Posts Tagged ‘scientific literature’

Doctor:    Therein the patient

  Must minister to himself.

Macbeth: Throw physic [medicine] to the dogs; I’ll none of it.

— William Shakespeare, Macbeth

The quality of nutrition papers even in the major scientific and medical journals is so variable and the lack of restraint in the popular media is so great that it is hard to see how the general public or even scientists can find out anything at all. Editors and reviewers are the traditional gate-keepers in science but in an area where rigid dogma has reached Galilean proportions, it is questionable that any meaningful judgement was made: it is easy to publish papers that conform to the party line (“Because of the deleterious effects of dietary fructose, we hypothesized that…”) and hard to publish others: when JAMA published George Bray’s “calorie-is-a-calorie” paper and I pointed out that the study more accurately supported the importance of carbohydrate as a controlling variable, the editor declined to publish my letter.  In this, the blogs have performed a valuable service in providing an alternative POV but if the unreliability is a problem in the scientific literature, that problem is multiplied in internet sources. In the end, the consumer may feel that they are pretty much out there on their own. I will try to help.  The following was posted on FatHead’s Facebook page:

 How does one know if a study is ‘flawed’? I see a lot of posts on here that say a lot of major studies are flawed. How? Why? What’s the difference if I am gullible and believe all the flawed studies, or if I (am hopefully not a sucker) believe what the Fat Heads are saying and not to believe the flawed studies — eat bacon.

Where are the true studies that are NOT flawed…. and how do I differentiate? : /

 My comment was that it was a great question and that it would be in the next post so I will try to give some of the principles that reviewers should adhere to.  Here’s a couple of guides to get started. More in future posts:

 1“Healthy” (or “healthful”) is not a scientific term. If a study describes a diet as “healthy,” it is almost guaranteed to be a flawed study.  If we knew which diets were “healthy,” we wouldn’t have an obesity epidemic. A good example is the paper by Appel, et al. (2005). “Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial,” whose conclusion is:

“In the setting of a healthful diet, partial substitution of carbohydrate with either protein or monounsaturated fat can further lower blood pressure, improve lipid levels, and reduce estimated cardiovascular risk.”

 It’s hard to know how healthful the original diet, a “carbohydrate-rich diet used in the DASH trials … currently advocated in several scientific reports” really is if removing carbohydrate improved everything.

Generally, understatement  is good.  One of the more famous is from Watson & Cricks’s 1953 paper in which they proposed the DNA double helix structure. They said “It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material.”  A study with the word “healthy” is an infomercial.

2. Look for the pictures (figures).  Presentation in graphic form usually means the author wants to explain things to you, rather than snow you.  This is part of the Golden Rule of Statistics that I mentioned in my blogpost “The Seventh Egg”  which discusses a very flawed study from Harvard on egg consumption. The rule comes from the book PDQ Statistics:

“The important point…is that the onus is on the author to convey to the reader an accurate impression of what the data look like, using graphs or standard measures, before beginning the statistical shenanigans.  Any paper that doesn’t do this should be viewed from the outset with considerable suspicion.”

The Watson-Crick  paper cited above had the diagram of the double-helix  which essentially became the symbol of modern biology.  It was drawn by Odile, Francis’s wife, who is described as being famous for her nudes, only one of which I could find on the internet.

Krauss, et. al. Separate effects of reduced carbohydrate intake and weight loss on atherogenic dyslipidemia.

The absence of a figure may indicate that the authors are not giving you a chance to actually see the results, that is, the experiment may not be flawed but the interpretation may be misleading, intentionally or otherwise.  An important illustration of the principle is a paper published by Krauss. It is worth looking at this paper in detail because the experimental work is very good and the paper directly — or almost directly — confronts a big question in diet studies: when you reduce calories by cutting out carbohydrate, is the effect due simply  to lowering calories or is there a specific effect of carbohydrate restriction.  The problem is important since many studies compare low-carbohydrate and low-fat diets where calories are reduced on both. Because the low-carbohydrate diet generally has the better weight loss and better improvement in HDL and triglycerides, it is said that it was the weight loss that caused the lipid improvements.

So Krauss compared the effects of carbohydrate restriction and weight loss on the collection of lipid markers known collectively as atherogenic dyslipidemia.  The markers of atherogenic dyslipidemia, which are assumed to predispose to cardiovascular disease, include high triglycerides (triacylglycerol), low HDL and high concentrations of the small dense LDL.

