28 posts · 11,454 views
Chronic health is achievable for almost everyone. It's the opposite of the epidemic of chronic diseases which plagues us today. This blog is all about how we can turn the tide into an epidemic of chronic health. With the tools and the knowledge of health sciences.
Lutz Kraushaar
28 posts
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- September 3, 2012
- 12:30 AM
- 338 views
Why We Are Slaves Of Food Obsession.
by Lutz Kraushaar in Chronic Health
A 95 years old psychology article holds the key to solving the obesity epidemic. It's not about a long forgotten medicine or an ancient psycho-trick. It's a simple observation about the dynamics of feeding. Vindicated by neurohormonal research, here is what it means to your struggle with extra pounds.... Read more »
Craig W. (1917) Appetites and Aversions as Constituents of Instincts. Proceedings of the National Academy of Sciences of the United States of America, 3(12), 685-8. PMID: 16586767
Seeley RJ, Payne CJ, & Woods SC. (1995) Neuropeptide Y fails to increase intraoral intake in rats. The American journal of physiology, 268(2 Pt 2). PMID: 7864237
Ammar AA, Sederholm F, Saito TR, Scheurink AJ, Johnson AE, & Södersten P. (2000) NPY-leptin: opposing effects on appetitive and consummatory ingestive behavior and sexual behavior. American journal of physiology. Regulatory, integrative and comparative physiology, 278(6). PMID: 10848532
- August 20, 2012
- 01:30 AM
- 301 views
The Truth About The Genetics Of Obesity.
by Lutz Kraushaar in Chronic Health
Evolutionary selection favored those who became fat easily. That's the essence of the "thrifty gene hypothesis". It's like Madonna. On the wrong side of 50, and ripe to be dethroned by something with greater sex appeal. In this case the contender's name is the "drifty gene hypothesis". Here is why you shouldn't be too dazzled about it.... Read more »
NEEL JV. (1962) Diabetes mellitus: a "thrifty" genotype rendered detrimental by "progress"?. American journal of human genetics, 353-62. PMID: 13937884
Speakman JR, & Westerterp KR. (2012) A mathematical model of weight loss under total starvation and implications of the genetic architecture of the modern obesity epidemic for the thrifty-gene hypothesis. Disease models . PMID: 22864023
Segal NL, & Allison DB. (2002) Twins and virtual twins: bases of relative body weight revisited. International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity, 26(4), 437-41. PMID: 12075568
Speakman JR. (2006) Thrifty genes for obesity and the metabolic syndrome--time to call off the search?. Diabetes , 3(1), 7-11. PMID: 16784175
Xue Y, Wang Q, Long Q, Ng BL, Swerdlow H, Burton J, Skuce C, Taylor R, Abdellah Z, Zhao Y.... (2009) Human Y chromosome base-substitution mutation rate measured by direct sequencing in a deep-rooting pedigree. Current biology : CB, 19(17), 1453-7. PMID: 19716302
- August 6, 2012
- 01:30 AM
- 358 views
What Infants Teach Us About Preventing Obesity.
by Lutz Kraushaar in Chronic Health
Public health has been telling you for years: you are fat because you move too little and eat too much. And yes, it's your fault if you don't break a sweat every day to keep our waist line in check. But research says, that's not the entire truth. In fact, public health might have taken the easy way out, and here is how it could finally make amends...... Read more »
Eriksson B, Henriksson H, Löf M, Hannestad U, & Forsum E. (2012) Body-composition development during early childhood and energy expenditure in response to physical activity in 1.5-y-old children. The American journal of clinical nutrition. PMID: 22836033
Levine JA, Eberhardt NL, & Jensen MD. (1999) Role of nonexercise activity thermogenesis in resistance to fat gain in humans. Science (New York, N.Y.), 283(5399), 212-4. PMID: 9880251
- July 23, 2012
- 01:30 AM
- 432 views
Why Medicine Might Be Wrong About Salt, Fat & BMI
by Lutz Kraushaar in Chronic Health
Salt and fat kill you early, and your BMI tells you how early. That has been the wisdom for years, but wisdoms have an expiry date, too. Particularly medical wisdoms. Recent research says those three are probably well beyond their use-by date.... Read more »
Routh CH. (1849) On the Causes of the Endemic Puerperal Fever of Vienna. Medico-chirurgical transactions, 27-40. PMID: 20895917
Alderman MH, & Cohen HW. (2012) Dietary sodium intake and cardiovascular mortality: controversy resolved?. American journal of hypertension, 25(7), 727-34. PMID: 22627176
Khaw KT, Friesen MD, Riboli E, Luben R, & Wareham N. (2012) Plasma Phospholipid Fatty Acid Concentration and Incident Coronary Heart Disease in Men and Women: The EPIC-Norfolk Prospective Study. PLoS medicine, 9(7). PMID: 22802735
Siri-Tarino PW, Sun Q, Hu FB, & Krauss RM. (2010) Meta-analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease. The American journal of clinical nutrition, 91(3), 535-46. PMID: 20071648
- July 16, 2012
- 01:30 AM
- 432 views
How The Media Monkeys Get You Panicked About Sitting Too Long!
by Lutz Kraushaar in Chronic Health
From "man is made to move" to "man is not made to sit" is a very recent transition of scientific insight. Let's get our readers panicked over more than not doing exercise, is the response of the media. Here is why you should sit down and get the facts straight before jumping up in fear.... Read more »
Katzmarzyk PT, & Lee IM. (2012) Sedentary behaviour and life expectancy in the USA: a cause-deleted life table analysis. BMJ open, 2(4). PMID: 22777603
Katzmarzyk PT, Church TS, Craig CL, & Bouchard C. (2009) Sitting time and mortality from all causes, cardiovascular disease, and cancer. Medicine and science in sports and exercise, 41(5), 998-1005. PMID: 19346988
Patel AV, Bernstein L, Deka A, Feigelson HS, Campbell PT, Gapstur SM, Colditz GA, & Thun MJ. (2010) Leisure time spent sitting in relation to total mortality in a prospective cohort of US adults. American journal of epidemiology, 172(4), 419-29. PMID: 20650954
Brown WJ, Trost SG, Bauman A, Mummery K, & Owen N. (2004) Test-retest reliability of four physical activity measures used in population surveys. Journal of science and medicine in sport / Sports Medicine Australia, 7(2), 205-15. PMID: 15362316
- July 9, 2012
- 12:30 AM
- 419 views
Supplements: Nutrition Science Or Nutrition Crap?
by Lutz Kraushaar in Chronic Health
Nutritionists claim they are doing science, consumers buy it, and the supplements industry makes a healthy living from it. Only you probably won't. Here is why: ... Read more »
Peto R, Doll R, Buckley JD, & Sporn MB. (1981) Can dietary beta-carotene materially reduce human cancer rates?. Nature, 290(5803), 201-8. PMID: 7010181
The Alpha-Tocopherol Beta Carotene Cancer Prevention Study Group. (1994) The effect of vitamin E and beta carotene on the incidence of lung cancer and other cancers in male smokers. The Alpha-Tocopherol, Beta Carotene Cancer Prevention Study Group. The New England journal of medicine, 330(15), 1029-35. PMID: 8127329
Goodman GE, Thornquist MD, Balmes J, Cullen MR, Meyskens FL Jr, Omenn GS, Valanis B, & Williams JH Jr. (2004) The Beta-Carotene and Retinol Efficacy Trial: incidence of lung cancer and cardiovascular disease mortality during 6-year follow-up after stopping beta-carotene and retinol supplements. Journal of the National Cancer Institute, 96(23), 1743-50. PMID: 15572756
Gonzalez CA, & Riboli E. (2010) Diet and cancer prevention: Contributions from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. European journal of cancer (Oxford, England : 1990), 46(14), 2555-62. PMID: 20843485
Li K, Kaaks R, Linseisen J, & Rohrmann S. (2012) Associations of dietary calcium intake and calcium supplementation with myocardial infarction and stroke risk and overall cardiovascular mortality in the Heidelberg cohort of the European Prospective Investigation into Cancer and Nutrition study (EPIC-Hei. Heart (British Cardiac Society), 98(12), 920-5. PMID: 22626900
Galan P, Kesse-Guyot E, Czernichow S, Briancon S, Blacher J, Hercberg S, & SU.FOL.OM3 Collaborative Group. (2010) Effects of B vitamins and omega 3 fatty acids on cardiovascular diseases: a randomised placebo controlled trial. BMJ (Clinical research ed.). PMID: 21115589
- June 28, 2012
- 12:30 AM
- 523 views
Will The Polypill Prevent Your Heart Attack?
by Lutz Kraushaar in Chronic Health
Giving the polypill to everybody above the age of 55 kills two birds with one stone: cardiovascular risk and preventive medicine. That's what the proponents of the polypill say. The medical establishment is in uproar. Here is why you should be, too. But for a different reason. ... Read more »
Wald NJ, Simmonds M, & Morris JK. (2011) Screening for future cardiovascular disease using age alone compared with multiple risk factors and age. PloS one, 6(5). PMID: 21573224
- June 25, 2012
- 12:30 AM
- 560 views
Why Risk Screening For Heart Disease Is As Good As Crystal Ball Gazing
by Lutz Kraushaar in Chronic Health
If weather forecasts were as reliable as cardiovascular risk prediction tools, meteorologists would miss two thirds of all hurricanes, expect rain for 8 out of 10 sunny days, and fail to see the parallels to fortune telling. ... Read more »
Collins GS, & Altman DG. (2012) Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ (Clinical research ed.). PMID: 22723603
- June 21, 2012
- 01:00 AM
- 558 views
Are You A Unique Medical Case?
