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Cerebral Aβ deposition precedes reduced cerebrospinal fluid and serum Aβ42/Aβ40 ratios in the AppNL−F/NL−F knock-in mouse model of Alzheimer’s disease

Alzheimer's Research & Therapy volume 15, Article number: 64 (2023) Cite this article

Abstract

Background

Aβ42/Aβ40 ratios in cerebrospinal fluid (CSF) and blood are reduced in preclinical Alzheimer’s disease (AD), but their temporal and correlative relationship with cerebral Aβ pathology at this early disease stage is not well understood. In the present study, we aim to investigate such relationships using App knock-in mouse models of preclinical AD.

Methods

CSF, serum, and brain tissue were collected from 3- to 18-month-old AppNL−F/NL−F knock-in mice (n = 48) and 2–18-month-old AppNL/NL knock-in mice (n = 35). The concentrations of Aβ42 and Aβ40 in CSF and serum were measured using Single molecule array (Simoa) immunoassays. Cerebral Aβ plaque burden was assessed in brain tissue sections by immunohistochemistry and thioflavin S staining. Furthermore, the concentrations of Aβ42 in soluble and insoluble fractions prepared from cortical tissue homogenates were measured using an electrochemiluminescence immunoassay.

Results

In AppNL−F/NL−F knock-in mice, Aβ42/Aβ40 ratios in CSF and serum were significantly reduced from 12 and 16 months of age, respectively. The initial reduction of these biomarkers coincided with cerebral Aβ pathology, in which a more widespread Aβ plaque burden and increased levels of Aβ42 in the brain were observed from approximately 12 months of age. Accordingly, in the whole study population, Aβ42/Aβ40 ratios in CSF and serum showed a negative hyperbolic association with cerebral Aβ plaque burden as well as the levels of both soluble and insoluble Aβ42 in the brain. These associations tended to be stronger for the measures in CSF compared with serum. In contrast, no alterations in the investigated fluid biomarkers or apparent cerebral Aβ plaque pathology were found in AppNL/NL knock-in mice during the observation time.

Conclusions

Our findings suggest a temporal sequence of events in AppNL−F/NL−F knock-in mice, in which initial deposition of Aβ aggregates in the brain is followed by a decline of the Aβ42/Aβ40 ratio in CSF and serum once the cerebral Aβ pathology becomes significant. Our results also indicate that the investigated biomarkers were somewhat more strongly associated with measures of cerebral Aβ pathology when assessed in CSF compared with serum.

Introduction

The presence of aggregated beta-amyloid (Aβ) proteins in the form of extracellular senile plaques in the brain is one of the key neuropathological hallmarks of Alzheimer’s disease (AD) [1]. Aβ derives from sequential cleavage of the transmembrane amyloid precursor protein (APP) by the enzymes β- and γ-secretase [2]. This generates Aβ peptides of different lengths, where the isoform containing 40 amino acids (Aβ40) is the most prevalent while the one containing 42 amino acids (Aβ42) is highly aggregation-prone [3] and found to a large extent in the extracellular senile plaques [4].

Cerebral Aβ pathology can be assessed in vivo by the measurement of the concentration of Aβ42 as well as the Aβ42/Aβ40 ratio in cerebrospinal fluid (CSF), which are well-established fluid biomarkers of AD [5]. The concentration of Aβ42 in CSF is reduced by approximately 50% in patients with AD [6], and longitudinal studies have shown that this change occurs at least a decade before cognitive symptoms manifest, i.e., in the preclinical stage of the disease [7, 8]. In contrast to Aβ42, the concentration of Aβ40 in CSF shows no or minor alteration along the AD continuum [6]. Nevertheless, this peptide can be used to normalize for individual variability in Aβ production, and using the CSF Aβ42/Aβ40 ratio has repeatedly shown to more accurately identify individuals with abnormal cerebral Aβ burden compared with CSF Aβ42 alone [9,10,11,12,13].

The early changes in CSF Aβ42 concentration and the Aβ42/Aβ40 ratio have opened the possibility to identify cognitively healthy individuals who are at high risk of developing dementia due to AD later in life. This has important implications for both clinical management and clinical trials, as disease-modifying therapies probably are most effective when introduced in this early disease stage, before neurodegeneration is widespread [5]. However, the assessment of Aβ in CSF requires invasive lumbar puncture and the alternative use of amyloid positron emission tomography (PET), which is a well-validated imaging biomarker of AD, is expensive and has limited availability. This has led to an intense search for cost-effective and easily accessible blood-based biomarkers that would facilitate biomarker implementation in clinical practice and enable a more efficient screening of potential participants in clinical trials [5, 14]. Indeed, recent development of ultrasensitive immunoassays and high-precision immunoprecipitation mass spectrometry (IP-MS) methods have made it possible to reliably measure Aβ in blood in addition to CSF for the detection of cerebral Aβ pathology in AD [15,16,17,18,19]. The Aβ42/Aβ40 ratio in plasma is highly predictive of cerebral amyloidosis [16, 17, 19] and declines, like the corresponding ratio in CSF, in the preclinical stage of the disease [15, 18, 20]. Indeed, it has been suggested that the Aβ42/Aβ40 ratio in both CSF and plasma may be changed before significant deposition of fibrillar Aβ in the brain, as measured by amyloid PET, is reached [12, 17]. However, it remains uncertain whether these findings reflect different thresholds employed across the techniques used for in vivo measurement of Aβ in fluids and brain or if reduced Aβ42/Aβ40 ratios in CSF and blood serve as indicators of Aβ-related pathological processes that precede the formation of cerebral fibrillar Aβ. Moreover, the ability of the Aβ42/Aβ40 ratio in blood to reflect cerebral Aβ pathology in the brain compared with the corresponding ratio in CSF during the earliest disease stage is not fully elucidated. Further studies are therefore needed to better understand the temporal and correlative relationships between changes in CSF and blood Aβ42/Aβ40 ratios and cerebral Aβ pathology in early preclinical AD.

Although currently available amyloid PET ligands have a high affinity for fibrillar Aβ [21], which is the form of Aβ that dominates in the center of dense-core senile plaques [1], their binding to non-fibrillar plaques is limited [22, 23] and detection of soluble forms of Aβ using this imaging technique is not possible [21]. Thus, to gain a more detailed understanding of how well fluid biomarkers reflect the earliest stages of the Aβ aggregation cascade in AD, animals that recapitulate AD-related pathologies may provide an important model system. A few studies have investigated early changes in CSF and blood Aβ using transgenic mouse models that overexpress mutant human APP under the control of certain promoters [24,25,26]. However, this overexpression paradigm may result in artificial phenotypes that are not related to AD [27], possibly affecting the trajectories of fluid biomarkers in the early pathogenic stage in mice. To overcome this issue, new App knock-in mouse models that express endogenous levels of APP while producing pathogenic human Aβ have been introduced to the field [28]. These have in many aspects been shown to better recapitulate AD-related pathologies [27, 28] and may therefore provide a more relevant tool to gain further insight into the dynamics of fluid biomarkers for AD in relation to cerebral Aβ pathology during the early preclinical stage of the disease.