Here is how the experiment was set up: subjects first consumed a baseline diet of  54% of energy as carbohydrate, for 1 week. They were then assigned to one of four groups.  Either they continued the baseline diet, or they kept calories constant but reduced carbohydrate by putting fat in its place.  The three lower carbohydrate diets had 39 % or 26 % carbohydrate or 26 % carbohydrate with higher saturated fat.  After 3 weeks on constant calories but reduced carbohydrate, calories were decreased by 1000 kcal/d for 5 week and, finally, energy was stabilized for 4 weeks and the features of atherogenic dyslidemia were measured at week 13.  The protocol is shown in the figure from Krauss’s paper:

The Abstract of the paper describes the outcomes and the authors’ conclusions.

Results: The 26%-carbohydrate, low-saturated-fat diet reduced [atherogenic dylipidemia]. These changes were significantly different from those with the 54%-carbohydrate diet. After subsequent weight loss, the changes in all these variables were significantly greater…(my italics)

 Conclusions: Moderate carbohydrate restriction and weight loss provide equivalent but non-additive approaches to improving atherogenic dyslipidemia. Moreover, beneficial lipid changes resulting from a reduced carbohydrate intake were not significant after weight loss.

Now there is something odd about this.  It is the last line of the conclusion that is really weird. If you lose weight, the effect of carbohydrate is not significant?  As described below, Jeff Volek and I re-analyzed this paper so I have read that line a dozen times and I have no idea what it means.  In fact, the whole abstract is strange.  It will turn out that the lower (26 %) is certainly “significantly different from.. the 54%-carbohydrate diet” but it is not just different but much better. Why would you not say that?  The Abstract is generally written so that it sounds negative about low carbohydrate effects but it is already known from Krauss’s previous work and others that carbohydrate restriction has a beneficial effect on lipids and the improvements in lipid markers occur on low-carbohydrate diets whether weight is lost or not.  The last sentence doesn’t seem to make any sense at all.    For one thing, the experiment wasn’t done that way.  As set up, weight loss came after carbohydrate restriction.  So, let’s look at the data in the paper.  There are few figures in the paper and Table 2 in the paper presents the results in a totally mind-numbing layout.  Confronted with data like this, I sometimes stop reading.  After all, if the author doesn’t want to conform to the Golden Rule of Statistics, if they don’t want to really explain what they accomplished, how much impact is the paper going to have.  In this case, however, it is clear that the experiment was designed correctly and it just seems impossible from previous work that this wouldn’t support the benefits of carbohydrate restriction and the negative tone of the Abstract needs to be explained.  So we all had to slog through those tables.  Let’s just look at the triglycerides since this is one of the more telling attributes of atherogenic dyslpidemia.  Here’s the section from the Table:

Well this looks odd in that the biggest change is in the lowest carb group with high SF but it’s hard to tell what the data look like.  First it is reported as logarithms. You sometime take logs of your data in order to do a statistical determination but that doesn’t change the data and it is better to report the actual value.  In any case, it’s easy enough to take antilogs and we can plot the data.  This is what it looks like:

It’s not hard to see what the data really show: Reducing carbohydrate has an overwhelming effect on triglycerides even without weight loss.  When weight loss is introduced, the high carbohydrate diets still can’t equal the performance of the carbohydrate reduction phase.  (The dotted line in the figure are data from Volek’s earlier work which Krauss forgot to cite).

The statements in the Conclusion from the Abstract are false and totally misrepresent the data.  It is not true as it says “carbohydrate restriction and weight loss provide equivalent…” effects. The carbohydrate-reduction phase is dramatically better than the calorie restriction phase and it is not true that they are “non-additive”  Is this an oversight?  Poor writing?  Well, nobody knows what Krauss’s motivations were but Volek and I plotted all of the data from Krauss’s paper and we published a paper in Nutrition & Metabolism providing an interpretation of Krauss’s work (with pictures).  Our conclusion:

Summary Although some effort is required to disentangle the data and interpretation, the recent publication from Krauss et al. should be recognized as a breakthrough. Their findings… make it clear that the salutary effects of CR on dyslipidemia do not require weight loss, a benefit that is not a feature of strategies based on fat reduction. As such, Krauss et al.  provides one of the strongest arguments to date for CR as a fundamental approach to diet, especially for treating atherogenic dyslipidemia.

