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Defining and predicting transdiagnostic categories of neurodegenerative disease.

ABSTRACT: The prevalence of concomitant proteinopathies and heterogeneous clinical symptoms in neurodegenerative diseases hinders the identification of individuals who might be candidates for a particular intervention. Here, by applying an unsupervised clustering algorithm to post-mortem histopathological data from 895 patients with degeneration in the central nervous system, we show that six non-overlapping disease clusters can simultaneously account for tau neurofibrillary tangles, α-synuclein inclusions, neuritic plaques, inclusions of the transcriptional repressor TDP-43, angiopathy, neuron loss and gliosis. We also show that membership to the six transdiagnostic disease clusters, which explains more variance in cognitive phenotypes than can be explained by individual diagnoses, can be accurately predicted from scores of the Mini-Mental Status Exam, protein levels in cerebrospinal fluid, and genotype at the APOE and MAPT loci, via cross-validated multiple logistic regression. This combination of unsupervised and supervised data-driven tools provides a framework that could be used to identify latent disease subtypes in other areas of medicine.

SUBMITTER: Cornblath EJ

PROVIDER: S-EPMC7946378 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Defining and predicting transdiagnostic categories of neurodegenerative disease.

Cornblath Eli J EJ Robinson John L JL Irwin David J DJ Lee Edward B EB Lee Virginia M-Y VM Trojanowski John Q JQ Bassett Danielle S DS

Nature biomedical engineering 20200803 8

The prevalence of concomitant proteinopathies and heterogeneous clinical symptoms in neurodegenerative diseases hinders the identification of individuals who might be candidates for a particular intervention. Here, by applying an unsupervised clustering algorithm to post-mortem histopathological data from 895 patients with degeneration in the central nervous system, we show that six non-overlapping disease clusters can simultaneously account for tau neurofibrillary tangles, α-synuclein inclusion ...[more]

PMID: 32747831

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Project description:BackgroundNeurodegenerative diseases (NDDs) are increasingly prevalent and radically alter the lives of individuals and their informal care partners (together called a dyad). As symptoms progress, dyads are at risk for elevated emotional distress and declines in relationship functioning and quality of life. Psychosocial interventions delivered to dyads early after diagnosis have successfully prevented chronic emotional distress across several chronic illnesses including cancer and acute brain injury. Dyads with NDD could benefit from such interventions, however, they are limited. Because NDDs have symptom profiles that are distinct from other chronic illnesses, they require a unique framework and interventions. Given the limited dyadic interventions and unified symptoms across NDDs, a transdiagnostic framework may help to enhance scalability and efficiency. To address this problem, we developed a transdiagnostic framework that cuts across NDD physical and emotional diagnoses to inform cost-effective and sustainable NDD dyadic interventions.MethodsTo develop this framework, we conducted: (1) a narrative review on dyadic adjustment and existent dyadic interventions for those with NDDs, and (2) integrated findings to develop our NDD transdiagnostic framework for dyadic interventions early after diagnosis.ResultsFindings revealed no existent dyadic interventions for NDDs delivered shortly after diagnosis. Among available interventions, all were delivered later in disease progression, thereby focusing on dyadic challenges at more advanced stages. In addition, although research emphasized the influence of individual, dyadic, and contextual factors on dyads' early adjustment to NDDs, no conceptual model has been developed. Informed by theory and current research, we introduce an NDD transdiagnostic framework for couples' early biopsychosocial adjustment. This framework includes NDD specific: contextual factors, illness-related factors, individual and dyadic stressors, adaptive coping strategies, and dyads' resources.ConclusionsOur NDD transdiagnostic framework can be used to inform early dyadic psychosocial interventions that cut across all NDDs. This approach has important implications for implementation and scalability.

Project description:The gut microbiota-brain axis is suspected to contribute to the development of Alzheimer's disease (AD), a neurodegenerative disease characterized by amyloid-β plaque deposition, neurofibrillary tangles, and neuroinflammation. To evaluate the role of the gut microbiota-brain axis in AD, we characterized the gut microbiota of female 3xTg-AD mice modeling amyloidosis and tauopathy and wild-type (WT) genetic controls. Fecal samples were collected fortnightly from 4 to 52 weeks, and the V4 region of the 16S rRNA gene was amplified and sequenced on an Illumina MiSeq. RNA was extracted from the colon and hippocampus, converted to cDNA, and used to measure immune gene expression using reverse transcriptase quantitative PCR (RT-qPCR). Diversity metrics were calculated using QIIME2, and a random forest classifier was applied to predict bacterial features that are important in predicting mouse genotype. Gene expression of glial fibrillary acidic protein (GFAP; indicating astrocytosis) was elevated in the colon at 24 weeks. Markers of Th1 inflammation (il6) and microgliosis (mrc1) were elevated in the hippocampus. Gut microbiota were compositionally distinct early in life between 3xTg-AD mice and WT mice (permutational multivariate analysis of variance [PERMANOVA], 8 weeks, P = 0.001, 24 weeks, P = 0.039, and 52 weeks, P = 0.058). Mouse genotypes were correctly predicted 90 to 100% of the time using fecal microbiome composition. Finally, we show that the relative abundance of Bacteroides species increased over time in 3xTg-AD mice. Taken together, we demonstrate that changes in bacterial gut microbiota composition at prepathology time points are predictive of the development of AD pathologies. IMPORTANCE Recent studies have demonstrated alterations in the gut microbiota composition in mice modeling Alzheimer's disease (AD) pathologies; however, these studies have only included up to 4 time points. Our study is the first of its kind to characterize the gut microbiota of a transgenic AD mouse model, fortnightly, from 4 weeks of age to 52 weeks of age, to quantify the temporal dynamics in the microbial composition that correlate with the development of disease pathologies and host immune gene expression. In this study, we observed temporal changes in the relative abundances of specific microbial taxa, including the genus Bacteroides, that may play a central role in disease progression and the severity of pathologies. The ability to use features of the microbiota to discriminate between mice modeling AD and wild-type mice at prepathology time points indicates a potential role of the gut microbiota as a risk or protective factor in AD.

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Defining and predicting transdiagnostic categories of neurodegenerative disease.

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Defining and predicting transdiagnostic categories of neurodegenerative disease.

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