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marylin monroe
Showing posts with label epidemiology. Show all posts
Showing posts with label epidemiology. Show all posts

"Milk Kills," Study Says and Everyone is Afraid. Is This More Than Fearmongering Bullsh*t? Methodological Issues & Conflicting Evidence Would Suggest the Answer is "No!"

After reading this article you won't have to be afraid of milk any longer.
The editor of the British Medical Journal (BMJ) will be rubbing his / her hands. The paper by Karl Michaëlsson et al. (2014) that was published earlier this week, made it to the mainstream news in the US and Europe and did - at least at first sight - reflect well on his or her magazine. "The British Medical Journal saves you from intoxicating yourself with milk!" - That's great, right?

Well, in today's SuppVersity article, I am going to take a closer look at how "great" it actually is that studies like this hit the mainstream media, while less exciting, because beneficial studies on milk are not being mentioned at all ... unless, of course, it's the morally superior and allegedly healthier soy milk we are talking about *sarcastic laughter*
You can learn more about dairy at the SuppVersity

Dairy Has Branched-Chain Fatty Acids!

Is There Sth. Like a Dairy Weight Loss Miracle?

There is Good A2 and Bad A1 Dairy, True or False?

Lactulose For Your Gut & Overall Health

Is There a "Fat Advantage" for Dairy Lovers

Dairy, Diabetes, Estrogen, IGF-1, Cancer & More
Before we get to a detailed analysis of the analysis, let's briefly remind ourselves of the type of data we are dealing with. Data from the Swedish Mammography Cohort (all female subjects) and Cohort of Swedish Men (all male subjects) that was complemented by data from food questionnaire that were send out back in the late nineteen eighties (women) and -nineties (men) along with the invitation to participate in the respective cohort studies.

Figure 1: Flow chart of the study sample (Michaëlsson. 2014).
As you can see in Figure 1 we are dealing with a hell lot of data. Data of which we still should not forget that it is based on data of which Thompson, et al. were able to show that it has an accuracy of 45-52%, specifically for dairy products (Thompson. 2002).

Now, in the study by Thompson the subjects were asked about what they ate in the last 30 days. The data in the study at hand, however, is based on what subjects said about how often they drank milk in the past 365 days! A fact that is not likely to make the data any more accurate.

