Beatriz Bosch’s research while affiliated with Consorci Institut D'Investigacions Biomediques August Pi I Sunyer and other places

Objectives Frontotemporal dementia (FTD) usually shows more asymmetric atrophy patterns than Alzheimer’s disease (AD). We aim to quantify this asymmetry to differentiate FTD, AD, and FTD subtypes. Methods We studied T1-MRI scans, including FTD (different phenotypes), AD, and healthy controls (CTR). We defined the Cortical Asymmetry Index (CAI) using measures based on a metric derived from information theory with the cortical thickness measures. Some participants had additional follow-up MRIs, cerebrospinal fluid (CSF), or plasma measures. We analysed differences at cross-sectional and longitudinal levels. We then clustered FTD and AD participants based on the CAI values and studied the patients’ fluid biomarker characteristics within each cluster. Results A total of 101 FTD patients (64 ± 8 years, 53 men), 230 AD patients (65 ± 10 years, 84 men), and 173 CTR (59 ± 15 years, 67 men) were studied. CAI differentiated FTD, AD, and CTR. It also distinguished the semantic variant primary progressive aphasia (svPPA) from the other FTD phenotypes. In FTD, the CAI increased over time. The cluster analysis identified two subgroups within FTD, characterised by different neurofilament-light (NfL) levels, and two subgroups within AD, with different plasma glial fibrillary acidic protein (GFAP) levels. In AD, CAI correlated with GFAP and Mini-Mental State Examination (MMSE); in FTD, the CAI was associated with NfL levels. Conclusions The proposed method quantifies asymmetries previously described visually. The CAI could define clinically and biologically meaningful disease subgroups in the differential diagnosis of AD and FTD and its subtypes. CAI could also be of interest in tracking disease progression in FTD. Key Points Question There is a need to find quantitative metrics from MRI that can identify disease subgroups, and that could be useful for diagnosis and tracking. Findings We propose a Cortical Asymmetry Index that differentiates Alzheimer’s disease (AD) from Frontotemporal dementia (FTD), distinguishes FTD subtypes, correlates with NFL and GFAP levels, and monitors FTD progression. Clinical relevance Our proposed index holds the potential to support clinical applications for diagnosis and disease tracking in AD and FTD, using a quantitative summary metric from MRI data. It also contributes to the understanding of these diseases.

PSEN1 mutations are the most frequent cause of autosomal dominant Alzheimer’s disease (ADAD), and show nearly full penetrance. There is presently increasing interest in the study of biomarkers that track disease progression in order to test therapeutic interventions in ADAD. We used white mater (WM) volumetric characteristics and diffusion tensor imaging (DTI) metrics to investigate correlations with the normalized time to expected symptoms onset (relative age ratio) and group differences in a cohort of 36 subjects from PSEN1 ADAD families: 22 mutation carriers, 10 symptomatic (SMC) and 12 asymptomatic (AMC), and 14 non-carriers (NC). Subjects underwent a 3T MRI. WM morphometric data and DTI metrics were analyzed. We found that PSEN1 MC showed significant negative correlation between fractional anisotropy (FA) and the relative age ratio in the genus and body of corpus callosum and corona radiate (p < 0.05 Family-wise error correction (FWE) at cluster level) and positive correlation with mean diffusivity (MD), axial diffusivity (AxD), and radial diffusivity (RD) in the splenium of corpus callosum. SMC presented WM volume loss, reduced FA and increased MD, AxD, and RD in the anterior and posterior corona radiate, corpus callosum (p < 0.05 FWE) compared with NC. No significant differences were observed between AMC and NC in WM volume or DTI measures. These findings suggest that the integrity of the WM deteriorates linearly in PSEN1 ADAD from the early phases of the disease; thus DTI metrics might be useful to monitor the disease progression. However, the lack of significant alterations at the preclinical stages suggests that these indexes might not be good candidates for early markers of the disease.

