# Comparing high and low amyloid producers in Alzheimer's disease: An in-depth analysis

Mélanie Leroy, Anne Laure Aziz, Susanna Schraen, Vincent Deramecourt, Emilie Skrobala, Simon Lecerf, Florence Pasquier, Vincent Huin, Maxime Bertoux, Thibaud Lebouvier

► To cite this version:

Mélanie Leroy, Anne Laure Aziz, Susanna Schraen, Vincent Deramecourt, Emilie Skrobala, et al. Comparing high and low amyloid producers in Alzheimer's disease: An in-depth analysis. Revue Neurologique, 2025, Revue Neurologique, 10.1016/j.neurol.2025.02.004. hal-05025065

HAL Id: hal-05025065

https://lilloa.hal.science/hal-05025065v1

Submitted on 8 Apr 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Original article

# Comparing high and low amyloid producers in Alzheimer's disease: An in-depth analysis

Mélanie Leroy<sup>a,b</sup>, Anne Laure Aziz<sup>a,1</sup>, Susanna Schraen<sup>a,b,1</sup>,
Vincent Deramecourt<sup>a</sup>, Emilie Skrobala<sup>b</sup>, Simon Lecerf<sup>a</sup>,
Florence Pasquier<sup>a,b</sup>, Vincent Huin<sup>a,b</sup>, Maxime Bertoux<sup>a,b,1</sup>,
Thibaud Lebouvier<sup>a,b,1,*</sup>

## INFO ARTICLE

Article history:

Received 22 November 2023

Received in revised form

15 November 2024

Accepted 14 February 2025

Available online xxx

Keywords:

Alzheimer's disease

Cerebrospinal fluid biomarkers

Aβ<sub>42/40</sub> ratio

A/T/N

## ABSTRACT

**Background.** – The cerebrospinal fluid (CSF) Aβ<sub>42/40</sub> ratio has proven to be a more reliable biomarker for amyloid pathology than CSF Aβ<sub>42</sub> in Alzheimer's disease (AD), helping to correctly classify patients with positive tau biomarkers (T+) that would otherwise have remained outside of the AD continuum. It was shown that the Aβ<sub>42/40</sub> ratio better captures a relative decrease of Aβ<sub>42</sub> in patients with high CSF Aβ. However, whether patients with high-amyloid (HiA) AD, in whom A+ is defined by the Aβ<sub>42/40</sub> ratio, exactly compare with their low-amyloid (LoA) counterparts, in whom A+ is defined by Aβ<sub>42</sub> solely, deserves further analysis.

**Methods.** – We retrospectively included patients with A+T+ AD and evidence of cognitive and neurodegenerative changes (N+). LoA patients were operationally defined as patients with T+N+ and low CSF Aβ<sub>42</sub>, while HiA patients were defined as patients with T+N+ and normal CSF Aβ<sub>42</sub> but abnormal Aβ<sub>42/40</sub> ratio. Tau CSF biomarkers, neuropsychological profile, rates of cognitive decline, structural and metabolic imaging, ApoE genotype and brain neuropathology were compared between the HiA and LoA groups.

**Results.** – At the time of the lumbar puncture, LoA patients were significantly younger than the HiA patients (68.9 ± 8.7 years vs. 71.8 ± 9.4; P = 0.0015) and had a lower Mini-Mental Status Examination (MMSE) (18.7 ± 6.4 vs. 20.7 ± 6.2; P = 0.0005). There was no difference in the neuropsychological profile nor in the annual rates of cognitive decline between the two groups with early AD. No differences were retrieved between groups on CSF Tau and P-Tau biomarkers, atrophy and brain metabolism, distribution of the APOE4 allele and APOE4/E4 genotype, and neuropathology.

**Conclusions.** – Overall, our study supports the surrogate use of the Aβ<sub>42/40</sub> ratio as an equivalent to Aβ<sub>42</sub> to define AD. We showed that HiA CSF profiles were not associated with

<footnote><sup>a</sup> Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, 59000 Lille, France</footnote>

<footnote><sup>b</sup> CHU de Lille, DISTALZ, Lille, France</footnote>

<footnote>* Corresponding author at: Lille Neuroscience & Cognition, Inserm UMRS1172, Université de Lille, CHU de Lille, Lille, France. E-mail address: thibaud.lebouvier@chu-lille.fr (T. Lebouvier).</footnote>

<footnote n="1">¹ ALA and SS, as well as MB and TL, contributed equally.</footnote>

<footnote>https://doi.org/10.1016/j.neurol.2025.02.004</footnote>

<footnote>0035-3787/© 2025 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</footnote>
differences in cognition, brain structures and metabolism, APOE genotype tau CSF biomarkers or the rates of cognitive decline, but may be the associated with later-onset and early-stage AD.

© 2025 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

## 1. Abbreviations

<table><tr><td>A</td><td>amyloid</td></tr><tr><td>Aβ</td><td>beta amyloid peptide</td></tr><tr><td>AD</td><td>Alzheimer's disease</td></tr><tr><td>APP</td><td>amyloid precursor protein</td></tr><tr><td>CSF</td><td>cerebrospinal fluid</td></tr><tr><td>DRS</td><td>dementia rating scale</td></tr><tr><td>FAB</td><td>frontal assessment battery</td></tr><tr><td>FCSRT</td><td>Free and Cued Selective Reminding Test</td></tr><tr><td>[18F]FDG-PET scan</td><td>2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography</td></tr><tr><td>FTLD</td><td>frontotemporal lobar degeneration</td></tr><tr><td>LP</td><td>lumbar puncture</td></tr><tr><td>MCI</td><td>mild cognitive impairment</td></tr><tr><td>MRI</td><td>magnetic resonance imaging</td></tr><tr><td>MMSE</td><td>Mini-Mental State Examination</td></tr><tr><td>MMRC</td><td>Lille Memory Resources and Research Centre</td></tr><tr><td>N</td><td>neuronal injury</td></tr><tr><td>NIA-AA</td><td>National Institute on Aging-Alzheimer's Association Research</td></tr><tr><td>T</td><td>tau pathology</td></tr><tr><td>Tau<sub>p181</sub></td><td>phosphorylated tau</td></tr><tr><td>VAT</td><td>Visual Association Test</td></tr><tr><td>VBM</td><td>voxel-based morphometry</td></tr></table>

## 2. Introduction

The amyloid/tau/neurodegeneration (ATN) framework has been proposed as a means of evidencing the biological state of Alzheimer's disease (AD) [1]. According to this framework, patients with suspected AD can be evaluated for the presence of β-amyloid (cerebrospinal fluid [CSF Aβ] or amyloid positron emission tomography [PET]: “A”), hyperphosphorylated tau (CSF p-tau or tau PET: “T”), and neurodegeneration (atrophy or hypometabolism at magnetic resonance imaging [MRI], ¹⁸fluorodeoxyglucose [FDG-PET], or CSF total tau: “N”). In standard clinical practice, the analysis of CSF biomarkers, prompted by a clinical presentation compatible with AD, is the primary clinical tool for assessing a single absolute value reflecting the degree of abnormality with respect to the ATN classification.