by Lutz Kraushaar in Chronic Health
Research says yes, public health doesn't listen, and you suffer the consequences: too little benefits from generic interventions. And it could be so simple. ... Read more »
King NA, Hopkins M, Caudwell P, Stubbs RJ, & Blundell JE. (2008) Individual variability following 12 weeks of supervised exercise: identification and characterization of compensation for exercise-induced weight loss. International journal of obesity (2005), 32(1), 177-84. PMID: 17848941
Yang, Q., Cogswell, M. E., Flanders, W. D., Hong, Y., Zhang, Z., Loustalot, F., Gillespie, C., Merritt, R., & Hu, F. B. (2012) Trends in Cardiovascular Health Metrics and Associations With All-Cause and CVD Mortality Among US Adults. JAMA : the journal of the American Medical Association. DOI: 10.1001/jama.2012.339
Dong C, Rundek T, Wright CB, Anwar Z, Elkind MS, & Sacco RL. (2012) Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across whites, blacks, and hispanics: the northern Manhattan study. Circulation, 125(24), 2975-84. PMID: 22619283
- June 18, 2012
- 01:32 AM
- 379 views
10 Good Reasons Not To Exercise?
by Lutz Kraushaar in Chronic Health
Exercise may actually be bad for you! A professor says he stumbled upon this "potentially explosive" insight. The New York Times has been quick to peddle it. And couch potatoes descend on it like vultures on road kill. But professors can get it wrong, too.
Before we judge the verity of the "exercise may be bad" claim, let's first look at how the media present it to us. We shall use the recent article in The New York Times, headlined "For Some, Exercise May Increase Heart Risk". The first paragraph confronts us with a journalist's preferred procedure for feeding us contentious scientific claims: presenting an authoritative author with stellar academic credentials and a publication list longer than your arm. While that is certainly better than having, say, Paris Hilton as the source of scientific insights, it is a far cry from actually investigating such claims. Which is what we want to do now.
The basis of the exercise-may-be-bad claim is a study which investigated the question "whether there are people who experience adverse changes in cardiovascular risk factors" in response to exercise [1]. The chosen risk factors in question were some of the usual suspects: systolic blood pressure, HDL-cholesterol, triglycerides and insulin. The research question: Are there people whose risk factors actually get worse when they change from sedentary to more active lifestyles?
Sounds simple enough to investigate. Put a group of couch potatoes on a work-out program for a couple of weeks and see how their risk factors change. Only it is not that simple. In the realm of biomedicine, every measurement of every biomarker is subject to (a) errors in measurement and (b) other sources of variability. This makes it virtually impossible for you to see exactly the same results on your lab report for, say, blood pressure, cholesterol, glucose or any other parameter, when you get them measured two or more days in a row. Even if you were to eat exactly the same food every day and to perform exactly the same activities.
Now imagine, if you conducted an intervention study on your couch-potato subjects and you found their risk factors changed after a couple of weeks of doing exercise, you could theoretically be seeing nothing else but a random variation caused by the error inherent in such measurement.
To avoid falsely interpreting such a variation as a change into one or the other direction, it makes good sense to know the bandwidth of these errors for each biomarker, before you embark on interpreting the results of your study. Which is what the authors of this particular study did. They took 60 people and measured their risk factors three times over three weeks. From these measurements they were able to calculate the margin of error. Actually, they didn't do this for this particular paper, they had done this measurement as an ancillary study in the HERITAGE study performed earlier. The HERITAGE study had investigated the effects of a 20-weeks endurance training program on various risk factors in previously sedentary adults. Whether heritability plays a role in this response was a key question. That's why this study recruited entire families, that is, parents up to the age of 65, together with their adult children.
I mention this because the paper, which we are deciphering now, is a re-hash of the HERITAGE study's results, to which the authors added the data of another 5 exercise studies. That's what is called a meta-analysis. In this case it covers more than 1600 people, with the HERITAGE study delivering almost half of them.
Fast forward to answering the question of how many of those participants had experienced a worsening of at least 1 risk factor. Close to 10%. That is, about 10% of the participants had an adverse change of a risk factor in excess of the margin of error, which I mentioned earlier. I'm going to demonstrate the results, using systolic blood pressure and the Heritage study as the example. I do this exemplification for three reasons: First, blood pressure is the more serious of the investigated risk factors. Secondly, the HERITAGE study delivers most of the participants, and thirdly, the effects seen and discussed with respect to blood pressure and HERITAGE apply similarly to the other 5 studies and risk factors.
But before we go there I need to familiarize you with a basic concept of statistics. It is called the "normal distribution of data". It is an amazing observation of how data are distributed when you take many measurements. Let's take blood pressure as an example.
If you were to measure the blood pressure values for every individual living in your village, city or country, you could easily calculate the average blood pressure for this group of people. You could put all those data into a chart such as the one in figure 1.
Figure 1
On the x-axis, the horizontal axis, you write down the blood pressure values, and on the y-axis (the vertical axis) you write down the number of observations, that is, how often a particular blood pressure reading has been observed. You will find that most people have a blood pressure value pretty close to the average. Fewer people will have values, which lie further away from this average, and very few people will have extreme deviations from the average.
It so turns out, that when you map almost any naturally occurring value, be it blood pressure, IQ or the number of hangovers over the past 12 months, the curve, which you get from connecting all the data points in your graph, will look very similar in shape. Some curves are a bit flatter and broader, while others are a bit steeper and narrower. But the underlying shape is called the "normal distribution", and it means just that: It's how data are normally distributed over a range of possible values. The curve's shape being reminiscent of a bell, has lead to this curve being called the "bell curve".
In statistics, especially when we use them to interpret study data, we always go through quite some effort to ensure that the data we measure are normally distributed. That's because many statistic tools don't give us reliable answers if the distribution is not normal.
... Read more »
Bouchard C, Blair SN, Church TS, Earnest CP, Hagberg JM, Häkkinen K, Jenkins NT, Karavirta L, Kraus WE, Leon AS.... (2012) Adverse metabolic response to regular exercise: is it a rare or common occurrence?. PloS one, 7(5). PMID: 22666405
Wilmore, J. H., Stanforth, P. R., Gagnon, J., Rice, T., Mandel, S., Leon, A. S., Rao, D. C., Skinner, J. S., & Bouchard, C. (2001) Heart rate and blood pressure changes with endurance training: the HERITAGE family study. Medicine and Science in Sports and Exercise. DOI: 10.1097/00005768-200101000-00017
Bouchard, C., & Rankinen, T. (2001) Individual differences in response to regular physical activity. Med Sci Sports Exerc. DOI: 10.1097/00005768-200106001-00013
- June 14, 2012
- 02:35 AM
- 420 views
Why You Should Arm Your Bullshit Alarm Before Reading Diet News.
by Lutz Kraushaar in Chronic Health
In the fight over best diet for health and weight loss, it's protein lovers vs. vegetarian zealots. So far, a clear winner has not emerged. Only one loser: you, the victim of biased research. Here is an example of why you should keep your bullshit alarm on high alert when reading about weight loss diets.
[tweet this].
Ellen M. Evans and colleagues wanted to know whether overweight men and women differ in their body composition responses to different weight loss diets [1]. So they enrolled 58 men and 72 women with a BMI greater than 26, and randomized them into two diet groups. One group was instructed to follow a high-protein low-carbohydrate diet, which delivered 1.6 g of protein per kg bodyweight per day. The high-carb group received only half that amount of protein, and both groups' fat intake was capped at 30% of total energy intake. Both diets contained the same amount of fiber. Women received a daily total of 1700 calories, men 1900 calories. The intervention lasted for 4 months, followed by an 8-months weight maintenance period. Fast forward to the 12-months results:
Both diet groups and both genders lost about 10% of their body weight. But expressing weight loss in kilos of body weight can be a deceptive thing. Ideally we want that loss to be fat loss rather than loss of lean mass, that is, muscle mass. In the study at hand, for men on the high-carb diet, a little over one third of their weight loss came from lean body mass. Meaning, of the 14 kilos, which they lost on average, 5 Kilos came from a reduction in muscle tissue. The high-protein guys maintained their muscle mass to a greater extent: only 20% of their weight loss came from wasted muscle. For the women the picture looked almost identical: muscle mass contributed 37% to the weight loss of the high-carb women, compared to 23% in the high-protein group.
You would be forgiven if you now agreed with the authors' statement that the high-protein diet "...was more effective in reducing percent body fat...". Or in other words, a high-protein diet is superior to a high-carb alternative, as losing lean mass isn't a good thing in weight loss. I'll get to that point shortly in a little more detail.
Before we go there, let me state, that, being a firm supporter of the high-protein low-carb dietary philosophy, I loved to read this study. But I'm an equally firm supporter of proper scientific methods. And they have been prostituted in this case, which is why I love this study a lot less than its results.
Here is why: When I read the tables in which the authors present the results, I was impressed by the fact that both groups not only managed to rescue the 4-months weight loss to the 12-months finish line, but even increased this weight loss a little. When you have read literally hundreds of studies on weight loss interventions, as I have done, you'll find this observation to be in stark contrast to what we typically see: a reversal of weight loss. That is, at least a partial post-intervention regain of the weight lost during the dietary period.
We find the explanation for this miraculous exception in the number of participants. Or rather in the number of disappearing participants. Of the 66 participants who started in the high-carb group, only 30 made it to the finish line 12 months later. That's a drop-out rate of more than 50%! And of the 64 participants in the high-protein group 23, or 36%, had dropped out by month 12.