Furthermore, I assume that all of you will have heard of people who change their dietary habits over time, right? Well, for Michaëlsson et al. this is obviously news. Otherwise they would not have relied exclusively data that was gathered, when the subjects were enlisted for the cohort study in the late 1980s / 1990s, when they were trying to identify the reason that 15,541 of the men and women died over the course of the 10-20 year follow-up.
Speaking of 20 years. That's the time that passed between being enlisted and speculating about their daily food intake when the 90 303 women aged 39-74 were enlisted in the Swedish Mammography Cohort and the 31st of December 2010, which was used as an end point for the analysis.
Figure 2: Mortality raters (raw data) according to milk intake in glasses / grams (Michaëlsson. 2014).
Malicious gossip would now probably have it that the additional 10 year gap, i.e. 10 more years to start eating completely differently, alone, could explain why we see a significant negative effect of drinking milk in the female, but not the male participants, for whom the interlude between the food frequency questionnaire and the end point of the study was ~50% smaller.
Figure 3: Adjusted predictions of urine 8-iso-PGF2α, a marker of oxidative stress, in 892 women (based on cross sectional data, mean age 70 years) and 700 men (Michaëlsson. 2014).
A similar criticism can be brought forward with respect to the allegedly "objective" measurements of 8-iso-PGF2α, a marker of oxidative stress, that was assessed in only 892 women (based on cross sectional data, mean age 70 years) and only 700 men, i.e. 1.4% of the female and 1.5% of the male participants, where the "trend" towards increased inflammation reached - once gain! - significance only in the female study participants (see Figure 3).
Homogenization may in fact be a problem. You find that's bogus? There is evidence that suggests that homogenization, not pasteurization is a serious problem | more.
What do commenters say? If you take a closer look at the hitherto published comments (retrieved on October 31, 2015) on the BMJ website, you will find commenters mentioning (1) the obvious association between having too little calcium <> fractures and the desire to increase ones calcium intake by drinking more milk (Kerr, J. Prof. of Epidemiology in Columbia), (2) the absence (or as Rom R. Hill from the Newcastle University says "significant omission") of a clear distinction between raw and pasteurized milk and low fat and full fat milk that makes the study, in Kerr's eyes, more or less meaningless, (3) last but not least, an unknown commenter mentions the issue of hormone, antibiotics and analgesic abuse in modern milk production and highlights that somatotropin (rBST) was still allowed in the EU, when the data from the study was collected. In view of the fact that rBST can affect hormonal and metabolic growth factors including human serum insulin-like growth factors (IGF), this could be another reason specifically for an increase in cancer related mortality (Allen. 2002; WHO. 2006).
Learn more about dairy from Liz in a previous SuppVersity article, i.e. "Dairy - The Good, Bad or Ugly?"
Moreover, the hazard ratios you read of in the news may have been adjusted, but the question remains, whether the adjustment could correctly make up for the fact that men and/or women who consumed more milk, ...
  1. consumed significantly more energy on a daily basis (39% more in women, 24% more in men),
  2. consumed significantly more saturated (36% more in women) and total fat, and
  3. were significantly less likely to use bone building calcium supplements (15 % less in women).
Of these three factors (1) + (2) could explain the increased mortality and cancer risk in women and (3) could explain why women, but not men have a higher risk of hip, but not general bone fracture (hypothetically!).
My recommendation: Don't overrate the results of the study at hand. It has truckloads of methodological shortcomings, a tinge of the hysterical attention grabbing sensationalism and, most importantly, it stand in stark contrast to previous results which indicate that...
Figure 4: If you look at all the evidence, you will see that milk is more likely to protect than to kill you (Elwood. 2008; Bonthuis. 2010; Goldbohm. 2011)
  • a high intake of milk is associated with a 16% reduced risk of cardiovascular disease and an 8% reduced risk of diabetes, two of the most important health issues that will have you pass away years, if not decades before your time (Elwood. 2008 | meta-analysis of 15 pertinent studies),
  • Australians with a high fat milk intake of of 339g/day or more have a 69% reduced risk of dying from cardiovascular disease than their peers (Bonthuis. 2010),
  • Dutch full-fat dairy connoisseurs have a 1% reduced all-cause mortality risk for each 10g of full fat dairy consumption per day (Goldbohm. 2011).
And in spite of the fact that several other studies find no beneficial effects of milk consumption of CVD or all-cause mortality (e.g. non-significant -23% in partly adjusted model in the Whitehall II study), it appears very unlikely that "milk kills". That this statement makes appalling headlines and will get a lot of clicks on the Internet, on the other hand, stands out of question | Read the whole paper for free @ the BMJ Website make up your mind and tell me on Facebook what you think.
References:
  • Allen, Naomi E., et al. "The associations of diet with serum insulin-like growth factor I and its main binding proteins in 292 women meat-eaters, vegetarians, and vegans." Cancer Epidemiology Biomarkers & Prevention 11.11 (2002): 1441-1448.
  • Bonthuis, M., et al. "Dairy consumption and patterns of mortality of Australian adults." European journal of clinical nutrition 64.6 (2010): 569-577.
  • Elwood, Peter C., et al. "The survival advantage of milk and dairy consumption: an overview of evidence from cohort studies of vascular diseases, diabetes and cancer." Journal of the American College of Nutrition 27.6 (2008): 723S-734S.
  • Goldbohm, R. Alexandra, et al. "Dairy consumption and 10-y total and cardiovascular mortality: a prospective cohort study in the Netherlands." The American journal of clinical nutrition (2011): ajcn-000430.
  • Michaëlsson, Karl, et al. "Milk intake and risk of mortality and fractures in women and men: cohort studies." BMJ 349 (2014): g6015.
  • Thompson, Frances E., et al. "Cognitive research enhances accuracy of food frequency questionnaire reports: results of an experimental validation study." Journal of the American Dietetic Association 102.2 (2002): 212-225.
  • WHO Expert Committee on Food Additives. "Toxicological evaluation of certain veterinary drug residues in food/prepared by the sixty-sixth meeting of the Joint FAO/WHO Expert Committee on Food Additives (JEFCA)." (2006).

Increasing Adolescent Obesity Among Girls: How School Stress is the New Scapegoat When the Skinny Fat Phenomenon and Twinkie Diets Really are to Blame.

