Heterogeneity of Regional Brain Atrophy Patterns Associated with Distinct Progression Rates in Alzheimer's Disease

Dysregulated interleukin networks drive immune heterogeneity in Alzheimer's disease: an immunogenomic approach to subgroup classification and predictive modeling

BACKGROUND

Alzheimer's Disease (AD) is marked by intricate immunological alterations, including the dysregulation of interleukin signaling. This study investigates the differential expression and potential roles of interleukins and their receptors in AD patients.

METHODS

We analyzed the GSE48350 dataset to assess the single-sample Gene Set Enrichment Analysis (ssGSEA) scores for interleukins and their receptors between normal and AD groups. Differentially expressed interleukin-related genes (DIGs) were identified. Enrichment analysis was conducted to understand functional implications. LASSO and logistic regression were used to identify key interleukin genes, which were employed to construct a predictive nomogram. This model was validated using the GSE132903 dataset. Unsupervised clustering and immune cell infiltration analyses were performed to examine AD patient heterogeneity.

RESULTS

The ssGSEA scores indicated significantly elevated interleukin and receptor levels in AD patients. A total of 23 DIGs were discovered, and the enrichment analysis emphasized their participation in immune signaling pathways. The nomogram based on key interleukin genes demonstrated strong predictive capability, with an AUC of 0.882 in the training set and 0.837 in the validation set. Unsupervised clustering revealed two AD subgroups with distinct immune profiles and pathway activities. Subgroup C2 exhibited higher immune cell infiltration and pathway activity than subgroup C1.

CONCLUSION

Interleukins and their receptors are significantly upregulated in AD patients, with distinct immune profiles identified in AD subgroups. The predictive nomogram effectively stratifies AD patients based on interleukin gene expression. These findings provide insights into AD's immunological landscape and suggest potential biomarkers for personalized therapeutic strategies.

Alterations in functional connectivity in individuals with subjective cognitive decline and hippocampal atrophy

OBJECTIVES

The aim of this study was to determine whether individuals with Subjective Cognitive Decline (SCD), particularly those with a neurostructural marker of risk for AD (SCD+), exhibit differences in the functional connectivity of the Default-Mode Network (DMN) relative to controls, as this network is known to be altered in the AD continuum.

DESIGN

Cross-sectional study.

SETTING

Galicia, Northwest Spain.

PARTICIPANTS

The sample compromised 133 participants: 69 controls, 51 SCD and 13 SCD+.

MEASUREMENTS

Seed-to-voxel analysis was conducted using four DMN ROIs. Dynamic independent component analysis of the DMN was also performed.

RESULTS

The SCD and SCD+ groups exhibited DMN hyperconnectivity, which was more extensive in the SCD+ group. Increased anti-correlations between DMN and task-positive parietal regions were related to poorer executive scores in SCD+ and a tendency for higher DMN recurrence in SCD+.

CONCLUSIONS

Hippocampal atrophy as a SCD+ biomarker is associated with extensive DMN hyperconnectivity and increased anti-correlations between DMN and task-positive network regions.

Brain microstructure alterations in subjective cognitive decline: a multi-component T2 relaxometry study

Previous research has revealed patterns of brain atrophy in subjective cognitive decline, a potential preclinical stage of Alzheimer's disease. However, the involvement of myelin content and microstructural alterations in subjective cognitive decline has not previously been investigated. This study included three groups of participants recruited from the Compostela Aging Study project: 53 cognitively unimpaired adults, 16 individuals with subjective cognitive decline and hippocampal atrophy and 70 with subjective cognitive decline and no hippocampal atrophy. Group differences were analysed across five MRI biomarkers derived from multi-component T2 relaxometry, each sensitive to variations in cerebral composition and microstructural tissue integrity. Although no significant differences in myelin content were observed between groups, the subjective cognitive decline with hippocampal atrophy group exhibited a larger free-water fraction, and reduced fraction and relaxation times of the intra/extracellular water compartment in frontal, parietal and medial temporal lobe brain regions and white matter tracts as compared with the other groups. Moreover, both subjective cognitive decline groups displayed lower total water content as compared with the control group and the subjective cognitive decline with hippocampal atrophy group showed lower total water content as compared with the subjective cognitive decline without hippocampal atrophy group. These changes are likely related to microstructural tissue differences related to neuroinflammation, axonal degeneration, iron accumulation or other physiologic variations, calling for further examinations.

Combined cognitive assessment and automated MRI volumetry improves the diagnostic accuracy of detecting MCI due to Alzheimer's disease

BACKGROUND

Mild cognitive impairment (MCI) confers a high annual risk of 10-15 % of conversion to Alzheimer's disease (AD) dementia. MRI atrophy patterns derived from automated ROI analysis, particularly hippocampal subfield volumes, were reported to be useful in diagnosing early clinical stages of Alzheimer's disease.

OBJECTIVE

The aim of the present study was to combine automated ROI MRI morphometry of hippocampal subfield volumes and cortical thickness estimates using FreeSurfer 6.0 with cognitive measures to predict disease progression and time to conversion from MCI to AD dementia.

METHODS

Baseline (Neuropsychology, MRI) and clinical follow-up data from 62 MCI patients were analysed retrospectively. Individual cortical thickness and volumetric measures were obtained from T1-weighted MRI. Linear discriminant analysis (LDA) of both, cognitive measures and MRI measures (hippocampal subfields, temporal and parietal lobe volumes), were performed to differentiate MCI converters from stable MCI patients.

RESULTS

Out of 62 MCI patients 21 (34 %) converted to AD dementia within a mean follow-up time of 74.7 ± 36.8 months (mean ± SD, range 12 to 130 months). LDA identified temporal lobe atrophy and hippocampal subfield volumes in combination with cognitive measures of verbal memory, verbal fluency and executive functions to correctly classify 71.4.% of MCI subjects converting to AD dementia and 92.7 % with stable MCI. Lower baseline GM volume of the subiculum and the superior temporal gyrus was associated with faster disease progression of MCI converters.

CONCLUSION

Combining cognitive assessment with automated ROI MRI morphometry is superior to using a single test in order to distinguish MCI due to AD from non converting MCI patients.

Clinical and biological underpinnings of longitudinal atrophy pattern progression in Alzheimer's disease

BACKGROUND

Magnetic resonance imaging (MRI) has recently enabled to identify four distinct Alzheimer's disease (AD) subtypes: hippocampal sparing (HpSp), typical AD (tAD), limbic predominant (Lp), and minimal atrophy (MinAtr). To date, however, the natural history of these subtypes, especially regarding the presence of subjects switching to other MRI patterns and their clinical and biological differences, remains poorly understood.

OBJECTIVE

To investigate the clinical and biological underpinnings of longitudinal atrophy pattern progression in AD.

METHODS

251 AD patients (16 with significant memory concern, 66 with early mild cognitive impairment (MCI), 125 with late MCI, and 44 with AD dementia) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were assigned to their baseline MRI atrophy subtype using Freesurfer-derived cortical:hippocampal volumes ratio. Switching to other MRI patterns was investigated on longitudinal scans, and patients were accordingly classified as "switching" and "stable". Logistic regression models were applied to identify predictors of switching to other MRI patterns.

RESULTS

40% of Lp, 26% of HpSp, and 35% of MinAtr cases switched to other MRI patterns, with tAD representing the destination subtype of all switching HpSp and Lp, and the majority of MinAtr. At baseline significant clinical, cognitive and biomarkers differences were observed across the four subtypes. Only clinical and cognitive variables, however, were significantly associated with switch to other MRI patterns.

CONCLUSIONS

Our results suggest convergent directions of disease progression across atypical and typical AD forms, at least in a subset of AD subjects, and highlight the importance of deep-phenotyping approaches to understand AD heterogeneity.

The study on cuproptosis in Alzheimer's disease based on the cuproptosis key gene FDX1

Background

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory and cognitive impairments. Previous studies have shown neuronal death in the brains of AD patients, but the role of cuproptosis and its associated genes in AD neurons remains unclear.

