Haidt-Love Relationship

Epistemic status: personal, exhortatory, expressive

Jonathan Haidt has an ideology.  In his academic life, he poses positive questions, but he definitely has a normative position as well. And you can see this most clearly in his speeches to young people, which are sermons on Haidtism.

Here is an example.

In it, he contrasts “Coddle U” with “Strengthen U,” two archetypal colleges. He’s clearly arguing in favor of psychological resilience, and against fragility. Let’s leave aside the question of whether feminists and other activists are actually oversensitive weenies, and whether trigger warnings are actually coddling, and engage with his main point, that it is better not to be an oversensitive weenie.

Haidt seems to see this as self-evident. The emotionally weak are to be mocked; the emotionally strong are to be respected.

I don’t find it as obvious.

Fragility can have a certain charm. Sensitive, romantic, tender spirits can be quite attractive.  The soft-hearted can be quick to show kindness. The easily-bruised can be alert to problems that more thick-skinned folks ignore.  We usually trust people’s sincerity more when they are moved to strong emotion.  A frail, innocent person is often a lovable person.  And who wouldn’t want to be lovable?

“Do you want to be strong or do you want to be fragile?” takes us back to Nietzsche’s old conflict of Herrenmoral and Sklavmoral.  Is it good to be successful, skilled, strong, powerful (as opposed to weak, cowardly, unhealthy, contemptible)?   Or is it good to be innocent, pure, gentle, kind (as opposed to oppressive, selfish, cruel)?

Of course it’s possible to be both kind and strong.  Herrenmoral and Sklavmoral are both pre-rational viewpoints, more like aesthetics than actual ethics.  It’s a question of whether you want to be this:
1920px-john_everett_millais_-_ophelia_-_google_art_project

or this:

Ultimately, the consideration in favor of strength is simply that the world contains threats.  Fragility may make you lovable, but it can also make you dead.  You don’t get to appreciate the benefits of sensitivity and tenderness if you’re dead.

Being strong enough to do well at the practicalities of the world — physical safety and health, economic security, enough emotional stability not to put yourself or others at risk — is, up to a point, an unalloyed good.

Think of it as a gambler’s ruin situation. You have to win or save enough to stay in the game.  Strength helps you stay in the game.

And because strength is necessary for survival, there’s something to respect in the pro-strength aesthetic.

From the outside, it can seem kind of mean and elitist. You’re scorning people for failure and pain? You think you’re better than the rest of us, just because you’re pretty or smart or tough or happy?

But another way of looking at it is having respect for the necessities of life.  If you consider that starvation is a thing, you’ll remember that food is valuable, and you’ll feel gratitude to the farmers who grow it. In the same way, you can have respect for intelligence, respect for competence, respect for toughness, respect for all skills.  You can be glad for them, because human skill drives out the darkness of death, the hard vacuum of space that surrounds us, and excellent humans are pinpricks of flame in the dark.  You can love that hard brilliance.

And if respect can tinge into love, love can shade into enjoyment. You can enjoy being awesome, or knowing people who are awesome.  It can be exhilarating.  It can be a high and heady pleasure.

And from that vantage point, it’s possible to empathize with someone who, like Haidt, scorns weakness. Maybe, once you’ve been paying attention to the high points of human ability, anything else seems rather dingy.  Maybe you think “It’s so much nicer here upon the heights, why would you want to be down in the valley?”  Maybe some of the people who seem “elitist” actually just want to be around the people who light them up, and have developed high standards for that.

Not to say that there doesn’t exist shallow, vindictive status-grabbing.  But there are also people who aren’t like that, who just prefer the excellent to the mediocre.

Or, on a smaller scale, there are those who seek out “positive people” and avoid “toxic people” — they’re orienting towards success and away from failure, towards strength and away from weakness, and this is an understandable thing to do.

An addict trying to get her life together would try hard to avoid weakness, temptation, backsliding — and this would be a good thing, and any decent person would cheer for her.  That kind of motivation is the healthy thing that drives people to choose strength over fragility.

So Haidt’s basic premise — that you want to be more strong than fragile — is believable.

His prescriptions for achieving that are risk tolerance and minimizing the negative.

I’m going to reframe his ideas somewhat so they refer to individuals.  He’s talking about a top-down perspective — how schools can make students stronger. I have an issue with that, because I think that “improving” people against their will is ethically questionable, and especially trying to “make people tough” by exposing them to adversity, if they have no intrinsic desire to toughen and no input into the type of “adversity” involved, is probably counterproductive.  However, people self-improve all the time, they make themselves tougher, and that’s a more fruitful perspective, in my opinion.

Risk tolerance is the self-motivated version of “we’re not going to coddle you.” It would mean seeking out challenges, looking for criticism, engaging with “hard truths”, going on adventures.  Trying things to test your mettle.

It’s pretty obvious why this works: small amounts of damage cause you to develop stronger defenses. Exercise produces micro-tears in muscle, so it grows back stronger.  Vaccines made of weakened virus stimulate immunity to that virus.  Intermittent, all-out efforts against fear or failure are good for you.

(You’re still playing to stay in the game, so an adversity that takes you out of the game altogether is not good for you. This is why I think it works much better if the individual’s judgment and motivation is engaged.  Voluntary choice is important. Authorities trying to “toughen kids up” against their will can kill them. )

Minimizing the negative means mentally shrinking the sources of your distress. Haidt cites Marcus Aurelius, Boethius, the Buddha, and the tenets of cognitive behavioral therapy as pointing at the same universal truths.

Now, of course, Stoicism, Buddhism, and modern psychology have very different visions of the good life. The ideal Stoic is a good citizen; the ideal Buddhist is an ascetic; the ideal psychological subject is “well.”  The ideal Stoic is protective of his soul; the ideal Buddhist is aware that his “self” does not exist.  Trying to be a serious Stoic is quite different from trying to be a serious Buddhist, and it’s not clear what it would even mean to try to be the “ideal person” by the standards of cognitive behavioral therapy.

What these philosophies have in common is a lot simpler than that: it’s just “Don’t sweat the small stuff.”

Don’t freak out over trivial shit. Remember that it’s trivial.

Stoicism and Buddhism both use meditation as a tactic; both suggest focusing on impermanence and even death, to remind oneself that trivial shit will pass.  CBT’s tactic is disputation — arguing with your fears and frustrations, telling yourself that the problem is not that big a deal.

Marcus Aurelius in particular uses pride a lot as a tactic, encouraging you to view getting upset as beneath the dignity of your soul.

Of course, “Don’t sweat the small stuff” imposed from without is a bit insulting.  Who are you, authority figure, to say what is and isn’t important?  Aren’t you telling me to ignore real problems and injustices?

But seen from within, “don’t sweat the small stuff” is just another perspective on “focus on your goals and values.”

You want to stay in the game, remember? So you can win, whatever that means to you.  So survival matters, robustness matters, because that keeps you in the game.  Freaking out takes you hors de combat.

Haidt tends not to push too hard on Christianity, perhaps because his audience is secular, but it is a very common source of comfort that does, empirically, make people happier.  My impression of Christian positivity, from a non-theological perspective, is that it says the good outweighs the bad. The bad exists; but the good is stronger and bigger and wins in the end.  And this is another way of not freaking out over trivial shit, which is quite different in aesthetic from the others, and maybe underappreciated by secular people.  Instead of trying to shrink your troubles by minimizing or disputing them, you can make them seem less important by contrast to something vast and Good. In a similar, albeit secular, spirit, there’s Camus’ famous line, “In the midst of winter, I found there was, within me, an invincible summer.”

Stripped of the sneering and the political angle and the paternalism, what we have here is a pretty solid message.

It’s a good idea to become stronger; in order to do that, try hard things, and don’t freak out about trivial shit.

Now, I immediately imagine a dialogue with my Weenie Self resisting this idea.

But…that sounds AWFUL!  I don’t want to!

Well, the thing is, “I’m not currently doing X” is not a valid argument against doing X. If it were, nobody would ever have a reason to change their behavior.  We’d all just follow the gradients of our current stimuli wherever they led.  There’s no choice in that world, no deliberate behavior. “But I’m currently freaking out about trivial shit!” doesn’t actually mean that you shouldn’t want to freak out less in future.

I know. It’s weird.  This is a way of thinking about things consciously and explicitly, even when they feel kind of awkward and wrong.

How can it be right when it doesn’t feel right?!  I am currently experiencing a sense of certainty that this is a bad idea! You want me to trust a verbal argument over this overwhelming feeling of certainty?

This, believe it or not, is what people mean when they talk about reason!

Trusting an argument that is correct as far as you can tell, over your feelings, even very strong feelings.  Being consciously aware that a thing is a good idea, and doing it, even when it’s awkward and unnatural and feels wrong.  You’re not used to doing things this way, because you usually discipline yourself with more feelings — guilt or fear, usually.  But there’s a way of making yourself do hard things that starts, simply, with recognizing intellectually that the hard thing is a good idea.

You can make yourself like things that you don’t currently like!  You can make yourself feel things that you aren’t currently feeling!

This bizarre, robotic, abstract business of making decisions on the basis of thoughts rather than feelings is a lot less crazy than it, um, feels.  It’s a tremendous power.

Some people luck into it by being naturally phlegmatic. The rest of us look at them and think “Man, that would suck, having practically no feelings.  Feelings are the spice of life!”  But we can steal a bit of their power, with time and effort, without necessarily becoming prosaic ourselves.

My overall instinctive response to Haidtism is negative.  The ideology initially comes across as smug and superficial.  But upon reflection, I have come to believe that it is right to aim towards self-transcendence, to do hard things and not sweat the small stuff. And I’m resolving to be more charitable towards people who support that creed even when they rub me the wrong way stylistically.  Ultimately, I want to do the things that are good ideas, even when that means awkward, deliberate change.

Nootropics

Epistemic status: medium

There are a lot of drugs and supplements reputed to improve cognitive function.  I was sick of relying on hearsay and anecdote, so I did my best attempt at a systematic overview of what works and what doesn’t.

BOTTOM LINES

Caffeine, modafinil, amphetamine, methylphenidate, and maybe a discontinued nicotinic-receptor agonist drug called ispronicline, have really big effects on cognitive function in healthy people.

Caffeine and modafinil work significantly better in sleep-deprived than non-sleep-deprived people.

Caffeine, nicotine, and amphetamine, in contrast to methylphenidate and modafinil, do not improve memory performance or accuracy on cognitive tasks in healthy people, but only reaction time.  In other words: caffeine, nicotine, and amphetamine make you more alert but not smarter; methylphenidate and modafinil also seem to improve memory.

Amphetamine and modafinil work better on people with the COMT val/val phenotype (who tend to be less intelligent) and may be ineffective or counterproductive on COMT met/met phenotype people.

All of the above (caffeine, nicotine, modafinil, amphetamine, and methylphenidate) cause some tolerance.

Cerebrolysin, a mixture of neural growth factors, apparently works really well on Alzheimer’s patients, though there’s fewer studies of it than more common Alzheimer’s drugs.  It might extrapolate to people with other kinds of neurodegenerative problems, or to slow the effects of aging.

Cognitive training (memorization practice including spaced repetition) works moderately well on Alzheimer’s patients and schizophrenics.  It’s quite plausible that it’s also good for healthy people.

Healthy people can get small positive effects from nicotine, possibly the herb Bacopa monniera, and from transcranial magnetic stimulation.

Alzheimer’s patients can get small effects from cholinesterase inhibitors (which are standard Alzheimer’s drugs); from a mixture of vitamins, fatty acids, choline, and uridine; from melatonin, the hormone which regulates sleep; and from the amino acid derivative acetyl-l-carnitine. Apart from the cholinesterase inhibitors (which have GI side effects) these are safe for healthy people to take, but it’s not known whether they affect cognitive function in healthy people.

