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J.-M. Pyun1, M.J. Kang2, S.J. Baek3, K. Lee1, Y.H. Park4, S.Y. Kim4 for the Alzheimer’s Disease Neuroimaging Initiative†


1. Department of Neurology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59, Daesagwan-ro, Yongsan-gu, Seoul, 04401, Republic of Korea; 2. Department of Neurology, Veterans Health Service Medical Center, 53, Jinhwangdo-ro 61-gil, Gangdong-gu, Seoul, 05368, Republic of Korea;
3. Department of Radiology, Bobath Memorial Hospital, 155-7, Daewangpangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13552, Republic of Korea; 4. Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea

Corresponding Author: SangYun Kim, Department of Neurology, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Republic of Korea, Tel.: +82 31 787 7462; Fax: +82 31 787 4059; E-mail:

J Prev Alz Dis 2024;
Published online March 5, 2024,



BACKGROUND: Cerebral amyloid angiopathy (CAA) pathology is becoming increasingly important in Alzheimer’s disease (AD) because of its potential link to amyloid-related imaging abnormalities, a critical side effect observed during AD immunotherapy. Identification of CAA without typical magnetic resonance imaging (MRI) markers (MRI-negative CAA) is challenging, and novel detection biomarkers are needed.
METHODS: We included 69 participants with high neuritic plaques (NP) burden, with and without CAA pathology (NP with CAA vs. NP without CAA) based on autopsy data from the Alzheimer’s Disease Neuroimaging Initiative. Two participants with hemorrhagic CAA markers based on MRI were excluded and the final analysis involved 36 NP without CAA and 31 NP with CAA. A logistic regression model was used to compare the cerebrospinal fluid (CSF) amyloid-β42 (Aβ42), phosphorylated tau181, and total tau levels, the amyloid positron emission tomography (PET) standardized uptake ratio (SUVR), and cognitive profiles between NP with and without CAA. Regression models for CSF and PET were adjusted for age at death, sex, and the last assessed clinical dementia rating sum of boxes score. Models for cognitive performances was adjusted for age at death, sex, and education level.
RESULTS: NP with CAA had significantly lower CSF Aβ42 levels when compared with those without CAA (110.5 pg/mL vs. 134.5 pg/mL, p-value = 0.002). Logistic regression analysis revealed that low CSF Aβ42 levels were significantly associated with NP with CAA (odds ratio [OR]: 0.957, 95% confidence interval [CI]: 0.928, 0.987, p-value = 0.005). However, amyloid PET SUVR did not differ between NP with CAA and those without CAA (1.39 vs. 1.48, p-value = 0.666). Logistic regression model analysis did not reveal an association between amyloid PET SUVR and NP with CAA (OR: 0.360, 95% CI: 0.007, 1.741, p-value = 0.606).
CONCLUSIONS: CSF Aβ42 is more sensitive to predict MRI-negative CAA in high NP burden than amyloid PET.

Key words: Alzheimer’s disease, cerebral amyloid angiopathy, neuropathology, cerebrospinal fluid, amyloid positron emission tomography.



