As I've posted previously, I have been recovering from multiple bypass heart surgery. I had some angina, vague superficial chest pain that is a symptom of impending heart attack (or, more properly) coronary artery blockage. Fortunately, I am educated enough to recognize that this chest pain wasn't from my doing a new kind of exercise, I went to the doc and--to make a long story short--was sent right off to the hospital for heart bypass surgery (replacing clogged coronary arteries).
The radiography showed that at least some of my heart arteries were clogged--with whatever radio-opaque goop, presumably including cholesterol, and by whatever clogging mechanism. These causal facts are, as I understand things, complex and not completely understood, but the upshot was clear: surgery.....or else!
Now, the doctors would say that, given this evidence, I was at high risk of potentially lethal heart disease. I'm sure had the opportunity been there (and it may be in some future doctor's appointment), I will be chided--or scolded--for my bad diet, too much cholesterol, etc. It will be assumed that my voluntary lifestyle choices caused my blockage and my need for preventive artery replacement. Bad boy! Bad diet! Tsk, tsk, tsk....
But is that right, or might it be the opposite of a more serious truth?
What is bad behavior, health-wise?
I am 77. This is beyond the usual 76-ish life expectancy for US males (searching the ad-laden web to find data has become mainly a challenge to wade through the relentless commercialism). So, my lifestyle cannot be viewed as bad behavior in this respect. Indeed, I have already lived longer than half my birth cohort! So perhaps my diet and whatever else can, or should be viewed as having been protective. After all, I was symptom-free until after my expected lifespan.
It is very difficult to understand what 'risk' means in such regards. If my lifestyle led to my artery becoming clogged up, but it didn't happen until after I'd out-lived my average peer, can I legitimately think of that lifestyle as having been protective rather than risky? We all have to get some final disorder and some point, so is the absolute cause the relevant fact, or is it the relative? How can we decide such questions, if indeed they are meaningful ones that can even have meaningful answers?
If my behavior (for whatever reason, including just plain luck) led to my surviving in very good health, except for one weakest-link, then does that link suggest I've behaved badly, or does my overall great state of health suggest the opposite? More to the point, how can such questions even be answered in a meaningful sense? They seem meaningful....until you think a bit more carefully about them. . . . .
The philosophical quick-sand doesn't stop there. If my arterial clog would have led to a relatively quick death--not a 'premature' death at my age!--but saved me from some worse, more prolonged or debilitating fate, can we seriously view that as preventive or protective, with me now facing those dreadful fates?
When we have competing causes and inevitable mortality, we have to view the causes, and what causes them, in a rather different light. That doesn't mean there are consensus, much less easy, answers. But it may mean that rules for 'healthy' behavior are not so obvious as they seem to be.
Showing posts with label risk. Show all posts
Showing posts with label risk. Show all posts
Friday, September 6, 2019
Thursday, October 4, 2018
Processed meat? Really? How to process epidemiological news
So this week's Big Story in health is that processed meat is a risk for breast cancer. A study has been published that finds it so.....so it must be true, right? After all, it's on CNN and in some research report. Well, read even CNN's headliner story and you'll see the caveats, the admissions, softened of course, that the excess risk isn't that great, but, at least, that the past studies have been 'inconsistent'.
Of course, with this sort of 'research' the weak associations with some named risk factors can easily be correlated with who knows how many other behavioral or other factors, and even if researchers tried to winnow them out, it is obvious that it's a guessing game. Too many aspects of our lives are unreported, unknown, or correlated. This is why week after week, it seems, do-this or don't-do-that stories hit the headlines. If you believe them, well, I guess you should stop eating bacon.....until next week when some story will say that bacon prevents some disease or other.
Why breast cancer, by the way? Why not intestinal or many other cancers? Why, if even the current story refers to past results as being 'inconsistent' do we assume this one's right and they, or some of them, were wrong? Could it be that this is because investigators want attention, journalists need news stories, and so on?
Why, by the way, is it always things that are actually pleasurable to eat that end up in these stories? Why is it never cauliflower, or rhubarb, or squash? Why coffee and not hibiscus tea? Could western notions of sin have anything to do with the design of the studies themselves?
But what about, say, protective effects?
Of course, the headlines are always about the nasty diseases to which anything fun, like a juicy bacon sandwich, not to mention alcohol, coffee, cookies, and so on seems to condemn us. This makes for 'news', even if the past studies have been 'inconsistent' and therefore (it seems) we can believe this new one.
However, maybe eating bacon sandwiches has beneficial effects that don't make the headlines. Maybe they protect us from hives, antisocial or even criminal behavior, raise our IQ, or get fewer toothaches. Who could look for all those things, when they're busy trying to find bad things that bacon sandwiches cause? Have investigators of this sort of behavioral exposure asked whether bacon and, say, beer raise job performance, add to longevity, or (heavens!) improve one's sex life? Are these studies, essentially, about bad outcomes from things we enjoy? Is that, in fact, a subtle, indirect effect of the Protestant ethic or something like that? Of the urge to find bad things in these studies because they're paid for by NIH and done by people in medical schools?
The serious question
There are the pragmatic, self-interested aspects to these stories, and indeed even to the publication of the papers in proper journals. If they disagree with previous work on the purportedly same subject, they get new headlines, when they should perhaps not be published without explicitly addressing the disagreement in real detail, as the main point of the work--rather than the subtle implication that now, finally, these new authors have got it right. Or at least, they should not headline their findings. Or something!
Instead, news sells, and thus we build a legacy of yes/yes/no/maybe/no/yes! studies. These may generally be ignored by our baconophilic society, or they could make lots of people switch to spinach sandwiches, or many other kinds of effects. This latter is somewhat akin to the quantum mechanical notion that measurement gives only incomplete information but affects what's being measured.
Epidemiological studies of this sort have been funded, at large expense, for decades now, and if there is anything consistent about them, it's that they are not consistent. There must be a reason! Is it really that the previous studies weren't well done? Is it that if you fish for enough items, you'll catch something--big questionnaire studies looking at too many things? Is it changing behaviors in ways not being identified by the studies?
Or, perchance, is it that these investigators need projects to get funded? This sort of yo-yo result is very, very common. There must be some explanation, and that inconsistency itself is likely as fundamental and important as any given study's findings. Maybe bacon-burgers only are bad for you in some cultural environments, and these change in unmeasured ways, and that varying results are not 'inconsistent' at all--maybe it's the expectation that there's one relevant truth, so that inconsistency suggests problems in study design. Maybe the problem is in simplistic thinking about risks.
Where do cynical possibilities meet serious epistemological ones, and how do we tell?
 |
| Yummy poison!! source: from the web, at Static.zoonar.com |
Why breast cancer, by the way? Why not intestinal or many other cancers? Why, if even the current story refers to past results as being 'inconsistent' do we assume this one's right and they, or some of them, were wrong? Could it be that this is because investigators want attention, journalists need news stories, and so on?
Why, by the way, is it always things that are actually pleasurable to eat that end up in these stories? Why is it never cauliflower, or rhubarb, or squash? Why coffee and not hibiscus tea? Could western notions of sin have anything to do with the design of the studies themselves?
But what about, say, protective effects?
Of course, the headlines are always about the nasty diseases to which anything fun, like a juicy bacon sandwich, not to mention alcohol, coffee, cookies, and so on seems to condemn us. This makes for 'news', even if the past studies have been 'inconsistent' and therefore (it seems) we can believe this new one.
However, maybe eating bacon sandwiches has beneficial effects that don't make the headlines. Maybe they protect us from hives, antisocial or even criminal behavior, raise our IQ, or get fewer toothaches. Who could look for all those things, when they're busy trying to find bad things that bacon sandwiches cause? Have investigators of this sort of behavioral exposure asked whether bacon and, say, beer raise job performance, add to longevity, or (heavens!) improve one's sex life? Are these studies, essentially, about bad outcomes from things we enjoy? Is that, in fact, a subtle, indirect effect of the Protestant ethic or something like that? Of the urge to find bad things in these studies because they're paid for by NIH and done by people in medical schools?
The serious question
There are the pragmatic, self-interested aspects to these stories, and indeed even to the publication of the papers in proper journals. If they disagree with previous work on the purportedly same subject, they get new headlines, when they should perhaps not be published without explicitly addressing the disagreement in real detail, as the main point of the work--rather than the subtle implication that now, finally, these new authors have got it right. Or at least, they should not headline their findings. Or something!
Instead, news sells, and thus we build a legacy of yes/yes/no/maybe/no/yes! studies. These may generally be ignored by our baconophilic society, or they could make lots of people switch to spinach sandwiches, or many other kinds of effects. This latter is somewhat akin to the quantum mechanical notion that measurement gives only incomplete information but affects what's being measured.
Epidemiological studies of this sort have been funded, at large expense, for decades now, and if there is anything consistent about them, it's that they are not consistent. There must be a reason! Is it really that the previous studies weren't well done? Is it that if you fish for enough items, you'll catch something--big questionnaire studies looking at too many things? Is it changing behaviors in ways not being identified by the studies?
Or, perchance, is it that these investigators need projects to get funded? This sort of yo-yo result is very, very common. There must be some explanation, and that inconsistency itself is likely as fundamental and important as any given study's findings. Maybe bacon-burgers only are bad for you in some cultural environments, and these change in unmeasured ways, and that varying results are not 'inconsistent' at all--maybe it's the expectation that there's one relevant truth, so that inconsistency suggests problems in study design. Maybe the problem is in simplistic thinking about risks.
Where do cynical possibilities meet serious epistemological ones, and how do we tell?
Wednesday, March 29, 2017
The (bad) luck of the draw; more evidence
A while back, Vogelstein and Tomasetti (V-T) published a paper in Science in which it was argued that most cancers cannot be attributed to known environmental factors, but instead were due simply to the errors in DNA replication that occur throughout life when cells divide. See our earlier 2-part series on this.
Essentially the argument is that knowledge of the approximate number of at-risk cell divisions per unit of age could account for the age-related pattern of increase in cancers of different organs, if one ignored some obviously environmental causes like smoking. Cigarette smoke is a mutagen and if cancer is a mutagenic disease, as it certainly largely is, then that will account for the dose-related pattern of lung and oral cancers.
This got enraged responses from environmental epidemiologists whose careers are vested in the idea that if people would avoid carcinogens they'd reduce their cancer risk. Of course, this is partly just the environmental epidemiologists' natural reaction to their ox being gored--threats to their grant largesse and so on. But it is also true that environmental factors of various kinds, in addition to smoking, have been associated with cancer; some dietary components, viruses, sunlight, even diagnostic x-rays if done early and often enough, and other factors.
Most associated risks from agents like these are small, compared to smoking, but not zero and an at least legitimate objection to V-T's paper might be that the suggestion that environmental pollution, dietary excess, and so on don't matter when it comes to cancer is wrong. I think V-T are saying no such thing. Clearly some environmental exposures are mutagens and it would be a really hard-core reactionary to deny that mutations are unrelated to cancer. Other external or lifestyle agents are mitogens; they stimulate cell division, and it would be silly not to think they could have a role in cancer. If and when they do, it is not by causing mutations per se. Instead mitogenic exposures in themselves just stimulate cell division, which is dangerous if the cell is already transformed into a cancer cell. But it is also a way to increase cancer by just what V-T stress: the natural occurrence of mutations when cells divide.
There are a few who argue that cancer is due to transposable elements moving around and/or inserting into the genome where they can cause cells to misbehave, or other perhaps unknown factors such as of tissue organization, which can lead cells to 'misbehave', rather than mutations.
These alternatives are, currently, a rather minor cause of cancer. In response to their critics, V-T have just published a new multi-national analysis that they suggest supports their theory. They attempted to correct for the number of at-risk cells and so on, and found a convincing pattern that supports the intrinsic-mutation viewpoint. They did this to rebut their critics.
This is at least in part an unnecessary food-fight. When cells divide, DNA replication errors occur. This seems well-documented (indeed, Vogelstein did some work years ago that showed evidence for somatic mutation--that is, DNA changes that are not inherited--and genomes of cancer cells compared to normal cells of the same individual. Indeed, for decades this has been known in various levels of detail. Of course, showing that this is causal rather than coincidental is a separate problem, because the fact of mutations occurring during cell division doesn't necessarily mean that the mutations are causal. However, for several cancers the repeated involvement of specific genes, and the demonstration of mutations in the same gene or genes in many different individuals, or of the same effect in experimental mice and so on, is persuasive evidence that mutational change is important in cancer.
The specifics of that importance are in a sense somewhat separate from the assertion that environmental epidemiologists are complaining about. Unfortunately, to a great extent this is a silly debate. In essence, besides professional pride and careerism, the debate should not be about whether mutations are involved in cancer causation but whether specific environmental sources of mutation are identifiable and individually strong enough, as x-rays and tobacco smoke are, to be identified and avoided. Smoking targets particular cells in the oral cavity and lungs. But exposures that are more generic, but individually rare or not associated with a specific item like smoking, and can't be avoided, might raise the rate of somatic mutation generally. Just having a body temperature may be one such factor, for example.
