SCIENTIFIC HUB

Follow the evidence.

SCIENTIFIC LITERATURE · MED

What we can learn from animal models about cerebral multi-morbidity.

Alzheimers Res Ther · 2015-01-29

DOI: 10.1186/s13195-015-0097-2PMID: 25810783

Open accessresearch-article; Journal ArticleOriginal source ↗

Research overview

The text below is the source article’s original abstract under a verified reuse license. cc by

Late-onset diseases such as Alzheimer's disease, Parkinson's disease, or frontotemporal lobar degeneration are considered to be protein-folding disorders, with the accumulation of protein deposits causing a gain-of-toxic function. Alzheimer's disease is characterized by two histological hallmark lesions: amyloid-β-containing plaques and tau-containing neurofibrillary tangles. However, signature proteins, including α-synuclein, which are found in an aggregated fibrillar form in the Lewy bodies of Parkinson's disease brains, are also frequently found in Alzheimer's disease. This highlights the fact that, although specific aggregates form the basis for diagnosis, there is a high prevalence of clinical overlap between neuropathological lesions linked to different diseases, a finding known as cerebral co- or multi-morbidity. Furthermore, the proteins forming these lesions interact, and this interaction accelerates an ongoing degenerative process. Here, we review the contribution that transgenic animal models have made to a better mechanistic understanding of the causes and consequences of co- or multi-morbidity. We discuss selected vertebrate and invertebrate models as well as the insight gained from non-transgenic senescence-accelerated mouse-prone mice. This article is part of a series on 'Cerebral multi-morbidity of the aging brain'.

Scientific assessment

01

Research question

Assess the question within the original study’s scope.

02

Methods & sample

Check the source article for study design and sample details.

03

Key findings

An editorially approved findings summary is not yet published for this record.

04

Limitations

Generalizability, bias and clinical meaning require separate assessment.

Methods, samples and clinical conclusions are not inferred from bibliographic metadata.