According to this framework, AD is defined as any situation with both abnormal amyloid and tau biomarkers (A+T+). In this regard, AD can theoretically be ruled out in patients without abnormal amyloid levels, even with positive tau biomarkers and a compatible phenotype. This situation, referred to as A-T+ [1], places physicians in a perplexing situation. Indeed,

when the amyloid biomarker considered is CSF Aβ₄₂, which is the most widely accepted marker of amyloid pathology in the literature, A-T+ is not an uncommon scenario, representing more than one-third of T+ patients [2]. Inter-individual variations of total Aβ were shown to be responsible for most of these cases [3]. Indeed, CSF Aβ₄₂ concentration is believed to depend both on Aβ sequestration in amyloid plaques and on the total CSF Aβ. Consequently, AD patients with intrinsically high total CSF Aβ can exhibit a relative decrease of Aβ₄₂ that still falls within the normal range [3].

To mitigate this potential bias, the Aβ₄₂/₄₀ ratio has been proposed as a more accurate marker of amyloid pathology. Aβ₄₀ being the most abundant isoform of Aβ, it does indeed reflect total Aβ. Therefore, a decrease in the Aβ₄₂/₄₀ ratio is thought to better indicate the presence of amyloid pathology, independently of the total CSF Aβ [4]. When used as an amyloid marker instead of Aβ₄₂, the Aβ₄₂/₄₀ ratio has demonstrated higher sensitivity and specificity for AD, enabling the reclassification of approximately half of the T+ patients with normal Aβ₄₂ into the AD spectrum (i.e. A+T+) [2,5,6]. Moreover, the ratio appears to have a stronger correlation with brain amyloid deposition, as assessed through PET, compared to Aβ₄₂ alone [7-9]. Consecutively, the 2018 NIA-AA criteria have recognized the Aβ₄₂/₄₀ ratio as an alternative amyloid biomarker, alongside Aβ₄₂ [1]. If CSF is used to guide the diagnosis, A+ is now defined as positive CSF Aβ₄₂ and/or a positive Aβ₄₂/₄₀ ratio. Thus, the Aβ₄₂/₄₀ ratio is increasingly used as a second-line amyloid biomarker when CSF Aβ₄₂ is normal despite the presence of positive tau biomarkers [10].

However, caution should be exercised when using the Aβ₄₂/₄₀ ratio as a substitute for Aβ₄₂. The amyloid cascade hypothesis, posited decades ago, suggests that Aβ deposition is the initial pathological event in AD [11]. Although alternate hypotheses have been proposed since, the amyloid cascade hypothesis has strong proponents. Notably, genetics provide support for the notion that amyloid precursor protein (APP) metabolism plays a central role in AD pathophysiology [12]. In this context, it remains unclear whether situations with normal Aβ₄₂ but an abnormal Aβ₄₂/₄₀ ratio (a) reflect differences in Aβ production and/or clearance and (b) result in clinically significant distinctions. In other words, it is yet uncertain whether A+T+ patients with normal Aβ₄₂ and an abnormal Aβ₄₂/₄₀ ratio, hereafter referred to as high-amyloid (HiA) patients, are clinically identical to those with low Aβ₄₂, hereafter referred to as low-amyloid (LoA) patients, in terms of demographic characteristics, biomarkers, APOE genotyping, cognitive functions, brain atrophy, metabolism, and neuropathology.

The objective of this retrospective single-center study was to characterize the CSF biomarker, cognitive, neuroimaging,
APOE genotyping and neuropathological profiles of AD HiA patients as compared with AD LoA patients. We retrospectively included A+T+ AD patients with evidence of cognitive and neurodegenerative changes (N+). Low amyloid patients were operationally defined as T+N+ patients with low CSF Aβ<sub>42</sub>, while HiA patients were defined as T+N+ patients with normal CSF Aβ<sub>42</sub> but an abnormal Aβ<sub>42/40</sub> ratio.

## 3. Methods

### 3.1. Population

Participants seen at the Lille Memory Clinic between September 2010 and December 2018 were retrospectively included in this study. The inclusion criteria were as follows: (i) availability of CSF samples obtained to aid in the etiological diagnosis of degenerative cognitive disorders, including Aβ<sub>42</sub>, Aβ<sub>40</sub>, T-tau and tau<sub>181P</sub>; (ii) elevated CSF tau<sub>181P</sub> levels (tau<sub>181P</sub> ≥ 60 pg/mL); and (iii) positive neurodegeneration markers (N+) defined as elevated CSF tau (T-tau ≥ 400 pg/mL) as shown in Fig. 1.

CSF, demographics, and Mini-Mental State Examination (MMSE) data were available for the entire population. However, neuropsychological examinations, neuroimaging analyses, genotyping and neuropathological examinations were not systematically available and the sample size (n) for both groups are specified in Supplementary Tables 2, 3 and 4.

### 3.2. CSF biomarkers

One milliliter of CSF was obtained by a lumbar puncture (LP) in the L3–L4 or L4–L5 intervertebral space using a 22- or 25-gauge needle. It was collected in Sarstedt 62.610.201 polypropylene tubes and transferred at 4 °C to the local laboratory within 4 hours. Routine (cytology, biochemistry, and bacteriology) analyses were run. The CSF was then centrifuged at 1000 g at 4 °C for 10 minutes, then transferred to 0.5 mL polypropylene tubes and stored at –80 °C until analysis.

