Archive for the ‘Crisis in Nutrition’ Category

The attack was quite sudden although it appeared to have been planned for many years. The paper was published last week (Augustin LS, Kendall CW, Jenkins DJ, Willett WC, Astrup A, Barclay AW, Bjorck I, Brand-Miller JC, Brighenti F, Buyken AE et al: Glycemic index, glycemic load and glycemic response: An International Scientific Consensus Summit from the International Carbohydrate Quality Consortium (ICQC). Nutr Metab Cardiovasc Dis 2015, 25(9):795-815.

Augustin_Stresa+Nov_27

As indicated by the title, responsibility was taken by the self-proclaimed ICQC.  It turned out to be a continuation of the long-standing attempt to use the glycemic index to co-opt the obvious benefits in control of the glucose-insulin axis while simultaneously attacking real low-carbohydrate diets. The authors participated in training in Stresa, Italy.

The operation was largely passive aggressive. While admitting the importance of dietary carbohydrate in controlling post-prandial glycemic,  low-carbohydrate diets were ignored. Well, not exactly. The authors actually had a strong attack.  The Abstract of the paper said (my emphasis):

Background and aims: The positive and negative health effects of dietary carbohydrates are of interest to both researchers and consumers.”

Methods: International experts on carbohydrate research held a scientific summit in Stresa, Italy, in June 2013 to discuss controversies surrounding the utility of the glycemic index (GI), glycemic load (GL) and glycemic response (GR).”

So, for the record, the paper is about dietary carbohydrate and about controversies.

The Results in Augustin, et al were simply

“The outcome was a scientific consensus statement which recognized the importance of postprandial glycemia in overall health, and the GI as a valid and reproducible method of classifying carbohydrate foods for this purpose…. Diets of low GI and GL were considered particularly important in individuals with insulin resistance.”

A definition is always a reproducible way of classifying things, and the conclusion is not controversial: glycemia is important.  Low-GI diets are a weak form of low-carbohydrate diet and they are frequently described as a politically correct form of carbohydrate restriction. It is at least a subset of carbohydrate restriction and one of the “controversies” cited in the Abstract is sensibly whether it is better or worse than total carbohydrate restriction. Astoundingly, this part of the controversy was ignored by the authors.  Our recent review of carbohydrate restriction in diabetes had this comparison:

 

 

15_Th_Westman_Jenkins_Mar25-2

A question of research integrity.

It is considered normal scientific protocol that, in a scientific field, especially one that is controversial, that you consider and cite alternative or competing points of view. So how do the authors see low-carbohydrate diets fitting in? If you search the pdf of Augustin, et al on “low-carbohydrate” or “low carbohydrate,” there are only two in the text:

“Very low carbohydrate-high protein diets also have beneficial effects on weight control and some cardiovascular risk factors (not LDL-cholesterol) in the short term, but are associated with increased mortality in long term cohort studies [156],”

and

“The lowest level of postprandial glycemia is achieved using very low carbohydrate-high protein diets, but these cannot be recommended for long term use.”

There are no references for the second statement but very low carbohydrate diets can be and frequently are recommended for long term use and have good results. I am not aware of “increased mortality in long term cohort studies” as in the first statement. In fact, low-carbohydrate diets are frequently criticized for not being subjected to long-term studies. So it was important to check out the studie(s) in reference 156:

[156] Pagona L, Sven S, Marie L, Dimitrios T, Hans-Olov A, Elisabete W. Low carbohydrate-high protein diet and incidence of cardiovascular diseases in Swedish women: prospective cohort study. BMJ 2012;344.

Documenting increased mortality.

The paper is not about mortality but rather about cardiovascular disease and, oddly, the authors are listed by their first names. (Actual reference: Lagiou P, Sandin S, Lof M, Trichopoulos D, Adami HO, Weiderpass E: . BMJ 2012, 344:e4026). This minor error probably reflects the close-knit “old boys” circle that functions on a first name basis although it may also indicate that the reference was not actually read so it was not discovered what the reference was really about.

Anyway, even though it is about cardiovascular disease, it is worth checking out. Who wants increased risk of anything. So what does Lagiou, et al say?

The Abstract of Lagiou says (my emphasis) “Main outcome measures: Association of incident cardiovascular diseases … with decreasing carbohydrate intake (in tenths), increasing protein intake (in tenths), and an additive combination of these variables (low carbohydrate-high protein score, from 2 to 20), adjusted for intake of energy, intake of saturated and unsaturated fat, and several non-dietary variables.”

Low-carbohydrate score? There were no low-carbohydrate diets. There were no diets at all. This was an analysis of “43, 396 Swedish women, aged 30-49 years at baseline, [who] completed an extensive dietary questionnaire and were followed-up for an average of 15.7 years.” The outcome variable, however, was only the “score” which the authors made up and which, as you might guess, was not seen and certainly not approved, by anybody with actual experience with low-carbohydrate diets. And, it turns out that “Among the women studied, carbohydrate intake at the low extreme of the distribution was higher and protein intake at the high extreme of the distribution was lower than the respective intakes prescribed by many weight control diets.” (In social media, this is called “face-palm”).

Whatever the method, though, I wanted to know how bad it was? The 12 years or so that I have been continuously on a low-carbohydrate diet might be considered pretty long term. What is my risk of CVD?

Results: A one tenth decrease in carbohydrate intake or increase in protein intake or a 2 unit increase in the low carbohydrate-high protein score were all statistically significantly associated with increasing incidence of cardiovascular disease overall (n=1270)—incidence rate ratio estimates 1.04 (95% confidence interval 1.00 to 1.08), 1.04 (1.02 to 1.06), and 1.05 (1.02 to 1.08).”

Rate ratio 1.04? And that’s an estimate.  That’s odds of 51:49.  That’s what I am supposed to be worried about. But that’s the relative risk. What about the absolute risk? There were 43 396 women in the study with 1270 incidents, or 2.9 % incidence overall.  So the absolute difference is about 1.48-1.42% = 0.06 % or less than 1/10 of 1 %.

Can such low numbers be meaningful? The usual answers is that if we scale them up to the whole population, we will save thousands of lives. Can we do that? Well, you can if the data are strong, that is, if we are really sure of the reliability of the independent variable. The relative risk in the Salk vaccine polio trial, for example, was in this ballpark but scaling up obviously paid off. In the Salk vaccine trial, however, we knew who got the vaccine and who didn’t. In distinction, food questionnaire’s have a bad reputation. Here is Lagiou’s description (you don’t really have to read this):

“We estimated the energy adjusted intakes of protein and carbohydrates for each woman, using the ‘residual method.’ This method allows evaluation of the “effect” of an energy generating nutrient, controlling for the energy generated by this nutrient, by using a simple regression of that nutrient on energy intake.…” and so on. I am not sure what it means but it certainly sounds like an estimate. So is the data itself any good? Well,

“After controlling for energy intake, however, distinguishing the effects of a specific energy generating nutrient is all but impossible, as a decrease in the intake of one is unavoidably linked to an increase in the intake of one or several of the others. Nevertheless, in this context, a low carbohydrate-high protein score allows the assessment of most low carbohydrate diets, which are generally high protein diets, because it integrates opposite changes of two nutrients with equivalent energy values.”

And “The long interval between exposure and outcome is a source of concern, because certain participants may change their dietary habits during the intervening period.”

Translation: we don’t really know what we did here.

In the end, Lagiou, et al admit “Our results do not answer questions concerning possible beneficial short term effects of low carbohydrate or high protein diets in the control of body weight or insulin resistance. Instead, they draw attention to the potential for considerable adverse effects on cardiovascular health of these diets….” Instead? I thought insulin resistance has an effect on CVD but if less than 1/10 of 1 % is “considerable adverse effects” what would something “almost zero” be.?

Coming back to the original paper by Augustin, et al, what about the comparison between low-GI diets and low-carbohydrate diets. The comparison in the figure above comes from Eric Westman’s lab. What do they have to say about that?

Augustin_

They missed this paper. Note: a comment I received suggested that I should have searched on “Eric” instead of “Westman.” Ha.