In the present study, we used AppNL−F/NL−F knock-in mice as a model of preclinical AD to gain further insight into changes in the Aβ42/Aβ40 ratio in CSF and serum and its relation to cerebral Aβ pathology in early stages of the disease. Ultrasensitive single molecule array (Simoa) immunoassays were used to measure the concentrations of Aβ42 and Aβ40 in CSF and serum collected at different time points. Subsequently, we investigated the trajectories of the Aβ42/Aβ40 ratio in the two fluid compartments and their association with different measures of cerebral amyloidosis over time.

Methods

A graphic overview of the experimental design is presented in Additional file 1: Fig. S1.

Animals

Experimental procedures were carried out in accordance with Swedish animal research regulations and approved by the committee of animal research at Lund University (ethical permit number: 7482/2017). Animals were housed in groups of 2–6 mice per cage under a 12:12-h light/dark cycle with food and water provided ad libitum.

Male and female AppNL−F/NL−F (3–18 months, n = 48) knock-in mice from which paired CSF and serum samples were available were used for the experiments. In these mice, the Aβ sequence of the endogenous APP gene has been humanized and two mutations associated with familial AD, the Swedish (KM670/671NL) and Beyreuther/Iberian (I716F), have been introduced. This results in an age-dependent deposition of extracellular amyloid plaques in the cortex and hippocampus starting at 6 months of age [28]. In addition, AppNL/NL (2–18 months, n = 35) knock-in mice that only harbor the Swedish mutation and show no sign of cerebral extracellular plaque deposition during the investigated time period [29] were used as controls.

Collection of CSF, serum, and brain tissue

All sample collection was performed between 9 AM and 1 PM to minimize the potential influence of the circadian cycle on Aβ40 and Aβ42 concentrations [30].

CSF (around 10 μl) was collected from cisterna magna with a tapered-tip glass capillary as previously described [31]. Following collection, the samples were immediately transferred to protein LoBind tubes, snap frozen on dry ice, and stored at − 80 °C until analysis.

Blood was collected terminally by cardiac puncture, transferred to protein LoBind tubes, allowed to clot for 2 h at room temperature, and centrifuged for 20 min at 2000 × g. The serum supernatant was collected, aliquoted in protein LoBind tubes, and stored at − 80 °C until analysis.

For the collection of brain tissue, transcardial perfusion of the mice with ice-cold 0.1 M phosphate buffer (PB) was performed. The brain was removed and the cortex from the right hemisphere was dissected, collected in protein LoBind tubes, snap frozen on dry ice, and stored at − 80 °C until analysis. The left hemisphere was fixed in 4% paraformaldehyde in 0.1 M PB, pH 7.4, for 48 h at 4 °C and then immersed in 30% sucrose solution for 48 h at 4 °C. Brains were serially cut into 30 μm thick sagittal sections using a sliding microtome (Leica Biosystems) and collected in antifreeze solution (30% sucrose and 30% ethylene glycol in PB) for storage at − 20 °C.

CSF and serum analysis

The concentrations of human Aβ40 and Aβ42 in CSF and serum were measured using the Simoa Aβ40 and Aβ42 assay kits (Quanterix) on the Simoa HD-1 Analyzer (Quanterix) according to instructions provided by the manufacturer. CSF and serum samples were diluted 1:100 and 1:5, respectively, prior to analysis. The samples were run in singlicates and all measurements were performed in one round of experiment using the same batch of reagents. Intra-assay coefficients of variation, determined using duplicate measurements of internal quality control samples on each plate, were around 5%.

Histology and immunohistochemistry

30 µm thick free-floating sagittal brain sections were washed 3 × 10 min in TBS, treated 8 min with 88% formic acid (FA), permeabilized 3 × 10 min in TBS containing 0.25% Triton X-100 (TBSX), and blocked 1 h in TBSX containing 5% normal donkey serum (NDS). The sections were then incubated with anti-Aβ42 primary antibody (H31L21, Invitrogen) diluted 1:1000 in TBSX containing 2.5% NDS overnight at 4 °C. Following overnight incubation, the sections were washed 3 × 10 min in TBSX, incubated with appropriate Alexa-fluorophore-conjugated secondary antibody diluted 1:500 in TBSX containing 2.5% NDS, washed 3 × 10 min in TBSX, mounted on glass slides, and coverslipped with ProLong™ Diamond Antifade Mountant (Invitrogen) according to the recommendations from the manufacturer.

For the detection of fibrillar dense-core plaques, free-floating sagittal sections were stained with 0.01% thioflavin S in 50% ethanol for 10 min and then washed 2 × 1 min in 50% ethanol, 3 × 1 min in ddH2O, and finally 10 min in TBS. The stained specimens were mounted on glass slides and coverslipped with SlowFade™ Diamond Antifade Mountant (Invitrogen) according to the manufacturer’s recommendations.

Image acquisition and analysis

Fluorescence images of whole brain sections were acquired using a 10 × objective lens on the Operetta® CLS™ High Content Analysis System (PerkinElmer). The cortex and hippocampus from 3–4 brain sections per mouse were manually segmented and the area covered by Aβ42-positive staining, as well as thioflavin S-positive fibrillar dense-core plaques, was quantified using the Fiji software by applying an automated local threshold that was maintained for all images analyzed. For each mouse, the total cortical and hippocampal area (%) covered was determined by calculating the average of all captured sections.

Brain tissue homogenization

The cortex from the right hemisphere was homogenized at 10% (w/v) in TBS (50 mM Tris–HCl, 150 mM NaCl, pH 7.6) containing Halt™ Protease and Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific) using the FastPrep-24™ Classic bead beating grinder and lysis system (MP Biomedicals). The homogenized cortical tissue was aliquoted in protein LoBind tubes and stored at − 80 °C until analysis.

For extraction of Aβ, the prepared homogenates were thawed on ice and centrifuged at 14,000 × g for 30 min at 4 °C. The supernatant was collected as the TBS-soluble fraction, aliquoted in protein LoBind tubes, and stored at − 80 °C until analysis. The remaining pellet was re-suspended at 10% (v/w) in ice-cold 70% FA containing Halt™ Protease and Phosphatase Inhibitor Cocktail, sonicated on ice for 6 × 10 s, and centrifuged at 14,000 × g for 1 h at 4 °C. The supernatant was collected as the FA-soluble fraction, neutralized 1:20 in 1 M Tris-base at room temperature, aliquoted in protein LoBind tubes, and stored at − 80 °C until analysis.

Biochemical analysis of Aβ42 in soluble and insoluble brain tissue extracts

The concentration of Aβ42 in the TBS- and FA-soluble fractions prepared from cortical brain tissue homogenates was measured using the MSD V-PLEX Human Aβ42 Peptide (6E10) Kit according to the manufacturer’s recommendations. Samples from the TBS-soluble fraction were diluted 1:16, whereas those from the FA-soluble fraction were diluted up to 1:640. All samples were measured in singlicates, as this assay consistently has shown a low intra-plate coefficient of variation in previous analyses.

