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How to use human history as a nutritional prior?
Nutrition is a case where we have to try to make the best possible use of the data we have no matter how terrible, because we have to eat something now to sustain us while we plan and conduct more experiments.
I want to apply Bayes theorem to make rational health decisions from relatively weak data. I am generally wondering how one can synthesize historical human experiences with incomplete scientific data, in order to make risk-adverse and healthy decisions about human nutrition given limited research.
Example question/hypothesis: Does gluten cause health problems (ie exhibit chronic toxicity) in non-coeliac humans? Is there enough evidence to suggest that avoiding gluten might be a prudent risk-adverse decision for non-coeliacs?
We have some (mostly in vitro) scientific data suggesting that gluten may cause health problems in non-coeliac humans (such as these articles http://evolvify.com/the-case-against-gluten-medical-journal-references/). Let's say for the sake of arguing, that I can somehow convert these studies into a non-unity likelihood ratio for gluten toxicity in humans (although suggestions are welcome here too).
However, we also have prior information that a population of humans has been consuming gluten containing foods for at least 10,000 years, without any blatantly obvious toxic effects. Is there some way to convert this observation (and observations like this) into a prior probability distribution?
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Comments (49)
The Romans used cups made of lead, not knowing the effects of lead poisoning. It also took a while to notice the effects of cigarette smoking. If wheat gluten was Bad, but the effects weren't immediately obvious, it could easily go unnoticed for a long, long time.
(As far as I can tell, you can make at least a halfway decent case against basically any kind of food; it wouldn't be much of an exaggeration to say that science shows that eating, in general, is bad for you.)
All foods both exhibit toxicity, and provide nutrients. I think the goal of nutrition is to choose the right foods in the right amounts to balance nutritional needs with toxic effects.
Unfortunately, this is easier said than done. When you really look into it we don't know much about human nutrition, but some choices are still better than others with the information we do have. The problem is I am having trouble finding a rigorous way to weigh these different choices. I guess the real question isn't "is gluten toxic?" but "are gluten containing foods more or less toxic than other alternatives which meet the same nutritional requirements?"
That's a great point about the lead cups, and smoking. It certainly makes me wonder what other things are hurting our health right now, that we potentially have the clues to identify but haven't managed to connect the dots yet.
There are other examples besides lead cups. Most nutritional deficiencies have only recently been recognized; scurvy's solution was famously lost for a century, and while there were folk remedies even back in the Roman era for curing goiters with seaweed, none of them indicate any understanding of the subtler effects on intelligence.
Nutritional and economic requirements. I'd guess gluten-free foods are no cheaper than otherwise-equivalent regular foods, so unless you're willing to spend an arbitrarily large amount of money on food that's also relevant.
While tobacco was used by Europeans in some quantity since the early colonization of North America, it was the mass-production of cigarettes in the late 19th century that made heavy smoking a possibility for a large number of people. The first medical work showing a link between smoking and cancer came out in 1912 and 1929.
But smoking didn't get really huge in the U.S. until the mid-20th century, the era of mass media marketing of cigarettes (L.S./M.F.T.!); the British Doctors Study in 1956 was statistically pretty darned conclusive from a Bayesian standpoint, but was famously opposed by R. A. Fisher. Much of the later controversy on the subject of tobacco and cancer was manufactured by the tobacco industry, well after the matter was scientifically settled.
Sources:
http://en.wikipedia.org/wiki/Health_effects_of_tobacco_smoking
http://www.cdc.gov/tobacco/data_statistics/tables/economics/consumption/
http://en.wikipedia.org/wiki/British_Doctors_Study
Thanks for the update.
I've often wondered if a large-userbase data collecting website could help solve problems like this by looking for very weak statistical correlations among coinciding events over large datafields. I.e. see how often people self-report eating X, see how often people self-report feeling Y, see how often one precedes the other and when they happen independently. The function to users would be letting them track their own actions (e.g. diet, health, etc) according to preset (or high-karma member-submitted) input:data -sets. I should think with members in the thousands such a thing would become useful. Especially were the service entangled with some social app to get users and some very good statistics processing to get results. Does anything like this at all exist? (Any obvious ideas why it doesn't, barring there possibly being lack of incentive to use it, lack of an incentive for a company/person to program it?)