High drop-out rates are nothing unusual in weight loss trials, but it is good practice for researchers to tell their readers, how they accounted for these drop outs in the statistics, with which they interpret the data. Nothing of that in this paper. So, we don't know whether the drop-outs simply did not show up for their measurements, or whether the researchers did not consider the data of those participants, who failed to achieve some arbitrary weight loss threshold. The latter is an absolute no-no. It enables researchers to skew the results every which way they want. And the former is reason to investigate whether the drop-outs differed in some way significantly from the adherent participants. Such differences often affect the interpretation of the results.
One interpretation emerges right away, when checking the differences of relative fat loss while considering the drop-out rates: the smaller relative loss of muscle mass in the high-protein diet is not significantly different from the loss observed in the high-carb group. That does not mean, there is no difference between these two diet types. It only means, the study was underpowered to detect such difference, if there was any. And if it was underpowered to detect the difference between diet groups, it was certainly underpowered to differentiate between men and women in this respect.
If you still want the final verdict on high-carb vs. high-protein, I'm afraid I can't give it to you, even though I'm heavily leaning in favor of the high-protein version. I base my judgment on a 2009 systematic review of all randomized controlled trials, which were performed between 2000 and 2007, and which had pitted high-carb vs. high-protein strategies [2]. This review demonstrated that high-protein diets are more effective with respect to weight loss and probably with respect to cardiovascular risk factors than high-carb diets. At least over observation periods of 6 to 12 months.
Only long-term observations, comparing hard endpoints, can decide which diet may be better. Those studies are a long way off. To complicate matters, we might find that different people react differently to the same type of dietary strategy. Until we know better, we need to go with what we know:
The preservation of lean body mass certainly is a key aspect. Muscle tissue is an important endocrine organ, which, when exercised, produces potent anti-inflammatory substrates and hormones. These are the key elements of physical activity's protection against the initiating step of heart disease: atherosclerosis. Muscle tissue is also the body's primary site to store dietary carbohydrate in the form of glucose. The other site being the liver. With a high-carb diet, these storage sites are easily overwhelmed, which leads to conversion of carbs to fat. When, ironically, a high-carb diet nibbles away at the body's carb storage sites, you can imagine what this means to the body's relative fat content. Another aspect is that muscle tissue consumes energy, even at rest. The loss of this "burner" during weight loss makes weight rebound more likely.
So, if all these matters are known and understood, why perform a study, which is underpowered and fraud with questionable interpretations? Why produce the food equivalent of a scientology propaganda piece?
Beats me. Maybe because part of the study's funding ca... Read more »
Evans, Ellen, Mojtahedi, Mina, Thorpe, Matthew, Valentine, Rudy, Kris-Etherton, Penny, & Layman, Donald. (2012) Effects of protein intake and gender on body composition changes: a randomized clinical weight loss trial. Nutrition and Metabolism. info:/doi:10.1186/1743-7075-9-55
Hession, M., Rolland, C., Kulkarni, U., Wise, A., & Broom, J. (2009) Systematic review of randomized controlled trials of low-carbohydrate vs. low-fat/low-calorie diets in the management of obesity and its comorbidities. Obesity Reviews, 10(1), 36-50. DOI: 10.1111/j.1467-789X.2008.00518.x
- June 11, 2012
- 01:50 AM
- 512 views
Can Chocolate Save You From Heart Attack?
by Lutz Kraushaar in Chronic Health
The media says yes. Science says maybe. In the end, you decide. Here are the facts:
A truffle treatment for heart disease is imminent. That's what a recent article suggests, headlined in the New York Daily News as: "Dark chocolate cuts heart deaths; Study shows benefits for high risk cardiac patients."
The funny thing is, the cited study does not show what the media geniuses claim it does. So, let's look at this master piece of research journalism and do a little fact check.
[tweet this].
The cited study was performed by Ella Zomer and colleagues in Australia [1]. The researchers wanted to answer the question, what the daily consumption of dark chocolate would do to the heart health of a given population. Contrary to what you might believe, the researchers didn't pit chocolate eaters against abstainers. They simply ran an algorithmic model on the computer. In this case, a 10-year forward projection of what might happen, heart-wise, in a given population. Nothing wrong with that, as long as we keep in mind that such models are based purely on assumptions. You need to know those assumptions before you start investing a part of your daily food budget into chocolate. So, let's take a more detailed look than the anonymous AFP writer did, whose master piece the NYDN bought to educate their readers.
The researchers selected the data sets of 2013 AusDiab study participants who were free from cardiovascular disease and diabetes, but who had the metabolic syndrome. The latter is not a disease in itself but an arbitrary risk definition along 5 risk factors: abdominal obesity, elevated triglycerides, blood sugar and blood pressure, and low "good" cholesterol (high-density lipoprotein, HDL). Have any three of those 5, and you are said to have the metabolic syndrome.
To calculate the risk of suffering a heart attack or stroke, the researchers used the algorithms developed from the Framingham study. Those risk calculations are widely used in clinical practice. They inform your doctor about the need and urgency of treating you to prevent a heart attack or stroke. I have written about the sense and nonsense of such risk factors in my earlier post "When risk factors for heart attack really suck!". Now, in order to calculate what the blissful consumption of chocolate will do to prevent such heart attacks, we need some more data. The researchers took those from 13 studies, which investigated the effects of chocolate consumption on blood pressure and on cholesterol levels.
Now here is the first problem: While the Framingham study's algorithms have been tuned on the correlation of risk factors with with hard outcomes (real heart attacks and strokes) for more than half a century, the longest clinical trial on the effects of chocolate lasted just 18 weeks. Meaning, for the effects of chocolate consumption, we don't have anything remotely equivalent to the Framingham data. And we will probably never have, because it is difficult to imagine a study in which we expose half the participants to a daily chocolate load for many years, while the other half doesn't get any, with us waiting and watching what happens in terms of heart disease. Which is to say, the 13 studies used by the researchers are the next best choice. It informs us about the effects of chocolate consumption on blood pressure and cholesterol. The researchers plugged those data into the mathematical model, together with the Framingham algorithms and the life tables available for their Australian population. The entire model is based on a so-called "Markov model", which is simply a probability-based simulation of processes over time.
Now that we are clear about the methods and assumptions, let's look at what you read in the article:
"Australian researchers have found that eating a block of chocolate daily over 10 years has 'significant' benefits for high risk cardiac patients and could prevent heart attacks and strokes." Well, didn't I just tell you that the participants' data sets had been selected such that only those who were not cardiac patients, were considered in the model? Yep, that's what it says in the methods section of the study. But methods are tedious to read and, admittedly, a bit difficult to understand sometimes, so we forgive our writer for this little slip-up. Also, none of the participants had eaten a chocolate bar daily for 10 years, so really nobody could "have found" what that would have done for heart risk. But let's not dwell on such trifles. On to the next paragraph:
"A study .... found that the consumption of ... chocolate ... was an effective measure to reduce risk." Whoa, that one we can't forgive. What we do have are 13 studies, which show that a daily chocolate consumption of about 100 grams (3.5 ounces) reduces systolic blood pressure in hypertensive people by 5 mmHg on average, and total cholesterol by 0.21 mmol/L (8mg/dL). What these studies do not show, is a reduction of risk for heart disease, that is, a reduction of real heart attacks and strokes.
Given the size of the improvements, I have serious doubts about those effects anyway. What I observe as blood pressure measurements in the daily clinical practice, a 5mmHg difference is within the error margin of many physicians' and nurses' measurement skills. And a 0.21 mmol/L difference in cholesterol is deep within the bandwidth of variations, which most people would see if they were to measure their cholesterol levels for a few days in a row. We have done that in our lab, and found the intra-individual variability to be way above those 0.21 mmol/L. In other words, if your cholesterol level is measured today, and tomorrow, and day after tomorrow, the values will vary by more than those 0.21 mmol/L, even if your blood was drawn at the same time of day, and always after an overnight fast.
On to the next paragraph: "Lead researcher Ella Zomer said the team found 70 fatal and 15 non-fatal cardiovascular events per 10,000 people could be prevented over 10 years if patients at risk of having a heart attack or stroke ate dark chocolate." Throwing out numbers always looks good, but what do these numbers mean for YOU? I operate under the assumption that you are not interested in the 85 events among the 10,000 people, but that your interest is with the ONE possible event in YOU, right?
OK, let's look at that. Of course, I don't have the data set of Zomer and colleagues, but we can make quite an educated calculation using the Framingham risk algorithm, the average risk profile of the participant and the researchers' statement of the effect size: 85 prevented events per 10,000 people. And here is what it means to you:
If you... Read more »
Zomer, E., Owen, A., Magliano, D., Liew, D., & Reid, C. (2012) The effectiveness and cost effectiveness of dark chocolate consumption as prevention therapy in people at high risk of cardiovascular disease: best case scenario analysis using a Markov model. BMJ, 344(may30 3). DOI: 10.1136/bmj.e3657
- June 7, 2012
- 01:47 AM
- 433 views
Can A Genetic Test Say Why You Are Fat?
by Lutz Kraushaar in Chronic Health
With the decoding of the human genome came the hope of getting a lever on the chronic diseases, which kill most of us today: heart disease, stroke, diabetes and many cancers. And since overweight and obesity are a common cause of those diseases, many obese people were, and still are, yearning for that exculpatory headline: "It's all in your genes!" Why and how this headline is unlikely to ever appear in any serious media, was a subject of my earlier post "It's not your genes, stupid!".
