Apolipoprotein E ɛ4-related effects on cognition are limited to the Alzheimer's disease spectrum

Artificial Intelligence-Assisted Hippocampal Segmentation and Its Diagnostic Value for Alzheimer's Disease: A Meta-analysis

BACKGROUND

Hippocampal atrophy is a key marker of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Diverse artificial intelligence (AI) architectures for automated hippocampal segmentation have been increasingly reported in neuroimaging research. Different hippocampal automated segmentation methods can be of added value for the AD diagnostic work-up and treatment planning. This study aims to conduct a thorough meta-analysis to evaluate the segmentation accuracy and diagnostic performance of AI-assisted hippocampal segmentation in AD and MCI.

METHODS

We searched PubMed, Embase, Web of Science, and the Cochrane Library up to December 2024. Studies using neuroimaging data to assess AI algorithms for hippocampal segmentation and diagnosis in AD or MCI populations were included. Pooled segmentation accuracy was estimated using the Dice similarity coefficient (DSC) through a random-effects model, while diagnostic performance (sensitivity, specificity, and area under the curve [AUC]) was evaluated using a bivariate mixed-effects model.

RESULTS

A total of 27 studies were included. For segmentation accuracy, pooled DSC values were 0.82 (95% CI: 0.80-0.85) for AD, 0.85 (0.83-0.88) for MCI, and 0.86 (0.84-0.88) for normal controls (NC). Subgroup analyses indicated comparable performance between left and right hippocampi (both DSC: 0.87). Diagnostic meta-analysis demonstrated the highest accuracy for AD vs. NC (sensitivity: 0.87, specificity: 0.91, AUC: 0.95), but lower performance for AD vs. MCI (AUC: 0.80) and MCI vs. NC (AUC: 0.83).

CONCLUSION

AI-assisted hippocampal segmentation achieves good accuracy and demonstrates promising diagnostic capabilities for distinguishing AD from NC, though differentiation between AD and MCI remains challenging. Future high-quality research that applied standardized protocols, external validation, and clinical integration is needed to improve reliability in clinical practice.

The role of Apolipoprotein E4 on cognitive impairment in Parkinson's disease and Parkinsonisms

Cognitive impairment is a prevalent non-motor symptom of Parkinson's disease (PD), increasing the risk of dementia as the disease progresses. Despite its clinical significance, the etiology of cognitive impairment in PD remains unclear. Apolipoprotein E4 (APOE4), a well-known genetic risk factor of Alzheimer's disease, has been studied for its potential role in PD-related cognitive impairment. However, findings have been conflicting and thus inconclusive, highlighting a need to critically evaluate the current research. Several studies using neuroimaging modalities have explored the brains of individuals with PD and atypical parkinsonian disorders who have APOE4. Some of these studies have identified distinct neuropathological changes that have been previously reported to be associated with cognitive impairments in those with Parkinsonisms. Here, we review the role of APOE4 on cognitive impairment in PD and atypical Parkinsonisms using neuroimaging evidence. We will examine how APOE4 may contribute to pathological changes within the brain and its association with cognitive impairment.

APOE4 and infectious diseases jointly contribute to brain glucose hypometabolism, a biomarker of Alzheimer's pathology: New findings from the ADNI

BACKGROUND

Impaired brain glucose metabolism is a preclinical feature of neurodegenerative diseases such as Alzheimer's disease (AD). Infections may promote AD-related pathology. Therefore, we investigated the interplay between infections and APOE4, a strong genetic risk factor for AD.

METHODS

We analyzed data on 1,509 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using multivariate linear regression models. The outcomes were rank-normalized hypometabolic convergence index (HCI), statistical regions of interest (SROI) for AD, and mild cognitive impairment (MCI). Marginal mean estimates for infections, stratified by APOE4 carrier status, were then computed.

RESULTS

Prior infections were associated with greater HCI [β = 0.15, 95% CI: 0.03, 0.27, p = 0.01]. The combined effects of infections and APOE4 carriers on HCI levels were significantly greater than either variable alone. Among APOE4 carriers, the estimated marginal mean was 0.62, rising to 0.77, with infections (p<0.001), indicating an interaction effect. Carriers with multiple infections showed greater hypometabolism (higher HCI), with an estimate of 0.44 (p = 0.01) compared to 0.11 (p = 0.08) for those with a single infection, revealing a dose-response relationship. The estimates for the association of infections with SROI AD and SROI MCI were β = -0.01 (p = 0.02) and β = -0.01 (p = 0.04), respectively.