I would say that we are inevitably exposed to chemicals and so on that will potentially damage cells, mutation being one such effect. V-T are substantially correct, from what the data look like, in saying that (in our words) namable, specific, and avoidable environmental mutations are not the major systematic, organ-targeting cause of cancer. Vague and/or generic exposure to mutagens will lead to mutations more or less randomly among our cells (maybe, depending on the agent, differently depending on how deep in our bodies the cells are relative to the outside world or other means of exposure). The more at-risk cells, the longer they're at risk, and so on, the greater the chance that some cell will experience a transforming set of changes.
Most of us probably inherit mutations in some of these genes from conception, and have to await other events to occur (whether these are mutational or of another nature as mentioned above). The age patterns of cancers seem very convincingly to show that. The real key factor here is the degree to which specific, identifiable, avoidable mutational agents can be identified. It seems silly or, perhaps as likely, mere professional jealousy, to resist that idea.
These statements apply even if cancers are not all, or not entirely, due to mutational effects. And, remember, not all of the mutations required to transform a cell need be of somatic origin. Since cancer is mostly, and obviously, a multi-factor disease genetically (not a single mutation as a rule), we should not have our hackles raised if we find what seems obvious, that mutations are part of cell division, part of life.
There are curious things about cancer, such as our large body size but delayed onset ages relative to the occurrence of cancer in smaller, and younger animals like mice. And different animals of different lifespans and body sizes, even different rodents, have different lifetime cancer risks (some may be the result of details of their inbreeding history or of inbreeding itself). Mouse cancer rates increase with age and hence the number of at-risk cell divisions, but the overall risk at very young ages despite many fewer cell divisions (yet similar genome sizes) shows that even the spontaneous mutation idea of V-T has problems. After all, elephants are huge and live very long lives; why don't they get cancer much earlier?
Overall, if if correct, V-T's view should not give too much comfort to our 'Precision' genomic medicine sloganeers, another aspect of budget protection, because the bad luck mutations are generally somatic, not germline, and hence not susceptible to Big Data epidemiology, genetic or otherwise, that depends on germ-line variation as the predictor.
Related to this are the numerous reports of changes in life expectancy among various segments of society and how they are changing based on behaviors, most recently, for example, the opiod epidemic among whites in depressed areas of the US. Such environmental changes are not predictable specifically, not even in principle, and can't be built into genome-based Big Data, or the budget-promoting promises coming out of NIH about such 'precision'. Even estimated lifetime cancer risks associated with mutations in clear-cut risk-affecting genes like BRCA1 mutations and breast cancer, vary greatly from population to population and study to study. The V-T debate, and their obviously valid point, regardless of the details, is only part of the lifetime cancer risk story.
ADDENDUM 1
Just after posting this, I learned of a new story on this 'controversy' in The Atlantic. It is really a silly debate, as noted in my original version. It tacitly makes many different assumptions about whether this or that tinkering with our lifestyles will add to or reduce the risk of cancer and hence support the anti-V-T lobby. If we're going to get into the nitty-gritty and typically very minor details about, for example, whether the statistical colon-cancer-protective effect of aspirin shows that V-T were wrong, then this really does smell of academic territory defense.
Why do I say that? Because if we go down that road, we'll have to say that statins are cancer-causing, and so is exercise, and kidney transplants and who knows what else. They cause cancer by allowing people to live longer, and accumulate more mutational damage to their cells. And the supposedly serious opioid epidemic among Trump supporters actually is protective, because those people are dying earlier and not getting cancer!
The main point is that mutations are clearly involved in carcinogenesis, cell division life-history is clearly involved in carcinogenesis, environmental mutagens are clearly involved in carcinogenesis, and inherited mutations are clearly contributory to the additional effects of life-history events. The silly extremism to which the objectors to V-T would take us would be to say that, obviously, if we avoided any interaction whatsoever with our environment, we'd never get cancer. Of course, we'd all be so demented and immobilized with diverse organ-system failures that we wouldn't realize our good fortune in not getting cancer.
The story and much of the discussion on all sides is also rather naive even about the nature of cancer (and how many or of which mutations etc it takes to get cancer); but that's for another post sometime.
ADDENDUM 2
I'll add another new bit to my post, that I hadn't thought of when I wrote the original. We have many ways to estimate mutation rates, in nature and in the laboratory. They include parent-offspring comparison in genomewide sequencing samples, and there have been sperm-to-sperm comparisons. I'm sure there are many other sets of data (see Michael Lynch in Trends in Genetics 2010 Aug; 26(8): 345–352. These give a consistent picture and one can say, if one wants to, that the inherent mutation rate is due to identifiable environmental factors, but given the breadth of the data that's not much different than saying that mutations are 'in the air'. There are even sex-specific differences.
The numerous mutation detection and repair mechanisms, built into genomes, adds to the idea that mutations are part of life, for example that they are not related to modern human lifestyles. Of course, evolution depends on mutation, so it cannot and never has been reduced to zero--a species that couldn't change doesn't last. Mutations occur in plants and animals and prokaryotes, in all environments and I believe, generally at rather similar species-specific rates.
If you want to argue that every mutation has an external (environmental) cause rather than an internal molecular one, that is merely saying there's no randomness in life or imperfection in molecular processes. That is as much a philosophical as an empirical assertion (as perhaps any quantum physicist can tell you!). The key, as asserted in the post here, is that for the environmentalists' claim to make sense, to be a mutational cause in the meaningful sense, the force or factor must be systematic and identifiable and tissue-specific, and it must be shown how it gets to the internal tissue in question and not to other tissues on the way in, etc.
Given how difficult it has been to chase down most environmental carcinogenic factors, to which exposure is more than very rare, and that the search has been going on for a very long time, and only a few have been found that are, in themselves, clearly causal (ultraviolet radiation, Human Papilloma Virus, ionizing radiation, the ones mentioned in the post), whatever is left over must be very weak, non tissue-specific, rare, and the like. Even radiation-induced lung cancer in uranium minors has been challenging to prove (for example, because miners also largely were smokers).
It is not much of a stretch to simply say that even if, in principle, all mutations in our body's lifetime were due to external exposures, and the relevant mutagens could be identified and shown in some convincing way to be specifically carcinogenic in specific tissues, in practice if not ultra-reality, then the aggregate exposures to such mutations are unavoidable and epistemically random with respect to tissue and gene. That I would say is the essence of the V-T finding.
Quibbling about that aspect of carcinogenesis is for those who have already determined how many angels dance on the head of a pin.
Essentially the argument is that knowledge of the approximate number of at-risk cell divisions per unit of age could account for the age-related pattern of increase in cancers of different organs, if one ignored some obviously environmental causes like smoking. Cigarette smoke is a mutagen and if cancer is a mutagenic disease, as it certainly largely is, then that will account for the dose-related pattern of lung and oral cancers.
This got enraged responses from environmental epidemiologists whose careers are vested in the idea that if people would avoid carcinogens they'd reduce their cancer risk. Of course, this is partly just the environmental epidemiologists' natural reaction to their ox being gored--threats to their grant largesse and so on. But it is also true that environmental factors of various kinds, in addition to smoking, have been associated with cancer; some dietary components, viruses, sunlight, even diagnostic x-rays if done early and often enough, and other factors.
Most associated risks from agents like these are small, compared to smoking, but not zero and an at least legitimate objection to V-T's paper might be that the suggestion that environmental pollution, dietary excess, and so on don't matter when it comes to cancer is wrong. I think V-T are saying no such thing. Clearly some environmental exposures are mutagens and it would be a really hard-core reactionary to deny that mutations are unrelated to cancer. Other external or lifestyle agents are mitogens; they stimulate cell division, and it would be silly not to think they could have a role in cancer. If and when they do, it is not by causing mutations per se. Instead mitogenic exposures in themselves just stimulate cell division, which is dangerous if the cell is already transformed into a cancer cell. But it is also a way to increase cancer by just what V-T stress: the natural occurrence of mutations when cells divide.
There are a few who argue that cancer is due to transposable elements moving around and/or inserting into the genome where they can cause cells to misbehave, or other perhaps unknown factors such as of tissue organization, which can lead cells to 'misbehave', rather than mutations.
These alternatives are, currently, a rather minor cause of cancer. In response to their critics, V-T have just published a new multi-national analysis that they suggest supports their theory. They attempted to correct for the number of at-risk cells and so on, and found a convincing pattern that supports the intrinsic-mutation viewpoint. They did this to rebut their critics.
This is at least in part an unnecessary food-fight. When cells divide, DNA replication errors occur. This seems well-documented (indeed, Vogelstein did some work years ago that showed evidence for somatic mutation--that is, DNA changes that are not inherited--and genomes of cancer cells compared to normal cells of the same individual. Indeed, for decades this has been known in various levels of detail. Of course, showing that this is causal rather than coincidental is a separate problem, because the fact of mutations occurring during cell division doesn't necessarily mean that the mutations are causal. However, for several cancers the repeated involvement of specific genes, and the demonstration of mutations in the same gene or genes in many different individuals, or of the same effect in experimental mice and so on, is persuasive evidence that mutational change is important in cancer.
The specifics of that importance are in a sense somewhat separate from the assertion that environmental epidemiologists are complaining about. Unfortunately, to a great extent this is a silly debate. In essence, besides professional pride and careerism, the debate should not be about whether mutations are involved in cancer causation but whether specific environmental sources of mutation are identifiable and individually strong enough, as x-rays and tobacco smoke are, to be identified and avoided. Smoking targets particular cells in the oral cavity and lungs. But exposures that are more generic, but individually rare or not associated with a specific item like smoking, and can't be avoided, might raise the rate of somatic mutation generally. Just having a body temperature may be one such factor, for example.
I would say that we are inevitably exposed to chemicals and so on that will potentially damage cells, mutation being one such effect. V-T are substantially correct, from what the data look like, in saying that (in our words) namable, specific, and avoidable environmental mutations are not the major systematic, organ-targeting cause of cancer. Vague and/or generic exposure to mutagens will lead to mutations more or less randomly among our cells (maybe, depending on the agent, differently depending on how deep in our bodies the cells are relative to the outside world or other means of exposure). The more at-risk cells, the longer they're at risk, and so on, the greater the chance that some cell will experience a transforming set of changes.
Most of us probably inherit mutations in some of these genes from conception, and have to await other events to occur (whether these are mutational or of another nature as mentioned above). The age patterns of cancers seem very convincingly to show that. The real key factor here is the degree to which specific, identifiable, avoidable mutational agents can be identified. It seems silly or, perhaps as likely, mere professional jealousy, to resist that idea.
These statements apply even if cancers are not all, or not entirely, due to mutational effects. And, remember, not all of the mutations required to transform a cell need be of somatic origin. Since cancer is mostly, and obviously, a multi-factor disease genetically (not a single mutation as a rule), we should not have our hackles raised if we find what seems obvious, that mutations are part of cell division, part of life.
There are curious things about cancer, such as our large body size but delayed onset ages relative to the occurrence of cancer in smaller, and younger animals like mice. And different animals of different lifespans and body sizes, even different rodents, have different lifetime cancer risks (some may be the result of details of their inbreeding history or of inbreeding itself). Mouse cancer rates increase with age and hence the number of at-risk cell divisions, but the overall risk at very young ages despite many fewer cell divisions (yet similar genome sizes) shows that even the spontaneous mutation idea of V-T has problems. After all, elephants are huge and live very long lives; why don't they get cancer much earlier?
Overall, if if correct, V-T's view should not give too much comfort to our 'Precision' genomic medicine sloganeers, another aspect of budget protection, because the bad luck mutations are generally somatic, not germline, and hence not susceptible to Big Data epidemiology, genetic or otherwise, that depends on germ-line variation as the predictor.
Related to this are the numerous reports of changes in life expectancy among various segments of society and how they are changing based on behaviors, most recently, for example, the opiod epidemic among whites in depressed areas of the US. Such environmental changes are not predictable specifically, not even in principle, and can't be built into genome-based Big Data, or the budget-promoting promises coming out of NIH about such 'precision'. Even estimated lifetime cancer risks associated with mutations in clear-cut risk-affecting genes like BRCA1 mutations and breast cancer, vary greatly from population to population and study to study. The V-T debate, and their obviously valid point, regardless of the details, is only part of the lifetime cancer risk story.
ADDENDUM 1
Just after posting this, I learned of a new story on this 'controversy' in The Atlantic. It is really a silly debate, as noted in my original version. It tacitly makes many different assumptions about whether this or that tinkering with our lifestyles will add to or reduce the risk of cancer and hence support the anti-V-T lobby. If we're going to get into the nitty-gritty and typically very minor details about, for example, whether the statistical colon-cancer-protective effect of aspirin shows that V-T were wrong, then this really does smell of academic territory defense.
Why do I say that? Because if we go down that road, we'll have to say that statins are cancer-causing, and so is exercise, and kidney transplants and who knows what else. They cause cancer by allowing people to live longer, and accumulate more mutational damage to their cells. And the supposedly serious opioid epidemic among Trump supporters actually is protective, because those people are dying earlier and not getting cancer!
The main point is that mutations are clearly involved in carcinogenesis, cell division life-history is clearly involved in carcinogenesis, environmental mutagens are clearly involved in carcinogenesis, and inherited mutations are clearly contributory to the additional effects of life-history events. The silly extremism to which the objectors to V-T would take us would be to say that, obviously, if we avoided any interaction whatsoever with our environment, we'd never get cancer. Of course, we'd all be so demented and immobilized with diverse organ-system failures that we wouldn't realize our good fortune in not getting cancer.