Yes, it exists: http://genomera.com
They're actively running experiments and collecting data but are in "beta testing" and are very exclusive on whom they allow to join. I'm disappointed they didn't choose me when I filled out their request for a beta invite.
A huge problem with collecting data like this in the US population, is that everyone has a similar diet. There's so few people totally excluding gluten, you can't expect to measure it's effects with epidemiological diet surveys: you need to actually do a controlled trial where you tell people to avoid it.
In China where only about half of people eat foods with gluten the biggest epidemiological study ever performed (the China Study) did find that wheat intake was independently correlated with overall mortality (http://rawfoodsos.com/the-china-study/). They never published this finding themselves, but the correlation is clearly there in the data.
There's a lot of question about their methodology- they didn't keep or report data on individuals, but lumped whole communities together as single data points. There's likely a lot of highly correlated regional habits that weren't on the questionnaire, and I tend to find the whole study pretty questionable. For the most part, it's just comparing the health of rural farmers with wealthier urban Chinese- the two groups have radically different health, lifestyle, and diets and we can only control for the few questions they actually asked.
Perhaps now that gluten avoidance seems to be becoming a "fad diet" in western countries, suddenly it will be possible to actually collect good data on this.
That looks like it could prove really useful / interesting; thanks for linking.
I guess the entry requirements for beta are strict because they're trying to keep to a small set of variables for the people to check? It would have been really interesting to spy in on though. Regarding the China study, it sounds either like there was no effort to control for other obvious/statistically-true correlates or that there is no possible overlap at all to abstract a controlled comparison from. A fraction of that data might be useful (all data is useful! ...yum!). I think with sufficient (though perhaps improbably large) sample size even user-submitted data with large amounts of noise becomes useful. Any empirical paradigm more open and faster than the current is bound to be a good thing, even despite inaccuracy, for reasons of sheer brute force.
At least with user submitted noisy data you have individual data points, and potential to track individuals over time... unlike the China Study where entire communities were just averaged into a single point.
There's some usable information in the China Study, but not as much as people think... it's being touted as "proof" that all animal-based foods cause cancer (in a popular diet book by the primary investigator Dr. Campbell) because the two were well correlated in the data, when it's nothing of the sort.
The thing about toxicity though - not all poisons have any effect whatsoever in low doses. Some do have effects that don't go to zero at low doses - radiation for example - because it does not dilute below 1 particle track through nucleus, which is harmful. The carcinogens in general, as they don't dilute below causing 1 mutation to dna in 1 cell, which has probability of harm. But organic compounds which are not carcinogenic - not as in natural but as in organic chemistry - and especially those from plants, they may well be entirely harmless at the doses below those that actually result in toxicity. Of course there may be situations where they are 'the last straw', but in general not everything is harmful all the way down.
It's not quite that the toxicity effects don't go to zero at low doses for things like radiation — the effects of low radiation exposure may indeed be zero. The toxicity may be zero at even relatively high doses. Rather, the value of radiation's dosage curve at the low end is stochastic, where for chemical toxins the value of the dosage curve on the entire domain is the severity of toxicity.
Radiation actually appears to exhibit hormesis- small doses of ionizing radiation likely activate some protective response, which decreases rather than increases cancer risk.
I think allergens, and compounds which initiate autoimmune disease (gluten in coeliacs) are a good example of something which remains toxic at low doses. There hasn't been a lot of research into it, but even extremely low doses of gluten seem to be harmful to coeliacs.
Well, that's other way to see it. The effects of carcinogens at low doses are increase in cancer probability, and is almost definitely linear (as it is added to existing mutations, and you are effectively dealing with a small piece of a mutation vs cancer risk curve, at a non-special point).
You have to be careful what you mean by carcinogen. I suspect what you're saying is true for some chemical carcinogens and ionizing radiation in single burst exposures, but not for other time varying patterns of ionizing radiation exposure.
It hasn't been confirmed in humans, but ionizing radiation exposure most certainly doesn't have a linear dose response with in vitro cell lines. Well, it does for single doses- but initial doses to be protective against future doses (hormesis): http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1533272/?tool=pmcentrez
1: that happens for quite significant doses only (1 centigray = 0.01 gray = 10 millisievert for gamma and x-rays = 1000 hours of exposure to 10 microsievert/hour background)
2: on single cell level there is no small doses, there's small probabilities of 1 particle track through nucleus (edit: or 1 Compton scattering electron track for gamma rays). Non-linear response to probability would indeed be quite extraordinary.
