bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance on the graded naming test in a population‐based sample of 72‐year‐olds: Associations with life‐course predictors and β‐amyloid pathology
Authors: Street Rebecca E. , Lu Kirsty , Huckvale Mark , Brotherhood Emilie V. , Pavisic Ivanna M. , James Sarah‐Naomi , Keuss Sarah E. , Buchanan Sarah M. , Parker Thomas D. , Lane Christopher A. , Keshavan Ashvini , Murray‐Smith Heidi , Cash David M. , Coath William , Wong Andrew , Fox Nick C. , Richards Marcus , Schott Jonathan M. , Crutch Sebastian J.
Published: December 2020

Background

Word‐finding difficulties are a common early feature of Alzheimer’s disease (AD) and may be detectable during the preclinical stage. However, the relationship between changes in naming ability and accumulation of β‐amyloid pathology is not fully understood, and questions remain about the role of factors such as sex and education.

Method

Participants in Insight 46, a sub‐study of the British 1946 birth cohort, completed baseline cognitive assessments and neuroimaging (combined MRI/18F‐Florbetapir‐PET) at age 69‐71. Follow‐up assessments are currently underway (mean interval 28.9 months, SD 2.1) and include an audio‐recorded version of the 30‐item Graded Naming Test (GNT), which was not administered at baseline. Preliminary interim analyses have been conducted based on 211 cognitively‐normal individuals with complete neuroimaging data (see Table 1 for characteristics). A multivariable regression model was used to investigate predictors of picture naming accuracy, where the outcome was GNT score (max. 30) and predictors were sex, age at follow‐up assessment, β‐amyloid status at baseline (positive / negative), APOE genotype (ε4 carrier/non‐carrier), and prospectively‐collected measures of childhood cognitive ability, education and socioeconomic position (based on occupation). Due to the negatively‐skewed distribution of GNT scores, bootstrapping was used to produce bias‐corrected and accelerated 95% confidence intervals from 2,000 replications.

Results will be updated to include the full sample before the conference. We also plan to include data on naming latency extracted from the audio‐recordings, which may be a more sensitive measure of early changes than naming accuracy.

Result

Higher childhood cognitive ability predicted higher GNT scores over 60 years later (Table 2, Figure 1). Men scored 0.9 points higher than women on average. Amyloid‐positive participants scored 1.2 points lower than amyloid‐negative participants on average. These effects were all significant at the 5% level and were mutually independent.

Conclusion

Subtle changes in naming accuracy associated with β‐amyloid pathology are detectable in cognitively‐normal individuals as early as age 72. Performance is additionally influenced by sex and general cognitive ability, so these factors should be accounted for where possible in future studies and clinical trials that seek to detect and track the emergence of naming deficits.
Cognitive and functional progression along the continuum from mild cognitive impairment (MCI) to Alzheimer’s disease (AD): A latent class analysis using the U.S. National Alzheimer’s Coordinating Center (NACC) data set
Authors: Mouchet Julie , Betts Keith A. , Ionescu‐Ittu Raluca , Butler Lesley M. , Delmar Paul , Georgieva Mihaela V. , Kulalert Thomas , Desai Urvi
Published: December 2020

Background

The trajectory of AD progression is heterogeneous between individuals, and the predictors of cognitive and functional decline associated with rapid progression remain poorly understood. Better understanding of the cognitive and functional trajectories and predictors of decline is critical for articulating the natural history of the disease, identifying subgroups of individuals with MCI at higher risk of developing dementia, and contextualizing clinical trial data for AD treatments.

Method

Subjects newly diagnosed with MCI in the NACC uniform data set from September 2005 to February 2019 were identified. Cognition and function were measured by the Clinical Dementia Rating‐Sum of Boxes (CDR‐SB). CDR‐SB score trajectories were fitted using a linear multi‐step growth mixture model, which was then used to identify three latent classes of subjects based on the similarity of progression patterns. Multivariate multinomial logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for baseline predictors across the latent classes.

Result

Of 830 subjects included in the study sample, 42% were male, and the median age was 79.0 years. Median CDR‐SB at the time of MCI diagnosis was 0.5 (interquartile range=0.5–1.0; Table). Average follow‐up after initial MCI diagnosis was 3.6 years (standard deviation=2.5). Subjects were classified in three latent classes with differing CDR‐SB trajectories: fast progressors (21% of sample; average CDR‐SB increase=1.87 points/year), slow progressors (22%; average CDR‐SB increase=0.51 points/year), and non‐progressors (57%; no CDR‐SB increase) (Figure). Independent factors at MCI diagnosis associated with fast progressors vs non‐progressors were: older age (OR 1.04, 95% CI 1.01–1.07), female sex (2.00, 1.23–3.23), one copy of apo ε4 (1.89, 1.18–3.02), higher baseline CDR‐SB score (3.20, 2.25–4.54), and lower baseline Mini Mental State Examination (MMSE) score (0.89, 0.81–0.98).

