Maybe it's a waste of time to point out the biased preconceptions one sees in various science journals; most of the intelligent people I assume are the primary readers of this blog can spot them for themselves. But they annoy me so much I can't not point them out.
The latest one occurred in an article titled "Overweight Primarily a Problem Among Wealthier Women in Low To Middle-Income Countries" and published in the American Journal of Clinical Nutrition.
Researchers at the Harvard School of Public Health reported that in less affluent countries, being overweight is more common among women with higher incomes (they studied only women). In contrast, in more affluent countries like the United States, obesity is associated with poverty.
I think this is intuitively obvious. When it's difficult to get enough food, then only richer people will obtain enough calories to become fat. When food is plentiful but starchy and fatty food is cheaper than meat and vegetables, then the poorer you are, the more apt you are to be fat.
A famous photo of an emaciated boy holding out a bowl and begging for rice, while behind him a fat (by the standards of those days) merchant woman sits among huge bags of rice, illustrates this. The boy isn't counting calories; he's starving.
So initially, I found the study pretty ho-hum. Then I came on this attempt at an explanation:
"The researchers theorize that these findings could be due to a number of factors, including that women in higher income groups are more likely to have diets richer in animal fats than lower-income women."
In other words, they started with the assumption that obesity (and probably all the other ills of a "Western" diet) stem from too much animal fat. So that must surely be the explanation here too.
Couldn't it also be because the richer women were able to buy white bread and jam instead of fiber-filled vegetables the poorer people probably grew themselves?
They do make a couple of other suggestions:
"Also, cultural norms in developing countries may favor fatty body shapes among wealthier women. Richer women are also less likely than poor women to engage in regular physical labor."
I'm sure the difference in physical work does make a difference. But if cultural norms in developing countries favored fatty body shapes among wealthier women (as a sign that you could afford a lot of food), wouldn't you think the same would be true among poor people as well? Wouldn't poor people want to look as if they were rich?
Sometimes the logic in nutrition papers boggles my mind.
Wednesday, November 24, 2010
Saturday, November 13, 2010
Free Full Text
Mary Ann Liebert, Inc, publisher of many journals, is offering free full text of their diabetes-related journals through the end of November. The offer is in recognition of World Diabetes Day.
The three journals are Diabetes Technology & Therapeutics, Metabolic Syndrome and Related Disorders, and Childhood Obesity.
I've often seen abstracts in the first journal and wished I could read the full texts, but access was too expensive. So this is a good chance to download the articles that interest you, if any.
The three journals are Diabetes Technology & Therapeutics, Metabolic Syndrome and Related Disorders, and Childhood Obesity.
I've often seen abstracts in the first journal and wished I could read the full texts, but access was too expensive. So this is a good chance to download the articles that interest you, if any.
Friday, October 22, 2010
Are Parasites in Charge?
Parasites can influence the behavior of the organisms they inhabit.
For example, mice infected with the protozoan Toxoplasma gondii, the organism that causes toxoplasmosis, become lethargic and lose their fear of cats, the primary host of the parasite
Clearly, in a cat-infested environment, such mice don't last very long. And the cats that eat the infected mice become infected themselves and then spread the eggs (oocysts) through their feces.
The behavior modification caused by other parasites in other organisms are even more bizarre.
So, could human behavior also be influenced by some of the parasites we all carry? Some people think yes.
Our guts are filled with bacteria. Many of these bacteria are beneficial. For example, gut bacteria produce most of the B vitamin biotin that we need. Other bacteria can cause obvious harm, for example, gut inflammation, pain, and diarrhea. The diarrhea benefits the bacteria because it increases the probability that other people will come in contact with the abundant fluid and become infected themselves.
Effects on behavior could be more subtle. We know that animals infected with rabies virus behave differently. They become more aggressive and tend to bite. Because the virus colonizes the salivary gland, such bites pass the infection on.
But why am I babbling about all this, interesting though it might be?
It's because I'm wondering if it's gut bacteria that program some people to eat more than normal, causing obesity. Why would the bacteria do that? Well, the more you eat, the more food there will be in the gut, which means the more the bacteria could grow.
There is some evidence that gut bacteria are related to obesity: overweight people tend to have different types of bacteria than normal-weight people. And some animal studies showed that transferring the gut bacteria from mice prone to metabolic syndrome into normal mice caused the normal ones to develop metabolic syndrome too.
So this idea that gut bacteria are associated with obesity is not new. Whether the bacterial population causes the obesity or the obesity provides a gut environment friendly to certain types of bacteria, or perhaps both in a vicious circle, has not yet been definitively proved.
A recent study reported at the Stockholm meeting of the European Association for the Study of Diabetes showed that transplanting fecal matter from thin people into obese people with prediabetes did not result in any weight loss. However, the recipients did see their insulin resistance decrease.
Clearly, obesity, type 2 diabetes, and gut populations are related somehow. One possibility is that certain bacteria are especially efficient converters of food and fiber into compounds that can easily be taken up in the gut, essentially adding calories to whatever we eat.
But I'm wondering if there's more than a metabolic effect. I wonder if the gut bacteria, like the parasites that change behavior in mice and spiders, are subtly changing the behavior of their hosts.
If the bacteria made the hosts feel sluggish, they wouldn't want to move around a lot and burn off calories. If the bacteria made the hosts hungry all the time, they would eat more than they needed to maintain their weight.
