Galectin-3 is upregulated in frontotemporal dementia patients with subtype specificity.

INTRODUCTION: Neuroinflammation is a major contributor to the progression of frontotemporal dementia (FTD). Galectin-3 (Gal-3), a microglial activation regulator, holds promise as a therapeutic target and potential biomarker. Our study aimed to investigate Gal-3 levels in patients with FTD and assess its diagnostic potential. METHODS: We examined Gal-3 levels in brain, serum, and cerebrospinal fluid (CSF) samples of patients with FTD and controls. Multiple linear regressions between Gal-3 levels and other FTD markers were explored. RESULTS: Gal-3 levels were increased significantly in patients with FTD, mainly across brain tissue and CSF, compared to controls. Remarkably, Gal-3 levels were higher in cases with tau pathology than TAR-DNA Binding Protein 43 (TDP-43) pathology. Only MAPT mutation carriers displayed increased Gal-3 levels in CSF samples, which correlated with total tau and 14-3-3. DISCUSSION: Our findings underscore the potential of Gal-3 as a diagnostic marker for FTD, particularly in MAPT cases, and highlights the relation of Gal-3 with neuronal injury markers.

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.

Sex differences in early-onset Alzheimer's disease.

BACKGROUND AND PURPOSE: Sex is believed to drive heterogeneity in Alzheimer's disease (AD), although evidence in early-onset AD (EOAD; <65 years) is scarce. METHODS: We included 62 EOAD patients and 44 healthy controls (HCs) with core AD cerebrospinal fluid (CSF) biomarkers, neurofilament light chain levels, neuropsychological assessment, and 3-T magnetic resonance imaging. We measured cortical thickness (CTh) and hippocampal subfield volumes (HpS) using FreeSurfer. Adjusted linear models were used to analyze sex-differences and the relationship between atrophy and cognition. RESULTS: Compared to same-sex HCs, female EOAD subjects showed greater cognitive impairment and broader atrophy burden than male EOAD subjects. In a direct female-EOAD versus male-EOAD comparison, there were slight differences in temporal CTh, with no differences in cognition or HpS. CSF tau levels were higher in female EOAD than in male EOAD subjects. Greater atrophy was associated with worse cognition in female EOAD subjects. CONCLUSIONS: At diagnosis, there are sex differences in the pattern of cognitive impairment, atrophy burden, and CSF tau in EOAD, suggesting there is an influence of sex on pathology spreading and susceptibility to the disease in EOAD.

Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC. METHODS: We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity. RESULTS: We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability. CONCLUSION: We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.

Impact of demographics and comorbid conditions on plasma biomarkers concentrations and their diagnostic accuracy in a memory clinic cohort.

Plasma biomarkers have emerged as promising tools for identifying amyloid beta (Aß) pathology. Before implementation in routine clinical practice, confounding factors modifying their concentration beyond neurodegenerative diseases should be identified. We studied the association of a comprehensive list of demographics, comorbidities, medication and laboratory parameters with plasma p-tau181, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) on a prospective memory clinic cohort and studied their impact on diagnostic accuracy for discriminating CSF/amyloid PET-defined Aß status. Three hundred sixty patients (mean age 66.5 years, 55% females, 53% Aß positive) were included. Sex, age and Aß status-adjusted models showed that only estimated glomerular filtration rate (eGFR, standardized ß -0.115 [-0.192 to -0.035], p = 0.005) was associated with p-tau181 levels, although with a much smaller effect than Aß status (0.685 [0.607-0.763], p < 0.001). Age, sex, body mass index (BMI), Charlson comorbidity index (CCI) and eGFR significantly modified GFAP concentration. Age, blood volume (BV) and eGFR were associated with NfL levels. p-tau181 predicted Aß status with 87% sensitivity and specificity with no relevant increase in diagnostic performance by adding any of the confounding factors. Using two cut-offs, plasma p-tau181 could have spared 62% of amyloid-PET/CSF testing. Excluding patients with chronic kidney disease did not change the proposed cut-offs nor the diagnostic performance. In conclusion, in a memory clinic cohort, age, sex, eGFR, BMI, BV and CCI slightly modified plasma p-tau181, GFAP and NfL concentrations but their impact on the diagnostic accuracy of plasma biomarkers for Aß status discrimination was minimal.

Diagnostic Performance and Clinical Applicability of Blood-Based Biomarkers in a Prospective Memory Clinic Cohort.