Methods

Intersection analysis was conducted using the AD transcriptome dataset GSE63060, neuron dataset GSE147528, and reported cuproptosis-related genes to identify the cuproptosis key gene FDX1 highly expressed in AD. Subsequently, cell experiments were performed by treating SH-SY5Y cells with Aβ25-35 to establish AD cell model. The real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) and western blotting (WB) assays were employed to detect the expression levels of FDX1, DLAT, and DLST. Cell proliferation was analyzed by counting Kit-8 (CCK8), mitochondrial ROS levels were analyzed using flow cytometry. shRNA was used to downregulate FDX1 expression, followed by repetition of the aforementioned experiments. Clinical experiments utilized qPCR to detect FDX1 mRNA levels in peripheral venous blood of patients, and analyzed FDX1 expression differences in different APOE genotypes of AD patients. Finally, a protein-protein interaction (PPI) network of FDX1 was constructed based on the GeneMANIA database, immune infiltration analysis was conducted using R language, and transcription factors prediction for FDX1 was performed based on the ENCODE database.

Results

The cuproptosis key gene FDX1 showed significantly higher expression in peripheral blood and neuron models of AD compared to non-AD individuals, with significantly higher expression in APOE ε4/ε4 genotype than other APOE genotype of AD patients. Knockdown of FDX1 expression reduced the lipidation levels of DLAT and DLST in neurons, alleviated ROS accumulation in mitochondria, improved cell viability, and mitigated cuproptosis. Immune infiltration analysis results indicated a high enrichment of peripheral blood γδ-T lymphocytes in AD, and FDX1 was significantly associated with the infiltration of four immune cells and may be regulated by three transcription factors.

Conclusion

The cuproptosis key gene FDX1 is highly expressed in AD and may promote cuproptosis in AD neurons by regulating the lipidation levels of DLAT and DLST, thereby participating in the onset and development of AD. This provides a potential target for the diagnosis and treatment of AD.

Amygdala-predominant α-synuclein pathology is associated with exacerbated hippocampal neuron loss in Alzheimer's disease

Misfolded α-synuclein protein accumulates in 43-63% of individuals with symptomatic Alzheimer's disease. Two main patterns of comorbid α-synuclein pathology have been identified: caudo-rostral and amygdala-predominant. α-Synuclein aggregates have been shown to interact with the transactive response DNA-binding protein 43 (TDP-43) and abnormally phosphorylated tau protein. All these proteins accumulate in the amygdala, which is anatomically connected with the hippocampus. However, the specific role of amygdala-predominant α-synuclein pathology in the progression of Alzheimer's disease and hippocampal degeneration remains unclear. In this cross-sectional study, we analysed 291 autopsy brains from both demented and non-demented elderly individuals neuropathologically. Neuronal density in the CA1 region of the hippocampus was assessed for all cases. We semiquantitatively evaluated α-synuclein pathology severity across seven brain regions and calculated a ratio of limbic to brainstem α-synuclein pathology severity, which was used to stratify the cases into two distinct spreading patterns. In the 99 symptomatic Alzheimer's disease cases, we assessed severity of limbic-predominant age-related TDP-43 neuropathological changes and CA1 phosphorylated tau density. We performed triple fluorescence staining of medial temporal lobe samples with antibodies against phosphorylated TDP-43, α-synuclein and phosphorylated tau. Finally, we employed path analysis to determine the association network of various parameters of limbic pathology in Alzheimer's disease cases and CA1 neuronal density. We identified an association between the amygdala-predominant αSyn pathology pattern and decreased neuronal density in the CA1 region. We found that Alzheimer's disease cases with an amygdala-predominant α-synuclein pattern exhibited the highest TDP-43 severity and prevalence of TDP-43 inclusions in the dentate gyrus among all groups, while those with the caudo-rostral pattern had the lowest severity of Alzheimer's disease neuropathological changes. We observed colocalization of TDP-43, aggregated α-synuclein and hyperphosphorylated tau in cytoplasmic inclusions within hippocampal and amygdala neurons of Alzheimer's disease cases. Path analysis modelling suggests that the relationship between amygdala-predominant α-synuclein pathology and CA1 neuron loss is partially mediated by hippocampal tau and TDP-43 aggregates. Our findings suggest that Alzheimer's disease cases with amygdala-predominant α-synuclein pathology may constitute a distinct group with more severe hippocampal damage, a higher TDP-43 burden and potential interactions among α-synuclein, TDP-43 and hyperphosphorylated tau.

Divergent neurodegenerative patterns: Comparison of [18F] fluorodeoxyglucose-PET- and MRI-based Alzheimer's disease subtypes

[18F] fluorodeoxyglucose (FDG)-PET and MRI are key imaging markers for neurodegeneration in Alzheimer's disease. It has been well established that parieto-temporal hypometabolism on FDG-PET is closely associated with medial temporal atrophy on MRI in Alzheimer's disease. Substantial biological heterogeneity, expressed as distinct subtypes of hypometabolism or atrophy patterns, has been previously described in Alzheimer's disease using data-driven and hypothesis-driven methods. However, the link between these two imaging modalities has not yet been explored in the context of Alzheimer's disease subtypes. To investigate this link, the current study utilized FDG-PET and MRI scans from 180 amyloid-beta positive Alzheimer's disease dementia patients, 339 amyloid-beta positive mild cognitive impairment and 176 amyloid-beta negative cognitively normal controls from the Alzheimer's Disease Neuroimaging Initiative. Random forest hierarchical clustering, a data-driven model for identifying subtypes, was implemented in the two modalities: one with standard uptake value ratios and the other with grey matter volumes. Five hypometabolism- and atrophy-based subtypes were identified, exhibiting both cortical-predominant and limbic-predominant patterns although with differing percentages and clinical presentations. Three cortical-predominant hypometabolism subtypes found were Cortical Predominant (32%), Cortical Predominant+ (11%) and Cortical Predominant posterior (8%), and two limbic-predominant hypometabolism subtypes found were Limbic Predominant (36%) and Limbic Predominant frontal (13%). In addition, little atrophy (minimal) and widespread (diffuse) neurodegeneration subtypes were observed from the MRI data. The five atrophy subtypes found were Cortical Predominant (19%), Limbic Predominant (27%), Diffuse (29%), Diffuse+ (6%) and Minimal (19%). Inter-modality comparisons showed that all FDG-PET subtypes displayed medial temporal atrophy, whereas the distinct MRI subtypes showed topographically similar hypometabolic patterns. Further, allocations of FDG-PET and MRI subtypes were not consistent when compared at an individual level. Additional analysis comparing the data-driven clustering model with prior hypothesis-driven methods showed only partial agreement between these subtyping methods. FDG-PET subtypes had greater differences between limbic-predominant and cortical-predominant patterns, and MRI subtypes had greater differences in severity of atrophy. In conclusion, this study highlighted that Alzheimer's disease subtypes identified using both FDG-PET and MRI capture distinct pathways showing cortical versus limbic predominance of neurodegeneration. However, the subtypes do not share a bidirectional relationship between modalities and are thus not interchangeable.

Temporal atrophy together with verbal encoding impairment is highly predictive for cognitive decline in typical Alzheimer's dementia - a retrospective follow-up study

Introduction

The increasing prevalence of Alzheimer's disease (AD) has created an urgent need for rapid and cost-effective methods to diagnose and monitor people at all stages of the disease. Progressive memory impairment and hippocampal atrophy are key features of the most common so-called typical variant of AD. However, studies evaluating detailed cognitive measures combined with region of interest (ROI)-based imaging markers of progression over the long term in the AD dementia (ADD) stage are rare.

Method

We conducted a retrospective longitudinal follow-up study in patients with mild to moderate ADD (aged 60-92 years). They underwent magnetic resonance imaging (MRI; 3 Tesla, MPRAGE) as well as clinical and neuropsychological examination (Consortium to Establish a Registry for Alzheimer's Disease [CERAD] -Plus test battery) at baseline and at least one follow-up visit. ROI-based brain structural analysis of baseline MRIs was performed using the Computational Anatomy Toolbox (CAT) 12. Clinical dementia progression (progression index [PI]) was measured by the annual decline in the Mini Mental State Examination (MMSE) scores. MRI, demographic, and neuropsychological data were included in univariate and multiple linear regression models to predict the PI.

Results

104 ADD patients (age 63 to 90 years, 73% female, mean MMSE score 22.63 ± 3.77, mean follow-up 4.27 ± 2.15 years) and 32 age- and gender-matched cognitively intact controls were included. The pattern of gray matter (GM) atrophy and the cognitive profile were consistent with the amnestic/typical variant of ADD in all patients. Deficits in word list learning together with temporal lobe GM atrophy had the highest predictive value for rapid cognitive decline in the multiple linear regression model, accounting for 25.4% of the PI variance.

