish Etsin and Qvain services, as required by the open access policy by the Academy of Finland. The de-identified data used during the current study are available to qualified researchers from the corresponding author on reasonable request.

Abbreviations

Aβ:

Beta-amyloid peptide

APCC:

Alzheimer’s prevention Initiative Preclinical Composite Cognitive test

APOE :

Apolipoprotein E gene

ApoE:

Apolipoprotein E

BMI:

Body mass index

CERAD:

The Consortium to Establish a Registry for Alzheimer’s Disease

CNS:

Central nervous system

DVR:

Distribution volume ratio

GFAP:

Glial fibrillary acidic protein

MMSE:

Mini-mental state examination

MRI:

Magnetic resonance imaging

PET:

Positron emission tomography

PVE:

Partial volume effect

Simoa:

Single molecule array

SUVR:

Standardized uptake value ratio

TSPO:

18-KDa translocator protein

References

  1. Gustavsson A, Norton N, Fast T, Frolich L, Georges J, Holzapfel D, et al. Global estimates on the number of persons across the Alzheimer’s disease continuum. Alzheimers Dement. 2022;19(2):658-70. https://doi.org/10.1002/alz.12694. Epub 2022 Jun 2.

  2. Heneka MT, Carson MJ, El Khoury J, Landreth GE, Brosseron F, Feinstein DL, et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 2015;14(4):388–405.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Leng F, Edison P. Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol. 2021;17(3):157–72.

    Article  PubMed  Google Scholar 

  4. Fan Z, Brooks DJ, Okello A, Edison P. An early and late peak in microglial activation in Alzheimer’s disease trajectory. Brain. 2017;140(3):792–803.

    PubMed  PubMed Central  Google Scholar 

  5. Albrecht DS, Sagare A, Pachicano M, Sweeney MD, Toga A, Zlokovic B, et al. Early neuroinflammation is associated with lower amyloid and tau levels in cognitively normal older adults. Brain Behav Immun. 2021;94:299–307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kumar A, Fontana IC, Nordberg A. Reactive astrogliosis: a friend or foe in the pathogenesis of Alzheimer’s disease. J Neurochem. 2023;164(3):309-24. https://doi.org/10.1111/jnc.15565. Epub 2022 Jan 7.

  7. Hamelin L, Lagarde J, Dorothee G, Leroy C, Labit M, Comley RA, et al. Early and protective microglial activation in Alzheimer’s disease: a prospective study using 18F-DPA-714 PET imaging. Brain. 2016;139(Pt 4):1252–64.

    Article  PubMed  Google Scholar 

  8. Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC, Small GW, et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science. 1993;261(5123):921–3.

    Article  CAS  PubMed  Google Scholar 

  9. Reiman EM, Chen K, Liu X, Bandy D, Yu M, Lee W, et al. Fibrillar amyloid-beta burden in cognitively normal people at 3 levels of genetic risk for Alzheimer’s disease. Proc Natl Acad Sci U S A. 2009;106(16):6820–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Castellano JM, Kim J, Stewart FR, Jiang H, DeMattos RB, Patterson BW, et al. Human apoE isoforms differentially regulate brain amyloid-beta peptide clearance. Sci Transl Med. 2011;3(89):89ra57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Mecca AP, Barcelos NM, Wang S, Bruck A, Nabulsi N, Planeta-Wilson B, et al. Cortical beta-amyloid burden, gray matter, and memory in adults at varying APOE epsilon4 risk for Alzheimer’s disease. Neurobiol Aging. 2018;61:207–14.

    Article  CAS  PubMed  Google Scholar 

  12. Koistinaho M, Lin S, Wu X, Esterman M, Koger D, Hanson J, et al. Apolipoprotein E promotes astrocyte colocalization and degradation of deposited amyloid-beta peptides. Nat Med. 2004;10(7):719–26.