Image 1: Was it school stress that cost Michelle Obama her famous "guns" (=muscular upper arms)? Probably not - and despite contrary conclusions in the study at hand, I doubt that it is school stress that leaves our daughters fat, but undermuscled. What do you say?
According to the results of a recent evaluation of the data from the European HELENA trial, school, or rather the stress your children are exposed to within the educational system, may be one of the reasons for the increasing number of obese adolescents (age 12-18 years) - at least if we trust the statistical finesse of Tineke De Vriendt, Els Clays and 10 other scientists who recently published a paper on European adolescents’ level of perceived stress and its relationship with body adiposity in the European Journal of Public Health (DeVriendt. 2011). The study was part of the Healthy Lifestyle in Europe by Nutrition in Adolescence cross-sectional study (HELENA-CSS), the aim of which is to obtain "reliable and comparable data from a selected cohort of European adolescents concerning a broad variety of parameters related to nutrition, health, physical activity and fitness", or in other words, to provide the epidemiological back-bone for the formulation of more or less mainstream hypotheses on "why we are getting sick and obese"... be that as it may, with a sample size that allows for a confidence level of 95% and <0.3 error and 3865 adolescents from more than 10 European cities, the data from the HELENA study is probably the best we can get at the moment and thus you may nonetheless be interested to hear that ...
School-related stress was demonstrated to be a main source of stress in European adolescents, [...and] in adolescent girls (but not in boys), a positive association was observed between their level of perceived stress and measures of general and abdominal obesity.
If you have a closer look at the stress data, expressed on a 6-scale Likert scale from 1 = ‘Not at all stressful’ over  2 = ‘A little stressful’, 3 = ‘Moderately stressful’, 4 = ‘Quite stressful’ to 5 = ‘Very
stressful’ (with 6 indicating ‘Is irrelevant to me’), you will notice that school and the closely related fear of an "uncertain future" are in fact the major stressors in the life of our adolescent children (and you may safely assume that the results won't be very different in the US).
Figure 1: Overview of perceived stressors; values expressed on a Likert-scale from 1 to 5 (data adapted from DeVriendt. 2011)
In that, it is yet interesting to observe that none of the stressors is on average perceived as "quite stressful" or even "very stressful". It is also noteworthy that on 5 out of 10 scales and on the summary scores girls experienced more stress than boys. In view of the scientists previously cited conclusion (of which I do not have to tell you that it is based on one of those sophisticated *cough* statistical models) that only "in adolescent girls (but not in boys), a positive association was observed between their level of perceived stress and measures of general and abdominal obesity", it may thus surprise you to hear that the obesity rate among boys is more than 2x higher than among adolescent girls (cf. figure 2). So how is that?
Figure 2: Body adiposity characteristic of study sample (n=1121) of adolescents from the HELENA trial (data adapted from DeVriendt. 2011)
Well, we all know that even with a perfectly "normal" BMI, you can easily be "skinny fat". A phenomenon of which I feel that it is becoming the norm, not the exception, among adolescent girls, who - after their nth an-apple-a-day diets have lost nothing but muscle and thus have a very high body-fat percentage with a low overall body-weight.

This hypothesis would be confirmed by the regression coefficients of the statistical models from the study, according to which the association of perceived stress with the body fat level of the girls is 7x higher than the association of perceived stress and BMI, which is something that should you make reconsider, whether
  1. body weight and BMI is an important biological measure, at all,
  2. the real "obesity" rate, including skinny fats, among girls is not way higher than the 1.9% reported in the study would suggest, and
  3. in how far school-stress, despite being the "major stressor" in this survey and not a misguided beauty-ideal is to blame for the increasingly unbalanced ratio of lean to fat mass in adolescent girls
The importance of the latter, i.e. a questionably beauty-ideal, in the etiology of (and this would be ironic if it was not so unfortunate) diet-induced obesity could also explain that the pubertal stage the girls were in and not stress or their diet had the greatest "explanatory value" (remember: we are talking of associations here) in regard of their body fat levels (cf. figure 3).
Image 2: Low self-esteem and false beauty-ideals pave the way into disordered eating and life-long misery. Something you want to spare your daughter and son, don't you?
Did you know that according to a 2000 national survey 45% of the girls, but only 20% of the boys from 5th to 12th grade reported to have tried one or more diets at some point in the past (Neumark-Sztainer)? And would you have guessed that 17% did even consider their own eating behavior as already "distorted"? Needless to say that both of these factors showed a high correlation with overweight status, low self-esteem, depression, suicidal ideation, and substance use; and certainly reason enough for you to help our children (girls and boys) not to fall victim to ill-advised beauty-ideals and false dietary recommendations.
And from the fact that the same variable had 7x less predictive value in boys, who obviously do not want to be skinny fat and refrain from Twinkie-style low calorie, low fat dieting, we can with some caution (due to hormonal effects on fat accumulation) conclude that it is not just a time factor, meaning that the older girls have more time to accumulate body fat...
Figure 3: Regression coefficients (=associative strength*100) of stress or pubertal stage with body-fat percentage in adolescent boys and girls from the HELENA trial - mind the logarithmic scaling! (data adapted from DeVriendt. 2011)
Now, what can you do about that? Well, without even knowing you probably have already done something! Assuming that you (just like every other reader of the SuppVersity ;-) are of above-average intelligence, your education has already provided your daughter, but not your son, with a better chance of staying lean than her peers from less educated parents (~80x higher explanatory value than stress!). All that is left now to make absolutely fat-proof, is to tell her that strong, not skinny fat is the new beautiful ;-)

EPIC Study Says: Vegetables, Fish, Dairy, Pasta & Rice Reduce, Softdrinks, Processed Meat, Margarine, Spirits and Potatoes Increase Waist Circumference - Really!?