Overall, this is the evidence used by ICQC to tell you that low-carbohydrate diets would kill you. In the end, Augustin, et al is a hatchet-job, citing a meaningless paper at random. It is hard to understand why the journal took it. I will ask the editors to retract it.

In  The World Turned Upside Down. The Second Low-Carbohydrate Revolution, I added my voice to the critiques of the low-fat hypothesis and the sorry state of nutritional science. I also provided specific strategies on how to analyze reports in the literature to find out whether the main point of the paper is valid or not. The deconstructions of traditional nutrition, the “also bought” of my book on the Amazon page, are numerous and continuing to proliferate as more and more people become aware of how bad things are. To me, the “surprise” in Nina Teicholz’s “Big Fat Surprise” is that, after all the previous exposés and my own research, there were deceptive practices and poor science that even I didn’t know about.

Even establishment voices are beginning to perceive how bad things are. So, with all these smoking guns why doesn’t anybody do anything? Why doesn’t somebody blow the whistle on them? It’s not like we are dealing with military intelligence.  What are they going to do? Not fund my grant? Not publish my paper? Ha.

Whistleblowing

“When you go to work today, imagine having a tape recorder attached to your body, a second one in your briefcase, and a third one in a special notebook, knowing that you will be secretly taping your supervisors, coworkers, and in some cases, your friends.” These are the opening lines of Mark Whitacre’s remarkable confession/essay/exposé (later a movie with Matt Damon) describing his blowing the whistle on Archer Daniels Midland (ADM) one of the largest food companies in the world; their motto at the time “ADM. Supermarket to the world.”

informant_lMatt Damon in The Informant!

It turned out that ADM had been colluding with its competitors to fix prices, in particular on the amino acid, lysine. Whitacre’s story is fascinating in detail.  Although relatively young, he was high up in the company, a division manager (“I lived in a huge home, which had an eight car garage filled with eight cars, and indoor horse-riding stables for my children”). He travelled around the world to big corporate meetings. At some point, encouraged by his wife whose ethical standards were quite a bit higher than his own, he became an FBI informant. Accompanying him in his business trips was a green lamp, housing a video feed. ”It is a good thing that all of the co- conspirators were men. A woman would have immediately noticed that this green lamp did not match the five star décor of some of the finest hotels, such as the Four Seasons in Chicago.” Ultimately, the lysine trial resulted in fines and three-year prison sentences for three of the executives of ADM as well as criminal fine for foreign companies worth $105 million, a record at the time. At the trial, things really went down-hill for the company when Whitacre produced a tape recording of the President of ADM telling executives that the company’s competitors were their friends and their customers were the enemy. Wits at the time suggested a new motto “ADM. Super mark-up to the world.” In the end, in a remarkable twist in the story, Whitacre’s whistle-blowing was compromised by the fact that he was on the take himself.

“I concluded that I would steal my own severance pay, and decided upon $9.5 million, which amounted to several years of my total compensation. …And I also considered what would happen if ADM learned of this theft. If they accused me, I thought that I had the perfect answer. How can you prosecute me for stealing $9.5 million when you are stealing hundreds of millions of dollars each year in the price fixing scheme? …. I decided to submit several bogus invoices to ADM, until I accumulated $9.5 million, which was meant to be my family’s financial security when I would be fired at a future date for being a whistleblower.”

As it turned out, a number of food and beverage companies, who had won hundreds of millions in settlements against ADM  were the ones who actually provided financial security for his family while Mark Whitacre spent nine years in prison.

Whistle blowing and imperial deshabillement

If it is not hidden, is it whistle-blowing? Did the kid “blow the whistle on the emperor’s new clothes?” If it is right out in the open, what is the scandal?  Well, there is open and there is open. Leaving out information may be a sign of a cover-up. I described, in my book, the case of the paper by Foster, et al., the conclusion of which was that “neither dietary fat nor carbohydrate intake influenced weight loss.”  I admitted, in the book, that:

“I had not read Foster’s paper very carefully before making the pronouncement that it was not very good. I was upbraided by a student for such a rush to judgment. I explained that that is what I do for a living. I explained that I usually don’t have to spend a lot of time on a paper to see the general drift…. but I was probably not totally convincing. So I read the paper, which is quite a bit longer than usual. The main thing that I was looking for was information on the nutrients that were actually consumed since it was their lack of effect that was the main point of the paper.…

In a diet experiment, the food consumed should be right up front but I couldn’t find it at all…. The data weren’t there. I was going to write to the authors when I found out…that this paper had been covered in a story in the Los Angeles Times. As reported by Bob Kaplan: ‘Of the 307 participants enrolled in the study, not one had their food intake recorded or analyzed by investigators. The authors did not monitor, chronicle or report any of the subjects’ diets. No meals were administered by the authors; no meals were eaten in front of investigators. There were no self‑reports, no questionnaires. The lead authors, Gary Foster and James Hill, explained in separate e-mails that self‑reported data are unreliable and therefore they didn’t collect or analyze any.’

I confess to feeling a bit shocked. I don’t like getting scientific information from the LA Times.  How can you say “neither dietary fat nor carbohydrate intake influenced weight loss” if you haven’t measured fat or carbohydrate? …. in fact, the whole nutrition field runs on self‑reported data. Is all that stuff from the Harvard School of Public Health, all those epidemiology studies that rely on food records, to be chucked out?”

So was this a breach of research integrity? It might be considered simply an error of omission. If you didn’t measure food consumed, you might think that you don’t necessarily have to put it in the methods. Was it just dumb not to realize that if you write a study of a diet comparison, you can’t leave out what people ate or at least admit that you didn’t measure what they ate. So can you blow the whistle on them for not telling the whole truth?  The authors were all well-known researchers, if party-liners.

The Office of Research Integrity is set up to police serious infractions in federally funded grants but it usually has to be clear cut and, sometimes, there is a whistle-blower. The Baltimore case is one of the better known if somewhat embarrassing cases for the agency — there was nothing to the whistle-blower’s allegations. In any case, there is a big gray area. If you falsify your data on a government research grant, you can go to jail.  If you make a dumb interpretation, however, if you say the data mean X when they show not-X, well, research is about unknowns, and you may have slipped up. Even Einstein admitted to the need to offer “sacrifices at the altar of Stupidity.” The NIH is supposed to not fund stuff like that. Editors and reviewers are supposed to see through the omission. What if they fell down on the job too? What if you have a field like nutrition where the NIH study sections are on the same wavelength as the researchers. There is, however, the question of the total impact. A lot of stuff is never cited and never does any harm. I enquired with the ORI, in a general way, about Foster’s paper. They said that if it is widely quoted, it could be an infraction. It has, in fact, been cited as evidence against low-carb diets. So am I going to be a whistle-blower? I don’t think so.

The problem is that only an insider can blow the whistle and although cooperation and collegiality remain very weak in the nutrition field, it is still our own nest and whistle-blowing makes everybody look bad. The “long blue line” does not form because the police think that corruption is okay. The problem is not just that there can be retribution, as in Serpico, but that it makes everybody look bad. It is simply that it reflects poorly on the whole police force. And while it is probable that, as Mark Whitacre said, “almost all of their 30,000 employees went to work each day doing the right thing morally and ethically,” the statement that “ADM was not a bad company” does not ring true. If we call attention to what is tolerated in medical nutrition, we are all looking like fools. And, of course, Foster’s paper is one of the more egregious but there is a lot of competition for worst. And it reflects badly on all of us in the field. “Is that what you do when you go to work?”

The parable of the big fish

I received an email from a physician in England. He has had consistently good results with low-carbohydrate diets.

“There is never a day when I don’t see the deleterious effects of too many carbs on those with the metabolic syndrome. And yet most doctors carry on as if it doesn’t exist !! …

Only yesterday I saw a man I have known for over 15 years. His GGT [gamma-glutamyl transferase; marker for liver disease] had always been about double normal. Embarrassingly I had assumed that he was a drinker, despite repeated denial, thinking his big belly was evidence!  He chose low carb on March 2013 and never looked back. Liver function normal now and an easy 7 Kg weight loss.”

