Comment by gregable | Hacker News Reader

Comment by gregable | Hacker News Reader

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I fully concede this (and jacobolus's) point. I'm not a health researcher either, so don't take my word for any of this.

Building a double-blind clinical trial regarding changing people's diets over their lifetime and observing cardiovascular mortality is very hard.

Some clinical studies do exist, but they are usually looking over shorter terms and at heart disease risk factors rather than mortality. These show similar results:

https://www.ncbi.nlm.nih.gov/pubmed/16368299 - Systolic blood pressure decreased from 144 (SE +/- 1.1) to 134 mm Hg (SE +/- 2, P < .001), and diastolic blood pressure decreased from 87.4 (SE +/- 1.2) to 83.4 mm Hg (SE +/- 1.2, P < .05) from tomato extract.

https://www.ncbi.nlm.nih.gov/pubmed/26328470 - In the subgroup of hypertensives, quercetin (onion skin extract) decreased 24 h systolic BP by -3·6 mmHg (P=0·022) when compared with placebo (mean treatment difference, -3·9 mmHg; P=0·049).

Many of the epidemiological studies also adjust for many of the features that you might look for. For example https://academic.oup.com/ije/article/30/1/130/619054 adjusts for "age, randomized treatment, body mass index (BMI), smoking, alcohol intake, physical activity, history of diabetes, history of hypertension, history of high cholesterol, and use of multivitamins" and comes to the same conclusion.

> Building a double-blind clinical trial regarding changing people's diets over their lifetime and observing cardiovascular mortality is very hard.

But it is the only diet research that can actually generate knowledge.

The studies you cite are nice and all, but the first two are for studies that measured outcomes after 6-8 weeks. If you assume an average lifespan of 70 years, the longest study represented ~1/450th of a human lifespan. And, as you mention, they only measure what we think are valid indicators, not actual outcomes! And the third study may control for all of the things you mention, but it doesn't control for the other foods the study participants ate!

Really the only piece of information that you need to know regarding red meat is that public health messaging for the last 50 years has been that people should limit red meat intake. And it was successful. According to this[0], red meat consumption has decreased from 133lbs per person in 1965 to 107lbs per person in 2016. But it is during this exact same time period that the obesity epidemic came into existence. So, perhaps red meat is marginally bad for you, but it is almost certainly not cause of all of the various afflictions that we think are caused in some way by our diets.

[0]: https://www.nationalchickencouncil.org/about-the-industry/st...

Using a defensive position from the "white" meat interest is likely inaccurate.

Red meat consumption is on the rise: https://www.bloomberg.com/news/articles/2018-01-02/have-a-me...

The article you linked is showing data for red meat + poultry, so it doesn’t support the statement that red meat consumption is on the rise.

If you assume red meat declined and white meat made up the difference, sure - but you'd be wrong.

https://www.ers.usda.gov/data-products/livestock-meat-domest...

Scroll down to the Beef pivot table. Increases year over year.

Yes, red meat consumption is on the rise, along with others.

Ok, so your first link was bad because it included poultry, your second link is worse because it is total production, not per capita consumption (and some production is exported). You might be right, but still don’t have any data to support your claim.

Domestic disappearance is a measure of how much beef is used on the domestic market. So unless the meat is being used for not consumption, then yes.

This link[0] also from the USDA, shows availability of red meat (pork + beef) as decreasing from >120 pounds per person in 1970 to about 100 pounds per person in 2014. The best I could gather from the spreadsheets on the link you provided was that red meat consumption has gone up in the last 4 years (2014-2017, not covered by my link), but the overall trend has been way down over the last 40 years, unless I'm grossly mistaken.

And, given that this same time frame has coincided with the obesity epidemic, it seems hard to make the case that red meat is bad for you. I think the best argument you could make is that red meat is bad for you in a small way, but something else is causing the Really Bad Stuff™. I'm open to changing my mind, but the data has to lead me there.

[0]: https://www.ers.usda.gov/amber-waves/2017/januaryfebruary/us...