The bacteria could then happily munch on the extra calories, rapidly multiply, and infect other people.
Is this really true? No one knows. But the idea intrigues me.
For example, mice infected with the protozoan Toxoplasma gondii, the organism that causes toxoplasmosis, become lethargic and lose their fear of cats, the primary host of the parasite
Clearly, in a cat-infested environment, such mice don't last very long. And the cats that eat the infected mice become infected themselves and then spread the eggs (oocysts) through their feces.
The behavior modification caused by other parasites in other organisms are even more bizarre.
So, could human behavior also be influenced by some of the parasites we all carry? Some people think yes.
Our guts are filled with bacteria. Many of these bacteria are beneficial. For example, gut bacteria produce most of the B vitamin biotin that we need. Other bacteria can cause obvious harm, for example, gut inflammation, pain, and diarrhea. The diarrhea benefits the bacteria because it increases the probability that other people will come in contact with the abundant fluid and become infected themselves.
Effects on behavior could be more subtle. We know that animals infected with rabies virus behave differently. They become more aggressive and tend to bite. Because the virus colonizes the salivary gland, such bites pass the infection on.
But why am I babbling about all this, interesting though it might be?
It's because I'm wondering if it's gut bacteria that program some people to eat more than normal, causing obesity. Why would the bacteria do that? Well, the more you eat, the more food there will be in the gut, which means the more the bacteria could grow.
There is some evidence that gut bacteria are related to obesity: overweight people tend to have different types of bacteria than normal-weight people. And some animal studies showed that transferring the gut bacteria from mice prone to metabolic syndrome into normal mice caused the normal ones to develop metabolic syndrome too.
So this idea that gut bacteria are associated with obesity is not new. Whether the bacterial population causes the obesity or the obesity provides a gut environment friendly to certain types of bacteria, or perhaps both in a vicious circle, has not yet been definitively proved.
A recent study reported at the Stockholm meeting of the European Association for the Study of Diabetes showed that transplanting fecal matter from thin people into obese people with prediabetes did not result in any weight loss. However, the recipients did see their insulin resistance decrease.
Clearly, obesity, type 2 diabetes, and gut populations are related somehow. One possibility is that certain bacteria are especially efficient converters of food and fiber into compounds that can easily be taken up in the gut, essentially adding calories to whatever we eat.
But I'm wondering if there's more than a metabolic effect. I wonder if the gut bacteria, like the parasites that change behavior in mice and spiders, are subtly changing the behavior of their hosts.
If the bacteria made the hosts feel sluggish, they wouldn't want to move around a lot and burn off calories. If the bacteria made the hosts hungry all the time, they would eat more than they needed to maintain their weight.
The bacteria could then happily munch on the extra calories, rapidly multiply, and infect other people.
Is this really true? No one knows. But the idea intrigues me.
Friday, October 15, 2010
Popular Press Spins
When I was in graduate school, way back in the 1960s, almost every news report about some scientific finding ended by trying to explain why this finding would help to cure cancer. This was the era of the War on Cancer, and scientists hoped that relating their research to curing cancer would increase their chances of getting big research grants.
In the virus course I took with Jim Watson, the exams usually included a question in which we had to explain why some newspaper report of a scientific finding was wrong, that it actually would have nothing to do with cancer. They were fun questions.
Today, instead of trying to show how new studies can help to cure cancer, most popular press stories I see suggest that the findings provide a new target for new drugs, probably hoping to increase their chances of getting funding from drug companies.
Many of the stories appearing in popular science releases like Eurekalert and Science Daily are written by PR people at the institutions where the research is done. Their goal is to call attention to their institutions, professors, and funding sources as well as to the research itself. As a result, usually more than half of the articles is garbage.
When I was a newspaper editor, we'd get tons of press releases like this, and part of our job was to rewrite them without the self-promoting garbage. But these science news sites don't do this. Most of them simply print the press releases verbatim; you can read exactly the same stories on myriad sites.
An example from Science Daily:
"Researchers at the University of Edinburgh report a new experimental compound that can improve memory and cognitive function in aging mice. The compound is being investigated with a view to developing a drug that could slow the natural decline in memory associated with aging.
"With support from the Wellcome Trust Seeding Drug Discovery award, the team has identified a preclinical condition that they hope to take into human trials within a year."
Note that in the first two paragraphs they've mentioned the institution, the potential for drug development, and the funding source. They haven't mentioned what we all want to know: what this compound is. You have to slog through a lot of other boring stuff before they'll reveal that. Some stories even list all the researchers, their degrees, and their positions at the university before they'll tell you what the new finding actually was.
Here's another one:
"University of Michigan scientists have identified events inside insulin-producing pancreatic cells that set the stage for a neonatal form of non-autoimmune type 1 diabetes, and may play a role in type 2 diabetes as well. The results point to a potential target for drugs to protect normally functioning proteins essential for producing insulin."
In this case the PR people managed to make the institution the first word of the article.
You may say, "So what!" and that's partially true. We just have to learn to skim most of these articles to get to the crux of the story. And these popular press releases are important in alerting us to new journal articles that we'd probably never know of otherwise. Most of the press releases do have links to the original articles, although in many cases we can only read the abstracts unless we want to pay.
