Veganmama's initial response to Denise's flawed study:
OH MY. By request of beautiful Freelee, I've taken a look at Denise's analysis. I'm an epidemiologist, and on top of that my research focuses on cancer (not that this makes me completely infallible, but at least I feel equipped to provide an informed critique of her statistical capability). Dr. Campbell was certainly gracious in his response to criticism, but I cannot be so kind. Denise is incredibly naive in her crude analysis of the raw data. She uses correlations and ecologic comparisons to draw conclusions about relationships between diet and outcome (cancer, cardiovascular disease, etc.). WRONG WRONG WRONG!!
A correlation does not an association make.
And, as epidemiologists, our studies are intended to determine associations between exposures and disease. (Yes, there are special methods to determine actual causes of disease, but for most of us, associations will do.) See point 1 below for more on this.
Denise, while meticulous, went through a series of exercises only to:
1) Provide a series of correlations, which honestly, is just the FIRST STEP of any good statistical analysis. Let me explain in a nutshell - a correlation is a linear (assumes a "straight-line" relationship - but not all things are related in this manner), unadjusted (does not account for multiple factors that could potentially confound the relationship between an exposure, like diet, and outcome, like cancer), and non-directional (it does not say if one caused the other or the other way around). An association, on the other hand, is generally adjusted for potential confounding factors and - if a study is properly conducted - gives us an idea of temporality or direction. While we certainly look at correlations between all factors (i.e. between the exposure, potential confounding factors, and the outcome), typically more complex modeling of the data ensues so that multiple factors can be accounted for when investigating the relationship between an exposure and the outcome.
2) Much of her conclusions are drawn from purely ecologic data - that is, data that is in aggregate - such as evaluating total cholesterol and colorectal cancer (as Denise does). Sure, it can be informative, but it doesn't tell us anything about some of the other factors that might be related to cholesterol and colorectal cancer. And while she does perform a stratified analysis (stratifying on schistomiasis), which is a form of "adjustment for confounding"), it still does not take into account other possible confounders and still only tells us about general patterns, but nothing of individual-level associations. Furthermore, she doesn't present results for regions with schistomiasis. What if there was also little correlation between cholesterol and colorectal cancer in these regions? There might be other factors unaccounted for.
Ecologic studies are considered to be at the bottom of the "epidemiologic study totem pole." And we can NOT draw individual-level conclusions from them, i.e. we cannot say that an individual who consumes less fat will, on average, be protected from breast cancer (even if that's true, we cannot draw this conclusion from an ecologic study - there's even a term for it: "ecologic fallacy").
OK, my disclaimer: I'm an epidemiologist, and yes, scientists are NOT objective (I'll say it: I'm an ardent veggie with a happy veggie family). Hell, science is not objective. I mean, you could be given a blob of numbers that mean nothing. It takes some context, interpretation, and data processing to make anything meaningful out of those numbers. Yes, scientists can be biased, and so can the studies they conduct, and the analysis of those studies. But good scientists do the best they can, are open about their methods, and fair when discussing their results. I applaud Dr. Campbell for making his raw data available - very few scientists do this. I will be totally honest and say I have not read "The China Study" (I guess I feel it'd just be preaching to the choir, but I think I will read it now...). But I know enough to know that Denise's analysis was crude at best and completely wrong at worst. No card-carrying epidemiologist would EVER be able to publish her results and draw the conclusions that she does.
I've posted the following comment on Denise's blog (which, was there for a few minutes, and now when I go back to the site, it is mysteriously not there anymore...):
Your analysis is completely OVER-SIMPLIFIED. Every good epidemiologist/statistician will tell you that a correlation does NOT equal an association. By running a series of correlations, you’ve merely pointed out linear, non-directional, and unadjusted relationships between two factors. I suggest you pick up a basic biostatistics book, download a free copy of “R” (an open-source statistical software program), and learn how to analyze data properly. I’m a PhD cancer epidemiologist, and would be happy to help you do this properly. While I’m impressed by your crude, and – at best – preliminary analyses, it is quite irresponsible of you to draw conclusions based on these results alone. At the very least, you need to model the data using regression analyses so that you can account for multiple factors at one time.
** Updated to include an example from Denise's analysis rather than my original example of fat consumption and breast cancer.