METHODOLOGY

only looked at published studies on cognitive outcomes in humans: tests of memory, reaction time, and the like.  No animal studies. No measurements of neural correlates or biomarkers. To show up in my list, it has to make humans perform better.  I didn’t restrict attention to healthy humans, however; a lot of the studies on cognitive enhancement are performed on subjects with diseases like Alzheimer’s or schizophrenia, so I included some of those, under the suspicion that they might generalize to healthy people.

I ranked nootropics by effect size. That is, Cohen’s d, the difference in mean outcome between treatment and control groups divided by the pooled standard error.

Assume that a trait, like your score on an exam, has a Gaussian distribution. Suppose you have some treatment that increases the mean score in the treatment vs. the control group. Then you can divide by the (pooled) standard deviation of the score to get an estimate of how big a difference the treatment makes, compared to the population variation in the trait. Does it increase your score by one standard deviation? That’s an effect size of one.  Does it increase your score by half a standard deviation? That’s an effect size of 0.5.

This allows us to compare “how big an effect” different interventions have, along one scale, even if they’re acting on different traits. If drug A improves your reaction times by two standard deviations, and drug B improves your memory by half a standard deviation, you can still say that drug A has a larger effect than drug B, even though the effect isn’t on the same thing.

Conventionally, an effect size of 0.2-0.3 is a “small” effect, around 0.5 is a “medium” effect, and anything greater than 0.8 is a “large” effect. Most drugs used in psychiatry have effect sizes around 0.5.  Intuitively, effect sizes of about 0.5 look like “sorta works” to the naked eye. Effect sizes greater than 1 look like “holy shit, that’s an unmistakable effect” to the naked eye.

Anything with a p-value of <0.05 (but not <0.01) I didn’t include in the table of best nootropics, because the vast majority of studies with such high p-values don’t replicate.  I also didn’t include things in the table if they were shown to not work on healthy subjects (even if they did work on ill subjects).  When there was conflict between studies, I erred on the conservative side and chose smaller effect sizes.

 

Drug Effect Size Trait
Modafinil, Caffeine 2-3 Executive function in sleep deprived people
Modafinil, Caffeine 2-3 Wakefulness in sleep deprived people
Ispronicline 2.5 Attention and episodic memory in healthy people
Amphetamine 2.3 Reaction time in healthy people
Cerebrolysin 1.8-2.2 ADAS-cognitive test in Alzheimer’s patients
Methylphenidate 1.4 Memory in healthy non-sleep-deprived people
Modafinil 1.22 Working memory in sleep deprived people
Caffeine 0.7 Reaction time in non-sleep-deprived healthy people
Nicotine 0.7 Attention in schizophrenics
Modafinil 0.56 Attention in non-sleep-deprived healthy people
Melatonin 0.56 ADL’s for Alzheimer’s patients
Cognitive training (including spaced repetition) 0.43-0.47 Various cognitive tests and ADL’s for Alzheimer’s patients and schizophrenic patients
Bacopa monniera 0.32 Learning rate in healthy people
Nicotine 0.3 Reaction times in smokers and nonsmokers
Cholinesterase inhibitors 0.2-0.5 ADAS-cognitive test in Alzheimer’s patients
rTMS 0.2-0.3 Working memory and reaction time in healthy subjects
Souvenaid 0.23 Memory in Alzheimer’s patients
Acetyl-L-carnitine 0.2 Various cognitive tests in Alzheimer’s patients

EFFECTIVE THERAPIES

ALCAR

ALCAR, or acetylcarnitine, is an amino acid derivative used in the metabolism of fatty acids.

A meta-study of 21 studies of Alzheimer’s patients found a median effect size of 0.2, with a total of 499 patients, across various cognitive tests.

Amphetamine

Amphetamine is a dopaminergic stimulant drug.

Amphetamine improved working-memory performance in healthy subjects only if they had low performance at baseline, and worsened it in those who had high performance at baseline.[4]

Improves working memory on healthy val/val COMT subjects, doesn’t, or deteriorates it, on met/met subjects. (“Warriors” benefit, “worriers” do not.)[38]

Improves reaction time on a movement estimation task (effect size: 2.3) but not digit span.[39]

Bacopa monniera

Bacopa monniera is a plant traditionally supposed to improve memory. The active ingredient is bacoside, a triterpenoid saponin.

Randomized study of 46 healthy adults, AVLT learning rate after 12 weeks is better, effect size 0.32, a significant effect at p < 0.01.  State anxiety also lower, p < 0.001. No effect on digit span.[33] No effects on memory.[34]

Caffeine

Caffeine is the most commonly used psychoactive chemical worldwide, and is a stimulant that works by adenosine receptor antagonism.

Cross-sectional study of 9003 adults finds that higher habitual coffee and tea consumption has a significant dose-response relationship (p < 0.001) with performance tests of memory, visuospatial reasoning, and reaction time, suggesting that tolerance to caffeine is incomplete and caffeine does cause higher absolute levels of cognitive performance.[1]

Metastudy found that caffeine had no effect on free recall in most short-term memory studies. It does reliably improve reaction time.  Reduces the risk of sleep-deprivation-related work accidents by about two-fold.  Generally improves cognitive performance more in sleep-deprived than in non-sleep-deprived subjects. Caffeine improves cognitive function in elderly subjects more than in young (20-60) subjects, and regular caffeine consumers have less (half as much) age-related cognitive decline.[20]

Caffeine improves reaction time over placebo with an effect size of 0.7[21]

Cerebrolysin

Cerebrolysin is a mixture of neurotrophic peptides derived from pig brains, including BDNF, GDNF, NGF, and CNTF. It may have a neuroprotective or neurorestorative effect.

Randomized study of 279 Alzheimer patients found scores on the cognitive subscale of the ADAS improved by 4 points on Cere vs. placebo, effect size of 1.86, p = 0.03.  Global clinical outcome significantly better than placebo (p < 0.001).[27]  A randomized trial of 149 Alzheimer patients found an effect size of 2.22, improvement of 3.2 on the ADAS-cog on Cerebrolysin vs. placebo, p < 0.001.[28]  Effect size of 2 on elderly controls on the ADAS-cog.[67]

Cholinesterase inhibitors

This is a class of drugs used for Alzheimer’s disease, including donepezil and galantamine.  A meta-study found they had median effect size 0.28 on the ADAS-Cog for high-dose studies, 0.15 for low dose.[48]  Another meta-study found they had mean effect size 0.1 for ADLs in Alzheimer’s and there’s no difference between cholinesterase inhibitors.

Cognitive Training

For Alzheimer’s disease. Mostly these are memory practice games or drills, many of which are spaced repetition. Across various measurements of outcome (CPT, memory tests, IADLs, etc) median effect size was 0.47.[50] A metastudy of cognitive remediation for schizophrenia found a median effect size of 0.43 across various cognitive tests.[56]

Donepezil

Donepezil is an acetylcholinesterase inhibitor used in Alzheimer’s.

Effect size of 1.25 on ADAS-Cog in Alzheimer’s patients (p < 0.001).[51]  Odd that it is so much better than “cholinesterase inhibitors” as a class.  Doesn’t affect progression to Alzheimer’s in mild cognitive impairment.[53] Effect size of 0.6 on the MMSE in Parkinson’s patients, p = 0.0013.[54]  One study showed that donepezil did not have an effect in mild cognitive impairment.[55] Doesn’t work on schizophrenics either. [58]  Did not have an effect on healthy elderly volunteers on cognitive tasks.[66]  I’m going to take the conservative, lower estimates that effect sizes are around 0.2 or 0.5.

Erythropoietin

Erythropoietin is a hormone that increases red blood cell production.

It improves working memory, verbal processing, and Wisconsin Card Sorting scores significantly over placebo in schizophrenic patients.[8]  Significantly improves (p < 0.01) sustained attention and information processing speed in bipolar patients.[72] “EPO acts in an antiapoptotic, anti-inflammatory, antioxidant, neurotrophic, angiogenetic, stem cell–modulatory fashion” so it’s investigated as a neuroprotective for stroke and neurodegenerative diseases, but so far mostly in animals.[42]

Galantamine

Galantamine is an acetylcholinesterase inhibitor used in Alzheimer’s.

Effect size of 8.18 (?!) in Alzheimer’s patients after 6 months; slows cognitive decline.[59] After 3 months, effect size of 2.4 in Alzheimer’s patients, p = 0.002.[63] Galantamine is better than donepezil for Alzheimer’s ADAS-Cog and MMSE.[64] On schizophrenics, effect size of 0.89 in schizophrenic patients on RBANS test, one standard deviation up on the memory subscale, effectively normalizing performance.[60] A much larger randomized study on schizophrenics, however, found no overall effect. [61]  Metastudy on galantamine vs. donezepil for Alzheimers found much weaker effects: 0.48 effect size for donepezil and 0.52 for galantamine.[65]

Ginseng

Panax ginseng is a plant traditionally used as an “adaptogen” to increase alertness and endurance; the active ingredients are triterpinoid saponins called ginenosides.

In a controlled trial of Alzheimer’s, ginseng improves performance on MMSE and ADAS scales after 12 weeks (p = 0.009 and 0.029 respectively) and declined to baseline after discontinuation.[6] Reduces blood glucose acutely (p < 0.001) in 30 healthy volunteers [40] and improves performance at p < 0.05 at “repeated sevens” task. Effect size of 1-2, but since effects were only slightly significant here and were not in other tasks, there’s some reason for skepticism.  This study found that it didn’t improve working memory or reaction time but did improve the “quality of memory” subscore.[41]

Ispronicline

Ispronicline is a nicotinic receptor agonist.  The company that produced it, Targacept, appears to have gone out of business, and the drug was discontinued after it failed to make progress on Alzheimer’s.

It significantly improves measures of attention & episodic memory on healthy male volunteers vs. placebo. Also increases upper alpha peak on EEGs.[44]  2.5 effect size, p < 0.01 for 50 mg AZD vs. placebo for elderly patients on attention, episodic memory, and SDI-cog.[46]   Not statistically significantly effective on Alzheimer’s.[45] 

L-Dopa

This is a precursor to dopamine, used as a treatment for Parkinson’s disease.

Slightly reduces reaction time in healthy subjects, p < 0.05.[74]  Some healthy subjects develop side effects of nausea and excitation under L-Dopa, and these have slower reaction time than placebo; those who don’t have adverse effects have faster reaction times, p = 0.02.[75]

Melatonin

Melatonin is the hormone that regulates sleep cycles, often taken as a sleep aid.

Significant (p = 0.004) improvement in IADL score (activities of daily living, effect size 0.56) on 80 Alzheimer’s patients.[25]

Methylphenidate

Methylphenidate is a stimulant that works by dopamine reuptake inhibition and is used as a treatment for ADD.

Meta-analysis finds a large effect size (1.4) in memory on healthy non-sleep-deprived subjects, but no other improvements on executive function, attention, or mood.  Does not reduce sleepiness after sleep deprivation.[17]

Modafinil

Modafinil is a stimulant that works primarily by histamine agonism.

Significantly improves digit span (by 1-2 digits) and improved pattern recognition (by 8 percentage points), fewer stop errors & lower stop signal reaction time, better spatial planning.[11]

Significant effects (in a meta-study) on working memory, digit span, reaction time, in most studies; no effect on Stroop, spatial planning, verbal fluency; no effect at all on high-IQ population.[14]

Does not cause overconfidence vs. placebo.[15]

Improves performance in a mean 100 IQ group, but not a mean 115 IQ group.[16]

Meta-study founds a moderate improvement on attention (0.56) in healthy non-sleep-deprived individuals. No changes in mood, memory, or motivation.  In sleep-deprived individuals, has a large (2-3) effect size on executive function, a large effect size (1.22) on memory, and a large effect size on wakefulness (2-3).[17]

Comparable alertness and performance effects for 200 or 400 mg modafinil vs. 600 mg caffeine (6 cups of coffee) in sleep-deprived patients.[18]  Caffeine, amphetamine, and modafinil are comparably effective in increasing alertness & reaction time in sleep-deprived patients.[9]

Nicotine

Nicotine is a stimulant and nicotinic acetylcholine receptor agonist.