But I think the important thing is to remember that these articles are written by PR people whose goal is different from our goal. Their goal is to publicize their institution and overemphasize the importance of the research there. Our goal is to understand as completely as possible how good the evidence supporting the claims in the summary article is.
Whenever possible, I try to get the full text of an important article. I don't make the effort for what I consider less important ones. Time is not infinite. I once spent 2 days researching the science behind a story about using lettuce and some complex molecular biology to give people insulin by eating lettuce. Most of the popular press summaries didn't really understand what the research showed.
But if I spent 2 days researching every article I read, I wouldn't be able to read very many, and in the long run I'm hoping that having a surface acquaintance with a lot of research will be more useful than having an in-depth acquaintance with just a little.
I'm sure most of you are already aware of the way the press spins news about science research. But it never hurts to examine it again.
It's a reader-beware situation out there.
In the virus course I took with Jim Watson, the exams usually included a question in which we had to explain why some newspaper report of a scientific finding was wrong, that it actually would have nothing to do with cancer. They were fun questions.
Today, instead of trying to show how new studies can help to cure cancer, most popular press stories I see suggest that the findings provide a new target for new drugs, probably hoping to increase their chances of getting funding from drug companies.
Many of the stories appearing in popular science releases like Eurekalert and Science Daily are written by PR people at the institutions where the research is done. Their goal is to call attention to their institutions, professors, and funding sources as well as to the research itself. As a result, usually more than half of the articles is garbage.
When I was a newspaper editor, we'd get tons of press releases like this, and part of our job was to rewrite them without the self-promoting garbage. But these science news sites don't do this. Most of them simply print the press releases verbatim; you can read exactly the same stories on myriad sites.
An example from Science Daily:
"Researchers at the University of Edinburgh report a new experimental compound that can improve memory and cognitive function in aging mice. The compound is being investigated with a view to developing a drug that could slow the natural decline in memory associated with aging.
"With support from the Wellcome Trust Seeding Drug Discovery award, the team has identified a preclinical condition that they hope to take into human trials within a year."
Note that in the first two paragraphs they've mentioned the institution, the potential for drug development, and the funding source. They haven't mentioned what we all want to know: what this compound is. You have to slog through a lot of other boring stuff before they'll reveal that. Some stories even list all the researchers, their degrees, and their positions at the university before they'll tell you what the new finding actually was.
Here's another one:
"University of Michigan scientists have identified events inside insulin-producing pancreatic cells that set the stage for a neonatal form of non-autoimmune type 1 diabetes, and may play a role in type 2 diabetes as well. The results point to a potential target for drugs to protect normally functioning proteins essential for producing insulin."
In this case the PR people managed to make the institution the first word of the article.
You may say, "So what!" and that's partially true. We just have to learn to skim most of these articles to get to the crux of the story. And these popular press releases are important in alerting us to new journal articles that we'd probably never know of otherwise. Most of the press releases do have links to the original articles, although in many cases we can only read the abstracts unless we want to pay.
But I think the important thing is to remember that these articles are written by PR people whose goal is different from our goal. Their goal is to publicize their institution and overemphasize the importance of the research there. Our goal is to understand as completely as possible how good the evidence supporting the claims in the summary article is.
Whenever possible, I try to get the full text of an important article. I don't make the effort for what I consider less important ones. Time is not infinite. I once spent 2 days researching the science behind a story about using lettuce and some complex molecular biology to give people insulin by eating lettuce. Most of the popular press summaries didn't really understand what the research showed.
But if I spent 2 days researching every article I read, I wouldn't be able to read very many, and in the long run I'm hoping that having a surface acquaintance with a lot of research will be more useful than having an in-depth acquaintance with just a little.
I'm sure most of you are already aware of the way the press spins news about science research. But it never hurts to examine it again.
It's a reader-beware situation out there.
Thursday, August 5, 2010
Lipids and Diet
Low-carb and low-fat diets result in similar weight loss at 2 years when both are accompanied by comprehensive lifestyle counseling, according to a recent research project by Gary Foster and colleagues published in the Annals of Internal Medicine.
But people in the low-carb diet showed better lipid profiles after 2 years.
You have to pay to read the full text of the article, but low-carb advocate Jimmy Moore has an extensive discussion of the results including some graphs.
This study is important because it shows that various improvements in people following low-carb diets are not simply temporary. Although 2 years can't really be called long term, it's heading in the right direction.
What I find fascinating is the reaction to this article. I can't find it at all on either Science Daily or Eurekalert, both popular summaries of science news. They're always quick to report studies that say red meat is bad or low-carb diets cause problems. Why aren't they summarizing this study? Many other media outlets are, so it's not that the study is obscure.
Others have put a positive or a negative spin or a neutral spin on the study (not mentioning the better lipid profiles is actually a negative spin), some by the way they write their headlines. If you support low-carb diets, you can emphasize the fact that the lipid levels improved on that diet. If you don't, you can focus on the fact that weight loss was the same. Many people remember only the headlines; here are a few of them:
Positive:
Low-carb diets improve cholesterol long term (Medicinenet)
Low-carbohydrate diet beats low-fat diet in decreasing heart risk (Thaindian news)
Low-carb diet better for heart health than low-fat diet (The Money Times)
Neutral
Low-carb, low-fat diets tied for long term weight loss (uamshealth.com) (University of Arkansas for Medical Sciences)
Study disputes low-carb diet concerns (Philadelphia Inquirer)
Low-carb diet as good as low-fat one (Press TV)
When people do studies of "low-carb" diets that include 150 grams of carbs, low-carb supporters scream that such diets aren't really low carb (and I agree with this), that if they'd tested 50 g or 30 g or 20 g a day, they would have seen different results.
