Genetic risk for Alzheimer's dementia predicts motor deficits through multi-omic systems in older adults

Alzheimer's Disease Diagnosis in the Preclinical Stage: Normal Aging or Dementia

Alzheimer's disease (AD) progressively impairs the memory and thinking skills of patients, resulting in a significant global economic and social burden each year. However, diagnosis of this neurodegenerative disorder can be challenging, particularly in the early stages of developing cognitive decline. Current clinical techniques are expensive, laborious, and invasive, which hinders comprehensive studies on Alzheimer's biomarkers and the development of efficient devices for Point-of-Care testing (POCT) applications. To address these limitations, researchers have been investigating various biosensing techniques. Unfortunately, these methods have not been commercialized due to several drawbacks, such as low efficiency, reproducibility, and the lack of accurate identification of AD markers. In this review, we present diverse promising hallmarks of Alzheimer's disease identified in various biofluids and body behaviors. Additionally, we thoroughly discuss different biosensing mechanisms and the associated challenges in disease diagnosis. In each context, we highlight the potential of realizing new biosensors to study various features of the disease, facilitating its early diagnosis in POCT. This comprehensive study, focusing on recent efforts for different aspects of the disease and representing promising opportunities, aims to conduct the future trend toward developing a new generation of compact multipurpose devices that can address the challenges in the early detection of AD.

A longitudinal study of factors associated with cognitive frailty in middle-aged and elderly population based on the health ecology model

BACKGROUND

Cognitive frailty (CF) is an important geriatric syndrome and is reversible. It is crucial to develop preventive interventions for CF. We aimed to explore the associations between CF and its associated factors in Chinese aged 45 years and above.

METHODS

Based on the available data of 3 waves in China Health and Retirement Longitudinal Study from 2011 to 2015, 16,071 individuals aged 45 years and above from 3 waves were included. Based on the health ecology model, the associated factors were classified as downstream, midstream and upstream factors. Generalized hierarchical linear model including time level, individual level, and province level was applied to analyze the associations between factors and CF.

RESULTS

Multilevel factors have different effects on physical and cognitive function. In the downstream, old age, female, underweight, chronic diseases, and depression were risk factors of reversible CF and potentially reversible CF, and overweight was their protective factor. In the midstream, short or long night sleep duration was their risk factor, and > 30 and ≤ 60 min afternoon naps, alcohol drinking, and participation in social activities were their protective factors. In the upstream, living in rural areas was their risk factor, and high educational level, household consumption and GDP per capita were their protective factors.

CONCLUSIONS

Physical function and cognitive function are affected differently by multiple factors. The occurrence and development of physical frailty and cognitive impairment may have some common mechanisms. CF can be influenced by multilevel factors, and multilevel and comprehensive management of CF should be achieved.

KEY POINTS

Cognitive frailty was correlated with multilevel factors, including downstream, midstream, and upstream factors. It is crucial to focus on individual interventions such as physiological factors, psychological factors and health behaviors, especially the elderly, women and those with depression. Socioeconomic status was associated with the lower prevalence of cognitive frailty.

Neuropathology, Neuroimaging, and Fluid Biomarkers in Alzheimer's Disease

An improved understanding of the pathobiology of Alzheimer's disease (AD) should lead ultimately to an earlier and more accurate diagnosis of AD, providing the opportunity to intervene earlier in the disease process and to improve outcomes. The known hallmarks of Alzheimer's disease include amyloid-β plaques and neurofibrillary tau tangles. It is now clear that an imbalance between production and clearance of the amyloid beta protein and related Aβ peptides, especially Aβ42, is a very early, initiating factor in Alzheimer's disease (AD) pathogenesis, leading to aggregates of hyperphosphorylation and misfolded tau protein, inflammation, and neurodegeneration. In this article, we review how the AD diagnostic process has been transformed in recent decades by our ability to measure these various elements of the pathological cascade through the use of imaging and fluid biomarkers. The more recently developed plasma biomarkers, especially phosphorylated-tau217 (p-tau217), have utility for screening and diagnosis of the earliest stages of AD. These biomarkers can also be used to measure target engagement by disease-modifying therapies and the response to treatment.

