We have a good deal of enthusiasm in the keto/paleo/low-carb community. We have the real sense that we can we use carbohydrate restriction to take advantage of the characteristic metabolic features of cancer — inflexible reliance on glucose. Enthusiasm may have outstripped the data, however, and several groups are trying to fill the gap. The barrier rests with the difficulty for anybody to obtain funding from NIH or other major government or private agencies. On top of this looms the long-standing resistance to low-carbohydrate diets making things particularly difficult. Our group is carrying out some good experiments and we employ a dedicated technician and we can efficiently use limited funds. Your backing can help.  A $ 15 donation gets us several days of supplies for the in vitro experiments that provide the biochemical underpinnings for attacking cancer in the clinic. Our project at experiment.com provides background, a place for discussion and reports from the lab.

The current metabolic point of view in cancer — emphasizing flexibility of fuel choices —  derives from renewed interest in the Warburg effect. Warburg saw that many cancer cells were producing lactic acid, the product of glycolysis. In other words, the tumors were not using the more efficient aerobic metabolism even when oxygen was present in the environment. The tumor cell’s requirement for glucose suggests the possibility of giving the host an advantage by restricting carbohydrate and offering ketone bodies as an alternative fuel.constant_ATP_UCP2

We showed previously that we could inhibit the growth of 7 different cancer cell lines and repress the production of ATP (the”‘energy molecule” in cell culture by adding acetoacetate (one of the ketone bodies) to the medium.  Control normal cell lines were not affected. In addition, we showed that ATP reduction was associated with the level of a molecule called uncoupling protein-2 (UCP-2).  I explain in other posts what “uncoupling” means and how it figures into energy efficiency. First, the big picture..

What is the context of inhibition by ketone bodies ?
The real context, of course, is human cancer. Our 28 day pilot human trial of 10 subjects with advanced cancers on a very low carbohydrate ketogenic diet (KD) was published in Nutrition (Elsevier) in 2012.  A small study — nominally just to show safety and feasibility, it was nonetheless well received. Of note, is that we found that those patients with the greatest extent of ketosis had stable cancers or partial remission, while those with the least ketosis showed continued progressive cancer. Despite a favorable editorial and the Metabolism Award from the journal. Unfortunately, our proposal to scale up to 65 patients was rejected by the NIH/NCI which we find very discouraging perhaps related to a commitment to drug therapy. In any case, we appeal now for help in supporting dietary cancer research.
You can help. To donate:  Our project ia at experiment.com

Image  —  Posted: August 22, 2016 in Uncategorized

 

One of my favorite legal terms, collateral estoppel, refers to procedures to prevent re-litigation of issues that have already been settled in court. From the same root as stopper, that is, cork, it prevents harassment and wasting of the court’s time. The context is the recent flap over a poster presented by Kevin Hall which has started re-trying the case of whether all diets have the same metabolic efficiency, a question which, in my view, has been adjudicated several times. I put it this way because frequently I have made an analogy between evidence-based-medicine (EBM) and evidence as presented in a court of law. My main point has been that, in the legal system, there are rules of evidence and there is a judge who decides on admissibility. You can’t just say, as in EBM, that your stuff constitutes evidence.  My conclusion is usually that EBM is one of the self-congratulatory procedures that allows people to say anything that they want without having to defend their position. EBM represents of of the many corruptions of research procedure now under attack by critics (perpetrators ?) as in the recent editorial by Richard Horton, editor of The Lancet. One thing that I  criticize medical nutrition for is its inability to be estopped from funding and endlessly re-investigating whether saturated fat causes heart disease, whether high protein diets hurt your kidneys, and whether a calorie is a calorie. It seems that the issue is more or less settled — there are dozens of examples of variable energy expenditure in the literature. It would be reasonable to move on by investigating the factors that control energy balance, to provide information on the mechanisms that predict great variability and, most important, the mechanisms that make it so small in biological systems — most of the time, a calorie is a calorie, at least roughly. Funding and performing ever more expensive experiments to decide whether you can lose more or less weight on one diet or another, as if we had never done a test before, is not helpful.

Several bloggers discussed Hall’s study which claims that either a calorie is a calorie or it is not depending on whether, as described by Mike Eades, you look at the poster itself or at a video of Kevin Hall explaining what it is about. Mike’s blog is excellent but beyond the sense of déja-vu, the whole thing reminded me of the old joke about the Polish mafia. They make you an offer that you can’t understand.  So, because this is how I got into this business, I will try to explain how I see the problem of energy balance and why we might want this trial estopped.