Conclusion

In this NACC‐based study, three latent classes of subjects were identified based on CDR‐SB progression trajectories. Older age, female sex, one copy of apo ε4, and worse baseline cognitive scores (CDR‐SB and MMSE) were associated with faster progression on CDR‐SB.
Modelling the cascade of biomarker changes in progranulin‐related frontotemporal dementia
Authors: Venkatraghavan Vikram , Panman Jessica L. , van der Ende Emma Louise , Steketee Rebecca , Jiskoot Lize C. , Poos Jackie M. , Dopper Elise G.P. , Meeter Lieke H.H. , Kaat Laura Donker , Rombouts Serge A.R.B. , Vernooij Meike W. , Kievit Anneke J.A. , Premi Enrico , Cosseddu Maura , Bonomi Elise , Olives Jaume , Rohrer Jonathan D. , Sanchez‐Valle Raquel , Borroni Barbara , Bron Esther E. , van Swieten John C. , Papma Janne M. , Klein Stefan ,   GENFI Consortium investigators
Published: December 2020

Background

Progranulin related frontotemporal dementia (FTD‐GRN) is a fast progressive disorder, in which pathophysiological changes precede overt clinical symptoms in only a short time period. Modelling the cascade of multimodal biomarker changes aids in understanding the etiology of this disease, enables monitoring of individual mutation carriers, and would give input for disease‐modifying treatments. In this cross‐sectional study, we estimated the temporal cascade of biomarker changes for FTD‐GRN, in a data‐driven way.

Method

We included 56 presymptomatic and 35 symptomatic GRN mutation carriers, and 35 healthy non‐carriers. Of the symptomatic subjects, 17 had behavioural variant FTD (bvFTD), 16 presented as non‐fluent variant primary progressive aphasia (nfvPPA). The selected biomarkers for establishing the cascade of changes were neurofilament light chain, regional grey matter volumes, fractional anisotropy of white matter tracts, and cognitive domains. We used a data‐driven analysis called discriminative event‐based modelling (Venkatraghavan, NeuroImage, 2019) with a novel modification to its Gaussian Mixture Model (GMM) called Siamese GMM, to estimate the cascade of biomarker changes for FTD‐GRN. Using cross‐validation, we estimated disease severities of individual mutation carriers in the test set based on their progression along the biomarker cascade established on the training set.

Result

Neurofilament light chain and white matter tracts were the earliest biomarkers to become abnormal in FTD‐GRN mutation carriers. Attention and executive functioning were also affected early on in the disease process. Based on the estimated individual disease severities, presymptomatic mutation carriers could be distinguished from symptomatic mutation carriers with a sensitivity of 95% and specificity of 100% in the cross‐validation experiment. There was a high correlation (r=0.94, p<0.001) between estimated disease severity and years since symptom onset in nfvPPA, but not in bvFTD (r=0.33, p=0.46).

Conclusion

In this study, we unravelled the temporal cascade of multimodal biomarker changes for FTD‐GRN. Our results suggest that axonal degeneration is one of the first disease events in FTD‐GRN, which calls for designing disease modifying treatments that strengthens the axons. We also demonstrated a good delineation between symptomatic and presymptomatic carriers using the estimated disease severities, which suggest that our model could enable monitoring of individual mutation carriers.
NLRP3 inflammasome activation regulates microglial migration
Authors: McManus Roisin M. , Griep Angelika , Gomez‐Cabrero David , Tegner Jesper , Heneka Michael T.
Published: December 2020

Background

This laboratory has demonstrated an important role for the NLRP3 inflammasome in the progression of Alzheimer’s disease (AD) with activation found in post‐mortem tissue from individuals with AD (Heneka et al., 2013) and in transgenic mouse models of AD expressing amyloid precursor protein (APP) and presenilin 1 (PS1; APP/PS1 mice). However, APP/PS1 mice deficient for NLRP3 were protected from AD‐pathology, with a significant reduction in cleaved‐caspase‐1 and IL‐1β. We wanted to examine the wider implications of NLRP3 inflammasome deficiency beyond IL‐1β, and uncover other pathways and signaling mechanisms employed by microglia to protect the brain from the build up of Aβ.

Method

RNA sequencing was performed on CD11b+ cells from the brains of 4, 6 and 12 month old wildtype, APP/PS1, NLRP3‐/‐ and APP/PS1.NLRP3‐/‐ mice. Microglia were prepared from the brains of 1‐day old wildtype and NLRP3‐/‐ mice for live cell imaging, migratory assays and flow cytometric analysis. In a separate series of experiments CD11b+ cells were isolated from the brains of wildtype, APP/PS1, NLRP3‐/‐ and APP/PS1.NLRP3‐/‐ mice and myeloid cells were also prepared from the blood of humans with and without AD to replicate all findings in these models.

Result

The RNA sequencing analysis found a significant increase in pathways related to chemokine signaling in NLRP3‐/‐ and APP/PS1. NLRP3‐/‐ mice. Wildtype microglia significantly migrated to Aβ, however NLRP3‐/‐ microglia or cells treated with NLRP3 inhibitors had increased migration to Aβ and greater Aβ phagocytosis, which was associated with CCL2 release and mediated via the receptors CCR2 and CCR4. Importantly, we were able to replicate these findings using monocyte‐derived microglia‐like cells from humans and in NLRP3‐/‐ and APP/PS1/NLRP3‐/‐ mice ex vivo.

Conclusion

We have identified a new mechanism where microglia release CCL2 to migrate to Aβ and phagocytose this peptide. Critically, this activity is increased in NLRP3‐/‐ cells or those treated with NLRP3‐specific inhibitors, together demonstrating a previously unknown role for the NLRP3 inflammasome in regulating cellular migratory and phagocytic behavior. These findings provide insights into the pathogenesis of AD and importantly highlight the NLRP3 inflammasome as a serious target for prevention and early treatment of AD.
The effect of educational intervention by tele‐nursing on caregiver burden in family caregivers of elderly with Alzheimer's disease
Authors: Sadeghmoghadam Leila , Shahriyan Fariba , Delshad Ali , Deluee Masoume Amiri
Published: December 2020

Background

It is estimated that 90% of people with Alzheimer's disease live in their homes. And in 80% of the cases, the burden of caring for these people falls on family members. Therefore, it is necessary to develop and evaluate intervention programs for home caregivers to prevent and relieve their caregiver burden. The aim of this study was to determine the effect of educational intervention by tele‐nursing on caregiver burden in family caregivers of elderly with Alzheimer's disease.

