Matt Lentzner, Janine Jagger and I have designed a survey for participants of Gluten-free January, using the online application StatCrunch. Janine is an epidemiologist who studies healthcare worker safety at the University of Virginia; she has experience designing surveys for data collection so we're glad to have her on board. The survey will allow us to systematically gather and analyze data on the results of Gluten-free January. It will be 100 percent anonymous-- none of your answers will be connected to your identity in any way.
This survey has the potential to be really informative, but it will only work if you respond! The more people who take the survey, the more informative it will be, even if you didn't avoid gluten for a single day. If not very many people respond, it will be highly susceptible to "selection bias", where perhaps the only people who responded are people who improved the most, skewing the results.
Matt will be sending the survey out to everyone on his mailing list. Please complete it, even if you didn't end up avoiding gluten at all! There's no shame in it. The survey has responses built in for people who didn't avoid gluten. Your survey will still be useful!
We have potential data from over 500 people. After we crunch the numbers, I'll share them on the blog.
Monday, 31 January 2011
Sunday, 30 January 2011
The China Study II: A look at mortality in the 35-69 and 70-79 age ranges
This post is based on an analysis of a subset of the China Study II data, using HealthCorrelator for Excel (HCE), which is publicly available for download and use on a free trial basis. You can access the original data on the HCE web site, under “Sample datasets”.
HCE was designed to be used with small and individual personal datasets, but it can also be used with larger datasets for multiple individuals.
This analysis focuses on two main variables from the China Study II data: mortality in the 35-69 age range, and mortality in the 70-79 range. The table below shows the coefficients of association calculated by HCE for those two variables. The original variable labels are shown.
One advantage of looking at mortality in these ranges is that they are more likely to reflect the impact of degenerative diseases. Infectious diseases likely killed a lot of children in China at the time the data was being collected. Heart disease, on the other hand, is likely to have killed more people in the 35-69 and 70-79 ranges.
It is also good to have data for both ranges, because factors that likely increased longevity were those that were associated with decreased mortality in both ranges. For example, a factor that was strongly associated with mortality in the 35-69 range, but not the 70-79 range, might simply be very deadly in the former range.
The mortalities in both ranges are strongly correlated with each other, which is to be expected. Next, at the very top for both ranges, is sex. Being female is by far the variable with the strongest, and negative, association with mortality.
While I would expect females to live longer, the strengths of the associations make me think that there is something else going on here. Possibly different dietary or behavioral patterns displayed by females. Maybe smoking cigarettes or alcohol abuse was a lot less prevalent among them.
Markedly different lifestyle patterns between males and females may be a major confounding variable in the China Study sample.
Some of the variables are redundant; meaning that they are highly correlated and seem to measure the same thing. This is clear when one looks at the other coefficients of association generated by HCE.
For example, plant food consumption is strongly and negatively correlated with animal food consumption; so strongly that you could use either one of these two variables to measure the other, after inverting the scale. The same is true for consumption of rice and white flour.
Plant food consumption is not strongly correlated with plant protein consumption; many plant foods have little protein in them. The ones that have high protein content are typically industrialized and seed-based. The type of food most strongly associated with plant protein consumption is white flour, by far. The correlation is .645.
The figure below is based on the table above. I opened a separate instance of Excel, and copied the coefficients generated by HCE into it. Then I built two bar charts with them. The variable labels were replaced with more suggestive names, and some redundant variables were removed. Only the top 7 variables are shown, ordered from left to right on the bar charts in order of strength of association. The ones above the horizontal axis possibly increase mortality in each age range, whereas the ones at the bottom possibly decrease it.
When you look at these results as a whole, a few things come to mind.
White flour consumption doesn’t seem to be making people live longer; nor does plant food consumption in general. For white flour, it is quite the opposite. Plant food consumption reflects white flour consumption to a certain extent, especially in counties where rice consumption is low. These conclusions are consistent with previous analyses using more complex statistics.
Total food is positively associated with mortality in the 35-69 range, but not the 70-79 range. This may reflect the fact that folks who reach the age of 70 tend to naturally eat in moderation, so you don’t see wide variations in food consumption among those folks.