Now, a group of researchers have looked at the data of a 30-year investigation of health and behavior, which you might call the New Zealand equivalent of the famous U.S. Framingham study [1]. If you ever wondered whether it would make sense to get your children, or yourself, tested for your genetic risk of obesity, you will be surprised to learn what this study tells you. But one step at a time. Let's first have a look at this outstanding piece of research.
[tweet this].
The study population consists of all the 1037 babies born in Dunedin, New Zealand, between 1st April 1972 and 31st March 1973 at the Queen Mary Maternity Hospital. Comprehensive health assessments were done at ages 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32 and 38. These investigations will be extended into the future and into the next generation. This is a massive and admirable effort. With data having been collected about virtually all aspects of health and behavior, this project provides a rare opportunity to match those data with genetic information. While genetic profiling wasn't possible in the seventies, it is possible and feasible now. And since study participants' genetic make-up hasn't changed since the time of their conception, we can retrospectively look at the correlation of biomarkers and genes, in this case those that correlate with obesity. To understand this study let me familiarize you with some facts and terms first.
So-called genome-wide association studies (GWAS) have thrown up more than 30 individual single-nucleotide polymorphisms (SNP, pronounced 'snip'), that's geneticists' speak for a variation of a single building block (nucleotide) of a gene. The draw-back: Those SNPs individually correlate only very weakly with obesity. That is, while there is a statistical correlation with obesity, there are obese people who don't carry the SNP, and there are carriers of the SNP who are not obese. To complicate matters a little further, not all SNPs which show statistical correlations in one population, say the U.S., do so in another, say New Zealand. Which is why the Dunedin researchers developed a risk score from the 32 SNPs known from other studies. Of those 32 they could find 29 in their study cohort, and so they developed their score from those 29 SNPs. Participants were grouped according to their score into either high- or low-risk.
The next step was to look at how the participants' genetic risk score (GRS) correlated with BMI in each decade, starting from 15-18 years of age, followed by 21-26 years, and then from 32-38 years. In the second decade (ages 15-18), people with a high risk score had 2.4 times the risk of being obese than those who scored low on the GRS. had this been you, having a high risk score would have made you almost two and a half times more likely to be obese as a teenager compared to your buddies of the low-risk persuasion. That sounds like a lot, and you might be tempted to think that screening your child for that risk score would help you to be more vigilant in watching over his or her BMI while he or she is still under your care.
The authors certainly seem to think so when they say that "These findings have implications for clinical practice..." and that "the results suggest promise for using genetic information in obesity risk assessments." I respectfully disagree, and so might you.
Let's simply take your point of view for a moment, and not the one of public health, where we are interested in one patient only, the population under our care. In contrast, the only patient you are interested in is you, or maybe your child. This value of a relative risk of 2.4 doesn't tell you much. What you rather want to know is, what a high- or low-risk score means to you. And the right question to ask would be along the line of "what are the chances of becoming obese when my risk score is high?". And also, "what are my chances of not becoming obese when my risk score is low?". The answers to these 2 questions come in the shape of values, which we call positive predictive value (PPV) and negative predictive value (NPV). Unfortunately the Dunedin researchers don't report those values. But we can calculate them, which I did.
And here is the surprising answer: if you had a high score, your risk of being obese as an adolescent is just about 10%. In other words, even with a high-risk score, you stand a 90% chance of not being obese as an adolescent. And if your risk score had been low you would have a 95% chance of not becoming obese. Beats me, but I can't see the benefit of genetic testing.
I deliberately talk only about the risk at the age of adolescence. There is a simple reason for this. The researchers found that the relative risk of obesity between the high- and low-risk categories diminished progressively from 2.4 in the second decade to 1.6 in the fourth (ages 32-38). That means, our looking at adolescents affords us a look at a time when study participants' exposure to environmental and behavioral influences had been relatively short. Over the years, environment and behavior further diminish the predictive power of the genetic score. Which is akin to saying: your lifestyle choices give you a greater power over your BMI than your genes. And by extension, the choices you make for your children's lifestyle beats their genes easily, too. In other words, it's not so much the luck of the draw, which determines your bodyweight, but rather your skill of playing the deck of (genetic) cards, which we have been dealt at the moment of conception. The study's data say the same thing just in other words: At birth the high-risk babies were not any heavier than their low-risk peers. Only once they were exposed to the outside worlds, did BMI careers begin to divert. For some of them.
This tells us one thing: when it comes to obesity, habits and environment are the key, not a potpourri of SNPs. Of course, if you are in the business of peddling genetic tests, you will disagree. And also when selling guilt-free conscience to obese readers is what pays your bills. Which is why I'm curious to see how the media will portray this study. Let's stay tuned.
[tweet this].
1. Belsky, D.W., et al., Polygenic Risk, Rapid Childhood Growth, and the Development of ObesityEvidence From a 4-Decade Longitudinal StudyPolygenic Risk for Adult Obesity. Archives of Pediatrics and Adolescent Medicine, 2012. 166(6): p. 515-521.
Belsky DW, Moffitt TE, Houts R, Bennett GG, Biddle AK, Blumenthal JA, Evans JP, Harrington H, Sugden K, Williams B, Poulton R, & Caspi A (2012). Polygenic Risk, Rapid Childhood Growth, and the Development of Obesity: Evidence From a 4-Decade Longitudinal StudyPolygenic Risk for Adult Obesity. Archives of pediatrics & adolescent medicine, 166 (6), 515-21 PMID: 22665028
... Read more »
Belsky DW, Moffitt TE, Houts R, Bennett GG, Biddle AK, Blumenthal JA, Evans JP, Harrington H, Sugden K, Williams B.... (2012) Polygenic Risk, Rapid Childhood Growth, and the Development of Obesity: Evidence From a 4-Decade Longitudinal StudyPolygenic Risk for Adult Obesity. Archives of pediatrics , 166(6), 515-21. PMID: 22665028
- June 4, 2012
- 01:52 AM
- 498 views
No Time To Exercise? You Are Not Alone!
by Lutz Kraushaar in Chronic Health
Lack of time is the most often cited excuse for not exercising. I deliberately chose the word "excuse" over its less judgmental alternative "obstacle". Simply because I cannot see an "obstacle" when I compare two simple metrics: the hours people spend watching TV and the minutes needed to maintain one's health with exercise. With high intensity interval training, or HIT, health enhancing exercise can be compressed into an amazingly short amount of time. When done right.
According to the Nielsen "Three Screen Report" Americans spend 5.1 hours daily in front of their TV. But they admit to "only" half that time, according to a survey of the Bureau of Labor Statistics. To be fair, I take the survey's figure of 2.7 hours for the comparison with the American College of Sports Medicine (ACSM) current guidelines for quantity and quality of exercise [1]. The ACSM's recommendations of 2.5 hours exercise PER WEEK vs. 2.7 hours in front of the TV PER DAY. Cut your 162 minutes of daily TV watching by just 21 minutes, and it still leaves you with more than 2 hours for mind numbing soaps.
Now, if you are my fellow German reader don't think for one minute that our TV habits are in any way better than those of our U.S. friends. According to statista's "Daten & Fakten zur Mediennutzung" we spend on average 220 minutes in front of the dumb tube. So, either we have, for once, outdone our U.S. friends, or their self-admitted 2.7 hours are an understatement. Anyway, those figures tell you why I talk about excuses and not obstacles.
But I'm a realist. Whatever my view on the issue of having time, it won't change other people's views. Which is why my colleagues in public health have begun to look into ways of how to get the same health punch out of dramatically shorter exercise routines. And, as I mentioned in my previous post, the solution might have been found. It is called high intensity interval training, or HIT.
HIT is an exercise routine, which consists of brief bouts of vigorous activity, alternating with "active recovery" periods of more moderate intensity. Until very recently, researchers focused on the comparison of HIT with the conventional continuous endurance exercise of moderate-to-vigorous intensity, which is what those public health guidelines are all about. Most studies comparing those two exercise alternatives matched them for energy expenditure. Since energy expenditure is higher during the intense bout the overall time needed to expand the same amount of energy is shorter in HIT than in continuous exercise.
Latest research efforts, however, try to answer the question whether those high-intensity bouts might even compensate for an overall lesser energy volume. In other words, could we reduce not only the time spent on exercise but also the total exercise volume simply by doing HIT? Which means, reducing the time required for doing exercise even further? The latest study, conducted by Katharine D. Currie and her colleagues seems to suggest just that [2]. Before I go into the details, let me explain why I find her line of investigation very appealing and important.
The overall purpose of exercise is to maintain functional health. The reason why exercise is key to human functional health is because humans are made to move. Only, today they don't move anymore. That's why my primary interest in exercise is about its link to health. Anything else, such as weight loss, is secondary. Because, if I can improve health by exercising, I have achieved my objective. Regardless of whether weight loss has materialized as a side effect or not. Weight loss for its own sake without any improvement in health is a purely cosmetic issue, which doesn't interest me that much.
One of the main health issues attached to exercise is arterial function. It's impairment is the first step that leads to atherosclerotic plaque build-up in your arteries and ultimately to heart attack or stroke. The entire process typically lasts decades, and our current portfolio of risk factors, such as high cholesterol, alert us way too late to this situation. I have written about this in my earlier post "When Risk Factors For Heart Attack Really Suck". Which is why I believe that arterial function is THE benchmark for testing the efficacy of exercise: It's an extremely sensitive early warning signal and a reliable tool to measure the effect of your exercise efforts. This is what Currie and colleagues had in mind. They wanted to see how a low-volume HIT routine affected the arterial function and fitness of 10 participants with existing heart disease.
