We’re going to take an odd detour into both chaos theory and traffic flow in order to understand Alzheimer’s disease, so fasten your seatbelt. The key cascade of pathology that we’re going to look at (and explain) is the presence of beta amyloid plaques in patients with Alzheimer’s, but the principle applies equally to tau tangles and several other hallmarks of pathology seen in aging human patients with cognitive decline. Chaos theory and traffic flow will serve as useful analogies and help clarify the dynamics involved in human pathology, as well as potential cures.
To start with, let’s consider a simple example of chaos theory, in which a continual, linear event results in a sudden inflection and an unexpected, non-linear outcome. Imagine that you are trying to retrieve your iPhone in the middle of the night in order to listen to, for example, an audible book. The lights are out, your spouse is asleep and you gently pull on the earphones, using them to pull the iPhone toward you. Realizing that the slower you pull it, the less noise you make (and the less likely it is that you will waken your spouse), you provide a very slow, gentle traction. Unfortunately, the iPhone is on the bedside table and once it gets to the edge, it suddenly falls and produces a terrible racket, regardless of how slowly and quietly you’ve pulled it up until you reached the edge. The point here is that regardless of how noise and speed were related until you got to the edge of the table, there will come a sudden inflection point with an unexpected and non-linear increase in noise. In short, the amount of noise correlates linearly with speed until the inflection occurs and then the relationship between speed and noise becomes suddenly non-linear. As we will see, much the same thing happens to the clearance of beta amyloid (or tau tangles) and its relationship to neuronal death. Things seem to be going fine until some inflection point is reached, after which there is a sudden, unexpected inflection and the pathology (and cognitive decline) begins.
For the next analogy, consider traffic flow and construction slowdowns. Commuting to work each day, you (and the traffic generally) are moving along at a steady 55 mph as you approach an area of construction. In this area, the traffic slows to a speed averaging 10 mph, as a result of a traffic light at which the speed is 20 mph half the time (green light) and zero half the time (red light). However, you notice that despite this construction slowdown (which has been going on for several weeks), the traffic congestion always becomes noticeable at about the same spot and it never actually backs up indefinitely (as it might if the road was completely closed while traffic continued to arrive). As you think about it, you realize that the actual speed (55 mph versus 10 mph) isn’t the key here. The key question is the number of cars passing per unit time as they approach and as they go through the congested area. If the 55 mph cars are approaching at a rate of (say) 30 cars per minute (with a good distance between them) and the 10 mph cars are getting through the construction and the traffic light at the exact same rate of 30 cars per minute (although they are almost bumper-to-bumper), then the line of slow moving cars will only grow to a certain length before it achieves an equilibrium. We might find, for example, that despite the traffic congestion and as long as the number of cars passing each point per unit time remains equal (e.g., 30 cars per minute, regardless of how close the cars are to one another), then the line will only grow so far and no further.
But this is only true to a point.
It might be, for example, that (as long as the number of cars per minute is equal both coming into and leaving the traffic congestion) the line will be a half-mile long if the construction zone has an average speed of 15 miles an hour twice as long at 10 miles an hour, but there comes a point – perhaps at 9 miles per hour, when the line suddenly has an inflection point and begins to grow wildly (and non-linearly) because the number of cars leaving per minute has no fallen below the number of cars arriving per minute. The relationship between speed (going through construction) and the length of the traffic line was linear until some critical point, at which the relationship took an inflection, the traffic backs up, and all hell breaks loose. Not merely an example of chaos theory, but chaos in action as traffic gridlock ensues.
Much the same is occurring in the brain as it ages. Microglial cells are perfectly adept at clearing beta amyloid as it is produced. Even as these cells senesce and their rate of clearance falls, the backup of beta amyloid “traffic” is not bad enough to cause pathology and it does not trigger neuronal death – or clinical Alzheimer’s disease. There comes a point, however, when chaos theory enters the picture, a sudden inflection occurs, neuronal death ensues, and inexorable cognitive decline becomes obvious.
Think of it this way. The key questions (with beta amyloid as an example) are these: 1) how fast is beta amyloid being produced (how many cars are coming down the highway per minute), 2) how likely are the beta amyloid molecules to be abnormal perhaps because of APOE4 genes (how fast are the cars moving), and 3) how well are the senescing microglia able to clear the beta amyloid molecules (how many cars can they get through the construction area per minute)?
These same questions play a role in understanding why current interventions (e.g., monoclonal antibodies) fail and why we might want to intervene directly in cell senescence. Most current experimental approaches, such a monoclonal antibodies, only serve to “tow away some of the backed-up cars in the traffic line”, while the critical variable is our ability to move cars through the area of congestion. In short, the problem is not a static one (can we remove cars), but a dynamic one (can we keep the cars moving). Once we get a problem with traffic flow (a non-linear accumulation of beta amyloid plaques), the key intervention is not “towing away cars”, but increasing the flow of traffic through the congested area. We should be treating microglia, not beta amyloid.
Curing Alzheimer’s requires that we understand the pathology and not in a naïve, static fashion. If we want to cure Alzheimer’s, we need to improve the traffic, not the cars. The most effective point of intervention is not beta-amyloid but microglia.
Which is how we plan to cure Alzheimer’s.