4-week nicotine skin patch improves performance on continuous performance test vs. placebo in 8-person trials of Alzheimer’s.[3]

In abstinent smokers, nicotine improves performance on all tests; in never-smokers, produces faster reaction times but more errors.

6-month trial on schizophrenics improves performance on the CPT with an effect size of 0.7.[22]

A meta-analysis found that nicotine improved working memory reaction time in both smokers and nonsmokers, effect size 0.34, but did not improve accuracy; also improved reaction time in orienting attention, effect size 0.34, and alerting attention, 0.3

Oxygen

Breathing high-oxygen air increases blood oxygen concentration.

It improves word recall vs. placebo in healthy subjects, but only at a p < 0.05 level.  Reaction time lowered, p < 0.0005.  No effect on working memory.[10]  Effect size on word recall and reaction time in another study on healthy subjects was ~2.5, p < 0.05.[43]

Piracetam

Piracetam has an unknown mechanism of action but is sometimes used as a nootropic.

In a metastudy of piracetam for cognitive impairment (mostly age-related), 63.9% were improved on piracetam vs. 34.1% on placebo. Fixed-effects model OR is 3.35.[29]  Doesn’t work on Alzheimer’s.[69]

PRL-8-53

PRL-8-53 is an experimental compound with some cholinergic properties.

Significant (p < 0.01) improvement in word retention over placebo; 30-45% improvements in # of words retained.[26]

rTMS

Repetitive transcranial magnetic stimulation involves placing a magnetic coil near the head of the subject and produces small electric currents in the brain.

A meta-study found improvements with effect size of 0.2-0.3 in working memory and response times on healthy subjects on n-back tasks.[12]

Semax

Semax is a Russian nootropic that seems to work by stimulate nerve growth factors.

Significant 74% improvement over placebo on memorization exam in power plant operators.[24]  Most of the other evidence about Semax is from Russian rat studies.

Souvenaid

Souvenaid is a cocktail containing essential fatty acids, vitamins, uridine, and choline, used to treat Alzheimer’s.  

A randomized 24-week trial on Alzheimer’s patients found that it improved the memory subscore on the NTB with an effect size of 0.23.[68]

Tandospirone

Tandospirone is a serotonin partial agonist, similar to buspirone, used for anxiety and depression.

In schizophrenic patients, improves performance on Wechsler Memory Scale and Wisconsin Card Sorting, p < 0.001 and 0.0001 respectively, effect sizes of 0.63 and 0.7.[4]  However, tandospirone impaired memory in healthy subjects.[71]

Tianeptine

Tianeptine is an antidepressant that seems to work by enhancing dopamine release, enhancing BDNF, and/or targeting opioid receptors.

In an uncontrolled trial of depressed patients, tianeptine improved working memory and reaction time.[23]  Did not affect memory, attention, or psychomotor performance on young healthy volunteers.

Tolcapone

Tolcapone is a COMT inhibitor used in the treatment of Parkinson’s.

Tolcapone improves memory for val/val COMT healthy subjects, but worsens it for met/met. (“Warriors” benefit, “worriers” don’t.)  Effect size of about 0.8, p < 0.05 on the val/val’s.[73]
INEFFECTIVE THERAPIES

B vitamins

No effect on elderly subjects. [7]

Creatine

Creatine is a compound that occurs naturally in vertebrates and supplies ATP to muscles.

No effect on cognitive function on healthy young adults.[35]  Does have effects on memory in the elderly [36] (d = 1.5, p < 0.001 for backward digit span) and vegetarians [37]

D-Cycloserine

D-cycloserine is an amino acid derivative and antibiotic.

Doesn’t improve cognitive function/digit span in schizophrenics.[57]

DHEA

DHEA is a steroid hormone and precursor to estrogen and testosterone.

No effect on elderly subjects.[5]

Dual N-Back

Dual N-back is a memory practice game.

Metastudy shows that, while performance on the N-back task improves, no crossover improvement on IQ tests occurs.[13]

Gingko Biloba

Fails to find effect on cognitive performance on Stroop test in MS patients.[2]  Also fails to prevent cognitive decline in older adults.[76]g

Oxiracetam

Oxiracetam is in the racetam class of drugs, unknown mechanism of action.

Doesn’t work on Alzheimer’s. [32]

Selegiline

Selegiline is an MAOB inhibitor used in Parkinson’s and depression.

Not effective on cognitive performance in Alzheimer’s.[30]  Doesn’t help in Parkinson’s either.[31]

Tarenflurbil

Tarenflurbil is a discontinued putative Alzheimer’s drug that destroys amyloid plaques.

Doesn’t slow cognitive decline in Alzheimer’s.[49]

NOTES

Unsurprisingly, the classic stimulants do quite well. (Caffeine, nicotine, amphetamine, methylphenidate, modafinil.)  Ispronicline is less well known and its evidence base is much smaller, but since it’s also a nicotinic receptor agonist, it’s possible that it also belongs in this category.

Cerebrolysin is interesting. It’s a legal anti-Alzheimer’s drug in Europe, and one of the few drugs that directly focuses on neural growth factors. These are known (mostly in animal studies) to be protective against brain damage, as from stroke or Parkinson’s.  Deficiency in BDNF is also one of the current hypotheses for what’s going wrong in depression.  “Just give people some growth factors” might be one of these simple obvious-in-retrospect things that could pan out to be widely effective.  In animal studies, growth factor gene therapy often has neuroprotective effects, and Nobel Prize-winning neuroscientist Rita Levi-Montalcini took daily NGF eyedrops.

There’s a common pattern in anything dopaminergic (such as: amphetamines, tolcapone, L-dopa, etc) that they improve cognitive performance in people who have “too little dopamine” (Parkinson’s patients, ADHD patients, val/val COMT genotypes) but are useless or worse in those who have “too much dopamine” (met/met COMT genotypes.)  This seems like a fairly robust finding, across many drugs as well as a lot of fMRI studies about dorsolateral prefrontal cortex activation.  How good dopaminergics are for your mental performance may depend a lot on who you are.

 

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Measures of Awesomeness

Epistemic status: exploratory. I’m building out a model.  I know zero anthropology, so my speculations may very well be reinventing some wheel.

A visit to the anthropological wings of the Museum of Natural History can cure you of cultural relativism in a hurry. Some cultures, in some times and places, made cooler stuff than others.  In other words, the concept of “technology level” refers to a real thing.

In the context of looking at ancient pottery or metalwork, a casual museumgoer won’t see anything too strange about that assumption.  But there are a lot of uncertainties smuggled in.  How do we know that this pot is superior to that pot?  Doesn’t that depend on who you are and what you value?  When we look at an object and consider it “primitive”, does that mean anything besides mere cultural chauvinism?

Tech trees

One potential way to make the idea of “more advanced/less advanced” technology objective is to talk about a dependency graph.  If one technology is a prerequisite for another, then the “child” technology can be identified as “more advanced” than the “parent” technology. This concept has been referred to as fabricatory depth.  You need kiln-firing technology before you can produce glazed pottery; therefore kilns are a prerequisite for glazing, and glazing is more technologically advanced than kilns.  If you see people who can only make unglazed pottery and not glazed pottery, then, in that particular respect, those people are lower-tech than their glazing neighbors.

The computer game concept of a tech tree (really, it’s a tech DAG) is a simplified version of this concept. The “roots” of the tree are primitive technologies; applications and advancements on these technologies take you to higher levels of the “tech tree”, which in turn can lead to even higher levels.

This puts a partial ordering but not a total ordering on technologies. Not every pair of technologies is directly comparable.  Which means that it’s more nuanced than categories like “Stone Age” — it’s possible for Culture A to be more advanced than Culture B in one sector, but less advanced in some other sector. We’re not assuming that technologies line up in one single March of Progress; but we are noticing that some technologies are structurally, by necessity, more “foundational” or “basic” or “primitive” than others.

Thinking in terms of dependencies/prerequisites means we can talk about technology level while keeping some distance away from value judgments. Forget what’s more “useful” or “higher quality.”  A high-tech object is just an object that depends on a lot of accumulated technologies.  It’s an object that requires a long chain of skills to produce.

Note that this isn’t quite equivalent to a high degree of skill. It takes very high skill to hunt with a throwing stick. But probably not a long sequence of techniques, each of which produces many applications.  We don’t have to assume that a low degree of technological advancement implies a low degree of effort or intelligence; it just means that, for whatever reason, you don’t have a big stack of technologies that build on each other.

Prowess Metrics

If you stroll through the museum and ask yourself what makes “higher-quality” objects, you’ll notice some commonalities.

Usually, finer, more precise work is intuitively higher quality. Finer brushwork or filigree or carving, smoother carvings, finer textiles with tighter weave, straighter or more symmetrical shapes, etc.

Stronger and more durable objects tend to be higher quality.  Steel is harder than iron.  Glazing makes pottery water- and stain-resistant.

Highly replicable objects tend to be higher quality. Molds and casts and potter’s wheels allow identical objects to be produced with little effort.

More efficient objects tend to be higher quality. Structures that are lighter relative to their strength. Machines that consume less fuel or physical effort.

Bigger objects can be higher quality.  Buildings or sculptures or cities on a colossal scale.

These kinds of criteria are still relevant even in the modern day. The semiconductor industry runs on making finer, more precise circuits. Materials science continues to make glass, ceramics, and other substances stronger, more durable, and lighter.  The software and manufacturing industries run on making objects more replicable.  “Big data” refers to the technologies necessary for handling information at scale.

There seem to be some simple qualities like these which continue to be valued in technologies, over time and across industries.  I’ll call them prowess metrics, inspired by Venkat Rao’s discussion, because they’re usually related to excelling at a single property rather than being very well suited to a market niche.

Human wants are enormously varied; but certain inputs tend to be common among them. At the most elemental level, almost anything anyone could want will require things like mass and energy; therefore mass and energy are close to universally valuable.  Prowess metrics are capacities which permit a wide variety of applications.

As you go up a tech tree, producing technologies that are necessary for technologies that are necessary for technologies, the technologies that have a lot of descendants will tend to be high on prowess metrics.  If you develop a technique for very reliable duplication, or a stronger construction material, there are a lot of technologies that can be derived from it.  In fact, we can even define prowess metrics as the qualities that predict having a lot of descendants on the tech tree.  They are what make a technology “generative”, productive of new technology.  Prowess metrics might also be expected to correlate with being high on the tech tree, which makes sense if you picture a long-tailed distribution of technology — most “chains” peter out early, but if you’ve reached a certain level of technology, that means you’re more likely to continue going to yet more advanced technologies.

Being high on a prowess metric is no guarantee that an object will be useful.  Usefulness is defined by humans and the context in which the object would be used.  The fastest cars in the world are novelty items, because most people don’t actually need or want the fastest cars in the world.  Identifying the usefulness of an object to actual humans is the basic function of marketing, and prowess metrics can’t substitute for that.  Usefulness is about utility and value judgments and all that squishy stuff.

However, I hypothesize, prowess metrics are decent predictors of the utility of objects. If you have a way to make your widget faster, bigger, finer, stronger, lighter, cheaper, etc, it’s at least worth privileging the hypothesis that there’s going to be demand for it.

The Innovator’s Dilemma defines a disruptive innovation as one which satisfices on a bunch of the standard metrics, optimizes hard on a different metric, and finds a new market that really values this new metric. Usually, the examples given in the book of all the above metrics fit the pattern of prowess metrics; things like size, speed, cost, etc.  Which prowess metrics matter depends on the market and the use case. But that prowess metrics matter is not really disputed.