An important question in this experiment, however, is whether even in the calorie reduction phase, calories are actually the important variable.  This is a general problem in calorie restriction studies if for no other reason than that there is no identified calorie receptor.  When we published this data, Mike Eades pointed out that in the phase in which Krauss reduced calories, it was done by reducing macronutrients across the board so carbohydrate was also reduced and that might be the actual controlling variable so we plotted the TAG against carbohydrate in each phase (low, medium and high carb (LC, MC, HC) without or with weight loss (+WL) and the results are shown below

This is remarkably good agreement for a nutrition study. When you consider carbohydrates as the independent variable, you can see what’s going on.  Or can you?  After all, by changing the variables you have only made an association between carbohydrate and outcome  of an experiment. So does this imply a causal relation between carbohydrate and triglycerides or not?  It is widely said that observational studies do not imply causality, that observational studies can only provide hypothesis for future testing. It certainly seems like causality is implied here.  It will turn out that a more accurate description is that observational studies do not necessarily imply causality and I will discuss that in the next posts.  The bottom line will be that there is flaw in grand principles like “Random controlled trials are the gold standard.” “Observational studies are only good for generating hypotheses,”  “Metabolic Ward Studies are the gold standard.” Science doesn’t run on such arbitrary rules.

Asher Peres was a physicist, an expert in information theory who died in 2005 and was remembered for his scientific contributions as well as for his iconoclastic wit and ironic aphorisms. One of his witticisms was that “unperformed research has no results ”  Peres had undoubtedly never heard of intention-to-treat (ITT), the strange statistical method that has appeared recently, primarily in the medical literature.  According to ITT, the data from a subject assigned at random to an experimental group must be included in the reported outcome data for that group even if the subject does not follow the protocol, or even if they drop out of the experiment.  At first hearing, the idea is counter-intuitive if not completely idiotic  – why would you include people who are not in the experiment in your data? – suggesting that a substantial burden of proof rests with those who want to employ it.  No such obligation is usually met and particularly in nutrition studies, such as comparisons of isocaloric weight loss diets, ITT is frequently used with no justification and sometimes demanded by reviewers.   Not surprisingly, there is a good deal of controversy on this subject.  Physiologists or chemists, hearing this description usually walk away shaking their head or immediately come up with one or another obvious reductio ad absurdum, e.g. “You mean, if nobody takes the pill, you report whether or not they got better anyway?” That’s exactly what it means.

On the naive assumption that some people really didn’t understand what was wrong with ITT — I’ve been known to make a few elementary mistakes in my life — I wrote a paper on the subject.  It received negative, actually hostile. reviews from two public health journals — I include an amusing example at the end of this post.  I even got substantial grief from Nutrition & Metabolism, where I was the editor at the time, but where it was finally published.  The current post will be based on that paper and I will provide a couple of interesting cases from the medical literature.  In the next post I will discuss a quite remarkable new instance — Foster’s two year study of low carbohydrate diets — of the abuse of common sense that is the major alternative to ITT.

To put a moderate spin on the problem, there is nothing wrong with ITT, if you explicitly say what the method shows — the effect of assigning subjects to an experimental protocol; the title of my paper was Intention-to-treat.  What is the question? If you are very circumspect about that question, then there is little problem.  It is common, however, for the Abstract of a paper to correctly state that patients “were assigned to a diet” but by the time the Results are presented, the independent variable has become, not “assignment to the diet,” but “the diet” which most people would assume meant what people ate, rather than what they were told to eat. Caveat lector.  My paper was a kind of over-kill and I made several different arguments but the common sense argument gets to the heart of the problem in a practical way.  I’ll describe that argument and also give a couple of real examples.

Common sense argument against intention-to-treat

Consider an experimental comparison of two diets in which there is a simple, discrete outcome, e.g. a threshold amount of weight lost or remission of an identifiable symptom. Patients are randomly assigned to two different diets: diet group A or diet group B and a target of, say, 5 kg weight loss is considered success. As shown in the table above, in group A, half of the subject are able to stay on the diet but, for whatever reason, half are not. The half of the patients in group A who did stay on the diet, however, were all able to lose the target 5 kg.  In group B, on the other hand, everybody is able to stay on the diet but only half are able to lose the required amount of weight. An ITT analysis shows no difference in the two outcomes, while just looking at those people who followed the diet shows 100 % success.  This is one of the characteristics of ITT: it always makes the better diet look worse than it is.