Participants were tested individually for their fitness on a cycle ergometer. The researchers used the results of the fitness test to set the parameters for the two exercise routines, which all participants had to perform. The endurance protocol was set at 55% of each participant's peak power output as determined during the fitness test. In the endurance exercise bout, participants had to cycle at this intensity for 30 consecutive minutes.
The HIT protocol consisted of 10 1-minute bouts of exercise at 80% of peak power output, separated by 1-minute bouts at 10% of peak power output. That's 30 minutes of continuous exercise vs. 19 minutes of HIT, not considering warm-up and cool-down which were the same for both protocols.
Interestingly, while all participants completed the HIT protocol, 2 participants were unable to last through the endurance protocol. Arterial function improved after both exercise protocols similarly, despite the fact that the total work performed in the endurance protocol was significantly greater than in the HIT protocol.
Now, 10 participants is a rather small number of subjects for such a study. The problem with a small number is insufficient statistical power to detect a difference in arterial function between the two protocols, if there was a difference. Which is why we will be looking forward to seeing larger trials investigating this question using more participants.
The researchers also show one thing which is always close to my heart but which is rarely reported in study publications: the very different outcomes between individuals. After the endurance exercise one participant saw a dramatic improvement in arterial function, 4 participants had a more modest improvement, and the remaining 5 no improvement. Following the HIT routine, there were 2 participants with a dramatic improvement of arterial function, 2 with a more moderate improvement, 1 whose arterial function actually got worse and the remaining 5 with no change. Unfortunately the researchers do not tell us whether those who improved or didn't improve in one routine showed corresponding effects in the other routine. My guess is, for at least some of the participants, the reaction will have been different. But even if that was not the case, we can see again, that the presentation of group results masks the fact that different people react very differently to the same type of intervention. I have presented an example of this effect in my earlier post "Am I shittin' you? Learn to be a skeptic".
A similar degree of inter-individual difference was seen in a study which used the same protocol of low-volume HIT, but this time on healthy sedentary adults. The question was whether 2 weeks of performing the HIT routine 3 times per week would improve the participants' ability to burn fat instead of carbohydrates. This so called oxidative capacity is a marker of metabolic health and gives you a clue about your diabetes risk. True enough, the results support the idea, that this minimal amount of exercise can substantially improve metabolic function. But again, the wide standard deviation of the group results points at substantial differences between the individuals [3].
These inter-individual differences make prescription of exercise always a trial-and-error effort. As much as you would like to hear from your coach or doctor that a specific type of exercise will have a specific effect on your health, n... Read more »
Garber, C., Blissmer, B., Deschenes, M., Franklin, B., Lamonte, M., Lee, I., Nieman, D., & Swain, D. (2011) Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults. Medicine , 43(7), 1334-1359. DOI: 10.1249/MSS.0b013e318213fefb
Currie KD, McKelvie RS, & Macdonald MJ. (2012) Flow-Mediated Dilation Is Acutely Improved following High-Intensity Interval Exercise. Medicine and science in sports and exercise. PMID: 22648341
Hood MS, Little JP, Tarnopolsky MA, Myslik F, & Gibala MJ. (2011) Low-volume interval training improves muscle oxidative capacity in sedentary adults. Medicine and science in sports and exercise, 43(10), 1849-56. PMID: 21448086
- May 31, 2012
- 01:56 AM
- 418 views
How to Live Longer And Exercise Shorter?
by Lutz Kraushaar in Chronic Health
Let's face it, if exercise was really that much fun, everybody would do it and we wouldn't be fat, diabetic or die of heart disease. So when your doctor tells you that you better start exercising, your immediate question might be: how much do I have to do? The answer is, it depends. It depends on whether you want to hear the polite version or the truth.
The polite version goes something like this: As long as you do some exercise, you will see some health benefits. When your doctor gives you this advice, he probably has studies in mind like the one performed by Hamer and colleagues [1]. They show us that as few as 1-2 exercise sessions per week protect against heart disease. I don't really buy it, and neither should you. Here is why:
The researchers took data from 23,747 people of the English and Scottish health surveys and grouped them into one of two groups, depending on the status of their metabolic health. The latter was defined along the risk markers of high blood pressure, low good cholesterol, diabetes status, high waist circumference and inflammatory status. People who had less than 2 of those risk factors made it into the metabolically healthy group, the rest into the unhealthy group.
Since these surveys had also asked people to self-report their physical activity levels, the researchers were able to investigate, how exercise volume correlates with health outcome. And, lo and behold, over the average follow-up period of 7 years those among the metabolically unhealthy people, who reported exercising just once or twice a week, had the same risk of developing heart disease as the metabolically healthy people. I'm not trying to discredit this study. It is a valid method to look at associations between exercise and health. But we have to keep in mind that it only answers the question whether PA, at this low volume of 1-2 times per week, is associated with heart health. What the study doesn't tell us is, whether this association is of a causal nature. In other words, it really does not tell us whether low-volume PA "...is protective in men and women with clustered metabolic abnormalities" as the authors suggest.
If studies like the one of Hamer and colleagues are used to entice the couch potatoes to pick up exercise, even if it is only once or twice a week, then, by all means, that's a good start. In public health we love this type of message for a simple reason: We can throw it at the media in the hope of encouraging sedentary people to take up exercise. If the message is effective, there will be fewer heart attacks and early deaths. What we deliberately do not tell you, though, is how effective this exercise is for YOU. We have a number for that. It is called the 'number needed to expose' (NNE). It tells you how many couch potatoes need "to be exposed" to a change in exercise habits in order to prevent one single case of heart attack or death. In the case of Hamer's study that number stands at more than 40. Meaning, for every 40 people, which we convince, we can prevent 1 death from any cause. Good for us. But probably not good enough for you. If you take up our advice, the 1 in 40 simply means a 2.5% chance that this avoided death would have been yours. Not very motivating. Which is why you don't read so much about these numbers.
Now, if you were my client, I would ask you, whether you were interested in getting the best out of the limited time you are willing to invest in exercise. Which brings us to the second version of the answer, which I promised you in the beginning of this post: the truth.
Evidence is accumulating that the intensity at which you exercise is far more important for your health than the total volume of exercise. In an earlier post (Shortcut to Longevity) I introduced the results of the Copenhagen City Heart Study, which showed an association between heart disease mortality and the intensity, but not the volume, of habitual cycling. Of course, what applies to the Hamer study, applies to this study too. An association is not necessarily of a causative nature. But if we take it as an indication that exercise intensity is so important, isn't that bad news and bad news for the couch potato? Not only does he have to exercise, he also has to exercise hard? No, this is where the good news are: There is method of milking this high-intensity effect to the point where it saves you oodles of time.
It is called high intensity interval training, or HIT for short. This acronym should get you excited, because it super-charges the benefit:time ratio of exercising. In fact, if done correctly, you can expect to improve your fitness and endurance to the same extent as you would with traditional continuous endurance exercise while spending 90% less time on exercise [2]! But let's take it a step at a time.
What is HIT? As the name implies, HIT sessions consist of alternating intervals of vigorous and moderate intensity exercise. One-minute intervals of sprinting, interspersed with 3 minutes of jogging at a moderate pace, would be one of a virtually infinite number of variations of HIT. Do this for 16 or 20 minutes thrice a week and I promise you, within 2 weeks, you'll be excited about the noticeable progress you make. That's 60 minutes a week! Should be possible for the tightest of time budgets. After all, time is the most often cited obstacle to taking up exercise. Understandably, because there are only 24 hours in a day, of which statistically, every German spends 4 hours in front of the TV and every American 6 hours. Which really leaves us so little time to do something meaningful, aside from working and sleeping. If that comes across as sarcastic, I'm guilty as charged.
Anyway, I haven't answered the next logical question, whether HIT also translates into real health benefits. You bet it does. In fact those benefits are so profound, that even heart attack and heart failure patients are now being put on HIT routines. Wisloff and colleagues randomized 27 heart failure patients into 3 training groups [3]: a HIT group which walked three times a week four 4-minute intervals at close to maximal heart rate, with 3-minute intervals of walking at 50% to 70% of maximal heart rate between the high intensity intervals; a moderate-intensity exercise group which walked thrice weekly continuously for 47 minutes at 70% to 75% of maximal heart rate; and a control group which met every 3 weeks for a 47-minute walk. After 12 weeks, the control group showed no improvement in fitness, measured as maximal oxygen uptake. The moderate-intensity group had improved fitness by 14%, whereas the HIT group, which had spent 50% less time on exercise, had an improvement of 54%. Moreover HIT improved arterial function, cholesterol and heart function, significantly better than the continuous moderate-intensity exercise protocol.
In another study, diabetics were put on a HIT protocol consisting ten 60-s sprints interspersed with 60-s moderate-intensity cycling. After only 6 sessions, participants' glucose metabolism had improved substantially and so had their muscles' oxidative capacity [4]. Unfortunately, this study was not controlled, meaning there was no control group to compare the relative benefits of HIT vs. continuous moderate intensity exercise. Which shows, we are still in the early days of finding our ways to optimal protocols for different people with different health issues.