    Article  CAS  PubMed  Google Scholar 

  13. Verghese PB, Castellano JM, Garai K, Wang Y, Jiang H, Shah A, et al. ApoE influences amyloid-beta (Abeta) clearance despite minimal apoE/Abeta association in physiological conditions. Proc Natl Acad Sci U S A. 2013;110(19):E1807–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Egensperger R, Kosel S, von Eitzen U, Graeber MB. Microglial activation in Alzheimer disease: association with APOE genotype. Brain Pathol. 1998;8(3):439–47.

    Article  CAS  PubMed  Google Scholar 

  15. Rodriguez GA, Tai LM, LaDu MJ, Rebeck GW. Human APOE4 increases microglia reactivity at Abeta plaques in a mouse model of Abeta deposition. J Neuroinflammation. 2014;11:111.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cosenza-Nashat M, Zhao ML, Suh HS, Morgan J, Natividad R, Morgello S, et al. Expression of the translocator protein of 18 kDa by microglia, macrophages and astrocytes based on immunohistochemical localization in abnormal human brain. Neuropathol Appl Neurobiol. 2009;35(3):306–28.

    Article  CAS  PubMed  Google Scholar 

  17. Nutma E, Fancy N, Weinert M, Marzin MC, Tsartsalis S, Muirhead RCJ, et al. Translocator protein is a marker of activated microglia in rodent models but not human neurodegenerative diseases bioRxiv preprint. 2022.

  18. Tournier BB, Tsartsalis S, Ceyzeriat K, Fraser BH, Gregoire MC, Kovari E, et al. Astrocytic TSPO upregulation appears before microglial TSPO in Alzheimer’s disease. J Alzheimers Dis. 2020;77(3):1043–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Venneti S, Lopresti BJ, Wang G, Hamilton RL, Mathis CA, Klunk WE, et al. PK11195 labels activated microglia in Alzheimer’s disease and in vivo in a mouse model using PET. Neurobiol Aging. 2009;30(8):1217–26.

    Article  CAS  PubMed  Google Scholar 

  20. Cagnin A, Brooks DJ, Kennedy AM, Gunn RN, Myers R, Turkheimer FE, et al. In-vivo measurement of activated microglia in dementia. Lancet. 2001;358(9280):461–7.

    Article  CAS  PubMed  Google Scholar 

  21. Edison P, Archer HA, Gerhard A, Hinz R, Pavese N, Turkheimer FE, et al. Microglia, amyloid, and cognition in Alzheimer’s disease: An [11C](R)PK11195-PET and [11C]PIB-PET study. Neurobiol Dis. 2008;32(3):412–9.

    Article  CAS  PubMed  Google Scholar 

  22. Fan Z, Okello AA, Brooks DJ, Edison P. Longitudinal influence of microglial activation and amyloid on neuronal function in Alzheimer’s disease. Brain. 2015;138(Pt 12):3685–98.

    Article  PubMed  Google Scholar 

  23. Femminella GD, Ninan S, Atkinson R, Fan Z, Brooks DJ, Edison P. Does microglial activation influence hippocampal volume and neuronal function in Alzheimer’s disease and Parkinson’s disease dementia? J Alzheimers Dis. 2016;51(4):1275–89.

    Article  CAS  PubMed  Google Scholar 

  24. Okello A, Edison P, Archer HA, Turkheimer FE, Kennedy J, Bullock R, et al. Microglial activation and amyloid deposition in mild cognitive impairment: a PET study. Neurology. 2009;72(1):56–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Parbo P, Ismail R, Hansen KV, Amidi A, Marup FH, Gottrup H, et al. Brain inflammation accompanies amyloid in the majority of mild cognitive impairment cases due to Alzheimer’s disease. Brain. 2017;140(7):2002–11.

    Article  PubMed  Google Scholar 

  26. Zou J, Tao S, Johnson A, Tomljanovic Z, Polly K, Klein J, et al. Microglial activation, but not tau pathology, is independently associated with amyloid positivity and memory impairment. Neurobiol Aging. 2020;85:11–21.