The story and much of the discussion on all sides is also rather naive even about the nature of cancer (and how many or of which mutations etc it takes to get cancer); but that's for another post sometime.
ADDENDUM 2
I'll add another new bit to my post, that I hadn't thought of when I wrote the original. We have many ways to estimate mutation rates, in nature and in the laboratory. They include parent-offspring comparison in genomewide sequencing samples, and there have been sperm-to-sperm comparisons. I'm sure there are many other sets of data (see Michael Lynch in Trends in Genetics 2010 Aug; 26(8): 345–352. These give a consistent picture and one can say, if one wants to, that the inherent mutation rate is due to identifiable environmental factors, but given the breadth of the data that's not much different than saying that mutations are 'in the air'. There are even sex-specific differences.
The numerous mutation detection and repair mechanisms, built into genomes, adds to the idea that mutations are part of life, for example that they are not related to modern human lifestyles. Of course, evolution depends on mutation, so it cannot and never has been reduced to zero--a species that couldn't change doesn't last. Mutations occur in plants and animals and prokaryotes, in all environments and I believe, generally at rather similar species-specific rates.
If you want to argue that every mutation has an external (environmental) cause rather than an internal molecular one, that is merely saying there's no randomness in life or imperfection in molecular processes. That is as much a philosophical as an empirical assertion (as perhaps any quantum physicist can tell you!). The key, as asserted in the post here, is that for the environmentalists' claim to make sense, to be a mutational cause in the meaningful sense, the force or factor must be systematic and identifiable and tissue-specific, and it must be shown how it gets to the internal tissue in question and not to other tissues on the way in, etc.
Given how difficult it has been to chase down most environmental carcinogenic factors, to which exposure is more than very rare, and that the search has been going on for a very long time, and only a few have been found that are, in themselves, clearly causal (ultraviolet radiation, Human Papilloma Virus, ionizing radiation, the ones mentioned in the post), whatever is left over must be very weak, non tissue-specific, rare, and the like. Even radiation-induced lung cancer in uranium minors has been challenging to prove (for example, because miners also largely were smokers).
It is not much of a stretch to simply say that even if, in principle, all mutations in our body's lifetime were due to external exposures, and the relevant mutagens could be identified and shown in some convincing way to be specifically carcinogenic in specific tissues, in practice if not ultra-reality, then the aggregate exposures to such mutations are unavoidable and epistemically random with respect to tissue and gene. That I would say is the essence of the V-T finding.
Quibbling about that aspect of carcinogenesis is for those who have already determined how many angels dance on the head of a pin.
Thursday, October 23, 2014
What is this 'risk variant' shared by 40% of people with Type 2 Diabetes?
I heard a surprising statistic the other day. At least it was surprising to me. The Oct 9 episode of the excellent BBC Radio 4 program "Inside Science" covered a new treatment for type 2 diabetes (T2D). The blurb about the segment said this:
But what I was really interested in was this 40% statistic. It was mentioned but not discussed -- where did it come from, and what did it mean? Do we now have a very significant explanation for the cause of type 2 diabetes? If it actually accounts for such a large fraction of cases, why haven't we heard more about it, after so many big genomewide studies? If not, in what sense is it a 'risk variant'? And what does it mean that it's causal "if you have the wrong lifestyle"? Presumably some lifestyle risk factor such as energy imbalance or a dietary component interacts with the variant (whatever that means), but that's true of anyone with T2D, so is the causal pathway different in people with the variant? Do people with the variant and T2D have a different disease in some sense than those without? What is the frequency of the variant in people without T2D and should we expect it to be much lower than in those who have T2D?
I tried to run down the statistic. I found a few 2010 papers that looked promising as the source, but I couldn't find it in any of them. I Tweeted Adam Rutherford, the Inside Science presenter, and he kindly replied with the reference he had seen, a paper in the October 8 issue of Science ("Genotype-based treatment of type 2 diabetes with an a2A-adrenergic receptor antagonist," Tang et al.). Indeed, the authors of the new, 2014 paper, write:
So I turned to the Supplemental information. Here's the best I could do, but it's not the 30% and 40% I was looking for, either:
The 'A' allele is the 'risk variant', so here 28% of the cases and 24% of the controls have at least one copy, are either GA or AA at the chromosomal locus. Not a large difference between the two groups, and not the figures I was looking for, either. I emailed the senior author of the 2010 paper last week to ask about these statistics, but haven't heard back.
I'm either overlooking something obvious, or it's not where I'm looking or it has somehow been misreported along the way. But this is just increasing my curiosity. What about this paper, reporting on a study of the ADRA2A association with T2D in Sweden? SNP rs553668 is the 'risk variant' of interest, according to the previous papers I'd read.
A meta-analysis in 2013 found that SNP rs553668 may be associated with T2D in Europeans, but no other ethnic groups. But what about this statistic: GWAS have explained only ~10% of "heritability" of T2D, including ADRA2A because that chromosome region is covered by genome-spanning markers used in GWAS. This doesn't seem to jibe with the idea that 40% of people with T2D have the ADRA2A 'risk variant.' And of course if 30% of the healthy population has the risk variant, either they went on to develop T2D after the study was completed, or it's simply not a significant risk variant.
This is an important point: if a large fraction of the population carries the variant, it's not a signifiant risk variant by itself, and doesn't cause 40% of cases, even if it may turn out to be a useful variant to know about if you have T2D, in making treatment decisions. The population risk of T2D is heavily dependent on lifestyles and very changeable over the years (it is rapidly becoming very much more common than even earlier in our own lifetimes). But if we were to say that 8% of the population will get T2D, the current estimate, then of these, 40%, or 3.2% of the population has an involvement of this particular gene. That may be important, but it doesn't explain the epidemic, and leaves unanswered questions.
I hoped to get to the bottom of this by the end of this post. Instead, I remain confused.
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*Update* Oct 26. Dr Rosengren replied to my email this afternoon. He said that "the major allele frequency for the variant is in Table S1 in the Science paper. " That's this table:
The risk variant is rs553668, for which the minor allele frequency is 0.143.
He also said that "T2D patients have a 40% higher frequency." I have to say, this doesn't clarify things for me.
Additionally, he said that 'controls' with the variant are at higher risk of developing diabetes, which means, at least to me, that they aren't good controls.
In 2010, a particular gene variant was associated with around 40% of Type 2 diabetics - not directly causal, but this so-called 'risk variant' increases the chance of developing the condition if you have the wrong lifestyle.The focus of the program was on what seems to be a promising new treatment targeted at people who carry a specific variant in the ADRA2A gene. The variant inhibits the secretion of insulin from beta cells, and the treatment, yohimbine, seems to reverse that in people with this particular risk variant. Yohimbine isn't a cure, or the only treatment that would be necessary for carriers of the variant, but it would be used in combination with other drugs. It seems as though it could potentially be a good addition to control of type 2 diabetes in perhaps a large segment of the population.
But what I was really interested in was this 40% statistic. It was mentioned but not discussed -- where did it come from, and what did it mean? Do we now have a very significant explanation for the cause of type 2 diabetes? If it actually accounts for such a large fraction of cases, why haven't we heard more about it, after so many big genomewide studies? If not, in what sense is it a 'risk variant'? And what does it mean that it's causal "if you have the wrong lifestyle"? Presumably some lifestyle risk factor such as energy imbalance or a dietary component interacts with the variant (whatever that means), but that's true of anyone with T2D, so is the causal pathway different in people with the variant? Do people with the variant and T2D have a different disease in some sense than those without? What is the frequency of the variant in people without T2D and should we expect it to be much lower than in those who have T2D?
I tried to run down the statistic. I found a few 2010 papers that looked promising as the source, but I couldn't find it in any of them. I Tweeted Adam Rutherford, the Inside Science presenter, and he kindly replied with the reference he had seen, a paper in the October 8 issue of Science ("Genotype-based treatment of type 2 diabetes with an a2A-adrenergic receptor antagonist," Tang et al.). Indeed, the authors of the new, 2014 paper, write:
A genetic variant in ADRA2A was recently associated with defective b cell function (7). The finding represents the first exact disease mechanism for type 2 diabetes associated with a common (30% of the normal population; 40% of patients with type 2 diabetes) risk variant and provides an opportunity to examine the feasibility of a “pharma- cogenetic” approach to treat complex polygenic disorders like type 2 diabetes.So, there were the numbers but not the actual source of the data. Citation number 7 turned out to be one of the 2010 papers I'd already looked at, a paper in Science from the same group ("Overexpression of Alpha2A-Adrenergic Receptors Contributes to Type 2 Diabetes," Rosengren et al.) reporting overexpression of an ADRA2A variant associated with suppression of insulin secretion in rats, and humans, and "increased type 2 diabetes risk" in humans. Rosengren et al. found that "...in a case-control material with 3740 nondiabetics and 2830 diabetics, rs553668 was associated with increased risk of T2D [recessive effect; odds ratio (OR) 1.42, confidence interval (CI) 1.01 to 1.99, P = 0.04]." That is, risk is 42% higher in those with rs553668 than those without. Ok, but that wasn't what I was looking for.
So I turned to the Supplemental information. Here's the best I could do, but it's not the 30% and 40% I was looking for, either:
 |
| Table S2; Effects of SNPs on plasma glucose and serum insulin, Supplemental information, Rosengren et al, 2010 |
I'm either overlooking something obvious, or it's not where I'm looking or it has somehow been misreported along the way. But this is just increasing my curiosity. What about this paper, reporting on a study of the ADRA2A association with T2D in Sweden? SNP rs553668 is the 'risk variant' of interest, according to the previous papers I'd read.
SNP rs553668 was associated with T2D in men (odds ratio [OR] = 1.47; 95% confidence interval [CI] = 1.08–2.01; P = 0.015) but this association was lost after adjusting for age and for body mass index (BMI). Associations were also detected when comparing obese NGT and lean NGT subjects (OR = 1.49; 95% CI = 1.07–2.07; P = 0.017), and in obese (OR = 1.62; 95% CI = 1.06–2.49; P = 0.026), but not in lean T2D. In women, multiple logistic regression regarding SNP rs521674 demonstrated an increased OR of 7.61 (95% CI = 1.70–34.17; P = 0.008) for T2D when including age as a covariant. Correcting for BMI removed the significant association. When age was included in the model, association also found when obese T2D patients were compared with lean NGT subjects (P = 0.041). ADRA2A mRNA expression in human pancreatic islets was detectable, but with no statistically significant difference between the diabetic and the control groups. [Highlighting is mine.]So 'the' risk variant is associated with obesity in men, but it's a different variant in this gene in women that's associated with obesity. Of course, obesity is associated with T2D, but that's a step removed from what the other papers are suggesting.
A meta-analysis in 2013 found that SNP rs553668 may be associated with T2D in Europeans, but no other ethnic groups. But what about this statistic: GWAS have explained only ~10% of "heritability" of T2D, including ADRA2A because that chromosome region is covered by genome-spanning markers used in GWAS. This doesn't seem to jibe with the idea that 40% of people with T2D have the ADRA2A 'risk variant.' And of course if 30% of the healthy population has the risk variant, either they went on to develop T2D after the study was completed, or it's simply not a significant risk variant.
This is an important point: if a large fraction of the population carries the variant, it's not a signifiant risk variant by itself, and doesn't cause 40% of cases, even if it may turn out to be a useful variant to know about if you have T2D, in making treatment decisions. The population risk of T2D is heavily dependent on lifestyles and very changeable over the years (it is rapidly becoming very much more common than even earlier in our own lifetimes). But if we were to say that 8% of the population will get T2D, the current estimate, then of these, 40%, or 3.2% of the population has an involvement of this particular gene. That may be important, but it doesn't explain the epidemic, and leaves unanswered questions.
I hoped to get to the bottom of this by the end of this post. Instead, I remain confused.
-------------
*Update* Oct 26. Dr Rosengren replied to my email this afternoon. He said that "the major allele frequency for the variant is in Table S1 in the Science paper. " That's this table:
 |
| Source |
He also said that "T2D patients have a 40% higher frequency." I have to say, this doesn't clarify things for me.
Additionally, he said that 'controls' with the variant are at higher risk of developing diabetes, which means, at least to me, that they aren't good controls.
Friday, October 3, 2014
An example of the problem of risk projection
One of the biggest problems in biomedical, including genomic, disease risk prediction is that it is almost always based on projections of past risks into the future. We wrote about that the other day (here), but here's yet another example--and they abound.
The Oct 1 NYTimes had a story about a boom in pre-school fitness programs. If parents, and it will largely be middle-class privileged parents, adopt this fad, it may have long-term, even lifelong, implications for the future health of the kids who partake. If the Times is right that this is a boom industry, one can imagine a whole generation of super healthy upper and upper middle class future adults in the making. That would be quite good (unless, of course, it turns out that various muscle, skeletal, or other traits are harmed by overdoing this early exercise), and so a beneficial practice for individual and public health.
But, even if they are healthier than today's adults, eventually these babies will develop diseases as they grow older. From our perspective as scientists who think about pitfalls to doing science, this raises some potential problems for future researchers doing disease genetics or environmental epidemiology, looking for risk factors associated with disease.
