Alzheimer's Disease Subtypes Research Articles

Effect of Age on Clinical Trial Outcome in Participants with Probable Alzheimer's Disease.

Background:Age may affect treatment outcome in trials of mild probable Alzheimer’s disease (AD).Objective:We examined age as a moderator of outcome in an exploratory study of deep brain stimulation targeting the fornix (DBS-f) region in participants with AD.Methods:Forty-two participants were implanted with DBS electrodes and randomized to double-blind DBS-f stimulation (“on”) or sham DBS-f (“off”) for 12 months.Results:The intervention was safe and well tolerated. However, the selected clinical measures did not differentiate between the “on” and “off” groups in the intent to treat (ITT) population. There was a significant age by time interaction with the Alzheimer’s Disease Assessment Scale; ADAS-cog-13 (p = 0.028). Six of the 12 enrolled participants < 65 years old (50%) markedly declined on the ADAS-cog-13 versus only 6.7%of the 30 participants≥65 years old regardless of treatment assignment (p = 0.005). While not significant, post-hoc analyses favored DBS-f “off” versus “on” over 12 months in the < 65 age group but favored DBS-f “on” versus “off” in the≥65 age group on all clinical metrics. On the integrated Alzheimer’s Disease rating scale (iADRS), the effect size contrasting DBS-f “on” versus “off” changed from +0.2 (favoring “off”) in the < 65 group to –0.52 (favoring “on”) in the≥65 age group.Conclusion:The findings highlight issues with subject selection in clinical trials for AD. Faster disease progression in younger AD participants with different AD sub-types may influence the results. Biomarker confirmation and genotyping to differentiate AD subtypes is important for future clinical trials.

Resting-State Network Alterations Differ between Alzheimer's Disease Atrophy Subtypes.

Several Alzheimer's disease (AD) atrophy subtypes were identified, but their brain network properties are unclear. We analyzed data from two independent datasets, including 166 participants (103AD/63 controls) from the DZNE-longitudinal cognitive impairment and dementia study and 151 participants (121AD/30 controls) from the AD neuroimaging initiative cohorts, aiming to identify differences between AD atrophy subtypes in resting-state functional magnetic resonance imaging intra-network connectivity (INC) and global and nodal network properties. Using a data-driven clustering approach, we identified four AD atrophy subtypes with differences in functional connectivity, accompanied by clinical and biomarker alterations, including a medio-temporal-predominant (S-MT), a limbic-predominant (S-L), a diffuse (S-D), and a mild-atrophy (S-MA) subtype. S-MT and S-D showed INC reduction in the default mode, dorsal attention, visual and limbic network, and a pronounced reduction of "global efficiency" and decrease of the "clustering coefficient" in parietal and temporal lobes. Despite severe atrophy in limbic areas, the S-L exhibited only marginal global network but substantial nodal network failure. S-MA, in contrast, showed limited impairment in clinical and cognitive scores but pronounced global network failure. Our results contribute toward a better understanding of heterogeneity in AD with the detection of distinct differences in functional connectivity networks accompanied by CSF biomarker and cognitive differences in AD subtypes.

Cerebral Microbleeds, Cerebrospinal Fluid, and Neuroimaging Markers in Clinical Subtypes of Alzheimer's Disease

Lobar cerebral microbleeds (CMBs) in Alzheimer's disease (AD) are associated with cerebral amyloid angiopathy (CAA) due to vascular amyloid beta (Aβ) deposits. However, the relationship between lobar CMBs and clinical subtypes of AD remains unknown. Here, we enrolled patients with early- and late-onset amnestic dominant AD, logopenic variant of primary progressive aphasia (lvPPA) and posterior cortical atrophy (PCA) who were compatible with the AD criteria. We then examined the levels of cerebrospinal fluid (CSF) biomarkers [Aβ1-42, Aβ1-40, Aβ1-38, phosphorylated tau 181 (P-Tau), total tau (T-Tau), neurofilament light chain (NFL), and chitinase 3-like 1 protein (YKL-40)], analyzed the number and localization of CMBs, and measured the cerebral blood flow (CBF) volume by 99mTc-ethyl cysteinate dimer single photon emission computerized tomography (99mTc ECD-SPECT), as well as the mean cortical standard uptake value ratio by 11C-labeled Pittsburgh Compound B-positron emission tomography (11C PiB-PET). Lobar CMBs in lvPPA were distributed in the temporal, frontal, and parietal lobes with the left side predominance, while the CBF volume in lvPPA significantly decreased in the left temporal area, where the number of lobar CMBs and the CBF volumes showed a significant inversely correlation. The CSF levels of NFL in lvPPA were significantly higher compared to the other AD subtypes and non-demented subjects. The numbers of lobar CMBs significantly increased the CSF levels of NFL in the total AD patients, additionally, among AD subtypes, the CSF levels of NFL in lvPPA predominantly were higher by increasing number of lobar CMBs. On the other hand, the CSF levels of Aβ1-38, Aβ1-40, Aβ1-42, P-Tau, and T-Tau were lower by increasing number of lobar CMBs in the total AD patients. These findings may suggest that aberrant brain hypoperfusion in lvPPA was derived from the brain atrophy due to neurodegeneration, and possibly may involve the aberrant microcirculation causing by lobar CMBs and cerebrovascular injuries, with the left side dominance, consequently leading to a clinical phenotype of logopenic variant.