Method

This quasi‐experimental study was performed on 44 home caregivers of elderly people with Alzheimer's disease in Neyshabur during In the first six months of 2019. The participants in the first stage were selected based on inclusion criteria and purposeful sampling method. In the second stage, they were randomly divided into intervention and control groups by by permutation blocks. For the intervention group, 6 weeks educational intervention was conducted through tele‐nursing. The control group received routine care. Data collection tools included demographic information form and standard Copenhagen Burnout Inventory questionnaire (CBI). Data were analyzed by SPSS version 22 and independent t‐test, paired t‐test and chi‐square tests.

Result

The mean score of total caregiver burden before intervention was not significantly different between the two groups (P = 0.425). After the intervention, a significant decrease was observed in the intervention group compared to the control group (P <0.0001). The control and intervention groups had a significant difference in the caregiver burden rating after the intervention (P <0.0001).

Conclusion

The findings of the present study indicate that training through telenursing is an effective and cost effective method of reducing the care burden of caregivers of older people with Alzheimer's disease. Since the issue of family care for Alzheimer's sufferers is a very sensitive and important issue for Alzheimer's policy makers and social service providers. And while this requires a great deal of cost and human resources, it can be suggested as a low‐cost and effective method of reducing the burden of care for caregivers of patients with Alzheimer's disease.
Is machine learning prediction of Aβ positivity consistent? An assessment of multiple datasets
Authors: Grecchi Elisabetta , Grisan Enrico , Buckley Christopher , Wolber Jan
Published: December 2020

Background

Recent applications of machine learning methodologies in AD suggest that synergistic utilisation of imaging and non‐imaging biomarkers may improve the ability to predict subject’s likelihood to present with amyloid pathology prior to performing a PET scan. In this work we developed an algorithm for prediction of patient amyloid positivity that shows robust performance across different databases, thus being more likely to be suitable for real‐world application.

Method

Machine learning (ML) algorithms that combine imaging (MRI volumes), genetic (ApoE status), psychometric (MMSE and CDR), and demographic (age/gender) data were developed to predict a patient’s probability of being Aβ positive. The patient’s [18F]flutemetamol PET scan served as the standard of truth (SoT). Two ML methodologies (LASSO and RUS‐BOOST) were tested to tackle both the unbalance between Aβ positive/negative patients and selection of volumetric brain regions. The algorithms were trained and tested using combination of 5 different databases, excluding all healthy controls: MCI Progression Phase III trial (204 MCI), AIBL (52 MCI, 16 pAD), Biofinder (117 SCD, 147 AD), and a subset of ADNI data selected for the ADNI evolution prediction data (564 MCI, 147 AD).

Result

The cross‐validation results and the performance of the algorithms with different test sets are reported in Tables 1 and 2. We report AUC, accuracy, sensitivity, specificity, PPV and NPV for a probability threshold of 0.5 (Table1). PPV and NPV are also reported for threshold optimized and selected by the algorithm to provide a sensitivity of approximately 0.75 (Table 2), which results in a lower rate of misclassified amyloid positive.

Conclusion

We show that the developed algorithms can be confidently used across several independently acquired datasets. Our results suggest that for optimal usage as screening tool in a specific clinical trial, ML techniques should be adjusted for the characteristic of the specific population under analysis. Despite fairly consistent performance of the two methods within cross validation, results can vary when applying the learned models to different datasets, suggesting that cohort selection criteria, composition, and geographical origin may additionally influence outcomes.
Relationships of time‐varying resting state network stability and cognitive function along the Alzheimer’s disease spectrum
Authors: Chumin Evgeny J. , Risacher Shannon L. , West John D. , Apostolova Liana G. , Farlow Martin R. , McDonald Brenna C. , Saykin Andrew J. , Sporns Olaf
Published: December 2020

Background

Resting‐state functional connectivity (rsFC) neuroimaging studies of Alzheimer’s Disease (AD) have reported alterations in network community structure and time‐varying rsFC (tvFC). However, the temporal stability of community organization in tvFC has not been investigated. Therefore, the purpose of this work was to characterize the relationship of tvFC and cognitive function (CF) along the spectrum of AD progression.

Methods

Data were part of the Indiana Alzheimer Disease Center and Indiana Memory and Aging study cohorts, randomly split into discovery and validation samples (Table1). After standard preprocessing, rsFC (10min acquisition) and tvFC (∼60sec tapered, partially overlapping windows) were estimated for two cortical brain region parcellations (200 and 300 nodes). Modularity was estimated for rsFC and for all tvFC windows, across a range of community scales, with final network stability metric calculated as area under the curve of the mean temporal agreement across scales, within and between seven canonical resting‐state networks (RSNs). The Montreal Cognitive Assessment score was used as an index of overall CF. Spearman partial correlation (adjusted for age, education, and sex) was used to investigate relationships of tvFC stability with CF.

Results

No significant relationships were identified between CF and rsFC or tvFC community structure metrics (quality and number of communities). For tvFC, temporal stability of two network blocks, between visual‐frontoparietal and within ventral attention, showed significant associations with CF across both samples and both parcellations. Additionally, in the 300‐node parcellation, visual network metrics correlated with CF (Figure1). CF was negatively associated with tvFC community stability of regions belonging to different RSNs (Figure1A) and positively with regions within RSNs (Figure1B,C).

