CSF Biomarker Profile of Cerebral Amyloid Angiopathy: Diagnostic Performance and Imaging Correlates in a Hospital-Based Neurology Cohort

CSF Biomarker Profile of Cerebral Amyloid Angiopathy: Diagnostic Performance and Imaging Correlates in a Hospital-Based Neurology Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CSF Biomarker Profile of Cerebral Amyloid Angiopathy: Diagnostic Performance and Imaging Correlates in a Hospital-Based Neurology Cohort Aida Fernández-Lebrero, Joan Jiménez-Balado, Greta García-Escobar, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7448531/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND Cerebral amyloid angiopathy (CAA) frequently co-occurs with Alzheimer’s disease (AD), which complicates diagnosis in patients with cognitive impairment. The cerebrospinal fluid (CSF) biomarker profile of CAA, particularly in the presence of AD co-pathology, remains poorly defined. We aimed to characterize CSF biomarkers in CAA, assess their diagnostic accuracy, and examine associations with neuroimaging markers of CAA. METHODS We included 261 participants from a hospital-based neurology cohort at Hospital del Mar (Barcelona, Spain), recruited from both memory-clinic outpatients (Cognitive and Behavioural Neurology Unit) and Neurology inpatients (including the Neurovascular Unit). Groups comprised healthy controls (HC, n = 35), CAA without AD co-pathology (CAA-nonAD, n = 27), CAA with AD co-pathology (CAA-AD, n = 30), and AD (n = 169). CSF Aβ40, Aβ42, p-tau181, and t-tau were quantified using automated immunoassays. Group differences were tested using analysis of covariance adjusted for age and sex. Receiver operating characteristic (ROC) analyses with 10-fold cross-validation and bootstrapping assessed diagnostic performance. Associations between CSF biomarkers and CAA-related MRI markers were examined using ANCOVA. RESULTS Aβ40 concentrations were lower in CAA-nonAD and CAA-AD compared to AD and HC ( p -value bf <0.05). Aβ42 was reduced in CAA-AD and AD versus HC, but did not differ between CAA-nonAD and AD. Aβ40 showed the highest diagnostic accuracy for CAA (AUC = 0.73; 95% CI: 0.66–0.80), followed by Aβ42 (AUC = 0.71; 95% CI: 0.64–0.78). In AD patients, Aβ42 best discriminated coexisting CAA (AUC = 0.77). Higher CAA-SVD burden scores were associated with lower Aβ40 ( p -value bf <0.05). CONCLUSIONS CSF Aβ40 and Aβ42 provide complementary diagnostic value for identifying CAA, both in isolation and in the presence of AD co-pathology. Reduced Aβ40 is associated with greater CAA-related vascular burden, supporting its role as a marker of vascular amyloid pathology. Amyloid-β Cerebral Amyloid Angiopathy Alzheimer’s disease Biomarker Diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Sporadic cerebral amyloid angiopathy (CAA) is a highly prevalent degenerative small vessel disease, defined by the progressive deposition of β-amyloid (Aβ) fibrillar aggregates in the walls of cortical and leptomeningeal blood vessels 1 . Alzheimer’s disease (AD) is neuropathologically characterized by the accumulation of extracellular Aβ plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau 2 . Although traditionally regarded as distinct clinical entities, CAA and AD frequently coexist, suggesting that cognitive impairment in these individuals may result from the combined effects of cerebrovascular and neurodegenerative mechanisms 2 . This overlap complicates clinical diagnosis and poses challenges for individualized prognosis and treatment. While the definitive diagnosis of CAA remains histopathological, clinical diagnosis currently relies on characteristic neuroimaging markers, as outlined in the Boston criteria version 2.0 3 . Recently updated diagnostic frameworks for AD 4 , along with recent recommendations for CAA 3 , have emphasized the incorporation of molecular biomarkers into diagnostic and staging criteria, highlighting their potential to improve diagnostic accuracy and patient stratification. Despite increasing interest in CAA, most prior studies have focused on vascular imaging markers and haemorrhagic risk, with limited investigation of molecular biomarkers. When cognitive profiles are assessed, CAA and AD are often examined in separate cohorts, overlooking their frequent co-occurrence in clinical practice. Pathology-based studies report moderate-to-severe CAA in up to 48% of AD patients and 6.4% of cognitively unimpaired individuals 5 . Similarly, neuroimaging studies indicate that CAA-related markers are common in AD patients and even in asymptomatic individuals with positive Aβ-PET 6 , suggesting that vascular amyloid deposition begins early and progresses alongside AD pathology. This subclinical burden may confound diagnosis and risk stratification in memory clinic populations. Therefore, understanding both distinct and overlapping biomarker signatures of CAA and AD is critical, particularly in the context of co-pathology. Importantly, while the identification of CAA in patients with AD was traditionally of academic interest, the emergence of anti-amyloid therapies 7 has made detecting concomitant CAA clinically imperative. CAA is a recognized risk factor for amyloid-related imaging abnormalities (ARIA) 8 , 9 , a potentially serious complication of monoclonal antibodies targeting amyloid pathology 10 , 11 . Current recommendations for aducanumab and lecanemab advise against treatment in individuals with > 4 cerebral microbleeds (CMBs) or any cortical superficial siderosis (cSS), although trial eligibility criteria vary 11 . In this context, improved tools to detect CAA—particularly in patients with suspected or established AD—are urgently needed. In this study, we aimed to characterize the CSF biomarker profile of patients with MRI-defined CAA, compared to healthy controls (HC) and individuals with AD. We then assessed the diagnostic performance of CSF biomarkers in identifying CAA. Finally, we investigated the associations between CSF biomarker concentrations and the extent of CAA-related vascular burden as assessed by neuroimaging. 2. METHODS 2.1. Study design and participants This study is part of the ANGMAR project (Characterization of Biomarkers Footprint in Cerebral Amyloid Angiopathy Patients), a prospective observational study embedded within the BIODEGMAR 12 biobanking protocol at Hospital del Mar. ANGMAR provides the clinical and imaging study framework, whereas BIODEGMAR ensures standardized biospecimen collection, processing, and storage across participants. Participants were recruited from the Neurology Service at Hospital del Mar, including memory-clinic outpatients (Cognitive and Behavioural Neurology Unit) and Neurology inpatients (including the Neurovascular Unit). Eligibility was based on the inclusion and exclusion criteria detailed below, and all participants followed the same standardized ANGMAR/BIODEGMAR workflow for clinical assessment, MRI acquisition, and CSF handling, with raters blinded to clinical classifications. Neuropathological confirmation was not available in this cohort; CAA was defined on MRI according to Boston criteria v2.0 3 , which served as the reference standard for analyses. Inclusion criteria were: (1) availability of CSF measurements of Aβ42, Aβ40, phosphorylated tau at threonine 181 (p-tau181), and total tau (t-tau), quantified using the LUMIPULSE G600II platform 13 (Fujirebio); and (2) fulfilment of one of the following diagnostic classifications: (i) individuals with probable or possible CAA, defined according to the Boston criteria v2.0 3 and further stratified based on CSF biomarker profiles into CAA with coexisting AD pathology (CAA-AD) or without it (CAA-nonAD), using elevated p-tau181 levels as a marker of tauopathy; (ii) patients with mild cognitive impairment (MCI) or Alzheimer’s disease dementia (AD-d), fulfilling the clinical criteria established by the National Institute on Aging and the Alzheimer’s Association (NIA-AA) 14 , 15 , presenting a CSF profile consistent with AD pathology—defined by abnormal levels of both amyloid and tau ‘core 1’ biomarkers (AD group) 4 —and not meeting criteria for either probable or possible CAA according to the Boston criteria v2.0.; and (iii) cognitively unimpaired healthy controls (HC), characterized by normal neuropsychological performance 16 and no history of stroke or transient ischemic attack. All participants underwent standardized clinical, neuropsychological, and neuroimaging assessments as part of the ANGMAR/BIODEGMAR protocol. Additional methodological details, including diagnostic workup and biomarker processing, are provided in the Supplementary Material. The study flowchart is presented in Fig. 1 . 2.2. Clinical and Neuropsychological Assessments All participants underwent a comprehensive clinical and neuropsychological assessment to establish diagnostic classifications based on standardized criteria. Diagnostic evaluations included the application of the Boston criteria v2.0 for the diagnosis of CAA 3 and the NIA-AA criteria for MCI 14 and AD-d 15 . For participants classified with CAA, additional clinical information was collected to characterize the disease phenotype, including the presence of cognitive impairment, lobar intracerebral haemorrhages, spontaneous convexity subarachnoid haemorrhage (cSAH), and transient focal neurological episodes (TFNEs) 3 . Neuropsychological evaluation was conducted using a standardized battery of cognitive tests and functional scales, including the Mini-Mental State Examination (MMSE) 17 and the Clinical Dementia Rating (CDR) scale 18 . 2.3. Neuroimaging Structural brain MRI was performed using either a 1.5T system (General Electric Signa Explorer) or a 3T system (Philips Achieva). Brain imaging protocol included T1- and T2-weighted sequences, high-resolution 3D T1, diffusion-weighted imaging (DWI), fluid-attenuated inversion recovery (FLAIR), and susceptibility-sensitive sequences (gradient echo and ven-BOLD). Neuroimaging markers were evaluated by a certified neuroradiologist, blinded to clinical diagnosis, to characterize CAA-related pathology: lobar cerebral microbleeds (CMBs) and cortical superficial siderosis (cSS; both on T2*-GRE), convexity subarachnoid haemorrhage (cSAH; on T2*-GRE and FLAIR), white matter hyperintensities (WMH; FLAIR), and enlarged perivascular spaces in the centrum semiovale (CSO-PVS; T2-weighted). WMHs were considered significant when the Fazekas score 19 ≥ 2. cSS was evaluated using the cSS multifocality score and considered diffuse when ≥ 2 cortical sulci were affected. CMBs burden was significant when ≥ 5 lobar CMBs 20 , 21 . The CAA–small vessel disease (CAA-SVD) Burden Score 20 , 21 was calculated to quantify overall CAA-related small vessel disease. 2.4. CSF Biomarkers Analysis Core AD CSF biomarkers, including Aβ42, Aβ40, p-tau181, and t-tau, were quantified using the LUMIPULSE G600II automated immunoassay system (Fujirebio) 13 at the Laboratori de Referència de Catalunya (Barcelona, Spain). All samples were processed in the same laboratory facilities using an automated platform, following harmonized procedures, which likely reduced the possibility of batch-related bias. Laboratory personnel performing the analyses were blinded to the participants’ clinical classifications. Cutoff values for individual biomarkers and derived ratios (Aβ42, Aβ42/Aβ40, p-tau181, t-tau, and Aβ42/p-tau181) were based on previously validated thresholds 12 . An AD CSF biomarker profile was defined as Aβ42/Aβ40 ratio 69.5 pg/mL. To evaluate concomitant CAA–AD pathology, p-tau181 was selected as the primary biomarker of AD-related tauopathy. Since CAA is an amyloid-associated condition, the use of Aβ-based ratios could be confounded by impaired perivascular amyloid clearance mechanisms 1 , 2 . In contrast, p-tau181 is more specifically associated to tau pathology associated with AD and less susceptible to the amyloid clearance alterations inherent to CAA. 2.5. Ethics The ANGMAR study and the BIODEGMAR cohort were approved by the local ethics committee (CEIm Hospital del Mar Research Institute, project codes 2021/9960/I and 2018/7805I, respectively). All participants signed a specific informed consent form. 2.6. Statistical analyses 2.6.1. Descriptive analyses Demographic and baseline characteristics were summarized as means and standard deviations (SD) for normally distributed continuous variables, or as medians and interquartile ranges (IQR) for non-normally distributed data. Categorical variables were reported as frequencies and percentages. Group comparisons were conducted using one-way analysis of variance (ANOVA) for continuous variables with normal distributions, or the Kruskal-Wallis test for non-normally distributed data. For categorical variables, Pearson’s χ² test or Fisher’s exact test was applied, as appropriate. 