In my lab, we wanted to know whether the high benefit:time ratio of HIT, together with its quickly noticeable results, would entice couch potatoes to do more than a prescribed weekly minimum of three 20-minute hit sessions. After 6 months 76% of our 120 study participants had acquired the habit of exercising more than 150 minutes per week. When they started on our program they had all been sedentary and mostly overweight, but they were otherwise healthy. Over the 6 months they not only improved their fitness substantially but also reduced their weight and improved their risk factors for heart diseases and dia... Read more »
Hamer, M., & Stamatakis, E. (2012) Low-Dose Physical Activity Attenuates Cardiovascular Disease Mortality in Men and Women With Clustered Metabolic Risk Factors. Circulation: Cardiovascular Quality and Outcomes. DOI: 10.1161/CIRCOUTCOMES.112.965434
Kent, W. (2011) The effects of sprint interval training on aerobic fitness in untrained individuals: a systematic review. British Journal of Sports Medicine, 45(15). DOI: 10.1136/bjsports-2011-090606.26
Wisloff, U., Stoylen, A., Loennechen, J., Bruvold, M., Rognmo, O., Haram, P., Tjonna, A., Helgerud, J., Slordahl, S., Lee, S.... (2007) Superior Cardiovascular Effect of Aerobic Interval Training Versus Moderate Continuous Training in Heart Failure Patients: A Randomized Study. Circulation, 115(24), 3086-3094. DOI: 10.1161/CIRCULATIONAHA.106.675041
Little, J., Gillen, J., Percival, M., Safdar, A., Tarnopolsky, M., Punthakee, Z., Jung, M., & Gibala, M. (2011) Low-volume high-intensity interval training reduces hyperglycemia and increases muscle mitochondrial capacity in patients with type 2 diabetes. Journal of Applied Physiology, 111(6), 1554-1560. DOI: 10.1152/japplphysiol.00921.2011
- May 28, 2012
- 03:18 AM
- 342 views
3 Ways to Spot Their Lies About Healthy Recipes
by Lutz Kraushaar in Chronic Health
Briefly: If I had to name the one word, that is most often used to
label something as what it is not, my vote would go to "healthy". Whether it's the issue of sugar vs. honey, of butter vs. oil or of calories vs. nutrients, science and evidence are clearly not playing the lead role in the culinary theater of the world wide web. Judging by its popularity, that's a missed opportunity.
I recently gave a talk on the lies and deceptions the food
industry uses in labeling and marketing their products. A German
corporate health insurance had asked me to give that presentation to their
clients. Naturally, a large percentage of the audience were women. Judging from
the lively and entertaining discussion, which followed my presentation, almost
all women prefer home cooked food for their families to take-out or eat-out.
The most often cited reason was that home cooked food is the healthier choice. I'm
not convinced that they get it. Not if they get their food information from
where they professed to search for it: the internet.
I know this, because in preparation for my talk I followed
my wife on one of her culinary search trips through the web.
The number of recipe sites is staggering. So is the degree
of misinformation disseminated by them. Most of it in the form of labeling
something as healthy when it clearly isn't. Let's look at three commonly
encountered mis-perceptions on randomly chosen recipe sites. I won't give you
the links, because to single them out would be unfair. What I found there is so
ubiquitous, that you will encounter it virtually everywhere once you start surfing the
culinary side of the web.
Honey vs. Sugar
A self-proclaimed holistic health counselor shares her
recipe for a "Healthier Flourless Chocolate Cake". Which immediately
begs the question: healthier compared to what? The answer comes in
brackets directly behind the title, where it says "refined-sugar free".
Reducing sugar in our daily diet is certainly a big step towards better health.
But you won't get there by replacing sugar with honey. The difference between
sugar and honey is simple: Sugar is 100% sugar, honey is 80% sugar. Admitted,
that's a little oversimplified. Honey does have ingredients which sugar
doesn't. But these are not an issue when it comes to reducing calories or the
metabolic impact of sugar. Whether you sprinkle granulated sugar into the dough
or fold honey into it, what your metabolism has to deal with is their common
denominator, the breakdown molecule, which ends up in your blood - glucose. Of
the recipe's remaining 4 ingredients - butter, eggs, cocoa powder and baking chocolate -
the butter is evidence that our holistic health counselor has missed out on
another common diet mis-perception:
Butter vs. Oil
On another website we find the "Best Ever Healthier
Chocolate Brownies". Honey isn't an issue for this lady. Her claim to
healthiness is based on the conviction that other recipes use "... butter
rather than olive oil", and that "olive oil contains healthier
fats". This butter vs. oil issue is not as straight forward as the glucose
theme. So let's look at it in greater detail.
The fats for human nutrition come either from animal or
plant sources, and you can think of them in 3 major categories: saturated fats,
and mono- and poly-unsaturated fats. We don't need to go into the molecular
details of the fats - or fatty acids (FA), as they are more correctly called. Suffice
it to say, that the "unsaturated" part of the descriptor refers to
one (mono) or more (poly) carbon atoms of the fatty acid molecule having less
than the maximally possible number of hydrogen atoms linked to them. Depending
on the position of the first "unsaturated" atom in the chain of
carbon atoms, the poly-unsaturated fats are called omega-3 or omega-6
poly-unsaturated fatty acids (PUFA). There is one more thing you should be
aware of: the human body can manufacture most of the fatty acids which it needs
for its metabolism and maintenance. But there are two, which we need to supply
through our food intake. These two are alpha linolenic acid (ALA), an omega-3
FA, and linoleic acid (LA), an omega-6 FA. Our organism uses them to produce
other fatty acid variants which are essential for our health.
Armed with this knowledge we can now ask ourselves an
obvious question: What's the health issue with fats? You have probably heard
that a high fat diet promotes high levels of cholesterol in your blood (partly
true) and that high cholesterol is the cause of heart disease (not true). You
have also heard that saturated fats, such as butter, are bad for you and that
replacing it with olive oil is good for your health.
Now let's hear the facts as we know them today: Dietary
trials in which saturated fat, such as butter, was replaced by PUFA lead to a
reduction in risks for cardiovascular disease [1].
However, when those PUFAs were mainly of the omega-6 version, there was no
reduction, or even a slight increase in risk for heart disease. Looks like
replacing saturated fats with oils isn't going to do you any good if you don't
chose the oils for their content of omega-3 FAs.
These observations match nicely with what we know from
evolutionary biology. Comparing the fat intake between our hunter/gatherer
ancestors and us, we notice that the ratio of omega-6 to omega-3 fatty acids has
undergone a dramatic change. While that ratio stood at 1:1 or even lower
throughout most of human evolution, our modern western diet has upped that
ratio to a whopping 16:1 [2], and even greater than that,
depending on where you live. When I now tell you that the downstream products
of your omega-6 FA intake are pro-inflammatory whereas the products of ALA have the opposite effects, you might begin to see the picture. With heart
disease and stroke being the late-stage consequences of chronic inflammation of
the arteries, as I highlighted in my earlier post "Your Shortcut To Longevity", the type of fat appears
to have an effect on your arterial health. And therefore on your risk of heart
disease.
How large this effect really is, remains unclear. In a
recently updated review of randomized clinical trials the Cochrane
Collaboration came to the conclusion that reducing the content of saturated fat
in favor of unsaturated fats had some effect on cardiovascular disease events
in men only (not in women) and only if such dietary habit change lasted at
least 2 years [3]. There was no detectable effect
on the risk of dying from cardiovascular disease. Importantly, it was unclear
whether the reduction in disease events was due to poly- or mono-unsaturated
fatty acids.
The... Read more »
Kuipers RS, de Graaf DJ, Luxwolda MF, Muskiet MH, Dijck-Brouwer DA, & Muskiet FA. (2011) Saturated fat, carbohydrates and cardiovascular disease. The Netherlands journal of medicine, 69(9), 372-8. PMID: 21978979
Simopoulos, A. (2008) The Importance of the Omega-6/Omega-3 Fatty Acid Ratio in Cardiovascular Disease and Other Chronic Diseases. Experimental Biology and Medicine, 233(6), 674-688. DOI: 10.3181/0711-MR-311
Hooper L, Summerbell CD, Thompson R, Sills D, Roberts FG, Moore HJ, & Davey Smith G. (2012) Reduced or modified dietary fat for preventing cardiovascular disease. Cochrane database of systematic reviews (Online). PMID: 22592684
- May 24, 2012
- 01:58 AM
- 361 views
The Death Of Good Cholesterol
by Lutz Kraushaar in Chronic Health
Briefly
There were always two types of cholesterol, the good and the
bad. Until now. A large new study tells us that good cholesterol might have
been an impostor. That's food for the media types. For those who think before
they type, the real news is that we are finally getting closer to uncovering
the impostors. Thanks to the genetics revolution which seems to be paying off
in an unexpected area.
HDL - The Knight in Shining Armor
In the cholesterol universe there are two camps: good
cholesterol, also known as HDL, and bad cholesterol, often referred to as
non-HDL cholesterol. The latter comes in a variety of flavors, of which LDL is
the most prominent and best known. From many large observational studies we
know that high levels of LDL and low levels of HDL associate with an elevated
risk for heart disease and stroke. Certain limits have been derived from these
studies, above which your LDL shouldn't rise and below which your HDL shouldn't
fall. The magic level for HDL is 60 mg/dL blood. Above that limit, we are
assured, HDL will even offset some other risk factor, such as age or being of
the male persuasion. Given that a large percentage of people fail to achieve
these desirable levels, researchers have been eagerly sourcing for
pharmaceutical means to increase HDL. Now a new study tells us, that HDL might
have to be stripped off its White-Knight title, much for the same reason as
"Dr." Karl-Theodor zu Guttenberg, the former German defense minister,
had to be stripped off his doctorate last year: for being an impostor.
Epidemiology 101
If you have been following biomedical research for a couple
of years, you will have noticed that results are often conflicting. So, you
might discount the findings of one study if hundreds of others come to a
different conclusion. Only in this case you should pay closer attention,
because what Voight and colleagues have produced strikes at the foundation of
how we do research in epidemiology, the science which studies the health of
populations [1].