He said that the information had been used in the production of the ABC Catalyst TV documentary from Australia, but:

“I am a very, very small fish! As smaller fish we GPs specialise in getting ideas across to ordinary folk. The Internet is democratising medicine faster than some big fish realise. I wrote my practical diabetes piece partly for the educated general public and insisted on open access.

Big fish will scoff at my small numbers (70) and lack of double blindness anyway.”

I assured him that he was making an impact, that n = 70 was fine and not to worry about the big fish. I related a story told to me by one of my colleagues in graduate school: he had gone fishing in the Gulf of Mexico and they had caught a very big fish (I no longer remember the kind) which was thrashing around on the deck and they could not contain it. There happened to be a rifle on board and somebody shot the fish. The bullet went through the bottom of the boat which sank.

“…789 deaths were reported in Doll and Hill’s original cohort. Thirty-six of these were attributed to lung cancer. When these lung cancer deaths were counted in smokers versus non-smokers, the correlation virtually sprang out: all thirty-six of the deaths had occurred in smokers. The difference between the two groups was so significant that Doll and Hill did not even need to apply complex statistical metrics to discern it. The trial designed to bring the most rigorous statistical analysis to the cause of lung cancer barely required elementary mathematics to prove his point.”

Siddhartha Mukherjee —The Emperor of All Maladies.

 Scientists don’t like philosophy of science. It is not just that pompous phrases like hypothetico-deductive systems are such a turn-off but that we rarely recognize it as what we actually do. In the end, there is no definition of science and it is hard to generalize about actual scientific behavior. It’s a human activity and precisely because it puts a premium on creativity, it defies categorization. As the physicist Steven Weinberg put it, echoing Justice Stewart on pornography:

“There is no logical formula that establishes a sharp dividing line between a beautiful explanatory theory and a mere list of data, but we know the difference when we see it — we demand a simplicity and rigidity in our principles before we are willing to take them seriously [1].”

A frequently stated principle is that “observational studies only generate hypotheses.” The related idea that “association does not imply causality” is also common, usually cited by those authors who want you to believe that the association that they found does imply causality. These ideas are not right or, at least, they insufficiently recognize that scientific experiments are not so easily wedged into categories like “observational studies.”  The principles are also invoked by bloggers and critics to discredit the continuing stream of observational studies that make an association between their favorite targets, eggs, red meat, sugar-sweetened soda and a metabolic disease or cancer. In most cases, the studies are getting what they deserve but the bills of indictment are not quite right.  It is usually not simply that they are observational studies but rather that they are bad observational studies and, in any case, the associations are so weak that it is reasonable to say that they are an argument for a lack of causality. On the assumption that good experimental practice and interpretation can be even roughly defined, let me offer principles that I think are a better representation, insofar as we can make any generalization, of what actually goes on in science:

 Observations generate hypotheses. 

Observational studies test hypotheses.

Associations do not necessarily imply causality.

In some sense, all science is associations. 

Only mathematics is axiomatic.

 If you notice that kids who eat a lot of candy seem to be fat, or even if you notice that candy makes you yourself fat, that is an observation. From this observation, you might come up with the hypothesis that sugar causes obesity. A test of your hypothesis would be to see if there is an association between sugar consumption and incidence of obesity. There are various ways — the simplest epidemiologic approach is simply to compare the history of the eating behavior of individuals (insofar as you can get it) with how fat they are. When you do this comparison you are testing your hypothesis. There are an infinite number of things that you could have measured as an independent variable, meat, TV hours, distance from the French bakery but you have a hypothesis that it was candy. Mike Eades described falling asleep as a child by trying to think of everything in the world. You just can’t test them all. As Einstein put it “your theory determines the measurement you make.”

Associations predict causality. Hypotheses generate observational studies, not the other way around.

In fact, association can be strong evidence for causation and frequently provide support for, if not absolute proof, of the idea to be tested. A correct statement is that association does not necessarily imply causation. In some sense, all science is observation and association. Even thermodynamics, that most mathematical and absolute of sciences, rests on observation. As soon as somebody observes two systems in thermal equilibrium with a third but not with each other (zeroth law), the jig is up. When somebody builds a perpetual motion machine, that’s it. It’s all over.

Biological mechanisms, or perhaps any scientific theory, are never proved. By analogy with a court of law, you cannot be found innocent, only not guilty. That is why excluding a theory is stronger than showing consistency. The grand epidemiological study of macronutrient intake vs diabetes and obesity shows that increasing carbohydrate is associated with increased calories even under conditions where fruits and vegetables also went up and fat, if anything went down. It is an observational study but it is strong because it gives support to a lack of causal effect of increased carbohydrate and decreased fat on outcome. The failure of total or saturated fat to have any benefit is the kicker here. It is now clear that prospective experiments have, in the past, and will continue to show, the same negative outcome. Of course, in a court of law, if you are found not guilty of child abuse, people may still not let you move into their neighborhood. It is that saturated fat should never have been indicted in the first place.

An association will tell you about causality 1) if the association is strong and 2) if there is a plausible underlying mechanism and 3) if there is no more plausible explanation — for example, countries with a lot of TV sets have modern life styles that may predispose to cardiovascular disease; TV does not cause CVD.

Re-inventing the wheel. Bradford Hill and the history of epidemiology.

Everything written above is true enough or, at least, it seemed that way to me. I thought of it as an obvious description of what everybody knows. The change to saying that “association does not necessarily imply causation” is important but not that big a deal. It is common sense or logic and I had made it into a short list of principles. It was a blogpost of reasonable length. I described it to my colleague Gene Fine. His response was “aren’t you re-inventing the wheel?” Bradford Hill, he explained, pretty much the inventor of modern epidemiology, had already established these and a couple of other principles. Gene cited The Emperor of All Maladies, an outstanding book on the history of cancer.  I had read The Emperor of All Maladies on his recommendation and I remembered Bradford Hill and the description of the evolution of the ideas of epidemiology, population studies and random controlled trials. I also had a vague memory, of reading the story in James LeFanu’s The Rise and Fall of Modern Medicine, another captivating history of medicine. However, I had not really absorbed these as principles. Perhaps we’re just used to it, but saying that an association implies causality only if it is a strong association is not exactly a scientific breakthrough. It seems an obvious thing that you might say over coffee or in response to somebody’s blog. It all reminded me of learning, in grade school, that the Earl of Sandwich had invented the sandwich and thinking “this is an invention?”  Woody Allen thought the same thing and wrote the history of the sandwich and the Earl’s early failures — “In 1741, he places bread on bread with turkey on top. This fails. In 1745, he exhibits bread with turkey on either side. Everyone rejects this except David Hume.”

At any moment in history our background knowledge — and accepted methodology —  may be limited. Some problems seem to have simple solutions. But simple ideas are not always accepted. The concept of the random controlled trial (RCT), obvious to us now, was hard won and, proving that any particular environmental factor — diet, smoking, pollution or toxic chemicals was the cause of a disease and that, by reducing that factor, the disease could be prevented, turned out to be a very hard sell, especially to physicians whose view of disease may have been strongly colored by the idea of an infective agent.

Hill_CausationThe Rise and Fall of Modern Medicine describes Bradford Hill’s two demonstrations that streptomycin in combination with PAS (para-aminosalicylic acid) could cure tuberculosis and that tobacco causes lung cancer as one of the Ten Definitive Moments in the history of modern medicine (others shown in the textbox). Hill was Professor of Medical Statistics at the London School of Hygiene and Tropical Medicine but was not formally trained in statistics and, like many of us, thought of proper statistics as common sense. An early near fatal case of tuberculosis also prevented formal medical education. His first monumental accomplishment was, ironically, to demonstrate how tuberculosis could be cured with the combination of streptomycin and PAS.  In 1941, Hill and co-worker Richard Doll undertook a systematic investigation of the risk factors for lung cancer. His eventual success was accompanied by a description of the principles that allow you to say when association can be taken as causation.

 Ten Definitive Moments from Rise and Fall of Modern Medicine.