2.6.2. Biomarker profiles in CAA We first examined differences in CSF biomarkers—Aβ40, Aβ42, Aβ42/Aβ40 ratio, p-tau181, t-tau, Aβ42/p-tau181 ratio—across diagnostic groups, including HC, AD, CAA without AD co-pathology (CAA-nonAD) and CAA with AD co-pathology (CAA-AD). To account for potential confounders, analysis of covariance (ANCOVA) models were implemented adjusting for age and sex, given their well-established influence on CSF biomarker levels 22 – 24 . Adjusted means and 95% confidence intervals (CI) were reported for each biomarker. Multiple comparisons were corrected using Bonferroni post hoc adjustments ( p -value bf ). Group differences in CSF biomarkers were tested using ANCOVA models adjusted for age and sex, with Bonferroni correction for multiple comparisons. Additional analyses adjusting for education and APOE ε4 were performed, with covariates selected based on principal component analysis (see Supplementary Methods for details). Finally, we also ran a series of sensitivity analyses excluding patients with possible CAA according to Boston criteria v2.0. 2.6.3. Diagnostic Performance of CSF biomarkers These series of analyses aimed to evaluate the diagnostic utility of CSF biomarkers for identifying CAA, using MRI (Boston criteria v2.0 3 ) as the reference standard for classification. For this purpose, diagnostic groups were dichotomized into CAA (with or without AD) and non-CAA (with or without AD). We focused on amyloid-related markers, given their direct pathophysiological link to CAA. Aβ40 and Aβ42 are the main isoforms implicated in vascular amyloid deposition, independently of AD co-pathology 2 . We evaluated the diagnostic performance of Aβ40 and Aβ42 using ROC analysis. Optimal cutoffs were derived by cross-validation with a custom cost function, and diagnostic metrics (AUC, sensitivity, specificity) were estimated through bootstrap resampling. Analyses were also performed within the subgroup of patients with AD (CAA-AD and AD groups). This analysis aimed to assess the discriminative value of CSF biomarkers in a clinically challenging context of overlapping pathologies (see Supplementary Methods for details). 2.6.4. CSF Biomarker associations with CAA neuroimaging markers Associations between CSF biomarkers and CAA neuroimaging markers were tested using ANCOVA models adjusted for age and sex, with Bonferroni correction for multiple comparisons. Analyses were conducted in CAA patients across CAA-SVD Burden Score 20 , 21 categories and individual MRI markers 3 (see Supplementary Methods for details). 3. RESULTS 3.1 Descriptive analyses The final study sample comprised 35 HC, 57 CAA (27 CAA-nonAD, 30 CAA-AD), and 169 AD cases (Fig. 1 ). As shown in Table 1 , no significant differences were observed in age, sex or ethnicity across groups. HC participants had a significantly higher educational attainment compared to the other groups. Hypertension was less prevalent among HC participants. The APOE ε4 allele was significantly more frequent in the AD (64.3%) and CAA-AD (67.9%) groups compared to CAA-nonAD (24%) and HC (16.1%) ( p -value < 0.001). Table 1 Participants’ characteristics All ( n = 261) HC ( n = 35) CAA-nonAD (n = 27) CAA-AD (n = 30) AD (n = 169) p -value Age, years 73.0 [70.0;76.0] 72.0 [66.5;74.0] 73.0 [69.0;76.5] 73.5 [71.0;76.8] 73.0 [70.0;76.0] 0.19 Sex, n (%) 0.19 Female 157 (60.2%) 18 (51.4%) 16 (59.3%) 14 (46.7%) 109 (64.5%) Male 104 (39.8%) 17 (48.6%) 11 (40.7%) 16 (53.3%) 60 (35.5%) Ethnicity, n (%) 0.41 Caucasian 256 (98.1%) 35 (100%) 26 (96.3%) 29 (96.7%) 166 (98.2%) Others 5 (1.9%) 0 (0.00%) 1 (3.7%) 1 (3.33%) 3 (1.78%) Education, years 8.00 [6.00;12.0] 11.0 [8.00;15.0] 8.00 [8.00;12.0] 8.00 [6.00;12.0] 8.00 [6.00;10.0] 0.005 HTA n (%) 146 (55.9%) 12 (34.3%) 15 (55.6%) 20 (66.7%) 99 (58.6%) 0.036 Diabetes Mellitus n (%) 51 (19.5%) 5 (14.3%) 2 (7.41%) 7 (23.3%) 37 (21.9%) 0.26 Dyslipidemia n (%) 142 (54.4%) 13 (37.1%) 14 (51.9%) 18 (60.0%) 97 (57.4%) 0.15 Ischemic heart disease n (%) 14 (5.4%) 2 (5.7%) 1 (3.7%) 2 (6.7%) 9 (5.3%) 0.97 Antiplatelet therapy n (%) 54 (20.7%) 5 (14.3%) 4 (14.8%) 9 (30.0%) 36 (21.3%) 0.38 MMSE 22.0 [19.0;26.0] 28.0 [27.0;29.5] 24.0 [21.0;28.0] 21.0 [17.8;24.0] 21.0 [18.0;23.0] < 0.001 CDR < 0.001 0 44 (16.9%) 35 (100%) 8 (29.6%) 2 (6.67%) 0 (0.00%) 0.5 67 (25.7%) 0 (0.00%) 9 (33.3%) 9 (30.0%) 48 (28.4%) 1 76 (29.1%) 0 (0.00%) 1 (3.70%) 2 (6.67%) 73 (43.2%) 2 61 (23.4%) 0 (0.00%) 6 (22.2%) 14 (46.7%) 41 (24.3%) 3 13 (4.98%) 0 (0.00%) 3 (11.1%) 3 (10.0%) 7 (4.14%) Fazekas score < 0.001 0–1 (n, %) 176 (69.0%) 26 (89.7%) 11 (40.7%) 11 (36.7%) 128 (75.7%) 2–3 (n, %) 79 (31.0%) 3 (10.3%) 16 (59.3%) 19 (63.3%) 41 (24.3%) APOE ε4 carriers, n (%) 113 (53.1%) 5 (16.1%) 6 (24.0%) 19 (67.9%) 83 (64.3%) < 0.001 APOE ε2 carriers, n (%) 22 (10.3%) 5 (16.1%) 4 (16.0%) 4 (14.3%) 9 (6.98%) 0.186 Data are expressed as median and interquartile range or percentage (%). Education was not available in 12 participants, APOE genotype in 48 participants, MMSE in 2 participants. CSF biomarker distributions are summarized in Supplementary Figure S1 -A. All biomarkers differed between groups at the univariate level (all p- values < 0.001, Supplementary Figure S1 -B). 3.2. Biomarker profiles in CAA We next examined whether patients with CAA showed distinct CSF biomarker profiles compared to HC and AD groups. As shown in Figs. 2 -A and 2 -B and Supplementary Table S1 , Aβ40 levels were significantly lower in CAA-nonAD compared to HC and AD ( p -value bf <0.05). In addition, CAA-AD showed lower Aβ40 levels than HC and AD (both p -value bf 0.05). Similarly, Aβ42 levels were also reduced in both CAA subgroups and AD subjects compared to HC ( p -value bf <0.05). Among CAA cases, CAA-AD exhibited significantly lower Aβ42 levels than CAA-nonAD and AD (both p -value bf <0.05; Fig. 2 -A and 2 -B), whereas there were no differences between CAA-nonAD and AD participants. The Aβ42/Aβ40 ratio was significantly lower in both CAA subgroups compared to HC and in AD compared to HC (both p -value bf <0.05). Within the CAA subgroups, CAA-AD had a lower Aβ42/Aβ40 ratio than CAA-nonAD, while CAA-nonAD showed a significantly higher ratio than AD (both p -value bf <0.05). No significant differences were observed between CAA-AD and AD. Regarding tau-related biomarkers, p-tau181 and t-tau were elevated in AD and CAA-AD patients as compared to subjects with CAA-nonAD and HC, with no differences between HC and CAA-nonAD participants (Fig. 2 -A and 2 -B). Similarly, the Aβ42/p-tau181 ratio was significantly lower in AD and both CAA-nonAD and CAA-AD subjects compared to HC (both p -value bf <0.05). CAA-AD had a significantly lower Aβ42/p-tau181 ratio than CAA-nonAD, but showed no differences as compared to AD subjects. In summary, patients with CAA—whether or not they had coexisting AD—exhibited lower Aβ40 levels compared to both HC and AD participants. Additionally, individuals with CAA-AD had the lowest Aβ42 concentrations, while CAA-nonAD showed similar Aβ42 levels to AD participants ( p -value bf >0.05), both lower than HC. These biomarker profiles are summarized in Fig. 2 -C. To assess the impact of potential diagnostic heterogeneity, we conducted sensitivity analyses excluding all patients who met only criteria for possible CAA (21.1% of the CAA group, n = 12). The results remained consistent with the primary analyses (see Supplementary Figure S2 ), supporting the robustness of the biomarker differences observed between groups. Finally, PCA revealed that two components accounted for 89.2% of the variance in CSF biomarkers (Supplementary Figure S3 ). These components showed significant associations with both education and APOE ε4 status (Supplementary Table S2 ). However, adjusting for these additional potential confounders did not meaningfully alter the results or the conclusions described above (Supplementary Figure S4 & Supplementary Table S1 ). 3.3. Diagnostic Performance of CSF Biomarkers We first explored the discriminative capacity of each CSF biomarker, including ratios, to identify CAA by constructing ROC curves across the whole sample. Aβ40 and Aβ42 demonstrated the highest discriminative performance, with AUCs of 0.77 and 0.71, respectively (Supplementary Figure S5 ). We further explored the optimal cutoff for Aβ40 and Aβ42 because these biomarkers are directly implicated in the pathophysiology of vascular amyloid deposition and showed the highest discriminative performance in ROC analyses. To that aim, we ran cross-validations to generate a distribution of optimal cutoffs according to a cost function designed to balance the AUC and stability of biomarkers (see the methods section). These cutoffs are shown in Fig. 3 -A. Subsequently, we estimated their performance and 95% CI by running a bootstrap (see methods section). Aβ40 and Aβ42 showed good discriminative performance, with AUCs of 0.73 (95% CI: 0.66–0.80) and 0.71 (95% CI: 0.64–0.78; Fig. 3 -B). We also checked the performance of these cutoffs in the subset of patients with AD, both with and without CAA (N = 199). As shown in Supplementary Figure S6 , in this subset Aβ42 had a better performance as compared to Aβ40 (AUC: 0.77 [0.68–0.85] vs 0.71 [0.62–0.80], respectively). 3.4. CSF Biomarker associations with CAA neuroimaging markers As a final step, we studied the association between CSF biomarkers and neuroimaging CAA burden. These analyses were conducted only in patients with CAA, either with or without AD co-pathology. Table 2 summarizes the clinical and radiological characteristics of patients with CAA-AD and CAA-nonAD. After adjusting for age and sex, we observed no statistically significant differences between groups in any of the radiological markers assessed, including the number of lobar CMBs ( p -value = 0.19), presence of cSS ( p -value = 0.26), or WMH ( p -value = 0.96). The overall CAA-SVD burden score was similarly distributed across groups ( p -value = 0.85). However, CAA-nonAD individuals more frequently had a previous history of lobar intracerebral haemorrhage (25.9% vs 3.3%, p -value = 0.02), despite a comparable radiological burden across groups. Table 2 CAA participants’ clinical and radiological characteristics Clinical manifestations CAA (n = 57) CAA-nonAD (n = 27) CAA-AD (n = 30) p -value Lobar ICH (n, %) 8 (14.0%) 7 (25.9%) 1 (3.3%) 0.02 Acute SAH (n, %) 1 (1.8%) 1 (3.7%) 0 (0.0%) 0.47 TFNEs (n, %) 5 (8.7%) 3 (11.1%) 2 (6.7%) 0.66 Cognitive impairment (n, %) 47 (82.5%) 21 (77.8%) 26 (86.7%) 0.49 CDR 0.12 0 10 (17.5%) 8 (29.6%) 2 (6.67%) 0.5 18 (31.6%) 9 (33.3%) 9 (30.0%) 1 3 (5.26%) 1 (3.70%) 2 (6.67%) 2 20 (35.1%) 6 (22.2%) 14 (46.7%) 3 6 (10.5%) 3 (11.1%) 3 (10.0%) Radiological characteristics Lobar CMBs (n, %) 0.19 0 3 (5.26%) 2 (7.41%) 1 (3.33%) 1–4 31 (54.4%) 14 (51.9%) 17 (56.7%) 5–10 6 (10.5%) 5 (18.5%) 1 (3.33%) >10 17 (29.8%) 6 (22.2%) 11 (36.7%) cSS (n, %) 18 (31.6%) 11 (40.7%) 7 (23.3%) 0.26 Focal cSS (n, %) 18 (31.6%) 11 (40.7%) 7 (23.3%) Diffuse cSS (n, %) 0 (0.0%) 0 (0.0%) 0 (0.0%) CSO severe PVS (n, %) 14 (24.6%) 9 (33.3%) 5 (16.7%) 0.25 Fazekas score 0.96 0–1 (n, %) 22 (38.6%) 11 (40.7%) 11 (36.7%) 2–3 (n, %) 35 (61.4%) 16 (59.3%) 19 (63.3%) CAA SVD Burden Score 0.85 0 (n, %) 5 (8.77%) 2 (7.41%) 3 (10.0%) 1 (n, %) 16 (28.1%) 7 (25.9%) 9 (30.0%) 2 (n, %) 11 (19.3%) 4 (14.8%) 7 (23.3%) 3 (n, %) 11 (19.3%) 6 (22.2%) 5 (16.7%) ≥4 (n, %) 14 (24.6%) 8 (29.6%) 6 (20.0%) Boston criteria v2.0 Possible CAA (n, %) 12 (21.1%) 4 (14.8%) 8 (26.7%) 0.44 Probable CAA (n, %) 45 (78.9%) 23 (85.2%) 22 (73.3%) Data are expressed as median and interquartile range or percentage (%). We further analysed CSF biomarkers concentration by CAA-SVD burden score and observed that as CAA cerebrovascular burden increased, Aβ40 levels decreased (ANCOVA p- value bf <0.05; Fig. 4 -A). No significant differences were found for the other CSF biomarkers after adjusting for multiple comparisons (Supplementary Table S3 ). Finally, we examined the association between specific CAA-related MRI lesions and CSF biomarkers. Individuals with ≥ 5 CMBs exhibited decreased Aβ40 and Aβ42 levels after Bonferroni correction as compared to those with < 5 CMBs (ANCOVA p- value bf <0.05; Fig. 4 -B & Supplementary Table S4 ). No significant associations were found for other CAA-related MRI lesions and CSF biomarkers after multiple testing correction. Further adjustment for education and APOE-ε4 carriership did not significantly change the results either for CAA-SVD burden score or individual MRI markers (Supplementary Figure S7 & Supplementary Tables S3 to S4). 