I just realized that there's still some trail left about "fat consumption and breast cancer". I should clarify. Denise looked at cholesterol level in each Chinese county and the corresponding incidence rates of colorectal cancer in that region (this is what makes it "ecologic" - each dot represents a county). But the statement still stands - we can't make individual-level conclusions about cholesterol, colorectal cancer, and schistomiasis.
I also just want to add that when she refers to "statistical significance", all that's being tested is the "null hypothesis" that there is no correlation (i.e. correlation = 0). it is not testing whether an exposure is or is not arisk factor for the outcome, even though Denise uses this term loosely.
Its purpose was to re-articulate the limitations of her analysis, but also to inform. Good science should prevail, after all.
As promised, I'm posting my response to your email [yes, she emailed me] on your site. You asked that I provide some tips on where to start and how to proceed. BTW, you mentioned "epidemiology secrets" and I just want to say: no "secrets"!! Epidemiology is just critical thinking, but with numbers. It's no different from many other disciplines. Maybe some time you can help me with writing (scientists are generally terrible writers, hehe).
Note: I've included some comments on what went wrong and how it can be corrected merely for demonstrative purposes - not at all malicious attacks, OK? This is how we all learn after all. In caps, I will highlight steps in the action plan for you.
STEP 0: Do a literature search. I find it helpful to keep an excel spreadsheet with columns for author, title, journal, year, summary of paper, strengths of the study, weaknesses, and concluding remarks. This is essential, as one shouldn't just blindly go into an analysis without having at least some background information on the subject matter. No need to be an expert, but good to know what's already out there, and what needs to be done.
For this discussion, the outcome will be colorectal cancer, since you used it on your post. Similarly, the primary exposure of interest will be total cholesterol. By by basing your conclusions on uncorrected correlations alone, you've made a huge leap that doesn't have much ground to stand on. The simple correlations are biased, as you yourself pointed out when evaluating total cholesterol, schistomiasis, and colorectal cancer. As such, if you don't adjust for potential confounders via multiple regression, the association you observe is biased. We almost always need to adjust for confounders, and this is very true in your case.
STEP 1: It's a good habit to evaluate the correlations between all exposures and also between all exposures and the outcome at the individual level. So, for *every* analysis you plan on doing, run create scatterplots for every X against X and every X against Y, using the *individual* data (where possible), and provide the correlation + 95% confidence interval for each.
STEP 2: Create histograms for every exposure of that is categoric and density plots (or you can create histograms with very narrow bars) for every exposure that is continuous. This will tell you how the variables are distributed and what the appropriate summary statistics for them would be. For example, if total cholesterol is not normally distributed (follow a bell curve) then *median* total cholesterol might be a better summary statistic then *mean* total cholesterol (good to know when you present descriptive statistics of the data you're using). Sometimes it's useful to present different stats for a single variable.
2. Individual data vs. aggregated data:
You stated you didn't see much curvature, but keep in mind that you were presenting with aggregated data (eg. average total cholesterol for all individuals) instead of including individual-level data (the exposure and outcome for a single individual). Consequently, there was a big loss in information, and you can't make accurate decisions on how to model your data if you plot aggregated data. Related to this, your analysis was ecologic (used aggregated/grouped data) but you made individual-level conclusions when you used the term "risk factor." This is referred to as an ecologic fallacy - and it's just that. A fallacy. For example, all we can say based on your cholesterol-colorectal cancer example (the one that doesn't account for schistomiasis) is that the counties with higher mean total cholesterol tend to have higher incidence rates of colorectal cancer. We can't make the leap to calling cholesterol a *risk factor* for colorectal cancer.
STEP 3: Don't aggregate your data in your analysis. Why? You lose A LOT of information when you aggregate data and you can bias your results. So keep that data at the individual-level. For descriptive tables, by all means, aggregated data is necessary for obvious reasons. But in your analysis, individual-level data when you've got it is essential.
3. The right regression model:
One of your outcomes was incidence rates of colorectal cancer. When you do your analysis with individual-level data, with incidence rates of colorectal cancer as your outcome, linear regression = WRONG model. Make sure you know which models to use and when. To start - when modeling "raw" rates (case counts and person time), we almost always use Poisson regression, and often we need to account for overdispersion as well. Get to know some of the other common regression models as well.
