Association of the ATN Research Framework With Clinical Profile, Ccognitive Decline, and Mortality in Patients With Dementia With Lewy Bodies

Background and Objectives: The ATN framework has been developed to categorize biological processes within the Alzheimer’s disease (AD) continuum. Since AD pathology often coincides with dementia with Lewy Bodies (DLB), we aimed to investigate the distribution of ATN profiles in DLB and associate ATN-profiles in DLB to prognosis. / Methods: We included 202 DLB patients from the Amsterdam Dementia Cohort (68±7yrs, 19%F, MMSE: 24±3, DAT-SPECT abnormal: 105/119). Patients were classified into eight profiles according to the ATN framework, using CSF Aβ42 (A), CSF p-tau (T) and medial temporal atrophy scores (N). We compared presence of clinical symptoms in ATN profiles and used linear mixed models to analyze decline on cognitive tests (follow-up 3±2yrs for n=139). Mortality risk was assessed using Cox proportional hazards analysis. Analyses were performed on both the eight profiles, as well as three clustered categories (normal AD biomarkers, non-AD pathologic change, AD continuum). / Results: Fifty (25%) DLB patients had normal AD biomarkers (A-T-N-), 37 (18%) had non-AD pathologic change (A-T+N-: 10%/A-T-N+: 6%/A-T+N+: 3%) and 115 (57%) were classified within the AD continuum (A+T-N-: 20%/A+T+N-: 16%/A+T-N+: 10%/A+T+N+: 9%). A+T+N+ patients were older and least often had RBD symptoms. Parkinsonism was more often present in A+T-, compared to A-T+ (independent of N). Compared to patients with normal AD biomarkers, patients in A+ categories showed steeper decline on memory tests and higher mortality risk. Cognitive decline and mortality did not differ between non-AD pathologic change and normal AD biomarkers. / Discussion: In our DLB cohort, we found clinically relevant associations between ATN categories and disease manifestation. Patients within the AD continuum had steeper cognitive decline and shorter survival. Implementing the ATN framework within DLB patients aids in subtyping patients based on underlying biological processes and could provide targets for future treatment strategies, e.g. AD modifying treatment. Expanding the framework by incorporating markers for alpha-synucleinopathy would improve the use of the framework to characterize dementia patients with mixed pathology, which could enhance proper stratification of patients for therapeutic trials

Parsing heterogeneity within dementia with Lewy bodies using clustering of biological, clinical, and demographic data

Dementia with Lewy bodies (DLB) includes various core clinical features that result in different phenotypes. In addition, Alzheimer's disease (AD) and cerebrovascular pathologies are common in DLB. All this increases the heterogeneity within DLB and hampers clinical diagnosis. We addressed this heterogeneity by investigating subgroups of patients with similar biological, clinical, and demographic features. We studied 107 extensively phenotyped DLB patients from the European DLB consortium. Factorial analysis of mixed data (FAMD) was used to identify dimensions in the data, based on sex, age, years of education, disease duration, Mini-Mental State Examination (MMSE), cerebrospinal fluid (CSF) levels of AD biomarkers, core features of DLB, and regional brain atrophy. Subsequently, hierarchical clustering analysis was used to subgroup individuals based on the FAMD dimensions. We identified 3 dimensions using FAMD that explained 38% of the variance. Subsequent hierarchical clustering identified 4 clusters. Cluster 1 was characterized by amyloid-β and cerebrovascular pathologies, medial temporal atrophy, and cognitive fluctuations. Cluster 2 had posterior atrophy and showed the lowest frequency of visual hallucinations and cognitive fluctuations and the worst cognitive performance. Cluster 3 had the highest frequency of tau pathology, showed posterior atrophy, and had a low frequency of parkinsonism. Cluster 4 had virtually normal AD biomarkers, the least regional brain atrophy and cerebrovascular pathology, and the highest MMSE scores. This study demonstrates that there are subgroups of DLB patients with different biological, clinical, and demographic characteristics. These findings may have implications in the diagnosis and prognosis of DLB, as well as in the treatment response in clinical trials. The online version contains supplementary material available at 10.1186/s13195-021-00946-w