Brain Atrophy Subtypes and the ATN Classification Scheme in Alzheimer’s Disease

Introduction: We investigated the association between atrophy subtypes of Alzheimer’s disease (AD), the ATN classification scheme, and key demographic and clinical factors in 2 cohorts with different source characteristics (a highly selective research-oriented cohort, the Alzheimer’s Disease Neuroimaging Initiative [ADNI]; and a naturalistic heterogeneous clinically oriented cohort, Karolinska Imaging Dementia Study [KIDS]). Methods: A total of 382 AD patients were included. Factorial analysis of mixed data was used to investigate associations between AD subtypes based on brain atrophy patterns, ATN profiles based on cerebrospinal fluid biomarkers, and age, sex, Mini Mental State Examination (MMSE), cerebrovascular disease (burden of white matter signal abnormalities, WMSAs), and APOE genotype. Results: Older patients with high WMSA burden, belonging to the typical AD subtype and showing A+T+N+ or A+T+N− profiles clustered together and were mainly from ADNI. Younger patients with low WMSA burden, limbic-predominant or minimal atrophy AD subtypes, and A+T−N− or A+T−N+ profiles clustered together and were mainly from KIDS. APOE ε4 carriers more frequently showed the A+T−N− and A+T+N− profiles. Conclusions: Our findings align with the recent framework for biological subtypes of AD: the combination of risk factors, protective factors, and brain pathologies determines belonging of AD patients to distinct subtypes.

Multiple Subtypes of Alzheimer's Disease Base on Brain Atrophy Pattern.

Alzheimer’s disease (AD) is a disease of a heterogeneous nature, which can be disentangled by exploring the characteristics of each AD subtype in the brain structure, neuropathology, and cognition. In this study, a total of 192 AD and 228 cognitively normal (CN) subjects were obtained from the Alzheimer’s disease Neuroimaging Initiative database. Based on the cortical thickness patterns, the mixture of experts method (MOE) was applied to the implicit model spectrum of transforms lined with each AD subtype, then their neuropsychological and neuropathological characteristics were analyzed. Furthermore, the piecewise linear classifiers composed of each AD subtype and CN were resolved, and each subtype was comprehensively explained. The following four distinct AD subtypes were discovered: bilateral parietal, frontal, and temporal atrophy AD subtype (occipital sparing AD subtype (OSAD), 29.2%), left temporal dominant atrophy AD subtype (LTAD, 22.4%), minimal atrophy AD subtype (MAD, 16.1%), and diffuse atrophy AD subtype (DAD, 32.3%). These four subtypes display their own characteristics in atrophy pattern, cognition, and neuropathology. Compared with the previous studies, our study found that some AD subjects showed obvious asymmetrical atrophy in left lateral temporal-parietal cortex, OSAD presented the worst cerebrospinal fluid levels, and MAD had the highest proportions of APOE ε4 and APOE ε2. The subtype characteristics were further revealed from the aspect of the model, making it easier for clinicians to understand. The results offer an effective support for individual diagnosis and prognosis.

Data-driven FDG-PET subtypes of Alzheimer\u2019s disease-related neurodegeneration

BackgroundPrevious research has described distinct subtypes of Alzheimer’s disease (AD) based on the differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes.MethodsHierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment (“prodromal AD”) according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months.ResultsThree main hypometabolic subtypes were identified: (i) “typical” (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern; (ii) “limbic-predominant” (44.6%), characterized by old age and a memory-predominant cognitive profile; and (iii) a relatively rare “cortical-predominant” subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline.ConclusionsThese findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.

A systematic review of atypical Alzheimer's disease including behavioural and psychological symptoms.

Alzheimer's disease (AD) is the commonest cause of dementia, characterized by the clinical presentation of progressive anterograde episodic memory impairment. However, atypical presentation of patients is increasingly recognized. These atypical AD include logopenic aphasia, behavioural variant AD, posterior cortical atrophy, and corticobasal syndrome. These atypical AD are more common in patients with young onset AD before the age of 65 years old. Since medical needs (including the behavioural and psychological symptoms of dementia) of atypical AD patients could be different from typical AD patients, it is important for clinicians to be aware of these atypical forms of AD. In addition, disease modifying treatment may be available in the future. This review aims at providing an update on various important subtypes of atypical AD including behavioural and psychological symptoms.