In engineering-focused domains like the excavator industry or the semiconductor industry, the technical performance of the machinery matters a lot to purchasers. As you move “up the tech tree” to higher-level applications and consumer-facing products, technical prowess becomes less obviously relevant, but still in some sense underlies what’s possible.  Computing power still ultimately determines limits on what software applications are available.

Prowess metrics seem to be behind intuitions that look like the labor theory of value.  A worthy or excellent object, you feel, gives you a lot of something you can measure: many tons of wheat, high tensile strength, etc.  Objects that are “merely” well adapted to their context and highly desirable to their users may be perceived as having “fake” or “superficial” value, as opposed to the “real” value captured by prowess metrics.  “I care about the fuel economy of my tractor, not what color it’s painted!”

From a conventional economic perspective, this is exactly backwards: the prowess metric is only a correlate, a proxy,  of the things that really matter, the supply and demand.  And it’s not even always a good proxy!  But it’s an understandable fallacy once you accept that prowess metrics are frequently good predictors of value.  Moreover, prowess metrics tend to indicate something like “downstream” value — they mean that future applications of the technology can go farther and likely be worth more.

This is the intuition behind “We wanted flying cars, instead we got 140 characters.” Getting better prowess metrics on basic technologies (as you’d need to, to build flying cars), is substantial because it tends to open the doors to a lot of future technology and future value. Getting good product-market fit on an app built from off-the-shelf parts is less valuable in the long term because it isn’t causally necessary for as much future innovation.  (Twitter’s not a great example of a non-technological “tech” company, but it’s easy to think of better ones.)

Obviously, a lot of this is influenced by glamour — modern logistics is arguably as big a technological advance as flying cars would have been — but there still may be a meaningful, semi-rigorous notion of a foundational rather than a trivial technological improvement, and it seems to have to do with prowess metrics and going to nodes that have a lot of descendants on the tech tree.

Mana

There’s an intuition that a civilization can have a certain amount of motive power or mana or ability to do stuff.  Thriving cultures are increasing it; declining cultures are stagnating or losing it. And of course trying to make this intuition rigorous is hard, and potentially impracticable. You can’t directly rank cultures on how awesome they are.

But an armchair-observer, outside-view perspective might point to a handful of prowess metrics (literacy rates, cost of a loaf of bread, etc) and try to use them to get a rough, multidimensional picture of “ok, how rich and powerful is this society really? How much mana is there here?”

Studying material culture in this way is how, for instance, Kenneth Pomeranz argued that China was richer than Europe until the 19th century.  The Chinese consistently consumed more calories and more meat, had more furniture in their homes, and even read more books, than the Europeans. Comparing the historical “GDP’s” of China and Europe is uncertain and subject to statistical shenanigans; but if the Chinese consistently seem to have more of all the necessities and luxuries of life, then it starts to seem undeniable that, for most definitions of “rich”, they were richer.

The “material culture” approach is pretty similar to the “look at a bunch of prowess metrics” approach.  You make no attempt to have a single metric of “intrinsic value”. You can only make pretty modest claims. You merely observe that if a culture seems to be booming along a lot of highly general and “upstream” metrics, then there’s probably something vaguely positive going on.  This is the heuristic behind the kinds of claims in The Great Stagnation — things like ‘maximum vehicular speed isn’t increasing’ or ‘life expectancy isn’t increasing.’  Taken together, a lot of stagnant metrics paint a dispiriting portrait.

With a tech-tree model, most of the dependencies are unobserved, including (of course) all the future ones.  It’s hard to work with empirically, and even if you did know the structure, it would be impossible to put a single number on “the tech level.” If we can talk about that kind of structure at all, it’ll be with simplifying models — things like prowess metrics that are shared across many technologies and correlate with technological advancement.  You still can’t say much objectively about “how much mana do we have?” — as always, there’s an irreducible element of selection and storytelling.  But this at least, I think, gives us a starting point to concretize the questions and hypotheses.

The Peril of the Sublime

The “sublime”, as defined by writers such as Burke, Kant, and Keats, is an experience of immensity and awe.  “THE PASSION caused by the great and sublime in nature, when those causes operate most powerfully, is astonishment; and astonishment is that state of the soul, in which all its motions are suspended, with some degree of horror.”  We experience the sublime when we see vast mountains, violent storms, towering pyramids, dazzling details of pattern,the infinity of space.

The standard psychedelic or religious experiences are classic examples of the sublime.  The impression of infinite hugeness or infinite smallness, the impression of endless fractal intricacy, the impression of infinite recursion, the impression of vast significance  — these are intimations of infinity.

Indeed, it may be appropriate to simply define the sublime as the subjective experience of infinity.

But what, concretely, is the experience of infinity?

I suspect that it is merely the experience of being unable to measure or count. A person can innately see one or two objects and recognize them as one or two, without counting; if you show her more than seven or so, her first perception is of “many”.  The experience of uncountable multiplicity is the experience of losing count. “And he brought him forth abroad, and said, Look now toward heaven, and tell the stars, if thou be able to number them: and he said unto him, So shall thy seed be.”  Our metaphor for impossibly many is the innumerable stars. We measure the size of infinities by trying (and failing) to put them into one-to-one correspondence with each other.  Countlessness provokes awe.

So, too, does scalelessness: when we cannot estimate size, we become dizzy with vastness, with smallness, with the scale-free multiplicity of fractals. Timelessness provokes awe, with thoughts of “eternity in a grain of sand.”  When something breaks our units of measure, when it appears to go beyond them, we experience that as infinity.

If you think of perception as working through convolutional neural nets, you notice that higher level nodes are averages or invariants over measurements — the same object, independent of position or rotation or color shift.  Allow the neural net to run its outputs into its inputs long enough, and you begin to see the kinds of images that show up in DeepDream — highly multiscale, intricately patterned.  Some of these higher-level invariants are, clearly, being activated very intensely if the network is allowed to “ruminate” on its own contents.

I might speculate that ordinary perception puts something like frames or limits on this kind of recursive rumination.  As an artist drawing a picture first sketches the proportions of the main objects, before filling in details, to make sure nothing is out of balance, in ordinary perception we put objects or ideas in proportion or in context with the rest of our world. They have a finite size, a particular place in time, a finite importance, and so on.  If this ability to gauge proportion is baffled or broken, we get the impression of infinity and sublimity.

The sublime naturally inspires worship. When something appears to be infinitely important, infinitely vast or complex, eternal or beyond time, how can we not ascribe it with huge significance?  We can easily claim that some particular sacred cow is not, in fact, sacred; but to deny the importance of the sublime is tantamount to saying sacredness itself is not sacred.  From the perspective of someone who has experienced raw barefaced wonder, an enemy of the sublime is a desecrator, a dirty vandal, trying to reduce us all to his level of prosaic blindness.

I am not a vandal. But I am a scientist by training. And so, I find myself in a complicated relationship with the sublime.

The danger of worshiping the sublime is that it can all too easily reduce to worshiping one’s own incapacity.  The sublime’s favorite phrase is “I can’t even.”  It is the inability to put things into context and perspective.  To be overwhelmed by a wildflower is a kind of elevated sensitivity and acuteness of observation; but if you can be overwhelmed by anything, then you have a failure to prioritize.  If you perceive “infinity” as simply beyond what you can measure or comprehend, then seeking a sense of infinity is seeking your own ignorance.  You find yourself looking backwards and inwards, towards childhood, towards faith, towards “unmediated” perception, trying to peel back the layers of ordinary reason towards something “beyond” the workaday world.

I suspect that this kind of a backwards mental move is a fundamental kind of error. I’ve done it myself, enough times to recognize the pattern. You remember experiencing something as awe-inspiring and mysterious; you want to recreate that experience; you try to come up with a rational structure that preserves that intuition of mystery, that delicious sublimity; and look! you find you have come to a dead end, and the facts force you to acknowledge failure.

The first and most canonical example of this pattern is trying to prove the existence of God.  I recognize a similar kind of flavor in trying to defend superrationality, trying to refuse the No-Free-Lunch theorem, and trying to argue against digital physics.  There’s a deep appeal in ideas that seem to cut through our finite, parochial, incremental limitations to something “beyond,” but I’ve frequently found those intuitions impossible to justify.

Beyondness is sublime; locality is mundane.  But “beyond” is not a place you can get to.  We always represent infinity in terms of the failure of the finite. “For every N, there exists an n such that a_n > N.”  In other words: every bound will break. This is a temptation towards falling in love with brokenness.

The danger is that in reaching for infinity, reaching for the sublime, you wind up committing a kind of self-harm. Stunting your actual, real-world powers; admitting frank impossibilities into your belief system; seeking not the universe’s bigness but your own smallness.

The universe really is vast and awe-inspiring — it is not an accident that Carl Sagan, our contemporary poet of transcendence, was an astronomer — but to experience awe at the genuinely vast, you have to actually be moving outward along with the scientists, claiming old territory as comprehensible and well-mapped even as you look toward uncharted skies.  There’s a robust, outward-facing experience of the sublime that is dual to the “stolid, prosaic” approach that treats the world as finite and moderate in importance; if you can be cool-headed and proportionate and realistic, you can take on grand adventures and explorations.

 

There Is No Secret Notebook

Once, a friend of mine asked me to help out on his organization’s project. I didn’t feel qualified; I had read everything they’d published, but I assumed that all the things I was uncertain about were solved and written up somewhere in a “secret notebook” by people much smarter than I. My friend laughed and said “that’s what used to think!  I assumed everything had already been written up in a secret notebook! and then I joined the project and found out that there is no secret notebook.  We’re all just figuring it out as we go.”

There seems to be a phenomenon here of a sense that a thing is known.

Known by whom? Unclear. Known by smarter people than you.

But because it is known, it is not your job to deal with, and probably a waste of time for you to try to personally find out the answer.

This is a failure mode when, for instance, everybody in a group thinks somebody else is responsible for a task (surely it is known, surely it is being taken care of) and then nobody does it.

It’s also a failure mode that leads people to underestimate their capacity to contribute.

“Surely this simple question must already have been answered, right?”  Yeah, probably, you should check and see.  Your first move should be to consult StackExchange, Wikipedia, Google Scholar, a textbook, the smart guy at your office, etc.

But if you try a bunch of avenues and don’t find the answer, and you still care, the mistake is to conclude (even subconsciously) that the answer is intrinsically inaccessible to you.  “This is something that insiders know, and since I’m not an insider, nobody will ever tell me.”  “Normal people get this intuitively, but I’m a weirdo and I just can’t understand.”

The feeling is that the answer to your question belongs to people who have some essential quality that you forever lack. You look at yourself, with your slow and fragile reasoning power, and you feel like you’re counting on your fingers, and imagine that someone out there has a supercomputer.  (Or maybe that everybody on Earth but you has a supercomputer.)

This is an illusion. Everybody’s brain is made out of the same stuff, more or less. Sure, different people have different talents and levels of experience. But humans have general intelligence. Counting on your fingers, checking things to see if they match up to facts, going through arguments to see if they’re valid, trying things to see how they work — that’s how everyone figures out what’s true. There aren’t people out there who have found a shortcut.

If something is important to you, you can’t just defer it to “it is known.” You, personally, should try to find out. If it does happen to be known, out there somewhere, by someone whom you have no way of contacting, then it still doesn’t help you.

This isn’t to say that one person is capable of understanding everything on earth. It’s inevitable that the answer to some questions is going to be “I have no idea, and I’ll leave it at that.”  The failure mode is taking on faith that someone else has it covered. If there is no evidence of a “secret notebook”, then there probably isn’t one.

There is probably nobody, for instance, with a secret plan to end global warming.  If I were really motivated to do so,  which I’m not, I’d look much more carefully to see who the players are, and what the most promising proposals are, and so on. But if, after a long and careful search, I could find nobody steering effectively, I wouldn’t imagine that there were, somewhere out of sight, never mentioned in the news, a cabal of wise men guiding Earth’s climate.  That would be a kind of “god of the gaps” fallacy.

