Genetic overlap between Alzheimer's disease and immune-mediated diseases: An atlas of shared genetic determinants and biological convergence

Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

Molecular Mechanisms Linking Osteoarthritis and Alzheimer's Disease: Shared Pathways, Mechanisms and Breakthrough Prospects

Alzheimer's disease (AD) is a progressive neurodegenerative disease mostly affecting the elderly population. It is characterized by cognitive decline that occurs due to impaired neurotransmission and neuronal death. Even though deposition of amyloid beta (Aβ) peptides and aggregation of hyperphosphorylated TAU have been established as major pathological hallmarks of the disease, other factors such as the interaction of genetic and environmental factors are believed to contribute to the development and progression of AD. In general, patients initially present mild forgetfulness and difficulty in forming new memories. As it progresses, there are significant impairments in problem solving, social interaction, speech and overall cognitive function of the affected individual. Osteoarthritis (OA) is the most recurrent form of arthritis and widely acknowledged as a whole-joint disease, distinguished by progressive degeneration and erosion of joint cartilage accompanying synovitis and subchondral bone changes that can prompt peripheral inflammatory responses. Also predominantly affecting the elderly, OA frequently embroils weight-bearing joints such as the knees, spine and hips leading to pains, stiffness and diminished joint mobility, which in turn significantly impacts the patient's standard of life. Both infirmities can co-occur in older adults as a result of independent factors, as multiple health conditions are common in old age. Additionally, risk factors such as genetics, lifestyle changes, age and chronic inflammation may contribute to both conditions in some individuals. Besides localized peripheral low-grade inflammation, it is notable that low-grade systemic inflammation prompted by OA can play a role in AD pathogenesis. Studies have explored relationships between systemic inflammatory-associated diseases like obesity, hypertension, dyslipidemia, diabetes mellitus and AD. Given that AD is the most common form of dementia and shares similar risk factors with OA-both being age-related and low-grade inflammatory-associated diseases, OA may indeed serve as a risk factor for AD. This work aims to review literature on molecular mechanisms linking OA and AD pathologies, and explore potential connections between these conditions alongside future prospects and innovative treatments.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach

Background

The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

Objective

To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

Methods

We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline.

Results

We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways.

Conclusions

Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.