1941: Penicillin

1949: Cortisone

1950: streptomycin, smoking and Sir Austin Bradford Hill

1952: chlorpromazine and the revolution in psychiatry

1955: open-heart surgery – the last frontier

1963: transplanting kidneys

1964: the triumph of prevention – the case of strokes

1971: curing childhood cancer

1978: the first ‘Test-Tube’ baby

1984: Helicobacter – the cause of peptic ulcer

Wiki says: “in 1965, built  upon the work of Hume and Popper, Hill suggested several aspects of causality in medicine and biology…” but his approach was not formal — he never referred to his principles as criteria — he recognized them as common sense behavior and his 1965 presentation to the Royal Society of Medicine, is a remarkably sober, intelligent document. Although described as an example of an article that, as here, has been read more often in quotations and paraphrases, it is worth reading the original even today.

Note: “Austin Bradford Hill’s surname was Hill and he always used the name Hill, AB in publications. However, he is often referred to as Bradford Hill. To add to the confusion, his friends called him Tony.” (This comment is from Wikipedia, not Woody Allen).

The President’s Address

Bradford Hill’s description of the factors that might make you think an association implied causality:

Hill_Environment1965

1. Strength. “First upon my list I would put the strength of the association.” This, of course, is exactly what is missing in the continued epidemiological scare stories. Hill describes

“….prospective inquiries into smoking have shown that the death rate from cancer of the lung in cigarette smokers is nine to ten times the rate in non-smokers and the rate in heavy cigarette smokers is twenty to thirty times as great.”

But further:

“On the other hand the death rate from coronary thrombosis in smokers is no more than twice, possibly less, the death rate in nonsmokers. Though there is good evidence to support causation it is surely much easier in this case to think of some features of life that may go hand-in-hand with smoking – features that might conceivably be the real underlying cause or, at the least, an important contributor, whether it be lack of exercise, nature of diet or other factors.”

Doubts about an odds ratio of two or less. That’s where you really have to wonder about causality. The progression of epidemiologic studies that tell you red meat, HFCS, etc. will cause diabetes, prostatic cancer, or whatever, these rarely hit an odds ratio of 2.  While the published studies may contain disclaimers of the type in Hill’s paper, the PR department of the university where the work is done, and hence the public media, show no such hesitation and will quickly attribute causality to the study as if the odds ratio were 10 instead of 1.2.

2. Consistency: Hill listed the repetition of the results in other studies under different circumstances as a criterion for considering how much an association implied causality. Not mentioned but of great importance, is that this test cannot be made independent of the first criterion. Consistently weak associations do not generally add up to a strong association. If there is a single practice in modern medicine that is completely out of whack with respect to careful consideration of causality, it is the meta-analysis where studies with no strength at all are averaged so as to create a conclusion that is stronger than any of its components.

3. Specificity. Hill was circumspect on this point, recognizing that we should have an open mind on what causes what. On specificity of cancer and cigarettes, Hill noted that the two sites in which he showed a cause and effect relationship were the lungs and the nose.

4. Temporality: Obviously, we expect the cause to precede the effect or, as some wit put it “which got laid first, the chicken or the egg.”  Hill recognized that it was not so clear for diseases that developed slowly. “Does a particular diet lead to disease or do the early stages of the disease lead to those peculiar dietetic habits?” Of current interest are the epidemiologic studies that show a correlation between diet soda and obesity which are quick to see a causal link but, naturally, one should ask “Who drinks diet soda?”

5. Biological gradient:  the association should show a dose response curve. In the case of cigarettes, the death rate from cancer of the lung increases linearly with the number of cigarettes smoked. A subset of the first principle, that the association should be strong, is that the dose-response curve should have a meaningful slope and, I would add, the numbers should be big.

6. Plausibilityy: On the one hand, this seems critical — the association of egg consumption with diabetes is obviously foolish — but the hypothesis to be tested may have come from an intuition that is far from evident. Hill said, “What is biologically plausible depends upon the biological knowledge of the day.”

7. Coherence: “data should not seriously conflict with the generally known facts of the natural history and biology of the disease”

8. Experiment: It was another age. It is hard to believe that it was in my lifetime. “Occasionally it is possible to appeal to experimental, or semi-experimental, evidence. For example, because of an observed association some preventive action is taken. Does it in fact prevent?” The inventor of the random controlled trial would be amazed how many of these are done, how many fail to prevent. And, most of all, he would have been astounded that it doesn’t seem to matter. However, the progression of failures, from Framingham to the Women’s Health Initiative, the lack of association between low fat, low saturated fat and cardiovascular disease, is strong evidence for the absence of causation.

9. Analogy: “In some circumstances it would be fair to judge by analogy. With the effects of thalidomide and rubella before us we would surely be ready to accept slighter but similar evidence with another drug or another viral disease in pregnancy.”

Hill’s final word on what has come to be known as his criteria for deciding about causation:

“Here then are nine different viewpoints from all of which we should study association before we cry causation. What I do not believe — and this has been suggested — is that we can usefully lay down some hard-and-fast rules of evidence that must be obeyed before we accept cause and effect. None of my nine viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required as a sine qua non. What they can do, with greater or less strength, is to help us to make up our minds on the fundamental question – is there any other way of explaining the set of facts before us, is there any other answer equally, or more, likely than cause and effect?” This may be the first critique of the still-to-be-invented Evidence-based Medicine.

Nutritional Epidemiology.

The decision to say that an observational study implies causation is equivalent to an assertion that the results are meaningful, that it is not a random association at all, that it is scientifically sound. Critics of epidemiological studies have relied on their own perceptions and appeal to common sense and when I started this blogpost, I was one of them, and I had not appreciated the importance of Bradford Hill’s principles. The Emperor of All Maladies described Hill’s strategies for dealing with association and causation “which have remained in use by epidemiologists to date.”  But have they? The principles are in the texts. Epidemiology, Biostatistics, and Preventive Medicine has a chapter called “The study of causation in Epidemiologic Investigation and Research” from which the dose-response curve was modified. Are these principles being followed? Previous posts in this blog and others have have voiced criticisms of epidemiology as it’s currently practiced in nutrition but we were lacking a meaningful reference point. Looking back now, what we see is a large number of research groups doing epidemiology in violation of most of Hill’s criteria.

The red meat scare of 2011 was Pan, et al and I described in a previous post, the remarkable blog from Harvard . Their blog explained that the paper was unnecessarily scary because it had described things in terms of “relative risks, comparing death rates in the group eating the least meat with those eating the most. The absolute risks… sometimes help tell the story a bit more clearly. These numbers are somewhat less scary.”  I felt it was appropriate to ask “Why does Dr. Pan not want to tell the story as clearly as possible?  Isn’t that what you’re supposed to do in science? Why would you want to make it scary?” It was, of course, a rhetorical question.

Looking at Pan, et al. in light of Bradford Hill, we can examine some of their data. Figure 2 from their paper shows the risk of diabetes as a function of red meat in the diet. The variable reported is the hazard ratio which can be considered roughly the same as the odds ratio, that is, relative odds of getting diabetes. I have indicated, in pink, those values that are not statistically significant and I grayed out the confidence interval to make it easy to see that these do not even hit the level of 2 that Bradford Hill saw as some kind of cut-off.

TheBlog_Cause_Pan_Fig2_

The hazard ratios for processed meat are somewhat higher but still less than 2. This is weak data and violates the first and most important of Hill’s criteria. As you go from quartile 2 to 3, there is an increase in risk, but at Q4, the risk goes down and then back up at Q5, in distinction to principle 5 which suggests the importance of dose-response curves. But, stepping back and asking what the whole idea is, asking why you would think that meat has a major — and isolatable role separate from everything else — in a disease of carbohydrate intolerance, you see that this is not rational, this is not science. And Pan is not making random observations. This is a test of the hypothesis that red meat causes diabetes. Most of us would say that it didn’t make any sense to test such a hypothesis but the results do not support the hypothesis.

What is science?

Science is a human activity and what we don’t like about philosophy of science is that it is about the structure and formalism of science rather than what scientists really do and so there aren’t even any real definitions. One description that I like, from a colleague at the NIH: “What you do in science, is you make a hypothesis and then you try to shoot yourself down.” One of the more interesting sidelights on the work of Hill and Doll, as described in Emperor, was that during breaks from the taxing work of analyzing the questionnaires that provided the background on smoking, Doll himself would step out for a smoke. Doll believed that cigarettes were unlikely to be a cause — he favored tar from paved highways as the causative agent — but as the data came in, “in the middle of the survey, sufficiently alarmed, he gave up smoking.” In science, you try to shoot yourself down and, in the end, you go with the data.