To appreciate the gravity of the
situation, I need to familiarize you with a basic concept of epidemiological
studies: Confounding. I'll use a very simple and hypothetical example.
Let's say we are interested to know the causes of health and
disease in children in the hypothetical and impoverished state of Maladipore.
The figure to the left represents our astonishing finding that children growing
up in a household which owns a TV are significantly less likely to die during
childhood than children growing up without the boob tube. The correlation
between TV ownership status and survival are very strong and compelling.
On the
face of it we could now recommend the prime minister to improve the health of
the nation by simply installing a TV in every household in which there are
children. If we know that this is nonsense, we take our epidemiology tools and
look for another factor which has an influence on TV ownership AND on survival
rate.
And so we discover that wealth is this third factor. We call it a
confounder. Wealth has confounded our original finding because the wealthy can
afford a TV and they can also afford medical care and immunization for their
children. Whereas the inability to buy a TV certainly reflects the inability to
buy medical care, too. When we repeat our analysis of the data, which we
gathered during our observational study, we find that the link between TV
ownership and survival disappears once we bring the third variable, wealth,
into the equation. Clearly, providing every household with a TV wouldn't have reduced
the rate of child deaths. Greater wealth however will.
In the case of Maladipore, common sense is all it takes to
suspect and find the confounder. In real life it is almost never as simple.
When we find an association between cholesterol and heart disease, then we
typically have some idea about the way cholesterol might contribute to heart
disease. At that stage our ideas are merely hypothetical. The classic way of
investigating them is through clinical trials in which we randomize
participants into 2 groups, one in which we lower (bad) cholesterol and another
in which we don't, the control group. Then we observe them for a period and
note the rate at which people in both groups develop heart disease or die from
it. If we find that the control group, the one which didn't receive the benefit
of having its cholesterol lowered, has a significantly higher rate of falling
ill, we conclude that lowering cholesterol is the way to go. Sounds easy, but
it isn't. For several reasons. In the case of cholesterol, the time between
developing high bad, or low good, cholesterol and suffering a heart attack or
stroke is measured in decades rather than in years. We also cannot just
experiment with people as we would like to in the name of science. Ethics
boards look very closely at the potential risks and benefits associated with
what we do in trials. We cannot simply withhold treatment from a control group,
with scientific curiosity as the motivation. With these obstacles, we had to draw our conclusions from
observational studies, which tell us a lot about associations but nothing about
cause and effect. Until now, we simply had no other choice. But not any more:
It's Mendel All Over Again
With larger and larger databases being
developed from genetic research we can now do something else: Mendelian randomization
studies. Which is what Voight and colleagues did. The concept behind it is
amazingly simple and elegant, though not as brand new as you might think. It
has been named after Gregor Mendel, the father of modern genetics, who first
observed and described how traits are inherited. As
always, a concept is best understood using an example. In the 1980s some
researchers thought that very low cholesterol levels might increase the risk of
cancer. There was definitely an association being observed between cancer and
low cholesterol, but nobody knew which was the cause and which the effect. Or
whether there was a third confounding variable, as yet unknown. Now, you can't
make a study in which you lower the cholesterol in some people, just to see
whether they will develop cancer. Go
and find volunteers for that one.
... Read more »
Voight, B., Peloso, G., Orho-Melander, M., Frikke-Schmidt, R., Barbalic, M., Jensen, M., Hindy, G., Hólm, H., Ding, E., Johnson, T.... (2012) Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. The Lancet. DOI: 10.1016/S0140-6736(12)60312-2
- May 21, 2012
- 04:12 AM
- 392 views
Individualized medicine, ignorant medics and an invitation to lose weight.
by Lutz Kraushaar in Chronic Health
Why individualized medicine will not be a reality anytime soon, how physicians often misinterpret published studies, and how individualized prevention is a clear and present benefit.
In my previous post I promised to talk about your
individualized way to achieving optimal health. If that made you think
about personalized medicine, you were right. Almost. Because personalized
medicine is still light-years away from us. That's the bad news. The good news,
personalized prevention is an emerging reality. At least in my lab. Which is
why I would like to invite you to become a part of it. No strings attached. But
before we get to this let's first get on the same page about the
personalization of medicine.
Two questions we need to ask ourselves: What is personalized
medicine and why would we want it?
Professor Jeremy K Nicholson of the Imperial College,
London, defined personalized medicine as "effective therapies that are
tailored to the exact biology or biological state of an individual" [1]. Such tailoring of a
treatment, say for your high blood pressure, would require your doctor to evaluate
your biochemical and metabolic profile in order to prescribe you the most
effective drug or treatment at the most effective dose, with the least possibility
of side effects.
Now, why would we want this?
Simply because we don't have it. Because our current drugs do not work optimally in most people [2]. But don't just take my word for it. Take that of Dr. Allen D. Roses, head of the Drug Discovery Institute at Duke University School of Medicine. In an interview he told a UK newspaper, The Independent, that more than 90% of
modern drugs work, at best, in 30-50% of the people. He said that in 2003. At the time,
Roses was also senior vice president for genetics research and pharmacogenetics
at GlaxoSmithKline.
Contrary to what you might think, Roses did not reveal any nasty industry secret. What he said is plainly visible for everyone who can read the results of clinical trials through the lens of statistics. I simply quote Roses for effect. After all, he knows what he is talking about. Contrary to
many medical doctors, who have an amusingly limited grasp of the basic
statistics used to interpret and present the results of clinical trials. Just how
limited, that has been recently demonstrated for the case of cancer screening in
a mock-up trial investigating the understanding of practicing physicians [3].
Before I tell you the results of this trial, let me make you
understand what it was about. One big question in cancer screening is whether
screening helps to reduce the number of people dying from cancer. Let's
take a hypothetical example, and here I reuse the one which the study's authors
used to explain statistical outcomes. Let's say, cancer was detected in a group
of people at age 67. All of them died of their cancer at age 70. The 5-year
survival rate from diagnosis would stand at 0% (they all died before 5 years were over). Now imagine that all those
cancer cases would have been detected at age 60 with a screening test. And also imagine that all of them still died at age 70. In this case the 5-year survival rate
would have been 100% (they were all still alive at 65). You see the issue: the survival rate was better with screening, but the rate of dying remained the same. In epidemiology we call
this sort of thing lead-time bias. That is, simply detecting a disease earlier
might lead to an improved survival rate which has, in fact, nothing to do with
improved survival. Such lead time bias is rarely an all-or-nothing thing as in
this hypothetical case. Most of the time it comes in degrees. But in any case,
it would help you as a patient, if your doctor was able to see through the
reporting, and to question the clinical relevance of the results so presented. Your
doctor should look for the mortality rate, the rate of dying, not the survival
rate.
Back to the results of the mock-up trial about physicians' interpretive skills of clinical research publications. If the results of this mock-up trial are representative of
the population of your doctors, then you should be worried. Of the over 200
practicing physicians enrolled in this trial, fully 76% would recommend you
this useless screening test. They considered an improved 5-year survival rate as prove
for the test's efficacy! These were not undereducated physicians of a third
world country, mind you. They were randomly selected from the Harris
Interactive Physician Panel, which is representative of the general U.S.
physician population.
OK, you may say that this was a test related to cancer
screening. What has it got to do with understanding the efficiency of a drug,
which your doctor prescribes you? Well, maybe your doctor aces the statistics
test on drug trials after he has flunked the one on cancer screening. If you believe that, you probably also believe in the tooth fairy and in Santa Claus. But you may have another question: Can trial results be
presented in such misleading ways? Aren't researchers supposed to report their
results honestly and correctly? And what use is the peer-review process which
every published paper has to go through?
With 70% of all medical research being financed by the
private sector, data are a commodity. So, whether you develop a screening test,
a drug or a treatment, you will want to dress it up as a magic bullet. Because
when you have the magic bullet for, say high blood pressure or high
cholesterol, it will make it into every physician's armory. That's where the
money is. It's certainly not in personalized medicine, which may find your
competitors' drugs as more suitable solutions for a variety of cases.
Which
brings us back to personalized medicine. I have told you in my previous post
how much it costs to develop a drug. Which is why Big Pharma would love to
concentrate its research on the areas where the probability of success is high
and the potential risk of failure is low. That's the area of follow-up drugs, drugs of the same class as established drugs, but with incremental
improvements over the older version. Ironically, our health care system discourages
this type of pharmacological research. Incrementally improved drugs are
typically reimbursed at the same rate as older drugs. Not much profit potential
there. Particularly when competition is fierce.
Which is why Big Pharma looks for new grounds, that is new
therapeutic classes, for which, of course, there need to exist a large market [4]. Again, individualization is
certainly not desirable, as it would fragment any market. There is another
draw-back: when you break new grounds, it takes a lot longer to get off that
ground with some new product. Which is what we see in the FDA's records of drug
approvals over the past 10-15 years [5]. Ten years... Read more »
Nicholson, J. (2006) Global systems biology, personalized medicine and molecular epidemiology. Molecular Systems Biology. DOI: 10.1038/msb4100095
Wegwarth O, Schwartz LM, Woloshin S, Gaissmaier W, & Gigerenzer G. (2012) Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States. Annals of internal medicine, 156(5), 340-9. PMID: 22393129
Pammolli, F., Magazzini, L., & Riccaboni, M. (2011) The productivity crisis in pharmaceutical R. Nature Reviews Drug Discovery, 10(6), 428-438. DOI: 10.1038/nrd3405
Loscalzo, J. (2012) Personalized Cardiovascular Medicine and Drug Development: Time for a New Paradigm. Circulation, 125(4), 638-645. DOI: 10.1161/CIRCULATIONAHA.111.089243
Yang, Q. (2012) Trends in Cardiovascular Health Metrics and Associations With All-Cause and CVD Mortality Among US Adults. JAMA: The Journal of the American Medical Association, 307(12), 1273. DOI: 10.1001/jama.2012.339
Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, & Hu FB. (2012) Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA : the journal of the American Medical Association, 307(12), 1273-83. PMID: 22427615
- May 18, 2012
- 11:17 AM
- 347 views
How to survive the health care system.
by Lutz Kraushaar in Chronic Health
You have heard about good and bad cholesterol. You have
heard that increasing the former and reducing the latter will cut your risk of
heart disease. You will now hear what's principally wrong with this strategy of
attacking risk factors. And how it prevents us from eradicating the heart
disease epidemic sweeping the globe.