> But it is the only diet research that can actually generate knowledge.

> The studies you cite are nice and all, but the first two are for studies that measured outcomes after 6-8 weeks. If you assume an average lifespan of 70 years, the longest study represented ~1/450th of a human lifespan. And, as you mention, they only measure what we think are valid indicators, not actual outcomes! And the third study may control for all of the things you mention, but it doesn't control for the other foods the study participants ate!

I fully agree with everything you wrote about the science here.

However, many of the epidemiological studies are over very large populations and time periods, so it's not as though the body of evidence consists of a 6-8 week studies. And honestly the fact that the effects are so strong in such a short period of time strengthen the argument in some ways.

> Really the only piece of information that you need to know regarding red meat is that public health messaging for the last 50 years has been that people should limit red meat intake. And it was successful. According to this, red meat consumption has decreased from 133lbs per person in 1965 to 107lbs per person in 2016. But it is during this exact same time period that the obesity epidemic came into existence. So, perhaps red meat is marginally bad for you, but it is almost certainly not cause of all of the various afflictions that we think are caused in some way by our diets.

Interesting response. I never claimed that meat was bad, only that leafy vegetables were good. I find it almost incredible that your argument against the nutritional value of leafy vegetables is that there aren't enough double blind clinical trials, and then your defense of red meat uses a time correlation that doesn't adjust for any confounding variables at all. It also cites a document from an meat industry group. If you look at USDA numbers (https://www.ers.usda.gov/data-products/food-availability-per...), overall meat consumption has been consistently and slowly rising, however beef is being replaced with chicken, so yes red meat consumption is declining.

> However, many of the epidemiological studies are over very large populations and time periods, so it's not as though the body of evidence consists of a 6-8 week studies. And honestly the fact that the effects are so strong in such a short period of time strengthen the argument in some ways.

The studies all tell us nothing about the long term implications of diet. The long study tells us nothing because it is purely correlational. The short studies tell us nothing because they are not measuring the thing we care about (long term health outcomes). You can't somehow generate knowledge by combining two types of studies which each are incapable of generating knowledge about the thing we care about.

> I never claimed that meat was bad, only that leafy vegetables were good.

Yeah, I clearly was hallucinating. Nowhere did you mention red meat. And yet I rambled on about it for a paragraph. :-) Sorry!

But:

> I find it almost incredible that your argument against the nutritional value of leafy vegetables is that there aren't enough double blind clinical trials, and then your defense of red meat uses a time correlation that doesn't adjust for any confounding variables at all.

That's because correlation cannot demonstrate causation, but it can demonstrate the lack of causation. It's all about confounding variables, as you point out. If you have two trend lines which correlate, there can always be a third, unmeasured variable which is actually causing the two measured variables. However, if you have trend lines which do not correlate, then the only way they can be related is by some extremely convoluted chain of causation. Which, I guess, is technically possible, but rapidly approaches 0 probability.

In other words, epidemiology is very useful for narrowing the possibly explanations for some phenomena, and it can suggest places where we should continue focusing our attention, but it can't actually tell us if we've found the right culprit.

Correlation cannot demonstrate lack of causation, only lack of linear dose response. (Incl. Multivariate)

Any nonlinear enough process will show with wrong low correlations.

Suppose the process is stepwise - silly sample hypothesis, eating over x grams meat a day is harmful. It is binary. Correlation is meaningless for such a process. You have to use significantly more powerful statistic tools. Logistic regression will somewhat work for such a process for instance if the boundary is somewhat fuzzy. (You can put exact mathematical bound on how fuzzy.)

To extend it for multivariate analysis you get to use such complex tools as nonlinear ICA. (Instead of CCA and PCA for linear.)

I’m using the wrong terminology, I’m certain, but what I’m getting at is that observational (which is probably what I mean instead of correlational) studies can negate a hypothesis, but they can never prove one. So, one hypothesis is that red meat consumption leads to heart disease. If the data shows the trend lines of heart disease and red meat consumption going in opposite directions, then you know your hypothesis is wrong. But if the trend lines are going in the same direction, the hypothesis hasn’t been proven; all you know is that it is a potential explanation.