Editors' Note: Biological Subtypes of Alzheimer Disease: A Systematic Review and Meta-analysis

In the article, “Biological Subtypes of Alzheimer Disease: A Systematic Review and Meta-analysis,” Dr. Ferreira et al. performed a systematic review and meta-analysis on Alzheimer disease (AD) subtype studies based on postmortem and neuroimaging data until July 2019. They identified 4 main subtypes of AD—typical, limbic-predominant, hippocampal-sparing (these 3 being based on the distribution of tau-related pathology and regional atrophy), and minimal atrophy AD. They concluded that 2 core dimensions of the heterogeneity were seen in AD—typicality and severity with further influence driven by a combination of protective factors, risk factors, and concomitant non-AD brain pathologies. In response, Dr. Uleman et al. identify 2 main points for expanding the work of this study—examining the details of the AD criteria used in the individual studies because this too would introduce heterogeneity and could potentially influence the review's conclusions and a reliance on 2 simple linear axes (severity and typicality), which do not capture the complex physiologic pathways of interaction that underlie additional aspects of resilience and resistance in AD. They suggest that drawing on insights about elucidating mechanisms of resilience and resistance to cognitive decline in concussion may be helpful in this regard. Responding to these comments, the authors counter that details on the AD criteria used in the studies included in their review are available in the supplementary material and briefly summarize the trends observed in these criteria. They note that inter-study variability in subtyping methods, rather than AD diagnostic criteria, were a major source of heterogeneity. They clarify that they cannot specify whether the severity and typicality axes are linear and postulate that most of the factors in their model have complex relationships to each other. The authors note the need to empirically test and expand their conceptual model to better understand heterogeneity in AD. This exchange provides a glimpse into the complexities of AD subtyping and the various interconnected factors that are at play. In the article, “Biological Subtypes of Alzheimer Disease: A Systematic Review and Meta-analysis,” Dr. Ferreira et al. performed a systematic review and meta-analysis on Alzheimer disease (AD) subtype studies based on postmortem and neuroimaging data until July 2019. They identified 4 main subtypes of AD—typical, limbic-predominant, hippocampal-sparing (these 3 being based on the distribution of tau-related pathology and regional atrophy), and minimal atrophy AD. They concluded that 2 core dimensions of the heterogeneity were seen in AD—typicality and severity with further influence driven by a combination of protective factors, risk factors, and concomitant non-AD brain pathologies. In response, Dr. Uleman et al. identify 2 main points for expanding the work of this study—examining the details of the AD criteria used in the individual studies because this too would introduce heterogeneity and could potentially influence the review's conclusions and a reliance on 2 simple linear axes (severity and typicality), which do not capture the complex physiologic pathways of interaction that underlie additional aspects of resilience and resistance in AD. They suggest that drawing on insights about elucidating mechanisms of resilience and resistance to cognitive decline in concussion may be helpful in this regard. Responding to these comments, the authors counter that details on the AD criteria used in the studies included in their review are available in the supplementary material and briefly summarize the trends observed in these criteria. They note that inter-study variability in subtyping methods, rather than AD diagnostic criteria, were a major source of heterogeneity. They clarify that they cannot specify whether the severity and typicality axes are linear and postulate that most of the factors in their model have complex relationships to each other. The authors note the need to empirically test and expand their conceptual model to better understand heterogeneity in AD. This exchange provides a glimpse into the complexities of AD subtyping and the various interconnected factors that are at play.

Usefulness of texture analysis in 123I-FP-CIT for the differentiation of AD and DLB subtypes.