4. DISCUSSION Our study provides a comprehensive characterization of CSF biomarker profiles in patients with CAA, with and without AD co-pathology, compared to AD and HC, integrating fluid and neuroimaging markers to improve in vivo characterization. This represents a novel approach to disentangling the frequent CAA-AD comorbidity by analysing divergent and overlapping CSF biomarker patterns. CSF Aβ40 emerged as the most informative biomarker for identifying CAA, both in isolation and in the presence of AD co-pathology. Aβ40 levels were significantly reduced in CAA-nonAD and CAA-AD compared to AD and HC, consistent with its preferential vascular deposition and impaired clearance. Aβ42 and the Aβ42/Aβ40 ratio were also decreased—particularly in CAA-AD—likely reflecting mixed pathology, though less discriminative than Aβ40. These findings align with prior studies reporting selective reductions of CSF Aβ40 in CAA, contrasting with its relative preservation in AD 25 – 28 . Aβ42 levels were also lower in CAA-AD than in AD, suggesting that concomitant vascular amyloid pathology may exacerbate Aβ42 depletion, possibly reflecting a higher total amyloid burden. The disproportionate reduction of Aβ40 reflects its preferential vascular deposition, while Aβ42 accumulates mainly in parenchymal plaques due to its greater hydrophobicity 2 , 29 . These distinct biochemical and anatomical properties support Aβ40 as a disease-specific marker for vascular amyloid pathology. However, despite its group-level discriminative value, Aβ40 showed overlap across individuals (Fig. 2 A), limiting its standalone diagnostic utility in clinical settings. Its clinical applicability may be enhanced when integrated with neuroimaging and other fluid biomarkers as part of a multimodal diagnostic approach to CAA. Nonetheless, previous studies have reported heterogeneous results, likely due to differences in phenotype, disease stage, and control group selection, highlighting the need for standardized criteria 25 , 30 . Interpretation of CSF tau levels is challenging in mixed pathologies, as tau is generally lower in CAA than in AD 26 , 27 . This reinforces the importance of including tau-based biomarkers, such as p-tau181, to detect AD co-pathology in CAA patients. Higher p-tau181 levels were observed in CAA-AD vs CAA-nonAD, as expected given its use in defining AD status. Given that p-tau181 levels were used to define AD co-pathology, group differences in this biomarker should be interpreted with caution, as this may introduce circularity in the comparison. In contrast, biomarkers not involved in group definitions—such as Aβ40 and the Aβ42/Aβ40 ratio—offer unbiased insights into vascular amyloid pathology. Similarly, t-tau levels were elevated in CAA-AD and AD, but not in CAA-nonAD or HC, supporting its association with AD-related injury 26 , 27 . These results reinforce that tau elevation is not a prominent feature of CAA in the absence of AD and support combining tau and amyloid biomarkers to disentangle overlapping phenotypes. To assess the diagnostic value of CSF biomarkers in CAA, we conducted ROC analyses and estimated optimal cutoffs using cross-validation and bootstrapping. Consistent with the group-level comparisons, Aβ40 showed the highest performance (AUC = 0.73), followed by Aβ42 (AUC = 0.71), supporting their role in identifying CAA. Notably, in the direct comparison between CAA-AD and AD, Aβ42 and Aβ40 retained diagnostic value, with AUCs showing good performance in this challenging subgroup, supporting their role in detecting CAA in AD. Patients with CAA and a high burden of CMBs (≥ 5 CMBs) exhibited significantly lower CSF Aβ40 and Aβ42 levels, likely reflecting advanced vascular amyloid pathology. Accumulation of lobar CMBs is associated with increased amyloid deposition in vessel walls, impairing clearance and promoting vascular Aβ retention, reducing CSF concentrations 2 . The persistence of these associations after Bonferroni correction reinforces their robustness and suggests a true biological link between vascular amyloid load and altered CSF Aβ. Similarly, we observed an inverse correlation between CSF Aβ40 and the global CAA–SVD burden score. Together, these results support Aβ40 as a marker of vascular amyloid burden and highlight the value of combining CSF and neuroimaging biomarkers for in vivo CAA staging. Clinical implications The need to detect CAA has become increasingly relevant in clinical practice, particularly in the context of anti-amyloid therapies and their associated risk of ARIA 8 – 11 . Our findings support the use of CSF Aβ40 and Aβ42 as complementary tools to identify patients at increased ARIA risk, potentially improving treatment safety. Neuroimaging markers remain essential for comprehensive haemorrhagic risk assessment and combining them with fluid biomarkers may help select the best candidates for anti-amyloid treatment. Beyond treatment selection, identifying CAA in patients with AD may offer insights into disease progression, given the vascular contribution to cognitive decline. As a major cause of ICH and vascular dementia, CAA carries substantial clinical burden. The reduction in Aβ40 levels in CAA-AD compared to AD without MRI evidence of CAA suggests that vascular co-pathology may influence clinical heterogeneity. These findings support the integration of CSF biomarker panels—including Aβ40—into AD diagnostic workflows. Looking forward, the development of blood-based biomarkers for CAA warrants further research to enhance diagnostic accessibility and scalability 31 , 32 . CSF biomarker profiling may support diagnosis in cases where neuroimaging is inconclusive or Boston criteria v2.0 are not fully met—such as patients with isolated lobar ICH, atypical imaging patterns, or low haemorrhagic burden 30 . These scenarios are particularly frequent in memory clinic populations, where CAA often presents with cognitive symptoms and coexisting neurodegenerative pathology, reducing the sensitivity of imaging-based criteria 33 . In such cases, fluid biomarkers may offer complementary diagnostic value. Early detection—before multiple haemorrhagic lesions emerge—remains challenging. In this context, a CSF biomarker signature—particularly reduced Aβ40—may support earlier identification of CAA, potentially preceding structural MRI markers 34 and supporting integration into diagnostic workflows. Combining Aβ42, Aβ40, p-tau181, and t-tau may help identify both vascular and neurodegenerative pathology, reinforcing diagnosis in suspected CAA. In addition to this, the increasing use of anticoagulants in older adults—many of whom may harbor unrecognized CAA—underscores the need for haemorrhagic risk biomarkers. If validated for this purpose, CSF profiles could guide safer anticoagulation and support personalized care. A key strength of this study lies in the use of a well-characterized, memory-clinic-based cohort, enhancing clinical relevance. Unlike previous studies that treated AD and CAA as separate entities, we defined biomarker-based diagnostic groups—AD, CAA-nonAD, and CAA-AD—allowing direct comparisons across isolated and overlapping phenotypes. Systematic MRI in all groups enabled consistent assessment of CAA markers, including haemorrhagic and non-haemorrhagic features. The integration of CSF and MRI data provides a robust multimodal framework for in vivo CAA characterization, reinforcing the applicability of our findings for diagnosis and risk stratification in memory clinic populations. Limitations Our study is not free of limitations. First, the absence of neuropathological confirmation of CAA diagnosis is an inherent constraint, as histopathology remains the gold standard. Although we applied the Boston criteria, version 2.0—widely used and validated—these lack the specificity of histological confirmation and may lead to diagnostic misclassification, particularly in memory clinic populations. The inclusion of patients fulfilling only criteria for possible CAA also introduces diagnostic uncertainty. However, sensitivity analyses excluding these cases yielded consistent results, supporting the robustness of our findings. Nevertheless, some degree of phenotypic heterogeneity remains unavoidable in the absence of neuropathological validation. Second, because CSF availability was an inclusion criterion, selection bias cannot be excluded: patients unwilling or ineligible for lumbar puncture may differ clinically and radiologically from those included. Although recruitment encompassed both memory-clinic outpatients and Neurology inpatients, this hospital-based cohort may still under-represent patients with the most haemorrhagic CAA phenotypes, which could limit the generalizability of our findings. Third, although our findings support the utility of Aβ40 and Aβ42 in identifying CAA, CSF biomarkers alone cannot fully capture the disease’s pathophysiological complexity, which also involves vascular dysfunction and impaired clearance. Neuroimaging—particularly Boston criteria, version 2.0—remains essential for comprehensive characterization. In addition, the relatively small size of the CAA subgroups may limit statistical power and generalizability, especially for subgroup and sensitivity analyses. Finally, the cross-sectional design precludes assessment of biomarker trajectories or prognostic value. Future longitudinal studies with larger cohorts and, ideally, postmortem validation will be essential to confirm and extend these findings. Conclusion In conclusion, this study reinforces the role of Aβ40 and Aβ42 as key CSF biomarkers for detecting CAA, revealing a distinct biomarker profile compared to AD without coexisting CAA and healthy controls. The use of a well-characterized clinical cohort, with systematic neuroimaging and biologically defined AD diagnoses, strengthens the reliability and translational value of our findings. Notably, the inverse association between vascular lesion burden and CSF Aβ40 levels suggests a biological continuum in which progressive vascular amyloid deposition drives soluble Aβ depletion in CSF, pointing to its potential as a marker of both diagnosis and disease severity. These results support the integration of CSF biomarker panels into diagnostic frameworks for CAA, particularly in memory clinic settings where vascular and neurodegenerative pathologies often overlap. In the context of precision medicine, future research should focus on combining CSF and plasma biomarkers with advanced neuroimaging to improve CAA detection, guide risk stratification and personalized management. Declarations ACKNOWLEDGMENTS The authors would like to express their most sincere gratitude to all participants and relatives involved in the ANGMAR/BIODEGMAR study, whose collaboration made this research possible. We thank the Neurology Department of Hospital del Mar, especially the Neurovascular Unit, for their support throughout the project. CONFLICTS OF INTEREST Marc Suárez-Calvet has received in the past 36mo consultancy/speaker fees (paid to the institution) from by Almirall, Eli Lilly, Quanterix, Novo Nordisk, and Roche Diagnostics. He has received consultancy fees or served on advisory boards (paid to the institution) of Eli Lilly, Grifols, Novo Nordisk, and Roche Diagnostics. He was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. In-kind support for research (to the institution) was received from ADx Neurosciences, Alamar Biosciences, ALZpath, Avid Radiopharmaceuticals, Eli Lilly, Fujirebio, Janssen Research & Development, Meso Scale Discovery, and Roche Diagnostics; MS-C did not receive any personal compensation from these organizations or any other for-profit organization. Albert Puig-Pijoan has served on advisory boards for Schwabe Farma Iberica. Aida Fernández-Lebrero, Joan Jiménez-Balado, Greta García-Escobar, José Contador, Isabel Estraguès-Gázquez, Laia Peraferrer-Montesinos, Antoni Suárez-Pérez, Rosa María Manero-Borràs, Brigitte Beltrán, Ana Rodríguez Campello, Abel Palacio-Gili, Oriol Grau-Rivera, Angel Ois, and Irene Navalpotro-Gómez declare that they have no conflicts of interest related to this manuscript. FUNDING SOURCES This work was supported by the Instituto de Salud Carlos III through the Fondo de Investigación Sanitaria (FIS), project number PI21/00194, co-funded by the European Union, and by the European Research Area Network on Cardiovascular Diseases (ERA-CVD_JTC2020-015, project AC20/00001). ETHICS APPROVAL AND CONSENT TO PARTICIPATE The ANGMAR study and the BIODEGMAR cohort were approved by the local ethics committee (CEIm Hospital del Mar Research Institute, project codes 2021/9960/I and 2018/7805I, respectively). All participants signed a specific informed consent form. DATA SHARING STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy and ethical restrictions. AUTHOR CONTRIBUTIONS Aida Fernández-Lebrero and Joan Jiménez-Balado are co–first authors. Irene Navalpotro-Gómez and Albert Puig-Pijoan are co–senior authors. Aida Fernández-Lebrero had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Irene Navalpotro-Gómez, Aida Fernández-Lebrero. Acquisition, analysis, or interpretation of data: Aida Fernández-Lebrero, Joan Jiménez-Balado, Greta García-Escobar, Angel Ois, José Contador, Isabel Estraguès-Gázquez, Laia Peraferrer i Montesinos, Antoni Suárez-Pérez, Rosa María Manero-Borràs, Brigitte Beltrán Mármol, Albert Puig-Pijoan, Irene Navalpotro-Gómez. Drafting of the manuscript: Aida Fernández-Lebrero, Joan Jiménez-Balado. Statistical analysis: Joan Jiménez-Balado, Angel Ois, Abel Palacio-Gili. Obtained funding: This work was supported by the Instituto de Salud Carlos III through the Fondo de Investigación Sanitaria (FIS), project number PI21/00194, co-funded by the European Union, and by the European Research Area Network on Cardiovascular Diseases (ERA-CVD_JTC2020-015, project AC20/00001). Administrative, technical, or material support: Aida Fernández-Lebrero, Joan Jiménez-Balado, Brigitte Beltrán Mármol, Ludovica Gramegna, Albert Puig-Pijoan. Supervision: Irene Navalpotro-Gómez, Ana Rodríguez Campello, Angel Ois. All authors critically reviewed and approved the final manuscript. Additional Information All requests for raw and analysed data and materials will be promptly reviewed by the senior authors to verify whether the request is subject to any intellectual property or confidentiality obligations. 