Now, it may be that there is a different relationship, like the one that you mention: that eating more than X grams per day is harmful. The data from an observational study can negate this hypothesis (if there is no threshold pattern evident in the data), but it cannot prove the hypothesis. All it can do, at best, is let you know that the hypothesis might be true.

Observational studies can be post- interventional and in this case can prove or not a hypothesis that a certain widespread intervention was successful.

This is very rare and hard to run, so more direct interventional short term studies are used more often.

Suppose a certain country believed an observational study pointing that eating more than certain amount of sugar is harmful (suppose backed by other evidence). They instated hard tax and limit on sugar in foods as an intervention. After some time a big observational study is ran and it is assessed if the intervention brought results.

This is precisely what observational studies (follow up) should do. Not really try to find hypotheses - these need more direct evidence.

Yeah, but if there is an intervention, then it is not purely an observational study. The big problem with nutrition science is that most of the long term studies are purely observational. There is no intervention at all. All they are measuring is how certain observed measurements correlate (or not) with other observed measurements. These are the types of studies I'm talking about.

Double blind is not quite the only way to acquire knowledge.

Even Karl Popper believed in astronomy.

I'm been chewing over your comment since last night, so thanks! These are just some thoughts, which I don't have a high degree of confidence in, but here goes anyway:

I guess the difference between astronomy and diet is that, in my conception, at least, the systems studied in astronomy tend to be less complex than the human body and, therefore, they can be modeled relatively easily. My hunch is that this has something to do the human body being a self regulating, though climate systems on earth are probably of a similar level of complexity, yet are not self regulating (or at least, not in the same way).

So, the difference in complexity between a astronomical system and the human body would be akin to the difference in complexity between a program which implements some relatively straightforward business logic vs. one that implements a system which can identify and hunt prey. You can probably prove the correctness of most programs which implement basic business logic, but I'm guessing a system which can identify and hunt prey probably cannot be proven to be formally correct. You would need to test it in the real world by tweaking inputs and measuring how it responds.

I think that's an interesting approach. For the record, I absolutely share your healthy suspicion that health sciences need a bit (much) more hand holding than some (most) other disciplines.

To needle you slightly further, though, I'd recommend you consider John Snow's investigation into cholera clusters near certain water sources in London: https://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outb...

What should we make of that?

Ok, more generally...

I'm glad you're giving this some thought, because this line of inquiry (or something like it) really affected the way I think about science and knowledge. It's an incredibly rewarding line to go down, though not entirely free of frustrating dead ends.

If you find this stuff interesting, you might want to poke around a bit in 20th century philosophy. There are some (needlessly lengthy) summaries at the Stanford Encyclopedia of Philosophy, esp. under "Logical Empiricism." I hesitate to recommend that, though, it's long on history and short on significant arguments and though-provoking examples.

"Two Dogmas of Empiricism" by Quine profoundly affected the way I think about all of this. It's not an easy piece, I had to read it a few times and kept turning it over in my head for months. At first I thought it was nonsense. Then I thought it was just wrong. Then it sort of wormed its way into my head and changed a few core things I believed about knowledge. Or reorganized them. Karl Popper, Rudolf Carnap, and Wittgenstein all had profound contributions here too.

At the end of Quine's paper he says that White basically said everything important on this already in "The Analytic and the Synthetic: An Untenable Dualism." I haven't read that, but maybe it's the accessible version. One could hope.

http://www.thatmarcusfamily.org/philosophy/Course_Websites/R...

Changing and tracking diet long-term is nigh impossible. Has there been any work that you know of using economic incentives as a proxy? i.e. test group is granted coupons good for fruits and vegetables, while control group is granted fully discretionary coupons

Obviously it's an imperfect experiment, but it seems like a pragmatic compromise.

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