Statistical analysis

The nonparametric Kruskal–Wallis H test was performed to study age-dependent changes of Aβ in the CSF, serum, and brain. If a statistical significance was found, post hoc analyses for group comparisons between all groups were performed using the Mann–Whitney U test. Correlation analyses were done using Spearman’s rank-ordered correlation coefficient. For comparisons between correlation coefficients, Meng’s Z-test for correlated correlations was performed [32]. Statistical analyses were conducted using IBM SPSS Statistics 27 and corresponding graphs were produced in GraphPad Prism 9.

Results

The Aβ42/Aβ40 ratio in CSF was reduced from 12 months of age in App NL−F/NL−F knock-in mice

In CSF collected from AppNL−F/NL−F knock-in mice, the Aβ42/Aβ40 ratio was significantly affected by age (H(5) = 30.9, p < 0.001), in which pairwise comparisons revealed a lower Aβ42/Aβ40 ratio from 12 months (Fig. 1A). Similar results were obtained for CSF Aβ42 (H(5) = 30.8, p < 0.001), while no significant change in CSF Aβ40 was observed (H(5) = 9.4, p > 0.05) (Additional file 1: Fig. S2A-B). In AppNL/NL knock-in mice, some fluctuations in the CSF Aβ42/Aβ40 ratio were found over time (H(4) = 15.9, p < 0.01), although age did not significantly affect the concentrations of CSF Aβ42 (H(4) = 2.0, p > 0.05) or Aβ40 (H(4) = 6.3, p > 0.05) (Additional file 1: Fig. S3A-C).

Fig. 1
figure 1

CSF and serum Aβ42/Aβ40 ratios in AppNL−F/NL−F knock-in mice. CSF and serum Aβ42/Aβ40 ratios were measured in 3 (n = 9)-, 6 (n = 7)-, 9 (n = 8)-, 12 (n = 7)-, 16 (n = 8)-, and 18 (n = 9)-month-old AppNL−F/NL−F knock-in mice. A The Aβ42/Aβ40 ratio in CSF showed a significant reduction from 12 months of age while B the  Aβ42/Aβ40 ratio in serum was significantly declined from 16 months of age. C The Aβ42/Aβ40 ratio in serum correlated significantly positive with the corresponding ratio in CSF. Data is presented as median and IQR. Whiskers represent data within 1.5IQR of the lower and upper quartiles. For comparison between groups, statistical analyses were performed using the Kruskal–Wallis H test followed by the Mann–Whitney U test for post hoc group comparisons (*p < 0.05, **p < 0.01, ***p < 0.001). Correlation analysis was performed using Spearman’s rank-ordered correlation coefficient. Abbreviations: Aβ, amyloid beta; CSF, cerebrospinal fluid; IQR, interquartile range

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The Aβ42/Aβ40 ratio in serum was reduced from 16 months of age and correlated with the corresponding ratio in CSF in App NL−F/NL−F knock-in mice

There was a significant age-dependent effect on the serum Aβ42/Aβ40 ratio in AppNL−F/NL−F knock-in mice (H(5) = 16.5, p < 0.01), in which pairwise comparisons revealed a lower Aβ42/Aβ40 ratio from 16 months (Fig. 1B). Similar results were found for serum Aβ42 (H(5) = 12.4, p < 0.05), while serum Aβ40 was significantly increased from 16 months (H(5) = 11.3, p < 0.05) (Additional file 1: Fig. S2C-D). In AppNL/NL knock-in mice, no significant age-dependent effect on the serum Aβ42/Aβ40 ratio (H(4) = 3.3, p > 0.05), Aβ42 (H(4) = 0.8, p > 0.05), or Aβ40 (H(4) = 6.5, p > 0.05) was observed (Additional file 1: Fig. S3D-F).

There was a significant, but moderate, positive correlation between serum and CSF Aβ42/Aβ40 ratios in AppNL−F/NL−F knock-in mice when measured over all age groups (rs = 0.36, p < 0.05) (Fig. 1C). A similar correlation was obtained between serum and CSF Aβ42 (rs = 0.33, p < 0.05), while no correlation for Aβ40 was found (rs =  − 0.11, p > 0.05) (Additional file 1: Fig. S2E-F). In AppNL/NL knock-in mice, there was no correlation between serum and CSF Aβ42/Aβ40 ratios (rs = 0.20, p > 0.05), Aβ42 (rs =  − 0.27, p > 0.05), or Aβ40 (rs =  − 0.047, p > 0.05) (Additional file 1: Fig. S3G-I).

Extracellular amyloid plaques were increased in an age-dependent manner and inversely correlated with CSF and serum Aβ42/Aβ40 ratios in App NL-F/NL-F knock-in mice

Immunohistochemical analysis revealed very minor initial deposition of extracellular amyloid plaques in cortical brain regions at 6 months of age in AppNL−F/NL−F knock-in mice. The burden of cortical and hippocampal Aβ42 immunoreactivity was increased in an age-dependent manner (H(5)Cortex = 51.0, p < 0.001; H(5)Hippocampus = 48.4, p < 0.001), in which it became more widespread from approximately 12 months of age (Fig. 2A and Additional file 1: Table S1). Evaluation of the burden of thioflavin S-positive fibrillar dense-core plaques in the two regions showed similar results with clear, but still rather modest, increases in plaque burden at 12 months of age (H(5)Cortex = 52.1, p < 0.001; H(5)Hippocampus = 47.7, p < 0.001) (Fig. 2B and Additional file 1: Table S1). No deposition of extracellular amyloid plaques was found in AppNL/NL knock-in mice over time (Additional file 1: Fig. S3J).

Fig. 2
figure 2

Cerebral Aβ plaque burden in AppNL−F/NL−F knock-in mice. Cerebral Aβ42 immunoreactivity and thioflavin S-positive fibrillar dense-core plaques were measured in 3 (n = 9)-, 6 (n = 7)-, 9 (n = 8)-, 12 (n = 7)-, 16 (n = 8)-, and 18 (n = 9)-month-old AppNL−F/NL−F knock-in mice. Minor initial deposition of extracellular Aβ plaques in cortical brain regions was observed from 6 months of age. The burden of cortical and hippocampal A Aβ42 immunoreactivity and B thioflavin S-positive fibrillar dense-core plaques was significantly increased in an age-dependent manner. Data is presented as median and IQR. Whiskers represent data within 1.5IQR of the lower and upper quartiles. For comparison between groups, statistical analyses were performed using the Kruskal–Wallis H test followed by the Mann–Whitney U test for post hoc group comparisons. Scale bars: 500 μm. Abbreviations: Aβ, amyloid beta; IQR, interquartile range

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In the whole study population, the Aβ42/Aβ40 ratio in both CSF and serum showed a significant inverse correlation with Aβ42 immunoreactivity in the cortex (rs(CSF Aβ42/Aβ40 ratio) =  − 0.70, p < 0.001; rs(serum Aβ42/Aβ40 ratio) =  − 0.51, p < 0.001) and hippocampus (rs(CSF Aβ42/Aβ40 ratio) =  − 0.72, p < 0.001; rs(serum Aβ42/Aβ40 ratio) =  − 0.51, p < 0.001) (Fig. 3A–D). These associations appeared hyperbolic, in which the Aβ42/Aβ40 ratio in CSF and serum stabilized toward a plateau while Aβ42 immunoreactivity steadily continued to increase as the mice age. Similar results were found when evaluating the correlations between the fluid biomarkers and the burden of thioflavin S-positive fibrillar dense-core plaques in the cortex (rs(CSF Aβ42/Aβ40 ratio) =  − 0.67, p < 0.001; rs(serum Aβ42/Aβ40 ratio) =  − 0.52, p < 0.001) and hippocampus (rs(CSF Aβ42/Aβ40 ratio) =  − 0.67, p < 0.001; rs(serum Aβ42/Aβ40 ratio) =  − 0.49, p < 0.001) (Fig. 3E–H). Correlations similar to those shown for the Aβ42/Aβ40 ratio were obtained for CSF and serum Aβ42, but not for Aβ40 (Additional file 1: Fig. S4).