Assessing fitness to drive:A validation study on patients with mild cognitive impairment

Objectives: There is no consensus yet on how to determine which patients with cognitive impairment are able to drive a car safely and which are not. Recently, a strategy was composed for the assessment of fitness to drive, consisting of clinical interviews, a neuropsychological assessment, and driving simulator rides, which was compared with the outcome of an expert evaluation of an on-road driving assessment. A selection of tests and parameters of the new approach revealed a predictive accuracy of 97.4% for the prediction of practical fitness to drive on an initial sample of patients with Alzheimer's dementia. The aim of the present study was to explore whether the selected variables would be equally predictive (i.e., valid) for a closely related group of patients; that is, patients with mild cognitive impairment (MCI).Methods: Eighteen patients with mild cognitive impairment completed the proposed approach to the measurement of fitness to drive, including clinical interviews, a neuropsychological assessment, and driving simulator rides. The criterion fitness to drive was again assessed by means of an on-road driving evaluation. The predictive validity of the fitness to drive assessment strategy was evaluated by receiver operating characteristic (ROC) analyses.Results: Twelve patients with MCI (66.7%) passed and 6 patients (33.3%) failed the on-road driving assessment. The previously proposed approach to the measurement of fitness to drive achieved an overall predictive accuracy of 94.4% in these patients. The application of an optimal cutoff resulted in a diagnostic accuracy of 100% sensitivity toward unfit to drive and 83.3% specificity toward fit to drive. Further analyses revealed that the neuropsychological assessment and the driving simulator rides produced rather stable prediction rates, whereas clinical interviews were not significantly predictive for practical fitness to drive in the MCI patient sample.Conclusions: The selected measures of the previously proposed approach revealed adequate accuracy in identifying fitness to drive in patients with MCI. Furthermore, a combination of neuropsychological test performance and simulated driving behavior proved to be the most valid predictor of practical fitness to drive.</p

CSF proteomics in autosomal dominant Alzheimer's disease highlights parallels with sporadic disease

Autosomal dominant Alzheimer's disease (ADAD) offers a unique opportunity to study pathophysiological changes in a relatively young population with few comorbidities. A comprehensive investigation of proteome changes occurring in ADAD could provide valuable insights into AD-related biological mechanisms and uncover novel biomarkers and therapeutic targets. Furthermore, ADAD might serve as a model for sporadic AD, but in-depth proteome comparisons are lacking. We aimed to identify dysregulated CSF proteins in ADAD and determine the degree of overlap with sporadic AD. We measured 1472 proteins in CSF of PSEN1 or APP mutation carriers (n = 22) and age- and sex-matched controls (n = 20) from the Amsterdam Dementia Cohort using proximity extension-based immunoassays (PEA). We compared protein abundance between groups with two-sided t-tests and identified enriched biological pathways. Using the same protein panels in paired plasma samples, we investigated correlations between CSF proteins and their plasma counterparts. Finally, we compared our results with recently published PEA data from an international cohort of sporadic AD (n = 230) and non-AD dementias (n = 301). All statistical analyses were false discovery rate-corrected. We detected 66 differentially abundant CSF proteins (65 increased, 1 decreased) in ADAD compared to controls (q &lt; 0.05). The most strongly upregulated proteins (fold change &gt;1.8) were related to immunity (CHIT1, ITGB2, SMOC2), cytoskeletal structure (MAPT, NEFL) and tissue remodelling (TMSB10, MMP-10). Significant CSF-plasma correlations were found for the upregulated proteins SMOC2 and LILR1B. Of the 66 differentially expressed proteins, 36 had been measured previously in the sporadic dementias cohort, 34 of which (94%) were also significantly upregulated in sporadic AD, with a strong correlation between the fold changes of these proteins in both cohorts (rs = 0.730, P &lt; 0.001). Twenty-nine of the 36 proteins (81%) were also upregulated among non-AD patients with suspected AD co-pathology. This CSF proteomics study demonstrates substantial biochemical similarities between ADAD and sporadic AD, suggesting involvement of the same biological processes. Besides known AD-related proteins, we identified several relatively novel proteins, such as TMSB10, MMP-10 and SMOC2, which have potential as novel biomarkers. With shared pathophysiological CSF changes, ADAD study findings might be translatable to sporadic AD, which could greatly expedite therapy development.</p