Eating in moderation does not mean practicing severe calorie restriction. This post suggests that calorie restriction doesn't seem to be associated with increased longevity in this sample. Eating well, but not too much, is.
The bar for rice (consumption) on the left chart is likely a mirror reflection of the white flour consumption, so it may appear to be good in the 35-69 range simply because it reflects reduced white flour consumption in that range.
Green vegetables seem to be good when you consider the 35-69 range, but not the 70-79 range.
Neither rice nor green vegetables seem to be bad either. For overall longevity they may well be neutral, with the benefits likely coming from their replacement of white flour in the diet.
Dietary fat seems protective overall, particularly together with animal foods in the 70-79 range. This may simply reflect a delayed protective effect of animal fat and protein consumption.
The protective effect of dietary fat becomes clear when we look at the relationship between carbohydrate calories and fat calories. Their correlation is -.957, which essentially means that carbohydrate intake seriously displaces fat intake.
Carbohydrates themselves may not be the problem, even if coming from high glycemic foods (except wheat flour, apparently). This post shows that they are relatively benign if coming from high glycemic rice, even at high intakes of 206 to 412 g/day. The problem seems to be caused by carbohydrates displacing nutrient-dense animal foods.
Interestingly, rice does not displace animal foods or fat in the diet. It is positively correlated with them. Wheat flour, on the other hand, displaces those foods. Wheat flour is negatively and somewhat strongly correlated with consumption of animal foods, as well as with animal fat and protein.
There are certainly several delayed effects here, which may be distorting the results somewhat. Degenerative diseases don’t develop fast and kill folks right away. They often require many years of eating and doing the wrong things to be fatal.
HCE was designed to be used with small and individual personal datasets, but it can also be used with larger datasets for multiple individuals.
This analysis focuses on two main variables from the China Study II data: mortality in the 35-69 age range, and mortality in the 70-79 range. The table below shows the coefficients of association calculated by HCE for those two variables. The original variable labels are shown.
One advantage of looking at mortality in these ranges is that they are more likely to reflect the impact of degenerative diseases. Infectious diseases likely killed a lot of children in China at the time the data was being collected. Heart disease, on the other hand, is likely to have killed more people in the 35-69 and 70-79 ranges.
It is also good to have data for both ranges, because factors that likely increased longevity were those that were associated with decreased mortality in both ranges. For example, a factor that was strongly associated with mortality in the 35-69 range, but not the 70-79 range, might simply be very deadly in the former range.
The mortalities in both ranges are strongly correlated with each other, which is to be expected. Next, at the very top for both ranges, is sex. Being female is by far the variable with the strongest, and negative, association with mortality.
While I would expect females to live longer, the strengths of the associations make me think that there is something else going on here. Possibly different dietary or behavioral patterns displayed by females. Maybe smoking cigarettes or alcohol abuse was a lot less prevalent among them.
Markedly different lifestyle patterns between males and females may be a major confounding variable in the China Study sample.
Some of the variables are redundant; meaning that they are highly correlated and seem to measure the same thing. This is clear when one looks at the other coefficients of association generated by HCE.
For example, plant food consumption is strongly and negatively correlated with animal food consumption; so strongly that you could use either one of these two variables to measure the other, after inverting the scale. The same is true for consumption of rice and white flour.
Plant food consumption is not strongly correlated with plant protein consumption; many plant foods have little protein in them. The ones that have high protein content are typically industrialized and seed-based. The type of food most strongly associated with plant protein consumption is white flour, by far. The correlation is .645.
The figure below is based on the table above. I opened a separate instance of Excel, and copied the coefficients generated by HCE into it. Then I built two bar charts with them. The variable labels were replaced with more suggestive names, and some redundant variables were removed. Only the top 7 variables are shown, ordered from left to right on the bar charts in order of strength of association. The ones above the horizontal axis possibly increase mortality in each age range, whereas the ones at the bottom possibly decrease it.
When you look at these results as a whole, a few things come to mind.