Cerebral amyloid angiopathy (CAA) and Alzheimer’s disease (AD) share a common characteristic of amyloid-β (Aβ) protein accumulation in the central nervous system. However, their contributions to central amyloidopathy involve different pathways targeting cerebral vessels and the brain parenchyma, respectively. Despite distinct features regarding related Aβ subtypes, risk APOE alleles, and clinical phenotypes, both CAA and AD interact and coexist, manifesting as Aβ-related diseases affecting Aβ clearance (1, 2).
With the development of disease-modifying immunotherapies for AD, a significant concern has arisen regarding a critical side effect known as amyloid-related imaging abnormalities (ARIA). ARIA is identified by brain MRI showing vasogenic edema or hemorrhage with vascular abnormalities. The mechanism behind ARIA is suspected to involve the antibody-mediated breakdown of neuritic plaques, resulting in the release of Aβ and its deposition within vessels; this leads to CAA, perivascular inflammation, and compromised Aβ clearance (3), thereby indicating a potential connection between CAA and ARIA. Furthermore, the similarity of ARIA and CAA-related inflammation (CAA-ri), assumed to result from a spontaneous anti-Aβ immune response, supports the interrelation between CAA and ARIA. Therefore, the identification of patients harboring CAA pathology might be important for AD immunotherapy, potentially enabling the prediction of ARIA risk.
A definite diagnosis of CAA is established by neuropathological assessment. In living individuals, the diagnosis of probable or possible CAA is based on brain MRI findings, including lobar microbleeds, cortical superficial siderosis, and convexity subarachnoid hemorrhage (4). However, a previous meta-analysis revealed a notable discordance in CAA detection rates depending on the diagnostic methods. While the prevalence of neuropathologically diagnosed CAA in AD is reported at 48%, that of MRI-based diagnosed CAA in AD stands at 22% (5). This suggests that patients with CAA who lack typical MRI markers, referred to as “MRI-negative CAA,” may have missed diagnosis due to the absence of appropriate biomarkers. Therefore, comprehending the clinical characteristics and biomarker profiles of patients with MRI-negative CAA is imperative.
This study included pathologically-confirmed participants with high neuritic plaques (NP) burden and those with hemorrhagic MRI markers for CAA were excluded. Next, we compared AD fluid and imaging biomarkers, including cerebrospinal fluid (CSF) levels of Aβ42, phosphorylated tau181 (p-Tau181), and total tau (t-Tau), and amyloid plaque burden measured by amyloid positron emission tomography (PET), and cognitive function profiles between patients with and without CAA pathology.




In this study, we used a database from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The ADNI was launched in 2003 as a public-private partnership led by Principal Investigator Michael W. Weiner, MD. The primary goal of the ADNI is to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessments can be combined to measure the progression of mild cognitive impairment and early AD. Written informed consent was obtained at the time of enrollment and included permission for analysis and data sharing. The protocol and informed consent forms were approved by the institutional review boards of each participating institution. The study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards (
We included participants who underwent autopsy (n = 100). The neuropathological data in the ADNI relevant to AD are derived from the application of the National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of AD (6). Formalin-fixed paraffin-embedded blocks of tissue from the following 16 areas from the left cerebrum were stained using hematoxylin and eosin and a modified Bielschowsky silver impregnation: middle frontal gyrus, superior and middle temporal gyri, inferior parietal lobe (angular gyrus), occipital lobe to include the calcarine sulcus and peristriate cortex, anterior cingulate gyrus at the level of the genu of the corpus callosum, posterior cingulate gyrus and precuneus at the level of the splenium, amygdala and entorhinal cortex, hippocampus and parahippocampal gyrus at the level of the lateral geniculate nucleus, striatum (caudate nucleus and putamen) at the level of the anterior commissure, lentiform nucleus (globus pallidus and putamen), thalamus and subthalamic nucleus, midbrain, pons, medulla oblongata, cerebellum with dentate nucleus, and spinal cord. Immunohistochemical staining was done using antibodies against Aβ (10D5, Eli Lilley, Indianapolis) as previously described (7). The details of the neuropathologic protocol can be found in the ADNI database ( The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) scores for the density of neocortical NP ranged from 0 to 3, with 0, 1, 2, and 3, indicating no neuritic plaques, sparse neuritic plaques, moderate neuritic plaques, and frequent neuritic plaques, respectively. We dichotomized the participants with CERAD scores of 0 and 1 as having low NP burdens and those with CERAD scores of 2 and 3 as having high NP burdens. CAA pathology was assessed based on a scale of 0 to 3, with 0, 1, 2, and 3 indicating none, mild degree, moderate degree, and severe degree, respectively. We dichotomized participants into the negative CAA (scores 0 and 1) and the positive CAA (scores: 2 and 3) groups. Participants with a high NP burden (n = 69), representing a high parenchymal amyloidopathy burden were included in the analysis and classified into the high NP burden with negative CAA (NP without CAA, n = 36) and the high NP burden with positive CAA (NP with CAA, n = 33) groups. We did not include the presence of neurofibrillary tangles representing the hyperphosphorylated tau protein when defining our study population because our aim focused on the comparison between parenchymal and vascular amyloidopathy in the brain, and the elucidation of potential markers for differentiating between them.
To exclude participants with MRI markers for CAA, we downloaded brain MRI scans from the ADNI site ( We used brain MRI, which was performed as the last before death, and examined the absence of any lobar hemorrhagic lesions, including intracerebral hemorrhage, microbleeds, superficial siderosis, or convexity subarachnoid hemorrhage, which are the cardinal MRI markers of probable and possible CAA according to the Boston criteria v2.0.4 Our exclusion criteria included hemorrhagic MRI features but not white matter features because the criteria for probable CAA suggest that white matter features only along with the presence of hemorrhagic features (4). The assessment was conducted by a neuroradiologist who was highly experienced in the field of brain MRI. Two participants with lobar microbleeds were excluded from the study. Finally, 31 participants in the NP with CAA group and 36 participants in NP without CAA were included for further analyses.

