Synaptic damage, axonal neurodegeneration, and neuroinflammation are common features in Alzheimer's disease (AD), frontotemporal dementia (FTD), and Creutzfeldt-Jakob disease (CJD).
Unicentric cohort ...of 353 participants included healthy control (HC) subjects, AD continuum stages, genetic AD and FTD, and FTD and CJD. We measured cerebrospinal fluid neurofilament light (NF-L), neurogranin (Ng), 14-3-3, and YKL-40 proteins.
Biomarkers showed differences in HC subjects versus AD, FTD, and CJD. Disease groups differed between them except AD versus FTD for YKL-40. Only NF-L differed between all stages within the AD continuum. AD and FTD symptomatic mutation carriers presented differences with respect to HC subjects. Applying the AT(N) system, 96% subjects were positive for neurodegeneration if 14-3-3 was used, 94% if NF-L was used, 62% if Ng was used, and 53% if YKL-40 was used.
Biomarkers of synapse and neurodegeneration differentiate HC subjects from neurodegenerative dementias and between AD, FTD, and CJD. NF-L and 14-3-3 performed similar to total tau when AT(N) system was applied.
•Neurofilament light (NF-L) levels are increased in neurodegenerative dementias.•14-3-3 protein is increased in Alzheimer's disease (AD) and frontotemporal dementia (FTD).•Neurogranin is decreased in FTD and increased in AD and Creutzfeldt-Jakob disease.•NF-L and 14-3-3 are good neurodegeneration markers when applied in the AT(N) system.•Only cerebrospinal fluid NF-L levels tracked disease progression in AD.
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.
We use machine learning tools in a multidisease approach to assess differential discrimination of Alzheimer's disease and frontotemporal dementia. Importantly, our approach includes a feature reduction strategy, making it computationally efficient and it also considers the study of associated brain patterns to gain explainability of the algorithm.
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
-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.
Sex differences in early‐onset Alzheimer's disease Contador, José; Pérez‐Millan, Agnès; Guillén, Nuria ...
European journal of neurology,
December 2022, 2022-12-00, 20221201, Letnik:
29, Številka:
12
Journal Article
Recenzirano
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.
The diagnosis of incipient symptomatic stages of early-onset dementia is challenging. The magnetic resonance imaging (MRI) is an easy-access biomarker.
We aim to determine the distribution and ...diagnostic performance of the existing atrophy visual rating scales on MRI in initial stages of the most frequent neurodegenerative early onset dementias.
We evaluated the visual atrophy scales usefulness in two hundred subjects: seventy sporadic early onset Alzheimer's disease (AD) patients (48 amnestic and 22 non-amnestic), 14 patients with autosomal-dominant AD (ADAD), 25 sporadic frontotemporal dementia patients 11 with behavioral variant (bvFTD), nine with semantic variant of primary progressive aphasia (svPPA), and 5 with non-fluent primary progressive aphasia (nfvPPA), 7 with genetically determined FTD (genetic FTD), 25 mild cognitive impairment due to non-degenerative disorders, and 59 healthy controls. All had MMSE≥18, 3T-brain MRI, and biomarker-supported diagnosis. Two raters evaluated six frontal, temporal, and parietal scales. Inter-rater reliability and diagnostic performance in terms of area under the receiver-operator curves and balanced accuracy were analyzed.
Best scales to discriminate AD from controls were the anterior cingulate scale for amnestic and the posterior atrophy scale for sporadic non-amnestic AD and ADAD. The anterior temporal scale was the best for sporadic bvFTD and svPPA and the anterior cingulate scale was for nfvPPA. All scales performed well for the genetic FTD. However, no scale demonstrated good performance at discriminating AD from FTD or non-degenerative disorders.
The clinicians should interpret with caution atrophy scale assessment in subjects with early-onset cognitive impairment given that none of the evaluated scales met the requirements for being a diagnostic biomarker.
•Posterior cortices, hippocampus and amygdala track atrophy in EOAD over two years.•The medial temporal cortex is unaltered in EOAD at early stages.•EOAD exhibited a posterior-to-anterior gradient of ...cortical loss after two years.•EOAD subcortical volume loss extends beyond hippocampus and amygdala after two years.•Cerebrospinal fluid biomarkers might predict atrophy rates in EOAD.