Conclusion

Overall, these findings demonstrate a loss in the stability of RSNs in tvFC with decreasing CF. The networks identified here are generally implicated in cognitive control as well as orientation and attention to salient stimuli. These findings demonstrate that tvFC stability (independent of community and parcellation size) is related to CF along a spectrum of AD risk. Temporal dynamics at rest have potential as a biomarker to characterize progression in prodromal AD. Longitudinal studies are needed to assess the predictive validity of RSN dynamics.
CSF 7‐ketocholesterol is related to β‐amyloid and white matter microstructure in healthy adults
Authors: Iriondo Ane , Garcia‐Sebastian Maite , Arrospide Arantzazu , Arriba Maria , Aurtenetxe Sara , Barandiaran Myriam , Clerigue Montserrat , Ecay Mirian , Estanga Ainara , Gabilondo Alazne , Izagirre Andrea , Saldias Jon , Tainta Mikel , Villanua Jorge A , Blennow Kaj , Zetterberg Henrik , Mar Javier , Abad‐García Beatriz , Dias Irundika , Goñi Félix María , Martinez‐Lage Pablo
Published: December 2020

Background

Abnormal cholesterol metabolism changes the neuronal membrane and interferes with amyloidogenesis. Oxysterols in CSF are related to Alzheimer’s disease (AD) biomarkers in mild cognitive impairment and dementia. Cholesterol turnover is important for axonal and white matter (WM) microstructure maintenance. We aim to demonstrate that the association of oxysterols, biomarkers and WM microstructure occurs early in asymptomatic individuals.

Method

We studied the association of inter‐individual variability of CSF 24‐hydroxycholesterol (24‐OHC), 27‐hydroxycholesterol (27‐OHC), 7‐ketocholesterol (7‐KC), 7β‐hydroxycholesterol (7β‐OHC), β‐amyloid1‐42 (Aβ42), total‐tau (t‐tau), phosphorylated‐tau (p‐tau), neurofilament (NfL) and WM microstructure in cognitive regions, using diffusion tensor imaging (DTI), generalized lineal models and moderation/mediation analyses in 153 healthy adults.

Result

7‐KC levels were related to Aβ42 (B = ‐1.41; p = 0.041). Higher 7‐KC levels were related to lower fractional anisotropy (FA) and higher mean (MD), axial (AxD) and radial (RD) diffusivity in cognitive regions. 7‐KC modulated the association between AxD and NfL in the corpus callosum splenium (B = 39.39, p = 0.017) and genu (B = 68.64, p = 0.000) and fornix (B = 10.97, p = 0.000). Lower Aβ42 levels were associated to lower FA and higher MD, AxD and RD in the fornix, corpus callosum, inferior longitudinal fasciculus and hippocampus. The association between AxD and Aβ42 was moderated by 7K‐C (p= 0.048).

Conclusion

This study adds clinical evidence to support the role of 7K‐C on axonal integrity and the involvement of cholesterol metabolism in the Aβ42 generation process.
STAT3 inhibition reverses neuroinflammation and Aβ metabolism induced by systemic inflammation
Authors: Millot Perine , San Carine , Bennana Evangeline , Hugon Jacques , Paquet Claire , Hosten Benoit , Mouton‐Liger Francois
Published: December 2020

Background

STAT3 is a transcriptional factor involved in various physiological processes, including immune reaction. Abnormal activation of the, STAT3, has been recently associated with Alzheimer Disease (AD). Phosphorylation of STAT3 is dramatically increased in the hippocampus of AD mouse model and in AD post‐mortem brain. STAT3 phosphorylation is critical for cytokines secretion known to be involved in neuroinflammation, such as Il‐1β, IL‐6 or TNF‐α. Moreover, STAT3 may act as a transcriptional regulator of BACE1, the key enzyme in amyloïd β (Aβ) production. Our group have previously shown that neuroinflammation and increased brain Aβ production induced by systemic inflammation in wild‐type (WT) mice is associated with an excessive activation of STAT3 (Rebillat et al., 2015). Inhibitors of STAT3 have already shown beneficial effects in several pathology, such as cancer. The main objective of this research project was to investigate if STAT3 inhibitors could reverse neuroinflammation and abnormal BACE1 regulation in vivo and be considered as potential drug candidates for treatment in AD.

Method

C57BL/6 wild type mice were injected intraperitoneally with LPS (1mk/kg) versus saline once a day for 3 days to induce neuroinflammation and STAT3 activation. Inhibition of STAT3 activation is mediated by intraperitoneal injection of STATTIC, a small molecule which selectively inhibits dimerization and nuclear translocation of STAT3. Brains were collected and dissected; immunohistochemistry and western blotting were performed for neuroinflammation, STAT3 activation and Aβ metabolism (as BACE1 level).

Result

STATTIC injection significantly decreases LPS‐induced microglial activation in the hippocampus. In addition, STAT3 inhibition reduces production of cytokines IL‐6, IL‐10 and TNF‐α triggered by LPS administration. We also observed a significative reduction of BACE1 level in the hippocampus of mice treated with LPS and STATTIC compared to those exposed to LPS alone.

Conclusion

Taking together, our results show that STATTIC can reverse neuroinflammation and BACE1‐Aβ signaling deregulation induced by systemic inflammation, and pave the way for further explorations of the therapeutic potential of STAT3 inhibition in AD.
An Alzheimer’s linked loss‐of‐function CLN5 variant impairs Cathepsin D maturation consistent with a retromer trafficking defect
Authors: Reitz Christiane
Published: December 2020

Background

While lysosomal storage diseases (LSDs) typically have been regarded as causing neurodegeneration early in life, recent observations showed that genetic variants that cause one type of LSD, Gaucher’s disease, can also cause Parkinson’s disease, strongly suggesting that genes causing LSDs might also act as risk factors for other late‐onset neurodegenerative disorders, notably Alzheimer’s disease (AD).