CSF measurement

The concentrations of CSF Aβ42, p-Tau181, and t-Tau were measured using the micro-bead-based multiplex immunoassay, the INNO-BIA AlzBio3 RUO test (Fujirebio, Ghent, Belgium) on the Luminex platform.8 Qualification of the analytical performance of CSF samples from ADNI was controlled, showing a within-center coefficient of variation (%CV) 95% confidence interval (CI) value (mean) of 4.0%–6.0% (5.3%) for Aβ42, 6.4%–6.8% (6.7%) for t-Tau, and 5.5%–18.0% (10.8%) for p-Tau181.9 The inter-center %CV 95% CI ranged from 15.9% to 19.8% (17.9%) for Aβ42, 9.6%–15.2% (13.1%) for t-Tau, and 11.3%–18.2% (14.6%) for p-Tau181 (9). CSF Aβ40 data were not available in the dataset that we used (“UPENNBIOMK_MASTER.csv”). Additional details of data processing are available online ( In case of multiple CSF measurements in the same individual, we used the last performed CSF measurement.

Amyloid PET

Amyloid imaging was acquired using [18F]florbetapir PET in four 5-min frames 50–70 min after the injection of 10 mCi and were spatially normalized to the statistical parametric mapping (SPM) template using SPM8 (Wellcome Trust Center for Neuroimaging, UCL, UK) in MATLAB R2013a (Mathworks, Natick, MA). Additional details of data processing are available online ( The standardized uptake value ratio (SUVR), which is the summary value of florbetapir retention, was determined using the global cortical target region of interest with the cerebellum reference regions.10 In case of multiple amyloid PET assessments in the same individual, we used the last performed amyloid PET SUVR.

Cognitive assessment

Global cognitive function was evaluated using the Mini-Mental State Examination (MMSE) (11). Disease severity was evaluated by the Clinical Dementia Rating (CDR) (12) and CDR Sum of Boxes (CDR SB). Memory and executive functions were assessed using the composite scores of memory (ADNI MEM) (13) and executive function (ADNI EF) (14). ADNI-MEM was generated from item-level data from the Rey Auditory Verbal Learning Test, the AD Assessment Scale-Cognitive Subscale, the Mini-Mental State Examination, and the Logical Memory test. ADNI-EF score was derived from item-level data from the Trail Making Test (A and B), Digit Span Backwards, Digit Symbol, Animal Naming, Vegetable Naming, and the Clock Drawing Test. We included the last implemented cognitive assessment.