Image 1: The EPIC project unites scientists
from all over Europe (EPIC, IARC. 2011)
I don't know whether it is a good idea, for an anti-epidemiologist like me (I mean, come on, epidemiology vs. controlled experiments is like astrology vs. astronomy) to post this, but a 13-year (1992-2005) meta-analysis (Romaguera. 2011) of data from 48,631 men and women from 5 countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study is probably as good as as epidemiology can ever get and with scientists from 12 different institutes all over Europe, there is at least some hope that the results of their investigation into the association of food groups/items consumption "on prospective annual changes in 'waist circumference for a given BMI' (WCBMI), prox for abdominal adiposity".

Unfortunately the first of the typical shortcomings of EPIC studies (pun intended) like this becomes obvious in the previous citation, already, as the quotation marks before "waist circumference" indicate that the latter "was measured either at the midway between the lowest rib and the iliac crest (the Netherlands, and Potsdam-Germany) or at the narrowest torso circumference (the other centres)." And even worse,
at follow-up examinations, participants in Norfolk (United Kingdom) and Doetinchem (the Netherlands) were measured by trained technicians using the same protocols as at baseline, whereas other centres provided self-reported data.
I guess you can imagine how "exact" these waist circumference are - if not, try and "measure" your personal waist circumference, you will be astonished how easy it is to "diet down" from 35" to 32"... it's a pitty that this kind of 'weight reduction' is not sustainable ;o)

Be that as it may, it argues in favor of the scientists that they did a validity test with 408 men and women from the Danish cohort beforehand and it turned out that "a high correlation between the self-reported and technician measured WC was found". Furthermore, the scientists were able to extrapolate association with baseline BMI and used these later in the regression analysis to make up for potential measuring "mistakes" on part of their subjects. If we thusly assume that at least this part of the data is reliable - I don't think I have to go into any detail as far as notoriously inaccurate food frequency questionnaires are concerned - the study provides some tangible insights into the influence the eating habits of the general population has on the accrual / loss of body fat.
Figure 1: Association (expressed as beta² coefficients, left axis) of certain foodstuffs and beverages (daily consumption in grams, right axis) with increases (positive beta²) and decreases (negative beta²) in waist circumference in 48,631 subjects from the EPIC-DiOGenes project (data adapted from Romaguera. 2011)
If you have a closer look at the data in figure 1, it's quite easy to recognize the major offenders, if you compare the association strengths (beta²) of a given food with the total amount of this foodstuff/beverage in g/day the men and women in the EPIC-DiOGenes project consumed. High beta² values indicate a positive association, which means that study participants who report (!) that they eat much of this food / drink much of this type of beverage, were more likely to increase their waist circumference.

Image 2: If you have any interest in milk, colostrum or dairy in general, make sure you read my recent write-up on milk and milk consumption!
Now, while spirits may have an even higher association with abdominal adiposity than soft drinks, the overall contribution of Jack Daniels and Co to the obesity epidemic, which has a similar handle on Europe as on the US, is nevertheless negligible, because even men "booze" way to seldom to get fat just from booze. On the other hand, the negative association of fish (beta²=-0.05) and waist circumference is much stronger than that of milk, but since fish consumption is equally low for both men and women, chances are that, overall, milk (beta²=-0.01) may contribute just as much or even more to keep (at least some of) Europeans, who love their milk (you can read more on milk if you follow the link beyond image 2) and despise fish, from bursting out of their pants and skirts than fish does, despite having the higher and thus less beneficial beta² coefficient.

I bet, some of you are just about to freak out that both "vegetable oils", as well as pasta and rice and (worst of all) breakfast cereals are associated with reductions in waist circumference. At least in the eyes of the paleo-cultists out there, this may disqualify the study as devil's work ;-) People like Robb Wolf or Matt Lalonde, whose pointed analyses go beyond the common black-and-white cave-paintings you will find on the bazillion of paleo-blogs out there, would yet notice that the men and women in the study actually consumed relatively low amounts of these foods. Roughly 63g of rice and pasta for men and 52g for women is certainly a tolerable amount. And to be honest, I have not seen anybody die from a well filled tablespoon of vegetable oil per day, either ;-)