So risk estimates are about the future, but future exposures can't even in principle be known. This is obvious, so why is awareness of the problem so low? And what, if anything, can we do about it besides discounting risk estimates and acknowledging that they usually have unknown precision?
 |
| Baby swimming; Wikipedia |
The Oct 1 NYTimes had a story about a boom in pre-school fitness programs. If parents, and it will largely be middle-class privileged parents, adopt this fad, it may have long-term, even lifelong, implications for the future health of the kids who partake. If the Times is right that this is a boom industry, one can imagine a whole generation of super healthy upper and upper middle class future adults in the making. That would be quite good (unless, of course, it turns out that various muscle, skeletal, or other traits are harmed by overdoing this early exercise), and so a beneficial practice for individual and public health.
But, even if they are healthier than today's adults, eventually these babies will develop diseases as they grow older. From our perspective as scientists who think about pitfalls to doing science, this raises some potential problems for future researchers doing disease genetics or environmental epidemiology, looking for risk factors associated with disease.
1. If it's predominantly parents of a given ancestry, European urbanites say, who enroll their kids, this can induce false positive genomic signals. Any other kind of clustering related to who enrolls can be equally problematic;
2. The kids themselves may not remember, or investigators decades from now may not be aware of these early fitness programs even to ask about them. The exposure to such programs' effects may as a result go under-reported in epidemiological or genetic association studies, leading to distorted estimates of other risk factors;
3. If parents who enroll their kids are, as seems likely, themselves into fitness plans, there can be a family association of altered risk with genotype that will be challenging to identify and correct for as they could seem to be genetic;
4. If the kids are inculcated with other health-habits, based on today's do-this/don't-do-that fashions (e.g., here's a story in the Times about 6 year olds choosing to be vegan), there will be correlations with later disease that will not necessarily be identifiable, and indeed, it may be the parents' attitudes that are responsible, not the kids' genotypes or behavioral choices freely made.Our society already spends much media ink and research resources in hyping risk estimates for genes and lifestyle factors alike, that are made retrospectively based on the behavioral and exposure antecedents of today's disease cases, as ascertained by means such as interview questionnaires (Did you smoke? How much, for how long? Did you get exercise when you were a child? How much, for how long? How many eggs did you eat per week when you were in your twenties?). Those are not only quite inaccurate, involving things occurring decades ago, but the chronic, complex disease risks we're exposed to today generally won't materialize for decades into the future. Indeed, if we read about a risky behavior or food, this makes a lot of us change our behavior, yet another complication--and one which operates regularly as we read advice from the latest research, not always aware of its potential weaknesses.
So risk estimates are about the future, but future exposures can't even in principle be known. This is obvious, so why is awareness of the problem so low? And what, if anything, can we do about it besides discounting risk estimates and acknowledging that they usually have unknown precision?
Monday, July 7, 2014
IRBs: Insider control can't do what's expected. Part II: Loss of control going viral
The virus that might roar
A story published in The Independent last week reported the controversial work of virologist Dr Yoshihiro Kawaoka at the University of Wisconsin-Madison. Kawaoka was in the news several years ago for manipulating the H5N1 strain of flu virus so that it would be able to evade the immune defenses that much of the world developed when the virus was pandemic in 2009, killing over 500,000 people (the story was covered at the time by ScienceInsider). That work was the subject of intense debate and scrutiny, and a moratorium was imposed while it underwent review. The moratorium was lifted last year, and the work eventually cleared for publication.
According to a recent piece in the Wisconsin State Journal, during the moratorium Kawaoka began to do the same kind of work with the virus that killed so many people globally in 1918. The results of that project were recently published in Cell Host and Microbe. Kawaoka's goal is to understand the kinds of genetic changes that would make these viruses circumvent human immunity to become even more infectious or more lethal. The rationale, according to The Independent, is that it will help in the development of vaccines if such genetic changes were to occur in the wild.
The problem, as many see it, is that there is no guarantee that these virulent strains won't escape from the lab and do much harm. While Kawaoka says this won't happen, other lethal experimental organisms have done, and for new technologies like this such risk is always a concern. Indeed, for old technologies -- the debate about whether to keep smallpox virus in labs has been going on for decades. Kawaoka's work was approved by the university institutional review board although, according to The Independent, at least one member of the board was not willing to approve his current project.
Does the fact that Kawaoka is a star on the faculty of the University of Wisconsin, where he has been treated extremely well, influence the IRB? He is well-known in the field of influenza research, and has been involved in much recent work on emerging viruses, and no doubt his track record should count when his work is evaluated, but it's also possible, as always when power may be an issue, that Kawaoka's proposals have an easier time passing review than, say, a new researcher's would.
But what is the IRB's role here? Is it the board's job to decide what kind of risk society should be subjected to when academics do their work? Or is that the job of an inter-institutional, or governmental agency, such as the U.S. National Science Advisory Board for Biosecurity (NSABB), which reviewed, and approved, Kawaoka's earlier work?
The risk of an inadvertent epidemic or even pandemic from this research may be small to very slight, but the consequences of such a thing would be so huge as to ask about the risk-benefit balance. The importance of the discovery, should the research be successful, could be very great as well. So there is no easy answer.
But that the University of Wisconsin allowed one of its very well-heeled faculty members to develop a modified pathogenic virus to which humans would no longer have resistance sounds like something out of Dr Strangelove. How often is this sort of thing being done in a university near you--with or without its noble IRB being aware of it?
As we noted above, the previous work that got Kawaoka and Dutch investigators into hot water involved tinkering with the H5N1 flu virus to see what it would take to escape our immune system. Their idea was essentially to test virus genomic modification on ferrets, who in many ways are similar to humans immunologically. The work was allowed to proceed after review, but in fact how can anyone guarantee that an accident won't happen? We don't happen to know the conditions of the lifting of the moratorium, but no matter how extensive the review, or how cautious the scientists promised to be, no one can be absolutely certain that an accidental release of these viruses won't occur. It reminds me of the time a little boy was getting on his bike to ride down the hill in front of our house. His father reminded him to put on his helmet before he went, in case he fell off the bike. "But Dad," he protested, "I'm not going to fall off!"
Similar concerns about recombinant DNA were raised a generation ago, and over time adequate protections were worked out and no disaster occurred that we know of. But recombinant DNA doesn't pose the kinds of dangers that virulent viruses do. And we have seen with other things, like stem cells, that scientists will do their best to find ways to do what they want to do. Scientists, and the private sector, are both anxious to find new cures and also, one must acknowledge, looking for the major profits that are to be made. The stem cell issue is more complicated because objections largely were religious. Scientists may sneer at such things as ignorance standing the way of progress, but religious people are citizens and taxpayers, and if they are the majority, and aren't in favor of such a project, in a democracy, perhaps that should rule, whether frustrated scientists like it or not.
And there are other issues. If a rock-star scientist threatens to leave the institution and go work elsewhere, this can be an incentive for an institution that treats faculty members like celebrities--particularly if they bring in big grant money--to compromise standards.
And, should a properly independent system, with zero vested interests, be allowed or instructed to impose research bans for some number of years, appropriate to the offense, for investigators about whom there is evidence of misleading the IRB, or doing things not approved or even disapproved?
It is, as in most similar kinds of situations, difficult to see how policy should be formed and implemented. After all, even amoral scientists are still scientists and citizens, and if they think something should be done, they have their votes, too. And major public good might often also entail risks.
The IRBs were started in the wake of abuses by Nazi and other scientists, including the most respected pillars of their society, and including in our country, as we mentioned last week. That showed that scientists can't automatically be trusted not to intentionally, or even inadvertently do harm. But many of us feel that the tenor of the committees has itself drifted from that proper gate-keeping job to a primary function to protect the institution against law-suits, part of a general trend in universities that is stifling in many ways, as well as costly in time and resources.
Our mistaken mixing of messages
Making decisions is not easy, but there should be a balance of power. However, in Thursday's post on IRBs, we mixed two aspects of bioethics. One was about treatment of research subjects, human or otherwise. The other was about priorities for spending society's resources (both are involved in our discussion here as well). The issues overlap somewhat but we probably should have kept them separate. IRBs are not mandated to deal with research priorities or societal concerns, though they do have to judge whether a project violates those concerns, and about whether doing some procedure on mice or other animals is warranted for the stated purpose of a project.
The peer review and policies of funders are the bodies that deal with research priorities. My view is, as stated in Part I and elsewhere is that our priorities often too much depend on vested interests. That is because agencies like NIH ask scientists what should be the next research priority. Indeed, as I have seen directly several times, an agency like the National Academy of Sciences, entrusted with advising the government, can be paid by an NIH agency to hold a meeting about priorities, at which the agency's funded clients, and agency administrators, attend. This is, essentially, insider trading and the NAS should not accept such contracts. However, how to set priorities is not an easy thing to decide, since asking scientists their view is begging for self-interest to be at play, yet scientists know better than the public what the issues are.
In this sense, humans or animals are involved in projects that subject them to conditions that are allowed because of the social politics of the funding and academic career apparatus. Are we out of proper alignment with what most would agree are appropriate societal priorities? The payoff in actual public or scientific good is often, I think, far below what is promised. This is of course a value judgment, but so are all IRB decisions and policies.
In any case, the Wisconsin issue that triggered these comments is more closely related to IRBs and its degree of real control of research ethics than about whether funds should be spent on this type of project rather than some other. Here, in fact, the story as written suggests serious abuse of what IRBs should rightly be policing. One can argue that the knowledge being sought would properly have very high societal priority (because it deals with dangerous infectious disease), but that's a separate question.
More generally, the funding priority issue may often even more important than the safer, local IRB protections. Billions of dollars go to feed the established research system, making it very self-aggrandizing and far less innovative than it might be if funding commitments, mega-longterm projects and the like were not so entrenched. Instead of spending mega-bucks on more Big Data surveys we might focus funding on problems that were well-posed enough to be soluble. This is again a societal issue about how resources are used, or captured, which does, of course, go beyond local IRB concerns that we were mainly intending to comment on.
So, while the ethical issues are not entirely separate, it confuses things to mix them as I did in our previous post.
A story published in The Independent last week reported the controversial work of virologist Dr Yoshihiro Kawaoka at the University of Wisconsin-Madison. Kawaoka was in the news several years ago for manipulating the H5N1 strain of flu virus so that it would be able to evade the immune defenses that much of the world developed when the virus was pandemic in 2009, killing over 500,000 people (the story was covered at the time by ScienceInsider). That work was the subject of intense debate and scrutiny, and a moratorium was imposed while it underwent review. The moratorium was lifted last year, and the work eventually cleared for publication.
According to a recent piece in the Wisconsin State Journal, during the moratorium Kawaoka began to do the same kind of work with the virus that killed so many people globally in 1918. The results of that project were recently published in Cell Host and Microbe. Kawaoka's goal is to understand the kinds of genetic changes that would make these viruses circumvent human immunity to become even more infectious or more lethal. The rationale, according to The Independent, is that it will help in the development of vaccines if such genetic changes were to occur in the wild.
The problem, as many see it, is that there is no guarantee that these virulent strains won't escape from the lab and do much harm. While Kawaoka says this won't happen, other lethal experimental organisms have done, and for new technologies like this such risk is always a concern. Indeed, for old technologies -- the debate about whether to keep smallpox virus in labs has been going on for decades. Kawaoka's work was approved by the university institutional review board although, according to The Independent, at least one member of the board was not willing to approve his current project.
Does the fact that Kawaoka is a star on the faculty of the University of Wisconsin, where he has been treated extremely well, influence the IRB? He is well-known in the field of influenza research, and has been involved in much recent work on emerging viruses, and no doubt his track record should count when his work is evaluated, but it's also possible, as always when power may be an issue, that Kawaoka's proposals have an easier time passing review than, say, a new researcher's would.
But what is the IRB's role here? Is it the board's job to decide what kind of risk society should be subjected to when academics do their work? Or is that the job of an inter-institutional, or governmental agency, such as the U.S. National Science Advisory Board for Biosecurity (NSABB), which reviewed, and approved, Kawaoka's earlier work?
The risk of an inadvertent epidemic or even pandemic from this research may be small to very slight, but the consequences of such a thing would be so huge as to ask about the risk-benefit balance. The importance of the discovery, should the research be successful, could be very great as well. So there is no easy answer.
But that the University of Wisconsin allowed one of its very well-heeled faculty members to develop a modified pathogenic virus to which humans would no longer have resistance sounds like something out of Dr Strangelove. How often is this sort of thing being done in a university near you--with or without its noble IRB being aware of it?
As we noted above, the previous work that got Kawaoka and Dutch investigators into hot water involved tinkering with the H5N1 flu virus to see what it would take to escape our immune system. Their idea was essentially to test virus genomic modification on ferrets, who in many ways are similar to humans immunologically. The work was allowed to proceed after review, but in fact how can anyone guarantee that an accident won't happen? We don't happen to know the conditions of the lifting of the moratorium, but no matter how extensive the review, or how cautious the scientists promised to be, no one can be absolutely certain that an accidental release of these viruses won't occur. It reminds me of the time a little boy was getting on his bike to ride down the hill in front of our house. His father reminded him to put on his helmet before he went, in case he fell off the bike. "But Dad," he protested, "I'm not going to fall off!"