I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl) nortropane (123I-FP-CIT) is well known to be a useful tracer for differentiating dementia with Lewy bodies (DLB) and Alzheimer disease (AD). However, clinically, there are some cases in which these diseases cannot be differentiated by ordinary quantitative methods. Therefore, in this study, we established an index that reflects not only the total count but also the distribution and heterogeneity of tracer uptake. We investigated whether assessment of the heterogeneous depletion of 123I-FP-CIT is useful for the differentiation of various types of dementia, i.e., probable DLB, possible DLB, and AD, using texture analysis. A total of 122 patients with either probable DLB (n=35), possible DLB (n=23), AD (n=44), and normal controls (n=20) were analyzed. Summated single photon emission computed tomography (SPECT) images (7 to 10 slices) of the patients, including the bilateral striatum, were analyzed using the gray-level histogram method (GLHM) of texture analysis. Mean, variance, skewness, and kurtosis of GLHM were compared with the specific binding ratio by Livia Tossici-Bolt's method (SBR). The sensitivity and specificity for differentiating probable DLB from possible DLB, AD, and normal controls were 97.1% and 77.0%, respectively, for skewness, using a cut-off point of 6.8%, and 97.1% and 81.6%, respectively, for kurtosis, using a cut-off point of 53.4%. The sensitivity and specificity for differentiating probable and possible DLB from AD and normal controls was 65.5% and 98.4%, respectively, for skewness, using a cut-off point of 6.4%, and 79.3% and 93.8%, respectively, for kurtosis, using a cut-off point of 53.4%. In the assessment of the efficacy of 123I-FP-CIT to differentiate AD and DLB subtypes, mean, variance, skewness, and kurtosis by GLHM was as useful as the SBR method. Moreover, possible DLB and probable DLB could be differentiated by skewness and kurtosis. Our results demonstrate that texture analysis is more useful than conventional quantitative methods for obtaining valuable information of the brain. Textural features as such may have considerable potential as imaging biomarkers of DLB progression.

Stage vs. Subtype Hypothesis in Alzheimer's Disease: A Multi-Cohort and Longitudinal Bayesian Clustering Study

Background: The heterogeneity in Alzheimer’s disease (AD) has recently become a topic of intensive research due to its implications in understanding the underlying pathophysiological mechanisms of AD. We aimed to assess whether AD atrophy subtypes reflect different disease stages or biologically distinct subtypes. Methods We used longitudinal magnetic resonance images of 1196 individuals (891 AD dementia patients and 305 cognitively unimpaired individuals) from ADNI, AIBL, J-ADNI, and AddNeuroMed cohorts. AD patients were divided in discovery and prediction datasets. In the discovery dataset we applied a novel longitudinal clustering model that estimates differential atrophy patterns from the age of clinical disease onset. We calculated cluster-specific cognitive trajectories and validated our model in the prediction dataset. Results We discovered five longitudinal atrophy patterns in 320 amyloid-β positive AD patients over 8 years, with different atrophy and cognitive trajectories. Our findings replicated the previously reported AD subtypes (typical, limbic predominant, hippocampal sparing, minimal atrophy), but featured only two distinct longitudinal neurodegeneration pathways (mediotemporal vs. cortical). AD subtypes with distinct atrophy trajectories converged in late disease stages. We found a substantial agreement between our model estimates and the prediction dataset’s (571 AD) atrophy levels. Conclusion: Two neurodegeneration pathways were defined, suggesting that some of the previously reported atrophy subtypes may reflect different disease stages rather than distinct subtypes. However, substantial heterogeneity exists within the two pathways with different rates of atrophy and cognitive decline, potentially caused by a complex combination of protective/risk factors and concomitant non-AD brain pathologies. Clearly distinct atrophy trajectories may converge in late disease stages. By shifting from the cross-sectional understanding of AD subtypes to the new perspective brought by longitudinal clustering, we set the ground to unravel the heterogeneity in AD, which may provide a platform for developing disease-modifying treatments in the future. Funding: No funding is declared. Declaration of Interest: None to declare. Ethical Approval: All participants provided written informed consent in accordance with the Helsinki declaration and approval for the studies was obtained by the local ethical committees.

A Review of Brain Atrophy Subtypes Definition and Analysis for Alzheimer’s Disease Heterogeneity Studies

Alzheimer's disease (AD) is a heterogeneous disease with different subtypes. Studying AD subtypes from brain structure, neuropathology, and cognition are of great importance for AD heterogeneity research. Starting from the study of constructing AD subtypes based on the features of T1-weighted structural magnetic resonance imaging, this paper introduces the major connections between the subtype definition and analysis strategies, including brain region-based subtype definition, and their demographic, neuropathological, and neuropsychological characteristics. The advantages and existing problems are analyzed, and reasonable improvement schemes are prospected. Overall, this review offers a more comprehensive view in the field of atrophy subtype in AD, along with their advantages, challenges, and future prospects, and provide a basis for improving individualized AD diagnosis.