Almost "experimental" seems the last finding of the study I want to mention, which is the (calculated) effect the replacement of (-100kcal energy from) soft drinks, margarine, processed meat and white breads (the major offenders) with either (+100kcal from) dairy or fruits would have on the changes in waist circumference in the course of one year.
Figure 2: Model calculation of the beneficial (lower beta² coefficients) effects of iso-caloric (100kcal/day) replacements of soft drinks, margarine, processed meat and white bread by fruits or dairy (data adapted from Romaguera. 2011)
As the data in figure 2 goes to show the effect the statistical model predicts that the effects of fruit would be more profound... this, however, is something I dare question, simply because the different macronutrient composition of dairy (moderate protein, moderate fat, relatively low carb) vs. fruit (no fat, no protein, high carb) simply forbids such statistical thimblering that neglects the macronutrient composition and focuses solely on the caloric value... ahh, one last word: Whenever, this crucial distinction between eating calories and eating food (I am starting to think that maybe some of the scientists actually eat calories?) will reach mainstream science, I will immediately let you know ;-)

Meat-Ology: A Brief Glance at the Latest Data on The Link Between Red Meat, Cooking Techniques & Prostate Cancer

Image 1: The mass media is already sharpening its knifes - finally some sensational headlines on the negative side effects of red meat! But is your prostate really at risk? Should you already tell your urologist to sharpen his surgical knife and schedule your operation? I have compiled the data for you, now it's up to you to decide.
I guess most of you will have noticed the upheaval around the latest egg-bashing study by Spence et al. that made the rounds a couple of days ago. In view of (1) the authors' outright statement that their intention was to go against the hazardous disregard for the potential dangers of cholesterol laden eggs that was spreading so rapidly (sounds more like pamphlet than like a study, right?), (2) the fact the authors present data with standard deviations that are larger than the actual differences in carotid plaque areas between the different quantiles of weekly egg intake and (3) the superior (lower total and LDL and higher HDL) cholesterol levels in those participants who ate the most of the oh-so-unhealthy eggs, I decided to leave it to a brief comment on the SuppVersity Facebook Wall, instead of a full blogpost (Mark from Mark's daily apple was not as lazy as me, so if you are interested check out his critique). When I hit onto a ScienceDaily article on its "red meat counterpart" today, though, I felt compelled not to leave you hanging with the sensational "red meat causes prostate cancer" reports of which I am pretty sure that they will soon pop up on all mass media outlets.

What's that about red meats, burgers and steaks?

Notes on the methodology: The scientists evaluated the raw data for their two cohorts, Los Angeles County site (LA), and San Francisco Bay Area (SFBA), individually.

The scientists excluded over- (>6,000kcal) and undereaters (<600kcal) and applied two different models with...
  • model 1 adjusting for age (years), BMI (<25, 25-29, ≥30), total calorie intake (kcal/ day) and family history of PCA (yes/ no) and  
  • model 2 adjusting for age (years), BMI (<25, 25-29, ≥30), total calorie intake(kcal/ day), family history of PCA (yes/ no), total fat intake (g/ day), alcohol consumption (g/ day), cigarette smoking (pack-years),total fruit consumption (g/ day), total vegetable consumption (g/ day)
Data was acquired by "professional interviewers" in home interviews.

Dietary data was queried by food frequency questionnaires and commonly used cooking module that would elicit the degree of doneness and browning (participants were given photos to pick from).
According to what I bet you have already or will soon read (and probably even see on TV), researchers from the UCLA have finally identified why red meat is so bad for you - it's the frying! Strangely, though, it's only bad if you fry hamburgers and what's more, you better be Hispanic if you want to make sure it will give you prostate cancer (see image 3)... if we take the ScienceDaily article on the matter as a reference, we will yet find this information only, if we are not satisfied with the "gist" the author gives us as an introduction:
"Research from the University of Southern California (USC) and Cancer Prevention Institute of California (CPIC) found that cooking red meats at high temperatures, especially pan-fried red meats, may increase the risk of advanced prostate cancer by as much as 40 percent."
And you bet that 90% of the readers will do just that: They read "40% increase in prostate cancer risk" and tell themselves either"I will finally go vegan, like Steve jobs" or "F*** it, I love my red meat!" The 10% who are still undecided will then go on and find a few neat quotes like
"The observations from this study alone are not enough to make any health recommendations, but given the few modifiable risk factors known for prostate cancer, the understanding of dietary factors and cooking methods are of high public health relevance"
- Mariana Stern, associate professor of preventive medicine at the Keck School of Medicine of USC
Which show us that (contrary to the mass media) the scientists are well aware of the exploratary nature of their study, so take the following elaborations of the study not as a critique of Stern, Joshi, Corral, et al. whose study is exemplary in the way they are finally going beyond the "all red meats are created equal" approach that is so prevalent in epidemiological studies, but as an example of why third-hand information (even if this is my hand ;-) is never absolutely reliable.

What statistical significant findings were there actually?

