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RESEARCH PRODUCT

With mouse age comes wisdom : a review and suggestions of relevant mouse models for age-related conditions

Rui E. CastroValter TucciErica BariniRoosmarijn E. VandenbrouckeHerminia González-navarroPaul PotterMarcin F. OsuchowskiMarina A. LynchSusana NovellaSilva HećimovićSusanne Drechsler

subject

0301 basic medicineGerontologyAgingPopulation ageingProcess (engineering)TRAUMATIC BRAIN-INJURYDiseaseBiologyMouse modelsMice03 medical and health sciences0302 clinical medicineAge relatedMedicine and Health SciencesAnimalsHumansCLOSED-BONE-FRACTURESENESCENCE-ACCELERATED MOUSEE-DEFICIENT MICECELL-MEDIATED-IMMUNITYTRIPLE-TRANSGENIC MODELBiology and Life SciencesNECROSIS-FACTOR-ALPHAAged patientsCell mediated immunityC-REACTIVE PROTEINACTIVATION IN-VIVODisease Models AnimalPatient populationAgeing030104 developmental biologyAgeingPhenotypingMouse models ; ageing ; phenotypingLONG-TERM POTENTIATION030217 neurology & neurosurgeryCognitive psychologyDevelopmental Biology

description

Ageing is a complex multifactorial process that results in many changes in physiological changes processes that ultimately increase susceptibility to a wide range of diseases. As such an ageing population is resulting in a pressing need for more and improved treatments across an assortment of diseases. Such treatments can come from a better understanding of the pathogenic pathways which, in turn, can be derived from models of disease. Therefore the more closely the model resembles the disease situation the more likely relevant the data will be that is generated from them. Here we review the state of knowledge of mouse models of a range of diseases and aspects of an ageing physiology that are all germane to ageing. We also give recommendations on the most common mouse models on their relevance to the clinical situations occurring in aged patients and look forward as to how research in ageing models can be carried out. As we continue to elucidate the pathophysiology of disease, often through mouse models, we also learn what is needed to refine these models. Such factors can include better models, reflecting the ageing patient population, or a better phenotypic understanding of existing models.

10.1016/j.mad.2016.07.005https://biblio.ugent.be/publication/8167026/file/8197337