Similar concerns about recombinant DNA were raised a generation ago, and over time adequate protections were worked out and no disaster occurred that we know of. But recombinant DNA doesn't pose the kinds of dangers that virulent viruses do. And we have seen with other things, like stem cells, that scientists will do their best to find ways to do what they want to do. Scientists, and the private sector, are both anxious to find new cures and also, one must acknowledge, looking for the major profits that are to be made. The stem cell issue is more complicated because objections largely were religious. Scientists may sneer at such things as ignorance standing the way of progress, but religious people are citizens and taxpayers, and if they are the majority, and aren't in favor of such a project, in a democracy, perhaps that should rule, whether frustrated scientists like it or not.
And there are other issues. If a rock-star scientist threatens to leave the institution and go work elsewhere, this can be an incentive for an institution that treats faculty members like celebrities--particularly if they bring in big grant money--to compromise standards.
And, should a properly independent system, with zero vested interests, be allowed or instructed to impose research bans for some number of years, appropriate to the offense, for investigators about whom there is evidence of misleading the IRB, or doing things not approved or even disapproved?
It is, as in most similar kinds of situations, difficult to see how policy should be formed and implemented. After all, even amoral scientists are still scientists and citizens, and if they think something should be done, they have their votes, too. And major public good might often also entail risks.
The IRBs were started in the wake of abuses by Nazi and other scientists, including the most respected pillars of their society, and including in our country, as we mentioned last week. That showed that scientists can't automatically be trusted not to intentionally, or even inadvertently do harm. But many of us feel that the tenor of the committees has itself drifted from that proper gate-keeping job to a primary function to protect the institution against law-suits, part of a general trend in universities that is stifling in many ways, as well as costly in time and resources.
Our mistaken mixing of messages
Making decisions is not easy, but there should be a balance of power. However, in Thursday's post on IRBs, we mixed two aspects of bioethics. One was about treatment of research subjects, human or otherwise. The other was about priorities for spending society's resources (both are involved in our discussion here as well). The issues overlap somewhat but we probably should have kept them separate. IRBs are not mandated to deal with research priorities or societal concerns, though they do have to judge whether a project violates those concerns, and about whether doing some procedure on mice or other animals is warranted for the stated purpose of a project.
The peer review and policies of funders are the bodies that deal with research priorities. My view is, as stated in Part I and elsewhere is that our priorities often too much depend on vested interests. That is because agencies like NIH ask scientists what should be the next research priority. Indeed, as I have seen directly several times, an agency like the National Academy of Sciences, entrusted with advising the government, can be paid by an NIH agency to hold a meeting about priorities, at which the agency's funded clients, and agency administrators, attend. This is, essentially, insider trading and the NAS should not accept such contracts. However, how to set priorities is not an easy thing to decide, since asking scientists their view is begging for self-interest to be at play, yet scientists know better than the public what the issues are.
In this sense, humans or animals are involved in projects that subject them to conditions that are allowed because of the social politics of the funding and academic career apparatus. Are we out of proper alignment with what most would agree are appropriate societal priorities? The payoff in actual public or scientific good is often, I think, far below what is promised. This is of course a value judgment, but so are all IRB decisions and policies.
In any case, the Wisconsin issue that triggered these comments is more closely related to IRBs and its degree of real control of research ethics than about whether funds should be spent on this type of project rather than some other. Here, in fact, the story as written suggests serious abuse of what IRBs should rightly be policing. One can argue that the knowledge being sought would properly have very high societal priority (because it deals with dangerous infectious disease), but that's a separate question.
More generally, the funding priority issue may often even more important than the safer, local IRB protections. Billions of dollars go to feed the established research system, making it very self-aggrandizing and far less innovative than it might be if funding commitments, mega-longterm projects and the like were not so entrenched. Instead of spending mega-bucks on more Big Data surveys we might focus funding on problems that were well-posed enough to be soluble. This is again a societal issue about how resources are used, or captured, which does, of course, go beyond local IRB concerns that we were mainly intending to comment on.
So, while the ethical issues are not entirely separate, it confuses things to mix them as I did in our previous post.
Friday, May 10, 2013
Good news for tanning salons - sunlight and health
New research lauding the benefits of sunlight, and not because of vitamin D, brings up a general question about reductive research. The work was reported in Edinburgh at the International Investigative Dermatology 2013 meeting, and suggests that sunlight helps reduce blood pressure, which leads to lower risk of heart attack and stroke.
Researchers found that UV rays from the sun caused their study subjects to release a compound that has this positive effect on blood pressure. That's nitric oxide, apparently, which is released into the circulation when sunlight touches the skin. The researchers note that hypertension and cardiovascular disease rates rise in the winter, and propose it's because of reduced exposure to sunlight.
And, Medical News Today quotes the lead author:
Whether these results are valid or not is not our interest here. Indeed, hypertension is high among African Americans and Afro-Caribbeans, though the data are equivocal for Africa itself.
But, ok, let's assume there's something to this. Indeed, let's assume that even a tenth of what people say about vitamin D is true, and that exposure to sunlight is good for our health for multiple reasons. And that wouldn't be surprising, given that we've lived most of our evolutionary history exposed to sunlight, and if it were as bad for us as dermatology says it is, we'd not have made it this far.
That aside, this brings up the question of competing effects. Sun exposure is bad because it causes cancer. No, it's good because we need the vitamin D, and now the nitric oxide. Red wine in moderation is good for us because it lowers heart disease risk, but it's bad because it causes breast cancer. Eating fish is good because of antioxidants, but bad because of mercury. Brown rice is good because it's a source of fiber and vitamins, but bad because it's loaded with arsenic.
The list could, and does, go on and on. In large part it's a product of reductive science, looking at single factors and determining single outcomes, ignoring complexity and context.
And life is a balancing of costs and benefits -- exercise is good for us, but running wears out knees, and bicycling brings risk of accidents. You might decide that the benefits outweigh the costs, but how informed is that decision, really? How do you decide whether or not to lie in the sun? You might not in fact be at risk of hypertension, so sun exposure isn't a great benefit to you in terms of lowering your risk of heart disease, and so the potential cost of skin cancer might be greater for you than the benefits.
But, how would you know? These results are based on population data, measuring only a subset of factors that are actually involved in the complex interactions that result in hypertension or skin cancer or breast cancer or heart disease, and certainly not measuring your personal set of factors, exposures and risk.
In the end, we probably should make these lifestyle and dietary decisions based less on this week's data -- indeed, a lot of the relevant data we don't have and don't even know we should have -- but on how much we enjoy that glass of wine, or lying in the sun.
Researchers found that UV rays from the sun caused their study subjects to release a compound that has this positive effect on blood pressure. That's nitric oxide, apparently, which is released into the circulation when sunlight touches the skin. The researchers note that hypertension and cardiovascular disease rates rise in the winter, and propose it's because of reduced exposure to sunlight.
And, Medical News Today quotes the lead author:
Richard Weller, Senior Lecturer in Dermatology, and colleagues, say the effect is such that overall, sun exposure could improve health and even prolong life, because the benefits of reducing blood pressure, cutting heart attacks and strokes, far outweigh the risk of getting skin cancer.It's of course notable that these results are being presented at a dermatology meeting, because dermatologists have been telling us for years to reduce our exposure to sunlight, a prime cause of skin cancer.
 |
| La promenade (1875) by Claude Monet |
But, ok, let's assume there's something to this. Indeed, let's assume that even a tenth of what people say about vitamin D is true, and that exposure to sunlight is good for our health for multiple reasons. And that wouldn't be surprising, given that we've lived most of our evolutionary history exposed to sunlight, and if it were as bad for us as dermatology says it is, we'd not have made it this far.
That aside, this brings up the question of competing effects. Sun exposure is bad because it causes cancer. No, it's good because we need the vitamin D, and now the nitric oxide. Red wine in moderation is good for us because it lowers heart disease risk, but it's bad because it causes breast cancer. Eating fish is good because of antioxidants, but bad because of mercury. Brown rice is good because it's a source of fiber and vitamins, but bad because it's loaded with arsenic.
The list could, and does, go on and on. In large part it's a product of reductive science, looking at single factors and determining single outcomes, ignoring complexity and context.
And life is a balancing of costs and benefits -- exercise is good for us, but running wears out knees, and bicycling brings risk of accidents. You might decide that the benefits outweigh the costs, but how informed is that decision, really? How do you decide whether or not to lie in the sun? You might not in fact be at risk of hypertension, so sun exposure isn't a great benefit to you in terms of lowering your risk of heart disease, and so the potential cost of skin cancer might be greater for you than the benefits.
But, how would you know? These results are based on population data, measuring only a subset of factors that are actually involved in the complex interactions that result in hypertension or skin cancer or breast cancer or heart disease, and certainly not measuring your personal set of factors, exposures and risk.
In the end, we probably should make these lifestyle and dietary decisions based less on this week's data -- indeed, a lot of the relevant data we don't have and don't even know we should have -- but on how much we enjoy that glass of wine, or lying in the sun.
Tuesday, August 14, 2012
If a butterfly flaps its wings....a tsunami! Or if a tsunami, the butterfly doesn't flap
There is the famous Lorenz effect that if a butterfly flaps its wings, its effect, though initially so trivial as to be barely unmeasurable, can cause a hurricane elsewhere several weeks later. Small causes with large later effects are particularly fearsome to us.
Ionizing radiation causes mutations. That is why there is debate about use of x-rays in various contexts. And, thought they may not know it explicitly, it is why so many people fear radiation and nuclear power.
It is no surprise that there is a report that butterflies in the Fukushima reactor's neighborhood are now showing up with mutations, at rates notably higher than those due to the normal mutation rate. This poor victim in the photo will not make any ripples in the air---but could it cause a storm of reaction?
The problem with nuclear reactors in terms of public perception is that there will be major disasters that will affect people in similarly grotesque ways. We know that no matter what protections we build in, some ill-alignment of small effects will occasionally cause a highly visible disaster.
Nobody worries about the risk of CT scans when there really is an injury or disease to diagnose and treat. In that instance it is life, or quality of life, vs small or even barely measurable risk. But what about routine preventive or diagnostic screening such as by CT scans, regular mammograms, airport whole-body security checks, or even dental x-rays? This is difficult to answer. There the individual risk is very small, but can be estimated on a population basis. If the risk is small, but happens to you, then it's a major Lorenz effect!
In the case of mammographic screening, the issues are not just about irrational fears, or even, surprisingly, about whether the x-rays cause more cancer than they detect. The issue is subtler: does routine radiation detect disease, that then is assumed to require therapeutic intervention which carries its own risk, but that would go away on its own? That's likely to occur much more often than radiogenic cancers, and it seems from several studies to be very measurable.
Reactor fear is similarly numerically irrational in that most alternative energy sources available today involve much higher risk than nuclear plants, even if there were to be a Fukushima every now and then. Illness and death due directly or indirectly to coal or oil mining, transporting, or use (including air pollution and global warming and risk to agriculture by bad farming practices reliant on petro-based fertilizer etc.) are vastly greater. But they're dispersed and not palbably connected directly to your local reactor. There, the risk of radiation illness, especially to any one person, is trivial.
These are statistical issues in a sense, but the probabilities involved are digests of many complex causal factors, so the probabilities themselves are hard to interpret. Also, the subjective way in which people react to probabilities are involved. And the issue is similar to the reasons why economists who assume 'rational man' have got things wrong so regularly: we are poorly educated about statistics, and we are emotional as well as rational beings. The two can't really be disconnected.
The 'butterfly effect' here is in a sense the emotional reaction to rare but gruesome results from a local event, that spread with unpredictable consequences.
Still, pity this poor butterfly, who committed no offense, but will never flit among the flowers.
Ionizing radiation causes mutations. That is why there is debate about use of x-rays in various contexts. And, thought they may not know it explicitly, it is why so many people fear radiation and nuclear power.
It is no surprise that there is a report that butterflies in the Fukushima reactor's neighborhood are now showing up with mutations, at rates notably higher than those due to the normal mutation rate. This poor victim in the photo will not make any ripples in the air---but could it cause a storm of reaction?
The problem with nuclear reactors in terms of public perception is that there will be major disasters that will affect people in similarly grotesque ways. We know that no matter what protections we build in, some ill-alignment of small effects will occasionally cause a highly visible disaster.
Nobody worries about the risk of CT scans when there really is an injury or disease to diagnose and treat. In that instance it is life, or quality of life, vs small or even barely measurable risk. But what about routine preventive or diagnostic screening such as by CT scans, regular mammograms, airport whole-body security checks, or even dental x-rays? This is difficult to answer. There the individual risk is very small, but can be estimated on a population basis. If the risk is small, but happens to you, then it's a major Lorenz effect!
In the case of mammographic screening, the issues are not just about irrational fears, or even, surprisingly, about whether the x-rays cause more cancer than they detect. The issue is subtler: does routine radiation detect disease, that then is assumed to require therapeutic intervention which carries its own risk, but that would go away on its own? That's likely to occur much more often than radiogenic cancers, and it seems from several studies to be very measurable.
Reactor fear is similarly numerically irrational in that most alternative energy sources available today involve much higher risk than nuclear plants, even if there were to be a Fukushima every now and then. Illness and death due directly or indirectly to coal or oil mining, transporting, or use (including air pollution and global warming and risk to agriculture by bad farming practices reliant on petro-based fertilizer etc.) are vastly greater. But they're dispersed and not palbably connected directly to your local reactor. There, the risk of radiation illness, especially to any one person, is trivial.