The assumption of “I don’t know, but someone must“, might be a habit learned in childhood. When you are a small child, you really are a lot less experienced than everyone else, and it really does make sense to assume that the grownups know things that you don’t.

But if you continue on into adulthood, and in particular if you continue to grow in expertise and achievement, and you keep running into situations where you feel like someone should know this and you can’t find anybody who does —

Maybe that’s because nobody actually knows.

Maybe that’s because you’re more capable than you think.

Maybe that’s because it’s your job to figure out.

 

Contra Science-Based Medicine

Epistemic status: hand-wavy, but making a serious point

TW: diets.

I recently did some reading about ketogenic diets for cancer, and I’d like to compare and contrast my approach with the explanation on the blog Science-Based Medicine, which consistently presents the “skeptical” perspective on alt-med questions.

David Gorski is a cancer biologist himself, as I am not; his posts are always informative, and I have no quarrel with his facts. I read the studies mentioned in the post, so we’re using pretty much the same set of data points. And I agree with the broad outlines of his claims: ketogenic diets have some promising but by no means conclusive preclinical evidence for brain cancers; they’re definitely not a substitute for chemotherapy in general; and Dr. Seyfried has been overselling his research as a cure for cancer in disreputable alt-med venues.

But I want to pick apart some points of perspective and interpretation.

The first part of the post is all about painting Seyfried as disreputable because of his associations with alt-med institutions. Gorski says of the American College for Advancement in Medicine, “this is not an organization with which a scientist who wishes to be taken seriously by oncologists associates himself.”

Now, I’m not defending the cancer quacks mentioned; these are people who pitch chelation and coffee enemas, things that are pretty clearly scientifically disproven.  However, I’m suspicious of the rhetorical trick of guilt by association and argument from consensus.  Surely we care about whether Seyfried is correct, not whether he is “taken seriously”, “reputable”, or “legitimate.”  These are all social words, not scientific ones, and constitute an emotional appeal to social conformity and authority.

To his credit,  Gorski doesn’t stop there; he does make substantive criticisms of Seyfried’s work.  But I think it’s worth pointing out when, as happens so often in the biomedical world, a social argument is conflated with a scientific one.

Gorski goes on to criticize Seyfried for “exaggerating how hostile the cancer research community is towards metabolism as an important, possibly critical, driver of cancer” when cancer metabolism is, in fact, an active area of current cancer research.  He goes on to say, “Dr. Seyfried, in my readings, appears all too often to speak of “cancer” as if it were a monolithic single disease. As I’ve pointed out many times before, it’s not. Indeed, only approximately 60-90% of cancers demonstrate the Warburg effect.”

None of these facts are wrong, but the interpretation is misleading. Cancer metabolism and metabolic mechanisms for cancer treatments are, in fact, common topics of cancer research; but this ought to be evidence in favor of Seyfried’s hypothesis, that it’s within the range of mainstream science and is supported by many cancer biologists, rather than being pure invention like most alt-med “cancer cures.”

I’d also argue with the statement that “cancer isn’t one disease.”  It’s true that not all cancers demonstrate the Warburg effect, but 60-90% is a lot of cancers; a drug that was effective in 60-90% of cancers would be as revolutionary an advance as chemotherapy.  An antibiotic that killed 60-90% of bacteria could fairly be said to “kill bacteria.” When most (if not all) cancers have structural features in common, that indicates that talking generally about “cancer” is meaningful, and that it doesn’t make sense to treat every sub-sub-type as though it is a completely different disease.  Cancer has both unity and diversity.  Saying “cancer isn’t one disease” is a rhetorically loaded move that means “don’t generalize from one type of cancer to another.”  But it’s not correct to never generalize; that would utterly paralyze research.  How much it’s safe to generalize depends on how common the relevant feature is across cancers; in the case of the Warburg effect, that’s a matter of current debate, but it’s fair to call it pervasive.

I don’t have much criticism of the way Gorski handles the ketogenic diet studies. He’s on the skeptical side, but skepticism is warranted. Mouse studies very frequently don’t generalize to humans; they’re suggestive, but only weak evidence. And while there were two case studies of patients who did notably better than typical glioblastoma patients on ketogenic diets, we don’t have enough patients to be confident that the improvements were a result of the diet.

But then Gorski says, “Clearly, ketogenic diets are not ready for prime time as a treatment for cancer.”

Now, wait a minute. What does that even mean?

As a cancer patient, does it make sense for you to try a ketogenic diet?  Well, there’s a plausible mechanism for it to work (particularly in brain cancer), there’s some suggestive evidence in mice and a few humans with brain cancers, and — crucially — it’s just a diet.  People go on ketogenic diets all the time, for no other reason than wanting to lose weight. It’s even been shown medically safe (though apparently hard to comply with) in cancer patients. Trying a special diet is pretty low risk, and a reasonable person aware of the evidence might very well choose to try it.

It doesn’t make sense to use a ketogenic diet as a replacement for chemotherapy or radiotherapy in cancers where those treatments work. That would be very unsafe.  But for certain advanced brain cancers, chemo barely extends life if at all, and is very unpleasant. If there’s anyone who has a good reason to refuse chemotherapy, it’s someone who’s almost certain to die soon and doesn’t want their last few months to be agonizing.

Is a ketogenic diet for cancer something that every oncologist in the world should be prescribing for his patients? No way. Should it be the “standard of care”? No; there isn’t enough evidence that it helps.  But is it worth trying for an individual who wants to? Quite possibly.

The distinction here is about where you put the reader’s locus of identity. Is a reader supposed to imagine herself as a potential cancer patient, considering whether or not to try the diet? Or as a potential administrator, considering whether or not to make the diet a policy for everyone?  The rhetorical trick Gorski’s using here is in identifying the reader with a nebulous “we”, as in “should we put cancer patients on ketogenic diets?”  You are meant to imagine a consensus, or an authoritative body.  The medical profession, the government, something like that.  This imagined “we” is the mirror image of the nebulous “they” that conspiracy theorists believe in, the “they” who doesn’t want you to know about cancer cures.

The overall effect of believing in an imagined “we” or an imagined “they” is to make social reality the primary reality.  “We” or “they” represents a vague model of “society” — the “respectable” people, the “legitimate” and “reputable” people, the “consensus”. In other words, the tribal elders. If you have a positive association with “the consensus”, as Gorski clearly does, then you want to expel the “disreputable” from the consensus.  If you have a negative association with “the consensus”, then you mistrust anything that sounds official and look for fellow mavericks and outsiders.  In neither case are you primarily evaluating claims of fact; you are evaluating people.

For instance, the existence of Phase I/II trials of the ketogenic diet on glioblastoma ought to be good news for ketogenic diets.  More evidence will soon come in; and the fact that the studies exist at all is further evidence that ketogenic diets are taken seriously by mainstream cancer researchers.  However, Gorski treats this as an indictment of Seyfried, because he wanted to do an (uncontrolled) case series of ketogenic diets rather than the more thorough controlled studies.  The overall intent of the blog post is to communicate Seyfried is disreputable, cancer is complicated, people who believe in cancer cures are beyond the pale, when one could have used exactly the same facts to make the point ketogenic diets are an exciting possibility for glioblastoma and the preliminary evidence is encouraging.

My own perspective can perhaps be summarized as “a contrarian worldview from mainstream sources.”  Looking at ordinary sources like journal articles and historical primary sources, looking at uncontroversial claims of fact, often gives me a view of the world that is quite different from the “we”-based view where “society” is more or less getting things right.  My object-level beliefs are rarely that unusual; the connotation of those beliefs is where I differ from most people.  I don’t feel myself to be safely nestled in the lamplit circle of “we”; I feel like I’m outside, tumbling in the abyss, with only the frail spark of my mind to illuminate a small patch around me.  And I think that, ultimately, the abyss is real, and the lamplit circle is imaginary.

What Is To Be Done?

Epistemic status: loose and speculative

This is the last post in my cancer series. On reflection, there’s a lot I want to edit and expand here, and I think the right format for this  is a book rather than a blog. So, over the next several months I’ll be working on that.

In the meantime, I want to draw some conclusions given what I’ve found out about cancer so far. What are the next steps? Where do we go from here? The world I see is one where the “efficient market hypothesis” doesn’t hold in cancer research. Just because an idea is promising doesn’t mean it’ll be tried, particularly in human clinical trials.

So what can you, a reader, do to cure cancer?

1. Do cancer research

This one’s kind of a no-brainer, but I put it here because I see a lot of young EA’s wondering what to do with their lives, and the most frequent ideas are things like “make a lot of money to give to charity” or “work at an EA organization”, but I really believe a broader range of object-level skills could be useful for bettering the world. Doctors and biologists are important. Ultimately, the things that kill the most human beings today are the noninfectious diseases of aging.  If your heroes are the people who eradicated smallpox, maybe you should take up the cause of ending another disease.

2. Invest in or donate to organizations doing undervalued cancer work

In my blog series, I pointed out some researchers that I thought were doing unusually promising early-stage research.  Getting preclinical studies to clinical trials takes funding.

I haven’t investigated any of these organizations as organizations — I don’t claim to know that they’re likely to be profitable investments or efficient charities. I’m just looking at the drug candidates that I’ve found promising and seeing which existing organizations are involved in researching them.

3-bromopyruvate, the glycolysis inhibitor I’m bullish on, is being developed by PreScience Labs.

The Cancer Research Institute, a nonprofit that accepts donations, has funded a great deal of important immunotherapy research, including most of the recent work on mixed bacterial vaccine by the late renowned immunologist Lloyd J. Old.

The Fibrolamellar Cancer Foundation, which focuses on the rare liver cancer fibrolamellar hepatocellular carcinoma, has Sanford Simon on its advisory board and funded his research on anti-IgG antibodies to precisely detect and potentially destroy cancer cells.

I expect that there are other avenues to funding particular lines of scientific research, from creating novel grant-giving foundations to crowdfunding experiments.

I’m also interested in institutions like IndieBio, which try to bring a radical, Silicon Valley spirit to the biotech industry, and get funding for biomedical startups working on hard problems.

3. Political reform

It would be easier to innovate in cancer research if the regulatory challenges were less onerous.  Lobbying and activism, in the US or elsewhere, could probably be helpful.

This is an area where think tanks and patient advocacy groups are relevant.  I don’t have a clear idea of which precise policy goals are the most useful and attainable, but people with more of a policy bent can probably answer that question.

A different kind of “political” approach is regulatory arbitrage — trying to find or negotiate a favorable political climate to research somewhere outside the US.

4. Further meta-research

We clearly need more evaluative work done on the questions “What types of cancer research are most promising? Where is the low-hanging fruit, if any?”  I’ve been doing that, but I’m only one perspective.  This seems valuable in the context of something like the Open Philanthropy Project, which tries to evaluate the tractability of entire goals.

 

Regulatory Problems with Cancer Research

Epistemic status: more argumentative than the other posts in this sequence. This is obviously informed by my own political views, but my intent is to be convincing to a range of audiences.

In this sequence I’ve been arguing that, while most cancer drugs developed over the past several decades are not very effective, there are potentially exciting avenues of research that haven’t gotten much attention or funding yet.

Why is this the case?  Cancer research is a huge field full of intelligent people. Cancer is a very common disease and there’s a lot of money to be made in treating it. “Curing cancer” is a byword for a lofty goal.  Why should there be any 20-dollar bills lying on the sidewalk at all?

In particular, why would there be less progress since the War on Cancer, which allocated much more federal funding to cancer research than was available before?

The conventional story is that cancer is simply hard. We already gathered the low-hanging fruit of radiotherapy and cytotoxic chemotherapy; now we’re trying to cure the tougher cancers, and it just takes more money and time.

I’ve been arguing that the “cancer is hard” story is incorrect. Targeted chemotherapy, the most popular approach for the past two decades, tends to fail because of the incredible diversity and mutability of cancer.  Approaches that focus on what cancers have in common, like their high glucose requirements or susceptibility to immune defenses, might turn out to work much better.