Cholinergic Receptor Muscarinic 1 Co-Localized with Mitochondria in Cultured Dorsal Root Ganglion Neurons, and Its Deletion Disrupted Mitochondrial Ultrastructure in Peripheral Neurons: Implications in Alzheimer's Disease

Background

Loss of Cholinergic Receptor Muscarinic 1 (CHRM1) has been linked to the pathogenesis of Alzheimer's disease (AD). Our recent study found significantly lower CHRM1 protein levels in AD patient cortices, linked to reduced survival. Furthermore, using knockout mice (Chrm1-/-) we demonstrated that deletion of Chrm1 alters cortical mitochondrial structure and function, directly establishing a connection between its loss and mitochondrial dysfunction in the context of AD. While CHRM1's role in the brain has been extensively investigated, its impact on peripheral neurons in AD remains a crucial area of research, especially considering reported declines in peripheral nerve conduction among AD patients.

Objective

The objective was to characterize Chrm1 localization and mitochondrial deficits in Chrm1-/- dorsal root ganglion (DRG) neurons.

Methods

Recombinant proteins tagged with Green or Red Fluorescent Protein (GFP/RFP) were transiently expressed to investigate the localization of Chrm1 and mitochondria, as well as mitochondrial movement in the neurites of cultured primary mouse DRG neurons, using confocal time-lapse live cell imaging. Transmission electron microscopy was performed to examine the ultrastructure of mitochondria in both wild-type and Chrm1-/- DRGs.

Results

Fluorescence imaging revealed colocalization and comigration of N-terminal GFP-tagged Chrm1 and mitochondrial localization signal peptide-tagged RFP-labelled mitochondria in the DRGs neurons. A spectrum of mitochondrial structural abnormalities, including disruption and loss of cristae was observed in 87% neurons in Chrm1-/- DRGs.

Conclusions

This study suggests that Chrm1 may be localized in the neuronal mitochondria and loss of Chrm1 in peripheral neurons causes sever mitochondrial structural aberrations resembling AD pathology.

Dysregulated glial genes in Alzheimer's disease are essential for homeostatic plasticity: Evidence from integrative epigenetic and single cell analyses

Synaptic homeostatic plasticity is a foundational regulatory mechanism that maintains the stability of synaptic and neural functions within the nervous system. Impairment of homeostatic regulation has been linked to synapse destabilization during the progression of Alzheimer's disease (AD). Recent epigenetic and transcriptomic characterizations of the nervous system have revealed intricate molecular details about the aging brain and the pathogenesis of neurodegenerative diseases. Yet, how abnormal epigenetic and transcriptomic alterations in different cell types in AD affect synaptic homeostatic plasticity remains to be elucidated. Various glial cell types play critical roles in modulating synaptic functions both during the aging process and in the context of AD. Here, we investigated the impact of glial dysregulation of histone acetylation and transcriptome in AD on synaptic homeostatic plasticity, using computational analysis combined with electrophysiological methods in Drosophila. By integrating snRNA-seq and H3K9ac ChIP-seq data from the same AD patient cohort, we pinpointed cell type-specific signature genes that were transcriptionally altered by histone acetylation. We subsequently investigated the role of these glial genes in regulating presynaptic homeostatic potentiation in Drosophila. Remarkably, nine glial-specific genes, which were identified through our computational method as targets of H3K9ac and transcriptional dysregulation, were found to be crucial for the regulation of synaptic homeostatic plasticity in Drosophila. Our genetic evidence connects abnormal glial transcriptomic changes in AD with the impairment of homeostatic plasticity in the nervous system. In summary, our integrative computational and genetic studies highlight specific glial genes as potential key players in the homeostatic imbalance observed in AD.

Mechanistic insights into the role of herpes simplex virus 1 in Alzheimer's disease

Alzheimer's Disease (AD) is an aging-associated neurodegenerative disorder, threatening millions of people worldwide. The onset and progression of AD can be accelerated by environmental risk factors, such as bacterial and viral infections. Human herpesviruses are ubiquitous infectious agents that underpin numerous inflammatory disorders including neurodegenerative diseases. Published studies concerning human herpesviruses in AD imply an active role HSV-1 in the pathogenesis of AD. This review will summarize the current understanding of HSV-1 infection in AD and highlight some barriers to advance this emerging field.

