Archive for January, 2011

From A Future History of Diabetes:

Posted: January 5, 2011 in American Diabetes Association, Evidence Based Medicine, The Nutrition Story
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I don’t believe in time travel, of course, so when somebody sent me the following article that was supposed to be a chapter from a Study of the History of Diabetes published in 2018, I didn’t think about it much.  Then I read an article about a woman who had been charged with neglect in the death of her son from complications due to diabetes.  It seems she “was trying to live by faith and felt like God would heal him.”

For some reason, that made me think of the Future History, so here is a chapter from the History.

Chapter IV.  ACCORD to The Court

We have seen how, early in the history of medicine, diabetes was recognized as a disease of carbohydrate intolerance and how, until the discovery of insulin, removing carbohydrate from the diet became the major treatment (Chapters I and II).  We chronicled the shift away from this medical practice under the influence of low fat recommendations and the ascendancy of pharmacology that followed the discovery of insulin.  Nonetheless, it persisted in the popular mind that you don’t give candy to people with diabetes, even as health agencies seemed to encourage sucrose (sugar) consumption.

The rather sudden reappearance of carbohydrate restriction, the so-called modern era in diabetes treatment, is usually dated to 2008, the precipitating event, publication of the ACCORD study in which a group undergoing  “intensive treatment” to lower blood glucose showed unexpected deaths [1].  ACCORD concluded that “These findings identify a previously unrecognized harm of intensive glucose lowering in high-risk patients with type 2 diabetes.” The intensive treatment turned out to be intensive pharmacologic therapy and this flawed logic lead to a popular uprising of sorts, a growing number of patients claiming that they had been hurt by intensive drug treatment and typically that they had only been able to get control of their diabetes by adherence to low carbohydrate diets. Blogs compared the ACCORD conclusion to an idea that alleviating headaches with intensive aspirin led to bleeding and we should therefore not treat headaches.

The conflict culminated in the large judgment for the plaintiff in Banting v. American Diabetes Association (ADA) in 2017, affirmed by the Supreme Court in 2018.  Dalton Banting, coincidentally a distant relative of the discoverer of insulin, was an adolescent with diabetes who took prescribed medications and followed a diet consistent with ADA recommendations.  He experienced worsening of his symptoms and ultimately had a foot amputated. At this point his parents found a physician who recommended a low carbohydrate diet which led to rapid and sustained improvement.  The parents claimed their son should have been offered carbohydrate-restriction as an option.  The case was unusual in that Banting had a mild obsessive-compulsive condition, expressed as a tendency to follow exactly any instructions from his parents or other authority figures.  Banting’s lawyers insisted that, as a consequence, one could rely on his having complied with the ADA’s recommendations.  Disputed by the defense, this was one of several issues that made Banting famous for vituperative courtroom interactions between academics.

Banting was a person with type 2 diabetes.  Unlike people with type 1 diabetes, he was able to produce insulin in response to dietary (or systemic) glucose but his pancreas was progressively dysfunctional and his body did not respond normally, that is, he was insulin-resistant.  Although most people with type 2 diabetes are at least slightly overweight, Banting was not, although he began gaining weight when treated with insulin.

The phrase “covered with insulin…” rocked the court: the president of the ADA, H. Himsworth, Jr., was asked to  read from the 2008 guidelines [2], never rescinded: “Sucrose-containing foods can be substituted for other carbohydrates in the meal plan or, if added to the meal plan, covered with insulin or other glucose lowering medications.”

Jaggers (attorney for Banting): “Are there other diseases where patients are counseled to make things worse so that they can take more drugs.”

Himsworth: “We only say ‘can be.’  We don’t necessarily recommend it.  We do say that ‘Care should be taken to avoid excess energy intake.’”

It soon became apparent that Himsworth was in trouble.  He was asked to read from the passage explaining the ADA’s opposition to low carbohydrate diets:

“Low-carbohydrate diets might seem to be a logical approach to lowering postprandial glucose. However, foods that contain carbohydrate are important sources of energy, fiber, vitamins, and minerals and are important in dietary palatability.”

Jaggers: “Important sources of energy?  I thought we wanted to avoid excess energy,” and “would you say that taking a vitamin pill is in the same category as injecting insulin?”

Finally,

Jaggers: “Dr. Himsworth, as an expert on palatability, could you explain the difference between Bordelaise sauce and Béarnaise sauce?” [laughter]

Damaging as this testimony was, the tipping point in the trial is generally considered to have been the glucometer demonstration.  Banting consumed a meal typical of that recommended by the ADA  and glucometer readings were projected on a screen for the jury, showing, on this day, so-called “spikes” in blood glucose.  The following day, Banting consumed a low carbohydrate meal and the improved glucometer readings were again projected for the jury.  Defense argued that one meal did not prove anything and that one had to look at the whole history of the lifestyle intervention but was unable to show any evidence of harm from continued maintenance of low blood sugar despite testimony of several expert witnesses.  In the end, the jury agreed that common sense overrides expert testimony and that Banting should have been offered the choice of a carbohydrate-restricted diet.

Banting was held in New York State which adheres to the Frye standard: in essence, the idea that scientific evidence is determined by “general acceptance.” The explicit inclusion of common sense was, in fact, a legal precedent [3].   The Supreme Court ultimately concurred and held that the more comprehensive standards derived from Daubert v. Merrill-Dow, could sensibly be seen to encompass common sense.

The final decision in Banting lead to numerous law suits.  The ADA and other agencies changed their tactics claiming that they never were opposed to low carbohydrate diets and, in fact, had been recommending them all along [4].  This is discussed in the next chapter.

References

1. Gerstein, H. C. et al., Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med 358 (24), 2545 (2008).

2. American Diabetes Association, Nutrition Recommendations and Interventions for Diabetes–2008. Diabetes Care 31 (Suppl 1), S61 (2008).

3. Berger, M, Expert Testimony: The Supreme Court’s Rules Issues in Science and Technology (2000).

4. American Diabetes Association, Nutrition Recommendations and Interventions for Diabetes–2018. Diabetes Care 40 (Suppl 1), S12 (2018).

Damned statistics and the nutrition literature

Posted: January 3, 2011 in Uncategorized
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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.