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Comparative methods for quantifying plasma biomarkers in Alzheimer’s disease: Implications for the next frontier in cerebral amyloid angiopathy diagnostics. Alzheimers Dement. 2024;20(2):1436–58. 10.1002/alz.13510 . Switzer AR, Charidimou A, McCarter S, et al. Boston Criteria v2.0 for Cerebral Amyloid Angiopathy Without Hemorrhage: An MRI-Neuropathologic Validation Study. Neurology. 2024;102(10):e209386. 10.1212/WNL.0000000000209386 . Van Etten ES, Verbeek MM, Van Der Grond J, et al. β-Amyloid in CSF: Biomarker for preclinical cerebral amyloid angiopathy. Neurology. 2017;88(2):169–76. 10.1212/WNL.0000000000003486 . Additional Declarations Competing interest reported. Marc Suárez-Calvet has received in the past 36mo consultancy/speaker fees (paid to the institution) from by Almirall, Eli Lilly, Quanterix, Novo Nordisk, and Roche Diagnostics. He has received consultancy fees or served on advisory boards (paid to the institution) of Eli Lilly, Grifols, Novo Nordisk, and Roche Diagnostics. He was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. In-kind support for research (to the institution) was received from ADx Neurosciences, Alamar Biosciences, ALZpath, Avid Radiopharmaceuticals, Eli Lilly, Fujirebio, Janssen Research & Development, Meso Scale Discovery, and Roche Diagnostics; MS-C did not receive any personal compensation from these organizations or any other for-profit organization. Albert Puig-Pijoan has served on advisory boards for Schwabe Farma Iberica. Aida Fernández-Lebrero, Joan Jiménez-Balado, Greta García-Escobar, José Contador, Isabel Estraguès-Gázquez, Laia Peraferrer-Montesinos, Antoni Suárez-Pérez, Rosa María Manero-Borràs, Brigitte Beltrán, Ludovica Gramegna, Ana Rodríguez Campello, Abel Palacio-Gili, Oriol Grau-Rivera, Angel Ois, and Irene Navalpotro-Gómez declare that they have no conflicts of interest related to this manuscript. Supplementary Files ANGMARCSFSupplementarymaterial.docx SuppFigS3.tiff SuppFigS6.tiff SuppFigS5.tiff SuppFigS2.tiff SuppFigS4.tiff SuppFigS7.tiff SuppFigS1.tiff TableS1.docx TableS3.docx TableS4.docx TableS2.docx SuppLegends.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":159313,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flowchart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were recruited from the Neurology Service at Hospital del Mar, including memory-clinic outpatients (Cognitive and Behavioural Neurology Unit) and Neurology inpatients (including the Neurovascular Unit). All participants followed the same diagnostic workflow (ANGMAR/BIODEGMAR). Abbreviations: HC, healthy controls; CAA, cerebral amyloid angiopathy; AD, Alzheimer’s disease; CSF, cerebrospinal fluid; MCI, mild cognitive impairment; NIA-AA, National Institute on Aging–Alzheimer’s Association; A+T+, positive amyloid and tau CSF biomarkers.\u003c/p\u003e","description":"","filename":"Fig1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7448531/v1/e71c2627dd2a03abcc181d43.jpeg"},{"id":94138492,"identity":"edac45a3-ee1f-4b49-8cc9-a5d3bc73a5a1","added_by":"auto","created_at":"2025-10-22 19:28:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5411164,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCSF biomarker profiles in the cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanel A presents the marginal means and 95% confidence intervals from ANCOVA models comparing CSF biomarkers across groups, adjusted for age and sex. Colours correspond to clinical diagnoses (HC, grey; CAA-nonAD, blue; CAA-AD, green; AD, orange). Panel B displays pairwise comparisons with Bonferroni correction, where blue tiles indicate a \u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e \u0026lt;0.05. Panel C illustrates the resulting CSF biomarker profile, with symbols indicating whether each marker's concentration is higher (red upward arrows) or lower (blue downward arrows) as compared to HC. The size of the symbols is scaled according to the magnitude of change.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7448531/v1/05b3ddc54878a6160738fcf4.png"},{"id":94136763,"identity":"c8c87b97-7efd-4d62-855d-fe7b72890116","added_by":"auto","created_at":"2025-10-22 19:20:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8323282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiscriminatory power of amyloid CSF biomarkers in CAA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanel A illustrates a cross-validation search for optimal cutoffs to detect CAA for each biomarker using 10-fold validation with 50 repeats. The optimal threshold was determined via a custom cost function, as explained in the Supplementary Methods. Red lines indicate the cutoff for each CSF biomarkers. Panel B presents the performance of these cutoffs in discriminating CAA, assessed by AUC, sensitivity, and specificity. Confidence intervals were obtained through bootstrapping (10,0000 resampled sets). In both panels, colours represent different biomarkers.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7448531/v1/fc49ef5c3e83364d11e2f621.png"},{"id":94136774,"identity":"056e6a9e-674a-4425-a93c-0065db1a8057","added_by":"auto","created_at":"2025-10-22 19:20:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14845952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between CSF biomarkers and CAA cerebrovascular burden\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanel A displays the marginal means and 95% confidence intervals from ANCOVA models comparing CSF biomarkers across groups, adjusted for age and sex. Colours are scaled according to the CAA burden score (X-axis), with blue indicating lower burden and orange indicating higher burden. Panel B extends this analysis to individual CAA-related MRI markers, where colours denote the presence (orange) or absence (blue) of each MRI marker. 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19:20:54","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":3655787,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7448531/v1/304e7f244dc3c351726735dc.docx"},{"id":94136788,"identity":"0914c074-5513-4c9f-9fd0-c636a76f1779","added_by":"auto","created_at":"2025-10-22 19:20:54","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":3655783,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7448531/v1/a0f585e330fa91b351f3f7aa.docx"},{"id":94140619,"identity":"4317d002-82b3-4d2f-a9f1-7e88fed9aa7d","added_by":"auto","created_at":"2025-10-22 19:44:54","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":3660626,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7448531/v1/fdc313097ddb0ad797e81a6a.docx"},{"id":94136806,"identity":"33085693-77eb-4944-8c79-a2e9153b7a47","added_by":"auto","created_at":"2025-10-22 19:20:54","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":3653790,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7448531/v1/181d82b15642b80908d72c4c.docx"},{"id":94138499,"identity":"2dc86a79-821f-499f-8ca6-76919f5df78f","added_by":"auto","created_at":"2025-10-22 19:28:54","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":16610,"visible":true,"origin":"","legend":"","description":"","filename":"SuppLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-7448531/v1/97324ae661d87650535a8f29.docx"}],"financialInterests":"Competing interest reported. Marc Suárez-Calvet has received in the past 36mo consultancy/speaker fees (paid to the institution) from by Almirall, Eli Lilly, Quanterix, Novo Nordisk, and Roche Diagnostics. He has received consultancy fees or served on advisory boards (paid to the institution) of Eli Lilly, Grifols, Novo Nordisk, and Roche Diagnostics. He was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. In-kind support for research (to the institution) was received from ADx Neurosciences, Alamar Biosciences, ALZpath, Avid Radiopharmaceuticals, Eli Lilly, Fujirebio, Janssen Research \u0026 Development, Meso Scale Discovery, and Roche Diagnostics; MS-C did not receive any personal compensation from these organizations or any other for-profit organization.\nAlbert Puig-Pijoan has served on advisory boards for Schwabe Farma Iberica. \nAida Fernández-Lebrero, Joan Jiménez-Balado, Greta García-Escobar, José Contador, Isabel Estraguès-Gázquez, Laia Peraferrer-Montesinos, Antoni Suárez-Pérez, Rosa María Manero-Borràs, Brigitte Beltrán, Ludovica Gramegna, Ana Rodríguez Campello, Abel Palacio-Gili, Oriol Grau-Rivera, Angel Ois, and Irene Navalpotro-Gómez declare that they have no conflicts of interest related to this manuscript.","formattedTitle":"CSF Biomarker Profile of Cerebral Amyloid Angiopathy: Diagnostic Performance and Imaging Correlates in a Hospital-Based Neurology Cohort","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSporadic cerebral amyloid angiopathy (CAA) is a highly prevalent degenerative small vessel disease, defined by the progressive deposition of β-amyloid (Aβ) fibrillar aggregates in the walls of cortical and leptomeningeal blood vessels\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Alzheimer\u0026rsquo;s disease (AD) is neuropathologically characterized by the accumulation of extracellular Aβ plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Although traditionally regarded as distinct clinical entities, CAA and AD frequently coexist, suggesting that cognitive impairment in these individuals may result from the combined effects of cerebrovascular and neurodegenerative mechanisms\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This overlap complicates clinical diagnosis and poses challenges for individualized prognosis and treatment.\u003c/p\u003e\u003cp\u003eWhile the definitive diagnosis of CAA remains histopathological, clinical diagnosis currently relies on characteristic neuroimaging markers, as outlined in the Boston criteria version 2.0\u003csup\u003e3\u003c/sup\u003e. Recently updated diagnostic frameworks for AD\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, along with recent recommendations for CAA\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, have emphasized the incorporation of molecular biomarkers into diagnostic and staging criteria, highlighting their potential to improve diagnostic accuracy and patient stratification.\u003c/p\u003e\u003cp\u003eDespite increasing interest in CAA, most prior studies have focused on vascular imaging markers and haemorrhagic risk, with limited investigation of molecular biomarkers. When cognitive profiles are assessed, CAA and AD are often examined in separate cohorts, overlooking their frequent co-occurrence in clinical practice. Pathology-based studies report moderate-to-severe CAA in up to 48% of AD patients and 6.4% of cognitively unimpaired individuals\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Similarly, neuroimaging studies indicate that CAA-related markers are common in AD patients and even in asymptomatic individuals with positive Aβ-PET\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, suggesting that vascular amyloid deposition begins early and progresses alongside AD pathology. This subclinical burden may confound diagnosis and risk stratification in memory clinic populations. Therefore, understanding both distinct and overlapping biomarker signatures of CAA and AD is critical, particularly in the context of co-pathology.\u003c/p\u003e\u003cp\u003eImportantly, while the identification of CAA in patients with AD was traditionally of academic interest, the emergence of anti-amyloid therapies\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e has made detecting concomitant CAA clinically imperative. CAA is a recognized risk factor for amyloid-related imaging abnormalities (ARIA)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, a potentially serious complication of monoclonal antibodies targeting amyloid pathology\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Current recommendations for aducanumab and lecanemab advise against treatment in individuals with \u0026gt;\u0026thinsp;4 cerebral microbleeds (CMBs) or any cortical superficial siderosis (cSS), although trial eligibility criteria vary\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In this context, improved tools to detect CAA\u0026mdash;particularly in patients with suspected or established AD\u0026mdash;are urgently needed.