Proteome-Wide Discovery of Cortical Proteins That May Provide Motor Resilience to Offset the Negative Effects of Pathologies in Older Adults

BACKGROUND

Motor resilience proteins have not been identified. This proteome-wide discovery study sought to identify proteins that may provide motor resilience.

METHODS

We studied the brains of older decedents with annual motor testing, postmortem brain pathologies, and proteome-wide data. Parkinsonism was assessed using 26 items of a modified United Parkinson Disease Rating Scale. We used linear mixed-effect models to isolate motor resilience, defined as the person-specific estimate of progressive parkinsonism after controlling for age, sex, and 10 brain pathologies. A total of 8 356 high-abundance proteins were quantified from dorsal lateral prefrontal cortex using tandem mass tag and liquid chromatography-mass spectrometry.

RESULTS

There were 391 older adults (70% female), mean age 80 years at baseline and 89 years at death. Five proteins were associated with motor resilience: A higher level of AP1B1 (Estimate -0.504, SE 0.121, p = 3.12 × 10-5) and GNG3 (Estimate -0.276, SE 0.068, p = 4.82 × 10-5) was associated with slower progressive parkinsonism. By contrast, a higher level of TTC38 (Estimate 0.140, SE 0.029, p = 1.87 × 10-6), CARKD (Estimate 0.413, SE 0.100, p = 3.50 × 10-5), and ABHD14B (Estimate 0.175, SE 0.044, p = 6.48 × 10-5) was associated with faster progressive parkinsonism. Together, these 5 proteins accounted for almost 25% of the variance of progressive parkinsonism above the 17% accounted for by 10 indices of brain pathologies.

DISCUSSION

Cortical proteins may provide more or less motor resilience in older adults. These proteins are high-value therapeutic targets for drug discovery that may lead to interventions that maintain motor function despite the accumulation of as yet untreatable brain pathologies.

Multi-omics data integration methods and their applications in psychiatric disorders

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

Multi-region brain transcriptomes uncover two subtypes of aging individuals with differences in Alzheimer risk and the impact of APOEε4

The heterogeneity of the older population suggests the existence of subsets of individuals which share certain brain molecular features and respond differently to risk factors for Alzheimer's disease, but this population structure remains poorly defined. Here, we performed an unsupervised clustering of individuals with multi-region brain transcriptomes to assess whether a broader approach, simultaneously considering data from multiple regions involved in cognition would uncover such subsets. We implemented a canonical correlation-based analysis in a Discovery cohort of 459 participants from two longitudinal studies of cognitive aging that have RNA sequence profiles in three brain regions. 690 additional participants that have data in only one or two of these regions were used in the Replication effort. These clustering analyses identified two meta-clusters, MC-1 and MC-2. The two sets of participants differ primarily in their trajectories of cognitive decline, with MC-2 having a delay of 3 years to the median age of incident dementia. This is due, in part, to a greater impact of tau pathology on neuronal chromatin architecture and to broader brain changes including greater loss of white matter integrity in MC-1. Further evidence of biological differences includes a significantly larger impact of APOEε4 risk on cognitive decline in MC-1. These findings suggest that our proposed population structure captures an aspect of the more distributed molecular state of the aging brain that either enhances the effect of risk factors in MC-1 or of protective effects in MC-2. These observations may inform the design of therapeutic development efforts and of trials as both become increasingly more targeted molecularly. One Sentence Summary: There are two types of aging brains, with one being more vulnerable to APOEε4 and subsequent neuronal dysfunction and cognitive loss.

The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer's disease

Beyond the core features of Alzheimer's disease (AD) pathology, i.e. amyloid pathology, tau-related neurodegeneration and microglia response, multiple other molecular alterations and pathway dysregulations have been observed in AD. Their inter-individual variations, complex interactions and relevance for clinical manifestation and disease progression remain poorly understood, however. Heterogeneity at both pathophysiological and clinical levels complicates diagnosis, prognosis, treatment and drug design and testing. High-throughput "omics" comprise unbiased and untargeted data-driven methods which allow the exploration of a wide spectrum of disease-related changes at different endophenotype levels without focussing a priori on specific molecular pathways or molecules. Crucially, new methodological and statistical advances now allow for the integrative analysis of data resulting from multiple and different omics methods. These multi-omics approaches offer the unique advantage of providing a more comprehensive characterisation of the AD endophenotype and to capture molecular signatures and interactions spanning various biological levels. These new insights can then help decipher disease mechanisms more deeply. In this review, we describe the different multi-omics tools and approaches currently available and how they have been applied in AD research so far. We discuss how multi-omics can be used to explore molecular alterations related to core features of the AD pathologies and how they interact with comorbid pathological alterations. We further discuss whether the identified pathophysiological changes are relevant for the clinical manifestation of AD, in terms of both cognitive impairment and neuropsychiatric symptoms, and for clinical disease progression over time. Finally, we address the opportunities for multi-omics approaches to help discover novel biomarkers for diagnosis and monitoring of relevant pathophysiological processes, along with personalised intervention strategies in AD.