CSF Aβ<sub>42</sub>, Aβ<sub>40</sub>, T-tau, tau<sub>p181</sub> were measured using the ELISA sandwich INNOTEST® (Fujirebio-Europe). CSF-Aβ<sub>40</sub> analysis was a routine analysis in Lille MMRC until July 2015 and thereafter became restricted to cases of discrepancy between tau and amyloid biomarkers. The Aβ<sub>42/40</sub> ratio was calculated whenever Aβ<sub>40</sub> was available. Cutoff values used in clinical routine were based on the results of the French multicenter study setting up the harmonization of sampling procedures and collection tubes [13]. Pathological results were defined as: Aβ<sub>42</sub> < 700 pg/mL, T-tau ≥ 400 pg/mL and tau<sub>181P</sub> ≥ 60 pg/mL. Cutoff values for Aβ<sub>42/40</sub> were set at < 0.065 following another French multicenter study [5]. LoA profiles were defined by tau<sub>181P</sub> ≥ 60 pg/mL and Aβ<sub>42</sub> < 700 pg/mL. HiA profiles were defined by tau<sub>181P</sub> ≥ 60 pg/mL, Aβ<sub>42</sub> ≥ 700 pg/mL and Aβ<sub>42/40</sub> ratio < 0.065, 0.065, congruent with the presence of amyloid pathology.

### 3.3. Neuropsychology and cognitive decline over time

All patients exhibited either mild cognitive impairment (MCI) or a major cognitive disorder at the time of the LP. Due to the variability in disease stages among the patients at the time of the LP, as well as the inconsistent availability of neuropsychological data in the entire population, we imposed additional inclusion criteria for the analyses related to neuropsychology. These criteria included a MMSE score of ≥ 20 [14]; the presence of a comprehensive neuropsychological evaluation at the initial referral; and ≥ 2 available follow-up MMSE measures to estimate the annual rate of cognitive decline. These criteria were implemented to restrict our analyses to patients with early AD, thereby maximizing the likelihood of identifying clinically significant differences and to prevent the influence of missing data in the dataset. The neuropsychological evaluation included the Mattis Dementia Rating Scale (MDRS) [15] to assess general cognitive efficiency, the Free and Cued Selective Reminding Test (FCSRT) [16] and the Visual Association Test (VAT) [17] to evaluate long-term episodic-like memory, the Trail Making Test [18] and the Stroop test [19] to explore executive functioning, and the

<figure><img src="image_2.png" /><figcaption>Fig. 1 – Study flowchart.</figcaption></figure>
Visual Object and Space Perception Battery (VOSP) [20] to assess visuo-spatial processing.

### 3.4. Neuroimaging

Only MRI and [18F]FDG-PET scans conducted within six months of the lumbar puncture (LP) were considered. A whole brain approach was chosen to determine the potential brain regions in which differences between groups in either gray matter density or metabolism could be observed. Prior to our analyses, each scan was carefully reviewed by a neuroradiologist to exclude patients with (i) significant traumatic, tumoral, or inflammatory brain lesions; (ii) hydrocephalus; (iii) substantial vascular disease, such as strategic brain infarctions, multiple lacunae, or extensive white matter hyperintensities reaching a score of 3 according to the Fazekas scale [21]; and (iv) imaging artifacts (e.g., motion artifacts) that could potentially affect the pre-processing of imaging data.

MRI data were all acquired with a standard quadrature head coil (eight channels). Parameters of each MRI sequence used on the three different machines are described in *Supplementary Material 1*, similarly to a previous study [22]. Regarding [18F]FDG-PET data, a computed tomography (CT) brain scan was acquired for attenuation correction of the [18F]FDG-PET data thirty minutes post-intravenous injection of 185 MBq of [18F]FDG, followed by a 15-minute emission scan acquisition. [18F]FDG-PET data were then acquired on a hybrid 4-ring Biograph mCT-Flow PET/CT system with 20-slice CT or on a hybrid Discovery RX16 PET/CT system. Patients were asked to fast and rest prior to the PET scan.

MRI data were analysed using FSL-VBM [23,24], part of the FSL software package [25]. Briefly, structural images were brain-extracted, segmented, and aligned in the MNI space. A study-specific template was created in which HiA and LoA patients, as well as images from each scanner were equally represented. The native gray matter images were registered non-linearly. Registered partial volumes maps were modulated by dividing them by the Jacobian of the warp field. Modulated images were smoothed with an isotropic Gaussian kernel with a 3-mm sigma. Quality checks were performed at each stage. The [18F]FDG-PET CT images were analyzed by SPM12 [26], realigned, AC-PC reoriented, and spatially normalized into the MNI space. All images were smoothed at 8 mm full-width at half-maximum with a Gaussian filter. Proportional scaling normalization was used.

### 3.5. APOE genotyping

Blood samples stored in the Lille memory clinic biobank at the Biological Resource Centre of Lille University Hospital (CRB du CIC 1403 du CHU de Lille, BRIF BB0033-00030) were genotyped for two APOE single nucleotide polymorphisms (rs429358 and rs7412), determining the three major APOE genotypes (APOE-ε2: rs429358-T, rs7412-T; APOE-ε3: rs429358-T, rs7412-C; APOE-ε4: rs429358-C, rs7412-C) using two dedicated TaqMan™ genotyping assays (Applied Biosystems, Villebon-sur-Yvette, France) according to the manufacturer's instructions and to the method previously described [27].

### 3.6. Neuropathology

Fresh tissue samples were obtained at autopsy from standardized brain regions for diagnosis, following a neuroanatomical atlas, and fixed overnight in 10% neutral buffered formalin, as previously described [28]. Subsequently, the tissue was embedded in paraffin blocks and cut into 6 µm sections for immunohistochemical staining. Well-characterized antibodies [28] were used for staining against tau, amyloid-β, transactive response DNA binding protein with $M_r$ 43 kD (TDP)-43 and α-synuclein. The neuropathological diagnoses were performed by expert neuropathologists using formal criteria [29,30].

### 3.7. Statistical analyses

Quantitative variables are presented in the manuscript as mean ± standard deviation (SD) unless stated otherwise. Qualitative variables are presented as numbers and percentages.