Ionization of DNA molecules is going to be linear in response to radiation, but you can't assume that will result in a linear cancer risk. Cells can up and down regulate their DNA repair mechanisms under varying conditions, allowing for a much more complicated relationship between radiation and cancer risk.
In this case, cells appear to up-regulate poly (ADP-ribose) polymerase (PARP) in response to past radiation exposure, increasing the chance of an accurate repair after an ionization event damages one strand.
//edit: The nucleus is also not the only potential site of cancer inducing mutations, the mitochondrial genome is another possibility.
Before cell can regulate it's DNA repair due to radiation, it has to detect the radiation, which at low levels in question (say, up to ~10x natural background, that's ~ 1..5 microsievert/hour, 2 000 .. 10 000 times less than centigray of gamma per hour) means detecting the probability before anything happens.
The issue with radiation is that people don't understand the units. You read the study you linked, you see, 1 centigray of x-rays, that's a 'low dose' they say, in god knows what context (Radiation therapy? Sure it's a low dose there). That's a 10 milliSieverts, okay? The average background dose a human receives per year, is , or 1/4 of that. Nobody's been proposing that 4 years worth of normal dose in a hour are going to still be linear.
edit: or actually, we do. We interpolate the low dose effects from the doses of somewhere around 0.1 Sv and up, based on various real world human data. Meaning that, if the effect outlined in your link is real, and there are some defence mechanisms activating at 0.01 Sv which prevent some of the DNA damage (at some other expense) - then we are underestimating the carcinogenicity of radiation at the low (near background) level, at which those defence mechanisms are not active. That is kind of scary to think about, in terms of potential extra cancer deaths.
Okay, I was misunderstanding what you were saying, and it makes sense now.
To paraphrase: Cancer risk in response to radiation levels can only be non-linear when the cell sees past radiation damage signaling it to mount a response. At low doses a given cell is unlikely to see any DNA ionization events, and therefore the risk must be linear.
That's a great point about the potential problem with extrapolating low doses from high dose data. That should really be investigated more carefully... if true "minor" radiation exposures could be a lot more risky than existing estimates suggest.
Yes. Instead what is happening, the nuclear lobby is citing studies like the one you linked as evidence of "radiation hormesis" i.e. generally beneficial effects of "low doses" of radiation (in your study the cells and the controls have to be irradiated with high dose afterwards to show any benefit; i'm very dubious that there exist any benefit versus the background), and lobbies for removal of strict EPA limits.
It gets even worse than this; the definition of Sievert and the procedures for calculating exposures of people rely on linear model - if the effects are linear then the average dose is all you need - and what the lobby wants is to use linear-model justified average doses together with threshold model, which just doesn't make sense. There literally can not be any consistent non-linear response to doses in Sieverts because the concept of dose and concept of Sievert presume linearity when it does not matter how the dose is distributed in space and time (well, up to organ scaling factors).
I've certainly seen several politicians argue that "radiation is actually good for you," but I've yet to hear any actual radiation health physicists argue that point...
The folk wisdom of "eat a bit of everything" seems to go well with humans being omnivores and with not overdosing on any particular harmful ingredient. That was before pollution and allergies were rampant, of course.
That only makes sense in cases where you have no additional data suggesting that some foods are healthier than others.
Also, if a few foods exist which exhibit chronic toxicity at low doses, but you don't know which foods those are, wouldn't it be safer to limit the total number of different foods you consume, as to limit the chance of consuming a particularly bad one by chance? While atypical, there could be cases where the toxicity curve is relatively flat, and lower doses don't really protect you. For example, an endocrine toxin that simulates a hormonal signal even at a low dose.
This would seem to require that most of the 'wisdom' being embedded in the 'everything' in question.
Good point, foods that have been historically clearly identified as toxic would have already moved into the non-food category. So this advice is effectively saying, "look only at your priors and don't consider or collect new data."
If our ancestors took this advice literally they'd have kept eating things that can obviously kill you, like digitalis.
In reality, I think the spirit of the advice is a warning not to under-estimate the importance of your priors: don't consider new data in isolation.
You have managed to extract a rather useful generalizable point from the subject!
As well as this we can assume that the advice also covers the idea of not putting all your eggs (be they epistemic or dietary) in one basket.