Fig. 3
figure 3

CSF and serum Aβ42/Aβ40 ratios and their associations with cerebral Aβ plaque burden in AppNL−F/NL−F knock-in mice. In the whole study population, the Aβ42/Aβ40 ratio in CSF and serum inversely correlated with A–D Aβ42 immunoreactivity and E–H thioflavin S-positive fibrillar dense-core plaques in the cortex and hippocampus. Correlation analyses were performed using Spearman’s rank-ordered correlation coefficient. Abbreviations: Aβ, amyloid beta; CSF, cerebrospinal fluid

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The correlations between the Aβ42/Aβ40 ratio in the two fluid compartments and pathological changes of cerebral plaque burden (measured by Aβ42 immunoreactivity and the burden of thioflavin S-positive fibrillar dense-core plaques) were numerically greater when the Aβ42/Aβ40 ratio was measured in CSF compared with serum, but the differences in correlation coefficients were not statistically significant (Table 1). Similar findings were obtained for Aβ42, although the concentrations in CSF were significantly more strongly associated with plaque burden in the hippocampus than those in serum (Additional file 1: Table S2).

Soluble and insoluble Aβ42 in cortical brain tissue were increased in an age-dependent manner and inversely correlated with CSF and serum Aβ42/Aβ40 ratios in App NL-F/NL-F knock-in mice

In the TBS- and FA-soluble fractions prepared from cortical brain tissue homogenates, the concentration of Aβ42 was increased in an age-dependent manner, with significant alterations from 12 and 9 months, respectively (H(5)TBS-soluble = 38.4, p < 0.001; H(5)FA-soluble = 44.1, p < 0.001) (Fig. 4A, D). The Aβ42/Aβ40 ratio in CSF and serum inversely correlated with the concentration of Aβ42 in the TBS-soluble fraction (rs(CSF Aβ42/Aβ40 ratio) =  − 0.74, p < 0.001; rs(serum Aβ42/Aβ40 ratio) =  − 0.46, p < 0.001) (Fig. 4B-C), as well as in the FA-soluble fraction (rs(CSF Aβ42/Aβ40 ratio) =  − 0.67, p < 0.001; rs(serum Aβ42/Aβ40 ratio) =  − 0.52, p < 0.001) (Fig. 4E-F), in a hyperbolic manner. Similar results as shown for the Aβ42/Aβ40 ratios were obtained for CSF and serum Aβ42, but not for Aβ40 (Additional file 1: Fig. S5).

Fig. 4
figure 4

Cortical TBS- and FA-soluble Aβ42 and their associations with CSF and serum Aβ42/Aβ40 ratios in AppNL−F/NL−F knock-in mice. Cortical TBS- and FA-soluble Aβ42 was measured in 3 (n = 9)-, 6 (n = 7)-, 9 (n = 8)-, 12 (n = 7)-, 16 (n = 8)-, and 18 (n = 9)-month-old AppNL−F/NL−F knock-in mice. A TBS-soluble and D FA-soluble Aβ42 increased in an age-dependent manner with significant changes from 12 and 9 months, respectively. In the whole study population, the Aβ42/Aβ40 ratio in CSF and serum inversely correlated with Aβ42 in the B-C TBS-soluble fraction and the E-F FA-soluble fraction prepared from cortical brain tissue homogenates. Data is presented as median and IQR. Whiskers represent data within 1.5IQR of the lower and upper quartiles. For comparison between groups, statistical analyses were performed using the Kruskal–Wallis H test followed by the Mann–Whitney U test for post hoc group comparisons (**p < 0.01, ***p < 0.001). Correlation analyses were performed using Spearman’s rank-ordered correlation coefficient. Abbreviations: Aβ, amyloid beta; CSF, cerebrospinal fluid; FA, formic acid; IQR, interquartile range; TBS, tris-buffered saline

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Table 1 Correlations between the Aβ42/Aβ40 ratio in CSF and serum and cerebral Aβ pathology in AppNL−F/NL−F knock-in mice
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The correlation with TBS-soluble Aβ42 was significantly greater for the Aβ42/Aβ40 ratio when measured in CSF compared with serum. However, although the Aβ42/Aβ40 ratio in CSF showed a greater correlation with FA-soluble Aβ42 than the corresponding ratio in serum, these were not statistically different (Table 1). Similar findings were obtained for Aβ42 when measured in CSF compared with serum (Additional file 1: Table S2).

Discussion

In the present study, our main findings showed that the Aβ42/Aβ40 ratio in both CSF and serum were reduced during early cerebral amyloidosis in AppNL−F/NL−F knock-in mice. Significant changes in the CSF Aβ42/Aβ40 ratio occurred when cerebral Aβ plaque burden started to become more widespread in cortical and hippocampal regions, while the corresponding ratio in serum was altered at a somewhat later time point. Furthermore, the initial decline of the CSF Aβ42/Aβ40 ratio coincided with increased concentrations of soluble and insoluble Aβ42 in cortical brain tissue. In both fluid compartments, the reduction in the Aβ42/Aβ40 ratio quickly started to stabilize towards a plateau although both insoluble and soluble forms of Aβ steadily continued to increase as the mice aged. Accordingly, we found inverse hyperbolic associations between cerebral Aβ and the Aβ42/Aβ40 ratio in both CSF and serum. These associations tended to be greater for the measures in CSF compared with serum. In general, similar results were obtained for CSF and serum Aβ42, but not Aβ40, when compared to those obtained for the Aβ42/Aβ40 ratio.