These are statistical issues in a sense, but the probabilities involved are digests of many complex causal factors, so the probabilities themselves are hard to interpret. Also, the subjective way in which people react to probabilities are involved. And the issue is similar to the reasons why economists who assume 'rational man' have got things wrong so regularly: we are poorly educated about statistics, and we are emotional as well as rational beings. The two can't really be disconnected.
The 'butterfly effect' here is in a sense the emotional reaction to rare but gruesome results from a local event, that spread with unpredictable consequences.
Still, pity this poor butterfly, who committed no offense, but will never flit among the flowers.
Thursday, July 26, 2012
Salt and stomach cancer -- the data aren't terribly convincing
Warning!
A new cancer scare has been all over the British press -- the World Cancer Research Fund (WCRF) is recommending that because salt has been linked with stomach cancer, "traffic light" color-coded food labeling should be required of all processed foods, the most significant source of dietary salt. The WCRF says that people should eat 6 grams of salt per day, or about a teaspoon; 75% of salt consumption is from processed food, only 25% added at the table.
How does salt cause cancer? The explanation that comes with these new warnings is that salt damages the stomach lining, which leads to disease -- as far as we can tell, this is based on experimental studies feeding rodents high salt diets. Of course, this doesn't really explain it since cancers involve genetic mutations, whether inherited or somatic.
Some papers, including some rodent studies, suggest it's an association with Helicobacter pylori, the very widespread bacterium that causes ulcers. Or, it's not added salt but salt-processed foods like meats and pickles. Whatever the mechanism, The Guardian reports that an estimated 14% of stomach cancers could be prevented if salt consumption were reduced (this estimate comes from the WCRF). We blogged about salt and cancer a while ago (here; sorry, ugly table in that post), but now that the story is back, we thought we'd take a look at how definitive the data are.
But...
First, as we point out every time we blog about dietary factors and epidemiology, it's extremely difficult to get reliable data on any food consumption, and salt is no exception. Data are almost all retrospective (taken after the fact) and either population based, with the correlation made between salt consumption and stomach cancer rates based on population-level data, or based on individual data from dietary recall (sometimes asking people to remember their diet a year ago or more) or food diaries, which are notoriously unreliable, or household food intake, which assume everyone in the household eats the same diet, and so on. There's no good way to figure out how much salt people eat in the long run, nor is it clear how long before a tumor excess salt intake would be carcinogenic, nor for how long. And, the few prospective studies of salt and cancer (following healthy people for years, and assessing their diet along the way) have had conflicting results. And so on.
So, at the very best, the data are rough. But don't just take our word for it. A 2011 paper in the British Journal of Cancer on lifestyle factors and cancer says this: "Although it is currently not possible to pinpoint exactly what constituents of diet are protective against cancer, there is a consensus that diet is an important component of cancer risk." Not terribly helpful.
The same paper states:
And, there's the problem of confounders, variables that may be relevant but aren't measured or are difficult to control for. Every story we've seen about the salt and cancer link this week includes this quote:
A book chapter on stomach cancer ("The Epidemiology of Stomach Cancer," in a 2009 book called Cancer Epidemiology) states
We're not convinced
So we, at least, are not entirely convinced that salt alone is a significant cancer risk. This, from the same British Journal of Cancer paper we cite above, doesn't help make the case:
There is some thought that the year round availability of fresh fruits and vegetables is responsible for this, and the reduction in mortality may be due in some part to improvements in cancer detection and treatment may be as well. But, if salt is the strong risk factor the WCFR is suggesting it is, given that average salt consumption in the UK is almost twice the recommended amount, stomach cancer rates should not have been falling.
So, a quick morning's review of the data on salt and stomach cancer leaves us unconvinced that the data are really solid on this.
A new cancer scare has been all over the British press -- the World Cancer Research Fund (WCRF) is recommending that because salt has been linked with stomach cancer, "traffic light" color-coded food labeling should be required of all processed foods, the most significant source of dietary salt. The WCRF says that people should eat 6 grams of salt per day, or about a teaspoon; 75% of salt consumption is from processed food, only 25% added at the table.
 |
| Source: BBC |
Some papers, including some rodent studies, suggest it's an association with Helicobacter pylori, the very widespread bacterium that causes ulcers. Or, it's not added salt but salt-processed foods like meats and pickles. Whatever the mechanism, The Guardian reports that an estimated 14% of stomach cancers could be prevented if salt consumption were reduced (this estimate comes from the WCRF). We blogged about salt and cancer a while ago (here; sorry, ugly table in that post), but now that the story is back, we thought we'd take a look at how definitive the data are.
But...
First, as we point out every time we blog about dietary factors and epidemiology, it's extremely difficult to get reliable data on any food consumption, and salt is no exception. Data are almost all retrospective (taken after the fact) and either population based, with the correlation made between salt consumption and stomach cancer rates based on population-level data, or based on individual data from dietary recall (sometimes asking people to remember their diet a year ago or more) or food diaries, which are notoriously unreliable, or household food intake, which assume everyone in the household eats the same diet, and so on. There's no good way to figure out how much salt people eat in the long run, nor is it clear how long before a tumor excess salt intake would be carcinogenic, nor for how long. And, the few prospective studies of salt and cancer (following healthy people for years, and assessing their diet along the way) have had conflicting results. And so on.
So, at the very best, the data are rough. But don't just take our word for it. A 2011 paper in the British Journal of Cancer on lifestyle factors and cancer says this: "Although it is currently not possible to pinpoint exactly what constituents of diet are protective against cancer, there is a consensus that diet is an important component of cancer risk." Not terribly helpful.
The same paper states:
The difficulties in estimating salt consumption in epidemiological studies probably contribute to the very heterogeneous findings; nevertheless, the consensus view, most recently expressed in the WCRF report (2007), is that salt intake (as well as sodium intake and salty and salted foods) is a probable cause of gastric cancer.
The calculation of excess risk assumes a simple log-linear increase in the risk of gastric cancer with increasing salt intake. The evidence for this is somewhat equivocal: it is apparent for total salt use in cohort but not case–control studies, whereas for sodium intake it was also apparent in case–control studies; for salted and salty foods, the reverse was observed (dose–response relationship in case–control but not cohort studies; WCRF, 2007).A book chapter on stomach cancer ("The Epidemiology of Stomach Cancer," in a 2009 book called Cancer Epidemiology) states
The best established risk factors for stomach cancer are Helicobacter pylori infection, the by far strongest established risk factor for distal stomach cancer, and male sex, a family history of stomach cancer, and smoking . While some factors related to diet and food preservation, such as high intake of salt-preserved foods and dietary nitrite or low intake of fruit and vegetables, are likely to increase the risk of stomach cancer, the quantitative impact of many dietary factors remains uncertain, partly due to limitations of exposure assessment and control for confounding factors [italics ours].And, confounders again...
And, there's the problem of confounders, variables that may be relevant but aren't measured or are difficult to control for. Every story we've seen about the salt and cancer link this week includes this quote:
Kate Mendoza, head of information at the [WCRF], said: "Stomach cancer is difficult to treat successfully because most cases are not caught until the disease is well-established.
"This places even greater emphasis on making lifestyle choices to prevent the disease occurring in the first place – such as cutting down on salt intake and eating more fruit and vegetables.Cutting down on salt and eating more non-processed foods. That's changing two factors, but it's hard to measure the effect. Are fruits and vegetables actually protective? Or is it that replacing the bad processed foods with neutral fresh foods decreases salt exposure, and thus cancer risk? How does that get sorted out?
A book chapter on stomach cancer ("The Epidemiology of Stomach Cancer," in a 2009 book called Cancer Epidemiology) states
The best established risk factors for stomach cancer are Helicobacter pylori infection, the by far strongest established risk factor for distal stomach cancer, and male sex, a family history of stomach cancer, and smoking . While some factors related to diet and food preservation, such as high intake of salt-preserved foods and dietary nitrite or low intake of fruit and vegetables, are likely to increase the risk of stomach cancer, the quantitative impact of many dietary factors remains uncertain, partly due to limitations of exposure assessment and control for confounding factors [italics ours].No one eats their daily 8.6 grams of salt alone. High salt consumption generally implies high processed food consumption, and/or high nitrates consumption. It's very hard to control for that -- that is, to determine that it's the salt and not any other component of the diet that might be associated with stomach cancer. This is true of most studies of dietary components. And, if the researcher just loads up rats with salt and measures its effects, that's an unnatural test of salt consumption and may have no correspondence with what's actually happening in human stomachs dealing with a normal diet.
We're not convinced
So we, at least, are not entirely convinced that salt alone is a significant cancer risk. This, from the same British Journal of Cancer paper we cite above, doesn't help make the case:
The likely adverse effect on cancer risk in the UK is small, as the incidence of gastric cancer is low (gastric cancer ranks only 13th in terms of incidence in the UK, with incidence rates well below the European average (CRUK, 2011)). Average [salt] consumption in the UK is around 10 g per day, and had shown little change between 1986–7 and 2001 (Food Standards Agency, 2004)...There is no direct evidence from intervention studies of the benefit of reduced salt intake with respect to gastric cancer. In Japan, the national dietary policy has resulted in declines in dietary salt intake, and there has been an equivalent reduction in the incidence of gastric cancer (Tominaga and Kuroishi, 1997); however, there have been other changes in prevalence of gastric cancer risk factors – notably in prevalence of infection with Helicobacter pylori (Kobayashi et al, 2004) – and thus the part played by salt reduction is far from clear.Note that the WCRF is recommending people consume 6g of salt daily, but consumption in the UK is now around 10 g per day -- and gastric cancer rates are low. In fact, until the 1990's stomach cancer deaths were the leading cause of cancer death worldwide, but mortality had been falling for decades, and currently stomach cancer is "relatively rare" in North America and Northern and Western Europe, although it is still high in Eastern Europe, Russia and parts of Central and South America or East Asia (data from a 2009 paper in the International Journal of Cancer).
There is some thought that the year round availability of fresh fruits and vegetables is responsible for this, and the reduction in mortality may be due in some part to improvements in cancer detection and treatment may be as well. But, if salt is the strong risk factor the WCFR is suggesting it is, given that average salt consumption in the UK is almost twice the recommended amount, stomach cancer rates should not have been falling.
So, a quick morning's review of the data on salt and stomach cancer leaves us unconvinced that the data are really solid on this.
Wednesday, May 30, 2012
Magical science: now you see it, now you don't. Part II: How real is 'risk'?
Why is it that after countless studies, we don't know whether to believe the latest hot-off-the-press pronouncement of risk factors, genetic or environmental, for disease, or of assertions about the fitness history of a given genotype? Or in social and behavioral science....almost anything! Why are scientific studies, if they really are science, so often not replicated when the core tenet of science is that causes determine outcomes? Why should we have to have so many studies of the same thing, even decade after decade? Why do we still fund more studies of the same thing? Is there ever a time when we say Enough!?
That time hasn't come yet, and partly that's because professors have to have new studies to keep our grants and our jobs, and we do what we know how to do. But there are deeper reasons, without obvious answers, and they're important to you if you care about what science is, or what it should be--or what you should be paying for.
Last Thursday, we discussed some aspects of the problem when a set of causes that we suspect work only by affecting the probability of an outcome we're interested in. The cause may truly be deterministic, but we just don't understand it well enough, so must view its effect in probability terms. That means we have to study a sample, of repeated individuals exposed to the risk factor we're interested in, in the same way you have to flip a coin many times to see if it's really fair--if its probability of coming up Heads is really 50%. You can't just look at the coin or flip it once.
Nowadays, reports are often of meta-analysis, in which, because it is believed that no single study is definitive (i.e., reliable), we pool them and analyze the lot, that is, the net result of many studies, to achieve adequate sample sizes to see what risk really is associated with the risk factor. It should be a warning in itself that the samples of many studies (funded because they claimed and reviewers expected them to be adequate to the task) are now viewed as hopelessly inadequate. Maybe it's a warning that the supposed causes are weak to begin with--too weak for this kind of approach to be very meaningful?
Why, among countless examples, after having done many studies don't we know if HDL cholesterol does or doesn't protect from heart disease, or antioxidants from cancer, or coffee is a risk factor, or obesity is, or how to teach language or math, or avoid misbehavior of students, or whether criminality is genetic (or is a 'disease'), and so on--so many countless examples from the daily news, and you are paying for this, study after study without conclusive results, every day!
There are several reasons. These are serious issues, worthy of the attention of anyone who actually cares about understanding truth and the world we live in, and its evolution. The results are important to our society as well as to our basic understanding of the world.
So, then, why are so many results not replicable?
Here are at least some reasons to consider:
This situation--and our list is surely not exhaustive--is typical and pervasive in observational rather than experimental science. (In the same kinds of problems, lists just as long exist to explain why some areas even of experimental science don't do much better!)
A recent Times commentary and post of ours discussed these issues. The commentary says that we need to make social science more like experimental physical science with better replications and study designs and the like. But that may be wrong advice. It may simply lead us down an endless, expensive path that simply fails to recognize the problem. Social sciences already consider themselves to be real science. And presenting peer-reviewed work that way, they've got their fingers as deeply entrenched into the funding pot as, say genetics does.