Cholinergic network disruption in AD subtypes: A study using graph theory

AbstractBackgroundCholinergic dysfunction is central in Alzheimer’s disease (AD). Recent advances in neuroimaging techniques and availability of larger postmortem datasets have opened new opportunities to understand the role of the cholinergic network in AD pathogenesis. However, there is pathophysiological heterogeneity within AD. AD subtypes with different distribution of neurofibrilary tangles, regional atrophy, and network disruption have been revealed in neuropathological and structural magnetic resonance imaging (sMRI) studies. We aimed to identify the cholinergic network using graph theory on sMRI data, and investigate whether differential cholinergic network disruption underlies the subtypes of AD.MethodWe applied graph theoretical analysis to structural MRI data from 75 Ab‐positive AD patients and 64 Ab‐negative controls from ADNI‐1. AD patients were classified into three subtypes according to Murray et al., (2011): typical AD, limbic‐predominant and hippocampal‐sparing. We built brain structural networks from 68 cortical regions and 14 subcortical gray matter structures. Network disruption was investigated through modular analyses by applying the Newman algorithm on weighted graphs.ResultWe identified 43 AD patients with a brain atrophy pattern consistent with typical AD; 17 patients with a limbic‐predominant pattern; and 15 patients with a hippocampal‐sparing pattern. We identified the cholinergic network in healthy controls, which included the basal forebrain, medial temporal lobe, precuneus, lateral parietal and occipital cortex, basal ganglia and thalamus. This network was completely fragmented in AD patients, but disruption differed according to subtype. The basal forebrain retained correlations only with basal ganglia and medial temporal lobe regions in typical AD; while in hippocampal‐sparing and limbic‐predominant subtypes these correlations also extended to new regions in frontal and lateral temporal cortices.ConclusionGraph theory on sMRI is able to identify a cholinergic network similar to that previously described in postmortem and in vivo diffusion imaging studies. AD subtypes have a distinct signature of cholinergic network disruption largely associated to their atrophy patterns. It is thus possible that pathology targeting the cholinergic system is one of the factors contributing to the emergence of these distinct subtypes. Our results may help to further understand pathophysiological heterogeneity in AD, with potential applications to research, clinical work and drug decision.

Differential association between tau pathology and longitudinal cortical atrophy in subtypes of Alzheimer’s disease

AbstractBackgroundTau neurofibrillary tangle pathology and cortical atrophy progress at different rates when investigated cross‐sectionally or retrospectively (LaPoint 2017). However, convergence of the two over time remains unclear, especially in the subtypes of AD, which could inform about the underlying neural mechanisms. Here, we studied the association between tau pathology with positron emission tomography (PET) and cortical atrophy with retrospective and prospective structural magnetic resonance imaging (sMRI) in Alzheimer’s disease (AD) dementia. We identified subtypes using both modalities to compare their temporal evolution.MethodsWe identified 383 participants from the ADNI database, which comprised of 30 Aβ+ AD dementia patients, 54 Aβ+ prodromal AD patients, 99 Aβ+ preclinical AD and 200 Aβ‐ healthy individuals (HC). All subjects had tau PET (AV‐1451) and concurrent sMRI (T1‐weighted). Additionally, retrospective and prospective sMRI were available. Preliminarily, we analyzed data from the AD dementia group. Tau PET and longitudinal sMRI were preprocessed to generate regional measures (Desikan 2006) of partial volume corrected standardized uptake value ratio (SUVR) (Greve 2016) and cortical volume, thickness respectively. AD subtypes were characterized by regional volumes and tau SUVR as in a previous study (Byun 2015). Four subtypes were identified: typical AD (TAD), limbic predominant (LP), hippocampal sparing (HS) and minimal atrophy (MA). We investigated the association between tau SUVR and longitudinal cortical thickness in AD dementia and the four subtypes.ResultsIn the preliminary analysis, we observed a significant negative association between tau SUVR and longitudinal cortical thickness (baseline and prospectively) in the entorhinal cortex. Significant association between tau SUVR and cortical thickness was also observed in temporal and parietal areas prospectively. Additionally, subtypes identified based on tau PET and sMRI did not always coincide at baseline, but prospective tracking was indicative of potential convergence between the two (Figure 1).ConclusionThe association of increased tau PET binding with prospective cortical thinning suggests that including prospective imaging is critical to track disease progression. Preliminary evidence suggests that the convergence between tau pathology and cortical atrophy could occur at different rates in different subtypes. Learning subtype‐specific trajectory would be valuable for the design of disease‐slowing/modifying clinical trials.

N‐of‐1‐pathways transcriptomic analysis reveals distinct subtypes of Alzheimer’s disease