“I may have killed a dozen men but I never stole a horse.”

— last words of outlaw in the American West before being hanged.

The principle known as Occam’s Razor is usually understood as a statement that a simple explanation is preferable to one that is more complicated. The principle has many variations. It might be interpreted as saying that you have to have a sense of priorities. Occam’s Razor is not exactly a scientific idea so much as a principle of aesthetics expressing the value of elegance in scientific explanations. Named for William of Ockham (c. 1285–1349) — it is also referred to as Ockham’s Razor — the idea can be described mathematically by saying that if the outcome, Y, of an experiment can be expressed with a rough sort of equation: Y = A + B + C +… and if A explains Y, then you don’t want to drag in B, C, etc unless you absolutely have to. (A more compelling description might be to consider the principle in terms of a power series and if you are inclined to mathematics, Wikipedia has excellent description and animation).

Where we’re going. The bottom line on this post is that for obesity, diabetes and general health, the predominant effect of diet, the major contribution to the outcome — A in the equation above — is provided by substituting fat (any fat) for carbohydrate (any carbohydrate). That’s what the science says. That will give you the best effect. The B contribution (type of fat, type of carbohydrate) is strictly secondary. The practical consequence: if for some reason, you want to reduce fructose in the diet, the best advice is to reduce carbohydrate across the board. You can then add the additional advice “preferably sugar and high fructose corn syrup” but even if B doesn’t kick in, you will surely get a benefit. Most of all, if you take out Pepsi® and put in Pepperidge Farm® Whole Wheat Bread, you may not accomplish much.

In practical terms, confronted with a phenomenon that has many controlling variables, make sure you can’t do with one before you bring in the others. In nutrition, when people say that the phenomenon is very complicated, they frequently mean that they don’t want to look at a simple explanation. On its practical side, if a patients in a dietary experiment responds to the level of carbohydrate, you have to assume that carbohydrate across the board is the controlling variable. If, however, you think that it is specifically the fructose in the diet that caused the effect, or if you think that it was an additional effect of fructose beyond its role as carbohydrate, then that is something that you have to show separately. Until you do, the fructose effect is sliced off by Occam’s Razor. In terms of policy, you don’t want to go after fructose unless you are sure that it is not primarily the role of fructose acting as a carbohydrate.

So, there is a logical question surrounding recommendations against sugar and especially against fructose. What we know well in nutrition is that if you replace carbohydrate with fat, as in Krauss’s experiment described in the previous post, things improve and this is why we suggest low-carbohydrate diets as the “default diet,” the one to try first for diabetes and metabolic syndrome and probably for cardiovascular risk. I have, however, received at least two emails from well-known nutritionists saying that “the type of carbohydrate is more important than how much carbohydrate” and, of course, Rob Lustig is everywhere telling us how toxic sugar is but never suggesting that a low carbohydrate diet is any kind of ideal. On the face of it, the idea doesn’t make much sense. Fructose is a carbohydrate so the amount and type are not easily separable.

There are all kinds of strange things in nutrition. People actually say that the type of diet you are on is less important than whether you stay on the diet. While true, it is like saying that if you are baseball player, whether you get a hit depends less on who’s pitching than whether you remember to show up for the game. But anyway, I decided to ask the question about relative importance of type and amount of carbohydrate on facebook and on a couple of blogs where things like Hizzona’ Michael Bloomberg’s Big Bottle Ban or related questions was being discussed. Here’s how I put it.

For general health, should you change the type of carbohydrate or replace the carbohydrate with fat (any natural fat, no trans-fat)? It’s a thought experiment (not real world situation with subtleties). You only get three choices: For general health (no change in calories):

1. Change type of carbohydrate
2. Replace carbohydrate with fat
3. It doesn’t matter

Strangely enough, I did not get very many answers. I think that people didn’t like the question and even when they voted, they wanted to put in disclaimers:

ANS: 2. Replace carbohydrate with fat But I want to add; not replacing ALL the carbs. Only the worst ones. You know; Sugar, grains (bread and pasta) potatoes and rice.

RDF: You can do that in a real case but the question is about first-order strategies. You only get 3 choices.

ANS: okej 2. Replace carbohydrate with fat.

And James Krieger jumped in:

“Feinman, your ‘thought experiment’ is essentially a false trichotomy…same thing as a false dichotomy except you’ve arbitrarily limited it to 3 choices rather than 2, when in fact there are many more. This is why you aren’t getting answers…because you’re committing a common logical fallacy.”

I explained that

“It’s called Occam’s Razor…. I’m simply asking: if you could theoretically do only one thing, 1. or 2., which would be better? There are many other choices but in a thought experiment you imagine these to be held constant or to be the higher order terms in a power series.”

But, of course, Krieger was right. The question is not really answerable. Not because it is false so much as because it is confused. Fructose is a carbohydrate and whatever its unique contribution, it is hard to say it is more important than the contribution of the fructose as a carbohydrate. It is a screwy idea but, again, that’s how it was phrased to me in emails and probably in print someplace. Researchers in this field say: “it is not carbohydrate per se (or glycemic index/load) that is involved in adverse metabolic effects of dietary carbohydrates, but rather the type of carbohydrate,…” The kind of evidence that is used to support such an idea, the kind of result that is used to support fructophobia is in the paper by Stanhope, et al.

Stanhope, et al. measured the effects of chronic consumption of either glucose- or fructose-sweetened beverages providing 25% of energy requirements for 10 weeks in overweight and obese subjects. The figure below shows the superimposed outcomes in the response of triglycerides in the course of a day (red lines = fructose, blue = glucose). It is obvious that there is a difference — people consuming fructose had higher triglyceride responses (although fasting levels were not different). Looking at the figure, though, there is big variation in the data and it is not clear that everybody showed big differences between the glucose and fructose curves: the error bars represent standard error of the mean (SEM) which, while it shows you that there may be a statistically significant difference between the trials, doesn’t display very well the spread of the individual values, that is, whether a few individuals biased the grouped data. To convert to standard deviation, which gives you a better feel for the variation, you multiply, in this case, by about 4. In other words, there must have been big overlap between the fructose people and the glucose people.

So there is an effect of type of carbohydrate. But what to compare it to? The study of Krauss in the previous post showed much bigger changes when you substituted fat for carbohydrate and, in fact, those were fasting triglycerides which, in the fructose experiment, didn’t change at all but this is a different kind of experiment. So for comparison, we can look at a study from Jeff Volek’s lab where carbohydrate was replaced with fat in the carbohydrate restricted diet (CRD) in comparison to a low-fat diet (LFD). I described this study previously because it showed how carbohydrate, rather than dietary saturated fat, was actually controlling saturated fat in the blood. Here is what the responses to meals as seen in plasma triglycerides:

Maybe it’s the Fructose.

The fructose experiments can be shaved with Occam’s razor — insofar as we can tell, reducing carbohydrate across the board is more effective than changing type of carbohydrate. But how do we know that the effect of reducing carbohydrates wasn’t due to removing fructose — fructose is a carbohydrate so carbohydrate restriction may be due to the de facto removal of the fructose? Well, we don’t. It’s unlikely but possible. Where does this leave us? Wikipedia cites Bertrand Russell’s variation of Occam’s Razor: “Whenever possible, substitute constructions out of known entities for inferences to unknown entities.” This is a pompous way of saying: “don’t make things up.”