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to characterize the CSF biomarker profile of patients with MRI-defined CAA, compared to healthy controls (HC) and individuals with AD. We then assessed the diagnostic performance of CSF biomarkers in identifying CAA. Finally, we investigated the associations between CSF biomarker concentrations and the extent of CAA-related vascular burden as assessed by neuroimaging.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study design and participants\u003c/h2\u003e\u003cp\u003eThis study is part of the ANGMAR project (Characterization of Biomarkers Footprint in Cerebral Amyloid Angiopathy Patients), a prospective observational study embedded within the BIODEGMAR\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e biobanking protocol at Hospital del Mar. ANGMAR provides the clinical and imaging study framework, whereas BIODEGMAR ensures standardized biospecimen collection, processing, and storage across participants. Participants were recruited from the Neurology Service at Hospital del Mar, including memory-clinic outpatients (Cognitive and Behavioural Neurology Unit) and Neurology inpatients (including the Neurovascular Unit). Eligibility was based on the inclusion and exclusion criteria detailed below, and all participants followed the same standardized ANGMAR/BIODEGMAR workflow for clinical assessment, MRI acquisition, and CSF handling, with raters blinded to clinical classifications. Neuropathological confirmation was not available in this cohort; CAA was defined on MRI according to Boston criteria v2.0\u003csup\u003e3\u003c/sup\u003e, which served as the reference standard for analyses.\u003c/p\u003e\u003cp\u003eInclusion criteria were: (1) availability of CSF measurements of Aβ42, Aβ40, phosphorylated tau at threonine 181 (p-tau181), and total tau (t-tau), quantified using the LUMIPULSE G600II platform\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e (Fujirebio); and (2) fulfilment of one of the following diagnostic classifications: (i) individuals with probable or possible CAA, defined according to the Boston criteria v2.0\u003csup\u003e3\u003c/sup\u003e and further stratified based on CSF biomarker profiles into CAA with coexisting AD pathology (CAA-AD) or without it (CAA-nonAD), using elevated p-tau181 levels as a marker of tauopathy; (ii) patients with mild cognitive impairment (MCI) or Alzheimer\u0026rsquo;s disease dementia (AD-d), fulfilling the clinical criteria established by the National Institute on Aging and the Alzheimer\u0026rsquo;s Association (NIA-AA)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, presenting a CSF profile consistent with AD pathology\u0026mdash;defined by abnormal levels of both amyloid and tau \u0026lsquo;core 1\u0026rsquo; biomarkers (AD group)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u0026mdash;and not meeting criteria for either probable or possible CAA according to the Boston criteria v2.0.; and (iii) cognitively unimpaired healthy controls (HC), characterized by normal neuropsychological performance\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and no history of stroke or transient ischemic attack.\u003c/p\u003e\u003cp\u003eAll participants underwent standardized clinical, neuropsychological, and neuroimaging assessments as part of the ANGMAR/BIODEGMAR protocol. Additional methodological details, including diagnostic workup and biomarker processing, are provided in the Supplementary Material. The study flowchart is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Clinical and Neuropsychological Assessments\u003c/h2\u003e\u003cp\u003eAll participants underwent a comprehensive clinical and neuropsychological assessment to establish diagnostic classifications based on standardized criteria. Diagnostic evaluations included the application of the Boston criteria v2.0 for the diagnosis of CAA\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and the NIA-AA criteria for MCI\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and AD-d\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. For participants classified with CAA, additional clinical information was collected to characterize the disease phenotype, including the presence of cognitive impairment, lobar intracerebral haemorrhages, spontaneous convexity subarachnoid haemorrhage (cSAH), and transient focal neurological episodes (TFNEs)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Neuropsychological evaluation was conducted using a standardized battery of cognitive tests and functional scales, including the Mini-Mental State Examination (MMSE)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and the Clinical Dementia Rating (CDR) scale\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Neuroimaging\u003c/h2\u003e\u003cp\u003eStructural brain MRI was performed using either a 1.5T system (General Electric Signa Explorer) or a 3T system (Philips Achieva). Brain imaging protocol included T1- and T2-weighted sequences, high-resolution 3D T1, diffusion-weighted imaging (DWI), fluid-attenuated inversion recovery (FLAIR), and susceptibility-sensitive sequences (gradient echo and ven-BOLD). Neuroimaging markers were evaluated by a certified neuroradiologist, blinded to clinical diagnosis, to characterize CAA-related pathology: lobar cerebral microbleeds (CMBs) and cortical superficial siderosis (cSS; both on T2*-GRE), convexity subarachnoid haemorrhage (cSAH; on T2*-GRE and FLAIR), white matter hyperintensities (WMH; FLAIR), and enlarged perivascular spaces in the centrum semiovale (CSO-PVS; T2-weighted). WMHs were considered significant when the Fazekas score\u003csup\u003e19\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;2. cSS was evaluated using the cSS multifocality score and considered diffuse when \u0026ge;\u0026thinsp;2 cortical sulci were affected. CMBs burden was significant when \u0026ge;\u0026thinsp;5 lobar CMBs\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The CAA\u0026ndash;small vessel disease (CAA-SVD) Burden Score\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e was calculated to quantify overall CAA-related small vessel disease.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. CSF Biomarkers Analysis\u003c/h2\u003e\u003cp\u003eCore AD CSF biomarkers, including Aβ42, Aβ40, p-tau181, and t-tau, were quantified using the LUMIPULSE G600II automated immunoassay system (Fujirebio)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e at the Laboratori de Refer\u0026egrave;ncia de Catalunya (Barcelona, Spain). All samples were processed in the same laboratory facilities using an automated platform, following harmonized procedures, which likely reduced the possibility of batch-related bias. Laboratory personnel performing the analyses were blinded to the participants\u0026rsquo; clinical classifications. Cutoff values for individual biomarkers and derived ratios (Aβ42, Aβ42/Aβ40, p-tau181, t-tau, and Aβ42/p-tau181) were based on previously validated thresholds\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. An AD CSF biomarker profile was defined as Aβ42/Aβ40 ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.062 and p-tau181\u0026thinsp;\u0026gt;\u0026thinsp;69.5 pg/mL. To evaluate concomitant CAA\u0026ndash;AD pathology, p-tau181 was selected as the primary biomarker of AD-related tauopathy. Since CAA is an amyloid-associated condition, the use of Aβ-based ratios could be confounded by impaired perivascular amyloid clearance mechanisms\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In contrast, p-tau181 is more specifically associated to tau pathology associated with AD and less susceptible to the amyloid clearance alterations inherent to CAA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Ethics\u003c/h2\u003e\u003cp\u003e The ANGMAR study and the BIODEGMAR cohort were approved by the local ethics committee (CEIm Hospital del Mar Research Institute, project codes 2021/9960/I and 2018/7805I, respectively). All participants signed a specific informed consent form.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Statistical analyses\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.6.1. Descriptive analyses\u003c/h2\u003e\u003cp\u003eDemographic and baseline characteristics were summarized as means and standard deviations (SD) for normally distributed continuous variables, or as medians and interquartile ranges (IQR) for non-normally distributed data. Categorical variables were reported as frequencies and percentages.\u003c/p\u003e\u003cp\u003eGroup comparisons were conducted using one-way analysis of variance (ANOVA) for continuous variables with normal distributions, or the Kruskal-Wallis test for non-normally distributed data. For categorical variables, Pearson\u0026rsquo;s χ\u0026sup2; test or Fisher\u0026rsquo;s exact test was applied, as appropriate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2. Biomarker profiles in CAA\u003c/h2\u003e\u003cp\u003eWe first examined differences in CSF biomarkers\u0026mdash;Aβ40, Aβ42, Aβ42/Aβ40 ratio, p-tau181, t-tau, Aβ42/p-tau181 ratio\u0026mdash;across diagnostic groups, including HC, AD, CAA without AD co-pathology (CAA-nonAD) and CAA with AD co-pathology (CAA-AD). To account for potential confounders, analysis of covariance (ANCOVA) models were implemented adjusting for age and sex, given their well-established influence on CSF biomarker levels\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Adjusted means and 95% confidence intervals (CI) were reported for each biomarker. Multiple comparisons were corrected using Bonferroni post hoc adjustments (\u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e). Group differences in CSF biomarkers were tested using ANCOVA models adjusted for age and sex, with Bonferroni correction for multiple comparisons. Additional analyses adjusting for education and APOE ε4 were performed, with covariates selected based on principal component analysis (see Supplementary Methods for details). Finally, we also ran a series of sensitivity analyses excluding patients with possible CAA according to Boston criteria v2.0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.6.3. Diagnostic Performance of CSF biomarkers\u003c/h2\u003e\u003cp\u003eThese series of analyses aimed to evaluate the diagnostic utility of CSF biomarkers for identifying CAA, using MRI (Boston criteria v2.0\u003csup\u003e3\u003c/sup\u003e) as the reference standard for classification. For this purpose, diagnostic groups were dichotomized into CAA (with or without AD) and non-CAA (with or without AD). We focused on amyloid-related markers, given their direct pathophysiological link to CAA. Aβ40 and Aβ42 are the main isoforms implicated in vascular amyloid deposition, independently of AD co-pathology\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe evaluated the diagnostic performance of Aβ40 and Aβ42 using ROC analysis. Optimal cutoffs were derived by cross-validation with a custom cost function, and diagnostic metrics (AUC, sensitivity, specificity) were estimated through bootstrap resampling. Analyses were also performed within the subgroup of patients with AD (CAA-AD and AD groups). This analysis aimed to assess the discriminative value of CSF biomarkers in a clinically challenging context of overlapping pathologies (see Supplementary Methods for details).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.6.4. CSF Biomarker associations with CAA neuroimaging markers\u003c/h2\u003e\u003cp\u003eAssociations between CSF biomarkers and CAA neuroimaging markers were tested using ANCOVA models adjusted for age and sex, with Bonferroni correction for multiple comparisons. Analyses were conducted in CAA patients across CAA-SVD Burden Score\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e categories and individual MRI markers\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e (see Supplementary Methods for details).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Descriptive analyses\u003c/h2\u003e\u003cp\u003eThe final study sample comprised 35 HC, 57 CAA (27 CAA-nonAD, 30 CAA-AD), and 169 AD cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, no significant differences were observed in age, sex or ethnicity across groups. HC participants had a significantly higher educational attainment compared to the other groups. Hypertension was less prevalent among HC participants. The APOE ε4 allele was significantly more frequent in the AD (64.3%) and CAA-AD (67.9%) groups compared to CAA-nonAD (24%) and HC (16.1%) (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipants\u0026rsquo; characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;261)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCAA-nonAD\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCAA-AD\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAD\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;169)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73.0 [70.0;76.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.0 [66.5;74.