AbstractBackgroundData from recent studies supports the hypothesis that Alzheimer’s disease (AD) is a heterogenous spectrum disorder characterized by multiple genotypes and phenotypes. Several subtypes of AD can be identified based on genetic makeup together with clinical phenotypes, neuroimaging and molecular signatures at different stages of the disease. N‐of‐1‐pathways approaches have been proposed for single‐subject gene expression analysis enabling disease subtyping at the transcriptome level.MethodWe analyzed existing RNA‐Seq data generated from post‐mortem brain tissue in the Religious Order Study and the Memory and Aging Project (ROSMAP). We calculated Mahalanobis distance based similarity measures between pair‐wise subjects per gene, and also per KEGG pathway. Using these similarity measures, we applied k‐means clustering algorithm to identify distinct subtypes of AD‐relevant transcriptomic signatures. Alignment of the subtypes with eight different mouse models of AD enabled determination of the mouse model(s) best representing each subtype.ResultIn the ROSMAP cohort, 144 and 81 subjects were labeled as AD and Control, respectively, according to predefined thresholds for COGDX, BRAAK and CERAD scores. A total of 186 KEGG pathways were assessed and AD subjects were clustered into four subtypes based on the Mahalanobis distance n‐of‐1‐pathway scores. Neuropathology related pathways, including neurotrophin signaling, axon guidance and long‐term potentiation, were altered in Subtype3 (n = 15). Transcriptomic signatures in the brain of 5XFAD and CR1 mouse models represented similarity with Subtype3. Subtype2 (n = 45) revealed a strong metabolic response with altered lysine and glycan metabolisms similar to ABCA7 and TREM2 mouse models. Immunity related pathways were enriched in Subtype3 (n = 48) mapping to ABCA7 and APOE4 mouse models. Subtype4 (n = 36) did not reveal a significant pathway enrichment.ConclusionAccurate identification of disease subtypes is critical for understanding the distinct molecular mechanisms of AD to provide effective personalized treatment options. To our knowledge, this is the first time that n‐of‐1‐pathways approach has been utilized for subtyping AD based on single‐subject transcriptomic signatures. The AD‐relevant pathways altered in each subtype was associated with specific mouse models suggesting a potential for utilization of the matching “mouse model/AD subtype” pairs for preclinical testing of therapeutic targets.

Disentangling neurodegeneration subtypes of Alzheimer’s disease using data‐driven methods

AbstractBackgroundResearch on understanding heterogeneity in Alzheimer’s disease (AD) has traditionally relied on ‘a priori’ categorizations of recognized clinical or pathologic disease variants. More recently, application of clustering methods to high‐dimensional spatial features from neuroimaging data has allowed data‐driven discovery of AD subtypes based on differentiated regional patterns of neurodegeneration.MethodsWe provide an up‐to‐date review of structural MRI studies that leveraged data‐driven approaches to examine heterogeneity in regional neurodegeneration patterns among AD patients. Findings will be discussed in relation to different families of clustering methods employed across studies. We further present primary data from our ongoing studies characterizing AD neurodegeneration subtypes by means of FDG‐PET as a potentially more sensitive molecular imaging marker of neurodegenerative processes.ResultsData‐driven studies across multiple independent cohorts and employed methodologies provide converging evidence for the existence of distinct MRI atrophy subtypes that broadly resemble the differentiation into atypical medial temporal‐ vs neocortical‐predominant patterns characterizing established neuropathological subtypes. These studies not only allowed for a regionally more detailed characterization of the respective atrophy patterns, but also pointed to the existence of more differentiated atrophy profiles within these broad subtype categories. Concordantly, our cluster analyses of FDG‐PET data confirmed a broad distinction into AD subtypes characterized by medial temporal/limbic‐ vs neocortical‐predominant hypometabolism, and at a higher resolution further distinguished a rare frontal variant from the typical temporo‐parietal neocortical pattern (Fig‐1). In a pilot imaging‐pathologic association study based on ante‐mortem FDG‐PET of 34 pathologic AD cases, the neocortical‐predominant subtype was more likely to have neocortical tau Braak stage VI and to have higher CERAD ratings of cortical neuritic plaques, whereas Thal amyloid phase and diffuse plaque ratings did not differ from the limbic‐predominant subtype. The limbic‐predominant subtype had a higher burden of comorbid cerebrovascular disease, but TDP‐43 or Lewy body comorbidity did not differ significantly between subtypes.ConclusionsData‐driven approaches applied to neuroimaging data can give an unbiased and regionally detailed estimate of the distinct neurodegeneration subtypes present among AD dementia patients. Methodological consensus on the optimal clustering techniques and input features is needed to define the most robust subtype‐defining neurodegeneration patterns to be used for patient stratification.