Discussion

Our results show that temporal atrophy together with deficits in the encoding of verbal material, rather than in immediate or delayed recall, is highly predictive for rapid cognitive decline in patients with mild to moderate amnestic/typical ADD. These findings point to the relevance of combining detailed cognitive and automated structural imaging analyses to predict clinical progression in patients with ADD.

The relationship between adult hippocampal neurogenesis and cognitive impairment in Alzheimer's disease

Neurogenesis persists throughout adulthood in the hippocampus and contributes to specific cognitive functions. In Alzheimer's disease (AD), the hippocampus is affected by pathology and functional impairment early in the disease. Human AD patients have reduced adult hippocampal neurogenesis (AHN) levels compared to age-matched healthy controls. Similarly, rodent AD models show a decrease in AHN before the onset of the classical hallmarks of AD pathology. Conversely, enhancement of AHN can protect against AD pathology and ameliorate memory deficits in both rodents and humans. Therefore, impaired AHN may be a contributing factor of AD-associated cognitive decline, rather than an effect of it. In this review we outline the regulation and function of AHN in healthy individuals, and highlight the relationship between AHN dysfunction and cognitive impairments in AD. The existence of AHN in humans and its relevance in AD patients will also be discussed, with an outlook toward future research directions. HIGHLIGHTS: Adult hippocampal neurogenesis occurs in the brains of mammals including humans. Adult hippocampal neurogenesis is reduced in Alzheimer's disease in humans and animal models.

Adaptive Subtype and Stage Inference for Alzheimer's Disease

Subtype and Stage Inference (SuStaIn) is a useful Event-based Model for capturing both the temporal and the phenotypical patterns for any progressive disorders, which is essential for understanding the heterogeneous nature of such diseases. However, this model cannot capture subtypes with different progression rates with respect to predefined biomarkers with fixed events prior to inference. Therefore, we propose an adaptive algorithm for learning subtype-specific events while making subtype and stage inference. We use simulation to demonstrate the improvement with respect to various performance metrics. Finally, we provide snapshots of different levels of biomarker abnormality within different subtypes on Alzheimer's Disease (AD) data to demonstrate the effectiveness of our algorithm.

Disentangling brain atrophy heterogeneity in Alzheimer's disease: A deep self-supervised approach with interpretable latent space

Alzheimer's disease (AD) is heterogeneous, but existing methods for capturing this heterogeneity through dimensionality reduction and unsupervised clustering have limitations when it comes to extracting intricate atrophy patterns. In this study, we propose a deep learning based self-supervised framework that characterizes complex atrophy features using latent space representation. It integrates feature engineering, classification, and clustering to synergistically disentangle heterogeneity in Alzheimer's disease. Through this representation learning, we trained a clustered latent space with distinct atrophy patterns and clinical characteristics in AD, and replicated the findings in prodromal Alzheimer's disease. Moreover, we discovered that these clusters are not solely attributed to subtypes but also reflect disease progression in the latent space, representing the core dimensions of heterogeneity, namely progression and subtypes. Furthermore, longitudinal latent space analysis revealed two distinct disease progression pathways: medial temporal and parietotemporal pathways. The proposed approach enables effective latent representations that can be integrated with individual-level cognitive profiles, thereby facilitating a comprehensive understanding of AD heterogeneity.

Patterns of Early Neocortical Amyloid-β Accumulation: A PET Population-Based Study

The widespread deposition of amyloid-β (Aβ) plaques in late-stage Alzheimer disease is well defined and confirmed by in vivo PET. However, there are discrepancies between which regions contribute to the earliest topographic Aβ deposition within the neocortex. Methods: This study investigated Aβ signals in the perithreshold SUV ratio range using Pittsburgh compound B (PiB) PET in a population-based study cross-sectionally and longitudinally. PiB PET scans from 1,088 participants determined the early patterns of PiB loading in the neocortex. Results: Early-stage Aβ loading is seen first in the temporal, cingulate, and occipital regions. Regional early deposition patterns are similar in both apolipoprotein ε4 carriers and noncarriers. Clustering analysis shows groups with different patterns of early amyloid deposition. Conclusion: These findings of initial Aβ deposition patterns may be of significance for diagnostics and understanding the development of Alzheimer disease phenotypes.

Multimodal subtypes identified in Alzheimer's Disease Neuroimaging Initiative participants by missing-data-enabled subtype and stage inference

Alzheimer's disease is a highly heterogeneous disease in which different biomarkers are dynamic over different windows of the decades-long pathophysiological processes, and potentially have distinct involvement in different subgroups. Subtype and Stage Inference is an unsupervised learning algorithm that disentangles the phenotypic heterogeneity and temporal progression of disease biomarkers, providing disease insight and quantitative estimates of individual subtype and stage. However, a key limitation of Subtype and Stage Inference is that it requires a complete set of biomarkers for each subject, reducing the number of datapoints available for model fitting and limiting applications of Subtype and Stage Inference to modalities that are widely collected, e.g. volumetric biomarkers derived from structural MRI. In this study, we adapted the Subtype and Stage Inference algorithm to handle missing data, enabling the application of Subtype and Stage Inference to multimodal data (magnetic resonance imaging, positron emission tomography, cerebrospinal fluid and cognitive tests) from 789 participants in the Alzheimer's Disease Neuroimaging Initiative. Missing-data Subtype and Stage Inference identified five subtypes having distinct progression patterns, which we describe by the earliest unique abnormality as 'Typical AD with Early Tau', 'Typical AD with Late Tau', 'Cortical', 'Cognitive' and 'Subcortical'. These new multimodal subtypes were differentially associated with age, years of education, Apolipoprotein E (APOE4) status, white matter hyperintensity burden and the rate of conversion from mild cognitive impairment to Alzheimer's disease, with the 'Cognitive' subtype showing the fastest clinical progression, and the 'Subcortical' subtype the slowest. Overall, we demonstrate that missing-data Subtype and Stage Inference reveals a finer landscape of Alzheimer's disease subtypes, each of which are associated with different risk factors. Missing-data Subtype and Stage Inference has broad utility, enabling the prediction of progression in a much wider set of individuals, rather than being restricted to those with complete data.

Small extracellular vesicles from young plasma reverse age-related functional declines by improving mitochondrial energy metabolism

Recent investigations into heterochronic parabiosis have unveiled robust rejuvenating effects of young blood on aged tissues. However, the specific rejuvenating mechanisms remain incompletely elucidated. Here we demonstrate that small extracellular vesicles (sEVs) from the plasma of young mice counteract pre-existing aging at molecular, mitochondrial, cellular and physiological levels. Intravenous injection of young sEVs into aged mice extends their lifespan, mitigates senescent phenotypes and ameliorates age-associated functional declines in multiple tissues. Quantitative proteomic analyses identified substantial alterations in the proteomes of aged tissues after young sEV treatment, and these changes are closely associated with metabolic processes. Mechanistic investigations reveal that young sEVs stimulate PGC-1α expression in vitro and in vivo through their miRNA cargoes, thereby improving mitochondrial functions and mitigating mitochondrial deficits in aged tissues. Overall, this study demonstrates that young sEVs reverse degenerative changes and age-related dysfunction, at least in part, by stimulating PGC-1α expression and enhancing mitochondrial energy metabolism.

Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease

Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular profile to be present in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes, endocytosis, phagosome, and mTOR signaling pathways altered in AD early and late stages. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, we leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. Lastly, we identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition. Our cross-omics analyses provide novel critical molecular insights into AD.

A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis

Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.

The Q/R editing site of AMPA receptor GluA2 subunit acts as an epigenetic switch regulating dendritic spines, neurodegeneration and cognitive deficits in Alzheimer's disease

BACKGROUND

RNA editing at the Q/R site of GluA2 occurs with ~99% efficiency in the healthy brain, so that the majority of AMPARs contain GluA2(R) instead of the exonically encoded GluA2(Q). Reduced Q/R site editing infcreases AMPA receptor calcium permeability and leads to dendritic spine loss, neurodegeneration, seizures and learning impairments. Furthermore, GluA2 Q/R site editing is impaired in Alzheimer's disease (AD), raising the possibility that unedited GluA2(Q)-containing AMPARs contribute to synapse loss and neurodegeneration in AD. If true, then inhibiting expression of unedited GluA2(Q), while maintaining expression of GluA2(R), may be a novel strategy of preventing synapse loss and neurodegeneration in AD.

