CNS biomarkers are extremely difficult. I say that with experience developing drugs in the neuroscience arena. Not cycling specific experience but relevant enough to shape the understanding.
When I say biomarkers in the cycling performance context, am thinking about better ways to know how much potential is being extracted and what physiological effect different types of training are having.
For performance, to try and isolate, to some extent, CNS effects from muscular potential. Particularly when there is a brain vs muscular disconnect.
Let me try to explain - and this might not go well 
We have a decent measure of “performance” in the power meter and the stopwatch. The problem is that performance is a combination of physiology and psychology.
For the example of the 3 w/kg rider, that is a number, but we don’t know if that rider is extracting 99.9% of what their legs can do, or if their brain is intervening (pain response) and they are only extracting a lower percentage of their performance. Say 80% just for argument.
Point is, when we don’t have quantitative biomarkers we rely on surrogates. Since those surrogates are almost always performance, and performance is a combination of multiple body functions, it’s not necessarily going to provide the data, or metric that we need to improve.
But if we can’t measure the components (brain v brawn), how can any of this be useful other than academic argument (or Internet forum discussion amongst friends as the case my be)?
Well, in my limited subset of lactate testing and helping friends train, have seen some interesting phenotypes. I don’t have population frequencies for these phenotypes because sample set is too small.
The biomarker I have access to is lactate. It’s not perfect, but we can establish MLSS for a rider and see how that correlates to FTP. We can look at LT1 and see how RPE and HR correlate. RPE is interesting as a performance factor as we’ll see in another 3-4 thousand words…
Phenotype #1 This rider has a well calibrated RPE meter. Their brain and their brawn are in sync. When riding at MLSS they report an RPE in the 8-9 range (10 scale). It is hard, it hurts a bit, they can ride at this level for a fairly long time.
Usually with my gang we’re looking at 40k ITT performance. If I have these riders ride at say 90%, the RPE is 7-8 (which still hurts if done for long enough durations). If I push them to 110% they report RPE in the 9-10 range and they can ride this pace for reasonable time periods but eventually hit a wall and give up.
OK… Group #1 is hopefully the majority, and for those riders, FTP/MLSS and RPE will be very useful proxies. They can guess that they are performing at current capability, and devise training programs to try and improve.
As always, the fun stuff is in the outliers.
Phenotype #2. These guys have much less RPE sensation at relatively hard levels. They’ll report 6-7 at MLSS. That’s not a bad thing per se, but in a race situation it can be defeating. These guys will go too hard and drastically overshoot their MLSS. If its a 40km TT, these guys are happily chugging along at RPE 8-9-10 for the first 10 miles, but instead of being at MLSS, they are way over. They are going to blow up well before the end of the race because while the brain is happy, the legs are not.
Have a very good friend who is in this camp. He doesn’t report RPE 8-9-10 until he’s in the 115-120% and above range. He always blew up in longer races. We tested him and I showed him that while his brain is happy at 325w, his MLSS is actually 300w. We recalibrated and his performances improved. He can work on improving that MLSS, and know to calibrate his race pace to his physiology. His brain is not a limiter but since it’s not calibrated to his brawn, causes an issue in performance. These guys, BTW, will crush ramp tests and even 20 min tests so it looks like they are better than they really are.
Phenotype #3 is the opposite. These guys are reporting RPE 9-10 well below MLSS. They have the legs but they don’t have the brain!! If you can show them that there is a disconnect, there is a potential to raise their performance, sometimes by a lot. These guys will struggle with racing and testing and undershoot potential by a lot.
Take home:
(1) quantitative biomarkers are useful for isolating physiology from psychology and can be used to guide training and improve performance.
(2) It would be nice to have better and more accessible biomarkers and to be better able to measure and predict potential and understand how much of a riders potential we are extracting. (I’d also like a pony for my birthday)
(3) Different training drives different adaptations and causes different stress and limiters. ISM seems to argue that excursions into higher wattages has longer term effects on a workout and prevents adaptations. It would be great to know, and be able to measure if our workouts are having the intended outcomes.
Better markers, accessible markers, would be wonderful if we understood the systems and replace some of the multi-modal and surrogate proxies with actual quantifiable and specific data.
Might be something useful in there. I know I helped two guys improve with testing and changing their training and racing. Am taking that as a win and if the discussion helps someone else that’s great.
Hopefully am not wrong as don’t want to do any harm!! Also, I wrote that between meetings so hopefully it’s not too difficult to parse.
). I work remotely and I don’t have kids.
Gets to the heart of the limitation.
The constraints might be a little more forgiving than hiring in tech because you’re really just going for “I need sufficient data”, not “CRAP WE’RE BUILDING BIAS INTO EVERYTHING HOW DO WE STOP DOING THAT”
Sport Science research is especially applicable to our “default subject” white/‘Western’ male 20-something uni student… but the further you are from that demographic? The less relevant it is for you. The less you fit our understanding of ‘reality’. That’s a bit scary.