I won't bore you any longer with additional moralizing and try my very best to refrain from too much interpretation... ah, let's just take a look at what we have
  • age is a factor for LA residents, not for San Francisco residents (older = more cancer)
  • cigarette smoking makes a significant difference only in LA
  • race is highly predictive in both LA and the San Francisco bay area
  • high dietary fat intake increases the risk significantly in LA and borderline significantly in SFBA
  • alcohol intake, fruit, veggies, and dairy don't make a difference
So far for the baseline characteristics, now what about the hazard ratios for meat consumption? What's going to make your prostate grow? Well, let's start with what won't make it grow, or I should say, "parameters that did not show significant increases in hazard ratios (HR)":
Image 2: The total amount of red meat (irrespective of how it was prepared) did not stat. significant increase prostate cancer risk
  • the absolute amounts of red meat, hamburgers, steaks, poultry, processed meat, bacon, sausages and homemade gravy regardless of how it's processed
  • red meat, when it is grilled, broiled or baked
  • hamburgers, when they are well done
  • steaks, when they are well done or high temperature cooked
  • poultry, when it is grilled, oven broiled, pan-fried, baked, high temperature cooked or well-done
Similarly, in a non-genomic analysis across all ethnicities, none of the purported meat mutagens, i.e. HCA 29amino919methyl1969phenylimidazo[4,59b]pyridine (PhIP), PAH Benzo9a9pyrene (BaP), HA 2-amino-3, 8-dimethylimidazo[4, 5-fquinoxaline (MelQx), DiMelQx or the total mutagenic activity had statistically significant influence on the the hazard risks (they did for G/C genotype and C/C genotype, but the results are inconclusive, because median intakes of were associated with increases of up to 50%, while very high intakes were associated with decreases in of up to 40% specifically in the C/C genotype of the PTGS2 SNP (rs20417), a gene that controls inflammatory processes and has been implicated in the etiology of various cancers)

So where are those 40% increases then? I see only statistically insignificant stuff!

You do in fact have to get your glasses out to find p-values with p < 0.05, which mean that the chances that the given increase in the risk of developing prostate cancer are just coincidence are below 5% and therefore "statistically significant"... now I did just that for you, got my glasses and hope I did not overlook one of the few that are actually in there - and since this is what the laypress considers the most important, I even drew a graph for ya ;-)
Figure 1: There are exactly three items in the study, for which the scientists observed statistical significant increases in advanced (an only advanced not total!) prostate cancer risk - red meat cooked at high temperatures (low = 0.0-9.4, medium = 9.4-16.9, high = 16.9+), hamburgers cooked at high temperatures (low = 0.0-4.9, medium = 4.4-7.9, high = 7.9+) and red meat low = 0.0-5,0, medium = 5.0-9.8, high = 9.8+ that was fried in a pan; intakes in g/1,000kcal diet (data based on model 2 from Joshi. 2012)
At first it my appear schizophrenic that red mead cooked at high temperatures, which obviously includes pan-fried red meat, has it's own category and is associated with a higher increase in hazard ratios than it's pan-fried counterpart . If you do yet take a closer look at the quantiles you will realize that the +40% increase in advanced cancer risk in the high red meat group is brought about by intake of more than 16.0g/1,000kcal, the +30% increase from pan-fried meat, on the other hand, is the result of only 9.6g/1,000kcal per day of pan-fried red meat.

Image 3 (getty images): The hispanic guy in the back could increase his  PC risk by 40, 80 and 160% (low, medium, high intakes), almost 2x more than non-hispanic whites; African Americans are "immune" against high-temperature cooked burgers -20, -10 and +10% nonsignificant changes in HR (p = .725)
Without knowing exactly how the scientists processed their data, which is by the way based on assessments of photographs of the food the study participants ate (make up your own mind about that!?), I will just treat it as a statistical artifact that the model yields a +40% increase in advanced cancer risk for total "high temperature-cooked meat", when two of the individual sub-categories, grilled meat and oven broiled meat did not yield a statistical significant association and the third one, i.e. pan-fried meat had a very clear association, albeit for a lower increase in hazard risk. Be that as it may, since I am about to conclude this mini-overview with the benefits that were spit out by the very same statistics, and artifacts like that are actually one of the reasons it is never prudent to think of statistical associations or correlations as if they were causations, I won't dig further into it.