The Aβ42/Aβ40 ratio in CSF was significantly reduced earlier than in blood in App NL-F/NL-F knock-in mice

The Aβ42/Aβ40 ratio in human CSF declines at least a decade before cognitive symptoms due to both sporadic and familial AD develop [7, 8], and recent studies using highly sensitive biochemical assays suggest that the corresponding measure in plasma also is reduced during preclinical sporadic AD [15, 18, 20]. Our findings in the AppNL−F/NL−F knock-in mice are in good agreement with these studies, demonstrating an age-dependent decline in CSF and serum Aβ42/Aβ40 ratios, changes that occur prior to the time at which Aβ-dependent spatial memory deficits develop in these mice [28]. The Aβ42/Aβ40 ratio in CSF was steadily reduced from 12 months of age, while the corresponding ratio in serum significantly declined somewhat later, at 16 months of age. The decline in serum Aβ42/Aβ40 ratio was partially due to elevated concentrations of Aβ40 in the oldest age groups. This is in contrast to previous studies in sporadic AD, in which the concentrations of this Aβ peptide have been reported to remain unaltered during the preclinical stage of the disease [15, 18]. Nevertheless, considering that the increase in serum Aβ40 coincided with the decrease in serum Aβ42 and that we found no age-dependent effect on serum Aβ40 in control AppNL/NL knock-in mice, it is possible that the concentrations of Aβ40 are elevated in response to AD-related pathological processes in AppNL−F/NL−F knock-in mice. Future studies should address the potential impact of the Beyreuther/Iberian mutation and the relevance of this finding for sporadic and familial AD.

In AppNL−F/NL−F knock-in mice, the Aβ42/Aβ40 ratio in serum was positively correlated with that in CSF when assessed over all age groups, although the association was relatively modest. These results are well in line with previous findings in which the Simoa platform was used to measure plasma Aβ42 and Aβ40 in a clinical cohort consisting of cognitively healthy individuals as well as patients with mild cognitive impairment (MCI) and AD dementia [15]. The modest association may to some extent be explained by physiological confounding factors that influence the measurement of Aβ in blood compared with CSF, such as degradation in the liver or by circulating enzymes, matrix effects, and renal clearance [33]. Moreover, it is likely that the production of these peptides in peripheral organs significantly contributes to the circulating pool, as it has been estimated that a maximum of 30–50% of Aβ present in blood derives from the central nervous system [34]. The choice of analytical platform may also play a role, as the use of methods based on IP-MS has recently been reported to generate higher associations between the Aβ42/Aβ40 ratio in the two fluid compartments compared to different Simoa immunoassays [35].

CSF and serum Aβ42/Aβ40 ratios in relation to cerebral amyloidosis

In agreement with previous findings [28], a few isolated extracellular Aβ plaques first appeared in cortical areas of the brain at 6 months of age in AppNL−F/NL−F knock-in mice. At 9 months, the cerebral Aβ plaque burden remained sparse and did not associate with changes in the Aβ42/Aβ40 ratio in either CSF or serum. Instead, the decline of this biomarker in the two fluid compartments at 12 and 16 months, respectively, occurred in relation to a more pronounced Aβ plaque load in both cortex and hippocampus. In a recent study in autopsy-confirmed AD cases, the authors reported that a decline in the CSF Aβ42/Aβ40 ratio was initiated in Thal phase 2 [36], which is characterized by the presence of Aβ deposits in neocortex and allocortical regions [37]. No changes in CSF Aβ42/Aβ40 ratio were found in cases in which the pathology was restricted to the neocortex, i.e., in Thal phase 1 [36]. The study also showed that a reduced CSF Aβ42/Aβ40 ratio was associated with a moderate Aβ plaque burden, as estimated in accordance with CERAD (Consortium to Establish a Registry for Alzheimer’s Disease) recommendations, a finding that is similar to what has been observed for the Aβ42/Aβ40 ratio in plasma [38]. These results are in line with those from the present study, suggesting a temporal sequence of events in which initial deposition of Aβ aggregates in restricted brain regions is followed by a decline in CSF and serum Aβ42/Aβ40 ratios once the Aβ pathology is somewhat more widespread but still relatively low to moderate. In addition, studies have suggested that CSF Aβ42—alone or in ratio with Aβ40 [12, 39,40,41,42]—as well as the Aβ42/Aβ40 ratio in plasma [17] are significantly changed before the threshold for abnormal fibrillar dense-core plaque burden in the brain, as measured by amyloid PET, is reached. It is possible that the sensitivity of amyloid PET to detect fibrillar Aβ species in the brain in early preclinical AD is limited, as the results from the present study suggest a sparse burden of thioflavin S-positive fibrillar dense-core plaques in the brain prior to changes in the investigated fluid biomarkers.

Insoluble forms of Aβ found predominantly in plaques can be measured biochemically in FA extract from brain tissue homogenates. As expected, cortical FA-soluble Aβ42 increased in an age-dependent manner with significant changes from 9 months of age, which is the same time from which we also observed a sparse burden of cortical Aβ plaques in AppNL−F/NL−F knock-in mice. In addition, cortical TBS-soluble Aβ42 increased from 12 months of age and thereby coincided with the initial decline in the CSF Aβ42/Aβ40 ratio. Although both insoluble and soluble forms of Aβ steadily increased over the studied time period, the reduced Aβ42/Aβ40 ratio in both CSF and serum quickly started to stabilize toward a plateau. Indeed, we found an inverse hyperbolic association between cerebral amyloidosis and the Aβ42/Aβ40 ratio in both CSF and serum, which is consistent with multiple cross-sectional studies in humans investigating the association between Aβ42—alone or in ratio with Aβ40—in the two fluid compartments and amyloid PET [9, 12, 17, 43,44,45]. Our results are also in good agreement with longitudinal studies suggesting that once a decline in CSF Aβ42 has occurred in early preclinical AD, the concentration remains fairly stable as the disease progresses [8, 46, 47]. Together, these findings may to some extent challenge the proposed hypothesis that the reduction in the investigated fluid biomarkers is due to the deposition of Aβ into extracellular plaques [48], as cerebral plaque load is only linearly associated with the Aβ42/Aβ40 ratio in CSF and blood during a very limited time-frame in the early disease stage. However, our findings that no changes in CSF or serum Aβ were found in AppNL/NL mice with age confirm that biological processes related to Aβ pathology are required for these biomarkers to decline. Future studies should further address the underlying cause of these changes in the preclinical stage of AD.

The inverse correlation with cerebral amyloidosis tended to be greater for the Aβ42/Aβ40 ratio in CSF when compared with serum. These results imply that the Aβ42/Aβ40 ratio in CSF more reliably may reflect Aβ pathology in the brain than the corresponding ratio in blood in preclinical AD. In agreement with these findings, a study conducted by Schindler et al. reported that the Aβ42/Aβ40 ratio in CSF was a better predictor of and showed a greater correlation with amyloid PET than the Aβ42/Aβ40 ratio in plasma in a cohort consisting of mainly cognitively healthy individuals [17]. Furthermore, although the ratio between plasma Aβ42 and Aβ40 has shown higher correspondence with cerebral amyloidosis than Aβ42 alone when studied in clinical cohorts [16], the correlations between cerebral amyloidosis and these two measures in serum were similar in AppNL−F/NL−F knock-in mice. As the concentrations of Aβ42 and Aβ40 in blood may be affected by comorbidities and other confounding factors [15, 49], the limited biological variation in the AppNL−F/NL−F knock-in mice compared to a human study population may to some extent explain these results.