In this case, the low-fat supporters are screaming that a diet with <30% fat isn't really a low-fat diet, that if they'd limited fat to 10%, they would have seen different results.
I agree that this study would have been better if they'd tested 4 different diets: very low carb ketogenic diet (<50 g carbs), low-carb diet (50-130 g), low-fat diet (<30% fat), very low fat (<10%).
But I think the researchers were trying to compare a standard Atkins diet (the diet many people think about when they think low carb, even though there are other low-carb diets that I think are better) with "standard care," which still often means following the old Food Pyramid with 60% carbs and <30% fat.
They may also have worried that few of the study subjects would have been willing to stick to the stricter diets for 2 years or more. Even with the more moderate diets, attrition was high: 32% for the low-fat diet and 42% for the low-carb diet at the end of 2 years.
The low-fat diet was limited in calories; the low-carb diet was not.
Both groups lost weight, rapidly at first, reaching a nadir at 6 months, and then regained some of the weight, so that by the end of 2 years, they'd lost an average of 7% of their starting weight.
Interestingly, this was the goal in the Diabetes Prevention Program weight-loss study of people at high risk of developing diabetes. The DPP showed that modest weight loss (the goal of 7% was not actually reached) could reduce the risk of progressing to overt diabetes. The same phenomenon occurred in that study too: subjects initially lost weight fast, then leveled off and started regaining again.
I think we've all experienced this phenomenon. We try a new diet and are enthusiastic and follow it exactly. Then after some time we hit a plateau and we also get bored with the diet and gradually revert to our old habits.
It's also possible that the body wants to maintain a certain weight that is higher than the weight we want it to have (the set-point theory), and after some time the metabolism changes to encourage the regaining of the weight.
People will never agree on the best diet for weight loss, partly because different people do better on different diets: YMMV, or "Your mileage may vary." But the more we know about various diets and various groups of dieters, preferable for as long as possible, the better.
Like weight loss, lipid levels tend to be different in the short term than they are in the long term. For example, in this study, triglycerides rapidly decreased in the low-carb group but then went up again and by the end of 2 years were about the same as the levels in the low-fat group. LDL levels went up in the low-carb group but later went down again. Only HDL levels were consistently higher on the low-carb diet throughout the study.
It's not clear whether the fluctuating lipid levels were because after 12 weeks the people on the low-carb diet were instructed to slowly start adding back carbs until their weight stabilized. This is what Atkins told people to do. But in fact, the weights in the low-carb group continued to drop after 12 weeks and then instead of "stabilizing" started to climb.
So it's also possible that the body was adapting to a different diet and adjusting the lipid levels after a certain amount of time on the diet. Perhaps we have a "lipid set-point" as well as a weight set-point, and the body keeps trying to reach it.
Regardless of the reasons for these shifts, in order to really understand how diets affect various heart health risks, we need longer-term data. Foster and colleagues have made a valuable first step. Perhaps even longer studies will follow.
But people in the low-carb diet showed better lipid profiles after 2 years.
You have to pay to read the full text of the article, but low-carb advocate Jimmy Moore has an extensive discussion of the results including some graphs.
This study is important because it shows that various improvements in people following low-carb diets are not simply temporary. Although 2 years can't really be called long term, it's heading in the right direction.
What I find fascinating is the reaction to this article. I can't find it at all on either Science Daily or Eurekalert, both popular summaries of science news. They're always quick to report studies that say red meat is bad or low-carb diets cause problems. Why aren't they summarizing this study? Many other media outlets are, so it's not that the study is obscure.
Others have put a positive or a negative spin or a neutral spin on the study (not mentioning the better lipid profiles is actually a negative spin), some by the way they write their headlines. If you support low-carb diets, you can emphasize the fact that the lipid levels improved on that diet. If you don't, you can focus on the fact that weight loss was the same. Many people remember only the headlines; here are a few of them:
Positive:
Low-carb diets improve cholesterol long term (Medicinenet)
Low-carbohydrate diet beats low-fat diet in decreasing heart risk (Thaindian news)
Low-carb diet better for heart health than low-fat diet (The Money Times)
Neutral
Low-carb, low-fat diets tied for long term weight loss (uamshealth.com) (University of Arkansas for Medical Sciences)
Study disputes low-carb diet concerns (Philadelphia Inquirer)
Low-carb diet as good as low-fat one (Press TV)
When people do studies of "low-carb" diets that include 150 grams of carbs, low-carb supporters scream that such diets aren't really low carb (and I agree with this), that if they'd tested 50 g or 30 g or 20 g a day, they would have seen different results.
In this case, the low-fat supporters are screaming that a diet with <30% fat isn't really a low-fat diet, that if they'd limited fat to 10%, they would have seen different results.
I agree that this study would have been better if they'd tested 4 different diets: very low carb ketogenic diet (<50 g carbs), low-carb diet (50-130 g), low-fat diet (<30% fat), very low fat (<10%).