STEP 4: Write out all of the primary exposures of interest you want to investigate and the corresponding outcome of interest and how you're setting up your outcome variable (are you interested in colorectal cancer *incidence rates*, *prevalence*, a simple yes/no the person has colorectal cancer?)
STEP 5: Write out what the appropriate regression model would be for the different analyses you plan to conduct.
These are factors that are related to the exposure and the outcome of interest such that *not* adjusting for them will produce a biased association between exposure and outcome. As you saw, schistomiasis might be a confounder. And in fact, county might be too - and is actually upstream of schistomiasis in some sense, right? Two confounders that almost *always* must be included in a model are AGE and SEX (provided your analysis isn't restricted to one sex). This is especially true for chronic disease (eg. cardiovascular disease and cancer). In this particular case, body mass index (BMI) would be very important to include as well. County may also be important.
STEP 6: For every analysis you do, write out all potential confounders you can think of and why. You know the data better than I do as you've worked with it extensively. And, from STEP 0, you'll know your context.
STEP 7: Write out *how* the confounders are related to the exposure and outcome. Is the confounder protective (i.e. decrease risk) for the outcome? Or is it a risk factor? How is it associated with the primary exposure of interest? This is where those scatterplots in STEP 1 come in handy! The purpose of this is to give you an idea of *how* an observed association might be biased if you *don't* adjust for certain confounders. It is tedious, but thorough and, like STEP 6, will allow you to approach your analyses with more contextual background.
5. "Cleaning" and "recoding" your data:
Raw data is not *in and of itself* a bad thing. It is simply the data in its original form. But in order to be useful for analysis we often need to "clean" it and "recode" it. When I say "clean" it, I mean setting up the *dataset* that is free (to the greatest extent possible) of unnecessary data (for example, if you're interested in ovarian cancer, you wouldn't include men), or mistakes (for example, if an individual in the data was coded as being a man with ovarian cancer, this is clearly wrong). In this case, you might either omit it since you don't have a way to check which is correct or, based on other data for that individual choose to change "man" to "woman" or "ovarian cancer" to "no ovarian cancer." "Recoding" means setting up the *variables* to be useful. For example, we might recode BMI in categories of underweight, normal, overweight, and obese rather than leave it as continuous. Some variables may already be categoric, if the corresponding data were collected that way.
STEP 8: Clean your data. You will likely need to set up multiple datasets.
STEP 9: Write out *how* you've cleaned your data. (This is good record keeping.)
STEP 10: Recode your data. This might include combining variables too.
STEP 11: Create a "data dictionary" similar to the one on the Oxford site. But in addition, include a description of how you've coded your data (eg. 1=underweight, 2=normal, 3=overweight, 4=obese). Again, good for record keeping, but also "keeps you honest" so others know how you set up your data. This will often be apparent when you present your results, but not always. It's a good habit to keep track of this, in any event.
STEP 12: Replot all newly *categorized* variables against the outcome(s) of interest. Why? Because the categorized data may reveal non-linear relationships with the outcome (in fact, this is a strength of categorizing data - that we can account for some non-linear relationships). For example, underweight might be a risk for something, whereas normal BMI is protective, while overweight and obese are a risk ("U-shaped").
6. Exploration of your data through descriptive statistics:
Almost all scientific papers start out with a "Table 1" which presents a description of the data. It tells us things like What's the % of women and men in our data, What is the proportion of people with and without the exposure and with and without the outcome?
STEP 13: Create descriptive tables of all relevant variables. This includes your primary exposure of interest, confounders, and outcome. Obviously, you will have different tables for each analysis as you're interested in different primary exposures (cholesterol? meat? total caloric intake?) and outcomes (cardiovascular disease? colorectal cancer? bladder cancer?). To save time, you might include all relevant exposures and confounders in rows, and cross-classify them with all outcomes of interest in columns.
The fun part.
STEP 14: Run your models. Keep track of what you include in your models b/c oftentimes we will evaluate several models for each analysis depending on what's called "fit statistics." Since you are familiar with p-values and I assume interpretation of beta coefficients, use these to help inform you of which variables to include in your final model *within the context of the analysis at hand* (this is key - if you have reason to believe that a confounder is important to include, keep it in the model even if it's non-significant).