    Article  CAS  PubMed  Google Scholar 

  27. Wiley CA, Lopresti BJ, Venneti S, Price J, Klunk WE, DeKosky ST, et al. Carbon 11-labeled Pittsburgh Compound B and carbon 11-labeled (R)-PK11195 positron emission tomographic imaging in Alzheimer disease. Arch Neurol. 2009;66(1):60–7.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Schuitemaker A, Kropholler MA, Boellaard R, van der Flier WM, Kloet RW, van der Doef TF, et al. Microglial activation in Alzheimer’s disease: an (R)-[(1)(1)C]PK11195 positron emission tomography study. Neurobiol Aging. 2013;34(1):128–36.

    Article  CAS  PubMed  Google Scholar 

  29. Knezevic D, Verhoeff NPL, Hafizi S, Strafella AP, Graff-Guerrero A, Rajji T, et al. Imaging microglial activation and amyloid burden in amnestic mild cognitive impairment. J Cereb Blood Flow Metab. 2018;38(11):1885–95.

    Article  PubMed  Google Scholar 

  30. Alawode DOT, Heslegrave AJ, Ashton NJ, Karikari TK, Simren J, Montoliu-Gaya L, et al. Transitioning from cerebrospinal fluid to blood tests to facilitate diagnosis and disease monitoring in Alzheimer’s disease. J Intern Med. 2021;290(3):583-601. https://doi.org/10.1111/joim.13332. Epub 2021 Jun 26.

  31. Zetterberg H, Schott JM. Blood biomarkers for Alzheimer’s disease and related disorders. Acta Neurol Scand. 2022;146(1):51–5.

    Article  CAS  PubMed  Google Scholar 

  32. Pereira JB, Janelidze S, Smith R, Mattsson-Carlgren N, Palmqvist S, Teunissen CE, et al. Plasma GFAP is an early marker of amyloid-beta but not tau pathology in Alzheimer’s disease. Brain. 2021;144(11):3505-16. https://doi.org/10.1093/brain/awab223.

  33. Cicognola C, Janelidze S, Hertze J, Zetterberg H, Blennow K, Mattsson-Carlgren N, et al. Plasma glial fibrillary acidic protein detects Alzheimer pathology and predicts future conversion to Alzheimer dementia in patients with mild cognitive impairment. Alzheimers Res Ther. 2021;13(1):68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Benedet AL, Mila-Aloma M, Vrillon A, Ashton NJ, Pascoal TA, Lussier F, et al. Differences between plasma and cerebrospinal fluid glial fibrillary acidic protein levels across the Alzheimer disease continuum. JAMA Neurol. 2021;78(12):1471-83.

  35. Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14(4):535–62.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Snellman A, Ekblad LL, Koivumaki M, Lindgren N, Tuisku J, Perala M, et al. ASIC-E4: interplay of beta-amyloid, synaptic density and neuroinflammation in cognitively normal volunteers with three levels of genetic risk for late-onset Alzheimer’s disease - study protocol and baseline characteristics. Front Neurol. 2022;13:826423.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Karjalainen T, Tuisku J, Santavirta S, Kantonen T, Bucci M, Tuominen L, et al. Magia: robust automated image processing and kinetic modeling toolbox for PET neuroinformatics. Front Neuroinform. 2020;14:3.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Turkheimer FE, Edison P, Pavese N, Roncaroli F, Anderson AN, Hammers A, et al. Reference and target region modeling of [11C]-(R)-PK11195 brain studies. J Nucl Med. 2007;48(1):158–67.

    PubMed  Google Scholar 

  39. Yaqub M, van Berckel BN, Schuitemaker A, Hinz R, Turkheimer FE, Tomasi G, et al. Optimization of supervised cluster analysis for extracting reference tissue input curves in (R)-[(11)C]PK11195 brain PET studies. J Cereb Blood Flow Metab. 2012;32(8):1600–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gunn RN, Lammertsma AA, Hume SP, Cunningham VJ. Parametric imaging of ligand-receptor binding in PET using a simplified reference region model. Neuroimage. 1997;6(4):279–87.

