Neils Bohr, a Danish physicist, is reputed to have said “It’s difficult to make predictions, especially about the future”. He was one of the smartest men to ever have lived. I guess he had an enormous respect for weathermen!
Well for us FAers, predictions are what we seek most in the world. It’s not hard to find out the list of things that’ll go wrong with us. What is hard though is getting an idea of when.
Unfortunately, the answers just aren’t available. There aren’t enough of us to have built a giant database yet with all our data so statistical analysis can be done. The only thing they’ll tell you is that speed of progression seems to be linked to the lower of your two repeat numbers.
The reason is variables. There are just too many. Going back to my earlier analogy, weather forecasts tend to be accurate a few days in advance. They’ll give guidance up to about a week but after that so many variables could have an impact that confidence in their predictions falls pretty rapidly. It’s the same with predicting when progression will effect specific change for an FAer.
This is a problem for two big reasons. The first of course is that when we ask “what’ll go wrong with me as my FA progresses and when are those things likely to happen?”, the answers we get are “We can’t be sure.” and “We can’t be sure”. The second reason is that unless they can describe how your progression is expected to go, a drug company can’t show that their drug changes that trajectory or by how much.
And that’s why almost every research project we FAers take part in which notes change over time has an element in its conclusions noting that what they measured might be useful as a biomarker. What they’re suggesting is the confidence level they have that what they measure will change according to a predictable path as FA progresses. So far it’s a bit like the weather – prediction seems to work fine short term but other variables have an impact in time so things become inaccurate.
Identifying biomarkers that support prediction as FA progresses is important for many reasons; for example, robust biomarkers are of immense value as outcome measures for clinical trials, preferably double-blind placebo-controlled trials: two cohorts, one given the drug and the other a placebo and no one, not even the scientists overseeing the study, knows who is in which group. So long as the groups are big enough, the averages can reduce the impact of an outlier individual result.