But I think the researchers were trying to compare a standard Atkins diet (the diet many people think about when they think low carb, even though there are other low-carb diets that I think are better) with "standard care," which still often means following the old Food Pyramid with 60% carbs and <30% fat.
They may also have worried that few of the study subjects would have been willing to stick to the stricter diets for 2 years or more. Even with the more moderate diets, attrition was high: 32% for the low-fat diet and 42% for the low-carb diet at the end of 2 years.
The low-fat diet was limited in calories; the low-carb diet was not.
Both groups lost weight, rapidly at first, reaching a nadir at 6 months, and then regained some of the weight, so that by the end of 2 years, they'd lost an average of 7% of their starting weight.
Interestingly, this was the goal in the Diabetes Prevention Program weight-loss study of people at high risk of developing diabetes. The DPP showed that modest weight loss (the goal of 7% was not actually reached) could reduce the risk of progressing to overt diabetes. The same phenomenon occurred in that study too: subjects initially lost weight fast, then leveled off and started regaining again.
I think we've all experienced this phenomenon. We try a new diet and are enthusiastic and follow it exactly. Then after some time we hit a plateau and we also get bored with the diet and gradually revert to our old habits.
It's also possible that the body wants to maintain a certain weight that is higher than the weight we want it to have (the set-point theory), and after some time the metabolism changes to encourage the regaining of the weight.
People will never agree on the best diet for weight loss, partly because different people do better on different diets: YMMV, or "Your mileage may vary." But the more we know about various diets and various groups of dieters, preferable for as long as possible, the better.
Like weight loss, lipid levels tend to be different in the short term than they are in the long term. For example, in this study, triglycerides rapidly decreased in the low-carb group but then went up again and by the end of 2 years were about the same as the levels in the low-fat group. LDL levels went up in the low-carb group but later went down again. Only HDL levels were consistently higher on the low-carb diet throughout the study.
It's not clear whether the fluctuating lipid levels were because after 12 weeks the people on the low-carb diet were instructed to slowly start adding back carbs until their weight stabilized. This is what Atkins told people to do. But in fact, the weights in the low-carb group continued to drop after 12 weeks and then instead of "stabilizing" started to climb.
So it's also possible that the body was adapting to a different diet and adjusting the lipid levels after a certain amount of time on the diet. Perhaps we have a "lipid set-point" as well as a weight set-point, and the body keeps trying to reach it.
Regardless of the reasons for these shifts, in order to really understand how diets affect various heart health risks, we need longer-term data. Foster and colleagues have made a valuable first step. Perhaps even longer studies will follow.
Wednesday, July 7, 2010
Is Avandia Safe?
Stastics is not my strong suit, so I confess that I find reading about these massive human trials of drugs and other treatments to be more of a chore than a pleasure. Each study may use a different population group, a different drug dosage, a different end point, and a different time to the end point.
Furthermore, most of these trials are supported by drug companies, and I don't trust the results. There are many ways to manipulate the data to make small differences sound like large differences, or to explain away results that aren't what you wanted.
And you can't trust the headlines written by popular science news services like Science Daily or those in general medical magazines. They'll just restate the conclusions emphasized by the authors of a particular study and then reiterate the background of the topic. In many diabetes news stories, more space is devoted to explaining the difference between type 1 and type 2, giving the numbers now suffering from these conditions, and then describing the "obesity epidemic" than in describing what's new.
Three Science Daily headlines about Avandia (rosiglitazone from studies reported at the recent American Diabetes Association meeting in Orlando, Florida, illustrate how difficult it is for us to know what is really going on. They were as follows:
1. No Link Between Diabetes Drug Rosiglitazone and Increased Rate of Heart Attack, Study Finds.
2. Type 2 Diabetes Medication Rosiglitazone Associated With Increased Cardiovascular Risks and Death, Study Finds.
3. New Meta-Analysis Demonstrates Heart Risks Associated With Rosiglitazone.
What's going on here?
First note that the first headline mentions "heart attack," the second refers to "cardiovascular risks and death," and the third refers to "heart risks." None say what the risks are compared to, and cardiovascular risks or heart risks could refer to a lot of different things. I suspect most people wouldn't delve deeply into the details and would interpret these headlines simply as (1) Avandia good, (2) Avandia bad, and (3) Avandia bad.
Let's start with study number 3. This is a meta-analysis, and like many other people, I don't trust meta-analyses. What they do is try to take a lot of small studies in which the results weren't statistically significant and pool them all together so that the results become significant.
This is because the statistical significance depends on both the magnitude of an effect and the number of people in the study. So, for example, let's say you were testing a drug called SugarDown and found that among 300,000 people matched to controls not taking the drug, 200,000 saw their A1cs decrease by at least 1 point and only 100,000 of the control subjects reached this endpoint. That is, twice as many people taking the drug had a desired result.
But if you gave the drug to only 3 people matched to controls, 2 of those taking the drug reached the end point and only 1 not taking the drug reached the end point, this might suggest to you that this drug was worth trying in a larger trial, but even though twice as many taking the drug had a desired result, just as in the larger trial, the results would obviously not be significant. The result could have been the result of chance.
When the results are more extreme -- let's say all 3 people taking the drug dropped dead 15 minutes after taking it and none of the controls did, then there's less chance that the results would be from chance.
These are obviously extremes; most studies have more realistic numbers. But no trial is perfect. Too many study patients make the studies too expensive, and too few patients make the results unreliable.