For ordinal data, such as clinical scores, or in cases where there was a deviation from normality, we used conventional Mann-Whitney tests to compare the median magnitudes of change. However, differences between groups were analyzed using unpaired two-tailed Student's t-tests. A Bonferroni correction was applied to comparisons between the HiA and LoA groups. The effect size ($d$) was calculated as the standardized mean difference. Given the large number of neuropsychological variables, we reduced the dataset dimensions for the analyses. Following a previously published method [31], we conducted a principal component analysis (PCA, after z-score transformation and relevant statistical checks) on all neuropsychological subscores, excluding the MMSE. Three components were identified, and their labels were deduced from the PCA structure: (i) the long-term memory component; (ii) the executive functioning component; and (iii) the visuo-spatial processing component.

The annual rate of MMSE decline was estimated in each group as well as its standard error, then compared between the groups using a linear mixed model with group, time, and time*group interactions as fixed effects. This model allows considering correlations between repeated measures.

Regarding imaging data, we conducted comparisons of gray matter density using voxel-based morphometry for MRI data and regional metabolism for [18F]FDG-PET scans. We first accounted for any potential scanner effects (direct contrasts across machines, which yielded no significant differences). For MRI data, differences in gray matter atrophy were then analyzed using two-sample t-tests models in FSL through permutation-based ($n = 5000$) non-parametric testing corrected for multiple comparisons (familywise error, $P = 0.05$). For regional differences in [18F]FDG-PET metabolism, two-sample t-test models were also employed, with a T-score threshold of 3.16 corresponding to a level of $P = 0.05$. In both VBM and metabolic comparisons, we considered age, sex, the MRI or [18F]FDG-PET scanner, and MMSE score as nuisance covariates.

Importantly, before each analysis, we checked that the subset of patients in whom neuropsychology, neuroimaging and genetics were assessed did not differ from the whole cohort.
<table><caption>Table 1 – Systematic comparison between high (HiA) and low-amyloid (LoA) Alzheimer's disease patients.</caption><thead><tr><th></th><th>LoA<br>n = 402</th><th>HiA<br>n = 146</th><th>P</th></tr></thead><tbody><tr><td>Women (%)</td><td>58.3</td><td>64.38</td><td>ns</td></tr><tr><td>Age at LP (years)</td><td>68.9 ± 8.7</td><td>71.8 ± 9.4</td><td>0.0015</td></tr><tr><td>Education</td><td>Same distribution across degrees</td><td></td><td>ns</td></tr><tr><td>MMSE at LP</td><td>18.7 ± 6.4</td><td>20.7 ± 6.2</td><td>0.0005</td></tr><tr><td>Time between LP and first symptoms (years)</td><td>4.94 ± 4.13</td><td>4.28 ± 3.18</td><td>ns</td></tr><tr><td>CSF Aβ42 (pg/mL)</td><td>555.2 ± 101.5</td><td>841.1 ± 170.2</td><td>&lt; 0.0001</td></tr><tr><td>CSF Aβ40 (pg/mL)</td><td>14,096 ± 4409<sup>a</sup></td><td>18,678 ± 5441</td><td>&lt; 0.0001</td></tr><tr><td>CSF tauP181 (pg/mL)</td><td>109.4 ± 41.7</td><td>109.3 ± 36.2</td><td>ns</td></tr><tr><td>CSF T-tau (pg/mL)</td><td>855.9 ± 447.9</td><td>803.6 ± 369.3</td><td>ns</td></tr></tbody></table>

LP: lumbar puncture; MMSE: Mini-Mental Status Examination; CSF: cerebrospinal fluid; ns: non significant.

# 4. Results

## 4.1. Population

Overall, 1290 patients underwent a CSF analysis. Among them, 625 had both abnormal CSF $\tau_{181P}$ and tau concentrations ($T+N+$). Within this $T+N+$ group, 402 patients (64%) had low CSF $A\beta_{42}$, forming the low-amyloid (LoA) group of $A+T+N+$ AD patients. Of the remaining 223 patients with normal $A\beta_{42}$, 146 had an abnormal $A\beta_{42/40}$ ratio (65% of the normal $A\beta_{42}$ $T+N+$ population and 23% of the entire $T+N+$ population), constituting the high-amyloid (HiA) group of $A+T+N+$ AD patients.

Comparisons between the 146 HiA and 402 LoA patients showed no significant between-group differences in gender distribution or level of education (Table 1). At the time of the LP, HiA patients were significantly older than the LoA patients ($71.8 \pm 9.4$ vs. $68.9 \pm 8.7$ years; $P = 0.0015$; $d = 0.3$) and had a higher MMSE than the LoA patients ($20.7 \pm 6.2$ vs. $18.7 \pm 6.4$; $P = 0.0005$; $d = 0.3$).

## 4.2. CSF biomarkers

The analysis was performed on 146 HiA and 402 LoA patients. HiA patients had higher $A\beta_{40}$ levels than LoA patients, reaching 133% of their LoA counterparts. CSF T-tau and $\tau_{P181}$ levels were not statistically different between the

groups (Table 1 and Fig. 2). Time between the first symptoms and the LP was slightly lower in the HiA group, however, this difference did not resist Bonferroni's correction for multiple comparisons.

## 4.3. Neuropsychology and cognitive decline over time

The analysis was performed on 135 HiA and 77 LoA patients. There were no differences regarding age, sex, education and MMSE scores between these groups (Supplementary Material 2). No statistical difference between the groups on executive, memory, and visuo-spatial processing components (Fig. 3) were observed.

Longitudinally, the mixed model did not show any group effect ($P = 0.37$) on the rate of MMSE decline over time, with an annual decline of $2.3 \pm 0.3$ and $2.1 \pm 0.2$ points/year in the LoA and the HiA groups respectively (Table 1).