STEP 15: Create tables for results from *all* analyses (including the models you decide to can in favor for another one) and what regression model was used. This is much more transparent than simply producing your final model.
There's more "post-analysis" stuff that should be done, but really Steps 1-15 is a pretty thorough.
I can't stress this enough. This is a long-term goal for sure, especially as you will likely end up with multiple papers! But once you think you've got the data set-up and analyses down, you need to write it up and send it on for peer-review. Peer-review is not perfect for sure, but it is the best measure we have for good science. It gives credibility to your efforts. Besides, you *do* want to be acknowledged for your efforts, right? By publishing in a peer-reviewed journal, you're more likely to gain more widely publicized attention, which I think should be the goal of most epidemiological studies; we want to improve public health through informing not only our peers, but also the public.
As a last note, I know this is a huge undertaking, but these are steps to a thorough analysis. I have no doubt you're capable of tackling it.
PS. I'm sure you already planned to do this, but make all of the above available. With your large readership you can make this a collaborative effort.
I visited this site to share our recent findings on Denise's study.
This is from the blog "Letthemeatmeat", below is a of the discussion between the Author of the blog post and "Paleo Robert" in regards to Denise's critique of THC.
Sorry it may be a bit disjointed but I found it challenging to copy and past the comments in the correct format.
To help her chances even more, I'd suggest she first send her manuscript to a few experts in the field for review, including at least one who is likely to disagree with her initial hypothesis (maybe even Campbell himself). It might seem odd to ask one's adversaries for help, but scientists do it all the time; they're trained to separate their own ideas from evaluations of the merits of others' work. An honest critic is far more likely to help you spot weaknesses in your methods than is a like-minded researcher, however competent.
I wish her luck. Despite my respect for credentials, I champion the cause of lay researchers everywhere.
Author: That isn't science, that's clinging to excuses to write her off. Shouldn't the lack of peer review be an incitement to questioning her claims, not just completely ignoring them?
Nope. The burden of proof lies with those challenging the established model; those defending it are under no obligation to accept the challenger's ideas as valid at face value. Step One after data-collection and experiment is submitting to peer-review, by a recognized body of experts in the field. If the work is good enough, it gets published. If it overturns long-held ideas with better evidence, it wins awards. If it fails to cut the mustard, it's ignored or sent back for revision.
That's the standard Campbell had to meet with all his research over his career. It's the standard that all medical and scientific researchers have to meet. Anyone else who wants their work seriously considered should meet the same minimum standard.
Author: To test her conclusions, all anyone needs is access to the original China Study data and an ability to interpret it.
Wrong. You'd also need years of training as an expert in the relevant fields of science and medicine. It really isn't something that can be done by just anyone.
Author: You're using the lack of peer review as an automatic disqualifier. Why not use it to say "this hasn't been fully settled -- let's see if other people can duplicate her conclusions"?
That's what happens after peer-review, not before it.
Author: It makes no sense to insist that she go through institutional peer-review channels for this. She's not a part of the institution that you say should be judging her claims.
First, yes it does make sense; it's the bare minimum that all other researchers in the field -- expert or layperson -- must meet. Why should her work get special treatment?
Second, she doesn't need to be part of the institution. Laymen often get their work published, if it's rigorous enough.
Author: Isn't putting something on the internet for anyone to question one way of submitting to peer-review?
No. Science isn't a popularity contest, and most laypeople really do not possess the expertise necessary to do peer-review properly. There's a damn good reason people spend thousands of dollars and man-hours getting science educations. It's complicated, rigorous and difficult. The general public really can't do it as well as trained experts can.
You wouldn't let just anyone diagnose your medical problems, or work on your car, would you?
Author: Have you read her China Study entries?
Not in detail. Her work looks fairly rigorous on its face, and she is clearly a very intelligent person. However, I have not checked her math, and I don't possess the qualifications necessary to evaluate her work the way an expert in the field could (neither, I suspect, do most readers of her blog or this one). While it's possible that she has made an important discovery, one should not assume so just because one likes what they're reading. Skepticism should be the default mindset towards all authoritative claims... especially if you're inclined agree with them (because it's easier to fool yourself otherwise).
Author: Simply hide behind a non-existent "scientific" consensus, and the fact that she's not working within a flawed, and sometimes, biased system.
