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Biology subjects

Howlett, S. E.

Publications and source records attributed to Howlett, S. E..

2 recordsLinked to original sources

Measurements of damage and repair in aging mice and humans reveals that robustness and resilience decrease with age, operate over broad timescales, and are affected differently by interventions

As an organism ages, its health-state is determined by a balance between the processes of damage and repair. Measuring these processes requires longitudinal data. We extract damage and repair rates from repeated observations of health deficits in mice and humans to explore the contribution of robustness and resilience, which respectively decrease damage rates and increase repair rates, to aging health. We find a conserved decline with age in robustness and resilience in mice and humans, implying that both contribute to worsening health in aging. A decline in robustness, however, has a greater effect than a decline in resilience on accelerating damage accumulation with age, and a greater association with reduced survival. We also find that deficits are damaged and repaired over a wide range of timescales ranging from the shortest measurement scales towards organismal lifetime timescales. We explore the effect of systemic interventions that have been shown to improve health, including the angiotensin-converting enzyme inhibitor enalapril and voluntary exercise for mice, and household wealth for humans. We find that these interventions affect both damage and repair rates - but in different proportions for different interventions. These findings have implications for how health in aging, and interventions targeting health, are conceptualized and assessed.

systems biology↗

Age and life expectancy clocks based on machine learning analysis of mouse frailty

The identification of genes and interventions that slow or reverse aging is hampered by the lack of non-invasive metrics that can predict life expectancy of pre-clinical models. Frailty Indices (FIs) in mice are composite measures of health that are cost-effective and non-invasive, but whether they can accurately predict health and lifespan is not known. Here, mouse FIs were scored longitudinally until death and machine learning was employed to develop two clocks. A random forest regression was trained on FI components for chronological age to generate the FRIGHT (Frailty Inferred Geriatric Health Timeline) clock, a strong predictor of chronological age. A second model was trained on remaining lifespan to generate the AFRAID (Analysis of Frailty and Death) clock, which accurately predicts life expectancy and the efficacy of a lifespan-extending intervention up to a year in advance. Adoption of these clocks should accelerate the identification of novel longevity genes and aging interventions.

physiology↗