On 30th November 2006, Jeff Kindler, the CEO of Pfizer,
praised their about-to-be released drug for increasing good cholesterol as
"...one of the most important compounds of our generation."
Three days later Pfizer halted the phase 3 clinical trial of
its hoped-for blockbuster drug. The simple reason: the drug's ingredient,
torcetrapib, did what it was supposed to: increase good cholesterol. But it
also increased patients' risk for heart attack, stroke or death from any cause [1].
You probably see the common theme behind this and the story of my previous post:
a drug improves a risk factor but worsens risk. Fortunately, Pfizer didn't
cover that up.
Now, before you admire Pfizer as an outstanding
citizen of the pharma world, let's look at their track record in the ethics
department: In 2009 Pfizer pleaded guilty to a felony violation of the Food,
Drug and Cosmetic Act for misbranding their drug Bextra and three others
"with the intent to defraud or mislead".
The anti-inflammatory drug Bextra had been pulled off the
market 4 years earlier, but Pfizer bribed its way into physicians' prescription
blocks. The consequence: a $ 2.3 Billion criminal fine, the largest ever
awarded. You would think of such a fine as putting a serious dent into a
company's balance sheet. Well, 4th quarter profits 2008 were only 10% of what
they used to be. Pfizer had made a provision for what they knew was coming. But
compared to the $ 50 Billion in annual sales that's nothing over which a CEO
would lose sleep.
Last month, Merck was fined $ 321 Million for
similar offences related to Vioxx, a drug of the same class as Bextra.
Altogether, Big Pharma has been fined $ 8 Billion over the past
10 years for repeatedly defrauding the U.S. health care system. If they do it
repeatedly, it's probably not because they are slow learners. It's because -
'cherchez l'argent' - there is money in it. If that's the case, then what has
been brought into the open, may just be the proverbial tip of the iceberg. Now,
an iceberg of monetary fraud is one thing, an iceberg of defrauding you of your
health is quite another. Let me backup my suspicions, again with a recent example:
Tamiflu
Roche's Tamiflu is the only orally administered
influenza antiviral drug in its class (neuraminidase inhibitors). Governments
all over the world had been stockpiling it before the 2009 influenza outbreak. So
did the U.S. government at a cost of $ 1.5 Billion for Tamiflu and Relenza.
... Read more »
Barter, P., Caulfield, M., Eriksson, M., Grundy, S., Kastelein, J., Komajda, M., Lopez-Sendon, J., Mosca, L., Tardif, J., Waters, D.... (2007) Effects of Torcetrapib in Patients at High Risk for Coronary Events. New England Journal of Medicine, 357(21), 2109-2122. DOI: 10.1056/NEJMoa0706628
Kaiser, L. (2003) Impact of Oseltamivir Treatment on Influenza-Related Lower Respiratory Tract Complications and Hospitalizations. Archives of Internal Medicine, 163(14), 1667-1672. DOI: 10.1001/archinte.163.14.1667
Doshi P, Jefferson T, & Del Mar C. (2012) The imperative to share clinical study reports: recommendations from the tamiflu experience. PLoS medicine, 9(4). PMID: 22505850
Wannamethee, S. (2005) Metabolic Syndrome vs Framingham Risk Score for Prediction of Coronary Heart Disease, Stroke, and Type 2 Diabetes Mellitus. Archives of Internal Medicine, 165(22), 2644-2650. DOI: 10.1001/archinte.165.22.2644
Ioannidis, J., & Tzoulaki, I. (2012) Minimal and Null Predictive Effects for the Most Popular Blood Biomarkers of Cardiovascular Disease. Circulation Research, 110(5), 658-662. DOI: 10.1161/RES.0b013e31824da8ad
Ioannidis JP, & Tzoulaki I. (2012) Minimal and null predictive effects for the most popular blood biomarkers of cardiovascular disease. Circulation research, 110(5), 658-62. PMID: 22383708
Paul, S., Mytelka, D., Dunwiddie, C., Persinger, C., Munos, B., Lindborg, S., & Schacht, A. (2010) How to improve R. Nature Reviews Drug Discovery. DOI: 10.1038/nrd3078
- May 14, 2012
- 02:11 AM
- 402 views
Why your heart attack may just be collateral damage in big pharma's turf wars.
by Lutz Kraushaar in Chronic Health
When a pharmaceutical company tells you that its drug is
safer than it really is, it probably plays with your health. And possibly with
your life. That's not a very nice thing to do. But it's also very profitable. Which
is why it happens more often that you care to know.
These days Takeda Pharmaceuticals has gotten some bad press
from a whistle blower suit which claims that TP deliberately withheld trial
data for Actos, a drug which treats diabetes. The active ingredient is Pioglitazone,
which improves the body's sensitivity to insulin and therefore your ability to
metabolize glucose.
You remember one of my earlier blogs, in which I introduced
you to the concept of "cherchez l'argent". The simple strategy of
finding motives behind actions. Particularly within the health care
environment. Which is why I want you to keep a few facts in mind before I tell
you a little suspense story which plays out more often in the universe of medicine,
than you and I would like to.
The Villains
TP's Actos generates annually 2.6 Billion Dollars for
TP.
It's patent expires in 2016.
GlaxoSmithKline's Avandia is, or was, Actos' competitor. Its
active ingredient is Rosiglitazone, whose patent expires this year. Until 2005,
Avandia saw yearly sales of 2.5 Billion dollars.
A few years back, the FDA became concerned with the entire
class of drugs, the so called thiazolidinediones, because of serious side
effects, such as an increased risk of heart disease, stroke and heart failure. Within
it's 'adverse events reporting system', the FDA collects data on serious
adverse events of drugs, which enables it to compare drugs based on benefits
AND risk.
The Beginning
In 2007 the New England Journal of Medicine published the
results of Dr. Nissen's meta-analysis of publicly available trial data on the cardiovascular
effects of Avandia [1]. In his analysis of 42
published studies he came to the conclusion that there was a significant
increase in the risk of heart attacks in patients taking Avandia.
Interestingly, Dr. Nissen also pointed out that he did not have access to the
source data of these trials, which prevented him from conducting a "more
statistically powerful time-to-event analysis". So his conclusion was,
correctly, that "more comprehensive evaluations are required" to
address these cardiovascular risks. Not an unreasonable demand, given that two
thirds of diabetics die of such events. Which is why they are prescribed drugs
like Avandia and Actos in the first place. Dr. Nissen expressed his hopes for
the yet to be published results of the RECORD trial, which was under way at that
time. Financed by GSK, mind you.
The Assassination
In a highly unethical move, one journal reviewer leaked the
draft of the Nissen paper to GSK a few days after its submission. The source of
the leak was no lesser than a professor of medicine at the University of Texas
Health Sciences Center, Dr. Steve Haffner. Why would he do such a thing? Maybe,
because he also happened to be a consultant to GSK? Your guess is as good as
mine. Cherchez l'argent.
GSK's own scientists found Nissen's statistical methods
beyond reproach. Not so GSK's marketing goons. They promptly showed up at Nissen's
Cleveland Clinics in Ohio. But Nissen was prepared for the worst, as he
expected GSK to apply some pressure tactics, as it had done in the past with
Dr. John Buse, a professor of medicine at the University of North Carolina.
Buse had been at the receiving end of GSK's
intimidation tactics for the same reason: openly voicing concerns about the
cardiovascular risks associated with Avandia. The issue had gone to head, with
the U.S. Senate Committee on Finance investigating the case and coming to the
conclusion that "Had Dr. Buse been able to continue voicing his concerns, without
being characterized as a “renegade” and without the need to sign a “retraction letter,”
it appears that the public good would have been better served".
Buse had warned Nissen in a private email about GSK's corporate
persuasion program. With this in mind Nissen secretly taped the entire
conversation with the GSK representatives.
... Read more »
Nissen, S.E. (2007) Effect of Rosiglitazone on the Risk of Myocardial Infarction and Death from Cardiovascular Causes. New England Journal of Medicine, 357(1), 100-100. DOI: 10.1056/NEJMx070038
Home, P., Pocock, S., Beck-Nielsen, H., Gomis, R., Hanefeld, M., Jones, N., Komajda, M., & McMurray, J. (2007) Rosiglitazone Evaluated for Cardiovascular Outcomes — An Interim Analysis. New England Journal of Medicine, 357(1), 28-38. DOI: 10.1056/NEJMoa073394
Home, P., Pocock, S., Beck-Nielsen, H., Curtis, P., Gomis, R., Hanefeld, M., Jones, N., Komajda, M., & McMurray, J. (2009) Rosiglitazone evaluated for cardiovascular outcomes in oral agent combination therapy for type 2 diabetes (RECORD): a multicentre, randomised, open-label trial. The Lancet, 373(9681), 2125-2135. DOI: 10.1016/S0140-6736(09)60953-3
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