Progression of Extrapyramidal Signs in Alzheimer’s Disease: Clinical and Neuropathological Correlates

Tosto, Giuseppe; Monsell, Sarah E.; Hawes, Stephen E.; Bruno, Giuseppe; Mayeux, Richard

doi:10.3233/jad-150244

Cited by 22 publications

(13 citation statements)

References 34 publications

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“…In a recent study, Tosto et al (2016) applied k-Means clustering algorithm on a dataset of 3,502 patients with AD with longitudinal assessments from the National Alzheimer’s Coordinating Center database, with 394 providing neuropathological data. The authors were interested in examining subgroups of patients with variable trajectories of extrapyramidal sign progression (which include movement disorders such as postural instability, tremors and rigidity, body restlessness, and abnormal gait, among others) and their clinical and neuropathological correlates.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

“…The authors were interested in examining subgroups of patients with variable trajectories of extrapyramidal sign progression (which include movement disorders such as postural instability, tremors and rigidity, body restlessness, and abnormal gait, among others) and their clinical and neuropathological correlates. Tosto et al (2016) observed the following three clusters of extrapyramidal sign progression: no/low ( n = 1,583), medium ( n = 1,259), and high ( n = 660) extrapyramidal burden. The high extrapyramidal cluster had greater cognitive and neuropsychiatric impairment (particularly hallucinations), relative to the other clusters.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

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The Application of Unsupervised Clustering Methods to Alzheimer’s Disease

Alashwal

Halaby

Crouse

et al. 2019

Front. Comput. Neurosci.

166

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Clustering is a powerful machine learning tool for detecting structures in datasets. In the medical field, clustering has been proven to be a powerful tool for discovering patterns and structure in labeled and unlabeled datasets. Unlike supervised methods, clustering is an unsupervised method that works on datasets in which there is no outcome (target) variable nor is anything known about the relationship between the observations, that is, unlabeled data. In this paper, we focus on studying and reviewing clustering methods that have been applied to datasets of neurological diseases, especially Alzheimer’s disease (AD). The aim is to provide insights into which clustering technique is more suitable for partitioning patients of AD based on their similarity. This is important as clustering algorithms can find patterns across patients that are difficult for medical practitioners to find. We further discuss the implications of the use of clustering algorithms in the treatment of AD. We found that clustering analysis can point to several features that underlie the conversion from early-stage AD to advanced AD. Furthermore, future work can apply semi-clustering algorithms on AD datasets, which will enhance clusters by including additional information.

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Section: Clustering Algorithmsmentioning

confidence: 99%

Section: Clustering Algorithmsmentioning

confidence: 99%

The Application of Unsupervised Clustering Methods to Alzheimer’s Disease

Alashwal

Halaby

Crouse

et al. 2019

Front. Comput. Neurosci.

166

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“…4 A ocorrência das DNs é favorecida pelo avançar da idade, sendo a doença de Alzheimer (DA) o tipo mais recorrente, podendo atingir cerca de 115,4 milhões de idosos em 2050. 5,6 Em 2009, Alzheimer's Disease International (ADI) publicou um relatório com estimativas de prevalência da DA até o ano de 2050. Já que em informações relativas ao ano de 2004 mostraram que a DA e outras demências colaboraram, em toda a população global com 0,8% de todos os anos de vida perdidos ajustados por incapacidade (DALYs), 1,6% dos anos vividos com incapacidade e 0,2% dos anos de vida perdida.…”

Section: Introductionunclassified

Efeito da suplementação de ômega 3 isolado ou associado em pacientes com doença de Alzheimer: uma revisão sistemática da literatura científica