Method

Capitalizing on data from a whole exome sequencing study of 31 multiplex Alzheimer’s disease (AD) families, we examined a select group of genes (CLN3, CLN5, CTSD) that cause another LSD, Neuronal ceroid lipofuscinosis (NCL), because they have been directly or indirectly associated with retromer trafficking, a key pathological mechanism in AD. To validate variant(s) identified in these analyses, we turned to cell culture to determine whether they have deleterious effects on normal function. Finally, because all three genes converge on Cathepsin D, we tested whether the abnormal function caused by identified variant(s) would affect the normal processing of this protein.

Result

We identified a missense variant in CLN5 c.A959G (p.Asn320Ser) that segregated with AD in multiplex families. We demonstrated that this variant causes glycosylation defects in the expressed protein, which causes it to be retained in the endoplasmic reticulum with reduced delivery to the endolysosomal compartment, CLN5’s normal cellular location. We showed that this variant reduces the normal processing of Cathepsin D and decreases levels of full‐length APP, in line with a defect in retromer‐dependent trafficking.

Conclusion

CLN5 is highly expressed in the brain, in particular in microglia. Retromer deficiency has been found in microglia of AD brains, but the mechanisms underlying this deficiency are unclear. While future studies expressing the identified CLN5 variant are required to further characterize its functional consequences, we postulate that it mediates its AD‐associated toxicity by affecting retromer function in microglia. Notably, independent supporting evidence for an etiologic overlap between NCL and AD comes from recent studies reporting a novel PSEN1 mutation in a family diagnosed with adult‐onset NCL (Dolzhanskaya et al. J Alzheimers Dis. 2014), and a recent whole‐exome sequencing study reporting a CLN13 (CTSF; Cathepsin F) mutation in a kindred with early‐onset Alzheimer’s disease (Bras et al., Neurobiol Aging. 2016).
Impaired neuroinflammatory response of ApoE4 in Alzheimer’s disease patients
Authors: Kloske Courtney M. , Dugan Adam , Woolums Abigail E. , Lee Tiffany , Anderson Sonya , Patel Ela , Abner Erin L. , Nelson Peter T , Fardo David W. , Wilcock Donna M
Published: December 2020

Background

Amyloid plaques and neurofibrillary tangles distinguish Alzheimer’s disease (AD) from other dementias. Additionally, there is a neuroinflammatory response to these hallmarks which induces the activation and proliferation of microglia. While this initially helps clear AD pathology, it can also lead to detrimental consequences depending on length of activation. In addition to the pathology, there are genetic risk factors for AD. One of the most influential is Apolipoprotein E (ApoE), a cholesterol and lipid transporter in the brain. There are three common isoforms: ApoE3 is believed to be the “control” phenotype, ApoE2 is believed to protect against AD, and ApoE4 to confer an increased risk for AD. Studies have shown that the ApoE isoforms have differing effects on many aspects of AD including, amyloid‐beta clearance and microglial activation.

Method

We used the Human Neuroinflammation NanoString panel to assess the neuroinflammatory profile in the superior medial temporal gyrus (SMTG) and cerebellar regions of age and sex matched patients with the following genotypes and pathology: ApoE3/3‐AD (Braak V/VI, Thal 5) (N=9); ApoE4/4‐AD (Braak V/VI, Thal 5) (N=10); and ApoE3/3‐control (Braak I/II, Thal 0) (N=5). RNA was extracted from frozen SMTG, and cerebellum. Nanostring results were analyzed with NanoStringDiff followed by additional statistical analysis. qPCR was performed to verify results.

Result

We first examined the inflammatory gene expression changes between ApoE3/3‐AD and ApoE3/3‐control patients to investigate differences that may be due to AD. Here we found significant gene changes specifically in pathways involved in microglial activation and apoptosis. Further, we examined differences between ApoE4/4‐AD and ApoE3/3‐control patients to determine the AD‐associated effects with ApoE4. These results showed no significant differences between the groups, suggesting that while ApoE4/4‐AD patients have both AD pathology and an impaired inflammatory response to the pathology. Next, we investigated ApoE3/3‐AD vs ApoE4/4‐AD to look at ApoE isoform‐associated effects with AD pathology. We found significant differences; however, the differences paralleled those between ApoE3/3‐AD and ApoE3/3‐control, once again suggesting ApoE4/4‐AD patients have an impaired inflammatory response to the pathology.

Conclusion

These results suggest differences in the neuroinflammation between ApoE3/3 and ApoE4/4 patients with AD and may be implicated in disease progression.
Type 2 diabetes and cognitive status: A causal inference approach to estimate effects
Authors: Katsumata Yuriko , Karanth Shama D. , Nelson Peter T. , Kryscio Richard J. , Schmitt Frederick A. , Fardo David W. , Abner Erin L.
Published: December 2020

Background

Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are two of the most common causes of death in the US. Multiple large epidemiological studies suggest that T2DM is strongly associated with dementia. However, the association between T2DM and dementia is still not well understood. In this study, we investigated the association of T2DM with cognitive performance using causal inference approaches.

Method

Participant data were drawn from the National Alzheimer’s Coordinating Center (NACC). Diabetes status was determined based on any reported diabetes diagnosis or diabetic treatment during annual assessments. Cognitive data obtained at the last clinical visit included CDR and MMSE for global function, verbal fluency (Animal and Vegetable Naming) and Boston Naming Test for language/fluency, WMS‐R Logical Memory – immediate and delayed for memory, WMS‐R Digit Span Forward and Backward trials, and Trail Making A for attention, and Trail Making B for executive function. We generated a score for each cognitive domain using principal component analysis (PCA). Analysis of covariance (ANCOVA) with either inverse‐variance (IV) or inverse probability (IP) weights was used to examine the associations between diabetes status and the cognitive domain scores represented by the first PC, controlling for gender, age at the last visit, years of education, APOE ε4 dosage, hypertension status, and study center.

