Patients with Alzheimer dementia display evidence of amyloid-related neurodegeneration. Our focus was to determine whether such patients also display evidence of a disease-targeting adaptive immune response mediated by CD4+ T cells. To test this hypothesis, we evaluated the CSF immune profiles of patients with Alzheimer clinical syndrome (ACS), who display clinically defined dementia. Innate and adaptive immune profiles of patients with ACS were measured using multicolor flow cytometry. CSF-derived CD4+ and CD8+ T-cell receptor repertoire genetics were measured using next-generation sequencing. Brain-specific autoantibody signatures of CSF-derived antibody pools were measured using array technology or ELISA. CSF from similar-age healthy controls (HCs) was used as a comparator cohort. Innate cells were expanded in the CSF of patients with ACS in comparison to HCs, and innate cell expansion increased with age in the patients with ACS, but not HCs. Despite innate cell expansion in the CSF, the frequency of total CD4+ T cells reduced with age in the patients with ACS. T-cell receptor repertoire genetics indicated that T-cell clonal expansion is enhanced, and diversity is reduced in the patients with ACS compared with similar-age HCs. Examination of CSF indicates that CD4+ T cell-mediated adaptive immune responses are altered in patients with ACS. Understanding the underlying mechanisms affecting adaptive immunity will help move us toward the goal of slowing cognitive decline.

The TAR DNA-binding protein 43 (TDP-43) has been identified as the major disease protein in amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with ubiquitin inclusions (FTLD-U), defining a novel class of neurodegenerative conditions: the TDP-43 proteinopathies. The first pathogenic mutations in the gene encoding TDP-43 (TARDBP) were recently reported in familial and sporadic ALS patients, supporting a direct role for TDP-43 in neurodegeneration. In this study, we report the identification and functional analyses of two novel and one known mutation in TARDBP that we identified as a result of extensive mutation analyses in a cohort of 296 patients with variable neurodegenerative diseases associated with TDP-43 histopathology. Three different heterozygous missense mutations in exon 6 of TARDBP (p.M337V, p.N345K, and p.I383V) were identified in the analysis of 92 familial ALS patients (3.3%), while no mutations were detected in 24 patients with sporadic ALS or 180 patients with other TDP-43-positive neurodegenerative diseases. The presence of p.M337V, p.N345K, and p.I383V was excluded in 825 controls and 652 additional sporadic ALS patients. All three mutations affect highly conserved amino acid residues in the C-terminal part of TDP-43 known to be involved in protein-protein interactions. Biochemical analysis of TDP-43 in ALS patient cell lines revealed a substantial increase in caspase cleaved fragments, including the approximately 25 kDa fragment, compared to control cell lines. Our findings support TARDBP mutations as a cause of ALS. Based on the specific C-terminal location of the mutations and the accumulation of a smaller C-terminal fragment, we speculate that TARDBP mutations may cause a toxic gain of function through novel protein interactions or intracellular accumulation of TDP-43 fragments leading to apoptosis.

Objective

To identify potential genetic modifiers contributing to the phenotypic variability that is detected in patients with repeat expansions in chromosome 9 open reading frame 72 (C9ORF72), we investigated the frequency of these expansions in a cohort of 334 subjects previously found to carry mutations in genes known to be associated with a spectrum of neurodegenerative diseases.

Methods

A 2-step protocol, with a fluorescent PCR and a repeat-primed PCR, was used to determine the presence of hexanucleotide expansions in C9ORF72. For one double mutant, we performed Southern blots to assess expansion sizes, and immunohistochemistry to characterize neuropathology.

Results

We detected C9ORF72 repeat expansions in 4 of 334 subjects (1.2% [or 1.8% of 217 families]). All these subjects had behavioral phenotypes and also harbored well-known pathogenic mutations in either progranulin (GRN: p.C466LfsX46, p.R493X, p.C31LfsX35) or microtubule-associated protein tau (MAPT: p.P301L). Southern blotting of one double mutant with a p.C466LfsX46 GRN mutation demonstrated a long repeat expansion in brain (>3,000 repeats), and immunohistochemistry showed mixed neuropathology with characteristics of both C9ORF72 expansions and GRN mutations.

Conclusions

Our findings indicate that co-occurrence of 2 evidently pathogenic mutations could contribute to the pleiotropy that is detected in patients with C9ORF72 repeat expansions. These findings suggest that patients with known mutations should not be excluded from further studies, and that genetic counselors should be aware of this phenomenon when advising patients and their family members.