I have taught nutrition and metabolism for many years but I got into nutrition research because the laws of thermodynamics were, and still are, invoked frequently in the discussion. Like most chemists, I wouldn’t claim to be a real expert but I like the subject and I teach the subject at some level. I could at least see that nobody in nutrition knew what they were talking about. I tried to show that the application of thermodynamics, if done correctly, more or less predicts that different diets will have different efficiencies (from the standpoint of storage, that is, weight gained per calorie consumed).

But you don’t really need thermodynamics to see this. Prof. Wendy Pogozelski at SUNY Geneseo pointed out that if you think about oxidative metabolic uncouplers, that is all you need to know. “Coupling,” in energy metabolism, refers to the sequence of reactions by which the energy from the oxidation of food is converted to ATP, that is, into useful biologic energy. The problem in energy metabolism is that the fuel, as in many “combustion engines,” is processed by oxidation — you put in oxygen and get out CO2 and water . The output, on the other hand  is a phosphorylation reaction — generation of ATP from ADP, its low energy form. The problem is how to couple these two different kins of reactions. It turns out that the mitochondrial membrane couples the two processes (together called oxidative phosphorylation). A “high energy” state is established across the membrane by oxidation and this energy is used to make ATP. Uncouplers are small molecules or proteins that disengage the oxidation of substrate (food) from ATP synthesis allowing energy to be wasted or channeled into other mechanisms, generation of reactive oxygen species, for example.

BLOG_car_analogy_May_16The car analogy of metabolic inhibitors. Figure from my lectures. Energy is generated in the TCA cycle and electron transport chain (ETC). The clutch plays the role of the membrane proton gradient, transmitting energy to the wheels which produce forward motion (phosphorylation of ADP). Uncouplers allow oxidation to continue — the TCA cycle is “racing” but to no effect. Other inhibitors (called oxidative phosphorylation inhibitors) include oligomycin which blocks the ATP synthase, analogous to a block under the wheels: no phosphorylation, no utilization of the gradient; no utilization, no gradient formation; no gradient, no oxidation. The engine “stalls.”

In teaching metabolism, I usually use the analogy of an automobile where the clutch connects the engine to the drive train . The German word for clutch is Kupplung and when you put a car in neutral your car is uncoupled, can process many calories of gasoline ‘in,’ but has zero efficiency, so that none of the ‘out’ does the useful work of turning the wheels. Biological systems can be uncoupled by external compounds — the classic is 2, 4-dinitrophenol which, if you are familiar with mitochondrial metabolism, is a proton ionophore, that is, destroys the proton gradient that couples oxidation to ADP-phophorylation.  There are natural uncouplers, the uncoupling proteins, of which there are five, named UCP-1 through UCP-5. Considered a family because of the homology to UCP-1, a known uncoupler, it has turned out that at least two others clearly have uncoupling activity. The take-home message is that whatever the calories in, the useful calories out (for fat storage or whatever) depends on the presence of added or naturally occurring uncouplers as well.

This is one of many examples of the mechanisms whereby metabolic calories-out per calorie-in could be variable.  The implication is that when somebody reports metabolic advantage (or disadvantage), there is no reason to disbelieve it. Conversely, this is one of the mechanisms that can reduce variability.

In fact, homeostatic mechanisms  are usually observed. You don’t have to have a metabolic chamber to know that your intake is variable day-to-day but your weight may be quite stable. The explanation is not in the physics which, again,  predicts variation, but rather in the biological system which is always connected in feedback so as to resist change. However strong the homeostasis (maintenance of steady-state), conversely, everybody has the experience of being in a situation where it doesn’t happen. “I don’t understand. I went on this cruise and I really pigged out on lobster and steak but I didn’t gain any weight.”  (It is not excluded, but nobody ever says that about the pancake breakfast). In other words, biochemistry and daily experience tells us that black swans are to be expected and, given that the system is set up for variability, the real question is why there are so many white swans.

So it is physically predicted that a calorie is not a calorie. When it has been demonstrated, in animal models where there is control of the food intake, or in humans, where there are frequently big differences that cannot reasonably be accounted for by the error in food records, there is no reason to doubt the effect. And, of course, a black swan is an individual. Kevin Hall’s study, as in much of the medical literature, reported group statistics and we don’t know if there were a few winners in with the group. The work has not been reviewed or published but, either way, I think it is likely to waste the court’s time.

 

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.