But, if I’m correct, why hasn’t some enterprising cancer researcher already come to the same conclusions? Even if I’m wrong, I’m not unique; a lot of my argument is just echoing James Watson’s views. Why haven’t any investors and funders decided it might be a good idea to try cancer research the way the discoverer of the double helix thinks we should do it?

This can be explained by an increase in the regulatory burden of cancer research in the past several decades. Clinical trials have become more expensive, require more paperwork, and allow less freedom of judgment from clinicians and researchers.  Only a large pharmaceutical company can afford to run Phase II and III clinical trials these days.  There’s more money in cancer research than ever, but it’s harder to try new things on sick people.  This tends to narrow cancer research to established players and drug classes.

In the world of early-stage tech startups, success follows a power-law distribution. Investors gain more money by funding a handful of huge successes than they lose by giving small investments to a lot of things that don’t work out. So it makes sense that, for instance, YCombinator keeps casting a wider net, accepting startups at earlier stages, and actively seeking outliers and mavericks. They want to make sure they don’t miss the next AirBnB.

It would seem to make sense that investing in drug candidates would work similarly; you don’t want to miss the next Gleevec either.  But if the cost of testing is too high, casting a wide net becomes much more expensive. You can’t just give the founders of an early-stage biotech company a little funding to see if they can do something awesome with it.  And so, medical research becomes much more conservative.

Regulation Has Increased Costs and Slowed Drug Development

90% or more of a typical drug’s costs come from Phase III clinical trials. So it makes sense to focus on the costs and barriers associated with clinical trials, to see if they’ve gone up over time and what the consequences have been.

As of 2005, the R&D cost of the average drug was $1.3 billion. In 1975, that figure was $100 million. (That is, drug trials have gotten on average more than 13 times more expensive over the past forty years.)  Phase III trials are becoming longer, involving more procedures and more hours of work, and have lower enrollment and retention due to more stringent enrollment criteria and trial protocols.  

Protocols for clinical trials are now over 200 pages long on average. The combined costs are $26,000 per patient.  The annual rate of cost increase is itself increasing, from an annual increase of 7.3% in 1970-1980 to 12.2% from 1980-1990 (inflation-adjusted.)  The estimated cost per life-year saved from current clinical cancer trials is approximately $2.7 million.

It now takes an average of 12-15 years from drug discovery to marketing, compared to an average of 8 years in the 1960’s.  Before the 1962 Kefauver-Harris amendment that vastly increased FDA powers, it took only 7 months. For oncology drugs, just the preclinical work takes 6 years; once early clinical trial data are in, it takes 26-27 months to proceed to Phase II or III; and it takes an average of 14.7 clinical trials (Phase I, II, or III) to get a drug approved.

Running a clinical trial requires protocols to be approved by the FDA, the NCI (National Cancer Institute, the primary funder of cancer research in the US), and various IRBs (institutional review boards, administered by the OHRP, or Office of Human Research Protections.)  On average, “16.8% of the total costs of an observational protocol are devoted to IRB interactions, with exchanges of more than 15,000 pages of material, but with minimal or no impact on human subject protection or on study procedures.”  Adverse events during trials require a time-consuming reporting and re-consent process. While protocols used to be guidelines for investigators to follow, they are now considered legally binding documents; so that, for instance, if a patient changes the dates of chemotherapy to schedule around family or work responsibilities, that is considered to be a violation of protocol that can void the whole trial.

To handle this regulatory burden, an entire industry of CROs (contract research organizations) has grown up, administering trials and handling paperwork to make the experimental drug look good to federal regulators. Like tax preparers, CROs have an incentive to keep the regulatory process complex and expensive.

The result of all this added cost is that fewer drugs get developed than otherwise would.  Sam Peltzman’s 1973 study of drug availability and safety before and after the 1962 Kefauver-Harris amendment (which significantly enhanced FDA powers) found that a model of drug development predicted a post-1962 average of 41 new drugs approved per year, while the actual average was 16 new drugs approved per year. The pre-1962 average number of drugs approved per year was 40.

 

Screen Shot 2015-12-06 at 10.57.51 AM

This is a graph of the number of new drug applications approved by the FDA every year from 1944 to the present. Note that the number of drugs approved has been largely flat since the 1962 Kefauver-Harris amendment, though the decline in drug approvals appears to precede the law by several years.

Increased Drug Regulation Has Not Meaningfully Decreased Risk

Peltzman’s study on the Kefauver-Harris amendment found that there was little evidence suggesting that more ineffective drugs reached the market pre-1962 compared to post-1962.

Comparing the US to Great Britain and Spain, each of which approve more drugs per year than the US, the other countries have no higher rates of postmarket withdrawals of drugs, suggesting that the extra regulatory scrutiny is not providing us with safer drugs.

Toxic death rates haven’t dropped much in Phase I trials. In 6639 patients, comprising 211 trials, between 1972 and 1987, the toxic death rate was 0.5%. In 11,935 patients, comprising 460 studies, between 1991 and 2002, the toxic death rate was also 0.5%.  

Between 1999 and 2006, the number of adverse drug reactions recorded in the US has actually been increasing, particularly  as the proportion of elderly patients taking many drugs has increased.

The most common severe drug interactions are often from old, well-known drugs, like insulin, warfarin, and digoxin.  “Antibiotics, anticoagulants, digoxin, diuretics, hypoglycaemic agents, antineoplastic agents and nonsteroidal anti-inflammatory drugs (NSAIDs) are responsible for 60% of ADRs leading to hospital admission and 70% of ADRs occurring in hospital.”  Increasing regulation on new drugs isn’t going to stop the problem of increasing adverse drug reactions, because most of those come from old drugs.

Cost-Benefit Tradeoffs Support Looser Regulations On Drugs

Gieringer’s 1985 study estimated the loss of life from FDA-related delay of drugs since 1962 to be in the hundreds of thousands.  This only includes the delay of drugs that were eventually approved, not the potentially beneficial drugs that were never approved or never developed, so it’s probably a vast underestimate.

In a recent paper, “Is the FDA Too Conservative Or Too Aggressive?“, the authors apply a Bayesian decision analysis to evaluate the overall cost of a trial based on the disease burden of Type I vs. Type II errors.

The classical approach used by the FDA is to constrain experiments to a maximum 2.5% risk of Type I error for all tests, and then choose a power for the alternative hypothesis by making the sample size large enough.  That is, no drug can be approved if there is a greater than 2.5% chance that it is ineffective.

This doesn’t make sense from a disease risk standpoint, because for very severe diseases, the risk of not trying a drug that might work is higher than the risk of trying a drug that doesn’t work.  The authors use data from the U.S. Burden of Disease study, which measures Years Lived with Disability to compute the “optimal” level of acceptable risk of inefficacy for drugs for different diseases. For instance, in pancreatic cancer, the BDA-optimal risk of Type 1 error is 27.9%, since the disease is so deadly.

Cancer in general, being both common and deadly, is an especially good area for looser drug regulation.  If a new therapy increased the cure rate of lung cancer by just 1% (through improved adjuvant therapy) and increased the average life expectancy of uncured patients by just 3 months, the [five-year] regulation-induced delay would cost more than 2,000,000 life-years worldwide.

Even this cost-benefit framing may be understating the case for FDA and OHRP reform, though. The problem seems to be less that the standards for efficacy are too high, than that the costs of compliance are too high because of redundant and excessive required documentation.  It would in principle be possible to streamline the process of conducting clinical trials without reducing its rigor.

We Think About Risk Wrong

In medical contexts, people often talk about the unknown as disrespectable. An “unapproved” drug, an “untested” drug, an “unproven” drug, a treatment that is “not indicated”, all sound unsettling.  Nobody wants to play cowboy in life-and-death situations.

But this kind of language is not actually about reducing risk.  Reality is probabilistic; all choices have potential risks and potential benefits. There’s no real wall, out in the universe, between the “safe/known” and the “unsafe/unknown”; that’s a human framing, akin to the Ellsberg paradox or the bias of ambiguity aversion. People prefer known risks to unknown risks.

In other words: death and disease are scary, and rightly so, but people will tend to be less frightened of risks that seem normal and natural (people have always died of cancer) than of risks that seem outlandish or like somebody’s fault (taking an experimental drug that might or might not work).  Chosen risk, conscious risk, stepping into the unknown, is viewed as worse than the risk of passively allowing harm to occur.  Even if the objective risk-benefit calculations actually work out the other way.

This is an instinct worth fighting.  Cancer is a common disease, yes; but the “normalcy” of it can blind us to the horrifying death toll.  As Bertrand Russell said, the mark of a civilized man is the capacity to read a column of numbers and weep.

Fear of action isn’t actually about making people safer. It’s about making people feel safer, because they aren’t looking at the whole picture.  It’s about making people feel like they can’t be blamed.

It’s Too Hard to Do Transformative Biomedical Research Today

Derek Lowe, an always insightful observer of the pharmaceutical scene,  comments on the VC firm Andreessen Horowitz’s first foray into biotech, “In this business, you work for years before you can have the tiniest hope of ever selling anything to anyone. And before you can do that, you have to (by Silicon Valley standards) abjectly crawl before the regulatory agencies in the US and every other part of the world you want to sell in. Even to get the chance to abase yourself in this fashion, you have to generate a mountain of carefully gathered and curated data, in which every part of every step must be done just so or the whole thing’s invalid, go back and start again and do it right this time. The legal and regulatory pressure is, by Valley standards, otherworldly.”

It shouldn’t be.

I am not a policy expert, so I don’t know what the appropriate next steps are.  What kinds of reforms in FDA and OHRP rules have a reasonable chance of being passed?  I don’t know at this point, and I hope some of my readers do.

I do know that committed activists can change things. In 1992, after a decade of heroic advocacy by AIDS patients, the FDA created the “accelerated approval” process, which can approve drugs for life-threatening diseases after Phase II studies.

We have to find a way to continue that legacy.

3-Bromopyruvate

Epistemic status: fairly confident

Most cancer cells, instead of using cellular respiration, get ATP  from glycolysis. This is called the “Warburg Effect.”

Glycolysis is less efficient than cellular respiration, which is why cancer cells have higher glucose requirements and why interventions that improve insulin sensitivity (like metformin) have anti-cancer effects.  The high glucose consumption of cancer cells is so reliable an indicator of cancer that it is the basis of detecting metastases in PET scans.

Tumor progression is associated with the emergence of the Warburg effect.  One hypothesis for why is that cancer cells lose mitochondrial function (and thus the capacity for cellular respiration) as they mutate; another is that the hypoxic environment of a tumor makes cellular respiration impossible.

Because  reliance on glycolysis is such a general feature of cancer, a natural type of drug to try on cancers is a glycolysis inhibitor, which would cut off tumors’ energy supply.  To the extent that the Warburg effect is a result of the inability to engage in cellular respiration, glycolysis inhibitors should not fail as cancers continue to advance, but should, if anything, become more effective; cancer cells which have lost mitochondrial function are unlikely to regain it, while cancer cells which have some distinctive surface marker targeted by a drug are likely to lose it over time.

An early-stage drug called 3-bromopyruvate is a glycolysis inhibitor which has shown promising animal results.

3-BP is a strong alkylating agent that inhibits the glycolysis enzyme GAPDH.

In a rat model of hepatocellular carcinoma [1], rats were treated with 2.0 mM of 3-BP; all treated rats survived more than seven months, while all control rats died within days.  The 3-BP eradicated large bulging tumors in the treated rats. The cellular ATP level of healthy cells stayed constant while the cellular ATP level of cancer cells dropped to 10% of its former level.

