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For centuries, aging was considered inevitable and immutable. Geroscience provides the conceptual framework to shift this focus toward a new view that regards aging as an active biological process, and the biological age of an individual as a modifiable entity. Significant steps forward have been made toward the identification of biomarkers for and measures of biological age, yet knowledge gaps in geroscience are still numerous. Animal models of aging are the focus of this perspective, which discusses how experimental design can be optimized to inform and refine the development of translationally relevant measures and biomarkers of biological age. We provide recommendations to the field, including: the design of longitudinal studies in which subjects are deeply phenotyped via repeated multilevel behavioral/social/molecular assays; the need to consider sociobehavioral variables relevant for the species studied; and finally, the importance of assessing age of onset, severity of pathologies, and age-at-death. We highlight approaches to integrate biomarkers and measures of functional impairment using machine learning approaches designed to estimate biological age as well as to predict future health declines and mortality. We expect that advances in animal models of aging will be crucial for the future of translational geroscience but also for the next chapter of medicine. [ABSTRACT FROM AUTHOR]

Advanced age is an independent risk factor for cardiovascular diseases in both sexes. This is thought to be due, in part, to age-dependent cellular, structural, and functional changes in the heart, a process known as cardiac aging. An emerging view is that cardiac aging leads to the accumulation of cellular and subcellular deficits that increase susceptibility to cardiovascular diseases. Still, people age at different rates, with those aging rapidly considered frail. Evidence suggests that frailty, rather than simply age, is a major risk factor for cardiovascular disease and predicts adverse outcomes in those affected. Recent studies in mouse models of frailty show that many adverse changes associated with cardiac aging are more prominent in mice with a high degree of frailty. This suggests that frailty sets the stage for late life cardiovascular diseases to flourish and raises the possibility that treating frailty may treat cardiovascular diseases. These studies show that ventricular dysfunction increases with frailty in males only, whereas atrial dysfunction increases with frailty in both sexes. These results may shed light on the reasons that men and women can be susceptible to different cardiovascular diseases as they age, and why frail individuals are especially vulnerable to these disorders. [ABSTRACT FROM AUTHOR]

BackgroundSymptomGuide Dementia (DGI Clinical Inc) is a publicly available online symptom tracking tool to support caregivers of persons living with dementia. The value of such data are enhanced when the specific dementia stage is identified. ObjectiveWe aimed to develop a supervised machine learning algorithm to classify dementia stages based on tracked symptoms. MethodsWe employed clinical data from 717 people from 3 sources: (1) a memory clinic; (2) long-term care; and (3) an open-label trial of donepezil in vascular and mixed dementia (VASPECT). Symptoms were captured with SymptomGuide Dementia. A clinician classified participants into 4 groups using either the Functional Assessment Staging Test or the Global Deterioration Scale as mild cognitive impairment, mild dementia, moderate dementia, or severe dementia. Individualized symptom profiles from the pooled data were used to train machine learning models to predict dementia severity. Models trained with 6 different machine learning algorithms were compared using nested cross-validation to identify the best performing model. Model performance was assessed using measures of balanced accuracy, precision, recall, Cohen κ, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). The best performing algorithm was used to train a model optimized for balanced accuracy. ResultsThe study population was mostly female (424/717, 59.1%), older adults (mean 77.3 years, SD 10.6, range 40-100) with mild to moderate dementia (332/717, 46.3%). Age, duration of symptoms, 37 unique dementia symptoms, and 10 symptom-derived variables were used to distinguish dementia stages. A model trained with a support vector machine learning algorithm using a one-versus-rest approach showed the best performance. The correct dementia stage was identified with 83% balanced accuracy (Cohen κ=0.81, AUPRC 0.91, AUROC 0.96). The best performance was seen when classifying severe dementia (AUROC 0.99). ConclusionsA supervised machine learning algorithm exhibited excellent performance in identifying dementia stages based on dementia symptoms reported in an online environment. This novel dementia staging algorithm can be used to describe dementia stage based on user-reported symptoms. This type of symptom recording offers real-world data that reflect important symptoms in people with dementia.

Frailty is considered a state of high vulnerability for adverse health outcomes for people of the same age. Those who are frail have higher mortality, worse health outcomes, and use more health care services than those who are not frail. Despite this, little is known about the biology of frailty, the effect of frailty on pharmacological or surgical outcomes, and potential interventions to attenuate frailty. In humans, frailty can be quantified using a frailty index (FI) based on the principle of deficit accumulation. The recent development of an FI in naturally ageing mice provides an opportunity to conduct frailty research in a validated preclinical model. The mouse FI has been successfully used across a wide range of applications; however, there are some factors that should be considered in implementing this tool. This review summarises the current literature, presents some original data, and suggests areas for future research on the current applications of the mouse FI, inter-rater reliability of the FI, the effect of observer characteristics and environmental factors on mouse FI scores, and the individual items that make up the FI assessment. The implementation of this tool into preclinical frailty research should greatly accelerate translational research in this important field. Key words: frailty index, deficit index, deficit accumulation, ageing, preclinical models, mouse models, translational. On considere que l'etat de fragilite rend tres vulnerable a des problemes de sante a l'interieur d'un groupe d'age donne. Les personnes fragiles presentent un taux de mortalite plus eleve et utilisent davantage de services de soins de sante que ceux qui ne le sont pas. En depit de cela, on connait peu de choses de la biologie de la fragilite, de l'effet de la fragilite sur les resultats des traitements pharmacologiques ou chirurgicaux, et des interventions permettant eventuellement d'attenuer la fragilite. Chez les humains, il est possible de quantifier la fragilite a l'aide d'un indice de fragilite (IF) fonde sur le principe d'accumulation des deficits. Le developpement recent d'un IF chez des souris vieillissant naturellement fournit l'occasion de mener des recherches sur la fragilite dans un modele preclinique valide. On a utilise l'IF de la souris avec succes dans une vaste gamme d'applications, mais il convient de prendre en compte certains facteurs pour l'utilisation de cet outil. Cet article de synthese constitue un resume de la litterature actuelle, presente certaines donnees originales et propose des champs de recherches quant aux applications actuelles de l'IF de la souris. Il traite aussi de la fiabilite de l'IF d'un evaluateur a l'autre, de l'effet des caracteristiques de l'observateur et des facteurs environnementaux sur les scores d'IF de la souris, ainsi que des elements individuels qui composent l'evaluation de l'IF. La mise en application de cet outil en recherche preclinique portant sur la fragilite devrait permettre d'accelerer grandement la recherche translationnelle dans ce domaine majeur. [Traduit par la Redaction] Mots-cles: indice de fragilite, indice de deficit, accumulation de deficits, vieillissement, modeles precliniques, modeles chez la souris, translationnel., Introduction The term 'frailty' is widely used in the clinical literature to describe a state of increased vulnerability to adverse health outcomes in older adults (Rockwood et al. 1994). Frailty [...]