There is evidence of longitudinal atrophy in posterior brain areas in early-onset Alzheimer’s disease (EOAD; aged < 65 years), but no studies have been conducted in an EOAD cohort with fluid biomarkers characterization. We used 3T-MRI and Freesurfer 6.0 to investigate cortical and subcortical gray matter loss at two years in 12 EOAD patients (A + T + N + ) compared to 19 controls (A-T-N-) from the Hospital Clínic Barcelona cohort. We explored group differences in atrophy patterns and we correlated atrophy and baseline CSF-biomarkers levels in EOAD. We replicated the correlation analyses in 14 EOAD (A + T + N + ) and 55 late-onset AD (LOAD; aged ≥ 75 years; A + T + N + ) participants from the Alzheimer's disease Neuroimaging Initiative. We found that EOAD longitudinal atrophy spread with a posterior-to-anterior gradient and beyond hippocampus/amygdala. In EOAD, higher initial CSF NfL levels correlated with higher ventricular volumes at baseline. On the other hand, higher initial CSF Aβ42 levels (within pathological range) predicted higher rates of cortical loss in EOAD. In EOAD and LOAD subjects, higher CSF t-tau values at baseline predicted higher rates of subcortical atrophy. CSF p-tau did not show any significant correlation. In conclusion, posterior cortices, hippocampus and amygdala capture EOAD atrophy from early stages. CSF Aβ42 might predict cortical thinning and t-tau/NfL subcortical atrophy.
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.
Plasma biomarkers have emerged as promising tools for identifying amyloid beta (Aβ) 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 Aβ status. Three hundred sixty patients (mean age 66.5 years, 55% females, 53% Aβ positive) were included. Sex, age and Aβ status-adjusted models showed that only estimated glomerular filtration rate (eGFR, standardized β −0.115 −0.192 to −0.035,
p
= 0.005) was associated with p-tau181 levels, although with a much smaller effect than Aβ 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 Aβ 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 Aβ status discrimination was minimal.
Background
The ongoing COVID‐19 pandemic and related care policies have affected dementia patients. The characteristics of early‐onset dementia (EOD, <65 years) patients in 2020 may provide insights ...on how to rearrange the provision of care.
Method
We retrospectively reviewed, from 2016 to 2020, the demographic and clinical data of the new referrals at our EOD clinic (Hospital Clínic Barcelona). We used Fisher’s Exact test and Mann–Whitney U test in R4.0.2 (http://www.R‐project.org/) to analyze differences between 2020 and the period 2016‐2019.
Result
In 2020, we did not visit any new referral from 15th march to 31th may. We evaluated 104 patients in 2020 and 392 patients in 2016‐2019 (mean=98(SD=11.8) patients/year). No differences were found in age at onset (AAO), sex, diagnostic delay and MMSE score (Table1). Significant differences were found in the diagnoses obtained in each period (p<0.000005, Figure1A). In 2020, 19.2% of the patients were diagnosed with neurodegenerative diseases (ND), 48.1% with non‐neurodegenerative diseases (NND) and 32.7% with subjective cognitive decline (SCD). On contrast, in 2016‐2019, 26% of the patients were diagnosed with ND, 22.2% with NND and 51.8% with SCD. Compared to 2016‐2019, ND, but not SCD or NND, presented longer diagnostic delay in 2020 (p<0.0005, Figure1B). ND, NND and SCD did not show differences between periods in AAO, sex or MMSE.
We did not find differences in the type of ND in each period (Figure1A). Compared to 2016‐2019, Frontotemporal Lobar Degeneration (FTLD) presented longer diagnostic delay in 2020 (p<0.005, Figure1B) while ND subgroups did not show differences in AAO, sex or MMSE. Cognitive disturbances in recovered COVID‐19 patients accounted for 16% of NND in 2020 N=8, AAO 50.63(12), 63% female, MMSE 26.8(2.3).
Conclusion
In 2020, albeit we were forced to stop our normal activity during 2.5 months, we visited a similar number of patients among which we observed an increase in NND, including cognitive disturbances in patients with recovered COVID‐19. On contrast, we found a reduction in SCD and, to a lesser extent, ND. ND showed a longer diagnostic delay in 2020 that mainly affected FTLD. Whether COVID‐19 pandemic entails a diagnostic delay in dementia patients must be confirmed in 2021.
Background
MRI atrophy predicts cognitive status in AD. However, this relationship has not been investigated in early-onset AD (EOAD, < 65 years) patients with a biomarker-based diagnosis.
Methods
...Forty eight EOAD (MMSE ≥ 15; A + T + N +) and forty two age-matched healthy controls (HC; A − T − N −) from a prospective cohort underwent full neuropsychological assessment, 3T-MRI scan and lumbar puncture at baseline. Participants repeated the cognitive assessment annually. We used linear mixed models to investigate whether baseline cortical thickness (CTh) or subcortical volume predicts two-year cognitive outcomes in the EOAD group.
Results
In EOAD, hemispheric CTh and ventricular volume at baseline were associated with global cognition, language and attentional/executive functioning 2 years later (
p
< 0.0028). Regional CTh was related to most cognitive outcomes (
p
< 0.0028), except verbal/visual memory subtests. Amygdalar volume was associated with letter fluency test (
p
< 0.0028). Hippocampal volume did not show significant associations.
Conclusion
Baseline hemispheric/regional CTh, ventricular and amygdalar volume, but not the hippocampus, predict two-year cognitive outcomes in EOAD.
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