Lack of peer-review is a perfectly legitimate basis on which to decline considering her points further. Bloggers never seem to get that this is the first standard by which all scientific claims are and should be measure. Sure, it's imperfect (what institution isn't?), but it's the best we've ever done. And it is astonishingly effective at both self-correction and BS-detecting over the long term.
Author: The book which she is critiquing is not itself a peer-reviewed publication.
Well, by your standard, why should it need to be? And anyway, all of its original research was peer-reviewed (this was Oxford and Cornell, remember).
Author: The China Study is constantly held up as a banner around which the vegetarian/vegan community rally, and is a very large part of what you call the current scientific consensus. To see it exposed for what it truly is- an agenda driven piece of work that draws dubious conclusions from a study that could have done so much to help people the world over- is heartwarming.
Except that it hasn't been exposed as any such thing. Again, until its critics can produce critiques they're willing to submit to peer-review, I decline to take them seriously. If they can't mean even this minimal standard, there is no good reason for anyone to do so.
And didn't you just tell me that the consensus was non-existent?
1) The author does not appear to be familiar with many of the
objectives, content and key concepts presented in The China Study.
The intent, message and implications of the book are misrepresented
to her readers and strawman arguments abound.
2) In spite of claiming to maintain neutrality, the author shows an
unmistakable bias in favor of animal foods in her analysis.
3) As a result of this (and lack of experience in the field of
epidemiology) much of the data examined and analyzed by the author is
misused and/or misinterpreted.
take a look this superb piece of analysis!
if you had any feelings that there was something fishy about the occasional ranting against the china study, this work will show you exactly why your feelings were spot on!
the work has been produced by B and its content is equally matched by the organization and presentation!
if you have a website or blog, post the link there.
if you have friends, tell them about it.
possibly with the linked heading and B's 3 point summary.
no need to put the other accolades i put on (that B's a modest fellow) unless you want to.
besides, they'll find out how well done it is once they read it.
it'd be good to get this out to the veg outlets too.
see what others think. no rush, imho - the crapaleo blogs will be there for a while still.
So, what if one takes a naturalistic approach and questions the applicability of the research, applying the available science to understand, experientially, the difference between conventional cooked food products that include animal parts as opposed to raw, plant diets? Isn't there some potential pay-off to considering elimination diets?
Denise (D): “I may lean more towards plant-based than paleo”
Chris (C): Hominidae co-evolved during the Miocene epoch, digesting plants (particularly succulent fruit) best. The Paleolithic era was a window of the past that didn’t change that.
“We have seen disease rates change over time so drastically that it is biologically impossible to put the blame on genes.” The China Study, p. 234.
“Just 15 crop plants provide 90 percent of the world’s food energy intake..” – Agriculture and Consumer Protection: Dimensions of need – An atlas of food and agriculture. Staple foods: What do people eat? http://www.fao.org/docrep/u8480e/u8480e07.htm
D: “but I would never call my diet anything near vegan”
C: Adopting vegan, vegetarian or omnivorous diets are behavior (psychological) choices reflecting sociological belief systems. Humans and all other great apes are biological frugivores, optimized to digest succulent fruit best.
D: “animal foods I do eat are what make the difference (for me)”
C: As opposed to other Hominidae? Eating connotes digestion. Human digestive systems are degraded with meat intake, which also contributes to numerous health conditions and diseases, otherwise preventable with biochemically compatible food. Surgery Today, Volume 18, Number 3 / May, 1988
J Nutr 1975 Jul;105(7):878-84
Surgery Today, Volume 18, Number 3 / May, 1988
The majority of differences in eating behavior (especially animal consumption) are merely the result of conditioning, addiction and relatively labile customs, not genetic instructions. “…[m]ost differences are psychological.”… “If people only ate because of internal eating cues, very few people would be overweight.” -Coon, D & Mitterer, J. Psychology: Modules for Active Learning (Eleventh Edition) 2009. Motivation and Emotion. Chapter 9; 348.
D: “...between thriving and falling apart.
C: Edible plants do not make humans ‘fall apart’ or prevent H. sapiens from thriving.
D: The non-vegan portion of my diet..
C: Non vegan portion? The diet portion that believes in meat and negates plants?