A few studies have previously investigated Aβ changes in CSF [24, 25] and blood [26] in relation to cerebral amyloidosis over time using transgenic mouse models that overexpress mutant human APP under the control of certain promotors. In line with our own findings in AppNL−F/NL−F knock-in mice, these studies have reported a decline in Aβ42—alone or in ratio with CSF Aβ40—in these fluid compartments that is initiated shortly after the onset of Aβ plaque deposition and inversely associates with the burden of Aβ in the brain. In one of the studies, increased concentrations of both CSF Aβ42 and Aβ40 prior to plaque deposition were observed, suggesting that the biphasic profile of this biomarker change potentially could be used for early identification of cognitively healthy individuals who are at risk of developing AD dementia [25]. Although we have observed similar findings in the well-characterized APP-overexpressing 3xTg mouse model (Additional file 1: Fig. S6 and Supplementary methods), this initial increase was not found in AppNL−F/NL−F knock-in mice. The concomitant increase in CSF Aβ42 and Aβ40 in APP-overexpressing mice suggests an elevated production or cleavage of APP and one may speculate that this early change to some extent is a result of an age-related overexpression of APP in these models. However, further studies are needed to elucidate these differences and their translational implication.

Limitations

A limitation of the study was the use of the Simoa platform for measurements of Aβ42 and Aβ40 in serum. Namely, it was recently demonstrated that the Aβ42/Aβ40 ratio in plasma more accurately identifies individuals with an abnormal burden of cerebral Aβ when assessed with certain methods based on IP-MS compared with Simoa immunoassays [35]. However, we could not sample enough volumes of serum from the mice to perform IP-MS. In addition, assessment of Aβ42 and Aβ40 in plasma instead of serum would have been preferable from a translational perspective, as plasma has been most commonly analyzed in previous clinical studies. Furthermore, we were not able to measure the concentrations of Aβ40 in TBS- and FA-soluble cortical extracts from AppNL−F/NL−F knock-in mice in the younger age groups with our current protocol, likely as a result of the low concentrations of this Aβ peptide due to the Beyreuther/Iberian mutation.

Conclusion

Taken together, our findings suggest that a low burden of cerebral Aβ pathology may be present before the Aβ42/Aβ40 ratio in CSF and serum starts to decline. These changes in fluid Aβ42/Aβ40 ratios seem to occur in association with a more widespread Aβ plaque burden in cortical and hippocampal regions, which occurs in parallel with increasing concentrations of insoluble and soluble Aβ42 in the brains of the AppNL−F/NL−F knock-in mice. The Aβ42/Aβ40 ratio in CSF seems to decline prior to the corresponding ratio in serum and may be the most reliable biomarker of early cerebral Aβ pathology of the two measures. Our findings raise the possibility that previous reports from human studies showing that an abnormal deposition of Aβ in the brain, as determined by amyloid PET, is reached after fluid Aβ42/Aβ40 ratios decline may be due to the limited sensitivity of this imaging technique to detect such lesions in the early preclinical phase of AD. This extends existing knowledge of the temporal relationship between early cerebral Aβ pathology and initial changes in fluid Aβ42/Aβ40 ratios and may have implications for the use of these biomarkers in future human studies aiming at better understanding the disease. Moreover, as disease-modifying therapies are likely to be more effective the earlier in the pathological process they are introduced, our results indicate the need for further research to identify fluid biomarkers reflecting the initial amyloidogenic phase of the disease. In this context, App knock-in mice, which possess less artifacts and may in many aspects more accurately recapitulate Aβ-related pathological processes in AD compared with first-generation APP-overexpressing transgenic mice, could provide a valuable translational tool. The use of such models may also contribute to important information on the underlying cause of changes in CSF biomarkers of Aβ pathology in preclinical AD and how these are affected by disease-modifying therapies.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.

Abbreviations

Aβ:

Beta-amyloid

Aβ40:

40 Amino acid beta-amyloid peptide

Aβ42:

42 Amino acid beta-amyloid peptide

AD:

Alzheimer’s disease

APP:

Amyloid precursor protein

CERAD:

Consortium to Establish a Registry for Alzheimer’s Disease

CSF:

Cerebrospinal fluid

FA:

Formic acid

IP-MS:

Immunoprecipitation mass spectrometry

MCI:

Mild cognitive impairment

MSD:

Meso Scale Discovery

NDS:

Normal donkey serum

PB:

Phosphate buffer

PET:

Positron emission tomography

Simoa:

Single molecule array

TBS:

Tris-buffered saline

TBSX:

Tris-buffered saline with Triton X-100

References

  1. DeTure MA, Dickson DW. The neuropathological diagnosis of Alzheimer’s disease. Mol Neurodegener. 2019;14(1):32.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Haass C, Kaether C, Thinakaran G, Sisodia S. Trafficking and proteolytic processing of APP. Cold Spring Harb Perspect Med. 2012;2(5):a006270.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Meisl G, Yang X, Hellstrand E, Frohm B, Kirkegaard JB, Cohen SI, et al. Differences in nucleation behavior underlie the contrasting aggregation kinetics of the Abeta40 and Abeta42 peptides. Proc Natl Acad Sci U S A. 2014;111(26):9384–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Iwatsubo T, Odaka A, Suzuki N, Mizusawa H, Nukina N, Ihara Y. Visualization of A beta 42(43) and A beta 40 in senile plaques with end-specific A beta monoclonals: evidence that an initially deposited species is A beta 42(43). Neuron. 1994;13(1):45–53.

    Article  CAS  PubMed  Google Scholar 

  5. Hansson O. Biomarkers for neurodegenerative diseases. Nat Med. 2021;27(6):954–63.

    Article  CAS  PubMed  Google Scholar 

  6. Olsson B, Lautner R, Andreasson U, Ohrfelt A, Portelius E, Bjerke M, et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurol. 2016;15(7):673–84.

    Article  CAS  PubMed  Google Scholar 

  7. Bateman RJ, Xiong C, Benzinger TL, Fagan AM, Goate A, Fox NC, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med. 2012;367(9):795–804.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Stomrud E, Minthon L, Zetterberg H, Blennow K, Hansson O. Longitudinal cerebrospinal fluid biomarker measurements in preclinical sporadic Alzheimer’s disease: a prospective 9-year study. Alzheimers Dement (Amst). 2015;1(4):403–11.

    Article  PubMed  Google Scholar 

  9. Janelidze S, Zetterberg H, Mattsson N, Palmqvist S, Vanderstichele H, Lindberg O, et al. CSF Abeta42/Abeta40 and Abeta42/Abeta38 ratios: better diagnostic markers of Alzheimer disease. Ann Clin Transl Neurol. 2016;3(3):154–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Doecke JD, Ward L, Burnham SC, Villemagne VL, Li QX, Collins S, et al. Elecsys CSF biomarker immunoassays demonstrate concordance with amyloid-PET imaging. Alzheimers Res Ther. 2020;12(1):36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Keshavan A, Wellington H, Chen Z, Khatun A, Chapman M, Hart M, et al. Concordance of CSF measures of Alzheimer’s pathology with amyloid PET status in a preclinical cohort: a comparison of Lumipulse and established immunoassays. Alzheimers Dement (Amst). 2020;12(1):e12097.