A meta-analysis tries to overcome these limitations.
The problem is that unlike the studies themselves -- which are usually "double-blinded," meaning that neither the patient nor the researchers know which ones got the real drug and which one got the placebo -- the researchers doing the meta-analyses have the results of all the trials in front of them.
They are able to pick which ones to use. This can be difficult when each study has a different end point and different parameters. And even a researcher without an ulterior motive might have unconscious biases that would result in rejection of one study that had an undesired result and the use of another that had a desired result.
With those caveats, here's what this study, by Steven Nissen and Kathy Wolski, published in the Archives of Internal Medicine, concluded: "Eleven years after the introduction of rosiglitazone, the totality of randomized clinical trials continued to demonstrate increased risk for myocardial infarction [heart attack] although not for cardiovascular or all-cause mortality. The current findings suggest an unfavorable benefit to risk ratio for rosiglitazone."
In other words, in this meta-analysis of rosiglitazone (Avandia) studies, more patients had heart attacks when on rosiglitazone, but no more died.
Study number 2, published in JAMA, which was not a meta-analysis, concluded that "Compared with prescription of pioglitazone, prescription of rosiglitazone was associated with an increased risk of stroke, heart failure, and all-cause mortality and an increased risk of the composite of acute myocardial infarction (heart attack), stroke, heart failure, or all-cause mortality in patients 65 years or older." [Italics mine}
So this study was limited to people over 65, and the results were compared with those of patients getting a similar drug, pioglitazone, not with those of patients not getting any of this type of drug. It's always possible that some drug might have a positive effect on some outcome compared with no drug, but it might have a less positive effect than another drug, so compared with the second drug the results would appear to be negative.
Their conclusions also refer to stroke, heart failure (not heart attack), and all-cause mortality. They found no increase in heart attack rates, but then they lump heart attacks together with the other end points and say there was an increase in this composite result!
That's like saying eating beans causes a lot of gas, and a composite consisting of people who ate beans, crackers, and chicken soup had more gas than people who ate none of these things. Many people reading that statement would avoid crackers and chicken soup before important meetings, even though the crackers and chicken soup had no effect on gas production.
The conclusion in the Abstract of the article doesn't specifically mention that the drug had no significant effect on heart attack rates. It just mentions the significant effect on stroke, heart failure, and all-cause mortality and the effect on the composite index.
Some busy physicians quickly reading this composite conclusion in the Abstract (and many people don't take the time to read an entire article, assuming the Abstract summarizes it correctly) might conclude that heart attack rates were increased as well as the other endpoints.
This article has other flaws. They mention in the Introduction that 7 previous studies showed that rosiglitazone increased heart attack rates, but they don't say 7 out of how many or increased compared with what. Seven studies out of 8 would be one thing; 7 studies out of 50 would be another.
The authors noted that most studies that showed an increase in heart attacks with rosiglitazone (again, they don't say increased compared to what) were done in younger patients (54 to 65 years old), whereas their study was in people older than 65 years.
This is another source of confusion for people trying to decide whether or not to use a drug. What helps or harms in one patient population might not do the same in another population.
Study number 1 seems to contradict the other two. It has not yet been published but was presented at the ADA meeting. According to this study, rosiglitazone had no effect on heart attack rates or mortality. Then they also used a composite outcome -- heart attack, mortality, and stroke -- and said rosiglitazone reduced this composite outcome.
But only stroke rates actually decreased, by 64%, whereas the "rates of heart attack and death on their own showed no significant difference between those who took rosiglitazone and those who did not." Once again, the composite outcome is confusing, and people may come to an erroneous conclusion.
This study was limited to patients with diabetes and existing cardiovascular disease. Some got revascularization for their cardiovascular disease. Some got insulin or metformin instead of rosiglitazone.
And the results reported at the ADA meeting were from a post-trial analysis of the results from the BARI-2D trial, which was not designed to test the safety of rosiglitazone. Hence the drug was not randomly assigned. Reanalysis of studies designed to test something else are somewhat questionable.
So is rosiglitazone safe to take? The evidence is not clear-cut. The FDA will soon meet to discuss the safety issue, presumably taking into account other studies in addition to the three discussed here.
But these three studies are a good example of the slippery slope we have to deal with when results of large clinical trials are published: confused statistics, biased authors with ties to drug companies, and different patient groups, comparisons, and end points.
I wonder how many bad drugs are on the market because of confusing clinical studies. So too, I wonder how many good drugs might have been dropped from the pipeline because of equally confusing clinical studies.
Evaluating risks vs benefits is not simple, and the best choice for a large population is not always the best choice for an individual patient. You might be allergic to a drug that helps most patients. Conversely, a drug that harms most patients might be wonderful for you.
All this is one reason that controlling with good food and exercise should always be the first choice. But this isn't always enough. Then we and our physicians have to evaluate which drugs will work best for us.
It is not a simple task.
Furthermore, most of these trials are supported by drug companies, and I don't trust the results. There are many ways to manipulate the data to make small differences sound like large differences, or to explain away results that aren't what you wanted.
And you can't trust the headlines written by popular science news services like Science Daily or those in general medical magazines. They'll just restate the conclusions emphasized by the authors of a particular study and then reiterate the background of the topic. In many diabetes news stories, more space is devoted to explaining the difference between type 1 and type 2, giving the numbers now suffering from these conditions, and then describing the "obesity epidemic" than in describing what's new.
