For many years it has been generally assumed that the rate of cell death within a population of neurons at risk for degeneration increases as the subject ages. This was attributed to a cumulative damage effect, where exposure to events such as oxidative and environmental stresses progressively brings a population of cells toward a critical threshold level from which it cannot recover. As time passes, the likelihood of any given cell in this population crossing that threshold value would increase. A recent study by Clarke et al. challenges this viewpoint, however, and provides compelling data to suggest that the risk of cell death remains constant, and does not increase over time in neuronal populations at risk for degeneration. In this report, Clarke et al. examined the percentage of neurons remaining in at risk populations over time in several neurodegenerative models. If the increasing risk hypothesis were to be correct, then a sigmoidal function would be predicted to best fit the progression of cell loss, as the rate at which cells die would increase progressively over time. Surprisingly, however, none of the 16 different models of neurodegeneration examined were best fit by a sigmoidal decay function. Rather, each was best fit by an exponential decay equation. Such a mathematical model excludes the increased risk model, and supports a model in which the rate of cell loss is either constant, or perhaps decreases, throughout the course of the condition.
To account for their results, Clarke et al. propose a model in which individual cells in subjects with a degenerative condition exist in a mutant steady state. Each individual condition would have its own mutant steady state, which would differ from the steady state observed in non-affected individuals. This mutant steady state would reside closer to a critical threshold that when reached, activates degenerative processes within the cell. The more severe the condition, the closer the mutant steady state would reside to this threshold value. Thus, any individual sporadically occurring event that pushes the cell over threshold would lead to its death. This ‘one hit‘ model is equally likely to affect any given cell in a population at any time, and is not influenced by previous events to which the cell may have been exposed.