Performance of a [18F]Flortaucipir PET Visual Read Method Across the Alzheimer Disease Continuum and in Dementia With Lewy Bodies

Background and Objectives: Recently, the US Food and Drug Administration approved the tau-binding radiotracer [18F]flortaucipir and an accompanying visual read method to support the diagnostic process in cognitively impaired patients assessed for Alzheimer disease (AD). Studies evaluating this visual read method are limited. In this study, we evaluated the performance of the visual read method in participants along the AD continuum and dementia with Lewy bodies (DLB) by determining its reliability, accordance with semiquantitative analyses, and associations with clinically relevant variables. // Methods: We included participants who underwent tau-PET at Amsterdam University Medical Center. A subset underwent follow-up tau-PET. Two trained nuclear medicine physicians visually assessed all scans. Inter-reader agreement was calculated using Cohen κ. To examine the concordance of visual read tau positivity with semiquantification, we defined standardized uptake value ratio (SUVr) positivity using different threshold approaches. To evaluate the prognostic value of tau-PET visual read, we performed linear mixed models with longitudinal Mini-Mental State Examination (MMSE). // Results: We included 263 participants (mean age 68.5 years, 45.6% female), including 147 cognitively unimpaired (CU) participants, 97 amyloid-positive participants with mild cognitive impairment or AD dementia (AD), and 19 participants with DLB. The visual read inter-reader agreement was excellent (κ = 0.95, CI 0.91–0.99). None of the amyloid-negative CU participants (0/92 [0%]) and 1 amyloid-negative participant with DLB (1/12 [8.3%]) were tau-positive. Among amyloid-positive participants, 13 CU participants (13/52 [25.0%]), 85 with AD (85/97 [87.6%]), and 3 with DLB (3/7 [42.9%]) were tau-positive. Two-year follow-up visual read status was identical to baseline. Tau-PET visual read corresponded strongly to SUVr status, with up to 90.4% concordance. Visual read tau positivity was associated with a decline on the MMSE in CU participants (β = −0.52, CI −0.74 to −0.30, p < 0.001) and participants with AD (β = −0.30, CI −0.58 to −0.02, p = 0.04). // Discussion: The excellent inter-reader agreement, strong correspondence with SUVr, and longitudinal stability indicate that the visual read method is reliable and robust, supporting clinical application. Furthermore, visual read tau positivity was associated with prospective cognitive decline, highlighting its additional prognostic potential. Future studies in unselected cohorts are needed for a better generalizability to the clinical population. // Classification of Evidence: This study provides Class II evidence that [18F]flortaucipir visual read accurately distinguishes patients with low tau-tracer binding from those with high tau-tracer binding and is associated with amyloid positivity and cognitive decline. // Glossary: Aβ=β-amyloid; AD=Alzheimer disease; CU=cognitively unimpaired; DLB=dementia with Lewy bodies; US FDA=US Food and Drug Administration; GMM=Gaussian mixture model; LMM=linear mixed model; MCI=mild cognitive impairment; MMSE=Mini-Mental State Examination; OR=odds ratio; ROI=region of interest; SCD=subjective cognitive decline; SUVr=standardized uptake value ratio

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Analysis of neurodegenerative disease-causing genes in dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a clinically heterogeneous disorder with a substantial burden on healthcare. Despite this, the genetic basis of the disorder is not well defined and its boundaries with other neurodegenerative diseases are unclear. Here, we performed whole exome sequencing of a cohort of 1118 Caucasian DLB patients, and focused on genes causative of monogenic neurodegenerative diseases. We analyzed variants in 60 genes implicated in DLB, Alzheimer's disease, Parkinson's disease, frontotemporal dementia, and atypical parkinsonian or dementia disorders, in order to determine their frequency in DLB. We focused on variants that have previously been reported as pathogenic, and also describe variants reported as pathogenic which remain of unknown clinical significance, as well as variants associated with strong risk. Rare missense variants of unknown significance were found in APP, CHCHD2, DCTN1, GRN, MAPT, NOTCH3, SQSTM1, TBK1 and TIA1. Additionally, we identified a pathogenic GRN p.Arg493* mutation, potentially adding to the diversity of phenotypes associated with this mutation. The rarity of previously reported pathogenic mutations in this cohort suggests that the genetic overlap of other neurodegenerative diseases with DLB is not substantial. Since it is now clear that genetics plays a role in DLB, these data suggest that other genetic loci play a role in this disease.Peer reviewe