Mechanisms of resilience against tau pathology in individuals across the Alzheimer's disease spectrum are insufficiently understood. Longitudinal data are necessary to reveal which factors relate to preserved cognition (i.e. cognitive resilience) and brain structure (i.e. brain resilience) despite abundant tau pathology, and to clarify whether these associations are cross-sectional or longitudinal. We used a longitudinal study design to investigate the role of several demographic, biological and brain structural factors in yielding cognitive and brain resilience to tau pathology as measured with PET. In this multicentre study, we included 366 amyloid-β-positive individuals with mild cognitive impairment or Alzheimer's disease dementia with baseline 18F-flortaucipir-PET and longitudinal cognitive assessments. A subset (n = 200) additionally underwent longitudinal structural MRI. We used linear mixed-effects models with global cognition and cortical thickness as dependent variables to investigate determinants of cognitive resilience and brain resilience, respectively. Models assessed whether age, sex, years of education, APOE-ε4 status, intracranial volume (and cortical thickness for cognitive resilience models) modified the association of tau pathology with cognitive decline or cortical thinning. We found that the association between higher baseline tau-PET levels (quantified in a temporal meta-region of interest) and rate of cognitive decline (measured with repeated Mini-Mental State Examination) was adversely modified by older age (Stβinteraction = -0.062, P = 0.032), higher education level (Stβinteraction = -0.072, P = 0.011) and higher intracranial volume (Stβinteraction = -0.07, P = 0.016). Younger age, higher education and greater cortical thickness were associated with better cognitive performance at baseline. Greater cortical thickness was furthermore associated with slower cognitive decline independent of tau burden. Higher education also modified the negative impact of tau-PET on cortical thinning, while older age was associated with higher baseline cortical thickness and slower rate of cortical thinning independent of tau. Our analyses revealed no (cross-sectional or longitudinal) associations for sex and APOE-ε4 status on cognition and cortical thickness. In this longitudinal study of clinically impaired individuals with underlying Alzheimer's disease neuropathological changes, we identified education as the most robust determinant of both cognitive and brain resilience against tau pathology. The observed interaction with tau burden on cognitive decline suggests that education may be protective against cognitive decline and brain atrophy at lower levels of tau pathology, with a potential depletion of resilience resources with advancing pathology. Finally, we did not find major contributions of sex to brain nor cognitive resilience, suggesting that previous links between sex and resilience might be mainly driven by cross-sectional differences.

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer's disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid-positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

Background

Amyloid deposition is a potential contributor to postoperative cognitive dysfunction. The authors hypothesized that 6-week global cortical amyloid burden, determined by F-florbetapir positron emission tomography, would be greater in those patients manifesting cognitive dysfunction at 6 weeks postoperatively.

Methods

Amyloid deposition was evaluated in cardiac surgical patients at 6 weeks (n = 40) and 1 yr (n = 12); neurocognitive function was assessed at baseline (n = 40), 6 weeks (n = 37), 1 yr (n = 13), and 3 yr (n = 9). The association of 6-week amyloid deposition with cognitive dysfunction was assessed by multivariable regression, accounting for age, years of education, and baseline cognition. Differences between the surgical cohort with cognitive deficit and the Alzheimer's Disease Neuroimaging Initiative cohorts (normal and early/late mild cognitive impairment) was assessed, adjusting for age, education, and apolipoprotein E4 genotype.

Results

The authors found that 6-week abnormal global cortical amyloid deposition was not associated with cognitive dysfunction (13 of 37, 35%) at 6 weeks postoperatively (median standard uptake value ratio [interquartile range]: cognitive dysfunction 0.92 [0.89 to 1.07] vs. 0.98 [0.93 to 1.05]; P = 0.455). In post hoc analyses, global cortical amyloid was also not associated with cognitive dysfunction at 1 or 3 yr postoperatively. Amyloid deposition at 6 weeks in the surgical cohort was not different from that in normal Alzheimer's Disease Neuroimaging Initiative subjects, but increased over 1 yr in many areas at a rate greater than in controls.

Conclusions

In this study, postoperative cognitive dysfunction was not associated with 6-week cortical amyloid deposition. The relationship between cognitive dysfunction and regional amyloid burden and the rate of postoperative amyloid deposition merit further investigation.

Background

Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.

Methods

The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.

Findings

The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).

Interpretation

Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.

Funding

The specific funding of this article is provided in the acknowledgements section.