APOE-MS4A genetic interactions are associated with executive dysfunction and network abnormality in clinically mild Alzheimer's disease

Identification of genetic drivers of plasma lipoprotein size in the Diversity Outbred mouse population

Despite great progress in understanding lipoprotein physiology, there is still much to be learned about the genetic drivers of lipoprotein abundance, composition, and function. We used ion mobility spectrometry to survey 16 plasma lipoprotein subfractions in 500 Diversity Outbred mice maintained on a Western-style diet. We identified 21 quantitative trait loci (QTL) affecting lipoprotein abundance. To refine the QTL and link them to disease risk in humans, we asked if the human homologs of genes located at each QTL were associated with lipid traits in human genome-wide association studies. Integration of mouse QTL with human genome-wide association studies yielded candidate gene drivers for 18 of the 21 QTL. This approach enabled us to nominate the gene encoding the neutral ceramidase, Asah2, as a novel candidate driver at a QTL on chromosome 19 for large HDL particles (HDL-2b). To experimentally validate Asah2, we surveyed lipoproteins in Asah2-/- mice. Compared to wild-type mice, female Asah2-/- mice showed an increase in several lipoproteins, including HDL. Our results provide insights into the genetic regulation of circulating lipoproteins, as well as mechanisms by which lipoprotein subfractions may affect cardiovascular disease risk in humans.

Identification and Prioritization of PET Neuroimaging Targets for Microglial Phenotypes Associated with Microglial Activity in Alzheimer's Disease

Activation of microglial cells accompanies the progression of many neurodegenerative disorders, including Alzheimer's disease (AD). Development of molecular imaging tools specific to microglia can help elucidate the mechanism through which microglia contribute to the pathogenesis and progression of neurodegenerative disorders. Through analysis of published genetic, transcriptomic, and proteomic data sets, we identified 19 genes with microglia-specific expression that we then ranked based on association with the AD characteristics, change in expression, and potential druggability of the target. We believe that the process we used to identify and rank microglia-specific genes is broadly applicable to the identification and evaluation of targets in other disease areas and for applications beyond molecular imaging.

Functional MRI and ApoE4 genotype for predicting cognitive decline in amyloid-positive individuals

BACKGROUND

In light of advancements in machine learning techniques, many studies have implemented machine learning approaches combined with data measures to predict and classify Alzheimer's disease. Studies that predicted cognitive status with longitudinal follow-up of amyloid-positive individuals remain scarce, however.

OBJECTIVE

We developed models based on voxel-wise functional connectivity (FC) density mapping and the presence of the ApoE4 genotype to predict whether amyloid-positive individuals would experience cognitive decline after 1 year.

METHODS

We divided 122 participants into cognitive decline and stable cognition groups based on the participants' change rates in Mini-Mental State Examination scores. In addition, we included 68 participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database as an external validation data set. Subsequently, we developed two classification models: the first model included 99 voxels, and the second model included 99 voxels and the ApoE4 genotype as features to train the models by Wide Neural Network algorithm with fivefold cross-validation and to predict the classes in the hold-out test and ADNI data sets.

RESULTS

The results revealed that both models demonstrated high accuracy in classifying the two groups in the hold-out test data set. The model for FC demonstrated good performance, with a mean F 1-score of 0.86. The model for FC combined with the ApoE4 genotype achieved superior performance, with a mean F 1-score of 0.90. In the ADNI data set, the two models demonstrated stable performances, with mean F 1-scores of 0.77 in the first and second models.

CONCLUSION

Our findings suggest that the proposed models exhibited promising accuracy for predicting cognitive status after 1 year in amyloid-positive individuals. Notably, the combination of FC and the ApoE4 genotype increased prediction accuracy. These findings can assist clinicians in predicting changes in cognitive status in individuals with a high risk of Alzheimer's disease and can assist future studies in developing precise treatment and prevention strategies.

Central Executive Network and Executive Function in Patients With Alzheimer's Disease and Healthy Individuals: Meta-Analysis of Structural and Functional MRI

OBJECTIVE

The neural architecture of executive function is of interest given its utility as a transdiagnostic predictor of adaptive functioning. However, a gap exists in the meta-analytic literature assessing this relationship in neuropsychiatric populations, concordance between structural and functional architecture, and the relationship with neuropsychological assessment of executive function. Given the importance of the central executive network (CEN) in Alzheimer's disease, this population may be useful in understanding this relationship in Alzheimer's disease pathology.