White flour consumption doesn’t seem to be making people live longer; nor does plant food consumption in general. For white flour, it is quite the opposite. Plant food consumption reflects white flour consumption to a certain extent, especially in counties where rice consumption is low. These conclusions are consistent with previous analyses using more complex statistics.
Total food is positively associated with mortality in the 35-69 range, but not the 70-79 range. This may reflect the fact that folks who reach the age of 70 tend to naturally eat in moderation, so you don’t see wide variations in food consumption among those folks.
Eating in moderation does not mean practicing severe calorie restriction. This post suggests that calorie restriction doesn't seem to be associated with increased longevity in this sample. Eating well, but not too much, is.
The bar for rice (consumption) on the left chart is likely a mirror reflection of the white flour consumption, so it may appear to be good in the 35-69 range simply because it reflects reduced white flour consumption in that range.
Green vegetables seem to be good when you consider the 35-69 range, but not the 70-79 range.
Neither rice nor green vegetables seem to be bad either. For overall longevity they may well be neutral, with the benefits likely coming from their replacement of white flour in the diet.
Dietary fat seems protective overall, particularly together with animal foods in the 70-79 range. This may simply reflect a delayed protective effect of animal fat and protein consumption.
The protective effect of dietary fat becomes clear when we look at the relationship between carbohydrate calories and fat calories. Their correlation is -.957, which essentially means that carbohydrate intake seriously displaces fat intake.
Carbohydrates themselves may not be the problem, even if coming from high glycemic foods (except wheat flour, apparently). This post shows that they are relatively benign if coming from high glycemic rice, even at high intakes of 206 to 412 g/day. The problem seems to be caused by carbohydrates displacing nutrient-dense animal foods.
Interestingly, rice does not displace animal foods or fat in the diet. It is positively correlated with them. Wheat flour, on the other hand, displaces those foods. Wheat flour is negatively and somewhat strongly correlated with consumption of animal foods, as well as with animal fat and protein.
There are certainly several delayed effects here, which may be distorting the results somewhat. Degenerative diseases don’t develop fast and kill folks right away. They often require many years of eating and doing the wrong things to be fatal.
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Thursday, 27 January 2011
The Diabetes Epidemic
The CDC just released its latest estimate of diabetes prevalence in the US (1):
These data are self-reported, and do not correct for differences in diagnosis methods, so they should be viewed with caution-- but they still serve to illustrate the trend. There was an increase in diabetes incidence that began in the early 1990s. More than 90 percent of cases are type 2 diabetics. Disturbingly, the trend does not show any signs of slowing.
The diabetes epidemic has followed on the heels of the obesity epidemic with 10-20 years of lag time. Excess body fat is the number one risk factor for diabetes*. As far as I can tell, type 2 diabetes is caused by insulin resistance, which is probably due to energy intake exceeding energy needs (overnutrition), causing a state of cellular insulin resistance as a defense mechanism to protect against the damaging effects of too much glucose and fatty acids (3). In addition, type 2 diabetes requires a predisposition that prevents the pancreatic beta cells from keeping up with the greatly increased insulin needs of an insulin resistant person**. Both factors are required, and not all insulin resistant people will develop diabetes as some people's beta cells are able to compensate by hypersecreting insulin.
Why does energy intake exceed energy needs in modern America and in most affluent countries? Why has the typical person's calorie intake increased by 250 calories per day since 1970 (4)? I believe it's because the fat mass "setpoint" has been increased, typically but not always by industrial food. I've been developing some new thoughts on this lately, and potentially new solutions, which I'll reveal when they're ready.
* In other words, it's the best predictor of future diabetes risk.
** Most of the common gene variants (of known function) linked with type 2 diabetes are thought to impact beta cell function (5).
