It took me a while to figure out what’s so disturbing about this graph, and I’m still not sure I get it. Is it strange and unexpected that attempted life-extending drugs shorten lifespans as often as they increase them? Or is it disturbing that the drugs are very likely to simply do nothing at all?
Or does this graph only represent trials of one drug? I see a single drug mentioned specifically, but the graph is also labeled as created from a massive compilation of data from multiple sources. Could someone explain this to me?
Is it strange and unexpected that attempted life-extending drugs shorten lifespans as often as they increase them?
The activities of people like Ray Kurzweil, who take literally hundreds of supplements, suggest that this is strange and unexpected, and cannot be deprecated enough. It also particularly cautions us against caring about any supplement or drug without dozens of positive studies (at a minimum).
Or is it disturbing that the drugs are very likely to simply do nothing at all?
That’s also disturbing. It suggests—pace Algernon’s law—that there is no good simple intervention.
The fact that this is a pretty smooth bellcurve also indicates, as far as I can tell, the general field has found no intervention at all that works—because then there would be dozens or hundreds of studies tweaking it and replicating it and investigating why it works. We don’t see a bimodal hump with most interventions at net 0%, and a second bell curve of caloric restriction/intermittent fasting centered at eg. +40%.
(This latter interpretation could be wrong, since the data is so heterogenous. It could be that the apparent excess of studies around +100% represent many of the CR/IF studies.)
Looks to me like the peak of the bell curve falls at about +10%. That’s in the same ballpark as what I’d expect from placebo, but nonetheless a little higher; if we’re instead looking at a lot of low-impact interventions, the really interesting question is how parallelizable they are. Unfortunately, if the natural variance in lifespan is anything to go by I suspect the answer is “not very”.
There’s also that really sharp spike at 0%, but I can think we can probably put some of that down to psychology.
It suggests—pace Algernon’s law—that there is no good simple intervention.
No major evolutionary incentive to extend lifespan much beyond the point people would be likely to have died from violence or accidents or disease, so Algernon’s law shouldn’t necessarily apply here.
For humans, it should; while average mortality is high even in the Paleolithic, this is the usual infant mortality skew. If you make it to adulthood… Old kin are still kin and can be useful, even if only a little bit—selection can still act on that.
Sure to a certain point. But there’s a limit to how much extension one will get just from that. Assume for example that post infancy there’s a 2% chance of random death due to violence, disease etc. in the native environment. Then there’s about a 3/4th chance that they will survive to age 75, assuming a rough constant. Given that, there’s little evolutionary incentive to push mortality down much past that. This is of course a toy-model, but the basic point is sound.
It took me a while to figure out what’s so disturbing about this graph, and I’m still not sure I get it. Is it strange and unexpected that attempted life-extending drugs shorten lifespans as often as they increase them? Or is it disturbing that the drugs are very likely to simply do nothing at all?
Or does this graph only represent trials of one drug? I see a single drug mentioned specifically, but the graph is also labeled as created from a massive compilation of data from multiple sources. Could someone explain this to me?
The activities of people like Ray Kurzweil, who take literally hundreds of supplements, suggest that this is strange and unexpected, and cannot be deprecated enough. It also particularly cautions us against caring about any supplement or drug without dozens of positive studies (at a minimum).
That’s also disturbing. It suggests—pace Algernon’s law—that there is no good simple intervention.
The fact that this is a pretty smooth bellcurve also indicates, as far as I can tell, the general field has found no intervention at all that works—because then there would be dozens or hundreds of studies tweaking it and replicating it and investigating why it works. We don’t see a bimodal hump with most interventions at net 0%, and a second bell curve of caloric restriction/intermittent fasting centered at eg. +40%.
(This latter interpretation could be wrong, since the data is so heterogenous. It could be that the apparent excess of studies around +100% represent many of the CR/IF studies.)
So this graph does represent comiled data about a lot of different drugs, rather than just one drug?
Yes.
Looks to me like the peak of the bell curve falls at about +10%. That’s in the same ballpark as what I’d expect from placebo, but nonetheless a little higher; if we’re instead looking at a lot of low-impact interventions, the really interesting question is how parallelizable they are. Unfortunately, if the natural variance in lifespan is anything to go by I suspect the answer is “not very”.
There’s also that really sharp spike at 0%, but I can think we can probably put some of that down to psychology.
Or stuff like publication bias (one reason I was interested in whether a funnel plot could be formed from the data).
I don’t understand what you’re trying to say:
No major evolutionary incentive to extend lifespan much beyond the point people would be likely to have died from violence or accidents or disease, so Algernon’s law shouldn’t necessarily apply here.
For humans, it should; while average mortality is high even in the Paleolithic, this is the usual infant mortality skew. If you make it to adulthood… Old kin are still kin and can be useful, even if only a little bit—selection can still act on that.
Sure to a certain point. But there’s a limit to how much extension one will get just from that. Assume for example that post infancy there’s a 2% chance of random death due to violence, disease etc. in the native environment. Then there’s about a 3/4th chance that they will survive to age 75, assuming a rough constant. Given that, there’s little evolutionary incentive to push mortality down much past that. This is of course a toy-model, but the basic point is sound.