As the nutrition world implodes, there are a lot of accusations about ulterior motives and personal gain. (A little odd, that in this period of unbelievable greed — CEO’s ripping off public companies for hundreds of millions of dollars, congress trying to give tax breaks to billionaires — book authors are upbraided for trying to make money). So let me declare that I am not embarrassed to be an author for the money — although the profits from my book do go to research, it is my own research and the research of my colleagues. So beyond general excellence (not yet reviewed by David Katz), I think “World Turned Upside Down” does give you some scientific information about red meat and cancer that you can’t get from the WHO report on the subject.

The WHO report has not yet released the evidence to support their claim that red meat will give you cancer but it is worth going back to one of the previous attacks.  Chapters 18 and 19 discussed a paper by Sinha et al, entitled “Meat Intake and Mortality.”    The Abstract says “Conclusion: Red and processed meat intakes were associated with modest increases in total mortality, cancer mortality, and cardiovascular disease mortality,” I had previously written a blogpost about the study indicating how weak the association was. In that post, I had used the data on men but when I incorporated the information into the book, I went back to Sinha’s paper and analyzed the original data. For some reason, I also checked the data on women. That turned out to be pretty surprising:

Sinha_Table3_Chapter18_Apr22

I described on Page 286: “The population was again broken up into five groups or quintiles. The lower numbered quintiles are for the lowest consumption of red meat. Looking at all cause mortality, there were 5,314 deaths [in lowest quintile] and when you go up to quintile 05, highest red meat consumption, there are 3,752 deaths. What? The more red meat, the lower the death rate? Isn’t that the opposite of the conclusion of the paper? And the next line has [calculated] relative risk which now goes the other way: higher risk with higher meat consumption. What’s going on? As near as one can guess, “correcting” for the confounders changed the direction….” They do not show most of the data or calculations but I take this to be equivalent to a multivariate analysis, that is, red meat + other things gives you risk. If they had broken up the population by quintiles of smoking, you would see that that was the real contributor. That’s how I interpreted it but, in any case, their conclusion is about meat and it is opposite to what the data say.

So how much do you gain from eating red meat? “A useful way to look at this data is from the standpoint of conditional probability. We ask: what is the probability of dying in this experiment if you are a big meat‑eater? The answer is simply the number of people who both died during the experiment and were big meat‑eaters …. = 0.0839 or about 8%. If you are not a big meat‑eater, your risk is …. = 0.109 or about 11%.” Absolute gain is only 3 %. But that’s good enough for me.

Me, at Jubilat, the Polish butcher in the neighborhood: “The Boczak Wedzony (smoked bacon). I’ll take the whole piece.”

Wedzony_Nov_8

Boczak Wedzony from Jubilat Provisions

Rashmi Sinha is a Senior Investigator and Deputy Branch Chief and Senior at the NIH. She is a member of the WHO panel, the one who says red meat will give you cancer (although they don’t say “if you have the right confounders.”)

So, buy my book: AmazonAlibris, or

Direct:  Personalized, autographed copy $ 20.00 free shipping USA only.  Use coupon code: SEPT16

 

…what metaphysics is to physics. The old joke came to mind when a reporter asked me yesterday to comment on a paper published in the BMJ. “Intake of saturated and trans unsaturated fatty acids and risk of all cause mortality, cardiovascular disease, and type 2 diabetes: systematic review and meta-analysis of observational studies” by de Souza, et al.

Now the title ““Intake of saturated and trans unsaturated fatty acids…” tells you right off that this is not good news; lumping together saturated fat and trans-fat is an indication of bias. A stand-by of Atkins-bashers, it is a way of vilifying saturated fat when the data won’t fit.  In the study that the reporter asked about, the BMJ provided a summary:

“There was no association between saturated fats and health outcomes in studies where saturated fat generally replaced refined carbohydrates, but there was a positive association between total trans fatty acids and health outcomes Dietary guidelines for saturated and trans fatty acids must carefully consider the effect of replacement nutrients.”

“But?” The two statements are not really connected. In any case the message is clear: saturated fat is Ok. Trans-fat is not Ok. So, we have to be concerned about both  saturated fat and trans-fat. Sad.

And “systematic” means the system that the author wants to use. This usually means a meta-analysis. Explained by the overly optimistic “What is …?” series:

Meta-anal_WhatIs

The jarring notes are “precise estimate” in combination with “combining…independent studies.” In practice, you usually only repeat an experiment exactly if you suspect that something was wrong with the original study or if the result is sufficiently outside expected values that you want to check it. Such a systematic examination involves an analysis of the experimental details. The idea underlying the meta-analysis, however, usually unstated is that the larger the number of subjects in a study, the more compelling the conclusion. One might make the argument, instead, that if you have two or more studies which are imperfect, combining them is likely to lead to greater uncertainty and more error, not less.  I am one who would make such an argument. So where did meta-analysis come from and what, if anything, is it good for?