Diabetes affects 8.3 percent of Americans of all ages, and 11.3 percent of adults aged 20 and older, according to the National Diabetes Fact Sheet for 2011. About 27 percent of those with diabetes—7 million Americans—do not know they have the disease. Prediabetes affects 35 percent of adults aged 20 and older.Wow-- this is a massive problem. The prevalence of diabetes has been increasing over time, due to more people developing the disorder, improvements in diabetes care leading to longer survival time, and changes in the way diabetes is diagnosed. Here's a graph I put together based on CDC data, showing the trend of diabetes prevalence (percent) from 1980 to 2008 in different age categories (2):
These data are self-reported, and do not correct for differences in diagnosis methods, so they should be viewed with caution-- but they still serve to illustrate the trend. There was an increase in diabetes incidence that began in the early 1990s. More than 90 percent of cases are type 2 diabetics. Disturbingly, the trend does not show any signs of slowing.
The diabetes epidemic has followed on the heels of the obesity epidemic with 10-20 years of lag time. Excess body fat is the number one risk factor for diabetes*. As far as I can tell, type 2 diabetes is caused by insulin resistance, which is probably due to energy intake exceeding energy needs (overnutrition), causing a state of cellular insulin resistance as a defense mechanism to protect against the damaging effects of too much glucose and fatty acids (3). In addition, type 2 diabetes requires a predisposition that prevents the pancreatic beta cells from keeping up with the greatly increased insulin needs of an insulin resistant person**. Both factors are required, and not all insulin resistant people will develop diabetes as some people's beta cells are able to compensate by hypersecreting insulin.
Why does energy intake exceed energy needs in modern America and in most affluent countries? Why has the typical person's calorie intake increased by 250 calories per day since 1970 (4)? I believe it's because the fat mass "setpoint" has been increased, typically but not always by industrial food. I've been developing some new thoughts on this lately, and potentially new solutions, which I'll reveal when they're ready.
* In other words, it's the best predictor of future diabetes risk.
** Most of the common gene variants (of known function) linked with type 2 diabetes are thought to impact beta cell function (5).
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Two Wheat Challenge Ideas from Commenters
Some people have remarked that the blinded challenge method I posted is cumbersome.
Reader "Me" suggested:
Reader "Me" suggested:
You can buy wheat gluten in a grocery store. Why not simply have your friend add some wheat gluten to your normal protein shake.Reader David suggested:
They sell empty gelatin capsules with carob content to opacify them. Why not fill a few capsules with whole wheat flour, and then a whole bunch with rice starch or other placebo. For two weeks take a set of, say, three capsules every day, with the set of wheat capsules in line to be taken on a random day selected by your friend. This would further reduce the chances that you would see through the blind, and it prevent the risk of not being able to choke the "smoothie" down. It would also keep it to wheat and nothing but wheat (except for the placebo starch).The reason I chose the method in the last post is that it directly tests wheat in a form that a person would be likely to eat: bread. The limitation of the gluten shake method is that it would miss a sensitivity to components in wheat other than gluten. The limitation of the pill method is that raw flour is difficult to digest, so it would be difficult to extrapolate a sensitivity to cooked flour foods. You might be able to get around that by filling the pills with powdered bread crumbs. Those are two alternative ideas to consider if the one I posted seems too involved.
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Monday, 24 January 2011
Blinded Wheat Challenge
Self-experimentation can be an effective way to improve one's health*. One of the problems with diet self-experimentation is that it's difficult to know which changes are the direct result of eating a food, and which are the result of preconceived ideas about a food. For example, are you more likely to notice the fact that you're grumpy after drinking milk if you think milk makes people grumpy? Maybe you're grumpy every other day regardless of diet? Placebo effects and conscious/unconscious bias can lead us to erroneous conclusions.
The beauty of the scientific method is that it offers us effective tools to minimize this kind of bias. This is probably its main advantage over more subjective forms of inquiry**. One of the most effective tools in the scientific method's toolbox is a control. This is a measurement that's used to establish a baseline for comparison with the intervention, which is what you're interested in. Without a control measurement, the intervention measurement is typically meaningless. For example, if we give 100 people pills that cure belly button lint, we have to give a different group placebo (sugar) pills. Only the comparison between drug and placebo groups can tell us if the drug worked, because maybe the changing seasons, regular doctor's visits, or having your belly button examined once a week affects the likelihood of lint.