Santos¹,

Cardoso²

2020

hu rev

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Introdução: O envelhecimento da população está associado ao aumento da incidência da doença de Alzheimer (DA), a qual causa graves complicações ao paciente. Os nutrientes imunomoduladores, como os ácidos graxos poli-insaturados (PUFAs) da série ômega 3 (w-3) podem auxiliar na melhora do quadro clínico da DA. Objetivo: Analisar o efeito da suplementação de PUFAs w-3 isolado ou associado na população idosa com DA. Material e métodos: Trata-se de uma revisão sistemática de literatura científica nas bases de dados Pubmed e Science Direct, que englobou ensaios clínicos em idosos com provável e/ou comprovado diagnóstico de DA e suplementados com PUFAS w-3 isolado ou associado, no idioma inglês e com os seguintes descritores em saúde (DECs): brain, Alzheimer’s disease, fatty docosahexaenoic acid (DHA), polyunsaturated fatty acids (PUFA), older and elderly people e termo de pesquisa: eicosapentaenoic acid (EPA). O recorte temporal das publicações foi delimitado de 2006 a 2017. Resultados: foram selecionados 10 ensaios clínicos, cuja suplementação de w-3 favoreceu menor declínio no score de miniexame de estado mental (MEEM), retardo da disfunção, melhora no domínio de agitação do inventário neuropsiquiátrico (NPI) e melhora dos sintomas depressivos pela Escala de Depressão de Montgomery-Asberg (MADRS). Foram observadas mudanças significativas como aumento de apetite, peso, índice de massa corporal (IMC), EPA e DHA, além de reduções nos níveis séricos de albumina, ácido araquidônico (AA), ácido mirístico, interleucina-6 (IL- 6), interleucina 1 ß (IL-1ß) e fator estimulante de colônias de granulócitos (G-CSF) e redução na liberação de prostaglandina F2α (PGF2α). Foram relatadas alterações positivas em alguns genes e em outros, redução de sua expressão, além de hipometilação de importantes genes. Conclusão: A suplementação de PUFAs w-3 exerceu efeito positivo em pacientes com DA grau leve a moderado.

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“…In the US alone, about 5.5 million individuals are affected with late-onset Alzheimer"s disease (AD) 1 and this number is destined to increase dramatically in the next few decades. The predominant clinical features of AD are a progressive, and ultimately fatal, worsening in cognition, function and behavior, but a broad phenotypic profile is commonly observed both in terms of timing of onset and rapidity of progression 2 . Pathological findings at brain autopsy are intracellular deposits of hyper-phosphorylated tau protein in the form of neurofibrillary tangles, and extracellular β-amyloid (Aβ) protein in diffusible oligomers and insoluble plaques.…”

Section: Introductionmentioning

confidence: 99%

“…Neuropathological findings are heterogeneous as well, with common observation of Lewy bodies and/or vascular changes 2 . Available drugs mostly belong to two classes of compounds, namely acetylcholinesterase inhibitors and NMDA receptor antagonists.…”

Section: Introductionmentioning

confidence: 99%

Genomics of Alzheimer's disease: Value of high-throughput genomic technologies to dissect its etiology

Tosto

Reitz

2016

Molecular and Cellular Probes

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Late-onset Alzheimer's disease (AD), the most common neurodegenerative disorder in western countries, is clinically defined by progressive worsening in cognitive functions along with function and behavioral impairment. This ultimately results in complete incapacity and death. AD is a clinically and pathologically heterogeneous disease, and this is reflected by the numerous genetic findings that point to several diverse molecular mechanisms and pathways. Linkage, genome-wide association and next-generation sequencing studies have led to the identification of more than 20 novel susceptibility loci for AD. While these observations have significantly increased the knowledge of pathogenic mechanisms and potential therapeutic targets, a large part of the genetic component underlying AD is still unexplained. This review will summarize and discuss the major genetic findings and their potential impact on AD diagnosis and prediction of prognosis.

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Progression of Extrapyramidal Signs in Alzheimer’s Disease: Clinical and Neuropathological Correlates

Cited by 22 publications

References 34 publications

The Application of Unsupervised Clustering Methods to Alzheimer’s Disease

The Application of Unsupervised Clustering Methods to Alzheimer’s Disease

Efeito da suplementação de ômega 3 isolado ou associado em pacientes com doença de Alzheimer: uma revisão sistemática da literatura científica

Genomics of Alzheimer's disease: Value of high-throughput genomic technologies to dissect its etiology

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