METHODS

A meta-analysis of studies (k=21) was conducted to elucidate the relationship between executive function and CEN for structural architecture (k=10; N=1,027) among patients with Alzheimer's disease (k=6; N=250) and healthy control subjects (HCs) (k=4; N=777) and for functional architecture (k=11; N=522) among patients with Alzheimer's disease (k=6; N=306) and HCs (k=5; N=216). Random-effects modeling was used to increase accuracy of conclusions about population means.

RESULTS

Analyses revealed a positive brain-behavior relationship (pr=0.032, 95% CI=0.07, 0.54), although there was a lack of statistically significant heterogeneity between functional and structural neuroimaging (Q=9.89, p=0.971, I²=0.00%) and between the Alzheimer's and HC groups in functional (Q=8.18, p=0.612, I²=0.00%) and structural (Q=1.60, p=0.996, I²=0.00%) neuroimaging. Similarly, a lack of statistically significant heterogeneity was revealed between functional and structural neuroimaging among patients with Alzheimer's disease (Q=3.59, p=0.980, I²=0.00%) and HCs (Q=3.67, p=0.885, I²=0.00%).

CONCLUSIONS

Structural and functional imaging in the CEN are predictive of executive function performance among patients with Alzheimer's disease and HCs. Regardless of how the CEN is affected, behavior is correlated to the degree to which the CEN is affected. Findings are significant in the context of methodological decisions in multimodal neuroimaging research.

Functional Connectivity, Physical Activity, and Neurocognitive Performances in Patients with Vascular Cognitive Impairment, No Dementia

BACKGROUND

Vascular Cognitive Impairment, No Dementia (VCIND) is a key stage at which early intervention will delay or prevent dementia. The pathophysiology of VCIND posits that a lesion in a single location in the brain has the ability to disrupt brain networks, and the subsequent abnormal Functional Connectivity (FC) of brain networks leads to deficits in corresponding neurobehavioral domains. In this study, we tested the hypothesis that disrupted anterior cingulate cortex and striatal networks mediated the effects of Physical Activity (PA) on neurobehavioral function.

METHODS

In 27 patients with VCIND, FC within the brain networks and neurobehavioral dysfunction were assessed. The relationship between the cognitive scores, FC, and PA was studied. The Fitbit Charge 2 was used to measure step counts, distance, and calories burned. In patients with VCIND, a cross-sectional Spearman's correlation to analyze the relationship among patient-level measures of PA, cognitive function scores, and FC strength within the brain networks.

RESULTS

Average step counts and average distance were associated with Trail Making Test B (TM-B) time to completion (seconds) and Instrumental Activities of Daily Living (IADL) score (P <0.05). The average calories burned were associated with IADL score (P = 0.009). The FC within the brain networks anchored by left caudal Anterior Cingulate Cortex (ACC) seeds (x= -5, y= 0, z= 36) and (x= -5, y= -10, z= 47) were positively correlated with average step counts and average distance, were negatively correlated with TMB time to completion (seconds), and were positively correlated with IADL score (P < 0.05). The FC within the brain networks anchored by left subgenual ACC seed (x= -5, y= 25, z= -10) were negatively correlated with average step counts and average distance were positively correlated with TMB time to completion (seconds), and were negatively correlated with IADL score (P < 0.05). The FC within the striatal networks was positively correlated with average calories burned and IADL score (P < 0.05).

Structural Covariance Network as an Endophenotype in Alzheimer's Disease-Susceptible Single-Nucleotide Polymorphisms and the Correlations With Cognitive Outcomes