I am trained in enzyme and protein chemistry but I have worked in a number of different  fields including invertebrate animal behavior. I never heard of meta-analysis until very recently, that is, until I started doing research in nutrition. In fact, in 1970 there weren’t any meta-analyses, at least not with that phrase in the title, or at least not as determined by my PubMed search. By 1990, there were about a 100 and by 2014, there were close to 10, 000 (Figure 1).

Meta-anal_Year_Mar9Figure 1. Logarithm of the number of papers in PubMed search with the title containing “meta-analysis” vs. Year of publication

This exponential growth suggests that the technique grew by reproducing itself. It suggests, in fact, that its origins are in spontaneous generation. In other words, it is popular because it is popular. (It does have obvious advantages; you don’t have to do any experiments). But does it give any useful information?

Meta-analysis

If you have a study that is under-powered, that is, if you only have a small number of subjects, and you find a degree of variability in the outcome, then combining the results from your experiment with another small study may point you to a consistent pattern. As such, it is a last-ditch, Hail-Mary kind of method. Applying it to large studies that have statistically meaningful results, however, doesn’t make sense, because:

  1. If all of the studies go in the same direction, you are unlikely to learn anything from combining them. In fact, if you come out with a value for the output that is different from the value from the individual studies, in science, you are usually required to explain why your analysis improved things. Just saying it is a larger n won’t cut it, especially if it is my study that you are trying to improve on.
  2. In the special case where all the studies show no effect and you come up with a number that is statistically significant, you are, in essence saying that many wrongs can make a right as described in a previous blog post on abuse of meta-analyses.  In that post, I re-iterated the statistical rule that if the 95% CI bar crosses the line for hazard ratio = 1.0 then this is taken as an indication that there is no significant difference between the two conditions that are being compared. The example that I gave was the meta-analysis by Jakobsen, et al. on the effects of SFAs or a replacement on CVD outcomes (Figure 2). Amazingly, in the list of 15 different studies that she used, all but one cross the hazard ratio = 1.0 line. In other words, only one study found that keeping SFAs in the diet provides a lower risk than replacement with carbohydrate. For all the others there was no significant difference.  The question is why an analysis was done at all.  What could we hope to find? How could 15 studies that show nothing add up to a new piece of information? Most amazing is that some of the studies are more than 20 years old. How could these have had so little impact on our opinion of saturated fat?  Why did we keep believing that it was bad?

SFA_Jakobsen_Sub_AJCN-2_2009

Figure 2. Hazard ratios and 95% confidence intervals for coronary events and deaths in the different studies in a meta-analysis from Jakobsen, et al.Major types of dietary fat and risk of coronary heart disease: a pooled analysis of 11 cohort studies. Am J Clin Nutr 2009, 89(5):1425-1432.

3. Finally, suppose that you are doing a meta-analysis on several studies and that they have very different outcomes, showing statistically significant associations in different directions. For example, if some studies showed substituting saturated fat for carbohydrate increased risk while some showed that it decreased risk. What will you gain by averaging them? I don’t know about you but it doesn’t sound good to me. It makes me think of the old story of the emerging nation that was planning to build a railroad and didn’t know whether to use a gauge that matched the country to the north or the gauge of the country to the south. The parliament voted to use a gauge that was the average of the two.

Following up from chapters in The World Turned Upside Down, I will try to provide a guide to getting through the medical literature. I will talk about relative risk first with an excerpt from the book and a quick way to get a feel for what relative risk really is.

It has been frequently pointed out that to succeed at major league baseball all you have to do is screw up no more than 70 % of the time. In fact ,it is almost 75 years since someone was able to come up with a failure rate as low as 60 %. (Ted Williams hit .406 in 1941). The point is that statistics is about interpretation. It is how you describe things. As in Paulos’s Once Upon a Number, mathematics and especially statistics is a story, and how you tell it.  The potential for being misled is obviously great. The idea that statistics is a cut-and-dried standard set of manipulations into which you pour your data and from which you are automatically given significance, importance and truth, is one of the major components in the failure of the medical nutrition literature. Read the rest of this entry »

In  The World Turned Upside Down. The Second Low-Carbohydrate Revolution, I add my voice to the critiques of the low-fat hypothesis and the sorry state of nutritional science. I also provide 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” 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.

Whistle-blowing

“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.