    Article  CAS  PubMed  Google Scholar 

  41. Gonzalez-Escamilla G, Lange C, Teipel S, Buchert R, Grothe MJ, Alzheimer’s Disease Neuroimaging I. PETPVE12: an SPM toolbox for Partial Volume Effects correction in brain PET - application to amyloid imaging with AV45-PET. Neuroimage. 2017;147:669–77.

    Article  PubMed  Google Scholar 

  42. Scholl M, Lockhart SN, Schonhaut DR, O’Neil JP, Janabi M, Ossenkoppele R, et al. PET imaging of Tau deposition in the aging human brain. Neuron. 2016;89(5):971–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Jack CR Jr, Lowe VJ, Senjem ML, Weigand SD, Kemp BJ, Shiung MM, et al. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain. 2008;131(Pt 3):665–80.

    Article  PubMed  Google Scholar 

  44. Rowe CC, Ellis KA, Rimajova M, Bourgeat P, Pike KE, Jones G, et al. Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiol Aging. 2010;31(8):1275–83.

    Article  PubMed  Google Scholar 

  45. Koikkalainen J, Rhodius-Meester H, Tolonen A, Barkhof F, Tijms B, Lemstra AW, et al. Differential diagnosis of neurodegenerative diseases using structural MRI data. Neuroimage Clin. 2016;11:435–49.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Lotjonen J, Wolz R, Koikkalainen J, Julkunen V, Thurfjell L, Lundqvist R, et al. Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer’s disease. Neuroimage. 2011;56(1):185–96.

    Article  PubMed  Google Scholar 

  47. Keshavan A, Pannee J, Karikari TK, Rodriguez JL, Ashton NJ, Nicholas JM, et al. Population-based blood screening for preclinical Alzheimer’s disease in a British birth cohort at age 70. Brain. 2021;144(2):434–49.

    PubMed  PubMed Central  Google Scholar 

  48. Meyer PF, Ashton NJ, Karikari TK, Strikwerda-Brown C, Kobe T, Gonneaud J, et al. Plasma p-tau231, p-tau181, PET biomarkers, and cognitive change in older adults. Ann Neurol. 2022;91(4):548–60.

    Article  CAS  PubMed  Google Scholar 

  49. Passamonti L, Rodriguez PV, Hong YT, Allinson KSJ, Bevan-Jones WR, Williamson D, et al. [(11)C]PK11195 binding in Alzheimer disease and progressive supranuclear palsy. Neurology. 2018;90(22):e1989–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Krasemann S, Madore C, Cialic R, Baufeld C, Calcagno N, El Fatimy R, et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity. 2017;47(3):566-81e9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ulrich JD, Ulland TK, Mahan TE, Nystrom S, Nilsson KP, Song WM, et al. ApoE facilitates the microglial response to amyloid plaque pathology. J Exp Med. 2018;215(4):1047–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Nguyen AT, Wang K, Hu G, Wang X, Miao Z, Azevedo JA, et al. APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer’s disease. Acta Neuropathol. 2020;140(4):477–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Ghisays V, Goradia DD, Protas H, Bauer RJ 3rd, Devadas V, Tariot PN, et al. Brain imaging measurements of fibrillar amyloid-beta burden, paired helical filament tau burden, and atrophy in cognitively unimpaired persons with two, one, and no copies of the APOE epsilon4 allele. Alzheimers Dement. 2020;16(4):598–609.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ekblad LL, Johansson J, Helin S, Viitanen M, Laine H, Puukka P, et al. Midlife insulin resistance, APOE genotype, and late-life brain amyloid accumulation. Neurology. 2018;90(13):e1150–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Jansen WJ, Janssen O, Tijms BM, Vos SJB, Ossenkoppele R, Visser PJ, et al. Prevalence estimates of amyloid abnormality across the Alzheimer disease clinical spectrum. JAMA Neurol. 2022;79(3):228–43.