METHODS

We engineered mice with the 'edited' arginine codon (CGG) in place of the unedited glutamine codon (CAG) at position 607 of the Gria2 gene. We crossbred this line with the J20 mouse model of AD and conducted anatomical, electrophysiological and behavioural assays to determine the impact of eliminating unedited GluA2(Q) expression on AD-related phenotypes.

RESULTS

Eliminating unedited GluA2(Q) expression in AD mice prevented dendritic spine loss and hippocampal CA1 neurodegeneration as well as improved working and reference memory in the radial arm maze. These phenotypes were improved independently of Aβ pathology and ongoing seizure susceptibility. Surprisingly, our data also revealed increased spine density in non-AD mice with exonically encoded GluA2(R) as compared to their wild-type littermates, suggesting an unexpected and previously unknown role for unedited GluA2(Q) in regulating dendritic spines.

CONCLUSION

The Q/R editing site of the AMPA receptor subunit GluA2 may act as an epigenetic switch that regulates dendritic spines, neurodegeneration and memory deficits in AD.

An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease

The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.

Identification of disulfidptosis-related genes and subgroups in Alzheimer's disease

Background

Alzheimer's disease (AD), a common neurological disorder, has no effective treatment due to its complex pathogenesis. Disulfidptosis, a newly discovered type of cell death, seems to be closely related to the occurrence of various diseases. In this study, through bioinformatics analysis, the expression and function of disulfidptosis-related genes (DRGs) in Alzheimer's disease were explored.

Methods

Differential analysis was performed on the gene expression matrix of AD, and the intersection of differentially expressed genes and disulfidptosis-related genes in AD was obtained. Hub genes were further screened using multiple machine learning methods, and a predictive model was constructed. Finally, 97 AD samples were divided into two subgroups based on hub genes.

Results

In this study, a total of 22 overlapping genes were identified, and 7 hub genes were further obtained through machine learning, including MYH9, IQGAP1, ACTN4, DSTN, ACTB, MYL6, and GYS1. Furthermore, the diagnostic capability was validated using external datasets and clinical samples. Based on these genes, a predictive model was constructed, with a large area under the curve (AUC = 0.8847), and the AUCs of the two external validation datasets were also higher than 0.7, indicating the high accuracy of the predictive model. Using unsupervised clustering based on hub genes, 97 AD samples were divided into Cluster1 (n = 24) and Cluster2 (n = 73), with most hub genes expressed at higher levels in Cluster2. Immune infiltration analysis revealed that Cluster2 had a higher level of immune infiltration and immune scores.

Conclusion

A close association between disulfidptosis and Alzheimer's disease was discovered in this study, and a predictive model was established to assess the risk of disulfidptosis subtype in AD patients. This study provides new perspectives for exploring biomarkers and potential therapeutic targets for Alzheimer's disease.

Clinical characteristics of patients with suspected Alzheimer's disease within a CSF Aß-ratio grey zone

BACKGROUND

The AT(N) research framework for Alzheimer's disease (AD) remains unclear on how to best deal with borderline cases. Our aim was to characterise patients with suspected AD with a borderline Aß1-42/Aß1-40 ratio in cerebrospinal fluid.

METHODS

We analysed retrospective data from two cohorts (memory clinic cohort and ADNI) of patients (n = 63) with an Aß1-42/Aß1-40 ratio within a predefined borderline area-Q1 above the validated cut-off value(grey zone). We compared demographic, clinical, neuropsychological and neuroimaging features between grey zone patients and patients with low Aß1-42 (normal Aß ratio but pathological Aß1-42, n = 42) and patients with AD (pathological Aß, P-Tau, und T-Tau, n = 80).

RESULTS

Patients had mild cognitive impairment or mild dementia and a median age of 72 years. Demographic and general clinical characteristics did not differ between the groups. Patients in the grey zone group were the least impaired in cognition. However, they overlapped with the low Aß1-42 group in verbal episodic memory performance, especially in delayed recall and recognition. The grey zone group had less severe medial temporal atrophy, but mild posterior atrophy and mild white matter hyperintensities, similar to the low Aß1-42 group.

CONCLUSIONS

Patients in the Aß ratio grey zone were less impaired, but showed clinical overlap with patients on the AD continuum. These borderline patients may be at an earlier disease stage. Assuming an increased risk of AD and progressive cognitive decline, careful consideration of clinical follow-up is recommended when using dichotomous approaches to classify Aß status.

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data

Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk factors for LOAD but not for LOAD subtypes. Here, we examined the genetic architecture of LOAD based on Japanese GWAS data from 1947 patients and 2192 cognitively normal controls in a discovery cohort and 847 patients and 2298 controls in an independent validation cohort. Two distinct groups of LOAD patients were identified. One was characterized by major risk genes for developing LOAD (APOC1 and APOC1P1) and immune-related genes (RELB and CBLC). The other was characterized by genes associated with kidney disorders (AXDND1, FBP1, and MIR2278). Subsequent analysis of albumin and hemoglobin values from routine blood test results suggested that impaired kidney function could lead to LOAD pathogenesis. We developed a prediction model for LOAD subtypes using a deep neural network, which achieved an accuracy of 0.694 (2870/4137) in the discovery cohort and 0.687 (2162/3145) in the validation cohort. These findings provide new insights into the pathogenic mechanisms of LOAD.

Differential response to donepezil in MRI subtypes of mild cognitive impairment

BACKGROUND

Donepezil is an approved therapy for the treatment of Alzheimer's disease (AD). Results across clinical trials have been inconsistent, which may be explained by design-methodological issues, the pathophysiological heterogeneity of AD, and diversity of included study participants. We investigated whether response to donepezil differs in mild cognitive impaired (MCI) individuals demonstrating different magnetic resonance imaging (MRI) subtypes.

METHODS

From the Hippocampus Study double-blind, randomized clinical trial, we included 173 MCI individuals (donepezil = 83; placebo = 90) with structural MRI data, at baseline and at clinical follow-up assessments (6-12-month). Efficacy outcomes were the annualized percentage change (APC) in hippocampal, ventricular, and total grey matter volumes, as well as in the AD cortical thickness signature. Participants were classified into MRI subtypes as typical AD, limbic-predominant, hippocampal-sparing, or minimal atrophy at baseline. We primarily applied a subtyping approach based on continuous scale of two subtyping dimensions. We also used the conventional categorical subtyping approach for comparison.

RESULTS

Donepezil-treated MCI individuals showed slower atrophy rates compared to the placebo group, but only if they belonged to the minimal atrophy or hippocampal-sparing subtypes. Importantly, only the continuous subtyping approach, but not the conventional categorical approach, captured this differential response.

CONCLUSIONS

Our data suggest that individuals with MCI, with hippocampal-sparing or minimal atrophy subtype, may have improved benefit from donepezil, as compared with MCI individuals with typical or limbic-predominant patterns of atrophy. The newly proposed continuous subtyping approach may have advantages compared to the conventional categorical approach. Future research is warranted to demonstrate the potential of subtype stratification for disease prognosis and response to treatment.

TRIAL REGISTRATION

ClinicalTrial.gov NCT00403520. Submission Date: November 21, 2006.

Structural and functional differences in the brains of patients with MCI with and without depressive symptoms and their relations with Alzheimer's disease: an MRI study

Background

The mild cognitive impairment (MCI) stage among elderly individuals is very complex, and the level of diagnostic accuracy is far from ideal. Some studies have tried to improve the 'MCI due to Alzheimer's disease (AD)' classification by further stratifying these patients into subgroups. Depression-related symptoms may play an important role in helping to better define the MCI stage in elderly individuals.

Objective

In this work, we explored functional and structural differences in the brains of patients with nondepressed MCI (nDMCI) and patients with MCI with depressive symptoms (DMCI), and we examined how these groups relate to AD atrophy patterns and cognitive functioning.

Methods

Sixty-five participants underwent MRI exams and were divided into four groups: cognitively normal, nDMCI, DMCI, and AD. We compared the regional brain volumes, cortical thickness, and white matter microstructure measures using diffusion tensor imaging among groups. Additionally, we evaluated changes in functional connectivity using fMRI data.