The cognitive manifestations of Alzheimer’s disease (AD) are related to brain network degeneration, and genetic differences may mediate network degeneration. Several AD-susceptible loci have been reported to involve amyloid or tau cascades; however, their relationships with gray matter (GM) volume and cognitive outcomes have yet to be established. We hypothesized that single-nucleotide polymorphism genotype groups may interact with apolipoprotein E4 (ApoE4) status or independently exert an effect on cognitive outcomes. We also hypothesized that GM structural covariance networks (SCNs) may serve as an endophenotype of the genetic effect, which, in turn, may be related to neurobehavior test scores. Gray matter SCNs were constructed in 324 patients with AD using T1 magnetic resonance imaging with independent component analysis (ICA). We assessed the effects of 15 genetic loci (rs9349407, rs3865444, rs670139, rs744373, rs3851179, rs11136000, rs3764650, rs610932, rs6887649, rs7849530, rs4866650, rs3765728, rs34011, rs6656401, and rs597668) using additive, recessive, and dominant models on cognitive outcomes. Statistical analysis was performed to explore the independent role of each locus, interactions with ApoE4 status, and relationships to GM ICA network intensity score. For outcome measures, we used the Mini-Mental State Examination (MMSE), Cognitive Abilities Screening Instrument (CASI) total score, and short-term memory (STM) subscores, adjusted for the covariates of education, disease duration, and age. Clinically, the CD2AP G allele showed a protective role in MMSE, CASI total, and CASI-STM scores independently or via interactions with non-ApoE4 status, while the CR1 A genotype group was associated with lower STM subscores independent of ApoE4 status. Three loci showed synergic interactions with ApoE4: BIN 1, MS4A6A, and FTMT. Of the 15 meaningful ICA components, 5 SCNs (anterior and posterior hippocampus, right temporal, left thalamus, default mode network) showed relationships with general cognitive performance, in which only the ApoE4 and MS4A6A genotype groups were independently related to the hippocampus network. The genetic loci MS4A6A, BIN1, CLU, CR1, BIN1, PICALM, and FGF1 influenced the networks independently or in synergy. This study suggests that AD-susceptible loci may each exert clinical significance independently through interactions with ApoE4 status or through SCNs as an endophenotype and that this effect is associated with the cognitive outcomes.

Role of Apolipoprotein E in the Clinical Profile of Atypical Parkinsonian Syndromes

INTRODUCTION

Atypical Parkinsonian syndromes (APS) encompass a spectrum of neurodegenerative diseases including dementia with Lewy bodies (DLB), progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and corticobasal syndrome (CBS). The effects of the Apolipoprotein E (APOE) gene on APS clinical features are controversial and understudied in several populations. We aimed to explore the influence of APOE genotype on clinical features in an APS Tunisian cohort.

METHODS

We included clinically diagnosed APS patients genotyped for APOE, and analyzed the clinical and APOE genotype associations.

RESULTS

A total of 328 APS patients were included, comprising 184 DLB, 58 PSP, 49 MSA, and 37 CBS. Significant differences in initial Mini-Mental State Examination and Frontal Assessment Battery scores according to APOE genotypes (P=0.05 and 0.0048) were found. Executive dysfunction (P=0.026) disorientation (P=0.025), and hallucinations (P<0.001) were more pronounced among APOE-ɛ4 carriers particularly in DLB. Memory disorders were also correlated to APOE-ɛ4 allele (P=0.048) and were more frequent among DLB and PSP carriers. Depression was associated to APOE-ε4 (P=0.042), more markedly in APOE-ε4-CBS and MSA carriers.

CONCLUSIONS

Our findings suggested a role of APOE-ε4 in defining a more altered cognitive phenotype with variable degrees across subgroups in APS patients, especially in DLB carriers. This effect mainly concerned executive, memory and orientation functions as well as hallucinations.

Microglia in Alzheimer's Disease: A Target for Therapeutic Intervention

Alzheimer’s disease (AD) is one of the most common types of age-related dementia worldwide. In addition to extracellular amyloid plaques and intracellular neurofibrillary tangles, dysregulated microglia also play deleterious roles in the AD pathogenesis. Numerous studies have demonstrated that unbridled microglial activity induces a chronic neuroinflammatory environment, promotes β-amyloid accumulation and tau pathology, and impairs microglia-associated mitophagy. Thus, targeting microglia may pave the way for new therapeutic interventions. This review provides a thorough overview of the pathophysiological role of the microglia in AD and illustrates the potential avenues for microglia-targeted therapies, including microglial modification, immunoreceptors, and anti-inflammatory drugs.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

Effect of APOE ε4 on multimodal brain connectomic traits: a persistent homology study

BACKGROUND

Although genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer's disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome.

RESULTS

Here, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers.

CONCLUSIONS

We have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.

Functional connectivity in default mode network correlates with severity of hypoxemia in obstructive sleep apnea

INTRODUCTION

Obstructive sleep apnea (OSA)-associated hypoxemia, sleep fragmentation, and cerebral vascular dysfunction are implicated in cognitive dysfunction. Functional connectivity within default mode network (DMN) is a possible mechanism underlying the cognitive impairment. The aim of this study was to investigate the impact of hypoxemia and sleep fragmentation on functional connectivity and on cognitive performance in patients with OSA.

METHODS

Twenty-eight patients with OSA were included (mean age = 58.0 ± 8.5 years). We correlated the functional connectivity in DMN with cognitive performances and further analyzed the relationship of functional connectivity in DMN with hypoxemia severity, as revealed by apnea-hypopnea index (AHI), oxygen desaturation index (ODI), and nadir SaO₂ (%), and with degree of sleep fragmentation, as shown by sleep efficiency and wake after sleep onset.