         Diet A         Diet B
Compliance (of 100 patients)   50   100
Success (reached target)   50    50
ITT success   50/100 = 50%   50/100 = 50%
“per protocol” (followed diet) success   50/50 = 100%   50/100 = 50%

Now, you are the doctor.  With such data in hand should you advise a patient: “well, the diets are pretty much the same. It’s largely up to you which you choose,” or, looking at the raw data (both compliance and success), should the recommendation be: “Diet A is much more effective than diet B but people have trouble staying on it. If you can stay on diet A, it will be much better for you so I would encourage you to see if you could find a way to do so.” Which makes more sense? You’re the doctor.

I made several arguments trying to explain that there are two factors, only one of which (whether it works) is clearly due to the diet. The other (whether you follow the diet) is under control of other factors (whether WebMD tells you that one diet or the other will kill you, whether the evening news makes you lose your appetite, etc.)  I even dragged in a geometric argument because Newton had used one in the Principia: “a 2-dimensional outcome space where the length of a vector tells how every subject did…. ITT represents a projection of the vector onto one axis, in other words collapses a two dimensional vector to a one-dimensional vector, thereby losing part of the information.” Pretentious? Moi?

Why you should care.  Case I. Surgery or Medicine?

Does your doctor actually read these academic studies using ITT?  One can only hope not.  Consider the analysis by Newell  of the Coronary Artery Bypass Surgery (CABS) trial.  This paper is astounding for its blanket, tendentious insistence on what is correct without any logical argument.  Newell considers that the method of

 “the CABS research team was impeccable. They refused to do an ‘as treated’ analysis: ‘We have refrained from comparing all patients actually operated on with all not operated on: this does not provide a measure of the value of surgery.”

Translation: results of surgery do not provide a measure of the value of surgery.  So, in the CABS trial, patients were assigned to Medicine or Surgery. The actual method used and the outcomes are shown in the Table below. Intention-to-treat analysis was, as described by Newell, “used, correctly.” Looking at the table, you can see that a 7.8% mortality was found in those assigned to receive medical treatment (29 people out of 373 died), and a 5.3% mortality (21 deaths out of 371) for assignment to surgery.  If you look at the outcomes of each modality as actually used, it turns out that that medical treatment had a 9.5% (33/349) mortality rate compared with 4.1% (17/419) for surgery, an analysis that Newell says “would have wildly exaggerated the apparent value of surgery.”

Survivors and deaths after allocation to surgery or medical treatment
Allocated medicine Allocated surgery
  Received surgery     Received medicine   Received surgery     Received medicine
Survived 2 years   48   296   354   20
Died    2    27    15    6
Total   50   323   369   26

Common sense suggests that appearances are not deceiving. If you were one of the 33-17 = 16 people who were still alive, you would think that it was the potential report of your death that had been exaggerated.  The thing that is under the control of the patient and the physician, and which is not a feature of the particular modality, is getting the surgery implemented. Common sense dictates that a patient is interested in surgery, not the effect of being told that surgery is good.  The patient has a right to expect that if they comply, the physician would avoid conditions where, as stated by Hollis,  “most types of deviations from protocol would continue to occur in routine practice.” The idea that “Intention to treat analysis is … most suitable for pragmatic trials of effectiveness rather than for explanatory investigations of efficacy” assumes that practical considerations are the same everywhere and that any practitioner is locked into the same abilities or lack of abilities as the original experimenter.

What is the take home message.  One general piece of advice that I would give based on this discussion in the medical literature: don’t get sick.

Why you should care.  Case II. The effect of vitamin E supplementation

A clear cut case of how off-the-mark ITT can be is a report on the value of antioxidant supplements. The Abstract of the paper concluded that “there were no overall effects of ascorbic acid, vitamin E, or beta carotene on cardiovascular events among women at high risk for CVD.” The study was based on an ITT analysis but,on the fourth page of the paper, it turns out that removing subjects due to

“noncompliance led to a significant 13% reduction in the combined end point of CVD morbidity and mortality… with a 22% reduction in MI …, a 27% reduction in stroke …. a 23% reduction in the combination of MI, stroke, or CVD death (RR (risk ratio), 0.77; 95% CI, 0.64–0.92 [P = 005]).”

The media universally reported the conclusion from the Abstract, namely that there was no effect of vitamin E. This conclusion is correct if you think that you can measure the effect of vitamin E without taking the pill out of the bottle.  Does this mean that vitamin E is really of value? The data would certainly be accepted as valuable if the statistics were applied to a study of the value of replacing barbecued pork with whole grain cereal. Again, “no effect” was the answer to the question: “what happens if you are told to take vitamin E” but it still seems is reasonable that the effect of a vitamin means the effect of actually taking the vitamin.

