Ageing is known to be the primary risk factor for most neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease and Huntington's disease. They are currently incurable and worsen over time, which has broad implications in the context of lifespan and healthspan extension. Adding years to life and even to physical health is suboptimal or even insufficient, if cognitive ageing is not adequately improved. In this review, we will examine how interventions that have the potential to extend lifespan in animals affect the brain, and if they would be able to thwart or delay the development of cognitive dysfunction and/or neurodegeneration. These interventions range from lifestyle (caloric restriction, physical exercise and environmental enrichment) through pharmacological (nicotinamide adenine dinucleotide precursors, resveratrol, rapamycin, metformin, spermidine and senolytics) to epigenetic reprogramming. We argue that while many of these interventions have clear potential to improve cognitive health and resilience, large-scale and long-term randomised controlled trials are needed, along with studies utilising washout periods to determine the effects of supplementation cessation, particularly in aged individuals.

Mortality improvement has traditionally been analysed using an array of statistical methods, and extrapolated to make actuarial projections. This paper presents a forward-looking approach to longevity risk analysis which is based on stochastic modelling of the underlying causes of mortality improvement, due to changes in lifestyle, health environment, and advances in medical science. The rationale for this approach is similar to that adopted for modelling other types of dynamic insurance risk, e.g. natural catastrophes, where risk analysts construct a stochastic ensemble of events that might happen in the future, rather than rely on a retrospective analysis of the non-stationary and comparatively brief historical record.

Another feature of prospective longevity risk analysis, which is shared with catastrophe risk modelling, is the objective of capturing vulnerability data at a high resolution, to maximise the benefit of detailed modelling capability down to individual risk factor level. Already, the use by insurers of postcode data for U.K. flood risk assessment has carried over to U.K. mortality assessment. Powered by fast numerical computation and parameterised with high quality geographical data, hydrological models of flood risk have superseded the traditional statistical insurance loss models. A decade later, medically-motivated computational models of mortality risk can be expected to gain increasing prominence in longevity risk management.