Whether coffee is a risk factor for disease, or certain behaviors or diseases are genetically determined, or why some trait has evolved in our ancestry...these are all legitimate questions whose non-answers show that there may be something deeply wrong without current methods and ideas about science. We regularly comment on the problem. But there seems to be no real sense that there's an issue being recognized, in opposition to the forces that pressure scientists to continue business as usual---which means that we continue to do more and more and more-expensive studies of the same things.
One highly defensible solution would be to cut support for such non-productive science until people figure out a better way to view the world, and/or that we require scientists to be accountable for their results. No more, "I write the significance section of my grants with my fingers crossed behind my back" because I know that I'm not telling the truth (and the reviewers, who do the same themselves, know that you are doing that).
As it is, resources go to more and more and more studies of the same that yield basically little, students flock to large university departments that teach them how to do it, too, journals and funders make their careers reporting their research results, and policy makers follow the advice. Every day on almost any topic you will see in the news "studies show that....."
This is no secret: we all know the areas in which the advice goes little if anywhere. But politically, we haven't got the nerve to make such cuts and in a sense we would be lost if we had nobody assessing these issues. What to do is not an easy call, even if there were the societal will to act.
That time hasn't come yet, and partly that's because professors have to have new studies to keep our grants and our jobs, and we do what we know how to do. But there are deeper reasons, without obvious answers, and they're important to you if you care about what science is, or what it should be--or what you should be paying for.
Last Thursday, we discussed some aspects of the problem when a set of causes that we suspect work only by affecting the probability of an outcome we're interested in. The cause may truly be deterministic, but we just don't understand it well enough, so must view its effect in probability terms. That means we have to study a sample, of repeated individuals exposed to the risk factor we're interested in, in the same way you have to flip a coin many times to see if it's really fair--if its probability of coming up Heads is really 50%. You can't just look at the coin or flip it once.
Nowadays, reports are often of meta-analysis, in which, because it is believed that no single study is definitive (i.e., reliable), we pool them and analyze the lot, that is, the net result of many studies, to achieve adequate sample sizes to see what risk really is associated with the risk factor. It should be a warning in itself that the samples of many studies (funded because they claimed and reviewers expected them to be adequate to the task) are now viewed as hopelessly inadequate. Maybe it's a warning that the supposed causes are weak to begin with--too weak for this kind of approach to be very meaningful?
Why, among countless examples, after having done many studies don't we know if HDL cholesterol does or doesn't protect from heart disease, or antioxidants from cancer, or coffee is a risk factor, or obesity is, or how to teach language or math, or avoid misbehavior of students, or whether criminality is genetic (or is a 'disease'), and so on--so many countless examples from the daily news, and you are paying for this, study after study without conclusive results, every day!
There are several reasons. These are serious issues, worthy of the attention of anyone who actually cares about understanding truth and the world we live in, and its evolution. The results are important to our society as well as to our basic understanding of the world.
So, then, why are so many results not replicable?
Here are at least some reasons to consider:
1. If no one study is trustworthy, why on earth would pooling them be?Overall, when this is the situation, the risk factor is simply not a major one!
2. We are not defining the trait of interest accurately
3. We are always changing the definition of the trait or how we determine its presence or absence
4. We are not measuring the trait accurately
5. We have not identified the relevant causal risk factors
6. We have not measured the relevant risk factors accurately
7. The definition of the risk factors is changing or vague
8. The individual studies are each accurate, and our understanding of risk is in error
9. Some of the studies being pooled are inaccurate
10. The first study or two that indicated risk were biased (see our post on replication), and should be removed from meta-analysis....and if that were done the supposed risk factor would have little or no risk.
11. The risk factor's effects depend on its context: it is not a risk all by itself
12. The risk factor just doesn't have an inherent causal effect: our model or ideas are simply wrong
13. The context is always changing, so the idea of a stable risk is simply wrong
14. We have not really collected samples that are adequate for assessing risk (they may not be representative of the population at-risk)
15. We have not collected large enough samples to see the risk through the fog of measurement error and multiple contributing factors
16. Our statistical models of probability and sampling are not adequate or are inappropriate for the task at hand (usually, the models are far too simplified, so that at best they can be expected only to generate an approximate assessment of things)
17. Our statistical criteria ('significance level') are subjective but we are trying to understand an objective world
18. Some causes that are really operating are beyond what we know or are able to measure or observe (e.g., past natural selection events)
19. Negative results are rarely published, and so meta-analyses cannot include them, so a true measure of risk is unattainable
20. The outcome has numerous possible causes; each study picks up a unique, real one (familial genetic diseases, say), but it won't be replicable in another population (or family) with a different cause that is just as real
21. Population-based studies can never in fact be replicated because you can never study the same population--same people, same age, same environmental exposures--at the same time, again
22. The effect of risk factors can be so small--but real--that it is swamped by confounding, unmeasured variables.
This situation--and our list is surely not exhaustive--is typical and pervasive in observational rather than experimental science. (In the same kinds of problems, lists just as long exist to explain why some areas even of experimental science don't do much better!)
A recent Times commentary and post of ours discussed these issues. The commentary says that we need to make social science more like experimental physical science with better replications and study designs and the like. But that may be wrong advice. It may simply lead us down an endless, expensive path that simply fails to recognize the problem. Social sciences already consider themselves to be real science. And presenting peer-reviewed work that way, they've got their fingers as deeply entrenched into the funding pot as, say genetics does.
Whether coffee is a risk factor for disease, or certain behaviors or diseases are genetically determined, or why some trait has evolved in our ancestry...these are all legitimate questions whose non-answers show that there may be something deeply wrong without current methods and ideas about science. We regularly comment on the problem. But there seems to be no real sense that there's an issue being recognized, in opposition to the forces that pressure scientists to continue business as usual---which means that we continue to do more and more and more-expensive studies of the same things.
One highly defensible solution would be to cut support for such non-productive science until people figure out a better way to view the world, and/or that we require scientists to be accountable for their results. No more, "I write the significance section of my grants with my fingers crossed behind my back" because I know that I'm not telling the truth (and the reviewers, who do the same themselves, know that you are doing that).
As it is, resources go to more and more and more studies of the same that yield basically little, students flock to large university departments that teach them how to do it, too, journals and funders make their careers reporting their research results, and policy makers follow the advice. Every day on almost any topic you will see in the news "studies show that....."
This is no secret: we all know the areas in which the advice goes little if anywhere. But politically, we haven't got the nerve to make such cuts and in a sense we would be lost if we had nobody assessing these issues. What to do is not an easy call, even if there were the societal will to act.
Thursday, May 24, 2012
Magical science: now you see it, now you don't. Part I: What we mean by 'risk'
The life, social, and evolutionary sciences have a problem. We posted about the issue of their non-replicability last Friday but that is only part of the problem. They also have non-predictability (see a recent Times commentary), but both replicability and predictability are key elements of science as we know it.
It is difficult to make rigorous assertions that have the kind of predictive power we have come (rightly or wrongly) to expect of science, on the typical if often unstated assumption that our world is law-like, the way it seems that the physical and chemical universe are.
A clear manifestation of the problem is the way that findings in epidemiology and genetic risk say yes factor X is risky, then a few years later no, then yes again, and so on. Can't we ever know if factor X is a cause of some outcome Y? In particular, is X a risk factor? X could be a reason for natural selection, a component of some disease, and so on.
Often, we think of this as a probability. As we've posted before, that means that exposure to that factor yields some probability that the outcome will be observed. If you have two copies of a gene, there is usually a 50% risk or probability that a given one of your children (or parents, or sibs) will also have it.
So, in a strange turn of phrase, coffee or a given HDL cholesterol level is said to be a risk factor for heart disease, or a given genetic variant is a risk factor for cancer. And we try to estimate the level of that risk--the probability that if that factor is present, you will manifest the outcome. Among various possible risk factors, the modern concept of science has it that you are at some net or overall risk of the outcome, like having a heart attack, depending on your exposure to those risk factors.
In evolutionary terms, having a particular genetic variant can have some probability (or some similar measure) of reproducing, or surviving to a particular age. Among various possible genetic risk factors, what you have puts you at some net risk of such outcome, which is your evolutionary fitness in the face of natural selection, for example.
If we assume that enumerated causes of this sort, and that they really are causes, are responsible for a trait then your exposure level can be specified. The causes might truly be deterministic, in the way gravity determines the rate an object will fall--here or anywhere in the universe--but that our incomplete level of knowledge is such that we can only express its effect in terms of probability.
Still, we assume that probabilistic causation is real. When things are the result of probabilities, we can know the causes but can't predict the specific outcome of any given instance. This is the sense in which we know a fair coin will come up Heads 50% of the time, but can't predict the result of a given flip. Actually, and we've posted about this before but the issue of probability is so central to much of science that we keep repeating it, the coin may be perfectly deterministic but we just don't know enough, so that for all practical purposes the result is probabilistic.
In such cases, which are clearly at the foundation of evolutionary inference and of genetic and other biomedical problems, we must estimate the risk associated with a given cause by choosing a sample from all those at risk, and seeing what happened to them. Then, we assume we know the causal structure and can then do what we must be able to do, if this is actual science: predict the outcome. This must be so if the world is causal, even if our predictions are expressed in terms of probabilities: given your genotype you have xx probability of getting yy disease.
So, with our huge and munificently funded science establishment, why is it that day after day the media tout the latest Dramatic Finding....that is just as noisily touted the next day when the previous assertion is overturned?
Why is it that we don't know if coffee is a risk factor for disease? Or isn't? Or isn't for the moment until some new study comes along? Or maybe until some environmental factor changes, like the type of filter paper McDonald's uses in its coffee maker, say--but how would we ever know whether that explains the flip-flopping findings?
Why indeed do we have to continue doing studies of the same purported risk factor to see if they are really, truly risks? These are fundamental questions not about the individual studies, but about the current practice of science itself.
If we look at the reasons, which is tomorrow's post, we'll see how shaky our knowledge really is in these areas, and we can ask whether it is even 'science'.
It is difficult to make rigorous assertions that have the kind of predictive power we have come (rightly or wrongly) to expect of science, on the typical if often unstated assumption that our world is law-like, the way it seems that the physical and chemical universe are.
A clear manifestation of the problem is the way that findings in epidemiology and genetic risk say yes factor X is risky, then a few years later no, then yes again, and so on. Can't we ever know if factor X is a cause of some outcome Y? In particular, is X a risk factor? X could be a reason for natural selection, a component of some disease, and so on.
Often, we think of this as a probability. As we've posted before, that means that exposure to that factor yields some probability that the outcome will be observed. If you have two copies of a gene, there is usually a 50% risk or probability that a given one of your children (or parents, or sibs) will also have it.
So, in a strange turn of phrase, coffee or a given HDL cholesterol level is said to be a risk factor for heart disease, or a given genetic variant is a risk factor for cancer. And we try to estimate the level of that risk--the probability that if that factor is present, you will manifest the outcome. Among various possible risk factors, the modern concept of science has it that you are at some net or overall risk of the outcome, like having a heart attack, depending on your exposure to those risk factors.
In evolutionary terms, having a particular genetic variant can have some probability (or some similar measure) of reproducing, or surviving to a particular age. Among various possible genetic risk factors, what you have puts you at some net risk of such outcome, which is your evolutionary fitness in the face of natural selection, for example.
If we assume that enumerated causes of this sort, and that they really are causes, are responsible for a trait then your exposure level can be specified. The causes might truly be deterministic, in the way gravity determines the rate an object will fall--here or anywhere in the universe--but that our incomplete level of knowledge is such that we can only express its effect in terms of probability.
Still, we assume that probabilistic causation is real. When things are the result of probabilities, we can know the causes but can't predict the specific outcome of any given instance. This is the sense in which we know a fair coin will come up Heads 50% of the time, but can't predict the result of a given flip. Actually, and we've posted about this before but the issue of probability is so central to much of science that we keep repeating it, the coin may be perfectly deterministic but we just don't know enough, so that for all practical purposes the result is probabilistic.
In such cases, which are clearly at the foundation of evolutionary inference and of genetic and other biomedical problems, we must estimate the risk associated with a given cause by choosing a sample from all those at risk, and seeing what happened to them. Then, we assume we know the causal structure and can then do what we must be able to do, if this is actual science: predict the outcome. This must be so if the world is causal, even if our predictions are expressed in terms of probabilities: given your genotype you have xx probability of getting yy disease.
So, with our huge and munificently funded science establishment, why is it that day after day the media tout the latest Dramatic Finding....that is just as noisily touted the next day when the previous assertion is overturned?
Why is it that we don't know if coffee is a risk factor for disease? Or isn't? Or isn't for the moment until some new study comes along? Or maybe until some environmental factor changes, like the type of filter paper McDonald's uses in its coffee maker, say--but how would we ever know whether that explains the flip-flopping findings?
Why indeed do we have to continue doing studies of the same purported risk factor to see if they are really, truly risks? These are fundamental questions not about the individual studies, but about the current practice of science itself.
If we look at the reasons, which is tomorrow's post, we'll see how shaky our knowledge really is in these areas, and we can ask whether it is even 'science'.
Thursday, January 19, 2012
Probability does not exist! Part IV. Here's to your health!
Probability and unique events
Probability and statistics are very sophisticated, technical, often very mathematical sciences. The field is basically about the frequency of occurrence of different possible outcomes of repeatable events.