    Article  PubMed  Google Scholar 

  56. Toppala S, Ekblad LL, Tuisku J, Helin S, Johansson JJ, Laine H, et al. Association of early beta-amyloid accumulation and neuroinflammation measured with [(11)C]PBR28 in elderly individuals without dementia. Neurology. 2021;96(12):e1608–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Dani M, Wood M, Mizoguchi R, Fan Z, Walker Z, Morgan R, et al. Microglial activation correlates in vivo with both tau and amyloid in Alzheimer’s disease. Brain. 2018;141(9):2740–54.

    PubMed  Google Scholar 

  58. Kreisl WC, Lyoo CH, McGwier M, Snow J, Jenko KJ, Kimura N, et al. In vivo radioligand binding to translocator protein correlates with severity of Alzheimer’s disease. Brain. 2013;136(Pt 7):2228–38.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Pascoal TA, Benedet AL, Ashton NJ, Kang MS, Therriault J, Chamoun M, et al. Microglial activation and tau propagate jointly across Braak stages. Nat Med. 2021;27(9):1592–9.

    Article  CAS  PubMed  Google Scholar 

  60. Chatterjee P, Pedrini S, Stoops E, Goozee K, Villemagne VL, Asih PR, et al. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer’s disease. Transl Psychiatry. 2021;11(1):27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The participants of ASIC-E4 study are warmly acknowledged for their commitment to the study. The authors would also like to acknowledge the staff of Turku PET Centre and Auria biobank for their assistance during the recruitment and data collection for this study. We also want to thank Dr Tomi Karjalainen for his help with the automated analysis pipeline and Jyrki Lötjönen from Combinostics Ltd for assistance concerning the cNeuro software.

Funding

AES was supported by the Emil Aaltonen foundation, the Paulo Foundation, the Orion Research Foundation sr, Finnish Governmental Research Funding (ERVA) for Turku University Hospital and Academy of Finland (#341059). LLE was supported by the Emil Aaltonen foundation and the Juho Vainio foundation. MS is supported by the Knut and Alice Wallenberg Foundation (Wallenberg Centre for Molecular and Translational Medicine; KAW2014.0363), the Swedish Research Council (2017–02869, 2021–02678 and 2021–06545), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (ALFGBG-813971 and ALFGBG-965326), the Swedish Brain Foundation (FO2021-0311), and the Swedish Alzheimer Foundation (AF-740191). HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018–02532), the European Research Council (#681712 and #101053962), Swedish State Support for Clinical Research (#ALFGBG-71320), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809–2016862), the AD Strategic Fund and the Alzheimer’s Association (#ADSF-21–831376-C, #ADSF-21–831381-C and #ADSF-21–831377-C), the Bluefield Project, the Olav Thon Foundation, the Erling-Persson Family Foundation, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2022-0270), the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), the European Union Joint Programme – Neurodegenerative Disease Research (JPND2021-00694), and the UK Dementia Research Institute at UCL (UKDRI-1003). KB is supported by the Swedish Research Council (#2017–00915), the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809–2016615), the Swedish Alzheimer Foundation (#AF-742881), Hjärnfonden, Sweden (#FO2017-0243), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986), the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466–236), the National Institute of Health (NIH), USA, (grant #1R01AG068398-01), and the Alzheimer’s Association 2021 Zenith Award (ZEN-21–848495). JR has received funding from the Academy of Finland (#310962), Sigrid Juselius Foundation, and Finnish Governmental Research Funding (VTR) for Turku University Hospital.