The ITT controversy

Advocates of ITT see its principles as established and may dismiss a common sense approach as naïve. The issue is not easily resolved; statistics is not axiomatic: there is no F=ma, there is no zeroth law.  A good statistics book will tell you in the Introduction that what we do in statistics is to try to find a way to quantify our intuitions. If this is not appreciated, and you do not go back to consideration of exactly what the question is that you are asking, it is easy to develop a dogmatic approach and insist on a particular statistic because it has become standard.

As I mentioned above, I had a good deal of trouble getting my original paper published and one  anonymous reviewer said that “the arguments presented by the author may have applied, maybe, ten or fifteen years ago.” This criticism reminded me of Molière’s Doctor in Spite of Himself:

Sganarelle is disguised as a doctor and spouts medical double-talk with phony Latin, Greek and Hebrew to impress the client, Geronte, who is pretty dumb and mostly falls for it but:

Geronte: …there is only one thing that bothers me: the location of the liver and the heart. It seemed to me that you had them in the wrong place: the heart is on the left side but the liver is on the right side.

Sgnarelle: Yes. That used to be true but we have changed all that and medicine uses an entirely new approach.

Geronte: I didn’t know that and I beg your pardon for my ignorance.

 In the end, it is reasonable that scientific knowledge be based on real observations. This has never before been thought to include data that was not actually in the experiment. I doubt that nous avons changé tout cela.

The study of metabolic pathways provides an insight into chemical reactions and the way they function in living systems but, in the end, even a biochemistry professor still has to answer the question  “What should I eat.”  Adam Cambell, an editor at Men’s Health magazine once asked me: “You’ve just had a meal that conforms to your idea of good nutrition and satisfying portions of food but you’re still hungry.  What should you do?”

“Think of a perfectly-cooked juicy steak or perfectly-prepared fish, or some similar high protein food that you usually like,” I suggested.  “If that sounds good, you’re hungry and you should eat something.  If it doesn’t sound good, you’re not hungry.  You may want desert.  You may want something feeling good in your mouth, but you’re not hungry.” What I meant, of course, is that foods that are high in protein, and lower in carbohydrate, tend to be more filling. This satiating effect of protein is well-known in the biochemical literature and is one of the advantages of diets that restrict carbohydrates and keep protein high.  The fact that protein is satisfying also means that it provides its own control over intake and, for that reason, “concerns” about high protein intake that you hear from nutritional expert are not usually a real problem.  In the obesity epidemic where there was a large increase in carbohydrate consumption and a general decline in fat consumption, protein stayed about the same.  When nutritionists carry out experiments in which people can eat freely, they generally do not change their protein consumption.  In fact, it now seems likely that most people are not getting enough protein.  Recent studies show that people benefit from replacing carbohydrate in their diet with protein, the benefit is in better weight control, in an improved ratio to lean body mass compared to fat and in better control of blood insulin and glucose.  I will describe some of the features of this problem with references to papers in the scientific literature that are either open access or have been made publicly available and public and do not require a subscription.

Nutritionists who study protein think that we need modification of official recommendations for protein consumption.  Donald Layman at the University of Illinois has reviewed some of the important research on this question and he came up with several important points:

•    Protein is a critical part of the adult diet. Beyond physical growth which is only important for a brief period in your life, there is a continuing need to repair and remodel muscle and bone

•    Protein needs for adults relate to body weight not, as you sometimes see, as a per cent of total calories. So, if you are reducing calories, protein needs to stay high and may be a higher percentage of total calories. In choosing a diet, you should establish the grams of protein first.

•    The amount of protein at each meal can be important.  Research indicates that an ideal is 30 g of protein per meal although this may not be practical for everybody. It is recommended that breakfast be high in protein.

•    Most adults benefit from protein intakes above the minimum RDA (recommended daily allotment) and this is especially true for an aging population with increased risks of poor health.  The RDA represents a minimum daily intake for active healthy adults but most people will benefit from replacing at lest some carbohydrate in the diet with protein.

The full story on protein recommendations can be found at Nutrition & Metabolism (no subscription required.