“..non vegan portion of my diet may not seem significant”
It seems very significant for all Hominidae, (requiring opposing digestive processes) in terms of disease risks.
D: ..my diet may not seem significant in terms of quantity..
C: So after all the number crunching, it is practically not significant and an 'effect[ive]' philosophical alternative is practiced and preferred, instead...
D: ...but it *is* significant in terms of effect.
C: I also noticed the physical and psychological effects when I consumed small amounts of meat, in terms of anxiety, pleural effusions, social acquisition, fibroepithelial polyps, weight gain, blood pressure, contagious disease susceptibility, congestion, functional constipation, body odor, acidosis, hiccups, burps, overall digestion, egestion and inflammation, etc.
D: I try to avoid listing out what I eat in detail because I don’t want to promote my current diet as ideal…
C: That shouldn't be a problem... But if one includes meat in an 'ideal diet' and STILL can't meet that ideal, what is the point?
D: Like you said- veritable buttload of variance in individual responses.”
C: Now that concept seems both cryptic and irksome. The only extant human variants are psychological typologies. Chimps and most mammals have far more genetic variation than humans, yet one doesn't see the vast ‘buttload’ differences in dietary requirements within any mammalian species, even those closely related apes with more genetic variation than humans. Chimpanzee Subspecies Are Genetically Mixed And More Diverse Than Humans. ScienceDaily Nov. 8, 1999.
Humans are a relatively homogenous species, often interbreeding across cultures with a genetic distance less than .01% between any 2 people. To put this into perspective, the number of genetic differences between humans and chimps is 10 times less than between the mouse and rat. On the other hand, the number of genetic differences between a human and a chimp is about 10 times more than between any two humans.(2)2. http://www.genome.gov/15515096
D: “Once you nix the modern atrocities of so-called food, there’s a lot of wiggle room in terms of building a successful diet..”
C: You meant to say ‘less wiggle room’, I’m sure. The need to build a successful diet is more, not less critical after the assaults of ‘modern atrocities’. For example, impairing insulin response or burning intestinal flora with acidifying meat intake reduces nutrient absorption and pancreatic function. The ‘atrocities’ of acidifying foods, lacking fiber and fermenting gut flora reduce ‘wiggle room’ to build successful digestion/diet.
If one forgoes the opportunity to apply the numbers to develop an ideal diet, what is all the debate worth, if not social acquisition (potentially at the expense of health)?
So, assuming one side or the other promotes the diet that minimizes disease risks, why forgo the opportunity to apply it personally (after all the fuss) and successfully?
this intelligent and well-constructed post by chris drew a response within 8 hrs by someone named kat: did you just use a quote from The China Study to try to prove a point…on THIS blog? bahahahahaha
the level of eloquence and reasoning capacity of the other side is truly fascinating!
Chris (C): Hominidae co-evolved during the Miocene epoch, digesting plants (particularly succulent fruit) best. The Paleolithic era was a window of the past that didn’t change that.
CPM: "And your point is...?"
That the original poster is not unique (in terms of genetic instructions) in leaning toward plant food.
C: “We have seen disease rates change over time so drastically that it is biologically impossible to put the blame on genes.” The China Study, p. 234.
CPM: And your point is...?
That human diets have changed faster than genetic instructions. The original poster singled herself out as '"...mak[ing] the difference" with animal product intake. Later, I elaborate, explaining that there is no possible mechanism by which it would be theoretically possible for some members within the species or biological family to adapt to meat (which requires opposing digestive processes). The slight variance in genes among H. sapiens does not allow some humans to digest animal products without disease risks (acidosis, anxiety, obesity, putrefaction dysbiosis, high blood pressure, increased cancer, hemorrhoids, Alzheimer's, etc).
Based on consumption of red, white, and processed meats, higher red and processed meat consumption representing a higher risk. After 10 years of annual follow-up, 47,976 men and 23,276 women died. Men in the highest quintile of red meat consumption had a 31% higher risk of death from any cause (95% confidence interval, 27%-35%), and women had a comparably elevated risk. Journal of Clinical Outcomes Management. May 2009. Vol. 16, Iss. 5; pg. 208
CPM: Many paleo people blame the food of the 20th century, not their genes"
Then the 'paleo people' wouldn't claim they have special (unexplained) biological reasons for consuming meat. The Paleolithic era was a small window (after the Miocene, in which ancestors co-evolved w/fruit consumption) during a time in which there was no selective pressure for meat intake (as you admit later) and that ancestors just resorted to whatever they could get their hands on during the ice age. Likewise, people can resort to 20th century sodas for 20,000 more centuries and will not mutate into natural soda consumers.