Investigating the genetic architecture of dementia with Lewy bodies: a two-stage genome-wide association study

Background Dementia with Lewy bodies is the second most common form of dementia in elderly people but has been overshadowed in the research field, partly because of similarities between dementia with Lewy bodies, Parkinson’s disease, and Alzheimer’s disease. So far, to our knowledge, no large-scale genetic study of dementia with Lewy bodies has been done. To better understand the genetic basis of dementia with Lewy bodies, we have done a genome-wide association study with the aim of identifying genetic risk factors for this disorder. Methods In this two-stage genome-wide association study, we collected samples from white participants of European ancestry who had been diagnosed with dementia with Lewy bodies according to established clinical or pathological criteria. In the discovery stage (with the case cohort recruited from 22 centres in ten countries and the controls derived from two publicly available database of Genotypes and Phenotypes studies [phs000404.v1.p1 and phs000982.v1.p1] in the USA), we performed genotyping and exploited the recently established Haplotype Reference Consortium panel as the basis for imputation. Pathological samples were ascertained following autopsy in each individual brain bank, whereas clinical samples were collected by clinical teams after clinical examination. There was no specific timeframe for collection of samples. We did association analyses in all participants with dementia with Lewy bodies, and also in only participants with pathological diagnosis. In the replication stage, we performed genotyping of significant and suggestive results from the discovery stage. Lastly, we did a meta-analysis of both stages under a fixed-effects model and used logistic regression to test for association in each stage. Findings This study included 1743 patients with dementia with Lewy bodies (1324 with pathological diagnosis) and 4454 controls (1216 patients with dementia with Lewy bodies vs 3791 controls in the discovery stage; 527 vs 663 in the replication stage). Results confirm previously reported associations: APOE (rs429358; odds ratio [OR] 2·40, 95% CI 2·14–2·70; p=1·05 × 10–⁴⁸), SNCA (rs7681440; OR 0·73, 0·66–0·81; p=6·39 × 10–¹⁰), and GBA (rs35749011; OR 2·55, 1·88–3·46; p=1·78 × 10–⁹). They also provide some evidence for a novel candidate locus, namely CNTN1 (rs7314908; OR 1·51, 1·27–1·79; p=2·21 × 10–⁶); further replication will be important. Additionally, we estimate the heritable component of dementia with Lewy bodies to be about 36%. Interpretation Despite the small sample size for a genome-wide association study, and acknowledging the potential biases from ascertaining samples from multiple locations, we present the most comprehensive and well powered genetic study in dementia with Lewy bodies so far. These data show that common genetic variability has a role in the disease

A comprehensive screening of copy number variability in dementia with Lewy bodies

The role of genetic variability in dementia with Lewy bodies (DLB) is now indisputable; however, data regarding copy number variation (CNV) in this disease has been lacking. Here, we used whole-genome genotyping of 1454 DLB cases and 1525 controls to assess copy number variability. We used 2 algorithms to confidently detect CNVs, performed a case-control association analysis, screened for candidate CNVs previously associated with DLB-related diseases, and performed a candidate gene approach to fully explore the data. We identified 5 CNV regions with a significant genome-wide association to DLB; 2 of these were only present in cases and absent from publicly available databases: one of the regions overlapped LAPTM4B, a known lysosomal protein, whereas the other overlapped the NME1 locus and SPAG9. We also identified DLB cases presenting rare CNVs in genes previously associated with DLB or related neurodegenerative diseases, such as SNCA, APP, and MAPT. To our knowledge, this is the first study reporting genome-wide CNVs in a large DLB cohort. These results provide preliminary evidence for the contribution of CNVs in DLB risk. (C) 2019 Elsevier Inc. All rights reserved.Peer reviewe