A look at the science

Proteins are generally more complicated molecules than fat or carbohydrate.  Like starch, they are polymers (think chain of beads).  Most starches are homopolymers (all the beads are the same, glucose in this case), but protein molecules are made of 20 different kinds of beads (amino acids).  About half are interchangeable or can be made from other nutrients and are said to be non-essential.  The other half are required in the diet and are said to be essential amino acids, or, for some reason, the more modern term is indispensable.  Now your body is continually breaking down and re-synthesizing its own proteins, the most obvious function of dietary protein is supplying amino acids to replenish body proteins so high quality dietary proteins will be those that supply all the essential amino acids.  Meat, fish and eggs are high quality proteins but combinations of vegetables can also supply the full complement of amino acids.  Many web sites and other sources will give you information about how vegetables can be combined to supply amino acids, but there is another aspect of protein nutrition that should be considered.  Amino acids, like carbohydrate are not just sources of cell material but may have a signaling function.  Remember that it is not just that glucose supplies energy but that it stimulates the release of insulin which further controls metabolism.  Amino acids also perform this function and stimulate insulin release and trigger other physiologic processes, in particular, synthesis of new body protein and provide control over blood glucose.  One essential amino acid in particular, leucine, is of greatest importance in this role.  In comparative studies, diets that are high in leucine improve the ratio of lean body mass to fat. Whey and other milk proteins are particularly high in leucine; red meat is also a good source.

The benefit in substituting protein for carbohydrate is greatest for people with diabetes.  The studies from the laboratories of Mary Gannon and Frank Nuttall are pretty remarkable and I show you a picture of the actual results from their experiments.  They studied the effect of reducing dietary carbohydrate on responses of people with diabetes.  The figure shows that after 5 weeks on a diet with 20 % available glucose (circles in the figure), the response to meals is drastically improved compared to the response if the traditional diet is continued (triangles).  As the diet proceeded, hemoglobin A1c was also reduced.  Gannon and Nuttall have also showed that diets with slightly higher glucose may be effective but the response depends on how much glucose is in the diet.  A very important feature of the studies of this study is that the diets were designed so that patients maintained their weight, in other words, benefit accrued even though no weight was lost.  Given how hard it is to lose weight, this has to be considered a real plus for the higher protein, lower carbohydrate diet.  You can see the whole study, again, without subscription here.

Is there a danger of too much protein?

How many times have you read an article in the media, or even in the medical literature, warning you about the dangers of high protein diets for your kidneys, or for kidney stones, or whatever.   Probably quite a few.  Are they for real?  To answer that question, think of how often you have read an article in the media describing somebody who actually had kidney problems or stones due to a high protein diet? That number is zero or close (there’s always a case study someplace with an isolated patient).

To understand the danger in a high protein diet for people with normal kidneys, consider the following conversation I had with an expert on kidney disease when I was the editor of Nutrition & Metabolism.

RF: I received a manuscript today that rather strongly and categorically says that there is no danger in high protein diets for people with normal kidneys.

Nephrologist: That’s right.

RF: It is?  Can we document that?

N: How do you document that there are no people with three eyes.  We have looked very hard for it and we never found it.

So, what’s wrong.  Mostly what’s wrong is that we never got around to agreeing on what high protein is.  Diets that encourage you to replace carbohydrate with protein are only trying to counteract the high carbohydrate message. Few people actually eat huge amounts of protein.  As discussed above, protein tends to be more satisfying than carbohydrate and what might be considered high protein is pretty average.

In other words, there is common sense.  A healthy high protein diet is currently estimated to have a daily intake of about 1 to 4 g of protein for every kg (2.2 lbs) of body weight while the USDA recommendation is only 0.8 g/kg).  So, if you weigh 175 lbs., an optimal level of protein will be at least 80.  The diet shown below is actually quite a bit higher. Is this really unusual?  In fact, if you ate 3 eggs or even bigger portion of  brisket, do you think something terrible will happen.  Is this dangerous?  To say that normal eating, even with occasional over-indulgence, is dangerous requires real proof and that’s what’s always been missing.

Finally, it is likely that for people with diabetes, there is great danger to kidneys from continued high blood sugar and most physicians would say that this risk is real while any risk from high protein is conjecture.

The bottom line: Substituting protein for carbohydrate in the diet improves blood glucose and insulin control.  As part of a weight loss diet, higher protein preserves lean mass compared to higher carbohydrate diets but the benefits of higher protein, lower carbohydrate diets provide benefit even in the absence of weight loss.