Results

In comparison to the nDMCI group, the DMCI patients had more pronounced atrophy in the hippocampus and amygdala. Additionally, DMCI patients had asymmetric damage in the limbic-frontal white matter connection. Furthermore, two medial posterior regions, the isthmus of cingulate gyrus and especially the lingual gyrus, had high importance in the structural and functional differentiation between the two groups.

Conclusion

It is possible to differentiate nDMCI from DMCI patients using MRI techniques, which may contribute to a better characterization of subtypes of the MCI stage.

Associations between different tau-PET patterns and longitudinal atrophy in the Alzheimer's disease continuum: biological and methodological perspectives from disease heterogeneity

BACKGROUND

Subtypes and patterns are defined using tau-PET (tau pathology) and structural MRI (atrophy) in Alzheimer's disease (AD). However, the relationship between tau pathology and atrophy across these subtypes/patterns remains unclear. Therefore, we investigated the biological association between baseline tau-PET patterns and longitudinal atrophy in the AD continuum; and the methodological characterization of heterogeneity as a continuous phenomenon over the conventional discrete subgrouping.

METHODS

In 366 individuals (amyloid-beta-positive cognitively normal, prodromal AD, AD dementia; amyloid-beta-negative cognitively normal), we examined the association between tau-PET patterns and longitudinal MRI. We modeled tau-PET patterns as a (a) continuous phenomenon with key dimensions: typicality and severity; and (b) discrete phenomenon by categorization into patterns: typical, limbic predominant, cortical predominant and minimal tau. Tau-PET patterns and associated longitudinal atrophy were contextualized within the Amyloid/Tau/Neurodegeneration (A/T/N) biomarker scheme.

RESULTS

Localization and longitudinal atrophy change vary differentially across different tau-PET patterns in the AD continuum. Atrophy, a downstream event, did not always follow a topography akin to the corresponding tau-PET pattern. Further, heterogeneity as a continuous phenomenon offered an alternative and useful characterization, sharing correspondence with the conventional subgrouping. Tau-PET patterns also show differential A/T/N profiles.

CONCLUSIONS

The site and rate of atrophy are different across the tau-PET patterns. Heterogeneity should be treated as a continuous, not discrete, phenomenon for greater sensitivity. Pattern-specific A/T/N profiles highlight differential multimodal interactions underlying heterogeneity. Therefore, tracking multimodal interactions among biomarkers longitudinally, modeling disease heterogeneity as a continuous phenomenon, and examining heterogeneity across the AD continuum could offer avenues for precision medicine.

Preliminary exploration of the co-regulation of Alzheimer's disease pathogenic genes by microRNAs and transcription factors

Background

Alzheimer's disease (AD) is the most common form of age-related neurodegenerative disease. Unfortunately, due to the complexity of pathological types and clinical heterogeneity of AD, there is a lack of satisfactory treatment for AD. Previous studies have shown that microRNAs and transcription factors can modulate genes associated with AD, but the underlying pathophysiology remains unclear.

Methods

The datasets GSE1297 and GSE5281 were downloaded from the gene expression omnibus (GEO) database and analyzed to obtain the differentially expressed genes (DEGs) through the "R" language "limma" package. The GSE1297 dataset was analyzed by weighted correlation network analysis (WGCNA), and the key gene modules were selected. Next, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis for the key gene modules were performed. Then, the protein-protein interaction (PPI) network was constructed and the hub genes were identified using the STRING database and Cytoscape software. Finally, for the GSE150693 dataset, the "R" package "survivation" was used to integrate the data of survival time, AD transformation status and 35 characteristics, and the key microRNAs (miRNAs) were selected by Cox method. We also performed regression analysis using least absolute shrinkage and selection operator (Lasso)-Cox to construct and validate prognostic features associated with the four key genes using different databases. We also tried to find drugs targeting key genes through DrugBank database.

Results

GO and KEGG enrichment analysis showed that DEGs were mainly enriched in pathways regulating chemical synaptic transmission, glutamatergic synapses and Huntington's disease. In addition, 10 hub genes were selected from the PPI network by using the algorithm Between Centrality. Then, four core genes (TBP, CDK7, GRM5, and GRIA1) were selected by correlation with clinical information, and the established model had very good prognosis in different databases. Finally, hsa-miR-425-5p and hsa-miR-186-5p were determined by COX regression, AD transformation status and aberrant miRNAs.

Conclusion

In conclusion, we tried to construct a network in which miRNAs and transcription factors jointly regulate pathogenic genes, and described the process that abnormal miRNAs and abnormal transcription factors TBP and CDK7 jointly regulate the transcription of AD central genes GRM5 and GRIA1. The insights gained from this study offer the potential AD biomarkers, which may be of assistance to the diagnose and therapy of AD.

Imaging markers of cerebral amyloid angiopathy and hypertensive arteriopathy differentiate Alzheimer disease subtypes synergistically

Background

Both cerebral amyloid angiopathy (CAA) and hypertensive arteriopathy (HA) are related to cognitive impairment and dementia. This study aimed to clarify CAA- and HA-related small vessel disease (SVD) imaging marker associations with cognitive dysfunction and Alzheimer disease (AD) subtypes.

Methods

A sample of 137 subjects with clinically diagnosed late-onset AD identified from the dementia registry of a single center from January 2017 to October 2021 were enrolled. Semi-quantitative imaging changes (visual rating scale grading) suggestive of SVD were analyzed singularly and compositely, and their correlations with cognitive domains and AD subtypes were examined.

Results

Patients with typical and limbic-predominant AD subtypes had worse cognitive performance and higher dementia severity than minimal-atrophy subtype patients. Deep white matter hyperintensity (WMH) presence correlated inversely with short-term memory (STM) performance. The three composite SVD scores correlated with different cognitive domains and had distinct associations with AD subtypes. After adjusting for relevant demographic factors, multivariate logistic regression (using minimal-atrophy subtype as the reference condition) revealed the following: associations of the typical subtype with periventricular WMH [odds ratio (OR) 2.62; 95% confidence interval (CI), 1.23–5.57, p = 0.012], global SVD score (OR 1.67; 95%CI, 1.11–2.52, p = 0.009), and HA-SVD score (OR 1.93; 95%CI, 1.10–3.52, p = 0.034); associations of limbic-predominant subtype with HA-SVD score (OR 2.57; 95%CI, 1.23–5.37, p = 0.012) and most global and domain-specific cognitive scores; and an association of hippocampal-sparing subtype with HA-SVD score (OR 3.30; 95%CI, 1.58–6.85, p = 0.001).

Conclusion

Composite SVD imaging markers reflect overall CAA and/or HA severity and may have differential associations with cognitive domains and AD subtypes. Our finding supports the possibility that the clinical AD subtypes may reflect differing burdens of underlying CAA and HA microangiopathologies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01083-8.

Cumulative effect of simvastatin, L-arginine, and tetrahydrobiopterin on cerebral blood flow and cognitive function in Alzheimer's disease

BACKGROUND AND OBJECTIVES

Vascular disease is a known risk factor for Alzheimer's disease (AD). Endothelial dysfunction has been linked to reduced cerebral blood flow. Endothelial nitric oxide synthase pathway (eNOS) upregulation is known to support endothelial health. This single-center, proof-of-concept study tested whether the use of three medications known to augment the eNOS pathway activity improves cognition and cerebral blood flow (CBF).

METHODS

Subjects with mild AD or mild cognitive impairment (MCI) were sequentially treated with the HMG-CoA reductase synthesis inhibitor simvastatin (weeks 0-16), L-arginine (weeks 4-16), and tetrahydrobiopterin (weeks 8-16). The primary outcome of interest was the change in CBF as measured by MRI from baseline to week 16. Secondary outcomes included standard assessments of cognition.

RESULTS

A total of 11 subjects were deemed eligible and enrolled. One subject withdrew from the study after enrollment, leaving 10 subjects for data analysis. There was a significant increase in CBF from baseline to week 8 by ~13% in the limbic and ~15% in the cerebral cortex. Secondary outcomes indicated a modest but significant increase in the MMSE from baseline (24.2±3.2) to week 16 (26.0±2.7). Exploratory analysis indicated that subjects with cognitive improvement (reduction of the ADAS-cog 13) had a significant increase in their respective limbic and cortical CBF.

CONCLUSIONS

Treatment of mild AD/MCI subjects with medications shown to augment the eNOS pathway was well tolerated and associated with modestly increased cerebral blood flow and cognitive improvement.