Result

After excluding participants who reported any other type of diabetes, whose age at the last visit were less than 65 years, and who had rare neuropathological diseases, a total of 33,756 NACC participants were included in this study. 4,885 cases were determined to have positive diabetes status. Participants with positive diabetes status had significantly worse domain scores in language/fluency (p = 3.0 × 10‐5), attention (p = 5.3 × 10‐22), and executive function (p = 6.7 × 10‐16) when IV weights were used. Analysis with IP weights revealed that positive diabetes status was additionally associated with memory (p = 0.0075).

Conclusion

There was a clear deficit in language/fluency, attention, and executive domain functions for participants with diabetes. After taking into account potential selection bias, diabetes was also associated with tests assessing the memory domain.
APOE‐ε4 carriers have superior recall on the ‘What was where?’ visual short‐term memory binding test at age 70, despite a detrimental effect of β‐amyloid
Authors: Lu Kirsty , Nicholas Jennifer M. , Pertzov Yoni , Grogan John , Husain Masud , Pavisic Ivanna M. , James Sarah‐Naomi , Parker Thomas D. , Lane Christopher A , Keshavan Ashvini , Keuss Sarah E. , Buchanan Sarah M. , Murray‐Smith Heidi , Cash David M. , Malone Ian B. , Coath William , Wong Andrew , Henley Susie M.D. , Crutch Sebastian J. , Fox Nick C. , Richards Marcus , Schott Jonathan M.
Published: December 2020

Background

While APOE‐ε4 carriers are at higher risk of Alzheimer’s disease (AD), there is evidence that APOE‐ε4 may have some beneficial effects across the life‐span, including on cognition. It is unclear how such effects may relate to subtle memory decline during the preclinical phase of AD. Two previous studies reported that APOE‐ε4 carriers recalled object locations more accurately than non‐carriers on the “What was where?” visual short‐term memory binding test (10.1016/j.cortex.2016.12.016; 10.1016/j.neurobiolaging.2018.09.017), but these studies did not account for preclinical AD pathology.

Method

The “What was where?” task (Figure 1) was administered to participants in Insight 46 – a sub‐study of the British 1946 birth cohort – who were all born during the same week (aged 69‐71 at assessment (Table 1)). Outcomes included object identification and a sensitive analogue measure of localisation error (the distance between the location reported by the participant and the true location). Two‐dimensional mixture models (10.31234/osf.io/q57fm) were used to isolate three sources of localisation error: imprecision, guessing, and misbinding (swapping an object’s location with that of a different object). β‐Amyloid status (positive / negative) was determined from 18F‐Florbetapir‐PET. Multivariable regression models were used to investigate differential effects of APOE genotype (ε4‐carrier / non‐carrier) and β‐amyloid status on performance in 398 cognitively‐normal participants, adjusting for confounders including a prospectively‐collected measure of childhood cognitive ability.

Result

APOE‐ε4 and β‐amyloid had opposing effects on object identification, with APOE‐ε4 predicting better recall and β‐amyloid‐positivity predicting poorer recall. APOE‐ε4 carriers also recalled object locations more precisely, but a subtle detrimental effect of β‐amyloid on localisation was seen only among non‐carriers (Table 2, Figure 2). Childhood cognitive ability also predicted performance over 60 years later (Table 2).

Conclusion

In this large population‐based sample of cognitively‐normal ∼70‐year‐olds, a positive association between APOE‐ε4 and short‐term visual memory was observed. For the localization measure, this appeared to be protective against a subtle deficit associated with β‐amyloid pathology. This is consistent with the antagonistic pleiotropy hypothesis – whereby a gene controls both beneficial and detrimental traits – and provides novel evidence that these effects persist into older age, even among individuals who may be in the preclinical stages of AD.
Calibration of a simplified ARIA‐E MRI severity scale suitable for clinical practice
Authors: Klein Gregory , Bracoud Luc , Purcell Derk D. , Delmar Paul , Bullain Szofia S. , Abi‐Saab Danielle , Barakos Jerome , Baudler Monika , Fontoura Paulo , Doody Rachelle , Kerchner Geoffrey A.
Published: December 2020

Background

Vasogenic edema and sulcal effusions detected with magnetic resonance imaging (MRI) are known and manageable possible side effects of many anti‐amyloid immunotherapies. Periodic MRI scans and severity assessment of these imaging findings, known as amyloid‐related imaging abnormalities – edema (ARIA‐E) are currently mandated in clinical trials evaluating these therapies. Previous studies of gantenerumab have used a 60‐point severity scale (the Barkhof Grand Total Scale [BGTS]),1 which is effective for research purposes with good inter‐rater reliability (ICC = 0.78, 95%CI[0.36‐0.94]) between trained neuroradiologists. Other anti‐amyloid studies have used a simpler 3‐point severity scale, which has excellent inter‐rater reliability (ICC = 0.93, 95%CI[0.89‐0.96])2 and is likely to be of more practical use in clinical practice. This work compares the BGTS with the 3‐point scale, and with a more refined, 5‐point scale.2

Method

T2‐FLAIR and T2*‐GRE MRI scans (5 mm slices, no gap, 256 × 256 matrix) from 100 participants (87 with previously detected ARIA‐E, 13 without ARIA from original BGTS assessment) in the Marguerite RoAD gantenerumab study (NCT02051608) were selected. After excluding 3 scans due to missing data or unacceptable quality, all valid scans were re‐read using all three severity scales by one neuroradiologist with extensive experience (>500 reads) on all three scales. The reader was presented with pairs of baseline/follow‐up scans, blinded to prior read results. Assessment of ARIA‐H was also completed. Tables 1 and 2 summarize the scale definitions.