In a study of rabbits with liver tumors [2], mean survival in the control group was 18 days while mean survival in the treated group (with intraarterial 3-BP) was 55 days.  The tumors in treated animals had not grown and were mostly necrotic; the livers in control animals was almost completely replaced by tumor, with extension into the diaphragm and lungs.  Death in the treated animals was believed to be due to extrahepatic disease (by the time of treatment, the tumors were already metastatic, both in control and treated animals.)  Three animals were treated “earlier” (one week after implantation): one survived, the other two survived 80 days and died of lung metastases.  Intravenous administration, by contrast, was not effective.

Another rabbit study [3] found that intravenous 3-BP doesn’t kill liver tumors but does eliminate their lung metastases.

3-BP  also enhances survival of nude mice injected with human mesothelioma.[4]  There was a control group, a 3-BP treated group, a cisplatin-treated group, and a combined 3-BP and cisplatin group.   In the control group, 3-BP only group, and cisplatin only group, all mice died before day 45. In the combined group, survival was better than control (p = 0.0021), 3-BP alone (p = 0.0024), and cisplatin alone (p = 0.0161).  

In a study of 3-BP on mice with lymphoma[5], a significant reduction in tumor activity was found among those given repeated treatments (p = 0.0043 at day 7) but in those given only a single treatment, the effect was only significant at the second day (p = 0.0152 at day 2) and afterwards the tumors returned to normal.  The only manifestation of toxicity was lower body weight in the 3-BP-treated groups.

Microencapsulated 3-BP, delivered systemically, into a mouse model of pancreatic ductal carcinoma[6], causes minimal to no tumor progression, compared to animals treated with gemcitabine, which had a 60-fold increase in BLI, a measure of tumor activity.

Glioma cells are refractory to most treatment. D-amino acid oxidase combined with 3-bromopyruvate [7] decreased proliferation and viability in rats.

Aerosolized 3-BP also prevents the development of lung cancer in carcinogen-treated mice, without causing liver toxicity.[11]

There has been one human case study of 3-bromopyruvate, [8] on a young man with terminal fibrolamellar carcinoma. At the time of treatment, he was on a feeding tube, he had ascites, and his spleen was swollen due to complete blockage of the renal artery. He had already been treated with chemotherapy.  The University of Frankfurt’s ethics committee permitted him to be treated with a 3-BP, through the TACE delivery method (transcatheter arterial chemoembolism). He began having symptoms of tumor lysis syndrome, a dangerous condition associated with tumor necrosis.  CT scans showed that the tumors were necrotic after administering 3-BP, and the tumor areas were encapsulated and showed fibrosis.   His mobility was limited due to pre-existing ascites and edema, but he began to go out in his wheelchair. He died of liver failure 2 years after his initial diagnosis.   A fluid sample from his ascites found some mesothelial cells, but detected no tumor cells.

There are also a few negative results about 3-BP, but these are explainable by differences in protocols from the successful studies. One study of rabbits with liver cancer found 3-BP didn’t significantly increase tumor necrosis relative to controls, but it only used a single administration rather than repeated administration.[9]  One study of rabbits with liver cancer found that 3-BP killed them, but that was with a dose of 12x the dose in the successful studies.[10]  

This is fairly strong animal evidence, across a variety of cancers, of the most confident kind (tumor eradication and prolonged survival, not just inhibition of growth).  The single human study is not strongly conclusive but leans towards a positive effect.

One major issue is that intra-arterial administration for liver cancer seems to work while systemic administration doesn’t, though other types of cancer do seem to respond to systemic, microencapsulated, or aerosolized delivery. Clearly, attention needs to be paid to optimizing dosage, administration schedule, and delivery method.

As well as being a glycolysis inhibitor, 3-BP is also an alkylating agent, like most cytotoxic chemotherapies; this suggests it may work along multiple pathways. It also suggests we might see similar side effects to cytotoxic chemotherapy in humans.

3-BP is not the only glycolysis-inhibiting drug that has been tried.

Imatinib, which is by far the most successful targeted cancer therapy, is, among other things, a glycolysis inhibitor.

Dichloroacetic acid had some promising animal studies and stabilized five patients with recurrent glioblastoma on palliative care[12] for fifteen months (note that median survival time after the first round of radiation + chemo is only 15 months itself). But DCA has carcinogenic effects in animals, so research was halted. It also lost credibility due to alt-med practitioners selling DCA without FDA approval.

Lonidamine, another glycolysis inhibitor, is currently in clinical trials for brain cancer, but its past clinical results have been unspectacular.  I would have to look in more detail to understand whether there’s any mechanistic reason why 3-BP could be expected to do better than lonidamine in human trials; but for now, the failure of an earlier glycolysis inhibitor is admittedly an argument against 3-BP.

The case for 3-BP may be somewhat weakened but not wholly dismantled by alternate hypotheses about what causes the increased rate of glycolysis in cancer.

The “Reverse Warburg Effect” is a theory that argues that in some epithelial cancers (such as types of breast cancer), it is not the cancer cells but a type of neighboring healthy cells called fibroblasts that have elevated levels of glycolysis.  Glycolysis inhibitors still work on such cancers, however; DCA blocks cancer growth in vitro in fibroblast-induced breast cancer tumor growth.[13]

The Crabtree effect[14] is the phenomenon that some tumor cells have a reversible, short-term shift to glycolysis in the presence of glucose; in such cells, glycolysis inhibitors might not work because the cells could switch back to cellular respiration.  The overall effectiveness of glycolysis inhibitors on cancer will depend on whether the shift to glycolysis is mostly a facultative or an obligate phenomenon.  We can expect glycolysis inhibition to slow growth even if it doesn’t kill tumors, because glycolysis allows for faster cell growth than respiration; but if tumor cells can switch back and forth from glycolysis to respiration, then glycolysis inhibitors wouldn’t be likely to eradicate late-stage tumors, but only to stall early ones.

Overall, 3-BP looks like an unusually strong early-stage cancer drug. It appears likely to be broadly effective among many types of cancer. It is simple, has dramatic results (in animals), and is upstream.

Mechanistically, glycolysis inhibitors seem to be a strategy that is robust to different hypotheses about the cause of the high rate of glycolysis in cancer, and glycolysis inhibitors that are also generally cytotoxic would be even more robust to mechanistic uncertainty.

While there are many preclinical drugs that never succeed in humans, 3-BP is fairly novel in mechanism and seems likelier to pan out than the average new targeted therapy, precisely because it relies on a strategy of attacking cancer that is quite simple, should be broadly applicable across cancers and shouldn’t change over time as tumors evolve.  In order for a tumor to “work around” 3-BP, it would have to create an alternate enzyme to catalyze the 6th step of glycolysis, which seems unlikely; while in order for a tumor to work around an angiogenesis inhibitor, for instance, it would only have to find a different way to stimulate blood vessel growth, which, given the wide variety of growth factors in the human body, is not too unlikely.

References

[1]Ko, Young H., et al. “Advanced cancers: eradication in all cases using 3-bromopyruvate therapy to deplete ATP.” Biochemical and biophysical research communications 324.1 (2004): 269-275.

[2]Vali, Mustafa, et al. “Targeting of VX2 rabbit liver tumor by selective delivery of 3-bromopyruvate: a biodistribution and survival study.” Journal of Pharmacology and Experimental Therapeutics 327.1 (2008): 32-37.

[3]Geschwind, Jean-Francois H., et al. “Novel Therapy for Liver Cancer Direct Intraarterial Injection of a Potent Inhibitor of ATP Production.” Cancer research62.14 (2002): 3909-3913.

[4]Zhang, Xiaodong, et al. “Novel therapy for malignant pleural mesothelioma based on anti-energetic effect: an experimental study using 3-Bromopyruvate on nude mice.” Anticancer research 29.4 (2009): 1443-1448.

[5]Schaefer, Niklaus G., et al. “Systemic administration of 3-bromopyruvate in treating disseminated aggressive lymphoma.” Translational Research 159.1 (2012): 51-57.

[6]Chapiro, Julius, et al. “Systemic Delivery of Microencapsulated 3-Bromopyruvate for the Therapy of Pancreatic Cancer.” Clinical Cancer Research (2014): clincanres-1271.

[7]El Sayed, S. M., et al. “D-amino acid oxidase gene therapy sensitizes glioma cells to the antiglycolytic effect of 3-bromopyruvate.” Cancer gene therapy 19.1 (2011): 1-18.

[8]Ko, Y. H., et al. “A translational study “case report” on the small molecule “energy blocker” 3-bromopyruvate (3BP) as a potent anticancer agent: from bench side to bedside.” Journal of bioenergetics and biomembranes 44.1 (2012): 163-170.

[9]Shin, S. W., et al. “Hepatic intra-arterial injection of 3-bromopyruvate in rabbit VX2 tumor.” Acta Radiologica 47.10 (2006): 1036-1041.

[10]Chang, Jung Min, et al. “Local toxicity of hepatic arterial infusion of hexokinase II inhibitor, 3-bromopyruvate: in vivo investigation in normal rabbit model.”Academic radiology 14.1 (2007): 85-92.

[11]Zhang, Qi, et al. “Aerosolized 3-bromopyruvate inhibits lung tumorigenesis without causing liver toxicity.” Cancer Prevention Research 5.5 (2012): 717-725.

[12]Michelakis, E. D., et al. “Metabolic modulation of glioblastoma with dichloroacetate.” Science translational medicine 2.31 (2010): 31ra34-31ra34.

[13]Bonuccelli, Gloria, et al. “The reverse Warburg effect: glycolysis inhibitors prevent the tumor promoting effects of caveolin-1 deficient cancer associated fibroblasts.” Cell cycle 9.10 (2010): 1960-1971.

[14]Diaz-Ruiz, Rodrigo, Michel Rigoulet, and Anne Devin. “The Warburg and Crabtree effects: On the origin of cancer cell energy metabolism and of yeast glucose repression.” Biochimica et Biophysica Acta (BBA)-Bioenergetics 1807.6 (2011): 568-576.

Metformin, Fasting, and Exercise

Epistemic status: fairly confident.

TW: discussion of diet and exercise

In the post on anti-IgG, I talked about the strategy of routing around the complexity of cancer by finding a way to detect and kill a wide range of cancer cells at once. Another set of strategies for routing around complexity focuses on starving a wide range of cancer cells of the resources they need to grow.

Some strategies of this kind, like VEGF inhibitors to prevent angiogenesis, don’t work as well as one might hope: bevacizumab does not prolong survival more than a few months in any cancer.

However, one might hope that it’s possible to do better by looking at a very fundamental aspect of cancer cells: their extremely high energy requirements.  Obviously cells growing and proliferating rapidly need more energy than typical cells; this is especially true given the Warburg effect in which most cancer cells use glycolysis, which is less efficient than cellular respiration and requires more glucose.  Insulin insensitivity, in which tissues are slow to absorb glucose from the blood, leaves more resources available for cancer cells.  (This is a vastly oversimplified model and I do not fully understand how insulin relates to cancer growth.  There are multiple possible mechanisms whereby high insulin levels and insulin resistance contribute to cancer.)

Some simple methods that improve insulin sensitivity — exercise, fasting, and the type 2 diabetes drug metformin — seem to have anti-cancer effects.

Exercise

The effect of exercise on cancer is extremely confounded by other factors, so I won’t discuss it in much detail, except to say that observational studies have found that regular exercise has a preventative effect on some types of cancer.

At least 170 observational studies have been conducted on the association between exercise and cancer risk. The evidence is strongest for colon, breast, and prostate cancer.  Colon cancer had 43 of 51 studies demonstrate a reduced risk of cancer with physical activity, with a 40-50% reduction in risk; breast cancer had 32 of 44 studies demonstrate a reduced risk of cancer with physical activity, with a 30-40% reduction in risk; prostate cancer had 17 of 30 studies demonstrate a reduced risk of cancer with physical activity, with a 10-30% reduction in risk.[0]

In colon cancer, the effect of exercise was found in both recreational and occupational activity, and was observed even after controlling for BMI and dietary intake.