When events can in fact be repeated, a typical use of statistical theory is to estimate the properties of what's being observed and assume, or believe, that these will pertain to future sets of similar observations. If we know how a coin flipped in repeated observations in the past, we extrapolate that to future flips of that coin--or even to flips of other 'similar' coins. If we observe thousands of soup cans coming off an assembly line, and know what fraction were filled slightly below specified weight, we can devise tests for efficiency of the machinery, or methods for detecting and rejecting under-weight cans. And there are countless other situations in which repeatable events are clearly amenable to statistical decision-making.
When events cannot be or haven't been repeated, a common approach is to assume that they could be, and use the observed single-study data to infer the likely outcomes of possible repetitions. As before, we extend our inference to to new situations in which similar conditions apply. In both truly and singular events there is similar reasoning, regardless of the details about which statisticians vigorously argue.
Everyone acknowledges that there is a fundamentally subjective element in making judgments, as we've described in the previous parts of this series of posts. They are called, for example, significance tests from which one must choose a cutoff level or decision level. But in well-controlled, relatively simple, especially repeatable situations, the theory at least provides some rigorous criteria for making the subjective choices.
The issues become much more serious and problematic when the situation we want to understand is either not replicable, not simple, not well understood, or in which even our idea of the situation is that the probabilities of different possible outcomes are very similar to each other. Unfortunately, these are basic problems in much of biology.
Like dice, outcome probabilities are estimated from empirical data--past experience or experiments and finite (limited) samples. Estimation is a mathematical procedure that depends on various assumptions and values, like averages of some measured trait, have measurement error and so on. One might question these aspects of any study of the real world, but the issue for us here is that these estimates rest on some assumptions and are retrospective, because they are based on past experience. But what we want those estimates for is to predict, that is to use them prospectively.
This is perhaps trivial for dice--we want to predict the probability of a 6 or 3 in the next roll, based on our observations of previous rolls. We can be confident that the dice will 'behave' similarly. Remarkably, we can also extrapolate this to other dice fresh from a new pack, that have never been rolled before, but only on the assumption that the new dice are just like the ones our estimates were derived from. We can never be 100% sure, but it seems usually a safe bet--for coin-flips and dice.
Predicting disease outcomes
But this is far from the case in genetics, evolution, and epidemiology. There, we know that no two people are genetically alike, no two have exactly the same environmental or lifestyle histories. So that people are not exactly like dice. Further, genes change (by mutation) and environments change, and these changes are inherently unpredictable as far as is known. Thus, unlike dice, we cannot automatically extrapolate estimates from past experience such as genes or lifestyle factors and disease outcomes, to the future -- or from past observations to you. That is, often or even typically, we simply cannot know how accurate an extrapolation will be, even if we completely believe in the estimated risks (probabilities) that we have obtained.
And, any risk estimation is inherently elusive anyway because people respond. If you're told your risk of heart disease is 12%, that might make you feel pretty safe and you might stop exercising so much, or add more whipped cream to your cocoa, or take up smoking, but if you're told your risk is 30% you might do the opposite. Plus, there's some thought that heart disease might have an infectious component, and that's never included in risk estimators, and is inherently stochastic anyway. And, if there's a genetic component to risk, that can vary to the extent that many families might have an allele unique to them, which can't be included in the model because models are built on prior observations that won't apply to that family.
A second issue is that even if the other things are orderly, in genetics and epidemiology and trying to understand natural selection and evolution, we are trying to understand outcomes whose respective probabilities are usually small and usually very similar. As we've tried to show with the very similar (or identical?) probabilities of Heads vs Tails, or of 6 vs 3 on a die, this is very difficult even in highly controlled, easily repeatable situations. But this simply is often not nearly the case in biology.
Here the risks of this vs that genotype, at many different genes simultaneously, are very indivdually small and similar, and that's why GWAS requires large samples, often gets apparently inconsistent results from study to study, accounts for small fractions of heritability (the estimated overall genetic contribution). This means that it is very difficult to identify genetic contributions that are statistically significant--that have strong enough effects to pass some subjective decision-making criterion.
This means it's very difficult to estimate a statistically reliable risk probability to persons based on their genotype, and certainly makes it difficult to assign a future risk. Or to know whether each person with that genotype has the same risk as the average for the group. That is why many of us think that the current belief system, and that's what it is!, in personalized genomic medicine, is going to cost a lot for relatively low payoff, compared to other things that can be done with research funds---for example, to study traits that really are genetic: for which the risk of a given genotype is so great, relative to other genotypes, that we can reliably infer causation that is hugely important to individuals with the genotype, and for which the precision of risk estimates is not a big issue.
Probabilities and evolution
Similarly, in reconstructing evolution, if the differences among contemporary genotypes in terms of adaptive (reproductive) success are very similar, the actual success of the bearers of the different genotypes will be very similar, and these are probabilities (of reproduction or survival). And if we want to estimate selection situations in the distant, unobserved past, from net results we see today, the problems are much more challenging even if we thoroughly believe in our theories about adaptive determinism or genetic control of traits. Past adaptation also occurs, usually we think, very slowly over many many generations, making it very difficult to apply simple theoretical models. Even to look for contemporary selection, other than in clear situations such as the evolution of antibiotic or pesticide resistance, is very challenging. Selective differences must be judged only from data we have today, and directly observing causes for reproductive differences in the wild today is difficult and requires sample conditions rarely achievable. So naturally it is hard to detect a pattern, hard to make causal assertions that are more than storytelling.
And, finally
We hope to have shown in this series of posts why we think we have to accept that 'probability' is an elusive notion, often fundamentally subjective and not different from 'belief'. We set up criteria for believability (statistical significance cutoff values) upon which decisions--and in health, lives--depend. The stability of the evidence and vagaries of cutoff-criteria, and our often reluctance to accept results we don't like (treating evidence that doesn't pass our cutoff criterion but is close to it as 'suggestive' of our idea rather than rejecting our idea), all conspire to raise very important issues for science. The issues have to do with allocation of resources, egos, and other vested interests upon which serious decisions must be made.
In the end, causation must exist (we're not solopsists!), but randomness and probability may not exist other than in our heads. The concept provides a tool for evaluating things that do exist, but in ways that are fundamentally subjective. But we are in such a hurry in the system of science and its use that has evolved that we are not nearly humble enough in regard to what we know about what we don't know. That is a fact that exists, whether probability does or not!
It is for these kinds of reasons that we feel research investment should concentrate on areas where the causal 'signal' is strong and basically unambiguous--traits and diseases for which a specific genetic causation is much more 'probable' than for the complex traits that are soaking up so many resources. Even the 'simple' genetic traits, or simple cases of evolutionary signal, are hard enough to understand.
Probability and statistics are very sophisticated, technical, often very mathematical sciences. The field is basically about the frequency of occurrence of different possible outcomes of repeatable events.
When events can in fact be repeated, a typical use of statistical theory is to estimate the properties of what's being observed and assume, or believe, that these will pertain to future sets of similar observations. If we know how a coin flipped in repeated observations in the past, we extrapolate that to future flips of that coin--or even to flips of other 'similar' coins. If we observe thousands of soup cans coming off an assembly line, and know what fraction were filled slightly below specified weight, we can devise tests for efficiency of the machinery, or methods for detecting and rejecting under-weight cans. And there are countless other situations in which repeatable events are clearly amenable to statistical decision-making.
When events cannot be or haven't been repeated, a common approach is to assume that they could be, and use the observed single-study data to infer the likely outcomes of possible repetitions. As before, we extend our inference to to new situations in which similar conditions apply. In both truly and singular events there is similar reasoning, regardless of the details about which statisticians vigorously argue.
Everyone acknowledges that there is a fundamentally subjective element in making judgments, as we've described in the previous parts of this series of posts. They are called, for example, significance tests from which one must choose a cutoff level or decision level. But in well-controlled, relatively simple, especially repeatable situations, the theory at least provides some rigorous criteria for making the subjective choices.
The issues become much more serious and problematic when the situation we want to understand is either not replicable, not simple, not well understood, or in which even our idea of the situation is that the probabilities of different possible outcomes are very similar to each other. Unfortunately, these are basic problems in much of biology.
Like dice, outcome probabilities are estimated from empirical data--past experience or experiments and finite (limited) samples. Estimation is a mathematical procedure that depends on various assumptions and values, like averages of some measured trait, have measurement error and so on. One might question these aspects of any study of the real world, but the issue for us here is that these estimates rest on some assumptions and are retrospective, because they are based on past experience. But what we want those estimates for is to predict, that is to use them prospectively.
This is perhaps trivial for dice--we want to predict the probability of a 6 or 3 in the next roll, based on our observations of previous rolls. We can be confident that the dice will 'behave' similarly. Remarkably, we can also extrapolate this to other dice fresh from a new pack, that have never been rolled before, but only on the assumption that the new dice are just like the ones our estimates were derived from. We can never be 100% sure, but it seems usually a safe bet--for coin-flips and dice.
Predicting disease outcomes
But this is far from the case in genetics, evolution, and epidemiology. There, we know that no two people are genetically alike, no two have exactly the same environmental or lifestyle histories. So that people are not exactly like dice. Further, genes change (by mutation) and environments change, and these changes are inherently unpredictable as far as is known. Thus, unlike dice, we cannot automatically extrapolate estimates from past experience such as genes or lifestyle factors and disease outcomes, to the future -- or from past observations to you. That is, often or even typically, we simply cannot know how accurate an extrapolation will be, even if we completely believe in the estimated risks (probabilities) that we have obtained.
And, any risk estimation is inherently elusive anyway because people respond. If you're told your risk of heart disease is 12%, that might make you feel pretty safe and you might stop exercising so much, or add more whipped cream to your cocoa, or take up smoking, but if you're told your risk is 30% you might do the opposite. Plus, there's some thought that heart disease might have an infectious component, and that's never included in risk estimators, and is inherently stochastic anyway. And, if there's a genetic component to risk, that can vary to the extent that many families might have an allele unique to them, which can't be included in the model because models are built on prior observations that won't apply to that family.
A second issue is that even if the other things are orderly, in genetics and epidemiology and trying to understand natural selection and evolution, we are trying to understand outcomes whose respective probabilities are usually small and usually very similar. As we've tried to show with the very similar (or identical?) probabilities of Heads vs Tails, or of 6 vs 3 on a die, this is very difficult even in highly controlled, easily repeatable situations. But this simply is often not nearly the case in biology.
Here the risks of this vs that genotype, at many different genes simultaneously, are very indivdually small and similar, and that's why GWAS requires large samples, often gets apparently inconsistent results from study to study, accounts for small fractions of heritability (the estimated overall genetic contribution). This means that it is very difficult to identify genetic contributions that are statistically significant--that have strong enough effects to pass some subjective decision-making criterion.
This means it's very difficult to estimate a statistically reliable risk probability to persons based on their genotype, and certainly makes it difficult to assign a future risk. Or to know whether each person with that genotype has the same risk as the average for the group. That is why many of us think that the current belief system, and that's what it is!, in personalized genomic medicine, is going to cost a lot for relatively low payoff, compared to other things that can be done with research funds---for example, to study traits that really are genetic: for which the risk of a given genotype is so great, relative to other genotypes, that we can reliably infer causation that is hugely important to individuals with the genotype, and for which the precision of risk estimates is not a big issue.
Probabilities and evolution
Similarly, in reconstructing evolution, if the differences among contemporary genotypes in terms of adaptive (reproductive) success are very similar, the actual success of the bearers of the different genotypes will be very similar, and these are probabilities (of reproduction or survival). And if we want to estimate selection situations in the distant, unobserved past, from net results we see today, the problems are much more challenging even if we thoroughly believe in our theories about adaptive determinism or genetic control of traits. Past adaptation also occurs, usually we think, very slowly over many many generations, making it very difficult to apply simple theoretical models. Even to look for contemporary selection, other than in clear situations such as the evolution of antibiotic or pesticide resistance, is very challenging. Selective differences must be judged only from data we have today, and directly observing causes for reproductive differences in the wild today is difficult and requires sample conditions rarely achievable. So naturally it is hard to detect a pattern, hard to make causal assertions that are more than storytelling.
And, finally
We hope to have shown in this series of posts why we think we have to accept that 'probability' is an elusive notion, often fundamentally subjective and not different from 'belief'. We set up criteria for believability (statistical significance cutoff values) upon which decisions--and in health, lives--depend. The stability of the evidence and vagaries of cutoff-criteria, and our often reluctance to accept results we don't like (treating evidence that doesn't pass our cutoff criterion but is close to it as 'suggestive' of our idea rather than rejecting our idea), all conspire to raise very important issues for science. The issues have to do with allocation of resources, egos, and other vested interests upon which serious decisions must be made.
In the end, causation must exist (we're not solopsists!), but randomness and probability may not exist other than in our heads. The concept provides a tool for evaluating things that do exist, but in ways that are fundamentally subjective. But we are in such a hurry in the system of science and its use that has evolved that we are not nearly humble enough in regard to what we know about what we don't know. That is a fact that exists, whether probability does or not!
It is for these kinds of reasons that we feel research investment should concentrate on areas where the causal 'signal' is strong and basically unambiguous--traits and diseases for which a specific genetic causation is much more 'probable' than for the complex traits that are soaking up so many resources. Even the 'simple' genetic traits, or simple cases of evolutionary signal, are hard enough to understand.
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