## 4.4. Neuroimaging

The inclusion criteria resulted in a final selection of 42 LoA patients and 32 HiA patients. To achieve matched group sizes ($n = 32$), patients from the LoA group were randomly selected. Regarding imaging data acquisition, whole-brain MRI scans were obtained using either a 3T Philips Achieva ($n = 54$), a 3T Philips Ingenia ($n = 11$) or a 1.5T Philips Ingenia ($n = 5$). The analysis was performed on 32 HoA and 32 LiA patients. The systematic comparison of gender, education, age at LP, age at

<figure><img src="image_3.png" /><figcaption>Fig. 2 – Cerebrospinal fluid biomarkers in high (HiA) and low-amyloid (LoA) Alzheimer's disease patients.</figcaption></figure>

<footnote>a Only available for 146 patients (36%).</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 3 – Neuropsychological profile in high (HiA) and low-amyloid (LoA) Alzheimer's disease patients.</figcaption></figure>

MRI, age at 18FDG-PET, MRI field, delay between MRI and LP, and delay between 18FDG-PET and LP showed no significant difference between the LoA and HiA groups (Supplementary Material 3). The imaging analyses revealed that neither gray matter density nor brain metabolism significantly differed between the groups.

## 4.5. APOE genotype

The analysis was performed on 113 LoA and 36 HiA patients. A systematic comparison of sex ratio, age at first symptoms, and education did not show any significant difference between the groups (Supplementary Material 4). In the LoA group, 51.7% of patients had at least one APOE4 allele, and 17.3% were E4/E4. In the HiA group, 61.1% had at least one APOE4 allele and 19.4% were E4/E4. There were no significant differences in the presence of ≥ 1 APOE4 allele and APOE4/E4 genotype between the groups (Table 1).

## 4.6. Neuropathology

This exploration was performed on 14 LoA and 1 HiA patient. The median age of disease onset in LoA patients was 63.1 years (ranging from 44 to 70 years). The age of disease onset in the HiA patient was 75.8. In the LoA group, pure AD pathology was retrieved two cases and ten cases exhibited an association of AD pathology with either cerebral amyloid angiopathy, TDP-43 inclusions or Lewy bodies. In the HiA patient, a combination of AD and Lewy bodies pathologies was observed. Details of the neuropathological findings are presented in Supplementary Material 5.

# 5. Discussion

With the objective of performing an in-depth comparison of AD patients with normal and low Aβ42, this retrospective study compared A+T+N+ patients with normal CSF Aβ42 but abnormal Aβ42/40 ratio (HiA) to A+T+N+ patients with low CSF Aβ42 (LoA). We found no difference in CSF T-tau and tauP181 biomarker levels, neuropsychological profiles, rates of cognitive decline, brain atrophy and metabolism and APOE genotype distribution between groups. However, patients in

the HiA group were, on average, older and had higher MMSE scores at presentation.

Published in April 2018, the new NIA-AA criteria consider Aβ42/40 as an amyloid biomarker alongside Aβ42 [1]. Since its first reported use in 1998 [32], the Aβ42/40 ratio has consistently demonstrated superior diagnostic accuracy over Aβ42 for AD, particularly in the prodromal stage [33,34], and increased specificity for differential diagnosis [4,8,9,32-41]. In clinical contexts such as prodromal, early-onset [42,43], and atypical presentations [44-47], the inclusion of the Aβ42/40 ratio enhances the sensitivity for detecting AD. We have previously shown that the Aβ42/40 ratio reclassified 65% of previously considered “uninformative” normal Aβ42-T+ profiles as A+T+ [10,32,39], aligning with prior research. This highlights the benefit of using the Aβ42/40 ratio alongside Aβ42 alone to increase diagnostic sensitivity and reduce the distress caused by non-contributive LP results. However, while the Aβ42/40 ratio is a more reliable marker of amyloidopathy than Aβ42, whether it holds clinical equivalence to Aβ42 remains unclear. Observational studies in real-life settings are relevant to determine whether patients with AD and HiA show phenotypic differences from their LoA counterparts.

In our study, as expected, Aβ40, a proxy for total Aβ, was approximately 30% higher in the HiA group than in the LoA group. Surprisingly, it remains unknown whether variations in CSF Aβ levels reflect increased brain amyloid production or accumulation [3], and thus differences in APP metabolism, or unrelated differences in CSF clearance dynamics [48,49].

In the former scenario, it would be tentative to assume that higher Aβ concentrations is associated with a higher susceptibility to AD, leading to younger age at onset, faster clinical progression, or specific clinical features. Indeed, genetic mutations like APP duplication are known to cause early-onset AD through increased Aβ production [50], while APOE4 genotype, the strongest risk factor gene for AD, is associated with altered brain clearance of Aβ [51,52].

However, our data do not support this, as HiA patients were older with higher MMSE scores at baseline. Additionally, we found no APOE-linked differences between HiA and LoA patients.

Conversely, the differences in age at onset and baseline MMSE scores may suggest a protective effect of high CSF Aβ40 against the development of AD, as CSF Aβ40 was elevated by approximately 30% in the HiA group compared to the LoA
group. Although cohort studies have previously suggested that high CSF Aβ₄₀ might be a risk factor for AD and a potential biological characteristic of early AD [53,54], preclinical studies have indicated that increasing Aβ₄₀ may actually have a protective effect against amyloid deposition [55,56]. Our results seem in line with these findings. Additionally, the HiA profile might reflect an earlier point in the AD biomarkers' trajectory curves. There is conflicting evidence in the literature regarding changes in CSF Aβ₄₀. While a meta-analysis demonstrated a small decrease in CSF Aβ₄₀ in AD [57], other studies have shown increased levels of Aβ₄₀, possibly indicative or earlier stages of AD [53,54].

Our study has some limitations. First, our data originated from a single-center, which may limit generalization. However, it also helped minimize pre-analytical differences, as all procedures related to biomarkers, from LP to CSF analyses, were conducted according to the same standardized protocol [13]. Indeed, differences in Aβ concentrations may also arise from pre-analytical handling variations [51,52] although, in this study, they could not have accounted for the clinical differences between LoA and HiA groups. Second, our reliance on routine care data restricted the availability of neuroimaging and genotyping, although we confirmed that the subset analyzed did not differ from the broader cohort. Third, the cognitive comparison was limited to patients with MMSE ≥ 20, strengthening early-stage comparisons but possibly missing differences at later stages. Additionally, measuring the rate of cognitive decline is most accurate in the early stages to avoid the floor effect of cognitive scales. Fourth, the limited availability of postmortem brain analysis data prevented further pathological comparisons. Nevertheless, we confirmed the presence of AD pathology in all patients as well as found that HiA profiles represented a minority of pathologically proven cases with AD (1 out of 12 in our pathological series). Last, our study could have used a more accurate definition of HiA and LoA patients based only on Aβ₄₀, whatever the ratio. We chose however to adopt a more pragmatic and clinically meaningful approach: indeed, most memory clinics (including ours) use Aβ₄₀ only in patients with an ambiguous A–T+ profile (normal CSF Aβ₄₂ concentration). Hence, CSF Aβ₄₀ was not available in every patient.