Another tool is called blinding. This is where the patient, and often the doctor and investigators, don't know which pills are placebo and which are drug. This minimizes bias on the part of the patient, and sometimes the doctor and investigators. If the patient knew he were receiving drug rather than placebo, that could influence the outcome. Likewise, investigators who aren't blinded while they're collecting data can unconsciously (or consciously) influence it.
Back to diet. I want to know if I react to wheat. I've been gluten-free for about a month. But if I eat a slice of bread, how can I be sure I'm not experiencing symptoms because I think I should? How about blinding and a non-gluten control?
Procedure for a Blinded Wheat Challenge
1. Find a friend who can help you.
2. Buy a loaf of wheat bread and a loaf of gluten-free bread.
3. Have your friend choose one of the loaves without telling you which he/she chose.
4. Have your friend take 1-3 slices, blend them with water in a blender until smooth. This is to eliminate differences in consistency that could allow you to determine what you're eating. Don't watch your friend do this-- you might recognize the loaf.
5. Pinch your nose and drink the "bread smoothie" (yum!). This is so that you can't identify the bread by taste. Rinse your mouth with water before releasing your nose. Record how you feel in the next few hours and days.
6. Wait a week. This is called a "washout period". Repeat the experiment with the second loaf, attempting to keep everything else about the experiment as similar as possible.
7. Compare how you felt each time. Have your friend "unblind" you by telling you which bread you ate on each day. If you experienced symptoms during the wheat challenge but not the control challenge, you may be sensitive to wheat.
If you want to take this to the next level of scientific rigor, repeat the procedure several times to see if the result is consistent. The larger the effect, the fewer times you need to repeat it to be confident in the result.
* Although it can also be disastrous. People who get into the most trouble are "extreme thinkers" who have a tendency to take an idea too far, e.g., avoid all animal foods, avoid all carbohydrate, avoid all fat, run two marathons a week, etc.
** More subjective forms of inquiry have their own advantages.
The beauty of the scientific method is that it offers us effective tools to minimize this kind of bias. This is probably its main advantage over more subjective forms of inquiry**. One of the most effective tools in the scientific method's toolbox is a control. This is a measurement that's used to establish a baseline for comparison with the intervention, which is what you're interested in. Without a control measurement, the intervention measurement is typically meaningless. For example, if we give 100 people pills that cure belly button lint, we have to give a different group placebo (sugar) pills. Only the comparison between drug and placebo groups can tell us if the drug worked, because maybe the changing seasons, regular doctor's visits, or having your belly button examined once a week affects the likelihood of lint.
Another tool is called blinding. This is where the patient, and often the doctor and investigators, don't know which pills are placebo and which are drug. This minimizes bias on the part of the patient, and sometimes the doctor and investigators. If the patient knew he were receiving drug rather than placebo, that could influence the outcome. Likewise, investigators who aren't blinded while they're collecting data can unconsciously (or consciously) influence it.
Back to diet. I want to know if I react to wheat. I've been gluten-free for about a month. But if I eat a slice of bread, how can I be sure I'm not experiencing symptoms because I think I should? How about blinding and a non-gluten control?
Procedure for a Blinded Wheat Challenge
1. Find a friend who can help you.
2. Buy a loaf of wheat bread and a loaf of gluten-free bread.
3. Have your friend choose one of the loaves without telling you which he/she chose.
4. Have your friend take 1-3 slices, blend them with water in a blender until smooth. This is to eliminate differences in consistency that could allow you to determine what you're eating. Don't watch your friend do this-- you might recognize the loaf.
5. Pinch your nose and drink the "bread smoothie" (yum!). This is so that you can't identify the bread by taste. Rinse your mouth with water before releasing your nose. Record how you feel in the next few hours and days.
6. Wait a week. This is called a "washout period". Repeat the experiment with the second loaf, attempting to keep everything else about the experiment as similar as possible.
7. Compare how you felt each time. Have your friend "unblind" you by telling you which bread you ate on each day. If you experienced symptoms during the wheat challenge but not the control challenge, you may be sensitive to wheat.