CPM: Many are also suspicious of the new foods of the Neolithic era
But strangely, the 'suspicion' of the 'paleo people' ends at the Paleolithic era... Taking meat seems to impair the realization that the genetic instructions to digest plants best, was already established tens of millions of years before tool use or fire kindling and that the conditions of natural selection were present while co-evolving with fruit availability during the Miocene.
"Just 15 crop plants provide 90 percent of the world’s food energy intake..” – Agriculture and Consumer Protection: Dimensions of need – An atlas of food and agriculture. Staple foods: What do people eat? http://www.fao.org/docrep/u8480e/u8480e07.htm
CPM: "And your point is...?"
Still, the original poster is not unique (in terms of genetic instructions) in leaning toward plant food. It helps to understand what the point is when you quote in context to see what I was responding to.
"legume crops and their associated seed oils, maybe we should look closer at these to see where the problem is coming from"
I didn't advocate seed oil. I'm not even advocating legume crops specifically, although legumes do not pose the disease risks that meat does. What I'm saying is that it is not unusual for humans to lean toward plant food. After this is recognized, a discussion (not a pillow fight) can develop as to what the optimal plant foods are for humans.
So, while plant oils and refined grains may also be sources of digestive compromises (to varying degrees) it is no easier to digest meat and dairy and no surprise that one would lean toward plant food in general (as the original poster said). I am trying to agree where there is a common view. Trying to determine when and how a human would lean toward 10% or any amount of animal products (as opposed to available plant foods in general) is another matter...
CPM: "eating shellfish saved humans from extinction"
Consuming food out of ecological niche was largely a stimulus motive resulting from exploration, not increased selective pressure for meat, specifically.
CPM: "humans ate whatever they could find.."
Finally! After having established a digestive system optimized for plants, prior to the Paleo era, w/no positive selection for meat specifically, humans resorted to consuming whatever they could find to survive abandoning the ecological niche during the ice age. Some even resorted to consuming each other or their own urine, etc... Back to square one... - a plant diet. Humans are still optimized for fruit digestion.
CPM: "eating meat is what made humans what they are now"
Humans are not scavengers. This is Lamarckian evolution, which is not accepted in modern theories of biological evolution and leaves out the natural selection equation. You're confusing social evolution with biological evolution. Selective pressure was not increased for meat intake specifically but as you said humans "ate anything they could get their hands on" and largely a function of stimulus motives, exploring outside of ecological niche, so humans still can't digest meat. It isn't even possible in theory. Humans can smoke cigars and consume skittles candy for thousands of years and also not adapt to those, either.
D: "animal foods I do eat are what make the difference (for me)"
C: "As opposed to other Hominidae"
CPM: "And your point is?"
That the genetic differences within humans are relatively small and do not support the claim that there are biological typologies of meat-eating humans.
CPM- "The whole idea of meat causing disease is just a hypotheses"
"Systematic review of the prospective cohort studies on meat consumption and colorectal cancer risk: a meta-analytical approach... results indicate that a daily increase of 100 g of all meat or red meat is associated with a significant 12-17% increased risk of CRC. A significant 49% increased risk was found for a daily increase of 25 g of processed meat...the overall association between meat consumption and risk of CRC appears to be positive.Cancer Epidemiol Biomarkers Prev. 2001 May;10(5):439-46
CPM: "until recently humans ate to stay alive"
Humans and protohumans have always been under the influences of not just genetic instructions or to stay alive but cultural influences of rituals, social roles, political reasons and to explore polar regions and eventually the moon and beyond. The optimal food (in terms of digestion and reduced disease risks) are neither processed food, dairy nor meat, however.
CPM: "No matter the motivation, eating extra protein seems an important aspect to human evolution.