Another way of looking at Stanhope’s experiment is to recognize that it does not show, as the title says, “Consuming fructose-sweetened, not glucose-sweetened, beverages increases visceral adiposity and lipids… in overweight/obese humans.” What the paper really is about is “Consuming fructose-sweetened, not glucose-sweetened, beverages as part a high carbohydrate diet (55 % of energy) increases….” In other words, you don’t know whether you would get any benefit in changing from fructose to glucose if the total carbohydrate were lower.  In terms of our Occam’s Razor equation, you can’t say that you have proved that your results are due to A  (the major controlling variable (carbohydrate)) when all you have studied is A with the specific change in  the term (secondary effect of the type of carbohydrate). Stanhope’s experiment shows: if you are on a high carbohydrate diet, replacing glucose with fructose will make things worse but that’s different than saying that fructose is toxic. From a practical point of view, if you are on a high carbohydrate diet and it is not giving you the health benefit you want, replacing sugar with starch may give you disappointing results compared to simply cutting down on carbohydrates.

How to Reduce Fructose Consumption.

If you want to encourage fructose reduction, encourage carbohydrate restriction (this is where we have the most information) with the additional proviso of recommending fructose reduction as the first carbohydrate to remove (may also help but we have less data).

Flawed Studies.

In combination with the previous post, a summary of things to look for in a study to make sure that the authors are not misleading you and/or themselves:

1. Understatement is good. “Healthy” is a value judgement. “Fructose-sweetened” is not the same thing as “fructose-sweetened in a high carbohydrate diet.”

2. Where are the pictures? The author has an obligation to make things clear. A graphic representation is usually an indication of a desire to explain.

3. Has Occam’s Razor been applied? Are secondary effects taken as primary?

Crabtree’s Bludgeon

Finally, we should not forget Crabtree’s Bludgeon which is described by Wikipedia as “a foil to Occam’s Razor” and “attributed to the fictitious poet, Joseph Crabtree, after whom the Crabtree Foundation is named.” It may be expressed as:

‘No set of mutually inconsistent observations can exist for which some human intellect cannot conceive a coherent explanation, however complicated.’

TIME: You’re partnering with, among others, Harvard University on this. In an alternate Lady Gaga universe, would you have liked to have gone to Harvard?

Lady Gaga: I don’t know. I am going to Harvard today. So that’ll do.

— Belinda Luscombe, Time Magazine, March 12, 2012

There was a sense of déja-vu about the latest red meat scare and I thought that my previous post as well as those of others had covered the bases but I just came across a remarkable article from the Harvard Health Blog. It was entitled “Study urges moderation in red meat intake.” It describes how the “study linking red meat and mortality lit up the media…. Headline writers had a field day, with entries like ‘Red meat death study,’ ‘Will red meat kill you?’ and ‘Singing the blues about red meat.”’

What’s odd is that this is all described from a distance as if the study by Pan, et al (and likely the content of the blog) hadn’t come from Harvard itself but was rather a natural phenomenon, similar to the way every seminar on obesity begins with a slide of the state-by-state development of obesity as if it were some kind of meteorologic event.

When the article refers to “headline writers,” we are probably supposed to imagine sleazy tabloid publishers like the ones who are always pushing the limits of first amendment rights in the old Law & Order episodes.  The Newsletter article, however, is not any less exaggerated itself. (My friends in English Departments tell me that self-reference is some kind of hallmark of real art). And it is not true that the Harvard study was urging moderation. In fact, it is admitted that the original paper “sounded ominous. Every extra daily serving of unprocessed red meat (steak, hamburger, pork, etc.) increased the risk of dying prematurely by 13%. Processed red meat (hot dogs, sausage, bacon, and the like) upped the risk by 20%.” That is what the paper urged. Not moderation. Prohibition. Who wants to buck odds like that? Who wants to die prematurely?

It wasn’t just the media. Critics in the blogosphere were also working over-time deconstructing the study.  Among the faults that were cited, a fault common to much of the medical literature and the popular press, was the reporting of relative risk.

The limitations of reporting relative risk or odds ratio are widely discussed in popular and technical statistical books and I ran through the analysis in the earlier post. Relative risk destroys information.  It obscures what the risks were to begin with.  I usually point out that you can double your odds of winning the lottery if you buy two tickets instead of one. So why do people keep doing it?  One reason, of course, is that it makes your work look more significant.  But, if you don’t report the absolute change in risk, you may be scaring people about risks that aren’t real. The nutritional establishment is not good at facing their critics but on this one, they admit that they don’t wish to contest the issue.

Nolo Contendere.

“To err is human, said the duck as it got off the chicken’s back”

 — Curt Jürgens in The Devil’s General

Having turned the media loose to scare the American public, Harvard now admits that the bloggers are correct.  The Health NewsBlog allocutes to having reported “relative risks, comparing death rates in the group eating the least meat with those eating the most. The absolute risks… sometimes help tell the story a bit more clearly. These numbers are somewhat less scary.” Why does Dr. Pan not want to tell the story as clearly as possible?  Isn’t that what you’re supposed to do in science? Why would you want to make it scary?

The figure from the Health News Blog:

Deaths per 1,000 people per year

    1 serving unprocessed meat a week   2 servings unprocessed meat a day
    Women    

7.0

8.5
    3 servings unprocessed meat a week   2 servings unprocessed meat a day
    Men

12.3

13.0

Unfortunately, the Health Blog doesn’t actually calculate the  absolute risk for you.  You would think that they would want to make up for Dr. Pan scaring you.   Let’s calculate the absolute risk.  It’s not hard.Risk is usually taken as probability, that is, number cases divided by total number of participants.  Looking at the men, the risk of death with 3 servings per week is equal to the 12.3 cases per 1000 people = 12.3/1000 = 0.1.23 = 1.23 %. Now going to 14 servings a week (the units in the two columns of the table are different) is 13/1000 = 1.3 % so, for men, the absolute difference in risk is 1.3-1.23 = 0.07, less than 0.1 %.  Definitely less scary. In fact, not scary at all. Put another way, you would have to drastically change the eating habits (from 14 to 3 servings) of 1, 429 men to save one life.  Well, it’s something.  Right? After all for millions of people, it could add up.  Or could it?  We have to step back and ask what is predictable about 1 % risk. Doesn’t it mean that if a couple of guys got hit by cars in one or another of the groups whether that might not throw the whole thing off? in other words, it means nothing.

Observational Studies Test Hypotheses but the Hypotheses Must be Testable.

It is commonly said that observational studies only generate hypotheses and that association does not imply causation.  Whatever the philosophical idea behind these statements, it is not exactly what is done in science.  There are an infinite number of observations you can make.  When you compare two phenomena, you usually have an idea in mind (however much it is unstated). As Einstein put it “your theory determines the measurement you make.”  Pan, et al. were testing the hypothesis that red meat increases mortality.  If they had done the right analysis, they would have admitted that the test had failed and the hypothesis was not true.  The association was very weak and the underlying mechanism was, in fact, not borne out.  In some sense, in science, there is only association. God does not whisper in our ear that the electron is charged. We make an association between an electron source and the response of a detector.  Association does not necessarily imply causality, however; the association has to be strong and the underlying mechanism that made us make the association in the first place, must make sense.

What is the mechanism that would make you think that red meat increased mortality.  One of the most remarkable statements in the original paper:

“Regarding CVD mortality, we previously reported that red meat intake was associated with an increased risk of coronary heart disease2, 14 and saturated fat and cholesterol from red meat may partially explain this association.  The association between red meat and CVD mortality was moderately attenuated after further adjustment for saturated fat and cholesterol, suggesting a mediating role for these nutrients.” (my italics)

This bizarre statement — that saturated fat played a role in increased risk because it reduced risk— was morphed in the Harvard News Letters plea bargain to “The authors of the Archives paper suggest that the increased risk from red meat may come from the saturated fat, cholesterol, and iron it delivers;” the blogger forgot to add “…although the data show the opposite.” Reference (2) cited above had the conclusion that “Consumption of processed meats, but not red meats, is associated with higher incidence of CHD and diabetes mellitus.” In essence, the hypothesis is not falsifiable — any association at all will be accepted as proof. The conclusion may be accepted if you do not look at the data.

The Data

In fact, the data are not available. The individual points for each people’s red meat intake are grouped together in quintiles ( broken up into five groups) so that it is not clear what the individual variation is and therefore what your real expectation of actually living longer with less meat is.  Quintiles are some kind of anachronism presumably from a period when computers were expensive and it was hard to print out all the data (or, sometimes, a representative sample).  If the data were really shown, it would be possible to recognize that it had a shotgun quality, that the results were all over the place and that whatever the statistical correlation, it is unlikely to be meaningful in any real world sense.  But you can’t even see the quintiles, at least not the raw data. The outcome is corrected for all kinds of things, smoking, age, etc.  This might actually be a conservative approach — the raw data might show more risk — but only the computer knows for sure.