If you want to take this to the next level of scientific rigor, repeat the procedure several times to see if the result is consistent. The larger the effect, the fewer times you need to repeat it to be confident in the result.
* Although it can also be disastrous. People who get into the most trouble are "extreme thinkers" who have a tendency to take an idea too far, e.g., avoid all animal foods, avoid all carbohydrate, avoid all fat, run two marathons a week, etc.
** More subjective forms of inquiry have their own advantages.
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HealthCorrelator for Excel (HCE) is now publicly available for free trial
HealthCorrelator for Excel (HCE) is now publicly available for download and use on a free trial basis. For those users who decide to buy it after trying, licenses are available for individuals and organizations. If you are a gym member, consider asking your gym to buy an organizational site license; this would allow the gym to distribute individual licenses at no cost to you and your colleagues.
HCE is a user-friendly Excel-based software that unveils important associations among health variables at the click of a button. Here are some of its main features:
- Easy to use yet powerful health management software.
- Estimates associations among any number of health variables.
- Automatically orders associations by decreasing absolute strength.
- Graphs relationships between pairs of health variables, for all possible combinations.
The beta testing was successfully completed, with fairly positive results. (Thank you beta testers!) Among beta testers were Mac users. The main request from beta testers was for more illustrative material on how to use HCE for specific purposes, such as losing body fat or managing blood glucose levels. This will be coming in the future in the form of posts and linked material.
To download a free trial version, good for 30 use sessions (which is quite a lot!), please visit the HealthCorrelator.com web site. There you will also find the software’s User Manual and various links to demo YouTube videos. You can also download sample datasets to try the software’s main features.
HCE is a user-friendly Excel-based software that unveils important associations among health variables at the click of a button. Here are some of its main features:
- Easy to use yet powerful health management software.
- Estimates associations among any number of health variables.
- Automatically orders associations by decreasing absolute strength.
- Graphs relationships between pairs of health variables, for all possible combinations.
The beta testing was successfully completed, with fairly positive results. (Thank you beta testers!) Among beta testers were Mac users. The main request from beta testers was for more illustrative material on how to use HCE for specific purposes, such as losing body fat or managing blood glucose levels. This will be coming in the future in the form of posts and linked material.
To download a free trial version, good for 30 use sessions (which is quite a lot!), please visit the HealthCorrelator.com web site. There you will also find the software’s User Manual and various links to demo YouTube videos. You can also download sample datasets to try the software’s main features.
Sponsored by:
Thursday, 20 January 2011
Eating Wheat Gluten Causes Symptoms in Some People Who Don't Have Celiac Disease
Irritable bowel syndrome (IBS) is a condition characterized by the frequent occurrence of abdominal pain, diarrhea, constipation, bloating and/or gas. If that sounds like an extremely broad description, that's because it is. The word "syndrome" is medicalese for "we don't know what causes it." IBS seems to be a catch-all for various persistent digestive problems that aren't defined as separate disorders, and it has a very high prevalence: as high as 14 percent of people in the US, although the estimates depend on what diagnostic criteria are used (1). It can be brought on or exacerbated by several different types of stressors, including emotional stress and infection.
Maelán Fontes Villalba at Lund University recently forwarded me an interesting new paper in the American Journal of Gastroenterology (2). Dr. Jessica R. Biesiekierski and colleagues recruited 34 IBS patients who did not have celiac disease, but who felt they had benefited from going gluten-free in their daily lives*. All patients continued on their pre-study gluten-free diet, however, all participants were provided with two slices of gluten-free bread and one gluten-free muffin per day. The investigators added isolated wheat gluten to the bread and muffins of half the study group.
During the six weeks of the intervention, patients receiving the gluten-free food fared considerably better on nearly every symptom of IBS measured. The most striking difference was in tiredness-- the gluten-free group was much less tired on average than the gluten group. Interestingly, they found that a negative reaction to gluten was not necessarily accompanied by the presence of anti-gluten antibodies in the blood, which is a test often used to diagnose gluten sensitivity.