A contradiction of terms. If there is 'extra protein' then it wasn't important for evolution. Humans consume protein IN SPITE of the important aspects of human evolution. The gold standard for diet requirements is breast milk. There is a difference between a stimulus motive and a biological motive, just as there is a difference between a psychological craving and a biological craving. Please consider this before responding with the patented, "And your point is...?"
"Human breast milk in zoological perspective reflects status as a slow-growing species dependent on frequent infant feeds. Zoologically speaking, there aren’t a lot of calories in breast milk because human milk is relatively low in fat. It’s also low in protein.
• Human: 3.8% fat; 1% protein; 7% lactose
• Cow: 3.7% fat; 3.4% protein; 4.8% lactose
• Rat: 10.3% fat; 8.4% protein; 2.6% lactose
• Dog: 12.9% fat; 7.9% protein; 3.1% lactose
• Rabbit: 18.3% fat; 13.9% protein; 2.6% lactose
So, how 'important' is 'extra protein' for humans developing? Apparently, a lot less than for rabbits and virtually all other mammals (including other great apes).
CPM: Even chimps have developed tools to add more protein (insects) to their diet."
Adopting tools in spite of biological adaptation is the result of aberrant cultural processes, not evolution. Chimps didn't adapt to tools and meat, they adopted tools in spite of meat related diseases.
Chris – “Edible plants do not make humans ‘fall apart’ or prevent H. sapiens from thriving.”
CPM – She did not say that edible plants do this
D: “animal foods I do eat are what make the difference (for me) between thriving and falling apart.
The only other type of food mentioned besides animal foods was plant foods so by process of elimination, the difference between the animal food consumption is exactly the plant foods, as the original poster explained previously. In essence, the poster is claiming that an all plant food diet causes her to 'fall apart'. This is an irrational fear.
CPM: "The vast majority of her diet is edible plants"
Yet the poster claimed that an all plant diet makes her 'fall apart'... Essentially this exposes a lack of experience or success with plant diets.
CPM: "she felt healthier when she added some extra nutrition that plants were not providing her"
Chris – “It seems very significant for all Hominidae, (requiring opposing digestive processes) in terms of disease risks.”
CPM: "Hominidae appear to be rather opportunistic"
And have taken the opportunity to acquire rampant diseases of affluence, mostly avoidable with plant-based, raw diets.
CPM: "how may Hominidae other than modern humans suck down grain seed oil?"
Strawman. I'm not advocating oil.
Chris – “I also noticed the physical and psychological effects when I consumed small amounts of meat"
CPM: "The human genome is too tightly wound for one person to react to food differently"
The original poster mentioned a big difference of adding animal products. I agreed. And you are correct, there is so much interbreeding among humans that there are not metabolic typologies that can safely digest meat.
CPM: "eat and feel better"
Until one detoxes with a long-term, plant-based, raw diet, how can one contrast a particular diet with the biochemically compatible diet to determine objectively or experientially whether one actually feels better. What are you comparing your feelings to? Inexperience? Other poor diets you've cycled through?
CPM: you listed all the different physical and psychological effects that you got from meat that was different than Denise’s."
I never claimed the effects I experienced were much different from anyone else consuming meat. The topic was just the vague 'difference' experienced with meat intake. All humans acquire health conditions and diseases from meat intake. No human has adapted to eat meat. The original poster just chose not to specify the 'difference' experienced but I did allude to some differences that are common for humans consuming meat, in contrast to raw, plant-based food.
Deglaciating, detoxing, deprogramming, post ice age,
this is so thorough and well-constructed, chris!
even the little escape attempt with the "She did not say that edible plants do this" was dealt with masterfully!
and this oft-repeated verse: "eating meat is what made humans what they are now"!
where have we seen this before! :D
and i think the old ""humans ate whatever they could find.." bit was handled about as well as i have ever seen: "Some even resorted to consuming each other" :D :D
this point by you is actually a very revealing one: "This is Lamarckian evolution, which is not accepted in modern theories of biological evolution", because CPM is the name of an old operating system from the 70s. so old in fact that i couldn't find any google reference to it till p10. the irony of it is that these people are using old, outdated and 'incorrect' information in order to try to align themselves with an old, outdated and 'incorrect' model of existence. it is puzzling to say the least why some people would want to espouse a prehistoric way of life (with all the modern amenities, of course). corpse consumption is indeed a peculiar advocacy!