Overall, our study shows that while HiA patients were not linked to differences in tau CSF biomarkers, cognition, brain morphology, metabolism, APOE genotype, or brain pathology, HiA profiles were commoner in older patients at earlier AD stages. Further studies should uncover the determinants of CSF Aβ concentrations.

## Ethics approval and consent to participate

This study, based on routine biological analyses, was not considered as "biomedical research" under French regulations, and therefore did not require informed consent. Blood sample samples were, however, stored in a biobank in CRB/CIC1403 (BRIF BB0033-00030) in Lille with authorization from the French Ministry of Health. Consent for collection and storage was obtained for every patient.

The database was declared to the ad hoc commission (Commission Nationale Informatique et Libertés [CNIL])

protecting personal data (#2146189 V1). Privacy and confidentiality rules were respected.

## Funding

The study was partially funded by the laboratory of excellence DistAlz. ML thesis received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Framework Partnership Agreement No. 650003 (HBP FPA).

## Availability of data and materials

The datasets considered in the current study are available from the corresponding author on reasonable request.

## Authors contributions

ML did the study concept and design, the analysis and interpretation of data, drafted and revised the manuscript for important intellectual content, statistical analysis and administrative support.

ALA did the study concept and design, the analysis and interpretation of [18F]FDG-PET data and revised the manuscript for important intellectual content.

SS did the study concept and design and interpretation of the CSF results.

ES did the analysis and revised the manuscript for important intellectual content.

SL and FP revised the manuscript for important intellectual content.

VH did the APOE analysis and revised the manuscript for important intellectual content.

MB did the study concept and design, the analysis and interpretation of MRI and neuropsychological data, drafted and revised the manuscript for important intellectual content.

TL did the study concept and design, the analysis and interpretation of data, drafted the manuscript, revised the manuscript for important intellectual content and supervised the overall study.

## Disclosure of interest

The authors declare that they have no competing interest.

## Acknowledgements

We are grateful to all the patients for their participation in this study, as well as the entire Lille Memory Resource and Research Centre team for programing appointments, seeing patients, collecting blood and entering clinical data, to name few. The authors would also like to thank Lille Hospital Biological Resources Centre for storage and administration of the blood biobanking and to Alexandre Genet for his technical assistance in the APOE analysis.
## Appendix A. Supplementary material

Supplementary material associated with this article can be found in the online version available at https://doi.org/10.1016/j.neurol.2025.02.004.

## REFERENCES

[1] Jack CR, 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 [Internet] 2018;14(4):535-62 [cited 2019 Aug 5. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1552526018300724].

[2] Pouclet-Courtemanche H, Nguyen TB, Skrobala E, Boutoleau-Bretonnière C, Pasquier F, Bouaziz-Amar E, et al. Frontotemporal dementia is the leading cause of "true" A-/T+ profiles defined with Aβ42/40 ratio. Alzheimers Dement Diagn Assess Dis Monit [Internet] 2019;11:161-9 [cited 2019 Aug 5. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2352872919300016].

[3] Wiltfang J, Esselmann H, Bibl M, Hüll M, Hampel H, Kessler H, et al. Amyloid β peptide ratio 42/40 but not Aβ42 correlates with phospho-Tau in patients with low- and high-CSF Aβ40 load. J Neurochem [Internet] 2007;101(4):1053-9 [cited 2020 May 20. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1471-4159.2006.04404.x].

[4] Lewczuk P, Esselmann H, Otto M, Maler JM, Henkel AW, Henkel MK, et al. Neurochemical diagnosis of Alzheimer's dementia by CSF Aβ42, Aβ42/Aβ40 ratio and total tau. Neurobiol Aging [Internet] 2004;25(3):273-81 [cited 2020 May 20. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0197458003000861].

[5] Dumurgier J, Schraen S, Gabelle A, Vercruysse O, Bombois S, Laplanche JL, et al. Cerebrospinal fluid amyloid-β 42/40 ratio in clinical setting of memory centers: a multicentric study. Alzheimers Res Ther 2015;7(1):30.

[6] Lehmann S, Delaby C, Boursier G, Catteau C, Ginestet N, Tiers L, et al. Relevance of Aβ42/40 ratio for detection of Alzheimer disease pathology in clinical routine: the PLMR Scale. Front Aging Neurosci [Internet] 2018;10:138 [cited 2020 May 20. Available from: https://www.frontiersin.org/article/10.3389/fnagi.2018.00138/full].

[7] Lewczuk P, Matzen A, Blennow K, Parnetti L, Molinuevo JL, Eusebi P, et al. Cerebrospinal fluid Aβ42/40 corresponds better than Aβ42 to amyloid PET in Alzheimer's disease. Lehmann S, editor. J Alzheimers Dis [Internet] 2016;55(2):813-22 [cited 2020 May 20. Available from: https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-160722].

[8] Janelidze S, Zetterberg H, Mattsson N, Palmqvist S, Vanderstichele H, Lindberg O, et al. CSF A β 42/A β 40 and A β 42/A β 38 ratios: better diagnostic markers of Alzheimer disease. Ann Clin Transl Neurol [Internet] 2016;3(3):154-65 [cited 2020 May 20. Available from: http://doi.wiley.com/10.1002/acn3.274].

[9] Niemantsverdriet E, Ottoy J, Somers C, De Roeck E, Struyfs H, Soetewey F, et al. The cerebrospinal fluid Aβ1-42/Aβ1-40 ratio improves concordance with amyloid-PET for diagnosing Alzheimer's disease in a clinical setting. J Alzheimers Dis [Internet] 2017;60(2):561-76 [cited 2020 May

20. Available from: https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-170327].

[10] Dumurgier J, Schraen S, Gabelle A, Vercruysse O, Bombois S, Laplanche JL, et al. Cerebrospinal fluid amyloid-β 42/40 ratio in clinical setting of memory centers: a multicentric study. Alzheimers Res Ther [Internet] 2015;7(1):30 [cited 2019 Aug 5. Available from: http://alzres.com/content/7/1/30].