BACKGROUND AND OBJECTIVES: Blood-based biomarkers have emerged as minimally invasive options for evaluating cognitive impairment. Most studies to date have assessed them in research cohorts, limiting their generalization to everyday clinical practice. We evaluated their diagnostic performance and clinical applicability in a prospective, real-world, memory clinic cohort. METHODS: All patients referred with suspected cognitive impairment between July 2019 and June 2021 were prospectively invited to participate. Five plasma biomarkers (tau phosphorylated at threonine 181 [p-tau181], glial fibrillary acidic protein [GFAP], neurofilament light chain [NfL], total tau [t-tau], and ubiquitin C-terminal hydrolase L1 [UCH-L1]) were determined with single-molecule array. Performance was assessed in comparison to clinical diagnosis (blinded to plasma results) and amyloid status (CSF/PET). A group of cognitively unimpaired (CU) controls was also included. RESULTS: Three hundred forty-nine participants (mean age 68, SD 8.3 years) and 36 CU controls (mean age 61.7, SD 8.2 years) were included. In the subcohort with available Alzheimer disease (AD) biomarkers (n = 268), plasma p-tau181 and GFAP had a high diagnostic accuracy to differentiate AD from non-neurodegenerative causes (area under the receiver operating characteristic curve 0.94 and 0.92, respectively), with p-tau181 systematically outperforming GFAP. Plasma p-tau181 levels predicted amyloid status (85% sensitivity and specificity) with accurate individual prediction in approximately 60% of the patients. Plasma NfL differentiated frontotemporal dementia (FTD) syndromes from CU (0.90) and non-neurodegenerative causes (0.93), whereas the discriminative capacity with AD and between all neurodegenerative and non-neurodegenerative causes was less accurate. A combination of p-tau181 and NfL identified FTD with 82% sensitivity and 85% specificity and had a negative predictive value for neurodegenerative diagnosis of 86%, ruling out half of the non-neurodegenerative diagnoses. In the subcohort without AD biomarkers, similar results were obtained. T-tau and UCH-L1 did not offer added diagnostic value. DISCUSSION: Plasma p-tau181 predicted amyloid status with high accuracy and could have potentially avoided CSF/amyloid PET testing in approximately 60% of subjects in a memory clinic setting. NfL was useful for identifying FTD from non-neurodegenerative causes but behaved worse than p-tau181 in all other comparisons. Combining p-tau181 and NfL improved diagnostic performance for FTD and non-neurodegenerative diagnoses. However, the 14% false-negative results suggest that further improvement is needed before implementation outside memory clinics. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that plasma p-tau181 correlates with the presence or absence of AD and a combination of plasma p-tau181 and NfL correlates moderately well with a diagnosis of FTD.

Early-onset Alzheimer's disease shows a distinct neuropsychological profile and more aggressive trajectories of cognitive decline than late-onset.

OBJECTIVES: Early- and late-onset Alzheimer's disease (EOAD and LOAD) share the same neuropathological traits but show distinct cognitive features. We aimed to explore baseline and longitudinal outcomes of global and domain-specific cognitive function in a well characterized cohort of patients with a biomarker-based diagnosis. METHODS: In this retrospective cohort study, 195 participants were included and classified according to their age, clinical status, and CSF AD biomarker profile: 89 EOAD, 37 LOAD, 46 young healthy controls (age ≤ 65 years), and 23 old healthy controls (>65 years). All subjects underwent clinical and neuropsychological assessment, neuroimaging, APOE genotyping and lumbar puncture. RESULTS: We found distinct neuropsychological profiles between EOAD and LOAD at the time of diagnosis. Both groups showed similar performances on memory and language domains, but the EOAD patients displayed worsened deficits in visual perception, praxis, and executive tasks (p < 0.05). Longitudinally, cognitive decline in EOAD was more pronounced than LOAD in the global outcomes at the expense of these non-amnestic domains. We found that years of education significantly influenced the decline in most of the neuropsychological tests. Besides, the APOE ε4 status showed a significant effect on the decline of memory-related tasks within the EOAD cohort (p < 0.05). INTERPRETATION: Age of onset is a main factor shaping the cognitive trajectories in AD patients, with younger age driving to a steeper decline of the non-memory domains. Years of education are related to a transversal decline in all cognitive domains and APOE ε4 status to a specific decline in memory performance in EOAD.