One possible mechanism for exercise’s preventative effects is that it reduces the level of sex hormones, which play a role in promoting the growth of breast and prostate cancer.  Exercise also improves insulin sensitivity and decreases the circulating levels of insulin-like growth factor (IGF); IGF and circulating insulin play a role in cancer growth.

Metformin

Metformin is a drug for Type 2 diabetes. It targets the enzyme AMPK, which induces muscles to take up glucose from the blood. This inhibits the production of glucose by the liver, which is why it reduces hyperglycemia in diabetics. It also increases insulin sensitivity and decreases insulin-induced suppression of fatty acid oxidation.

Insulin promotes cancer growth; insulin affects tumor cells either directly or indirectly through sex hormones, insulin-like growth factors, or adipokines.  Cancer cells have high energy requirements because of their rapid growth rate and their dependence on glycolysis.

Epidemiologic Evidence

A retrospective study of 11,876 diabetes patients in a Scottish hospital found that taking metformin slightly reduced the risk of cancer. The odds ratio was 0.86.[1]

A subsequent cohort study at the same hospital, of 4085 metformin users vs. 4085 matched diabetics who didn’t take metformin, found 7.3% of the metformin users vs. 11.6% of the comparators got cancer within 10 years, an odds ratio of 0.63 after adjusting for sex, age, BMI, SES, A1C, smoking, and drug use.  Also, 3.0% of metformin users died of cancer, compared to 6.1% of comparators.[2]

Complete response rates in breast cancer (defined as no sign of invasive carcinoma at the time of surgery, after a course of chemotherapy) were 24% for the metformin group, 8% for the non-metformin diabetic group, and 16% for the nondiabetic group.  This is statistically significant for metformin vs. non-metformin (p = 0.007) but not for metformin vs. nondiabetic.

A study of 1353 patients with diabetes found a 0.43 hazard ratio for cancer mortality among those taking metformin.[8]

A meta-study of 11 epidemiologic studies of metformin and cancer found a pooled relative risk for cancer incidence of 0.55.  The relative risk varied by year of use: 0.77 for 1 year, 0.6 for 2 years, and 0.28 for 5 years.  These studies compared diabetics using metformin to diabetics using other treatments, and included all types of cancers.[6]

A meta-study of 4 cohort studies and 2 RCTs found a pooled risk ratio of 0.66 for all-cancer mortality, 0.67 for all-cancer incidence.[9]

In a cohort of 480,984 Taiwanese participants, cancer incidence was twice as high for diabetics not on metformin as for nondiabetics; diabetics on metformin had similar cancer risk to nondiabetics.  Metformin users vs. diabetic metformin non-users had a hazard ratio of 0.47.[7]

Experimental Human Evidence

In 55 breast cancer patients randomized to metformin or no drug before surgery,  Ki67 levels (a measure of cellular proliferation) in the tumors dropped in all but 2 metformin patients but remained stable in control patients.[12]  However, a different randomized trial found no effect of preoperative metformin on Ki67 levels.[13]

A meta-analysis of randomized controlled studies where diabetics were given either metformin or a comparator found that cancer incidence was no lower in patients given metformin.[14]  This is a nontrivial concern, given that non-metformin drugs tend to be given to patients with more severe diabetes, meaning that observational studies comparing metformin-treated patients to other diabetics may be biased.

In-Vitro Evidence

Metformin inhibits growth of breast cancer cells and upregulates AMP-kinase activity in those sells; siRNA specific to AMP-kinase (blocking its expression) makes the anti-cancer effect of metformin stop.[3]

Metformin preferentially kills breast cancer stem cells, and prevents their transformation into tumor cells.[4]

Metformin inhibits p53-/-  colon cancer cell lines.[5]  These are usually the hardest type of cancer to treat; cancers with the p53 gene mutated are usually advanced and not responsive to most chemotherapies.

Animal Evidence

Metformin + doxorubicin kills all tumors in mice injected with breast cancer stem cells, while doxorubicin alone cause only a 2-fold decrease in tumor volume and metformin alone has little effect.  Mice remain in remission for 60 days after doxorubicin + metformin, vs. 20 days for doxorubicin alone.[4]

Hamsters fed a high fat diet and a carcinogen got pancreatic cancer 50% of the time; hamsters fed the high fat diet, metformin, and the carcinogen didn’t get cancer.[10]

Mice given ovarian cancer xenographs had about half the total tumor mass when treated with metformin; it also inhibits proliferation, metastasis, and angiogenesis. Metformin + cisplatin  resulted in significantly less proliferation, tumor area, mitotic counts, and vasculature than cisplatin or metformin alone.[11]

Metformin Conclusions

Metformin is definitely simple in its mechanism, and quite cheap. It is also probably upstream, though I don’t know yet how early in the process of cancer development its unusually high energy requirements arise.  It’s not especially decisive, given that it only has a moderate preventative effect and only inhibits growth, rather than killing cancer cells.

We don’t yet have direct evidence of how it works in humans as an adjuvant to chemotherapy or what its long-term effects on non-diabetics are; these seem like obvious experiments to run. Moreover, given its good side-effect profile, it’s plausible that healthy people could take metformin as a cancer preventative.

Fasting

Short-term fasts (<2 days) while taking chemotherapy may make the side effects milder and the effects stronger.  There is very little clinical evidence about this because doctors are understandably concerned about causing excessive weight loss in cancer patients; however, from the information I have available, it appears that short-term fasts followed by eating freely are safe.

In a case study[16] of 10 humans who had fasted voluntarily before and after chemotherapy, patients reported fewer side effects from courses of chemotherapy during which they fasted compared to courses of chemotherapy during which they ate.  They had faster recovery of blood cell and platelet counts during the chemotherapy regimens when they fasted. 

Cancer cells are quicker to die during a period of fasting than healthy cells are; this phenomenon is known as differential stress resistance and has been observed repeatedly in animal and in-vitro studies.

An in-vitro study[15] of tumor cells and RAS-mutated yeast cells found that 48-hour fasts sensitized the cells to chemotherapy; in mice, tumors were less than half the size in fasted mice than fed mice after 34 days and 5 fasting cycles, and mice were able to regain normal weight.

Mice injected with glioma were more responsive to chemotherapy and radiotherapy when they were subjected to 48-hour fasts; by day 28, 85% of the fasting + chemo mice were alive, compared to 40% of the fasting alone and chemo alone mice.  89% of the fasting + radiotherapy mice were alive by day 32, compared to 40% each of the fasting alone and radiotherapy alone mice.[17]

The differential stress resistance response appears to be associated with the effect of fasting on reducing IGF-1, a growth factor which promotes cancer.  Mice injected with metastatic melanoma and treated with doxyrubicin died 40% of the time from the doxorubicin and by 90 days all were dead from either the chemotherapy or metastases; the mice that lacked the ability to produce IGF-1 (mimicking the effect of fasting) survived 60% of the time.[18]

A cohort of Ecuadorian people with IGF deficiency (who are of unusually short stature, due to lack of growth hormone sensitivity) had unusually low rates of cancer; 20% of their unaffected relatives died of cancer but none of the IGF-deficient subjects did, a statistically significant difference.[19]

Conclusions

There’s some kind of rough emerging picture around insulin, glucose levels, and the metabolic syndrome, whereby insulin and growth factors and high blood glucose are associated with cancer growth, and insulin-sensitivity-promoting things like metformin, exercise, and short-term fasts have anti-cancer effects. There are multiple possible causal pathways that seem to point in the same direction. There’s a sort of “antifragile” heuristic here — cancer cells are especially vulnerable in multiple ways to metabolic stress, and messing with their energy supply might be a robust way to attack cancer even though there are surely many undiscovered biochemical pathways. These strategies seem to be more targeted at cancer prevention and growth inhibition than cancer eradication, except for metformin’s surprising effects on advanced cancer cell lines.  As usual, I think the conclusions to draw are “more research here”, not “cancer is cured”, though in this case there are obvious lifestyle choices that people can experiment with. And it seems clear that clinical trials of metformin and fasting during chemotherapy would be useful.

References

[0]Friedenreich, Christine M., and Marla R. Orenstein. “Physical activity and cancer prevention: etiologic evidence and biological mechanisms.” The Journal of nutrition 132.11 (2002): 3456S-3464S.

[1]Evans, Josie MM, et al. “Metformin and reduced risk of cancer in diabetic patients.” Bmj 330.7503 (2005): 1304-1305.

[2]Libby, Gillian, et al. “New Users of Metformin Are at Low Risk of Incident Cancer A cohort study among people with type 2 diabetes.” Diabetes care 32.9 (2009): 1620-1625.

[3]Zakikhani, Mahvash, et al. “Metformin is an AMP kinase–dependent growth inhibitor for breast cancer cells.” Cancer research 66.21 (2006): 10269-10273.

[4]Hirsch, Heather A., et al. “Metformin selectively targets cancer stem cells, and acts together with chemotherapy to block tumor growth and prolong remission.”Cancer research 69.19 (2009): 7507-7511.

[5]Buzzai, Monica, et al. “Systemic treatment with the antidiabetic drug metformin selectively impairs p53-deficient tumor cell growth.” Cancer research 67.14 (2007): 6745-6752.

[6]DeCensi, Andrea, et al. “Metformin and cancer risk in diabetic patients: a systematic review and meta-analysis.” Cancer prevention research 3.11 (2010): 1451-1461.

[7]Lee, Meei-Shyuan, et al. “Type 2 diabetes increases and metformin reduces total, colorectal, liver and pancreatic cancer incidences in Taiwanese: a representative population prospective cohort study of 800,000 individuals.”BMC cancer 11.1 (2011): 20.

[8]Landman, Gijs WD, et al. “Metformin associated with lower cancer mortality in type 2 diabetes ZODIAC-16.” Diabetes care 33.2 (2010): 322-326.

[9]Noto, Hiroshi, et al. “Cancer risk in diabetic patients treated with metformin: a systematic review and meta-analysis.” PloS one 7.3 (2012): e33411.

[10]Schneider, Matthias B., et al. “Prevention of pancreatic cancer induction in hamsters by metformin.” Gastroenterology 120.5 (2001): 1263-1270.

[11]Rattan, Ramandeep, et al. “Metformin suppresses ovarian cancer growth and metastasis with enhancement of cisplatin cytotoxicity in vivo.” Neoplasia 13.5 (2011): 483-IN28.

[12]Hadad, Sirwan, et al. “Evidence for biological effects of metformin in operable breast cancer: a pre-operative, window-of-opportunity, randomized trial.” Breast cancer research and treatment 128.3 (2011): 783-794.

[13]Bonanni, Bernardo, et al. “Dual effect of metformin on breast cancer proliferation in a randomized presurgical trial.” Journal of Clinical Oncology30.21 (2012): 2593-2600.

[14]Stevens, R. J., et al. “Cancer outcomes and all-cause mortality in adults allocated to metformin: systematic review and collaborative meta-analysis of randomised clinical trials.” Diabetologia 55.10 (2012): 2593-2603.

[15] Lee, Changhan, et al. “Fasting cycles retard growth of tumors and sensitize a range of cancer cell types to chemotherapy.” Science translational medicine4.124 (2012): 124ra27-124ra27.

[16]Safdie, Fernando M., et al. “Fasting and cancer treatment in humans: A case series report.” Aging (Albany NY) 1.12 (2009): 988.

[17]Safdie, Fernando, et al. “Fasting enhances the response of glioma to chemo-and radiotherapy.” (2012): e44603.

[18]Lee, Changhan, et al. “Reduced levels of IGF-I mediate differential protection of normal and cancer cells in response to fasting and improve chemotherapeutic index.” Cancer research 70.4 (2010): 1564-1572.

[19]Guevara-Aguirre, Jaime, et al. “Growth hormone receptor deficiency is associated with a major reduction in pro-aging signaling, cancer, and diabetes in humans.” Science translational medicine 3.70 (2011): 70ra13-70ra13.