Statistical analysis

The demographics between the groups (NP without CAA and NP with CAA) were compared using the Mann–Whitney U test for continuous variables and chi-square tests for categorical variables. We compared the amyloid PET SUVR value, levels of CSF Aβ42, p-Tau181, and t-Tau, and ADNI MEM, and ADNI EF scores between groups using the Mann–Whitney U test. We performed association analyses using logistic regression models between groups and CSF measurements, amyloid PET SUVR values, and cognitive function. Models for CSF and PET were adjusted for age at death, sex, and the last assessed CDR SB. Models for cognitive performance analysis were corrected for age at death, sex, and education level, defined as a participant’s years of education. The time intervals between death and CSF study, amyloid PET, ADNI MEM, ADNI EF, and MRI were compared between the groups and summarized in Supplementary Table 1 (Table S1). The classification performance of CSF Aβ42 and amyloid PET, two distinct biomarkers for amyloidopathy, for CAA was evaluated using the area under the receiver operating characteristic curve (AUC). We used R software (version 4.1.3) for all analyses, and statistical significance was set at p < 0.05.



Demographics of the participants

The demographic characteristics of patients with NP with and without CAA are shown in Table 1; the two groups showed no significant differences in age at death, sex, educational level, and frequency of APOE ε4 and APOE ε2 carrier.

Table 1. Demographic characteristics of the participants

Data are presented as the median (interquartile range) unless otherwise specified. Abbreviations: CAA: Cerebral amyloid angiopathy, NP: Neuritic plaque


CSF Aβ42, p-Tau181, and t-Tau

The CSF Aβ42, p-Tau181, and t-Tau levels of the two groups are presented in Supplementary Table 2 (Table S2). The CSF Aβ42 level was significantly lower in patients with NP with CAA than in those with NP without CAA. Additionally, there were no significant differences in CSF p-Tau181 and t-Tau levels between the groups. In logistic regression analysis, a low CSF Aβ42 level was significantly associated with NP with CAA, with an odds ratio (OR) of 0.957 (95% CI: 0.928, 0.987; p-value = 0.005) (Table 2, Fig. 1).

Table 2. Association of CSF Aβ42, p-Tau181, t-Tau, and amyloid PET with groups using logistic regression models

Abbreviations: Aβ: Amyloid-β, AD: Alzheimer’s disease, CAA: Cerebral amyloid angiopathy, CSF: Cerebrospinal fluid, p-Tau181: Phosphorylated tau181, t-Tau: Total tau, PET: Positron emission tomography, SUVR: Standardized uptake value ratio

Figure 1. Comparison of CSF and PET biomarkers for amyloidopathy between NP with and without CAA

Abbreviations: Aβ: Amyloid-β, CAA: Cerebral amyloid angiopathy, CSF: Cerebrospinal fluid, NP: Neuritic plaque, PET: Positron emission tomography, SUVR: Standardized uptake value ratio


Amyloid PET

The amyloid PET SUVR values were not significantly different between patients with NP with and without CAA (Table S3). In logistic regression analysis, the association between groups and amyloid PET SUVR value remained insignificant (OR: 0.360, 95% CI: 0.007, 1.741; p-value = 0.606) (Table 2, Fig. 1).

Cognitive function

The MMSE, CDR, CDR SB, ADNI MEM, and ADNI EF scores were not significantly different between patients with NP with and without CAA (Table S4). Logistic regression analysis of ADNI MEM and ADNI EF, with groups adjusted for age, sex, and educational level revealed no significant association (Table 3).

Table 3. Association of ADNI MEM and ADNI EF with groups using the logistic regression model

Abbreviations: ADNI EF: Composite score of executive function in ADNI, ADNI MEM: Composite scores of memory in ADNI


Classification performance of CSF Aβ42 and amyloid PET for CAA

Classification performance of CSF Aβ42 and amyloid PET for CAA in NP was compared. The AUC value of CSF Aβ42 for CAA classification was 0.774 (95% CI 0.637, 0.912), whereas the AUC value of amyloid PET was 0.549 (95% CI 0.349, 0.748). Receiver operating characteristic curves for CAA classification are shown in Fig. 2.