Things you won't hear in the news: "Bad meat" that will reduce your prostate cancer risk

If we thus put faith in the negative news, we should yet also appreciate the positive ones of which you probably won't read anywhere else but at the SuppVersity. Actually quite a pitty, as even among the "bad red meats" there appear to be some that are not so bad after all - at least if you prepare them correctly:
  • poultry - 10-30% reduced hazard ratios (HR) for advanced prostate cancer for Q2, Q3 and Q4 intakes of poultry with the highest (=30% reduction) for Q4, and a daily intake of more than 35.7g / 1,000kcal
  • home made gravy - 30% reduced HR for local prostate cancer risk (borderline significant p = 0.06) after adjustment for age, BMI, calorie intake, family history of prostate cancer, total fat intake, alcohol, smoking, fruit and veggie consumption.
I guess by now you will have read enough to see the shades of grey and even the little white there is, so I'll leave it up to you to decide, whether or not you are from now on living on poultry and home made gravy, only. As funny as it may sound: If we took at the results for a "T" that and not veganism would namely be the "ultima ratio" ;-)
    Current Age 10 Years 20 Years 30 Years
    30 0.01 0.32 2.49
    40 0.31 2.52 8.30
    50 2.30 8.30 14.40
    60 6.62 13.36 16.11
    70 8.50 11.97 N/A
    Table 1: Percent of U.S. Men Who Develop Prostate Cancer over 10-, 20-, and 30-Year Intervals According to Their Current Age, 2005–2007 (according to Altekruse. 2010)
    Putting things into perspective: I know that I said, I would refrain from overt commenting and just give you the facts, but I won't let you get away without at least some quantitative considerations that will help you to put data like +30% advanced prostate cancer risk from the consumption of more than 9.6g / 1,000kcal energy intake per day into perspective. I mean, let's be honest 9.6g/1,000kcal per day? If we assume that the average piece of meat you would eat has ~120g that would mean that even if you had a piece of pan fried red meat every 12 days, only, you would still end up in the medium intake category and have a +20% increase in cancer risk.

    In view of the fact that the average omnivore male human being probably eats way more meat it may no seem as if 90%+ of us were doomed to develop advanced prostate cancer and that may well be the case if we would not be talking about  if we were not talking about increases in hazard risk from a certain baseline. If we take the 2010 study by Altekruse et al. as a baseline (see table 1) our +30% increase in hazard risk would thus mean that a man who is now 40 years old would have a +0.1% increase of developing cancer in 10 years, a +0.8% increase to suffer from prostate cancer when he is 60 and a +2.5% greater risk with 70 (please remember that this data is at best of qualitative nature as calculations like this are outright unscientific and that's not just because the "average American" who is captured in the SEER data from 1975-2007 will definitely be a red meat eater ;-)
    References:
    • Altekruse SF, Kosary CL, Krapcho M, Neyman N, Aminou R, Waldron W, Ruhl J, Howlader N, Tatalovich Z, Cho H, Mariotto A, Eisner MP, Lewis DR, Cronin K, Chen HS, Feuer EJ, Stinchcomb DG, Edwards BK (eds). SEER Cancer Statistics Review, 1975–2007, National Cancer Institute. Bethesda, MD, based on November 2009 SEER data submission, posted to the SEER Web site, 2010.
    • Joshi AD, Corral R, Catsburg C, Lewinger JP, Koo J, John EM, Ingles S, Stern MC. Red meat and poultry, cooking practices, genetic susceptibility and risk of prostate cancer: results from the California Collaborative Prostate Cancer Study. Carcinogenesis. 2012 Jul 20.
    • Spence JD, Jenkins DJ, Davignon J. Egg yolk consumption and carotid plaque. Atherosclerosis. 2012 Aug 1. 
    • University of Southern California - Health Sciences (2012, August 16). Pan-fried meat increases risk of prostate cancer, new study finds. ScienceDaily. Retrieved August 17, 2012, from http://www.sciencedaily.com­ /releases/2012/08/120816170404.htm#.UC3I26LCdzU.facebook

    The Obesity Paradox, a Fat White Lie: A Longer Life for the Obese Is Nothing But a Sick Exception to the Healthy Rule

    "But Dr. the TV guy said, I would live longer than my normal weight cousin."
    I guess, you will be familiar with the therm "obesity paradox". A term coined by researchers and picked up by the media to sooth the increasingly overweight majority of the US citizens to believe that even if they cross the border to obesity (which more and more people do), they can still live a healthy  and - assuming that they follow all the good expert panel advice and take their expensive meds, even outlive their normal-weight peers. Now, a recent paper from the Department of Health and Nutrition Sciences at the Brooklyn College of the City University of New York basically states:

    "There is no such thing as an obesity paradox"