CONCLUSION

Our findings suggest that infections and APOE4 jointly contribute to brain glucose hypometabolism and AD pathology, supporting a "multi-hit" mechanism in AD development.

APOE4 and Infectious Diseases Jointly Contribute to Brain Glucose Hypometabolism, a Biomarker of Alzheimer's Pathology: New Findings from the ADNI

Background

Impaired brain glucose metabolism is a preclinical feature of neurodegenerative diseases such as Alzheimer's disease (AD). Infections may promote AD-related pathology. Therefore, we investigated the interplay between infections and APOE4, a strong genetic risk factor for AD.

Methods

We analyzed data on 1,509 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using multivariate linear regression models. The outcomes were rank-normalized hypometabolic convergence index (HCI), statistical regions of interest (SROI) for AD, and mild cognitive impairment (MCI). Marginal mean estimates for infections, stratified by APOE4 carrier status, were then computed.

Results

Prior infections were associated with greater HCI [β=0.15, 95% CI: 0.03, 0.27, p=0.01]. The combined effects of infections and APOE4 carriers on HCI levels were significantly greater than either variable alone. Among APOE4 carriers, the estimated marginal mean was 0.62, rising to 0.77, with infections (p<0.001), indicating an interaction effect. Carriers with multiple infections showed greater hypometabolism (higher HCI), with an estimate of 0.44 (p=0.01) compared to 0.11 (p=0.08) for those with a single infection, revealing a dose-response relationship. The estimates for the association of infections with SROI AD and SROI MCI were β=-0.01 (p=0.02) and β=-0.01 (p=0.04), respectively.

Conclusion

Our findings suggest that infections and APOE4 jointly contribute to brain glucose hypometabolism and AD pathology, supporting a "multi-hit" mechanism in AD development.

Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants

A leading explanation for translational failure in neurodegenerative disease is that new drugs are evaluated late in the disease course when clinical features have become irreversible. Here, to address this gap, we cognitively profiled 21,051 people aged 17-85 years as part of the Genes and Cognition cohort within the National Institute for Health and Care Research BioResource across England. We describe the cohort, present cognitive trajectories and show the potential utility. Surprisingly, when studied at scale, the APOE genotype had negligible impact on cognitive performance. Different cognitive domains had distinct genetic architectures, with one indicating brain region-specific activation of microglia and another with glycogen metabolism. Thus, the molecular and cellular mechanisms underpinning cognition are distinct from dementia risk loci, presenting different targets to slow down age-related cognitive decline. Participants can now be recalled stratified by genotype and cognitive phenotype for natural history and interventional studies of neurodegenerative and other disorders.

Actigraphy Estimated Sleep Moderates the Relationship between Physical Activity and Cognition in Older Adults

Background and Aims

Physical inactivity and poor sleep are common in older adults and may interact to contribute to age- and disease-related cognitive decline. However, prior work regarding the associations among physical activity, and cognition in older adults is primarily limited to subjective questionnaires that are susceptible to inaccuracies and recall bias. Therefore, this study examined whether objectively measured physical activity and sleep characteristics, each estimated using actigraphy, are independently or interactively associated with cognitive performance.

Methods

The study included 157 older adults free of dementia (136 cognitively unimpaired; 21 MCI; M age = 71.7) from the BIOCARD cohort.

Results

Using multiple linear regression, cognition was regressed on estimated total volume of physical activity (TVPA), sleep efficiency (SE), wake after sleep onset (WASO), and total sleep time (TST) (adjusted for age, sex, education, diagnosis, vascular risk factors, and Apolipoprotein E (APOE)-e4 genetic status). Models were also run for domain-specific cognitive composite scores. TVPA and SE each were positively associated with a global cognitive composite score. TVPA was positively associated with executive function and language composites, and SE was positively related to executive function, visuospatial, and language composites. Importantly, a TVPA by SE interaction (p = 0.015) suggested that adults with the poorest SE experienced the greatest benefit from physical activity in relation to global cognition. The other sleep metrics were unrelated to cognitive performance.

Conclusion

These results suggest that TVPA and SE may synergistically benefit cognition in older adults.