Result

Median(inter‐quartile range) BGTS scores for the 3‐point scale were 0 = 0(0‐0), 1 = 1(1‐3), 2 = 8(5‐14), and 3 = 20(20‐20), and for the 5‐point scale were 0 = 0(0‐0), 1 = 1(1‐3), 2 = 6(4‐9), 3 = NA, 4 = 15(10‐20), and 5 = 20(20‐20) (Figure 1). Spearman’s rank correlation between BGTS and the 3‐point scale was 0.85 and 0.91 for the 5‐point scale. Regression analysis of ARIA‐E severity scale to ARIA‐H count showed moderate positive association with Pearson’s r of 0.37, 0.40, 0.50 for the 3‐point, 5‐point, and BGTS scales, respectively.

Conclusion

The simplified 3‐ and 5‐point scales are well correlated to the 60‐point BGTS ARIA‐E severity scale; the 5‐point scale had better correlation than the 3‐point scale. Further study is warranted to assess reliability for reads performed by non‐specialist radiologists in real‐world applications. References: (1) AJNR 2013. (2) Alz&Dementia 2017.
Beta‐amyloid deposition and Apolipoprotein E4 carrier status predict cerebral blood flow velocity response to exercise
Authors: Kaufman Carolyn S. , Ward Jaimie L. , Vidoni Eric D. , Morris Jill K. , Burns Jeffrey M. , Billinger Sandra A.
Published: December 2020

Background

Cerebral blood flow (CBF) provides crucial oxygen and nutrients and clears harmful metabolic waste products from the brain. The CBF decline observed in healthy aging is accelerated in Alzheimer’s disease (AD). We previously reported a lower CBF response during exercise in cognitively‐normal older adults with beta‐amyloid deposition. Recent studies suggest the Apolipoprotein E4 (ApoE4) gene may act mechanistically through vascular pathways to cause AD. Thus, we expanded our model to include ApoE4 carrier status. We hypothesized that beta‐amyloid deposition and positive ApoE4 carrier status would be associated with an attenuated CBF velocity response to exercise.

Methods

We conducted a secondary analysis on sedentary, cognitively‐normal adults (N = 39). For the parent study, individuals were recruited from the University of Kansas Alzheimer’s Disease Center. Genotyping was performed by LCG Genomics using KASP proprietary genotyping technology. Beta‐amyloid load was obtained from Positron Emission Tomography scans after [18F] Florbetapir administration. We recorded middle cerebral artery velocity (MCAv) using transcranial Doppler ultrasound at rest and during 8 minutes of moderate‐intensity exercise. We used these data to assess the CBF velocity response to exercise, defined as the percent change in MCAv from rest to exercise (%ΔMCAv = exercising MCAv – resting MCAv / resting MCAv). We ran a multiple regression to predict %ΔMCAv from age, gender, exercising carbon dioxide, exercising mean arterial pressure, beta‐amyloid load, and ApoE4 carrier status.

Results

Of the 39 participants (65‐86 years old), fourteen (36%) were ApoE4 carriers. The multiple regression model significantly predicted %ΔMCAv, F(6, 32) = 2.760, p = 0.028, adjusted R2 = 0.217. Regression coefficients and standard errors are found in Table 1. The standardized regression coefficients were β = ‐0.406 for beta‐amyloid load (p = 0.014) and β = 0.348 for ApoE4 carrier status (p = 0.025).

Conclusions

Higher beta‐amyloid load was associated with a smaller %ΔMCAv, as hypothesized. However, contrary to our hypothesis, positive ApoE4 carrier status was associated with a larger %ΔMCAv from rest to exercise. This may reflect a compensatory CBF response in these adults who have maintained normal cognition despite having an elevated genetic risk of AD.
Deep learning detection of informative features in [18F] flortaucipir PET for Alzheimer’s disease classification
Authors: Jo Taeho , Nho Kwangsik , Risacher Shannon L. , Saykin Andrew J.
Published: December 2020

Background

Deep learning has been applied to disease classification using neuroimaging data. To date, most classification of Alzheimer’s disease (AD) or mild cognitive impairment (MCI) by deep learning has focused on MRI, FDG PET, and amyloid PET. Here we developed a deep learning classification model using tau PET measured with [18F]flortaucipir to classify AD or MCI against cognitively normal older adults (CN). We also visualized the process of the deep learning classification to examine whether it could detect informative features or patterns that would potentially improve diagnostic classification.

Method

We downloaded 458 tau PET images (196 CN, 196 MCI, and 66 AD) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and included only one scan per individual. SPM12 was used to process the tau PET data using standard techniques. We used a 3D convolution neural network (CNN) method for the classification, and applied a layer‐wise relevance propagation (LRP) algorithm to identify informative features and to visualize the classification results (Fig. 1). Five‐fold cross validation was applied, where 70% of the entire data set was used for model training, 20% for model testing, and 10% for independent validation.

Result

Our deep learning‐based classification model distinguished AD from CN with an average accuracy of 96.2% (sensitivity: 95.4%; specificity: 96.9%) in an independent validation set, whereas MCI vs. CN showed 64.2% accuracy (sensitivity: 48.6%; specificity: 82.4%) (Table1). Visualization of deep learning decisions using the LRP algorithm identified patterns similar to the regions showing significant diagnostic group differences by conventional SPM analysis. Fig. 2 compared SPM contrasts to relevance heatmaps of 3D‐CNN classification.