    PubMed  Google Scholar 

  12. Lewczuk P, Matzen A, Blennow K, Parnetti L, Molinuevo JL, Eusebi P, et al. Cerebrospinal fluid Abeta42/40 corresponds better than Abeta42 to amyloid PET in Alzheimer’s disease. J Alzheimers Dis. 2017;55(2):813–22.

    Article  CAS  PubMed  Google Scholar 

  13. Hansson O, Zetterberg H, Buchhave P, Andreasson U, Londos E, Minthon L, et al. Prediction of Alzheimer’s disease using the CSF Abeta42/Abeta40 ratio in patients with mild cognitive impairment. Dement Geriatr Cogn Disord. 2007;23(5):316–20.

    Article  CAS  PubMed  Google Scholar 

  14. Leuzy A, Mattsson-Carlgren N, Palmqvist S, Janelidze S, Dage JL, Hansson O. Blood-based biomarkers for Alzheimer’s disease. EMBO Mol Med. 2022;14(1):e14408.

    Article  CAS  PubMed  Google Scholar 

  15. Janelidze S, Stomrud E, Palmqvist S, Zetterberg H, van Westen D, Jeromin A, et al. Plasma beta-amyloid in Alzheimer’s disease and vascular disease. Sci Rep. 2016;6:26801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Dore V, et al. High performance plasma amyloid-beta biomarkers for Alzheimer’s disease. Nature. 2018;554(7691):249–54.

    Article  CAS  PubMed  Google Scholar 

  17. Schindler SE, Bollinger JG, Ovod V, Mawuenyega KG, Li Y, Gordon BA, et al. High-precision plasma beta-amyloid 42/40 predicts current and future brain amyloidosis. Neurology. 2019;93(17):e1647–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Palmqvist S, Janelidze S, Stomrud E, Zetterberg H, Karl J, Zink K, et al. Performance of fully automated plasma assays as screening tests for Alzheimer disease-related beta-amyloid status. JAMA Neurol. 2019;76(9):1060–9.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ovod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, et al. Amyloid beta concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 2017;13(8):841–9.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Fandos N, Perez-Grijalba V, Pesini P, Olmos S, Bossa M, Villemagne VL, et al. Plasma amyloid beta 42/40 ratios as biomarkers for amyloid beta cerebral deposition in cognitively normal individuals. Alzheimers Dement (Amst). 2017;8:179–87.

    Article  PubMed  Google Scholar 

  21. Chapleau M, Iaccarino L, Soleimani-Meigooni D, Rabinovici GD. The role of amyloid PET in imaging neurodegenerative disorders: a review. J Nucl Med. 2022;63(Suppl 1):13S-S19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Seo SW, Ayakta N, Grinberg LT, Villeneuve S, Lehmann M, Reed B, et al. Regional correlations between [(11)C]PIB PET and post-mortem burden of amyloid-beta pathology in a diverse neuropathological cohort. Neuroimage Clin. 2017;13:130–7.

    Article  PubMed  Google Scholar 

  23. Scholl M, Wall A, Thordardottir S, Ferreira D, Bogdanovic N, Langstrom B, et al. Low PiB PET retention in presence of pathologic CSF biomarkers in Arctic APP mutation carriers. Neurology. 2012;79(3):229–36.

    Article  PubMed  Google Scholar 

  24. Maia LF, Kaeser SA, Reichwald J, Hruscha M, Martus P, Staufenbiel M, et al. Changes in amyloid-beta and Tau in the cerebrospinal fluid of transgenic mice overexpressing amyloid precursor protein. Sci Transl Med. 2013;5(194):194re2.

    Article  PubMed  Google Scholar 

  25. Maia LF, Kaeser SA, Reichwald J, Lambert M, Obermuller U, Schelle J, et al. Increased CSF Abeta during the very early phase of cerebral Abeta deposition in mouse models. EMBO Mol Med. 2015;7(7):895–903.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kawarabayashi T, Younkin LH, Saido TC, Shoji M, Ashe KH, Younkin SG. Age-dependent changes in brain, CSF, and plasma amyloid (beta) protein in the Tg2576 transgenic mouse model of Alzheimer’s disease. J Neurosci. 2001;21(2):372–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sasaguri H, Nilsson P, Hashimoto S, Nagata K, Saito T, De Strooper B, et al. APP mouse models for Alzheimer’s disease preclinical studies. EMBO J. 2017;36(17):2473–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Saito T, Matsuba Y, Mihira N, Takano J, Nilsson P, Itohara S, et al. Single App knock-in mouse models of Alzheimer’s disease. Nat Neurosci. 2014;17(5):661–3.

    Article  CAS  PubMed  Google Scholar 

  29. Masuda A, Kobayashi Y, Kogo N, Saito T, Saido TC, Itohara S. Cognitive deficits in single App knock-in mouse models. Neurobiol Learn Mem. 2016;135:73–82.

    Article  CAS  PubMed  Google Scholar 

  30. Kang JE, Lim MM, Bateman RJ, Lee JJ, Smyth LP, Cirrito JR, et al. Amyloid-beta dynamics are regulated by orexin and the sleep-wake cycle. Science. 2009;326(5955):1005–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Andersson E, Janelidze S, Lampinen B, Nilsson M, Leuzy A, Stomrud E, et al. Blood and cerebrospinal fluid neurofilament light differentially detect neurodegeneration in early Alzheimer’s disease. Neurobiol Aging. 2020;95:143–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Meng X, Rubin D, Rosenthal R. Comparing correlated correlation coefficients. Quantitative Methods Psychol. 1992;111(1):172–5.

    Google Scholar 

  33. Hampel H, O’Bryant SE, Molinuevo JL, Zetterberg H, Masters CL, Lista S, et al. Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol. 2018;14(11):639–52.

  34. Roberts KF, Elbert DL, Kasten TP, Patterson BW, Sigurdson WC, Connors RE, et al. Amyloid-beta efflux from the central nervous system into the plasma. Ann Neurol. 2014;76(6):837–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Janelidze S, Teunissen CE, Zetterberg H, Allue JA, Sarasa L, Eichenlaub U, et al. Head-to-head comparison of 8 plasma amyloid-beta 42/40 assays in Alzheimer disease. JAMA Neurol. 2021;78(11):1375–82.

    Article  PubMed  Google Scholar 

  36. Mattsson-Carlgren N, Grinberg LT, Boxer A, Ossenkoppele R, Jonsson M, Seeley W, et al. Cerebrospinal fluid biomarkers in autopsy-confirmed Alzheimer disease and frontotemporal lobar degeneration. Neurology. 2022;98(11):e1137–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Thal DR, Rub U, Orantes M, Braak H. Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology. 2002;58(12):1791–800.