Confounders

“…mathematically, though, there is no distinction between confounding and explanatory variables.”

  — Walter Willett, Nutritional Epidemiology, 2o edition.

You make a lot of assumptions when you carry out a “multivariate adjustment for major lifestyle and dietary risk factors.”   Right off , you assume that the parameter that you want to look at — in this case, red meat — is the one that everybody wants to look at, and that other factors can be subtracted out. However, the process of adjustment is symmetrical: a study of the risk of red meat corrected for smoking might alternatively be described as a study of the risk from smoking corrected for the effect of red meat. Given that smoking is an established risk factor, it is unlikely that the odds ratio for meat is even in the same ballpark as what would be found for smoking. The figure below shows how risk factors follow the quintiles of meat consumption.  If the quintiles had been derived from the factors themselves we would have expected even better association with mortality.

The key assumption is that the there are many independent risk factors which contribute in a linear way but, in fact, if they interact, the assumption is not appropriate.  You can correct for “current smoker,” but biologically speaking, you cannot correct for the effect of smoking on an increased response to otherwise harmless elements in meat, if there actually were any.  And, as pointed out before, red meat on a sandwich may be different from red meat on a bed of cauliflower puree.

This is the essence of it.  The underlying philosophy of this type of analysis is “you are what you eat.” The major challenge to this idea is that carbohydrates, in particular, control the response to other nutrients but, in the face of the plea of nolo contendere,  it is all moot.

Who paid for this and what should be done.

We paid for it. Pan, et al was funded in part by 6 NIH grants.  (No wonder there is no money for studies of carbohydrate restriction).  It is hard to believe with all the flaws pointed out here and, in the end, admitted by the Harvard Health Blog and others, that this was subject to any meaningful peer review.  A plea of no contest does not imply negligence or intent to do harm but something is wrong. The clear attempt to influence the dietary habits of the population is not justified by an absolute risk reduction of less than one-tenth of one per cent, especially given that others have made the case that some part of the population, particularly the elderly may not get adequate protein. The need for an oversight committee of impartial scientists is the most important conclusion of Pan, et al.  I will suggest it to the NIH.

The Office of Research Integrity is hosting a conference on the Quest for Research Excellence and, for the first time, there is session that directly confronts policy and The Crises in Nutrition. The Speakers will delineate the problem — the two worlds of establishment nutrition and the major challenge of low carbohydrate diets, the growing problems of childhood obesity and our failure to deal with it, the confusion in the popular press on scientific issues, and finally, the voice of the patient, the failure to listen to the people who are dissatisfied with official guidelines and who have found workable solutions themselves. Three specific goals are recommended: 1) open hearings in which all researchers are represented, 2) funding research in which all people in low carbohydrate research work with others and finally, 3) a new oversight agency from NSF or Office of Research and Technology Policy.

The three goals may be a useful crystallizing point for moving forward. What can you do?

  1. Contact your elected officials and copy one of the authors from the conference. Use the Abstracts below as a basis for your own version of what needs to be done. The three goals can be more narrowly focussed for your own interests.
  2. Encourage local media to cover the meeting. Information is at http://ori.hhs.gov and the speakers can be contacted directly.
  3. Publicize your version of the three goals on your blog, your facebook page or other social media.

2011 Office of Research Integrity Conference Washington DC

Quest for Research Excellence, March 15, 2012.

Session on Crisis in Nutrition.

Chair: Richard David Feinman Contact Information: feinman@mac.com (917) 554-7794

Introduction and Abstracts.

The interest in nutrition for general health and for the prevention and treatment of disease is probably greater than at any time in history. A fairly large research community has grown up to provide information on the subject but the excellence of the results and their ability to inform the general public is highly questionable. The prospect for the future quality of research is similarly discouraging. This session focusses on a crisis in nutrition: the confusion in the public’s mind and the lack of accountability of official agencies and their failure to consider minority points of view. Four areas are considered in this session: the need to consider work that has been done on carbohydrate restriction (the major alternative to current recommendations), the limitations of current media representations of research, the problem of childhood obesity, and finally, the failure to listen to the patients who have not been well served by current ideas and who have discovered alternatives for themselves. The public, athrough forums and comments to blogs and other social media, have expressed substantial dissatisfaction with the current state of medical nutrition.

Three approaches are suggested as first steps for resolving the current crises:

  1. First, we need hearings to be held by congress or HHS in which all major researchers in nutrition are represented. We have to have everybody in the game. The USDA guidelines committee, the American Health Association nutrition panels have to meet with their critics. In particular, researchers in dietary carbohydrate restriction should be able to meet and discuss issues with their critics. This is what the government can do. Better than taxation or other punitive measures, they can bring out the information. The NIH or congress should hold meaningful hearings where all sides are heard.
  1. Second, we need to fund a study in which researchers in dietary carbohydrate restriction and critics of such diets cooperate to design a long-term comparison of CRD and low-fat diets, Mediterranean diets or whatever. The groups agree on methods of procedure, make-up of the diets, how compliance will be effected, and what parameters will be measured. They “write the paper first, leaving room for the data,” that is, they agree in advance on what the possible outcomes are and what conclusions could be drawn from them. In this way, the public and other scientists will have a sense that the issues have adequately been addressed and the results reliably evaluated.
  1. Finally, what’s needed is the creation of a new oversight organization, possibly under the auspices of the National Science Foundation or the Office of Science and Technology Policy in which scientists with no personal stake in nutrition, assess bias in grant awards and publications. The scientific principles involved in nutrition are neither so technical nor so profound that accomplished scientists from other fields cannot evaluate them. Such organizations might make recommendations (or indicate the limitations in existing knowledge that prevent making recommendations) after hearing all credentialed experts.

In the end, we have to say whether there is really a problem or not. Is their really an epidemic of obesity and overweight? Is there a crisis in the incidence of diabetes, or not? Are our health problems, the rising cost, the patient suffering, real? If they’re real, we have to use everything we have. We have to have real science and we can’t continue with one expert committee after another making recommendations but taking no responsibility for outcomes and refusing to show any willingness to confront their critics.

Crisis in nutrition: I. Research Integrity and the Challenge of Carbohydrate Restriction.

Author: Richard David Feinman.

Objective: Research integrity extends beyond falsification of data and explicit misconduct. We assessed the extent to which established majority opinion recommending dietary fat and saturated fat reduction has failed to cite contradictory evidence, accepted undocumented conclusions and marginalized contributions of alternative points of view, specifically the role of dietary carbohydrate restriction, the major challenge to current recommendations..

Main points: Government and private health agencies have long recommended a reduction in dietary fat, particularly saturated fat, in the treatment or prevention of cardiovascular disease, obesity and diabetes. While there are many disclaimers, low-fat in some form remains the standard nutritional recommendation. Alternative strategies based on control of insulin fluctuations via carbohydrate restriction, while widely used by many in the community, have been discouraged if not actually attacked. This has contributed to a “two worlds” system that has increased confusion among scientists and the public. While there are many exceptions and some emerging acceptance of carbohydrate restriction — which frequently fails to cite earlier work — there is a perception of a majority opinion with pervasive control of the scientific infrastructure: editorial boards, study sections and health agency administration. Examples will be given of undocumented negative statements about low-carbohydrate diets, misrepresentation of data and extensive failure to cite relevant papers from the literature. Most troubling is the tendency to accept some of the conclusions previously demonstrated in low-carbohydrate research without, again, giving appropriate citations to that research. This has led a significant part of the population to distrust official recommendations and medical science.

There is a need to re-evaluate published data on carbohydrate restriction and to guarantee adequate peer review of future manuscripts and grant applications on macronutrient composition of the diet. More generally, better communication and cooperation between researchers and physicians with different opinions can only benefit science and society, a society that is admittedly not making good progress on obesity, diabetes and metabolic syndrome.