Figure 2. Classification performance of CSF Aβ42 and amyloid PET for CAA in high NP burden

Abbreviations: Aβ: Amyloid-β, AUC: area under the curve, CAA: Cerebral amyloid angiopathy, CSF: Cerebrospinal fluid, NP: Neuritic plaque, PET: Positron emission tomography



This study compared CSF and amyloid PET biomarkers and cognitive profiles in pathologically-confirmed high NP burden with and without CAA. Patients with NP with CAA presented significantly lower CSF Aβ42 levels compared to those without CAA. Moreover, the amyloid PET SUVR, cognitive function, and CSF t-Tau, p-Tau181 levels showed no significant difference between patients with AD with and without CAA. The time intervals between death and CSF study, amyloid PET, ADNI MEM, ADNI EF, and MRI tend to be longer in NP with CAA than in NP without CAA, although there were no significant differences between the groups.
CAA with typical MRI markers can be easily detected; however, identifying patients with AD with MRI-negative CAA is challenging because of the absence of biomarkers. Nonetheless, this issue is critically important as patients with AD with MRI-negative CAA may indicate a high risk of ARIA in Aβ immunotherapy for AD (2). However, there is limited evidence regarding the characteristics of AD with MRI-negative CAA, as previous studies on CAA have mainly been conducted in CAA with MRI markers. Our study highlights the clinical and biomarker profiles of patients with high NP burden with MRI-negative CAA using autopsy data. We also found that the CSF Aβ42 level was significantly lower in patients with NP with CAA than without CAA in the absence of MRI markers for CAA.
Our study excluded participants with hemorrhagic MRI features but not those with white matter features only, which have been suggested as a new MRI feature for CAA based on Boston criteria version 2.0.4 Although the presence of white matter features along with a hemorrhagic lesion might detect a subset of true-positive patients with CAA based on the Boston criteria, version 2.0, white matter features still need independent replication to establish their usefulness in CAA diagnosis (4).
Studies investigating fluid and imaging biomarkers to differentiate AD and CAA have reported varying results. While several studies have shown no significant differences in CSF Aβ42 level between CAA and AD (15–17), others have revealed significantly lower Aβ42 levels in CAA than AD (18, 19). However, these studies used MRI-based criteria for CAA and clinical diagnostic AD. Amyloid PET has recently emerged as an imaging biomarker in CAA and AD research. CAA has been shown to have a higher occipital/global SUVR than AD (20, 21). Moreover, amyloid PET-positive CAA has been shown to represent rapid cognitive decline, suggesting the clinical utility of amyloid PET in predicting worse prognosis (21). However, amyloid PET is limited to differentiate CAA from AD because PET tracers can bind amyloid in vessels or brain parenchymal (22).
Our study showed no significant difference in global cognitive function, executive function, and memory function between NP with and without CAA. A previous study reported that CAA showed poor executive function compared to AD, suggesting vascular cognitive impairment (23). Other neuropathological studies have shown that CAA is associated with a faster decline in perceptual speed, episodic memory, and semantic memory.1
Although the results of this study are strengthened based on neuropathological data, several limitations should be mentioned. First, the sample size was relatively small, and replication in a larger cohort is warranted. Second, our analysis was conducted in a cross-sectional manner, and exploring longitudinal changes of biomarkers depending on disease stages would be interesting in a future study. Additionally, our study shows a significant association between CSF Aβ42 and CAA pathology in the context of a high NP burden. Although we could not evaluate the association of CSF Aβ40 or Aβ42/Aβ40 with CAA pathology in the context of a high NP burden because of data unavailability, it would be interesting to examine the association in the further study. Moreover, this study did not include the presence of neurofibrillary tangles in the definition of the study population. However, it would be valuable to identify potential biomarkers for differentiating between AD with amyloidopathy and tauopathy with and without CAA in a larger cohort.
In conclusion, this neuropathological study focused newly NP with MRI-negative CAA, and demonstrated that the CSF Aβ42 level was significantly lower in patients with high NP burden with CAA than in those without CAA. The CSF Aβ42 level is more sensitive to predict MRI-negative CAA in high NP burden than amyloid PET.


†Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:

Funding: There is no funding.

Acknowledgements: Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. This work was supported by the Soonchunhyang University Research Fund.

Ethical standards: Ethical approval was given by the local ethical committees of
all involved sites of ADNI, and the research was conducted in accordance with the Helsinki Declaration.

Conflict of interest: There is nothing to declare.





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