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.0 [69.0;76.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e73.5 [71.0;76.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e73.0 [70.0;76.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e157 (60.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (51.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16 (59.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e109 (64.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104 (39.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (48.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11 (40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e60 (35.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaucasian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e256 (98.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26 (96.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29 (96.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e166 (98.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (3.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3 (1.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.00 [6.00;12.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.0 [8.00;15.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.00 [8.00;12.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.00 [6.00;12.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.00 [6.00;10.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTA \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e146 (55.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (34.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15 (55.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e99 (58.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Mellitus \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (19.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (7.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e142 (54.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14 (51.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e97 (57.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschemic heart disease \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntiplatelet therapy \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54 (20.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e36 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.0 [19.0;26.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.0 [27.0;29.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.0 [21.0;28.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.0 [17.8;24.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21.0 [18.0;23.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCDR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (6.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e48 (28.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76 (29.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (3.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (6.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e73 (43.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61 (23.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (4.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7 (4.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFazekas score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;1 (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e176 (69.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (89.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11 (40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11 (36.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e128 (75.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ndash;3 (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79 (31.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16 (59.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19 (63.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPOE ε4 carriers, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113 (53.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (16.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (24.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19 (67.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e83 (64.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPOE ε2 carriers, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (16.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9 (6.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are expressed as median and interquartile range or percentage (%). Education was not available in 12 participants, APOE genotype in 48 participants, MMSE in 2 participants.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCSF biomarker distributions are summarized in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-A. All biomarkers differed between groups at the univariate level (all \u003cem\u003ep-\u003c/em\u003evalues\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-B).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Biomarker profiles in CAA\u003c/h2\u003e\u003cp\u003eWe next examined whether patients with CAA showed distinct CSF biomarker profiles compared to HC and AD groups. As shown in Figs.\u0026nbsp;\u0026lt;link rid=\"fig2\"\u0026gt;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u0026lt;/link\u0026gt;\u003c/span\u003e-A and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Aβ40 levels were significantly lower in CAA-nonAD compared to HC and AD (\u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e \u0026lt;0.05). In addition, CAA-AD showed lower Aβ40 levels than HC and AD (both \u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e \u0026lt;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B), and no differences were found compared with CAA-nonAD (\u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e \u0026gt;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSimilarly, Aβ42 levels were also reduced in both CAA subgroups and AD subjects compared to HC (\u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e \u0026lt;0.05). Among CAA cases, CAA-AD exhibited significantly lower Aβ42 levels than CAA-nonAD and AD (both \u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e \u0026lt;0.05; Fig.\u0026nbsp;\u0026lt;link rid=\"fig2\"\u0026gt;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u0026lt;/link\u0026gt;\u003c/span\u003e-A and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B), whereas there were no differences between CAA-nonAD and AD participants.\u003c/p\u003e\u003cp\u003eThe Aβ42/Aβ40 ratio was significantly lower in both CAA subgroups compared to HC and in AD compared to HC (both \u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e \u0026lt;0.05). Within the CAA subgroups, CAA-AD had a lower Aβ42/Aβ40 ratio than CAA-nonAD, while CAA-nonAD showed a significantly higher ratio than AD (both \u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e \u0026lt;0.05). No significant differences were observed between CAA-AD and AD.\u003c/p\u003e\u003cp\u003eRegarding tau-related biomarkers, p-tau181 and t-tau were elevated in AD and CAA-AD patients as compared to subjects with CAA-nonAD and HC, with no differences between HC and CAA-nonAD participants (Fig.\u0026nbsp;\u0026lt;link rid=\"fig2\"\u0026gt;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u0026lt;/link\u0026gt;\u003c/span\u003e-A and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B). Similarly, the Aβ42/p-tau181 ratio was significantly lower in AD and both CAA-nonAD and CAA-AD subjects compared to HC (both \u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e \u0026lt;0.05). CAA-AD had a significantly lower Aβ42/p-tau181 ratio than CAA-nonAD, but showed no differences as compared to AD subjects.\u003c/p\u003e\u003cp\u003eIn summary, patients with CAA\u0026mdash;whether or not they had coexisting AD\u0026mdash;exhibited lower Aβ40 levels compared to both HC and AD participants. Additionally, individuals with CAA-AD had the lowest Aβ42 concentrations, while CAA-nonAD showed similar Aβ42 levels to AD participants (\u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e \u0026gt;0.05), both lower than HC. These biomarker profiles are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-C. To assess the impact of potential diagnostic heterogeneity, we conducted sensitivity analyses excluding all patients who met only criteria for possible CAA (21.1% of the CAA group, n\u0026thinsp;=\u0026thinsp;12). The results remained consistent with the primary analyses (see Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), supporting the robustness of the biomarker differences observed between groups.\u003c/p\u003e\u003cp\u003eFinally, PCA revealed that two components accounted for 89.2% of the variance in CSF biomarkers (Supplementary Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). These components showed significant associations with both education and APOE ε4 status (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). However, adjusting for these additional potential confounders did not meaningfully alter the results or the conclusions described above (Supplementary Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e \u0026amp; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Diagnostic Performance of CSF Biomarkers\u003c/h2\u003e\u003cp\u003eWe first explored the discriminative capacity of each CSF biomarker, including ratios, to identify CAA by constructing ROC curves across the whole sample. Aβ40 and Aβ42 demonstrated the highest discriminative performance, with AUCs of 0.77 and 0.71, respectively (Supplementary Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe further explored the optimal cutoff for Aβ40 and Aβ42 because these biomarkers are directly implicated in the pathophysiology of vascular amyloid deposition and showed the highest discriminative performance in ROC analyses. To that aim, we ran cross-validations to generate a distribution of optimal cutoffs according to a cost function designed to balance the AUC and stability of biomarkers (see the methods section). These cutoffs are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A. Subsequently, we estimated their performance and 95% CI by running a bootstrap (see methods section). Aβ40 and Aβ42 showed good discriminative performance, with AUCs of 0.73 (95% CI: 0.66\u0026ndash;0.80) and 0.71 (95% CI: 0.64\u0026ndash;0.78; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe also checked the performance of these cutoffs in the subset of patients with AD, both with and without CAA (N\u0026thinsp;=\u0026thinsp;199). As shown in Supplementary Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e, in this subset Aβ42 had a better performance as compared to Aβ40 (AUC: 0.77 [0.68\u0026ndash;0.85] vs 0.71 [0.62\u0026ndash;0.80], respectively).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4. CSF Biomarker associations with CAA neuroimaging markers\u003c/h2\u003e\u003cp\u003eAs a final step, we studied the association between CSF biomarkers and neuroimaging CAA burden. These analyses were conducted only in patients with CAA, either with or without AD co-pathology. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the clinical and radiological characteristics of patients with CAA-AD and CAA-nonAD. After adjusting for age and sex, we observed no statistically significant differences between groups in any of the radiological markers assessed, including the number of lobar CMBs (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.19), presence of cSS (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.26), or WMH (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.96). The overall CAA-SVD burden score was similarly distributed across groups (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.85). However, CAA-nonAD individuals more frequently had a previous history of lobar intracerebral haemorrhage (25.9% vs 3.3%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.02), despite a comparable radiological burden across groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCAA participants\u0026rsquo; clinical and radiological characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical manifestations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCAA (n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCAA-nonAD\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCAA-AD\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobar ICH (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (14.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (25.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute SAH (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTFNEs (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive impairment (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47 (82.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21 (77.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26 (86.