Conclusions & Recommendations:

Recommendations for better integration of different points of view include government-sponsored meetings where all scientific approaches can present their own opinions and address critics, representation on study sections and editorial boards of people with experience in carbohydrate restriction-insulin control diets and long term comparative trials that include PIs with experience and understanding of the role of the glucose-insulin axis in obesity, diabetes and metabolic syndrome. Agreement in advance between the “two worlds” as to the expected outcomes and interpretations would provide most benefit for the public and scientist-community interactions. Given the pervasiveness of the problem, in the end, intervention of new oversight agencies, e.g. from NSF or Office of Science and Technology, may be needed

Figure 1. Comparison of low-carbohydrate diets to low-GI diets and high cereal diets.

Crisis in nutrition: II. The popular media and research publications  

Author: Richard David Feinman.

Objective: The public relies on popular media for description of nutrition research. A major interest is the controversy over macronutrient composition of the diet and particularly the role carbohydrate-restriction, the major challenge to official recommendations. The goal is to assess the extent to which statements to the media and especially press releases from authors, author institutions and journals accurately represent the results of reported research. To determine the extent to which personal bias influences and is taken as fact by the media.

Main points: Authors of research papers should sensibly have great freedom in describing the implications of their research to the media, but it is important that the public be aware of when that opinion does or does not follow directly from the publication. Two examples are given. In one, an animal study (Foo, et al. Proc Natl Acad Sci USA 2009, 106: 15418-15423), the accompanying press release implied that it was motivated by observations of patients in a hospital which were not described, were unsubstantiated and would have been purely anecdotal. In a second example, a press release stated that carbohydrate-restricted diets (CRDs) were not included in a comparative study because of their low compliance (Sacks, et al. N Engl J Med 2009, 360: 859-873. No data were given to support this assertion and it is, in fact not true — CRDs have, on average, better compliance than other dietary interventions. The study concluded that the macronutrient composition of the diet was not important even though, as implemented, dietary intake was the same for the groups studied and, again, the CRD was not included in the study. It seems likely that that this would have an inhibiting effect on the willingness of individuals to choose a CRD, an outcome that was not justified by the published research.

Conclusions & recommendations: Practices should be evaluated and guidelines should be generated by academic societies, scientific journals and the popular media as to what constitutes appropriate press description of published research. Reasonable principle are that only those specific conclusions that derive directly from the publication. The generally accepted idea that authors make clear what is their personal opinion and what is the product of research should be the norm.

Biography: Richard David Feinman, PhD in Chemistry (University of Oregon) is Professor of Cell Biology at SUNY Downstate Medical Center. His current area of research is nutritional biochemistry and biochemical education especially as it relates to macronutrients and bioenergetics. He is founder of the Nutrition & Metabolism Society and former co-editor-in-chief of the journal Nutrition & Metabolism.

Figure 2. The world according to Reuters. Low-fat is good. It’s bad. It’s not as bad as we thought. Wait! Eat more fruits and vegetables. “The low-dat diet craze?” Is that what it’s been? Is?

Crisis in nutrition: III. Childhood Obesity: Prevention and Intervention 

Author: Wendy Knapp Pogozelski, Dept of Chemistry, SUNY Geneseo, Geneseo, NY 14454.

Objective: Almost one-third of American children aged 2-11 qualify as obese or overweight, with obesity-related diseases such as type 2 diabetes greatly on the rise in this population. Despite the labeling of the crisis as “epidemic,” funding to study childhood obesity has been limited and restricted to the traditional intervention strategies (to reduce calories, to reduce dietary fat and to exercise more) despite the fact that these efforts have been largely unsuccessful. The time has come for frank assessment of foundational beliefs about a) the causes of obesity in children and b) effective prevention and intervention strategies. This talk will discuss assumptions that are barriers to research and will compare results from traditional calorie-restriction programs with results from programs that have emphasized carbohydrate control and insulin reduction.

Main points: The current generation of children is predicted to be the first to experience a lower life expectancy than that of its parents. Children across the world are experiencing unparalleled rates of obesity, heart disease and type 2 diabetes. Relatively little formal research has addressed the causes of childhood obesity, perhaps due to an assumption that the problem is already understood. Despite reluctance to use children as subjects in studies that depart from the traditional “eat less and exercise more” philosophies, it has been noted that the current efforts, dietary recommendations, educational programs and mandates of school lunch programs could be characterized as experiments. These experiments, like the numerous interventions based on traditional strategies, have had poor results but it has been very difficult to implement or fund those approaches that focus on carbohydrate control despite demonstrable success in this area. We will examine typical meals given in schools and at home, compare data from various obesity interventions and discuss causes of obesity on a molecular level

Conclusions & recommendations: The crisis warrants policy change. 1) Funding for childhood obesity should be increased. 2) A broader range of methods and principal investigators should be instituted, with greater accountability required of funded investigators. 3) The USDA nutritional recommendations, a “one size fits all” guide for school meal programs should be reevaluated and reformulated to take into account all strategies for obesity prevention and intervention. 4) Education for physicians, dietitians and health care professionals, as well as the general public, should be altered to include an understanding of the most positive results in obesity prevention.

Biography: Wendy Pogozelski, PhD in Chemistry (Johns Hopkins University) is Professor of Chemistry at SUNY Geneseo. Her research has been in radiation effects, DNA damage, and DNA computing. Current efforts are directed toward biochemical-based nutrition education for health professionals, educators and the general public. In addition to developing teaching materials that incorporate nutrition research, Dr. Pogozelski writes and lectures on diabetes and works with local and national organizations to improve nutrition education.

Figure 3. Before and After from James Bailes’s No More Fat Kids

Crisis in nutrition: IV. Vox Populi

Authors: Tom Naughton, Jimmy Moore, Laura Dolson

Objective: Blogs and other social media provide insights into how the public views the current state of nutrition science and the official dietary recommendations. We ask what can be learned from online discussions among people who dispute and distrust the official recommendations.

Main points: A growing share of the population no longer trusts the dietary advice offered by private and government health agencies. They believe the supposed benefits of the low-fat, grain-based diets promoted by those agencies are not based on solid science and that benefits of low-carbohydrate diets have been deliberately squelched. The following is typical of comments the authors (whose websites draw a combined 1.5 million visitors monthly) receive daily:

“The medical and pharmaceutical companies have no interest in us becoming healthy through nutrition. It is in their financial interest to keep us where we are so they can sell us medications.”

Similar distrust of the government’s dietary recommendations has been expressed by doctors and academics. The following comments, left by a physician on one of the authors’ blogs, are not unusual:

“You and Denise Minger should collaborate on a book about the shoddy analysis put out by hacks like the Dietary Guidelines Advisory Committee.”

“Sometimes I wonder if people making these statements even took a basic course in biochemistry and physiology.”

Many patients have given up on their health care professionals and turn to Internet sites for advice they trust. This is particularly true of people with diabetes who find that a low-fat, high-carbohydrate diet is not helping them control their blood glucose. As one woman wrote about her experience with a diabetes center:

“I was so frustrated, I quit going to the center for check ups.”

The data suggest a serious problem in science-community interactions which needs to be

explored.

Conclusions & recommendations: Our findings document a large number of such cases pointing to the need for public hearings and or conference. The community is not well served by an establishment that refuses to address its critics from within the general population as well as health professionals.

Figure 4. Some comments from the Active Low-Carber Forums (140, 660 members on March 12, 2012).

Biographies:

Tom Naughton is a former writer for a health magazine, a contributor to the Encylopedia Britannica’s Health and Medical Annual, a documentary filmmaker, and popular blogger who specializes in health and nutrition issues.

Jimmy Moore’s top-rated “Livin’ La Vida Low-Carb” blog has drawn more than 6 million visitors since 2005. His podcast show, “The Livin’ La Vida Low-Carb Show with Jimmy Moore” has featured interviews with hundreds of respected doctors and researcher. He has also authored two books.

Laura Dolson, MS is a writer and cancer support provider at Mediconsult.com, and hastaught health and nutrition classes at a junior high charter school in California. Her About.com nutrition website draws hundreds of thousands of visitors monthly.