Dataset Information

Personalized Management and Treatment of Alzheimer's Disease.

ABSTRACT: Alzheimer’s disease (AD) is a priority health problem with a high cost to society and a large consumption of medical and social resources. The management of AD patients is complex and multidisciplinary. Over 90% of patients suffer from concomitant diseases and require personalized therapeutic regimens to reduce adverse drug reactions (ADRs), drug−drug interactions (DDIs), and unnecessary costs. Men and women show substantial differences in their AD-related phenotypes. Genomic, epigenetic, neuroimaging, and biochemical biomarkers are useful for predictive and differential diagnosis. The most frequent concomitant diseases include hypertension (>25%), obesity (>70%), diabetes mellitus type 2 (>25%), hypercholesterolemia (40%), hypertriglyceridemia (20%), metabolic syndrome (20%), hepatobiliary disorder (15%), endocrine/metabolic disorders (>20%), cardiovascular disorder (40%), cerebrovascular disorder (60−90%), neuropsychiatric disorders (60−90%), and cancer (10%). Over 90% of AD patients require multifactorial treatments with risk of ADRs and DDIs. The implementation of pharmacogenetics in clinical practice can help optimize the limited therapeutic resources available to treat AD and personalize the use of anti-dementia drugs, in combination with other medications, for the treatment of concomitant disorders.

SUBMITTER: Cacabelos R

PROVIDER: S-EPMC8951963 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Personalized Management and Treatment of Alzheimer's Disease.

Cacabelos Ramón R Naidoo Vinogran V Martínez-Iglesias Olaia O Corzo Lola L Cacabelos Natalia N Pego Rocío R Carril Juan C JC

Life (Basel, Switzerland) 20220321 3

Alzheimer’s disease (AD) is a priority health problem with a high cost to society and a large consumption of medical and social resources. The management of AD patients is complex and multidisciplinary. Over 90% of patients suffer from concomitant diseases and require personalized therapeutic regimens to reduce adverse drug reactions (ADRs), drug−drug interactions (DDIs), and unnecessary costs. Men and women show substantial differences in their AD-related phenotypes. Genomic, epigenetic, neuroi ...[more]

PMID: 35330211

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Project description:Alzheimer's disease involves many neurobiological alterations from molecular to macroscopic spatial scales, but we currently lack integrative, mechanistic brain models characterizing how factors across different biological scales interact to cause clinical deterioration in a way that is subject-specific or personalized. As important signalling molecules and mediators of many neurobiological interactions, neurotransmitter receptors are promising candidates for identifying molecular mechanisms and drug targets in Alzheimer's disease. We present a neurotransmitter receptor-enriched multifactorial brain model, which integrates spatial distribution patterns of 15 neurotransmitter receptors from post-mortem autoradiography with multiple in vivo neuroimaging modalities (tau, amyloid-β and glucose PET, and structural, functional and arterial spin labelling MRI) in a personalized, generative, whole-brain formulation. In a heterogeneous aged population (n = 423, ADNI data), models with personalized receptor-neuroimaging interactions showed a significant improvement over neuroimaging-only models, explaining about 70% (±20%) of the variance in longitudinal changes to the six neuroimaging modalities. In Alzheimer's disease patients (n = 25, ADNI data), receptor-imaging interactions explained up to 39.7% (P < 0.003, family-wise error-rate-corrected) of inter-individual variability in cognitive deterioration, via an axis primarily affecting executive function. Notably, based on their contribution to the clinical severity in Alzheimer's disease, we found significant functional alterations to glutamatergic interactions affecting tau accumulation and neural activity dysfunction and GABAergic interactions concurrently affecting neural activity dysfunction, amyloid and tau distributions, as well as significant cholinergic receptor effects on tau accumulation. Overall, GABAergic alterations had the largest effect on cognitive impairment (particularly executive function) in our Alzheimer's disease cohort (n = 25). Furthermore, we demonstrate the clinical applicability of this approach by characterizing subjects based on individualized 'fingerprints' of receptor alterations. This study introduces the first robust, data-driven framework for integrating several neurotransmitter receptors, multimodal neuroimaging and clinical data in a flexible and interpretable brain model. It enables further understanding of the mechanistic neuropathological basis of neurodegenerative progression and heterogeneity, and constitutes a promising step towards implementing personalized, neurotransmitter-based treatments.

Project description:Despite huge investments and major efforts to develop remedies for Alzheimer's disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1-3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.

Dataset Information

Personalized Management and Treatment of Alzheimer's Disease.

Publications

Personalized Management and Treatment of Alzheimer's Disease.

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