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCDR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (6.67%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (5.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (3.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (6.67%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20 (35.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRadiological characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobar CMBs (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (5.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (7.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (3.33%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31 (54.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (51.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17 (56.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (18.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (3.33%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17 (29.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11 (36.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecSS (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFocal cSS (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiffuse cSS (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSO severe PVS (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5 (16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFazekas score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;1 (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22 (38.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11 (36.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ndash;3 (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35 (61.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (59.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19 (63.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCAA SVD Burden Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0 (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (8.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (7.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (28.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (25.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3 (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5 (16.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;4 (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBoston criteria v2.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePossible CAA (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12 (21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8 (26.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProbable CAA (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45 (78.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23 (85.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22 (73.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are expressed as median and interquartile range or percentage (%).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe further analysed CSF biomarkers concentration by CAA-SVD burden score and observed that as CAA cerebrovascular burden increased, Aβ40 levels decreased (ANCOVA \u003cem\u003ep-\u003c/em\u003evalue\u003csub\u003ebf\u003c/sub\u003e \u0026lt;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A). No significant differences were found for the other CSF biomarkers after adjusting for multiple comparisons (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFinally, we examined the association between specific CAA-related MRI lesions and CSF biomarkers. Individuals with \u0026ge;\u0026thinsp;5 CMBs exhibited decreased Aβ40 and Aβ42 levels after Bonferroni correction as compared to those with \u0026lt;\u0026thinsp;5 CMBs (ANCOVA \u003cem\u003ep-\u003c/em\u003evalue\u003csub\u003ebf\u003c/sub\u003e \u0026lt;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B \u0026amp; Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). No significant associations were found for other CAA-related MRI lesions and CSF biomarkers after multiple testing correction.\u003c/p\u003e\u003cp\u003eFurther adjustment for education and APOE-ε4 carriership did not significantly change the results either for CAA-SVD burden score or individual MRI markers (Supplementary Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e \u0026amp; Supplementary Tables S3 to S4).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eOur study provides a comprehensive characterization of CSF biomarker profiles in patients with CAA, with and without AD co-pathology, compared to AD and HC, integrating fluid and neuroimaging markers to improve \u003cem\u003ein vivo\u003c/em\u003e characterization. This represents a novel approach to disentangling the frequent CAA-AD comorbidity by analysing divergent and overlapping CSF biomarker patterns.\u003c/p\u003e\u003cp\u003eCSF Aβ40 emerged as the most informative biomarker for identifying CAA, both in isolation and in the presence of AD co-pathology. Aβ40 levels were significantly reduced in CAA-nonAD and CAA-AD compared to AD and HC, consistent with its preferential vascular deposition and impaired clearance. Aβ42 and the Aβ42/Aβ40 ratio were also decreased\u0026mdash;particularly in CAA-AD\u0026mdash;likely reflecting mixed pathology, though less discriminative than Aβ40.\u003c/p\u003e\u003cp\u003eThese findings align with prior studies reporting selective reductions of CSF Aβ40 in CAA, contrasting with its relative preservation in AD\u003csup\u003e\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Aβ42 levels were also lower in CAA-AD than in AD, suggesting that concomitant vascular amyloid pathology may exacerbate Aβ42 depletion, possibly reflecting a higher total amyloid burden. The disproportionate reduction of Aβ40 reflects its preferential vascular deposition, while Aβ42 accumulates mainly in parenchymal plaques due to its greater hydrophobicity\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. These distinct biochemical and anatomical properties support Aβ40 as a disease-specific marker for vascular amyloid pathology. However, despite its group-level discriminative value, Aβ40 showed overlap across individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), limiting its standalone diagnostic utility in clinical settings. Its clinical applicability may be enhanced when integrated with neuroimaging and other fluid biomarkers as part of a multimodal diagnostic approach to CAA. Nonetheless, previous studies have reported heterogeneous results, likely due to differences in phenotype, disease stage, and control group selection, highlighting the need for standardized criteria\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInterpretation of CSF tau levels is challenging in mixed pathologies, as tau is generally lower in CAA than in AD\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This reinforces the importance of including tau-based biomarkers, such as p-tau181, to detect AD co-pathology in CAA patients. Higher p-tau181 levels were observed in CAA-AD vs CAA-nonAD, as expected given its use in defining AD status. Given that p-tau181 levels were used to define AD co-pathology, group differences in this biomarker should be interpreted with caution, as this may introduce circularity in the comparison. In contrast, biomarkers not involved in group definitions\u0026mdash;such as Aβ40 and the Aβ42/Aβ40 ratio\u0026mdash;offer unbiased insights into vascular amyloid pathology. Similarly, t-tau levels were elevated in CAA-AD and AD, but not in CAA-nonAD or HC, supporting its association with AD-related injury\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. These results reinforce that tau elevation is not a prominent feature of CAA in the absence of AD and support combining tau and amyloid biomarkers to disentangle overlapping phenotypes.\u003c/p\u003e\u003cp\u003eTo assess the diagnostic value of CSF biomarkers in CAA, we conducted ROC analyses and estimated optimal cutoffs using cross-validation and bootstrapping. Consistent with the group-level comparisons, Aβ40 showed the highest performance (AUC\u0026thinsp;=\u0026thinsp;0.73), followed by Aβ42 (AUC\u0026thinsp;=\u0026thinsp;0.71), supporting their role in identifying CAA. Notably, in the direct comparison between CAA-AD and AD, Aβ42 and Aβ40 retained diagnostic value, with AUCs showing good performance in this challenging subgroup, supporting their role in detecting CAA in AD.\u003c/p\u003e\u003cp\u003ePatients with CAA and a high burden of CMBs (\u0026ge;\u0026thinsp;5 CMBs) exhibited significantly lower CSF Aβ40 and Aβ42 levels, likely reflecting advanced vascular amyloid pathology. Accumulation of lobar CMBs is associated with increased amyloid deposition in vessel walls, impairing clearance and promoting vascular Aβ retention, reducing CSF concentrations\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The persistence of these associations after Bonferroni correction reinforces their robustness and suggests a true biological link between vascular amyloid load and altered CSF Aβ. Similarly, we observed an inverse correlation between CSF Aβ40 and the global CAA\u0026ndash;SVD burden score. Together, these results support Aβ40 as a marker of vascular amyloid burden and highlight the value of combining CSF and neuroimaging biomarkers for \u003cem\u003ein vivo\u003c/em\u003e CAA staging.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe need to detect CAA has become increasingly relevant in clinical practice, particularly in the context of anti-amyloid therapies and their associated risk of ARIA\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Our findings support the use of CSF Aβ40 and Aβ42 as complementary tools to identify patients at increased ARIA risk, potentially improving treatment safety. Neuroimaging markers remain essential for comprehensive haemorrhagic risk assessment and combining them with fluid biomarkers may help select the best candidates for anti-amyloid treatment.\u003c/p\u003e\u003cp\u003eBeyond treatment selection, identifying CAA in patients with AD may offer insights into disease progression, given the vascular contribution to cognitive decline. As a major cause of ICH and vascular dementia, CAA carries substantial clinical burden. The reduction in Aβ40 levels in CAA-AD compared to AD without MRI evidence of CAA suggests that vascular co-pathology may influence clinical heterogeneity. These findings support the integration of CSF biomarker panels\u0026mdash;including Aβ40\u0026mdash;into AD diagnostic workflows. Looking forward, the development of blood-based biomarkers for CAA warrants further research to enhance diagnostic accessibility and scalability\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCSF biomarker profiling may support diagnosis in cases where neuroimaging is inconclusive or Boston criteria v2.0 are not fully met\u0026mdash;such as patients with isolated lobar ICH, atypical imaging patterns, or low haemorrhagic burden\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These scenarios are particularly frequent in memory clinic populations, where CAA often presents with cognitive symptoms and coexisting neurodegenerative pathology, reducing the sensitivity of imaging-based criteria\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In such cases, fluid biomarkers may offer complementary diagnostic value. Early detection\u0026mdash;before multiple haemorrhagic lesions emerge\u0026mdash;remains challenging. In this context, a CSF biomarker signature\u0026mdash;particularly reduced Aβ40\u0026mdash;may support earlier identification of CAA, potentially preceding structural MRI markers\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and supporting integration into diagnostic workflows. Combining Aβ42, Aβ40, p-tau181, and t-tau may help identify both vascular and neurodegenerative pathology, reinforcing diagnosis in suspected CAA. In addition to this, the increasing use of anticoagulants in older adults\u0026mdash;many of whom may harbor unrecognized CAA\u0026mdash;underscores the need for haemorrhagic risk biomarkers. If validated for this purpose, CSF profiles could guide safer anticoagulation and support personalized care.