TRIAL REGISTRATION

This study is registered in https://www.

CLINICALTRIALS

gov ; registration identifier: NCT01439555; date of registration submitted to registry: 09/23/2011; date of first subject enrollment: 11/2011.

Association between inflammatory biomarkers and cognitive aging

Inflammatory cytokines and chemokines related to the innate and adaptive immune system have been linked to neuroinflammation in Alzheimer's Disease, dementia, and cognitive disorders. We examined the association of 11 plasma proteins (CD14, CD163, CD5L, CD56, CD40L, CXCL16, SDF1, DPP4, SGP130, sRAGE, and MPO) related to immune and inflammatory responses with measures of cognitive function, brain MRI and dementia risk. We identified Framingham Heart Study Offspring participants who underwent neuropsychological testing (n = 2358) or brain MRI (n = 2100) within five years of the seventh examination where a blood sample for quantifying the protein biomarkers was obtained; and who were followed for 10 years for incident all-cause dementia (n = 1616). We investigated the association of inflammatory biomarkers with neuropsychological test performance and brain MRI volumes using linear mixed effect models accounting for family relationships. We further used Cox proportional hazards models to examine the association with incident dementia. False discovery rate p-values were used to account for multiple testing. Participants included in the neuropsychological test and MRI samples were on average 61 years old and 54% female. Participants from the incident dementia sample (average 68 years old at baseline) included 124 participants with incident dementia. In addition to CD14, which has an established association, we found significant associations between higher levels of CD40L and myeloperoxidase (MPO) with executive dysfunction. Higher CD5L levels were significantly associated with smaller total brain volumes (TCBV), whereas higher levels of sRAGE were associated with larger TCBV. Associations persisted after adjustment for APOE ε4 carrier status and additional cardiovascular risk factors. None of the studied inflammatory biomarkers were significantly associated with risk of incident all-cause dementia. Higher circulating levels of soluble CD40L and MPO, markers of immune cell activation, were associated with poorer performance on neuropsychological tests, while higher CD5L, a key regulator of inflammation, was associated with smaller total brain volumes. Higher circulating soluble RAGE, a decoy receptor for the proinflammatory RAGE/AGE pathway, was associated with larger total brain volume. If confirmed in other studies, this data indicates the involvement of an activated immune system in abnormal brain aging.

Macrostructural brain alterations at midlife are connected to cardiovascular and not inherited risk of future dementia: the PREVENT-Dementia study

BACKGROUND

Macrostructural brain alterations in the form of brain atrophy or cortical thinning typically occur during the prodromal Alzheimer's disease stage. Mixed findings largely dependent on the age of the examined cohorts have been reported during the preclinical, asymptomatic disease stage. In the present study, our aim was to examine the association of midlife dementia risk with brain macrostructural alterations.

METHODS

Structural 3T MRI scans were acquired for 647 cognitively normal middle-aged (40-59 years old) participants in the PREVENT-Dementia study. Cortical thickness, volumes of subcortical structures, the hippocampus and hippocampal subfields were quantified using Freesurfer version 7.1. The clarity of the hippocampal molecular layer was evaluated based on T2-weighted hippocampal scans. Associations of structural measures with apolipoprotein ε4 (APOE4) genotype and dementia family history (FHD), were investigated using linear regression. Correlations between the CAIDE dementia risk score (incorporating information about blood pressure, cholesterol, physical activity, body mass index, education, age and sex) and structural measures were further investigated.

RESULTS

A higher CAIDE score was associated with thinner cortex and a larger hippocampal fissure. APOE4 genotype was associated with reduced molecular layer clarity.

CONCLUSIONS

Our findings suggest that a higher CAIDE score is associated with widespread cortical thinning. Conversely, APOE4 carriers and participants with FHD do not demonstrate prominent macrostructural alterations at this age range. These findings indicate that cardiovascular and not inherited risk factors for dementia are associated with macrostructural brain alterations at midlife.

Identification and immunological characterization of cuproptosis-related molecular clusters in Alzheimer's disease

Introduction

Alzheimer's disease is the most common dementia with clinical and pathological heterogeneity. Cuproptosis is a recently reported form of cell death, which appears to result in the progression of various diseases. Therefore, our study aimed to explore cuproptosis-related molecular clusters in Alzheimer's disease and construct a prediction model.

Methods

Based on the GSE33000 dataset, we analyzed the expression profiles of cuproptosis regulators and immune characteristics in Alzheimer's disease. Using 310 Alzheimer's disease samples, we explored the molecular clusters based on cuproptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were identified using the WGCNA algorithm. Subsequently, the optimal machine model was chosen by comparing the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting. Nomogram, calibration curve, decision curve analysis, and three external datasets were applied for validating the predictive efficiency.

Results

The dysregulated cuproptosis-related genes and activated immune responses were determined between Alzheimer's disease and non-Alzheimer's disease controls. Two cuproptosis-related molecular clusters were defined in Alzheimer's disease. Analysis of immune infiltration suggested the significant heterogeneity of immunity between distinct clusters. Cluster2 was characterized by elevated immune scores and relatively higher levels of immune infiltration. Functional analysis showed that cluster-specific differentially expressed genes in Cluster2 were closely related to various immune responses. The Random forest machine model presented the best discriminative performance with relatively lower residual and root mean square error, and a higher area under the curve (AUC = 0.9829). A final 5-gene-based random forest model was constructed, exhibiting satisfactory performance in two external validation datasets (AUC = 0.8529 and 0.8333). The nomogram, calibration curve, and decision curve analysis also demonstrated the accuracy to predict Alzheimer's disease subtypes. Further analysis revealed that these five model-related genes were significantly associated with the Aβ-42 levels and β-secretase activity.

Conclusion

Our study systematically illustrated the complicated relationship between cuproptosis and Alzheimer's disease, and developed a promising prediction model to evaluate the risk of cuproptosis subtypes and the pathological outcome of Alzheimer's disease patients.

Neuropathologic Features of Antemortem Atrophy-Based Subtypes of Alzheimer Disease

BACKGROUND AND OBJECTIVES

To investigate whether antemortem MRI-based atrophy subtypes of Alzheimer disease (AD) differ in neuropathologic features and comorbid non-AD pathologies at postmortem.

METHODS

From the Alzheimer's Disease Neuroimaging Initiative cohort, we included individuals with antemortem MRI evaluating brain atrophy within 2 years before death, antemortem diagnosis of AD dementia/mild cognitive impairment, and postmortem-confirmed AD neuropathologic change. Antemortem atrophy subtypes were modeled as continuous phenomena based on a recent conceptual framework: typicality (spanning limbic-predominant AD to hippocampal-sparing AD) and severity (spanning typical AD to minimal atrophy AD). Postmortem neuropathologic evaluation included AD hallmarks, β-amyloid, and tau as well as non-AD pathologies, alpha-synuclein and TAR DNA-binding protein 43 (TDP-43). We also investigated the overall concomitance across these pathologies. Partial correlations assessed the associations between antemortem atrophy subtypes and postmortem neuropathologic outcomes.

RESULTS

In 31 individuals (26 AD dementia/5 mild cognitive impairment, mean age = 80 years, 26% females), antemortem typicality was significantly negatively associated with neuropathologic features, including β-amyloid (rho = -0.39 overall), tau (rho = -0.38 regionally), alpha-synuclein (rho = -0.39 regionally), TDP-43 (rho = -0.49 overall), and concomitance of pathologies (rho = -0.59 regionally). Limbic-predominant AD was associated with higher Thal phase, neuritic plaque density, and presence of TDP-43 compared with hippocampal-sparing AD. Regionally, limbic-predominant AD showed a higher presence of tau and alpha-synuclein pathologies in medial temporal structures, a higher presence of TDP-43, and concomitance of pathologies subcortically/cortically compared with hippocampal-sparing AD. Antemortem severity was significantly negatively associated with concomitance of pathologies (rho = -0.43 regionally), such that typical AD showed higher concomitance of pathologies than minimal atrophy AD.

DISCUSSION

We provide a direct antemortem-to-postmortem validation, highlighting the importance of understanding atrophy-based heterogeneity in AD relative to AD and non-AD pathologies. We suggest that (1) typicality and severity in atrophy reflect differential aspects of susceptibility of the brain to AD and non-AD pathologies; and (2) limbic-predominant AD and typical AD subtypes share similar biological pathways, making them more vulnerable to AD and non-AD pathologies compared with hippocampal-sparing AD, which may follow a different biological pathway. Our findings provide a deeper understanding of associations of atrophy subtypes in AD with different pathologies, enhancing the prevailing knowledge of biological heterogeneity in AD and could contribute toward tracking disease progression and designing clinical trials in the future.