Three Science Daily headlines about Avandia (rosiglitazone from studies reported at the recent American Diabetes Association meeting in Orlando, Florida, illustrate how difficult it is for us to know what is really going on. They were as follows:
1. No Link Between Diabetes Drug Rosiglitazone and Increased Rate of Heart Attack, Study Finds.
2. Type 2 Diabetes Medication Rosiglitazone Associated With Increased Cardiovascular Risks and Death, Study Finds.
3. New Meta-Analysis Demonstrates Heart Risks Associated With Rosiglitazone.
What's going on here?
First note that the first headline mentions "heart attack," the second refers to "cardiovascular risks and death," and the third refers to "heart risks." None say what the risks are compared to, and cardiovascular risks or heart risks could refer to a lot of different things. I suspect most people wouldn't delve deeply into the details and would interpret these headlines simply as (1) Avandia good, (2) Avandia bad, and (3) Avandia bad.
Let's start with study number 3. This is a meta-analysis, and like many other people, I don't trust meta-analyses. What they do is try to take a lot of small studies in which the results weren't statistically significant and pool them all together so that the results become significant.
This is because the statistical significance depends on both the magnitude of an effect and the number of people in the study. So, for example, let's say you were testing a drug called SugarDown and found that among 300,000 people matched to controls not taking the drug, 200,000 saw their A1cs decrease by at least 1 point and only 100,000 of the control subjects reached this endpoint. That is, twice as many people taking the drug had a desired result.
But if you gave the drug to only 3 people matched to controls, 2 of those taking the drug reached the end point and only 1 not taking the drug reached the end point, this might suggest to you that this drug was worth trying in a larger trial, but even though twice as many taking the drug had a desired result, just as in the larger trial, the results would obviously not be significant. The result could have been the result of chance.
When the results are more extreme -- let's say all 3 people taking the drug dropped dead 15 minutes after taking it and none of the controls did, then there's less chance that the results would be from chance.
These are obviously extremes; most studies have more realistic numbers. But no trial is perfect. Too many study patients make the studies too expensive, and too few patients make the results unreliable.
A meta-analysis tries to overcome these limitations.
The problem is that unlike the studies themselves -- which are usually "double-blinded," meaning that neither the patient nor the researchers know which ones got the real drug and which one got the placebo -- the researchers doing the meta-analyses have the results of all the trials in front of them.
They are able to pick which ones to use. This can be difficult when each study has a different end point and different parameters. And even a researcher without an ulterior motive might have unconscious biases that would result in rejection of one study that had an undesired result and the use of another that had a desired result.
With those caveats, here's what this study, by Steven Nissen and Kathy Wolski, published in the Archives of Internal Medicine, concluded: "Eleven years after the introduction of rosiglitazone, the totality of randomized clinical trials continued to demonstrate increased risk for myocardial infarction [heart attack] although not for cardiovascular or all-cause mortality. The current findings suggest an unfavorable benefit to risk ratio for rosiglitazone."
In other words, in this meta-analysis of rosiglitazone (Avandia) studies, more patients had heart attacks when on rosiglitazone, but no more died.
Study number 2, published in JAMA, which was not a meta-analysis, concluded that "Compared with prescription of pioglitazone, prescription of rosiglitazone was associated with an increased risk of stroke, heart failure, and all-cause mortality and an increased risk of the composite of acute myocardial infarction (heart attack), stroke, heart failure, or all-cause mortality in patients 65 years or older." [Italics mine}
So this study was limited to people over 65, and the results were compared with those of patients getting a similar drug, pioglitazone, not with those of patients not getting any of this type of drug. It's always possible that some drug might have a positive effect on some outcome compared with no drug, but it might have a less positive effect than another drug, so compared with the second drug the results would appear to be negative.
Their conclusions also refer to stroke, heart failure (not heart attack), and all-cause mortality. They found no increase in heart attack rates, but then they lump heart attacks together with the other end points and say there was an increase in this composite result!
That's like saying eating beans causes a lot of gas, and a composite consisting of people who ate beans, crackers, and chicken soup had more gas than people who ate none of these things. Many people reading that statement would avoid crackers and chicken soup before important meetings, even though the crackers and chicken soup had no effect on gas production.
The conclusion in the Abstract of the article doesn't specifically mention that the drug had no significant effect on heart attack rates. It just mentions the significant effect on stroke, heart failure, and all-cause mortality and the effect on the composite index.
Some busy physicians quickly reading this composite conclusion in the Abstract (and many people don't take the time to read an entire article, assuming the Abstract summarizes it correctly) might conclude that heart attack rates were increased as well as the other endpoints.
This article has other flaws. They mention in the Introduction that 7 previous studies showed that rosiglitazone increased heart attack rates, but they don't say 7 out of how many or increased compared with what. Seven studies out of 8 would be one thing; 7 studies out of 50 would be another.
The authors noted that most studies that showed an increase in heart attacks with rosiglitazone (again, they don't say increased compared to what) were done in younger patients (54 to 65 years old), whereas their study was in people older than 65 years.
This is another source of confusion for people trying to decide whether or not to use a drug. What helps or harms in one patient population might not do the same in another population.
