Posts Tagged ‘nutrition science’


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 one 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.



I was walking on a very dark street and I assumed that the voice I heard was a guy talking on a cell phone. Apparently about a dinner party, he was saying “Remember, I don’t eat red meat.” Only a few years ago, that would have sounded strange. Of course, a few years ago a man talking to himself on the street would have been strange. He would have been assumed to be deranged, more so if he told you that he was talking on the telephone. But yesterday’s oddity pops up everywhere today. Neo-vegetarianism affects us all. It’s all described very well by Jane Kramer’s excellent review of veggie cookbooks in the April 14 New Yorker,

“…from one chili party to the next, everything changed. Seven formerly enthusiastic carnivores called to say they had stopped eating meat entirely…. Worse, on the night of that final party, four of the remaining carnivores carried their plates to the kitchen table, ignoring the cubes of beef and pancetta, smoky and fragrant in their big red bean pot, and headed for my dwindling supply of pasta. “Stop!” I cried. “That’s for the vegetarians!”

Illustration by Robin Feinman. Reference:


A guide for consumers and the media

For Mark Twain’s hierarchy of lies, damned lies and statistics, we should really add epidemiological lies, those reports showing that brown rice or trans-palmitoleic acid will prevent diabetes and diet soda will make you fat, which appear every week or so in ABCNews.  (I mean the generic media, but ABCNews and I have a close relationship: sometimes they even print what I tell them).  If you’ve been eating white rice instead of brown rice and you develop diabetes ten years later, it is the fault of your choice of rice. Everybody knows that this is ridiculous but the data are there showing an almost 4-fold increased risk, so how can you argue with the numbers.

These kinds of studies are always based on associations and the authors are usually quick to tell you that association doesn’t mean causality even as they interpret the data as a clear guide to action (“Substitution of whole grains, including brown rice, for white rice may lower risk of type 2 diabetes.”)   In fact, to most scientists, association can be a strong argument for causality.  That is not what’s wrong with them. Philosophically speaking, there are only associations.  All we really know is that there is a stream of particles and there is an association between the presence of a magnet and the appearance of a spot on a piece of photographic paper (anybody remember photographic paper?).  God does not whisper in your ear that the particle has a magnetic moment.  It is the strength of the idea behind the association and the presentation of the idea that determines whether the association implies causality.  What most people really mean is that “association does not necessarily imply causality.  You may need more information.” What’s wrong with the rice story is that the idea is lacking in common sense.  The idea that the type of rice you eat has any meaningful impact by itself, or even whether one can guess whether it has a positive or negative impact on a general lifestyle, is absurd.  But what about the statistics? Here the problem is really presentation of the data.  The number of papers in the literature pointing out the errors in interpretation of statistics is very large although it is still less than the number of papers making those errors.  There are numerous problems and many examples but let’s look at the simplest case: limitations of reporting relative risk and alternatives.

images-1Here’s a good example cited in a highly recommended popular statistics books, Gerd Gigerenzer’s “Calculated Risks.” He discusses a real case, the West of Scotland Coronary Prevention Study (WOSCOPS) comparing the statin drug, pravastatin to placebo in people with high cholesterol.  The study was started in 1989 and went on for about 5 years.  (These days, I think you can only compare different statins; everybody is so convinced that they are good that a placebo would be considered unethical):

1. First, the press release: “People with high cholesterol can rapidly reduce… their risk of death by 22 per cent by taking…pravastatin.”

2. Now, ask yourself what this means? If 1000 people with high cholesterol take pravastatin, how many people will be saved from a heart attack that might have otherwise killed them?  Think about this, then look at the data, the data that should have  been reported in the media.

3. The data:

Treatment        deaths during 5 years (per 1000 people with high cholesterol)

pravastatin             32

placebo                  41

Right off, it doesn’t look as good as you might have thought.  Overall, death from a heart attack is a major killer, but if you take a thousand people and watch them for five years, not that many people die from a heart attack. Now there are three standard ways of representing the data.

4. Data presentation – Relative risk reduction.

Risk is the number of cases divided by total number of people in the trial (or risk per total number). So you calculate a risk for 1000 people on the drug = 32/1000 = 03.2 % and similarly for people on the statin. Risk reduction for comparing treatments is\ the difference between the two risks.  The relative risk reduction here  is just the reduction in risk divided by the risk for the placebo:

Risk reduction (number of people saved per thousand)  = 41-32 = 9. Saving 9 lives doesn’t sound that great but lets get the per cent as reported.

Relative risk reduction = 9/41 = 22 % as indicated, and it does sound like a big deal but there are other ways to look at the data.

5.  Data presentation – Absolute risk reduction.  Again, you start with risk, the number of cases divided by total number but you calculate the actual fraction.  The absolute risk reduction is the difference between these two fractions.

For pravastatin, risk = 32/1000

For placebo, risk = 41/1000

Absolute risk reduction = (41/1000) – (32/1000) = 9/1000 = 0.9 % (less than 1 %)

6.  Data presentation – Number needed to treat (NNT): This is a good indicator of outcomes.  If you treat 1000 people, 9 will survive who might have otherwise died. So,

number that you have to treat  to save one life = NNT  =  1000/9 = 111 people .

7. Conclusion: 22 % risk reduction is true enough but it seems like it didn’t really tell you what you want to know.  Cutting to the chase, would you take a statin if you had high cholesterol (more than about 250 mg/dl) and, as in WOSCOPS, no history of heart attacks. On the basis of this study alone, it’s not clear.  First, the risk is low.  There is clearly a benefit but how predictable is that benefit?  In the study, 99 % of the people had no benefit.  Of course, if you are the one out of a hundred, the drug would be a good thing.  The question is not easy to answer but the point of what’s written here is that the statistics as reported in the media might have led you to jump to conclusions.  Before you jump, though, you might ask about side-effects.  This is a complicated subject because although the side-effects are rare, their incidence is not zero and they can be severe but this post is only about the statistics.