RESULTS

Functional connectivity in DMN was associated with AHI, ODI, and nadir SaO₂ (%) (p < .05) and was not associated with sleep fragmentation measures (p > .05). Functional connectivity that was associated with AHI, ODI, and nadir SaO₂ (%) was in the areas of bilateral middle temporal gyri, bilateral frontal pole, and bilateral hippocampus and was positively correlated with Cognitive Abilities Screening Instrument (CASI) total score (ρ = 0.484; p = .012), CASI-List-generating, CASI-Attention, and composite score of CASI-List-generating plus CASI-Attention (p < .05).

CONCLUSION

Functional connectivity in DMN is implicated in impairment of global cognitive function and of attention in OSA patients. The functional connectivity in the DMN is associated with hypoxemia rather than with sleep fragmentation.

Functional connectome and neuropsychiatric symptom clusters of Alzheimer's disease

BACKGROUND

Neuropsychiatric symptoms (NPSs) are important aspects of Alzheimer's disease (AD). Investigation of the effect of functional network abnormalities on clustered NPSs may uncover loci of altered connectivity for more targeted pharmacological and behavioral interventions in AD. The study aimed to investigate functional connectivity in AD and the clustered NPSs, as assessed by the Neuropsychiatric Inventory (NPI).

METHODS

In one hundred and fifty-nine patients with mild dementia stage of AD, graph metrics measuring functional connectivity at global network- and local network-level were assessed by closeness-centrality, betweenness-centrality, average-path-length, local-efficiency, and clustering-coefficient, respectively. The relationship between the NPI composite score and functional connectivity was assessed.

RESULTS

In AD, an increase in behavioral composite score was associated with changes in functional connectivity at local network-level, and regions displayed the changes was left lingual gyrus, left sub-genual ACC nodes, and left supra-genual ACC nodes (P < 0.05). An increase in affective composite score was associated with changes in functional connectivity at global network-level, and regions displayed the change was right caudate (P = 0.014). An increase in psychotic composite score was associated with changes in functional connectivity at global network-level, and regions displayed the change was left precuneus and right dorsolateral superior frontal gyrus (P < 0.05).

LIMITATIONS

Cognitively normal elderly subjects and longitudinal follow-up will be needed to see the evolution of NPS clusters and pathological changes in the functional connectivity at global or local network-level.

CONCLUSIONS

Different NPS clusters corresponded to distinct changes in functional connectivity at global and local network-level.

ABCA7 polymorphisms correlate with memory impairment and default mode network in patients with APOEε4-associated Alzheimer's disease

Background

Since both APOE and ABCA7 protein expression may independently reduce neuritic plaque burden and reorganize fibrillar amyloid burden-mediated disruption of functional connectivity in the default mode network, we aimed to investigate the effect of the APOE-ABCA7 interaction on default mode network in Alzheimer’s disease.

Methods

Two hundred and eighty-seven individuals with a diagnosis of typical Alzheimer’s disease were included in this study. Memory was characterized and compared between APOE-ε4+ carriers and APOE-ε4 non-carriers within ABCA7 rs3764650T allele homozygous carriers and ABCA7 rs3764650G allele carriers, respectively. Two-way analysis of variance was used to identify a significant interaction effect between APOE (APOE-ε4+ carriers versus APOE-ε4 non-carriers) and ABCA7 (ABCA7 rs3764650T allele homozygous versus ABCA7 rs3764650G allele carriers) on memory scores and functional connectivity in each default mode network subsystem.

Results

In ABCA7 rs3764650G allele carriers, APOE-ε4+ carriers had lower memory scores (t (159) = − 4.879; P < 0.001) compared to APOE-ε4 non-carriers, but APOE-ε4+ carriers and APOE-ε4 non-carriers did not have differences in memory (P > 0.05) within ABCA7 rs3764650T allele homozygous carriers. There was a significant APOE-ABCA7 interaction effect on the memory (F3, 283 = 4.755, P = 0.030). In the default mode network anchored by the entorhinal seed, the peak neural activity of the cluster that was significantly associated with APOE-ABCA7 interaction effects (P = 0.00002) was correlated with the memory (ρ = 0.129, P = 0.030).

Conclusions

Genetic-biological systems may impact disease presentation and therapy. Clarifying the effect of APOE-ABCA7 interactions on the default mode network and memory is critical to exploring the complex pathogenesis of Alzheimer’s disease and refining a potential therapy.