Study number 1 seems to contradict the other two. It has not yet been published but was presented at the ADA meeting. According to this study, rosiglitazone had no effect on heart attack rates or mortality. Then they also used a composite outcome -- heart attack, mortality, and stroke -- and said rosiglitazone reduced this composite outcome.
But only stroke rates actually decreased, by 64%, whereas the "rates of heart attack and death on their own showed no significant difference between those who took rosiglitazone and those who did not." Once again, the composite outcome is confusing, and people may come to an erroneous conclusion.
This study was limited to patients with diabetes and existing cardiovascular disease. Some got revascularization for their cardiovascular disease. Some got insulin or metformin instead of rosiglitazone.
And the results reported at the ADA meeting were from a post-trial analysis of the results from the BARI-2D trial, which was not designed to test the safety of rosiglitazone. Hence the drug was not randomly assigned. Reanalysis of studies designed to test something else are somewhat questionable.
So is rosiglitazone safe to take? The evidence is not clear-cut. The FDA will soon meet to discuss the safety issue, presumably taking into account other studies in addition to the three discussed here.
But these three studies are a good example of the slippery slope we have to deal with when results of large clinical trials are published: confused statistics, biased authors with ties to drug companies, and different patient groups, comparisons, and end points.
I wonder how many bad drugs are on the market because of confusing clinical studies. So too, I wonder how many good drugs might have been dropped from the pipeline because of equally confusing clinical studies.
Evaluating risks vs benefits is not simple, and the best choice for a large population is not always the best choice for an individual patient. You might be allergic to a drug that helps most patients. Conversely, a drug that harms most patients might be wonderful for you.
All this is one reason that controlling with good food and exercise should always be the first choice. But this isn't always enough. Then we and our physicians have to evaluate which drugs will work best for us.
It is not a simple task.
Monday, May 17, 2010
Stupid Fat Study
No this isn't about "stupid fats." It's a stupid study, in my opinion.
I try to give research scientists the benefit of the doubt, because I've done lab research myself, and I know how difficult it can be to get reliable results. It's even more complex today than it was when I was in graduate school.
Nevertheless, I think this study, reported in Science Daily, really takes the cake. The SD title is "High-Fat Meals a No-No for Asthma Patients, Researchers Find."
So what did the researchers do?
They fed two different meals to 40 people with asthma and measured any resulting inflammation. Diet 1 was 1000 calories, 52% fat, and consisted of fast-food burgers and hash brown potatoes. Diet 2 was 200 calories, 13% fat, and consisted of reduced-fat yogurt.
They found that people eating diet 1 had more inflammation. So they concluded that the inflammation was caused by fat!
How can you possibly assign blame when the diets differed in so many ways?
An equally valid headline might have been "High-calorie meals a no-no for asthma patients" or "Hash-brown potatoes a no-no for asthma patients" or "Dairy products good for asthma patients" or "Eating lots of fat in combination with lots of carbohyrate a no-no for asthma patients" or "Hamburger buns a no-no for asthma patients."
Instead, they focused on the one ingredient they probably started out believing would be bad and ignored the rest.
This study was presented at the American Thoracic Society 2o10 conference in New Orleans.
One of the researchers said, "This is the first study to show that a high fat meal increases airway inflammation." No it didn't. It showed that a high-fat, high-calorie, high-carbohydrate, commercial junk-food meal increased airway inflammation.
Unfortunately, headlines are all that many people read and remember. Keep in mind that headlines can be misleading. Before accepting the conclusions in any study you think might be important for you, read as much of the full text as you are able to and then make up your own mind.
We can't depend on other people to inform us correctly. We have to take control ourselves.
I try to give research scientists the benefit of the doubt, because I've done lab research myself, and I know how difficult it can be to get reliable results. It's even more complex today than it was when I was in graduate school.
Nevertheless, I think this study, reported in Science Daily, really takes the cake. The SD title is "High-Fat Meals a No-No for Asthma Patients, Researchers Find."
So what did the researchers do?
They fed two different meals to 40 people with asthma and measured any resulting inflammation. Diet 1 was 1000 calories, 52% fat, and consisted of fast-food burgers and hash brown potatoes. Diet 2 was 200 calories, 13% fat, and consisted of reduced-fat yogurt.
They found that people eating diet 1 had more inflammation. So they concluded that the inflammation was caused by fat!
How can you possibly assign blame when the diets differed in so many ways?
An equally valid headline might have been "High-calorie meals a no-no for asthma patients" or "Hash-brown potatoes a no-no for asthma patients" or "Dairy products good for asthma patients" or "Eating lots of fat in combination with lots of carbohyrate a no-no for asthma patients" or "Hamburger buns a no-no for asthma patients."
Instead, they focused on the one ingredient they probably started out believing would be bad and ignored the rest.
This study was presented at the American Thoracic Society 2o10 conference in New Orleans.
One of the researchers said, "This is the first study to show that a high fat meal increases airway inflammation." No it didn't. It showed that a high-fat, high-calorie, high-carbohydrate, commercial junk-food meal increased airway inflammation.
Unfortunately, headlines are all that many people read and remember. Keep in mind that headlines can be misleading. Before accepting the conclusions in any study you think might be important for you, read as much of the full text as you are able to and then make up your own mind.
We can't depend on other people to inform us correctly. We have to take control ourselves.
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