A novel type of amyloid‐beta plaques discovered in early‐onset AD patients

AbstractBackgroundThe clinical presentation of Alzheimer’s disease (AD), including age at onset, is remarkably heterogeneous. Besides clinical heterogeneity, we recognize distinct Aβ deposits in AD pathology. These deposits are called plaques when located in brain parenchyma or cerebral amyloid angiopathy (CAA) when located in cerebral vessels. Recently, we described a novel Aβ plaque‐type, referred to as ‘the coarse‐grained plaque’. In this study, we evaluate its clinical relevance and perform in‐depth morphological characterization.MethodThe coarse‐grained plaque’s presence was investigated in the frontal cortex of Aβ‐pathology‐positive cases (N = 74), including non‐demented controls (n = 15), early‐onset (EO)AD (n = 38), and late‐onset (LO)AD (n = 21), using a semi‐quantitative approach. Main plaque characteristics were compared to other well‐defined Aβ deposits; the classic cored plaque, the cotton wool plaque, and CAA. In‐depth characterization was done by studying the coarse‐grained plaque’s Aβ isoform (Aβ40, Aβ42, AβN3pE, and pSer8Aβ) composition, neuroinflammatory component, and vascular association using immunohistochemistry and 3D confocal laser scanning microscopy imaging.ResultThe plaque was only observed in cases with clinical symptoms, it was more prominent in EOAD (95%) than in LOAD (66%) and was associated with a homozygous ApoE4 status (Figure 1). The coarse‐grained plaque showed a multi‐cored Aβ morphology and was seen in the proximity of CAA‐affected sulci. Its predominant Aβ40 composition was distinct from other plaques, but showed similarities to that of CAA (Figure 2A). 3D imaging revealed a unique hollow Aβ40‐shell structure (Figure 2B) with microglia in Aβ‐devoid holes (Figure 3). Furthermore, the plaque’s immunoreactivity for the CAA‐Type 1 specific marker norrin suggests – although having a different morphology ‐ a close etiological relation with CAA.ConclusionThe coarse‐grained plaque is a clinically relevant Aβ deposit as it occurs only in clinical AD and is more frequent in EOAD. The plaque is strongly associated with neuroinflammatory and vascular changes. Differences in presence of coarse‐grained plaque among AD subtypes, contributes to the compiling evidence that also pathophysiology in AD is heterogeneous.

A novel type of amyloid‐beta plaques identified in early‐onset AD

AbstractBackgroundThe clinical presentation of Alzheimer’s disease (AD) including age at onset, is remarkably heterogeneous. In previous research, we showed that inflammation is differently distributed between typical and atypical AD (Boon et al., 2018). In addition, distinct Aβ deposits in AD pathology are recognized. These deposits are called plaques when located in brain parenchyma or cerebral amyloid angiopathy (CAA) when located in the cerebral vessels. Recently, we described a novel Aβ plaque‐type, referred to as ‘the coarse‐grained plaque’. In this study, we evaluate its clinical relevance and perform in‐depth morphological characterization.MethodThe coarse‐grained plaque’s presence was investigated in the frontal cortex of Aβ‐pathology‐positive cases (N=74), including non‐demented controls (n=15), early‐onset (EO)AD (n=38), and late‐onset (LO)AD (n=21), using a semi‐quantitative approach. Main plaque characteristics were compared to other well‐defined Aβ deposits; the classic cored plaque, the cotton wool plaque, and CAA. In‐depth characterization was done by studying the coarse‐grained plaque’s Aβ isoform (Aβ40, Aβ42, AβN3pE, and pSer8Aβ) composition, neuroinflammatory component, and vascular association using immunohistochemistry and 3D confocal laser scanning microscopy imaging.ResultThe plaque was only observed in cases with clinical symptoms, it was more prominent in EOAD (95%) than in LOAD (66%) and was associated with a homozygous ApoE4 status (Figure 1). The coarse‐grained plaque showed a multi‐cored Aβ morphology and was seen in the proximity of CAA‐affected sulci. Its predominant Aβ40 composition was distinct from other plaques, but showed similarities to that of CAA (Figure 2A). 3D imaging revealed a unique hollow Aβ40‐shell structure (Figure 2B) with microglia in Aβ‐devoid holes (Figure 3). Furthermore, the plaque’s immunoreactivity for the CAA‐Type 1 specific marker norrin suggests – although having a different morphology ‐ a close etiological relation with CAA.ConclusionThe coarse‐grained plaque is a clinically relevant Aβ deposit as it occurs only in clinical AD and is more frequent in early‐onset cases. The plaque is strongly associated with neuroinflammatory and vascular changes. Differences in presence of coarse‐grained plaque among AD subtypes, contributes to the compiling evidence that also pathophysiology in AD is heterogeneous.