[11] Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7(3):280-92.

[12] O'Brien RJ, Wong PC. Amyloid precursor protein processing and Alzheimer's disease. Annu Rev Neurosci 2011;34:185-204.

[13] Lehmann S, Schraen S, Quadrio I, Paquet C, Bombois S, Delaby C, et al. Impact of harmonization of collection tubes on Alzheimer's disease diagnosis. Alzheimers Dement 2014;10(5):S390-400.

[14] Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12(3):189-98.

[15] Mattis S. Dementia Rating Scale. Professional manual. Florida: Psychological Assessment Resources; 1988.

[16] Lemos R, Duro D, Simoes MR, Santana I. The free and cued selective reminding test distinguishes frontotemporal dementia from Alzheimer's disease. Arch Clin Neuropsychol [Internet] 2014;29(7):670-9 [cited 2020 May 20. Available from: https://academic.oup.com/acn/article-lookup/doi/10.1093/arclin/acu031].

[17] Lindeboom J. Visual association test to detect early dementia of the Alzheimer type. J Neurol Neurosurg Psychiatry [Internet] 2002;73(2):126-33 [cited 2020 May 20. Available from: http://jnnp.bmj.com/cgi/doi/10.1136/jnnp.73.2.126].

[18] Reitan RM. The relation of the trail making test to organic brain damage. J Consult Psychol 1955;19(5):393-4.

[19] Stroop JR. Studies of interference in serial verbal reactions. Journal of Experimental Psychology 1935;18(6):643-62.

[20] Warrington E, James M. Visual object and space perception battery. Bury St Edmunds (UK): Thmaes Valley Test Compagny; 1991.

[21] Fazekas F, Chawluk J, Alavi A, Hurtig H, Zimmerman R. MR signal abnormalities at 1.5T in Alzheimer's dementia and normal aging. Am J Roentgenol [Internet] 1987;149(2):351-6 [cited 2021 Jan 14. Available from: https://www.ajronline.org/doi/10.2214/ajr.149.2.351].

[22] Lebreton M, Bertoux M, Boutet C, Lehericy S, Dubois B, Fossati P, et al. A critical role for the hippocampus in the valuation of imagined outcomes. PLoS Biol [Internet] 2013;11(10):e1001684 [cited 2021 May 10. Available from: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001684].

[23] Ashburner J, Friston KJ. Voxel-based morphometry—The methods. NeuroImage 2000;11(6):805-21.

[24] Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ. A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage [Internet] 2001;14(1):21-36 [cited 2020 Aug 4. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1053811901907864].

[25] Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage [Internet] 2004;23:S208-19 [cited 2020 Aug 4. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1053811904003933].
[26] SPM - Statistical Parametric Mapping [Internet]. [cited 2021 Apr 27. Available from: https://www.fil.ion.ucl.ac.uk/spm/].

[27] Koch W, Ehrenhaft A, Griesser K, Pfeufer A, Müller J, Schömig A, et al. TaqMan systems for genotyping of disease-related polymorphisms present in the gene encoding apolipoprotein E. Clin Chem Lab Med [Internet] 2002;40(11) [cited 2020 Aug 5. Available from: https://www.degruyter.com/view/j/cclm.2002.40 issue-11/cclm.2002.197/cclm.2002.197.xml].

[28] Bertoux M, Cassagnaud P, Lebouvier T, Lebert F, Sarazin M, Le Ber I, et al. Does amnesia specifically predict Alzheimer's pathology? A neuropathological study. Neurobiol Aging [Internet] 2020;95:123-30 [cited 2020 Oct 26. Available from: https://linkinghub.elsevier.com/retrieve/pii/S019745802030227X].

[29] Hyman BT, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Carrillo MC, et al. National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease. Alzheimers Dement [Internet] 2012;8(1):1-13 [cited 2020 Jun 30. Available from: http://doi.wiley.com/10.1016/j.jalz.2011.10.007].

[30] Mackenzie IRA, Neumann M, Bigio EH, Cairns NJ, Alafuzoff I, Kril J, et al. Nomenclature and nosology for neuropathologic subtypes of frontotemporal lobar degeneration: an update. Acta Neuropathol (Berl) 2010;119(1):1-4.

[31] Bertoux M, Lagarde J, Corlier F, Hamelin L, Mangin JF, Colliot O, et al. Sulcal morphology in Alzheimer's disease: an effective marker of diagnosis and cognition. Neurobiol Aging 2019;84:41-9.

[32] Shoji M, Matsubara E, Kanai M, Watanabe M, Nakamura T, Tomidokoro Y, et al. Combination assay of CSF Tau, Ab1-40 and Ab1-42(43) as a biochemical marker of Alzheimer's disease. J Neurol Sci 1998;7:134-40.

[33] Hansson O, Zetterberg H, Buchhave P, Andreasson U, Londos E, Minthon L, et al. Prediction of Alzheimer's disease using the CSF Aβ42/Aβ40 ratio in patients with mild cognitive impairment. Dement Geriatr Cogn Disord [Internet] 2007;23(5):316-20 [cited 2020 May 27. Available from: https://www.karger.com/Article/FullText/100926].

[34] Welge V, Fiege O, Lewczuk P, Mollenhauer B, Esselmann H, Klafki HW, et al. Combined CSF tau, p-tau181 and amyloid-β 38/40/42 for diagnosing Alzheimer's disease. J Neural Transm [Internet] 2009;116(2):203-12 [cited 2020 May 27. Available from: http://link.springer.com/10.1007/s00702-008-0177-6].

[35] Baldeiras I, Santana I, Leitão MJ, Gens H, Pascoal R, Tábuas-Pereira M, et al. Addition of the Aβ42/40 ratio to the cerebrospinal fluid biomarker profile increases the predictive value for underlying Alzheimer's disease dementia in mild cognitive impairment. Alzheimers Res Ther [Internet] 2018;10(1):33 [cited 2020 May 27. Available from: https://alzres.biomedcentral.com/articles/10.1186/s13195-018-0362-2].

[36] Spies PE, Slats D, Sjögren JMC, Kremer BPH, Verhey FRJ. The cerebrospinal fluid amyloid 42/40 ratio in the differentiation of Alzheimer's disease from non-Alzheimer's dementia. J Curr Alzh Res 2010;7(5):470-6.