Conclusion

Our results showed that tau PET‐based classification yielded higher accuracies than other imaging modalities and the LRP approach detected informative features in tau PET for AD classification. MCI classification was much less accurate reflecting the known heterogeneity within this group. The LRP‐based deep learning strategy can help visualize patterns of tau deposition in AD, MCI, or CN to assist with identification of features and patterns. Future directions include multi‐modality analysis and prognostic prediction.
Predicting cognitive resiliency in older women using machine learning
Authors: Casanova Ramon , Gaussoin Sarah A , Wallace Robert B , Baker Laura D , Sachs Bonnie C , Justice Jamie , Chen Jiu‐Chiuan , Manson JoAnn E , Henderson Victor , Whitsel Eric A , Hayden Kathleen M , Rapp Stephen R
Published: December 2020

Background

Identification of factors that help preserve cognitive function in late life could facilitate interventions that can improve the lives of millions of people as they age. However, the large number of potential factors associated with cognitive resiliency pose an analytical challenge. Machine learning can be used to discover complex functional relationships in high dimensional data. We used data from the Women’s Health Initiative Memory Study (WHIMS) and Random Forests (RF) methods to investigate predictors of cognitive resiliency in 80+ year old women from a set of demographic, biological, behavioral, social, and psychological variables.

Method

We included WHIMS women who were at least 80 years old as of 01/01/18, and had at least one cognitive assessment following their 80th birthday. We created two groups of participants: a cognitively resilient group and an impaired group of participants. The cognitively resilient group (N=205) included participants: (a) not previously adjudicated to have mild cognitive impairment (MCI) or probable dementia; (b) had a score >39 on the most recent administration of the TICSm and (c) the average of scores in previous visits>=39. The cognitively impaired group (N=176) included participants: (a) adjudicated with probable dementia within the first 14 years of follow‐up or; (b) who had at least two classifications of MCI within the first 14 years of follow‐up and (c) whose last TICS score was<31. RF classification was used to identify discriminative predictors of these two groups based on 67 baseline variables available in WHIMS. Analyses used the randomForestSRC R library. The minimal depth index (MDI) was used to determine predictors’ relevance.

Result

Discrimination between the resilient and impaired groups based on area under the curve was 0.80(95% CI‐0.76‐0.85). Figure 1 shows that according to the MDI values the most relevant predictors were age, self‐reported forgetfulness, physical function, optimism, hemoglobin, glucose, sleep disturbances, education, systolic pressure and depression.

Conclusion

We investigated discrimination of cognitively resilient older women from women who develop cognitive impairment using machine learning applied to a large set of predictors. The potential contributors to cognitive resilience include demographic, psychological, physical, metabolic and vascular factors suggesting multiple influences contributing to cognitive resiliency.
The API Generation program: Biomarker phenotyping of cognitively unimpaired participants screened in Generation study 1 and Generation study 2
Authors: Rouzade‐Dominguez Marie‐Laure , Riviere Marie‐Emmanuelle , Borowsky Beth , Liu Fonda , Cazorla Pilar , Quinn Matt , Seneca Nicholas , Neumann Ulf , Eichenlaub Udo , Chien Yuchen , Viglietta Vissia , Reiman Eric M. , Sui Yihan , Caputo Angelika , Graf Ana
Published: December 2020

Background

API Generation Program evaluated the effectiveness of the BACE1 inhibitor umibecestat and the active immunotherapy CAD106 in delaying the onset of AD symptoms in APOE4 carriers. Data from imaging (vMRI and PET scans), and fluid biomarkers (blood and CSF) were collected during the 12‐week Screening phase. In this presentation, we will summarize results across the two studies and investigate the relationships between the various biomarkers and key baseline characteristics.

Method

Amyloid levels were assessed via CSF and/or Amyloid PET scan in both studies for the APOE4 carriers. Generation Study 2 enrolled heterozygotes based on the following elevated amyloid inclusion criteria: Roche Elecsys® CSF p‐Tau/Abeta42 ratio > 0.024 and/or PET scans with any of the 3 amyloid F18 tracers (florbetapir, flutemetamol or florbetaben). If the centralized visual read of PET scan was negative, the SUVr was calculated (cortical composite in reference to whole cerebellum) for the three tracers in order to assess borderline cases. Thresholds for amyloid positivity equivalent to SUVR of 1.1 with florbetapir were used for the final decision on inclusion.

Result

Across both studies, over 500 lumbar punctures, 2700 amyloid PET scans and about 150 tau PET scans (conducted using flortaucipir) were performed. In Generation Study 1, over 200 FDG PET scans were also performed. Participants underwent amyloid testing during screening, blood sampling (plasma and serum) and underwent MRI scans for safety and to measure brain volumes. All baseline biomarker data will be presented, including available concentrations from CSF Tau/p‐Tau/Aβ42/Aβ40 along with the eligibility ratio for pTau/ab42. In about 150 participants, concordance between elevated/non elevated amyloid status by CSF and PET (using centiloids) will be shown. Baseline brain volume (whole brain and hippocampus) will be summarized for the different co‐variates (age, sex, cognition, genotype and amyloid status). Correlations among the various fluid biomarkers and imaging will be discussed.

Conclusion

These data describe the biomarker signature of the world’s largest cohort of cognitively unimpaired APOE4 carriers with and without elevated amyloid. The anonymized study data, biomarker samples as well as images collected will be shared with the scientific community after study completion and reporting. ELECSYS is a registered trademark of Roche.