Here's what I take away from this study:
I don't expect everyone to benefit from avoiding gluten. But for those who are really sensitive, it can make a huge difference. Digestive, autoimmune and neurological disorders associate most strongly with gluten sensitivity. Avoiding gluten can be a fruitful thing to try in cases of mysterious chronic illness. We're two-thirds of the way through Gluten-Free January. I've been fastidiously avoiding gluten, as annoying as it's been at times***. Has anyone noticed a change in their health?
* 56% of volunteers carried HLA-DQ2 or DQ8 alleles, which is slightly higher than the general population. Nearly all people with celiac disease carry one of these two alleles. 28% of volunteers were positive for anti-gliadin IgA, which is higher than the general population.
** Some people feel they are reacting to the fructans in wheat, rather than the gluten. If a modest amount of onion causes the same symptoms as eating wheat, then that may be true. If not, then it's probably the gluten.
*** I'm usually about 95% gluten-free anyway. But when I want a real beer, I want one brewed with barley. And when I want Thai food or sushi, I don't worry about a little bit of wheat in the soy sauce. If a friend makes me food with gluten in it, I'll eat it and enjoy it. This month I'm 100% gluten-free though, because I can't in good conscience encourage my blog readership to try it if I'm not doing it myself. At the end of the month, I'm going to do a blinded gluten challenge (with a gluten-free control challenge) to see once and for all if I react to it. Stay tuned for more on that.
Maelán Fontes Villalba at Lund University recently forwarded me an interesting new paper in the American Journal of Gastroenterology (2). Dr. Jessica R. Biesiekierski and colleagues recruited 34 IBS patients who did not have celiac disease, but who felt they had benefited from going gluten-free in their daily lives*. All patients continued on their pre-study gluten-free diet, however, all participants were provided with two slices of gluten-free bread and one gluten-free muffin per day. The investigators added isolated wheat gluten to the bread and muffins of half the study group.
During the six weeks of the intervention, patients receiving the gluten-free food fared considerably better on nearly every symptom of IBS measured. The most striking difference was in tiredness-- the gluten-free group was much less tired on average than the gluten group. Interestingly, they found that a negative reaction to gluten was not necessarily accompanied by the presence of anti-gluten antibodies in the blood, which is a test often used to diagnose gluten sensitivity.
Here's what I take away from this study:
- Wheat gluten can cause symptoms in susceptible people who do not have celiac disease.
- A lack of circulating antibodies against gluten does not necessarily indicate a lack of gluten sensitivity.
- People with mysterious digestive problems may want to try avoiding gluten for a while to see if it improves their symptoms**.
- People with mysterious fatigue may want to try avoiding gluten.
I don't expect everyone to benefit from avoiding gluten. But for those who are really sensitive, it can make a huge difference. Digestive, autoimmune and neurological disorders associate most strongly with gluten sensitivity. Avoiding gluten can be a fruitful thing to try in cases of mysterious chronic illness. We're two-thirds of the way through Gluten-Free January. I've been fastidiously avoiding gluten, as annoying as it's been at times***. Has anyone noticed a change in their health?
* 56% of volunteers carried HLA-DQ2 or DQ8 alleles, which is slightly higher than the general population. Nearly all people with celiac disease carry one of these two alleles. 28% of volunteers were positive for anti-gliadin IgA, which is higher than the general population.
** Some people feel they are reacting to the fructans in wheat, rather than the gluten. If a modest amount of onion causes the same symptoms as eating wheat, then that may be true. If not, then it's probably the gluten.
*** I'm usually about 95% gluten-free anyway. But when I want a real beer, I want one brewed with barley. And when I want Thai food or sushi, I don't worry about a little bit of wheat in the soy sauce. If a friend makes me food with gluten in it, I'll eat it and enjoy it. This month I'm 100% gluten-free though, because I can't in good conscience encourage my blog readership to try it if I'm not doing it myself. At the end of the month, I'm going to do a blinded gluten challenge (with a gluten-free control challenge) to see once and for all if I react to it. Stay tuned for more on that.
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