Disentangling tau and brain atrophy cluster heterogeneity across the Alzheimer's disease continuum

Introduction

Neuroimaging heterogeneity in dementia has been examined using single modalities. We evaluated the associations of magnetic resonance imaging (MRI) atrophy and flortaucipir positron emission tomography (PET) clusters across the Alzheimer's disease (AD) spectrum.

Methods

We included 496 Alzheimer's Disease Neuroimaging Initiative participants with brain MRI, flortaucipir PET scan, and amyloid beta biomarker measures obtained. We applied a novel robust collaborative clustering (RCC) approach on the MRI and flortaucipir PET scans. We derived indices for AD-like (SPARE-AD index) and brain age (SPARE-BA) atrophy.

Results

We identified four tau (I-IV) and three atrophy clusters. Tau clusters were associated with the apolipoprotein E genotype. Atrophy clusters were associated with white matter hyperintensity volumes. Only the hippocampal sparing atrophy cluster showed a specific association with brain aging imaging index. Tau clusters presented stronger clinical associations than atrophy clusters. Tau and atrophy clusters were partially associated.

Conclusions

Each neuroimaging modality captured different aspects of brain aging, genetics, vascular changes, and neurodegeneration leading to individual multimodal phenotyping.

Characterization of Alzheimer’s Disease Subtypes Based on Magnetic Resonance Imaging and Perfusion Single-Photon Emission Computed Tomography

Background: Alzheimer’s disease (AD) is a biologically heterogenous disease. Previous studies have reported the existence of various AD subtypes, and the various clinical features of the subtypes. However, inconsistent results have been obtained. Objective: To clarify the clinical characteristics of the various AD subtypes, by classifying probable AD into subtypes based on magnetic resonance imaging (MRI) and single-photon emission computed tomography (SPECT) findings. Methods: A total of 245 patients with probable AD were classified into the typical AD (TAD) subtype, limbic-predominant (LP) subtype, hippocampal-sparing (HS) subtype, and minimal-change (MC) subtype, based on the presence of medial temporal lobe atrophy on MRI and posterior cerebral hypoperfusion on SPECT. Demographics, including age, sex, body mass index, disease duration, education years, comorbidities, frailty, leisure activity, and neuropsychological findings were compared between the AD subtypes. Results: he frequency of TAD, LP, HS, and MC subtypes was 49%, 20%, 18%, and 13%, respectively. Patients with the LP subtype were older and characterized by fewer major comorbidities, higher frailty, and slower progression of disease. Patients with the HS subtype were younger and characterized by shorter disease duration, lower frailty, and preserved memory, but had prominent constructional dysfunction. Patients of the MC subtype were characterized by shorter disease duration, lower education level, less leisure activity, less impaired memory and orientation, and slower progression. Conclusion: Patients with different AD subtypes differed in their demographic and clinical features. The characterization of patients’ AD subtypes may provide effective support for the diagnosis, treatment, and care of AD patients.

Potential Mechanisms Underlying Resistance to Dementia in Non-Demented Individuals with Alzheimer's Disease Neuropathology

Alzheimer’s disease (AD) is the most common form of dementia and typically characterized by the accumulation of amyloid-β plaques and tau tangles. Intriguingly, there also exists a group of elderly which do not develop dementia during their life, despite the AD neuropathology, the so-called non-demented individuals with AD neuropathology (NDAN). In this review, we provide extensive background on AD pathology and normal aging and discuss potential mechanisms that enable these NDAN individuals to remain cognitively intact. Studies presented in this review show that NDAN subjects are generally higher educated and have a larger cognitive reserve. Furthermore, enhanced neural hypertrophy could compensate for hippocampal and cingulate neural atrophy in NDAN individuals. On a cellular level, these individuals show increased levels of neural stem cells and ‘von Economo neurons’. Furthermore, in NDAN brains, binding of Aβ oligomers to synapses is prevented, resulting in decreased glial activation and reduced neuroinflammation. Overall, the evidence stated here strengthens the idea that some individuals are more resistant to AD pathology, or at least show an elongation of the asymptomatic state of the disease compared to others. Insights into the mechanisms underlying this resistance could provide new insight in understanding normal aging and AD itself. Further research should focus on factors and mechanisms that govern the NDAN cognitive resilience in order to find clues on novel biomarkers, targets, and better treatments of AD.

Concordance of Alzheimer’s Disease Subtypes Produced from Different Representative Morphological Measures: A Comparative Study

Background: Gray matter (GM) density and cortical thickness (CT) obtained from structural magnetic resonance imaging are representative GM morphological measures that have been commonly used in Alzheimer’s disease (AD) subtype research. However, how the two measures affect the definition of AD subtypes remains unclear. Methods: A total of 180 AD patients from the ADNI database were used to identify AD subgroups. The subtypes were identified via a data-driven strategy based on the density features and CT features, respectively. Then, the similarity between the two features in AD subtype definition was analyzed. Results: Four distinct subtypes were discovered by both density and CT features: diffuse atrophy AD, minimal atrophy AD (MAD), left temporal dominant atrophy AD (LTAD), and occipital sparing AD. The matched subtypes exhibited relatively high similarity in atrophy patterns and neuropsychological and neuropathological characteristics. They differed only in MAD and LTAD regarding the carrying of apolipoprotein E ε2. Conclusions: The results verified that different representative morphological GM measurement methods could produce similar AD subtypes. Meanwhile, the influences of apolipoprotein E genotype, asymmetric disease progression, and their interactions should be considered and included in the AD subtype definition. This study provides a valuable reference for selecting features in future studies of AD subtypes.

Cross-Sectional Association Between Blood Cell Phenotypes, Cognitive Function, and Brain Imaging Measures in the Community-Based Framingham Heart Study

BACKGROUND

Peripheral inflammation is associated with increased risk for dementia. Neutrophil to lymphocyte ratio (NLR), red cell distribution width (RDW), and mean platelet volume (MPV), are easily measured circulating blood cell phenotypes reflecting chronic peripheral inflammation, but their association with dementia status is unclear.

OBJECTIVE

We sought to investigate the cross-sectional association of these inflammatory measures with neuropsychological (NP) test performance, and brain magnetic resonance imaging (MRI) measures in the Framingham Heart Study (FHS) Offspring, Third-generation, and Omni cohorts.

METHODS

We identified FHS participants who attended an exam that included a complete blood cell count (CBC) and underwent NP testing (n = 3,396) or brain MRI (n = 2,770) within five years of blood draw. We investigated the association between NLR, RDW, and MPV and NP test performance and structural MRI-derived volumetric measurements using linear mixed effect models accounting for family relationships and adjusting for potential confounders.

RESULTS

Participants were on average 60 years old, 53% female, and about 80% attended some college. Higher NLR was significantly associated with poorer performance on visual memory, and visuospatial abilities, as well as with larger white matter hyperintensity volume. We also observed associations for higher RDW with poorer executive function and smaller total cerebral brain volume.

CONCLUSION

Chronic peripheral inflammation as measured by NLR and RDW was associated with worse cognitive function, reduced brain volume, and greater microvascular disease in FHS participants. If confirmed in other samples, CBC may provide informative and cost-effective biomarkers of abnormal brain aging in the community.

Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes

BACKGROUND

Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses.

OBJECTIVE

To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers.

METHODS

We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class.

RESULTS

For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ1-40, higher depressive symptomology, and lower body mass index.

CONCLUSION

Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.

Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models

Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.