Tau‐first subtype of Alzheimer’s disease progression consistently identified through PET and CSF

AbstractBackgroundSeveral studies contend that some Alzheimer’s disease (AD) subjects develop early‐stage tau pathology before amyloid pathology1,2, supporting a hypothesis that there are multiple distinct subtypes of amyloid and tau pathology progression within AD. We investigated this hypothesis, applying data‐driven disease subtyping models to both amyloid and tau PET as well as CSF measures.MethodWe performed two separate analyses using cross‐sectional data from ADNI. The first was a tau‐PET‐based analysis, which used eight regional amyloid PET (AV‐45) SUVRs and ten tau PET (AV‐1451) SUVRs from 402 subjects with both scans at the same visit. The second was a tau‐CSF‐based analysis, substituting CSF‐based prediction of tau PET SUVR (see Figure 2) in place of actual tau PET to give a larger dataset of 996 subjects. We used the Subtype and Stage Inference (SuStaIn3) algorithm to infer disease progression subtypes and individuals’ disease stages, and characterized demographic, cognitive and CSF differences across subtypes.ResultWe found two PET‐based subtypes in the tau‐PET‐based analysis: an amyloid‐first (84% of subjects) subtype and a tau‐first (16% of subjects) subtype in which Braak stage I‐III related tau SUVRs (hippocampus, amygdala, entorhinal cortex) become abnormal first (Figure 1A,B). The tau‐CSF‐based analysis confirms these subtypes: amyloid‐first (83% of subjects) and tau‐first (17% of subjects; Figure 2A,B). There were no significant differences in demographics or subject stages between subtypes in the tau‐PET‐based analysis (Figure 1C,D). In the tau‐CSF‐based analysis there was a small difference in age (amyloid‐first subjects 2.4 years older, Cohen’s f2 = 0.02, p &lt; 0.05). Amyloid‐first individuals also had slightly worse executive function (Cohen’s f2 = 0.02, p &lt; 0.05; Figure 3C) and, as expected, more abnormal CSF amyloid (Cohen’s f2 = 0.10, p &lt; 0.001; Figure 3D) while tau‐first individuals had more abnormal CSF tau (Cohen’s f2 = 0.06, p &lt; 0.001; Figure 3D).ConclusionWe identified amyloid‐first and tau‐first AD subtypes consistently across PET and CSF biomarkers. Our data‐driven approach supports the existence of a subtype of AD with tau accumulation prior to amyloid pathology.

Pathophysiological subtypes of Alzheimer's disease based on cerebrospinal fluid proteomics.

Alzheimer's disease is biologically heterogeneous, and detailed understanding of the processes involved in patients is critical for development of treatments. CSF contains hundreds of proteins, with concentrations reflecting ongoing (patho)physiological processes. This provides the opportunity to study many biological processes at the same time in patients. We studied whether Alzheimer's disease biological subtypes can be detected in CSF proteomics using the dual clustering technique non-negative matrix factorization. In two independent cohorts (EMIF-AD MBD and ADNI) we found that 705 (77% of 911 tested) proteins differed between Alzheimer's disease (defined as having abnormal amyloid, n = 425) and controls (defined as having normal CSF amyloid and tau and normal cognition, n = 127). Using these proteins for data-driven clustering, we identified three robust pathophysiological Alzheimer's disease subtypes within each cohort showing (i) hyperplasticity and increased BACE1 levels; (ii) innate immune activation; and (iii) blood-brain barrier dysfunction with low BACE1 levels. In both cohorts, the majority of individuals were labelled as having subtype 1 (80, 36% in EMIF-AD MBD; 117, 59% in ADNI), 71 (32%) in EMIF-AD MBD and 41 (21%) in ADNI were labelled as subtype 2, and 72 (32%) in EMIF-AD MBD and 39 (20%) individuals in ADNI were labelled as subtype 3. Genetic analyses showed that all subtypes had an excess of genetic risk for Alzheimer's disease (all P > 0.01). Additional pathological comparisons that were available for a subset in ADNI suggested that subtypes showed similar severity of Alzheimer's disease pathology, and did not differ in the frequencies of co-pathologies, providing further support that found subtypes truly reflect Alzheimer's disease heterogeneity. Compared to controls, all non-demented Alzheimer's disease individuals had increased risk of showing clinical progression (all P < 0.01). Compared to subtype 1, subtype 2 showed faster clinical progression after correcting for age, sex, level of education and tau levels (hazard ratio = 2.5; 95% confidence interval = 1.2, 5.1; P = 0.01), and subtype 3 at trend level (hazard ratio = 2.1; 95% confidence interval = 1.0, 4.4; P = 0.06). Together, these results demonstrate the value of CSF proteomics in studying the biological heterogeneity in Alzheimer's disease patients, and suggest that subtypes may require tailored therapy.

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression.

Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer’s disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that simultaneously: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows for study confounding effects, 5) provides cluster visualization, 6) measures clustering uncertainty. We used amyloid-β positive AD and negative healthy subjects, three longitudinal structural magnetic resonance imaging scans (cortical thickness and subcortical volume) over two years. We found three distinct longitudinal AD brain atrophy patterns: one typical diffuse pattern (n=34, 47.2%), and two atypical patterns: minimal atrophy (n=23 31.9%) and hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.