\u003c/p\u003e\u003cp\u003eA key strength of this study lies in the use of a well-characterized, memory-clinic-based cohort, enhancing clinical relevance. Unlike previous studies that treated AD and CAA as separate entities, we defined biomarker-based diagnostic groups\u0026mdash;AD, CAA-nonAD, and CAA-AD\u0026mdash;allowing direct comparisons across isolated and overlapping phenotypes. Systematic MRI in all groups enabled consistent assessment of CAA markers, including haemorrhagic and non-haemorrhagic features. The integration of CSF and MRI data provides a robust multimodal framework for \u003cem\u003ein vivo\u003c/em\u003e CAA characterization, reinforcing the applicability of our findings for diagnosis and risk stratification in memory clinic populations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur study is not free of limitations. First, the absence of neuropathological confirmation of CAA diagnosis is an inherent constraint, as histopathology remains the gold standard. Although we applied the Boston criteria, version 2.0\u0026mdash;widely used and validated\u0026mdash;these lack the specificity of histological confirmation and may lead to diagnostic misclassification, particularly in memory clinic populations. The inclusion of patients fulfilling only criteria for possible CAA also introduces diagnostic uncertainty. However, sensitivity analyses excluding these cases yielded consistent results, supporting the robustness of our findings. Nevertheless, some degree of phenotypic heterogeneity remains unavoidable in the absence of neuropathological validation.\u003c/p\u003e\u003cp\u003eSecond, because CSF availability was an inclusion criterion, selection bias cannot be excluded: patients unwilling or ineligible for lumbar puncture may differ clinically and radiologically from those included. Although recruitment encompassed both memory-clinic outpatients and Neurology inpatients, this hospital-based cohort may still under-represent patients with the most haemorrhagic CAA phenotypes, which could limit the generalizability of our findings.\u003c/p\u003e\u003cp\u003eThird, although our findings support the utility of Aβ40 and Aβ42 in identifying CAA, CSF biomarkers alone cannot fully capture the disease\u0026rsquo;s pathophysiological complexity, which also involves vascular dysfunction and impaired clearance. Neuroimaging\u0026mdash;particularly Boston criteria, version 2.0\u0026mdash;remains essential for comprehensive characterization. In addition, the relatively small size of the CAA subgroups may limit statistical power and generalizability, especially for subgroup and sensitivity analyses. Finally, the cross-sectional design precludes assessment of biomarker trajectories or prognostic value. Future longitudinal studies with larger cohorts and, ideally, postmortem validation will be essential to confirm and extend these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study reinforces the role of Aβ40 and Aβ42 as key CSF biomarkers for detecting CAA, revealing a distinct biomarker profile compared to AD without coexisting CAA and healthy controls. The use of a well-characterized clinical cohort, with systematic neuroimaging and biologically defined AD diagnoses, strengthens the reliability and translational value of our findings. Notably, the inverse association between vascular lesion burden and CSF Aβ40 levels suggests a biological continuum in which progressive vascular amyloid deposition drives soluble Aβ depletion in CSF, pointing to its potential as a marker of both diagnosis and disease severity. These results support the integration of CSF biomarker panels into diagnostic frameworks for CAA, particularly in memory clinic settings where vascular and neurodegenerative pathologies often overlap. In the context of precision medicine, future research should focus on combining CSF and plasma biomarkers with advanced neuroimaging to improve CAA detection, guide risk stratification and personalized management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their most sincere gratitude to all participants and relatives involved in the ANGMAR/BIODEGMAR study, whose collaboration made this research possible. We thank the Neurology Department of Hospital del Mar, especially the Neurovascular Unit, for their support throughout the project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICTS OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarc Su\u0026aacute;rez-Calvet has received in the past 36mo consultancy/speaker fees (paid to the institution) from by Almirall, Eli Lilly, Quanterix, Novo Nordisk, and Roche Diagnostics. He has received consultancy fees or served on advisory boards (paid to the institution) of Eli Lilly, Grifols, Novo Nordisk, and Roche Diagnostics. He was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. In-kind support for research (to the institution) was received from ADx Neurosciences, Alamar Biosciences, ALZpath, Avid Radiopharmaceuticals, Eli Lilly, Fujirebio, Janssen Research \u0026amp; Development, Meso Scale Discovery, and Roche Diagnostics; MS-C did not receive any personal compensation from these organizations or any other for-profit organization.\u003c/p\u003e\n\u003cp\u003eAlbert Puig-Pijoan has served on advisory boards for Schwabe Farma Iberica.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAida Fern\u0026aacute;ndez-Lebrero, Joan Jim\u0026eacute;nez-Balado, Greta Garc\u0026iacute;a-Escobar, Jos\u0026eacute; Contador, Isabel Estragu\u0026egrave;s-G\u0026aacute;zquez, Laia Peraferrer-Montesinos, Antoni Su\u0026aacute;rez-P\u0026eacute;rez, Rosa Mar\u0026iacute;a Manero-Borr\u0026agrave;s, Brigitte Beltr\u0026aacute;n, Ana Rodr\u0026iacute;guez Campello, Abel Palacio-Gili, Oriol Grau-Rivera, Angel Ois, and Irene Navalpotro-G\u0026oacute;mez declare that they have no conflicts of interest related to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING SOURCES\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Instituto de Salud Carlos III through the Fondo de Investigaci\u0026oacute;n Sanitaria (FIS), project number PI21/00194, co-funded by the European Union, and by the European Research Area Network on Cardiovascular Diseases (ERA-CVD_JTC2020-015, project AC20/00001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ANGMAR study and the BIODEGMAR cohort were approved by the local ethics committee (CEIm Hospital del Mar Research Institute, project codes 2021/9960/I and 2018/7805I, respectively). All participants signed a specific informed consent form.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA SHARING STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy and ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAida Fern\u0026aacute;ndez-Lebrero and Joan Jim\u0026eacute;nez-Balado are co\u0026ndash;first authors. Irene Navalpotro-G\u0026oacute;mez and Albert Puig-Pijoan are co\u0026ndash;senior authors. Aida Fern\u0026aacute;ndez-Lebrero had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConcept and design:\u003c/em\u003e Irene Navalpotro-G\u0026oacute;mez, Aida Fern\u0026aacute;ndez-Lebrero.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcquisition, analysis, or interpretation of data:\u003c/em\u003e Aida Fern\u0026aacute;ndez-Lebrero, Joan Jim\u0026eacute;nez-Balado, Greta Garc\u0026iacute;a-Escobar, Angel Ois, Jos\u0026eacute; Contador, Isabel Estragu\u0026egrave;s-G\u0026aacute;zquez, Laia Peraferrer i Montesinos, Antoni Su\u0026aacute;rez-P\u0026eacute;rez, Rosa Mar\u0026iacute;a Manero-Borr\u0026agrave;s, Brigitte Beltr\u0026aacute;n M\u0026aacute;rmol, Albert Puig-Pijoan, Irene Navalpotro-G\u0026oacute;mez.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDrafting of the manuscript:\u003c/em\u003e Aida Fern\u0026aacute;ndez-Lebrero, Joan Jim\u0026eacute;nez-Balado.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis:\u003c/em\u003e Joan Jim\u0026eacute;nez-Balado, Angel Ois, Abel Palacio-Gili.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eObtained funding:\u003c/em\u003e This work was supported by the Instituto de Salud Carlos III through the Fondo de Investigaci\u0026oacute;n Sanitaria (FIS), project number PI21/00194, co-funded by the European Union, and by the European Research Area Network on Cardiovascular Diseases (ERA-CVD_JTC2020-015, project AC20/00001).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAdministrative, technical, or material support:\u003c/em\u003e Aida Fern\u0026aacute;ndez-Lebrero, Joan Jim\u0026eacute;nez-Balado, Brigitte Beltr\u0026aacute;n M\u0026aacute;rmol, Ludovica Gramegna, Albert Puig-Pijoan.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSupervision:\u003c/em\u003e Irene Navalpotro-G\u0026oacute;mez, Ana Rodr\u0026iacute;guez Campello, Angel Ois.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll authors critically reviewed and approved the final manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll requests for raw and analysed data and materials will be promptly reviewed by the senior authors to verify whether the request is subject to any intellectual property or confidentiality obligations. Bulk Anonymized data can be shared by request from any qualified investigator for the sole purpose of replicating procedures and results presented in the article, providing data transfer agrees with EU legislation and decisions by the IRB of each participating center.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCharidimou A, Boulouis G, Gurol ME, et al. Emerging concepts in sporadic cerebral amyloid angiopathy. Brain. 2017;140(7):1829\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/brain/awx047\u003c/span\u003e\u003cspan address=\"10.1093/brain/awx047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreenberg SM, Bacskai BJ, Hernandez-Guillamon M, Pruzin J, Sperling R, Van Veluw SJ. Cerebral amyloid angiopathy and Alzheimer disease \u0026mdash; one peptide, two pathways. 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Neurology. 2017;88(2):169\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/WNL.0000000000003486\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000003486\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Amyloid-β, Cerebral Amyloid Angiopathy, Alzheimer’s disease, Biomarker, Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7448531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7448531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBACKGROUND\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCerebral amyloid angiopathy (CAA) frequently co-occurs with Alzheimer\u0026rsquo;s disease (AD), which complicates diagnosis in patients with cognitive impairment. The cerebrospinal fluid (CSF) biomarker profile of CAA, particularly in the presence of AD co-pathology, remains poorly defined. We aimed to characterize CSF biomarkers in CAA, assess their diagnostic accuracy, and examine associations with neuroimaging markers of CAA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMETHODS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe included 261 participants from a hospital-based neurology cohort at Hospital del Mar (Barcelona, Spain), recruited from both memory-clinic outpatients (Cognitive and Behavioural Neurology Unit) and Neurology inpatients (including the Neurovascular Unit). Groups comprised healthy controls (HC, n\u0026thinsp;=\u0026thinsp;35), CAA without AD co-pathology (CAA-nonAD, n\u0026thinsp;=\u0026thinsp;27), CAA with AD co-pathology (CAA-AD, n\u0026thinsp;=\u0026thinsp;30), and AD (n\u0026thinsp;=\u0026thinsp;169). CSF Aβ40, Aβ42, p-tau181, and t-tau were quantified using automated immunoassays. Group differences were tested using analysis of covariance adjusted for age and sex. Receiver operating characteristic (ROC) analyses with 10-fold cross-validation and bootstrapping assessed diagnostic performance. Associations between CSF biomarkers and CAA-related MRI markers were examined using ANCOVA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRESULTS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAβ40 concentrations were lower in CAA-nonAD and CAA-AD compared to AD and HC (\u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e\u0026lt;0.05). Aβ42 was reduced in CAA-AD and AD versus HC, but did not differ between CAA-nonAD and AD. Aβ40 showed the highest diagnostic accuracy for CAA (AUC\u0026thinsp;=\u0026thinsp;0.73; 95% CI: 0.66\u0026ndash;0.80), followed by Aβ42 (AUC\u0026thinsp;=\u0026thinsp;0.71; 95% CI: 0.64\u0026ndash;0.78). In AD patients, Aβ42 best discriminated coexisting CAA (AUC\u0026thinsp;=\u0026thinsp;0.77). Higher CAA-SVD burden scores were associated with lower Aβ40 (\u003cem\u003ep\u003c/em\u003e-value\u003csub\u003ebf\u003c/sub\u003e\u0026lt;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCONCLUSIONS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCSF Aβ40 and Aβ42 provide complementary diagnostic value for identifying CAA, both in isolation and in the presence of AD co-pathology. Reduced Aβ40 is associated with greater CAA-related vascular burden, supporting its role as a marker of vascular amyloid pathology.\u003c/p\u003e","manuscriptTitle":"CSF Biomarker Profile of Cerebral Amyloid Angiopathy: Diagnostic Performance and Imaging Correlates in a Hospital-Based Neurology Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:20:48","doi":"10.21203/rs.3.rs-7448531/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a34d6f63-001f-4e03-bbb8-b5ae90a7ea22","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-14T09:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 19:20:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7448531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7448531","identity":"rs-7448531","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}