Anatomically Standardized Detection of MRI Atrophy Patterns in Early-Stage Alzheimer's Disease

MRI studies have consistently identified atrophy patterns in Alzheimer’s disease (AD) through a whole-brain voxel-based analysis, but efforts to investigate morphometric profiles using anatomically standardized and automated whole-brain ROI analyses, performed at the individual subject space, are still lacking. In this study we aimed (i) to utilize atlas-derived measurements of cortical thickness and subcortical volumes, including of the hippocampal subfields, to identify atrophy patterns in early-stage AD, and (ii) to compare cognitive profiles at baseline and during a one-year follow-up of those previously identified morphometric AD subtypes to predict disease progression. Through a prospectively recruited multi-center study, conducted at four Austrian sites, 120 patients were included with probable AD, a disease onset beyond 60 years and a clinical dementia rating of ≤1. Morphometric measures of T1-weighted images were obtained using FreeSurfer. A principal component and subsequent cluster analysis identified four morphometric subtypes, including (i) hippocampal predominant (30.8%), (ii) hippocampal-temporo-parietal (29.2%), (iii) parieto-temporal (hippocampal sparing, 20.8%) and (iv) hippocampal-temporal (19.2%) atrophy patterns that were associated with phenotypes differing predominately in the presentation and progression of verbal memory and visuospatial impairments. These morphologically distinct subtypes are based on standardized brain regions, which are anatomically defined and freely accessible so as to validate its diagnostic accuracy and enhance the prediction of disease progression.

The latent factor structure underlying regional brain volume change and its relation to cognitive change in older adults

OBJECTIVE

Late-life changes in cognition and brain integrity are both highly multivariate, time-dependent processes that are essential for understanding cognitive aging and neurodegenerative disease outcomes. The present study seeks to identify a latent variable model capable of efficiently reducing a multitude of structural brain change magnetic resonance imaging (MRI) measurements into a smaller number of dimensions. We further seek to demonstrate the validity of this model by evaluating its ability to reproduce patterns of coordinated brain volume change and to explain the rate of cognitive decline over time.

METHOD

We used longitudinal cognitive data and structural MRI scans, obtained from a diverse sample of 358 participants (Mage = 74.81, SD = 7.17), to implement latent variable models for measuring brain change and to estimate the effects of these brain change factors on cognitive decline.

RESULTS

Results supported a bifactor model for brain change with four group factors (prefrontal, temporolimbic, medial temporal, and posterior association) and one general change factor (global atrophy). Atrophy in the global (β = 0.434, SE = 0.070), temporolimbic (β = 0.275, SE = 0.085), and medial temporal (β = 0.240, SE = 0.085) factors were the strongest predictors of global cognitive decline. Overall, the brain change model explained 59% of the variance in global cognitive slope.

CONCLUSIONS

The current results suggest that brain change across 27 bilateral regions of interest can be grouped into five change factors, three of which (global gray matter, temporolimbic, and medial temporal lobe atrophy) are strongly associated with cognitive decline. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Bioinformatic analysis of the human brain extracellular matrix proteome in neurodegenerative disorders

Alzheimer's, Parkinson's, and Huntington's diseases are characterized by selective degeneration of specific brain areas. Although increasing number of studies report alteration of the extracellular matrix on these diseases, an exhaustive characterization at the brain's matrix level might contribute to the development of more efficient cell restoration therapies. In that regard, proteomics-based studies are a powerful approach to uncover matrix changes. However, to date, the majority of proteomics studies report no or only a few brain matrix proteins with altered expression. This study aims to reveal the changes in the brain extracellular matrix by integrating several proteomics-based studies performed with postmortem tissue. In total, 67 matrix proteins with altered expression were collected. By applying a bioinformatic approach, we were able to reveal the dysregulated biological processes. Among them are processes related to the organization of the extracellular matrix, glycosaminoglycans and proteoglycans' metabolism, blood coagulation, and response to injury and oxidative stress. In addition, a protein was found altered in all three diseases-collagen type I alpha 2-and its binding partners further identified. A ClueGO network was created, depicting the GO groups associated with these binding partners, uncovering the processes that may consequently be affected. These include cellular adhesion, cell signaling through membrane receptors, inflammatory processes, and apoptotic cell death in response to oxidative stress. Overall, we were able to associate the contribution of the modification of extracellular matrix components to essential biological processes, highlighting the investment needed on proteomics studies with specific focus on the extracellular matrix in neurodegeneration.

Pathobiological Subtypes of Alzheimer Disease

Alzheimer disease (AD), the most common form of dementia, is a heterogenous disorder with various pathobiological subtypes. In addition to the 4 major subtypes based on the distribution of tau pathology and brain atrophy (typical, limbic predominant, hippocampal sparing, and minimal atrophy [MA]), several other clinical variants showing distinct regional patterns of tau burden have been identified: nonamnestic, corticobasal syndromal, primary progressive aphasia, posterior cortical atrophy, behavioral/dysexecutive, and mild dementia variants. Among the subtypes, differences were found in age at onset, sex distribution, cognitive status, disease duration, APOE genotype, and biomarker levels. The patterns of key network destructions parallel the tau and atrophy patterns of the AD subgroups essentially. Interruption of key networks, in particular the default-mode network that is responsible for cognitive decline, is consistent in hetero-genous AD groups. AD pathology is often associated with co-pathologies: cerebrovascular lesions, Lewy pathology, and TDP-43 proteinopathies. These mixed pathologies essentially influence the clinical picture of AD and may accel-erate disease progression. Unraveling the heterogeneity among the AD spectrum entities is important for opening a window to pathogenic mechanisms affecting the brain and enabling precision medicine approaches as a basis for developing preventive and ultimately successful disease-modifying therapies for AD.

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.

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.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

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.

AD Resemblance Atrophy Index as a Diagnostic Biomarker for Alzheimer's Disease: A Retrospective Clinical and Biological Validation

BACKGROUND

Magnetic resonance imaging (MRI) provides objective information about brain structural atrophy in patients with Alzheimer's disease (AD). This multi-structural atrophic information, when integrated as a single differential index, has the potential to further elevate the accuracy of AD identification from normal control (NC) compared to the conventional structure volumetric index.

OBJECTIVE

We herein investigated the performance of such an MRI-derived AD index, AD-Resemblance Atrophy Index (AD-RAI), as a neuroimaging biomarker in clinical scenario.

METHOD

Fifty AD patients (19 with the Amyloid, Tau, Neurodegeneration (ATN) results assessed in cerebrospinal fluid) and 50 age- and gender-matched NC (19 with ATN results assessed using positron emission tomography) were recruited in this study. MRI-based imaging biomarkers, i.e., AD-RAI, were quantified using AccuBrain®. The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of these MRI-based imaging biomarkers were evaluated with the diagnosis result according to clinical criteria for all subjects and ATN biological markers for the subgroup.

RESULTS

In the whole groups of AD and NC subjects, the accuracy of AD-RAI was 91%, sensitivity and specificity were 88% and 96%, respectively, and the AUC was 92%. In the subgroup of 19 AD and 19 NC with ATN results, AD-RAI results matched completely with ATN classification. AD-RAI outperforms the volume of any single brain structure measured.

CONCLUSION

The finding supports the hypothesis that MRI-derived composite AD-RAI is a more accurate imaging biomarker than individual brain structure volumetry in the identification of AD from NC in the clinical scenario.

Severity Distribution of Alzheimer's Disease Dementia and Mild Cognitive Impairment in the Framingham Heart Study

BACKGROUND

Studies providing Alzheimer's disease (AD) prevalence data have largely neglected to characterize the proportion of AD that is mild, moderate, or severe. Estimates of the severity distribution along the AD continuum, including the mild cognitive impairment (MCI) stage, are important to plan research and allocate future resources, particularly resources targeted at particular stages of disease.

OBJECTIVE

To characterize the distribution of severity of AD dementia and MCI among prevalent cases in the population-based Framingham Heart Study.

METHODS

Participants (aged 50-94) with prevalent MCI or AD dementia clinical syndrome were cross-sectionally selected from three time-windows of the population-based Framingham Heart Study in 2004-2005 (n = 381), 2006-2007 (n = 422), and 2008-2009 (n = 389). Summary estimates of the severity distribution were achieved by pooling results across time-windows. Diagnosis and severity were assessed by consensus dementia review. MCI-progressive was determined if the participant had documented progression to AD dementia clinical syndrome using longitudinal data.

RESULTS

Among AD dementia participants, the pooled percentages were 50.4%for mild, 30.3%for moderate, and 19.3%for severe. Among all MCI and AD participants, the pooled percentages were 29.5%, 19.6%, 25.7%, and 45.2%for MCI-not-progressive, MCI-progressive, mild AD dementia, and the combined group of MCI-progressive and mild AD dementia, respectively. Distributions by age and sex were presented.

CONCLUSION

The finding that half of the people living with AD have mild disease underscores the need for research and interventions to slow decline or prevent progression of this burdensome disease.