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Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods.

ABSTRACT: Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual's course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field.

SUBMITTER: Habes M

PROVIDER: S-EPMC7305953 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Currently, more than 55 million people around the world suffer from dementia, and Alzheimer's disease-related dementia (ADRD) accounts for nearly 60-70% of all those cases. The spread of AD pathology and progressive neurodegeneration in the hippocampus and cerebral cortex is strongly correlated with cognitive decline in AD patients; however, the molecular underpinning of the ADRD causality is still unclear. Studies of postmortem AD brains and animal models of AD suggest that elevated endoplasmic reticulum (ER) stress may have a role in ADRD pathology through altered neurocellular homeostasis in brain regions associated with learning and memory. To study the ER stress-associated neurocellular response and its effects on neurocellular homeostasis and neurogenesis, we modeled an ER stress challenge using Thapsigargin (TG), a specific inhibitor of sarco/endoplasmic reticulum Ca2+ ATPase (SERCA), in induced pluripotent stem cell (iPSC)-derived neural stem cells (NSCs) of two individuals from our Mexican American Family Study (MAFS). High-content screening and transcriptomic analysis of the control and ER stress-challenged NSCs showed that NSC’s ER stress response resulted in a significant decline in NSC self-renewal and an increase in apoptosis and cellular oxidative stress. A total of 2300 genes were significantly (moderated t statistics FDR corrected p-value ≤ 0.05 and Fold Change absolute ≥ 2.0) differentially expressed (DE). Pathway enrichment and gene network analysis of DE genes suggests that all three unfolded protein response (UPR) pathways (PERK, IRE1, and ATF6) were significantly activated and cooperatively regulated the NSC’s transcriptional response to ER stress. Our results show that IRE1/XBP1 mediated transcriptional regulation of E2F1 and its downstream targets have a dominant role in inducing G1/S-phase cell cycle arrest in the ER stress-challenged NSCs. The ER stress-challenged NSCs also showed activation of CHOP-mediated apoptosis and dysregulation of synaptic plasticity and neurotransmitter homeostasis-associated genes. Overall, our results suggest that ER stress-associated attenuation of NSC self-renewal, increased apoptosis, and dysregulated synaptic plasticity and neurotransmitter homeostasis plausibly play a role in the causation of ADRD.

Project description:Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The Proteograph™ Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.

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Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods.

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