Plasma tau phosphorylated at threonine 181 (p-tau181) and 217 (p-tau217) have demonstrated high accuracy for Alzheimer’s disease (AD) diagnosis, defined by CSF/PET amyloid beta (Aβ) positivity, but most studies have been performed in research cohorts, limiting their generalizability. We studied plasma p-tau217 and p-tau181 for CSF Aβ status discrimination in a cohort of consecutive patients attending an academic memory clinic in Spain (July 2019–June 2024). All patients had CSF AD biomarkers performed as part of their routine clinical assessment. Aβ positivity was defined with a local cut-off of CSF Aβ1–42 < 600 pg/mL; in patients with borderline Aβ1–42 values or when there was a mismatch between the Aβ and the T status (T + if CSF p-tau181 ≥ 65 pg/mL), a ratio Aβ1–42/Aβ1–40 < 0.07 was used. Plasma p-tau217 and p-tau181 were measured retrospectively, from blood samples collected at first visit, with Fujirebio Lumipulse and Quanterix Simoa assays, respectively. We included 468 patients (mean age 67 years, 50% female, 61% Aβ positive). Plasma p-tau217 outperformed plasma p-tau181 in discriminating CSF Aβ status (AUC 0.95 vs 0.90, p = 0.005). A 97.5% sensitivity and specificity plasma p-tau217 algorithm, classifying patients into three groups of Aβ probability (Low, Intermediate and High), resulted in 67% of patients in the Low and High groups, having their Aβ status predicted (as negative and positive, respectively) with 96% accuracy. The remaining 33% in the Intermediate group were candidates to undergo CSF/PET testing. A model with a 10% variation in p-tau217 levels yielded small changes in accuracy (95%). In conclusion, plasma p-tau217 could have discriminated CSF Aβ status in two-thirds of patients with very high accuracy in a memory clinic cohort. These results support the implementation of plasma p-tau217 as an initial diagnostic tool in memory clinics for AD diagnosis, reducing the need for more invasive/expensive testing.

INTRODUCTION Early‐onset Alzheimer's disease (EOAD) shows a higher burden of neuropsychiatric symptoms than late‐onset Alzheimer's disease (LOAD). We aim to determine the differences in the severity of neuropsychiatric symptoms and locus coeruleus (LC) integrity between EOAD and LOAD accounting for disease stage. METHODS One hundred four subjects with AD diagnosis and 32 healthy controls were included. Participants underwent magnetic resonance imaging (MRI) to measure LC integrity, measures of noradrenaline levels in cerebrospinal fluid (CSF) and Neuropsychiatric Inventory (NPI). We analyzed LC‐noradrenaline measurements and clinical and Alzheimer's disease (AD) biomarker associations. RESULTS EOAD showed higher NPI scores, lower LC integrity, and similar levels of CSF noradrenaline compared to LOAD. Notably, EOAD exhibited lower LC integrity independently of disease stage. LC integrity negatively correlated with neuropsychiatric symptoms. Noradrenaline levels were increased in AD correlating with AD biomarkers. DISCUSSION Decreased LC integrity negatively contributes to neuropsychiatric symptoms. The higher LC degeneration in EOAD compared to LOAD could explain the more severe neuropsychiatric symptoms in EOAD. Highlights LC degeneration is greater in early‐onset AD (EOAD) compared to late‐onset AD. Tau‐derived LC degeneration drives a higher severity of neuropsychiatric symptoms. EOAD harbors a more profound selective vulnerability of the LC system. LC degeneration is associated with an increase of cerebrospinal fluid noradrenaline levels in AD.

Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases, has been in the scope of several studies performed so far. However, there is a gap in regard to analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs). We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer’s disease (AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort was also performed. The results showed hypermethylation as the most frequent finding in both tissues studied in the patient groups. Biological significance analysis revealed common pathways altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction, and immune system pathways. These alterations were also found in LCL samples, suggesting the epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive correlation. This study enhances our understanding of the biological pathways that are associated with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue.

Background: Frontotemporal dementia (FTD) patients usually show more asymmetric atrophy patterns than Alzheimer’s Disease (AD) patients. Here, we define the individual Cortical Asymmetry Index (CAI) and explore its diagnostic utility. Methods: We collected structural T1-MRI scans from 554 participants, including FTD (different phenotypes), AD, and healthy controls, and processed them using Freesurfer. We defined the CAI using measures based on a metric derived from information theory with the cortical thickness measures. Different subsets of the study participants had additional follow-up MRIs, cerebrospinal fluid (CSF), or plasma measures. We analyzed differences at cross-sectional and longitudinal levels. We then clustered FTD and AD participants based on the CAI values and studied the patients’ fluid biomarker characteristics within each cluster. Results: CAI differentiated FTD, AD, and healthy controls. It also distinguished the semantic variant Primary Progressive Aphasia (svPPA) from the other FTD phenotypes. In FTD, the CAI increased over time. The cluster analysis identified two subgroups within FTD, characterized by different CSF and plasma neurofilament-light (NfL) levels, and two subgroups within AD, with different plasma Glial fibrillary acidic protein (GFAP) levels. In AD, CAI correlated with plasma-GFAP and Mini-Mental State Examination (MMSE); in FTD, the CAI was associated with NfL levels (CSF and plasma. Conclusions: The method proposed here is able to quantify asymmetries previously described visually. The CAI could define clinically and biologically meaningful disease subgroups. We highlight the potential clinical utility of CAI in the differential diagnosis between FTD and AD and the different FTD phenotypes.