Polygenic Risk Score Effectively Predicts Depression Onset in Alzheimer’s Disease Based on Major Depressive Disorder Risk Variants

Introduction

Depression is a common, though heterogenous, comorbidity in late-onset Alzheimer’s Disease (LOAD) patients. In addition, individuals with depression are at greater risk to develop LOAD. In previous work, we demonstrated shared genetic etiology between depression and LOAD. Collectively, these previous studies suggested interactions between depression and LOAD. However, the underpinning genetic heterogeneity of depression co-occurrence with LOAD, and the various genetic etiologies predisposing depression in LOAD, are largely unknown.

Methods

Major Depressive Disorder (MDD) genome-wide association study (GWAS) summary statistics were used to create polygenic risk scores (PRS). The Religious Orders Society and Rush Memory and Aging Project (ROSMAP, n = 1,708) and National Alzheimer’s Coordinating Center (NACC, n = 10,256) datasets served as discovery and validation cohorts, respectively, to assess the PRS performance in predicting depression onset in LOAD patients.

Results

The PRS showed marginal results in standalone models for predicting depression onset in both ROSMAP (AUC = 0.540) and NACC (AUC = 0.527). Full models, with baseline age, sex, education, and APOEε4 allele count, showed improved prediction of depression onset (ROSMAP AUC: 0.606, NACC AUC: 0.581). In time-to-event analysis, standalone PRS models showed significant effects in ROSMAP (P = 0.0051), but not in NACC cohort. Full models showed significant performance in predicting depression in LOAD for both datasets (P < 0.001 for all).

Conclusion

This study provided new insights into the genetic factors contributing to depression onset in LOAD and advanced our knowledge of the genetics underlying the heterogeneity of depression in LOAD. The developed PRS accurately predicted LOAD patients with depressive symptoms, thus, has clinical implications including, diagnosis of LOAD patients at high-risk to develop depression for early anti-depressant treatment.

Integrated lipidomics and proteomics network analysis highlights lipid and immunity pathways associated with Alzheimer's disease

BACKGROUND

There is an urgent need to understand the pathways and processes underlying Alzheimer's disease (AD) for early diagnosis and development of effective treatments. This study was aimed to investigate Alzheimer's dementia using an unsupervised lipid, protein and gene multi-omics integrative approach.

METHODS

A lipidomics dataset comprising 185 AD patients, 40 mild cognitive impairment (MCI) individuals and 185 controls, and two proteomics datasets (295 AD, 159 MCI and 197 controls) were used for weighted gene co-expression network analyses (WGCNA). Correlations of modules created within each modality with clinical AD diagnosis, brain atrophy measures and disease progression, as well as their correlations with each other, were analyzed. Gene ontology enrichment analysis was employed to examine the biological processes and molecular and cellular functions of protein modules associated with AD phenotypes. Lipid species were annotated in the lipid modules associated with AD phenotypes. The associations between established AD risk loci and the lipid/protein modules that showed high correlation with AD phenotypes were also explored.

RESULTS

Five of the 20 identified lipid modules and five of the 17 identified protein modules were correlated with clinical AD diagnosis, brain atrophy measures and disease progression. The lipid modules comprising phospholipids, triglycerides, sphingolipids and cholesterol esters were correlated with AD risk loci involved in immune response and lipid metabolism. The five protein modules involved in positive regulation of cytokine production, neutrophil-mediated immunity, and humoral immune responses were correlated with AD risk loci involved in immune and complement systems and in lipid metabolism (the APOE ε4 genotype).

CONCLUSIONS

Modules of tightly regulated lipids and proteins, drivers in lipid homeostasis and innate immunity, are strongly associated with AD phenotypes.