    Article  PubMed  Google Scholar 

  38. Smirnov DS, Ashton NJ, Blennow K, Zetterberg H, Simren J, Lantero-Rodriguez J, et al. Plasma biomarkers for Alzheimer’s disease in relation to neuropathology and cognitive change. Acta Neuropathol. 2022;143(4):487–503.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Palmqvist S, Mattsson N, Hansson O, Alzheimer’s Disease Neuroimaging I. Cerebrospinal fluid analysis detects cerebral amyloid-beta accumulation earlier than positron emission tomography. Brain. 2016;139(Pt 4):1226–36.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Fagan AM, Mintun MA, Shah AR, Aldea P, Roe CM, Mach RH, et al. Cerebrospinal fluid tau and ptau(181) increase with cortical amyloid deposition in cognitively normal individuals: implications for future clinical trials of Alzheimer’s disease. EMBO Mol Med. 2009;1(8–9):371–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Morris JC, Roe CM, Xiong C, Fagan AM, Goate AM, Holtzman DM, et al. APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging. Ann Neurol. 2010;67(1):122–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Mattsson N, Insel PS, Donohue M, Landau S, Jagust WJ, Shaw LM, et al. Independent information from cerebrospinal fluid amyloid-beta and florbetapir imaging in Alzheimer’s disease. Brain. 2015;138(Pt 3):772–83.

    Article  PubMed  Google Scholar 

  43. Fagan AM, Mintun MA, Mach RH, Lee SY, Dence CS, Shah AR, et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann Neurol. 2006;59(3):512–9.

    Article  CAS  PubMed  Google Scholar 

  44. Landau SM, Lu M, Joshi AD, Pontecorvo M, Mintun MA, Trojanowski JQ, et al. Comparing positron emission tomography imaging and cerebrospinal fluid measurements of beta-amyloid. Ann Neurol. 2013;74(6):826–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hansson O, Seibyl J, Stomrud E, Zetterberg H, Trojanowski JQ, Bittner T, et al. CSF biomarkers of Alzheimer’s disease concord with amyloid-beta PET and predict clinical progression: a study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement. 2018;14(11):1470–81.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Buchhave P, Blennow K, Zetterberg H, Stomrud E, Londos E, Andreasen N, et al. Longitudinal study of CSF biomarkers in patients with Alzheimer’s disease. PLoS ONE. 2009;4(7):e6294.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Buchhave P, Minthon L, Zetterberg H, Wallin AK, Blennow K, Hansson O. Cerebrospinal fluid levels of beta-amyloid 1–42, but not of tau, are fully changed already 5 to 10 years before the onset of Alzheimer dementia. Arch Gen Psychiatry. 2012;69(1):98–106.

    Article  CAS  PubMed  Google Scholar 

  48. Blennow K, Zetterberg H. Biomarkers for Alzheimer’s disease: current status and prospects for the future. J Intern Med. 2018;284(6):643–63.

    Article  CAS  PubMed  Google Scholar 

  49. Pichet Binette A, Janelidze S, Cullen N, Dage JL, Bateman RJ, Zetterberg H, et al. Confounding factors of Alzheimer’s disease plasma biomarkers and their impact on clinical performance. Alzheimers Dement. 2022;1–12. https://doi.org/10.1002/alz.12787.

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Acknowledgements

The authors thank Bodil Israelsson for technical support during sample collection.

Funding

Open access funding provided by Lund University. Work at Lund University was supported by the Swedish Research Council (2016–00906), the Knut and Alice Wallenberg foundation (2017–0383), the Marianne and Marcus Wallenberg foundation (2015.0125), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer Foundation (AF-939932), the Swedish Brain Foundation (FO2021-0293), The Parkinson foundation of Sweden (1280/20), the Cure Alzheimer’s fund, the Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, the Skåne University Hospital Foundation (2020-O000028), Regionalt Forskningsstöd (2020–0314), and the Swedish federal government under the ALF agreement (2018-Projekt0279). HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018–02532); the European Union’s Horizon Europe research and innovation programme under grant agreement no. 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 programme 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 and #2022–00732); the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809–2016615); the Swedish Alzheimer Foundation (#AF-930351, #AF-939721 and #AF-968270); Hjärnfonden, Sweden (#FO2017-0243 and #ALZ2022-0006); the Swedish state under the agreement between the Swedish government and the County Councils; the ALF agreement (#ALFGBG-715986 and #ALFGBG-965240); the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466–236); the National Institute of Health (NIH), USA (grant #1R01AG068398-01); the Alzheimer’s Association 2021 Zenith Award (ZEN-21–848495); and the Alzheimer’s Association 2022–2025 Grant (SG-23–1038904 QC).

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Authors and Affiliations

  1. Clinical Memory Research Unit, Lund University, 22184, Lund, Sweden

    Emelie Andersson, Nina Schultz & Oskar Hansson

  2. Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan

    Takashi Saito

  3. Laboratory for Proteolytic Neuroscience, RIKEN Brain Science Institute, Wako-Shi, Saitama, Japan

    Takaomi C. Saido

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

    Kaj Blennow & Henrik Zetterberg

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

    Kaj Blennow & Henrik Zetterberg

  6. Department of Experimental Medical Science, Experimental Dementia Research Unit, Lund University, Lund, Sweden

    Gunnar K. Gouras

  7. Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London, UK

    Henrik Zetterberg

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

    Henrik Zetterberg

  9. Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China

    Henrik Zetterberg

  10. Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA

    Henrik Zetterberg

  11. Memory Clinic, SkåneUniversity Hospital, 20502, Malmö, Sweden

    Oskar Hansson

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Contributions

EA contributed to the study design, completed the animal experiments, performed statistical analyses, interpreted data, and drafted the manuscript. NS performed statistical analyses and interpreted data. TS and TCS provided the AppNL−F/NL−F and AppNL/NL knock-in mice. GKG contributed to the study design. HZ and KB performed the biochemical analysis of CSF and serum from mice included in the study. OH was responsible for the design and concept of the study and overviewed collection, analyses, and interpretation of the data. All authors reviewed the manuscript for intellectual content and approved the submitted version.

Corresponding authors

Correspondence to Emelie Andersson or Oskar Hansson.

Ethics declarations

Ethics approval and consent to participate

Animal experimental procedures were carried out in accordance with the Swedish animal research regulations and approved by the committee of animal research at Lund University (ethical permit number: 7482/2017).

Consent for publication

Not applicable.

Competing interests

OH has acquired research support (for the institution) from ADx, AVID Radiopharmaceuticals, Biogen, Eli Lilly, Eisai, Fujirebio, GE Healthcare, Pfizer, and Roche. In the past 2 years, he has received consultancy/speaker fees from AC Immune, Amylyx, Alzpath, BioArctic, Biogen, Cerveau, Fujirebio, Genentech, Novartis, Roche, and Siemens.

HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, 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, BioArctic, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Ono Pharma, Pharmatrophix, Prothena, 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, outside the work presented in this paper.

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Andersson, E., Schultz, N., Saito, T. et al. Cerebral Aβ deposition precedes reduced cerebrospinal fluid and serum Aβ42/Aβ40 ratios in the AppNL−F/NL−F knock-in mouse model of Alzheimer’s disease. Alz Res Therapy 15, 64 (2023). https://doi.org/10.1186/s13195-023-01196-8

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Alzheimer's Research & Therapy

ISSN: 1758-9193