[37] Gabelle A, Roche S, Gény C, Bennys K, Labauge P, Tholance Y, et al. Decreased sAβPPβ, Aβ38, and Aβ40 cerebrospinal fluid levels in frontotemporal dementia. J Alzheimers Dis [Internet] 2011;26(3):553-63 [cited 2020 May 27. Available from: https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-2011-110515].

[38] Slaets S, Le Bastard N, Martin JJ, Sleegers K, Van Broeckhoven C, De Deyn PP, et al. Cerebrospinal fluid Aβ1-40 improves differential dementia diagnosis in patients with intermediate P-tau181P levels. J Alzheimers Dis 2013;36(4):759-67.

[39] Bousiges O, Cretin B, Lavaux T, Philippi N, Jung B, Hezard S, et al. Diagnostic value of cerebrospinal fluid biomarkers (Phospho-Tau181, total-Tau, Aβ42, and Aβ40) in prodromal stage of Alzheimer's disease and dementia with Lewy bodies. J Alzheimers Dis [Internet] 2016;51(4):1069-83 [cited 2020 May 27. Available from: https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-150731].

[40] Nutu M, Zetterberg H, Londos E, Minthon L, Nägga K, Blennow K, et al. Evaluation of the cerebrospinal fluid amyloid-β1-42/Amyloid-β 1-40 ratio measured by alpha-LISA to distinguish Alzheimer's disease from other dementia disorders. Dement Geriatr Cogn Disord [Internet] 2013;36(1-2):99-110 [cited 2020 May 27. Available from: https://www.karger.com/Article/FullText/353442].

[41] Lehmann S, Delaby C, Boursier G, Catteau C, Ginestet N, Tiers L, et al. Relevance of Aβ42/40 ratio for detection of Alzheimer disease pathology in clinical routine: the PLMR Scale. Front Aging Neurosci 2018;10:138.

[42] Buchhave P, Minthon L, Zetterberg H. Cerebrospinal Fluid Levels of N<sub>L</sub>-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):9.

[43] Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L, et al. Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 2006;5:228-34.

[44] Beaufils E, Dufour-Rainfray D, Hommet C, Brault F, Cottier JP, Ribeiro MJ, et al. Confirmation of the amyloidogenic process in posterior cortical atrophy: value of the Aβ42/Aβ40 ratio. J Alzheimers Dis [Internet] 2013;33(3):775-80 [cited 2020 May 27. Available from: https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-2012-121267].

[45] Seguin J, Formaglio M, Perret-Liaudet A, Quadrio I, Tholance Y, Rouaud O, et al. CSF biomarkers in posterior cortical atrophy. Neurology [Internet] 2011;76(21):1782-8 [cited 2020 May 27. Available from: http://www.neurology.org/cgi/doi/10.1212/WNL.0b013e31821ccc98].

[46] de Souza LC, Lamari F, Belliard S, Jardel C, Houillier C, De Paz R, et al. Cerebrospinal fluid biomarkers in the differential diagnosis of Alzheimer's disease from other cortical dementias. J Neurol Neurosurg Psychiatry [Internet] 2011;82(3):240-6 [cited 2020 May 27. Available from: http://jnnp.bmj.com/cgi/doi/10.1136/jnnp.2010.207183].

[47] Jack CR, Knopman DS, Chételat G, Dickson D, Fagan AM, Frisoni GB, et al. Suspected non-Alzheimer disease pathophysiology - concept and controversy. Nat Rev Neurol 2016;12(2):117-24.

[48] Eckerström C, Svensson J, Kettunen P, Jonsson M, Eckerström M. Evaluation of the ATN model in a longitudinal memory clinic sample with different underlying disorders. Alzheimers Dement Amst Neth 2021;13(1):e12031.

[49] Tijms BM, Gobom J, Reus L, Jansen I, Hong S, Dobricic V, et al. Pathophysiological subtypes of Alzheimer's disease based on cerebrospinal fluid proteomics [Internet]. Neurology 2020 [cited 2020 Dec 4. Available from: http://medrxiv.org/lookup/doi/10.1101/2020.03.25.20043158].

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

[51] Perret-Liaudet A, Pelpel M, Tholance Y, Dumont B, Vanderstichele H, Zorzi W, et al. Cerebrospinal fluid collection tubes: a critical issue for Alzheimer disease diagnosis. Clin Chem [Internet] 2012;58(4):787-9 [cited 2019
Aug 7. Available from: http://www.clinchem.org/cgi/doi/10.1373/clinchem.2011.178368].

[52] Janelidze S, Stomrud E, Brix B, Hansson O. Towards a unified protocol for handling of CSF before β-amyloid measurements. Alzheimers Res Ther [Internet] 2019;11(1):63 [cited 2021 Apr 29. Available from: https://doi.org/10.1186/s13195-019-0517-9].

[53] Kim J, Onstead L, Randle S, Price R, Smithson L, Zwizinski C, et al. Abeta40 inhibits amyloid deposition in vivo. J Neurosci 2007;27(3):627-33.

[54] Kuperstein I, Broersen K, Benilova I, Rozenski J, Jonckheere W, Debulpaep M, et al. Neurotoxicity of Alzheimer's disease Aβ peptides is induced by small changes in the Aβ42 to Aβ40 ratio. EMBO J 2010;29(19):3408-20.

[55] Olsson B, Lautner R, Andreasson U, Öhrfelt 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 [Internet] 2016;15(7):673-84 [cited 2020 May 5. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1474442216000703].

[56] Palmqvist S, Insel PS, Stomrud E, Janelidze S, Zetterberg H, Brix B, et al. Cerebrospinal fluid and plasma biomarker trajectories with increasing amyloid deposition in Alzheimer's disease. EMBO Mol Med 2019;11(12):e11170.

[57] Lehmann S, Dumurgier J, Ayrignac X, Marelli C, Alcolea D, Ormaechea JF, et al. Cerebrospinal fluid A beta 1-40 peptides increase in Alzheimer's disease and are highly correlated with phospho-tau in control individuals. Alzheimers Res Ther [Internet] 2020;12(1):123 [cited 2021 May 12. Available from: https://doi.org/10.1186/s13195-020-00696-1].