Author information

Authors and Affiliations

  1. Turku PET Centre, University of Turku, Turku University Hospital, Kiinamyllynkatu 4–8, 20520, Turku, Finland

    Anniina Snellman, Laura L. Ekblad, Jouni Tuisku, Mikko Koivumäki, Semi Helin, Marco Bucci & Juha O. Rinne

  2. Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden

    Anniina Snellman, Nicholas J. Ashton, Juan Lantero-Rodriguez, Thomas K. Karikari, Michael Schöll, Henrik Zetterberg & Kaj Blennow

  3. Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway

    Nicholas J. Ashton

  4. Department of Old Age Psychiatry, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, UK

    Nicholas J. Ashton

  5. NIHR Biomedical Research Centre for Mental Health & Biomedical Research Unit for Dementia at South London & Maudsley NHS Foundation, London, UK

    Nicholas J. Ashton

  6. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA

    Thomas K. Karikari

  7. Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden

    Marco Bucci

  8. Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

    Marco Bucci

  9. Department of Biostatistics, University of Turku, Turku, Finland

    Eliisa Löyttyniemi

  10. Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland

    Riitta Parkkola

  11. Department of Psychology, Åbo Akademi University, Turku, Finland

    Mira Karrasch

  12. Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK

    Michael Schöll

  13. Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden

    Michael Schöll & Henrik Zetterberg

  14. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden

    Henrik Zetterberg & Kaj Blennow

  15. UK Dementia Research Institute at UCL, London, UK

    Henrik Zetterberg

  16. Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China

    Henrik Zetterberg

  17. InFLAMES Research Flagship Center, University of Turku, Turku, Finland

    Juha O. Rinne

Authors
  1. Anniina Snellman
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  2. Laura L. Ekblad
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  3. Jouni Tuisku
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  4. Mikko Koivumäki
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  7. Thomas K. Karikari
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  8. Semi Helin
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  9. Marco Bucci
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  14. Henrik Zetterberg
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  15. Kaj Blennow
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  16. Juha O. Rinne
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Contributions

J.O.R and A.S. designed the study concept. A.S. drafted the manuscript. A.S., L.L.E., M.Ko., R.P., M.Ka., J.L., M.B. and S.H. contributed to data collection. A.S., L.L.E. and J.T. analyzed data. E.L. designed the statistical analysis plan. J.O.R, A.S., L.L.E, M.Ka., N.J.A., T.K.K., H.Z., K.B., and M.S. contributed to study design and interpretation of the data. J.O.R., H.Z. and K.B. supervised the study. All authors read and critically revised the manuscript for its content and approved the submitted version.

Corresponding author

Correspondence to Anniina Snellman.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethical Committee of the Hospital District of Southwest Finland. All participants signed a written informed consent according to the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Alector, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Passage Bio, Pinteon Therapeutics, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KB has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Roche Diagnostics, and Siemens Healthineers, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. MS has served on a scientific advisory board for Servier Pharmaceuticals (outside submitted work). AS, LLE, JT, MKo, NJA, JL, TKK, SH, MB, MKa, EL, RP, and JR report no competing interests.

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Supplementary Information

Additional file 1:

eTable 1. Data availability for all variables. eTable 2. Demographics stratified by Aβ positivity. eTable 3. Regional TSPO binding in Aβ+ and Aβ- participants. eFigure 1. Voxel-level differences in 11C-PiB binding between (A) APOE ε4 homozygotes and non-carriers, and (B) APOE ε4 homozygotes and APOE ε4 heterozygotes. eFigure 2. Interaction between Aβ status and APOE ε4 gene dose on regional 11C-PK11195 binding.

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Snellman, A., Ekblad, L.L., Tuisku, J. et al. APOE ε4 gene dose effect on imaging and blood biomarkers of neuroinflammation and beta-amyloid in cognitively unimpaired elderly. Alz Res Therapy 15, 71 (2023). https://doi.org/10.1186/s13195-023-01209-6

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Keywords

Alzheimer's Research & Therapy

ISSN: 1758-9193