    In fact the only paradox there is, is the ease with which scientists around the world have been fooled by numerous reports of a lower mortality risk for obese individuals than for normalweight individuals within the past decades (I guess that's wishful thinking controlled by their own beer-bellies ;-). As Greenberg points out, corresponding reports
    "[...] contradict the well-accepted, empirically based idea that obesity confers elevated mortality risk. Some of these paradoxical reports resulted from studies that involved cohorts of elderly free-living persons, such as elderly US residents, US veterans, and residents of Jerusalem. Other studies used data from seriously ill patients, including patients on kidney dialysis, post–coronary revascularization patients, livertransplant recipients, and patients with conditions such as wasting disease, AIDS, cancer, chronic obstructive pulmonary disease, heart failure, acute myocardial infarction, and peripheral arterial disease.
    So exactly those patients, ah... pardon me, subject who are relevant for the few really (not new standards) normal weight individuals out there, right? Accordingly, one of the most frequently heard and in fact reasonable explanations for these counter-intuitive observations was reverse causation:
    "Reverse causation is postulated to be caused by factors such as smoking and serious illness that simultaneously induce weight loss and increase mortality risk.These factors are theorized to increase mortality risk at low BMIs, and hence deflate mortality risks for obese individuals relative to normal-weight individuals, thereby yielding an artificially low mortality risk for obese persons. An analytic technique that is commonly used to abate reverse causation involves excluding smokers and participants with serious illness from the analysis. However, this technique cannot be applied in analyses involving samples of seriously ill participant." (Greenberg. 2013)
    To avoid being accused of falling for the same mistakes, Greenberg decided against the use of the usually subjects and resorted to data of which you'd expect that it had by now already been analyzed: data from the mortality-linked NHANES I, II, and III cohorts. Based on these datasets, which are based on 3 different 15-y follow-up periods: 1973–1988, 1978–1993, and 1991–2006., he set out to evaluate, whether the hypothesis that "mortality risk is lower for obesity than for normal weight only among elderly and/or serious ill Americans" (Greenberg. 2013) - and the results were intriguing:
    • compared with normal-weight participants obese participants are older, nonwhite, nonsmokers, and nondrinker
    • there was a (shockingly?) steady increase in the prevalence of obesity and a steady decrease in the prevalence of normal weight across the cohorts over time
    • significant 2-way interactions were found for cohort and serious illness, cohort and smoking, cohort and sex, or cohort and age for mortality because of all causes - one or more significant 2-way interactions involving age and serious illness, age and sex, illness and sex, smoking and age, smoking and serious illness, or smoking and sex 
    • a significantly lower mortality risk for obesity than for normal weight was found only among men with serious illness and only in NHANES III
    • of all other subgroups none achieved this "effect" - if present (e.g. older (aged.55 y)
      men and seriously ill participants) the fat advantage was non-significant, even in NHANES III
    • specifically for women, "[t]here was no evidence of lower mortality risk for obese women than for normal-weight women" (Greenberg. 2013)
    The question still remains, is it reverse causation, or not - or to be a little more straight forward: "Is it the pathological weight loss that comes hand in hand with serious illness or smoking that deflates the survival analysis risk ratios for obesity, when the normalweigt catgory is the referent category?"
    An often heard, logical, but hard to verify alternative to explain the "seriously ill obesity paradox", is that it could be a result of "clinicians using appropriate diagnostic and therapeutic techniques more intensively and earlier in the disease process for obese male patients than for other patients." (Greenberg. 2013) - If that was the case, even the advantage for seriously ill-patients was nothing but and epidemiological edifice.
    "If reverse causation is the explanation for the RR for all-cause mortality for obesity (with normal weight as the referent) being significantly lower than 1.00 only for men with serious illness, and only in NHANES III, then an increase in reverse causation would have occurred only in seriously ill men between NHANES I and III and would have manifested as a higher mortality rate among normal-weight, seriously ill men in NHANES III compared with NHANES I." (Greenberg. 2013)

    To evaluate this, Greenberg thus used NHANES I as a reference and found that the mortality risk normal-weight, seriously ill men in NHANES III, with normal-weight, seriously ill men was 1.11 (0.40, 3.08; compared to NHANES I). A result which would suggest that "an increase in reverse causation between NHANES I and III was unlikely to be the explanation" (Greenberg. 2013) for the "obesity paradox" in the study at hand.

    Usually this would be the place for a "suggested read", but in this case this is more of a "must read" or "obligatory read: "Study Reveals Unsettling Data About How Fat We've Already Gotten Over the Past 40 Years. Plus: Macro Analysis of the Diets of the Leanest & Fattest Yields Surprising Results" (read more)
    Bottom line: So, while Greenberg is not able to fully determine, what the exact underlying mechanism of the "seriously ill obesity paradox", as it would have to be called (you only benefit from being obese if you are "seriously ill" - great, hah) is, it goes without saying that being normal weight and healthy  is better than being sick and obese. After all, neither healthy, elderly, obese female nor healthy, obese male NHANES participants exhibited a lower relative mortality risk than their equivalent normal-weight participants - even in the outlier cohort of NHANES III  - the notion that you better fatten up now, not to die prematurely is thus haphazard and just another instance of an - in my humble opinion - literally life-threatening trend to tailor towards the obese masses' (all puns intended) desire to be told that "all is going to be good, it's not bad..." - trust me, it is that bad!

    References:
    • Greenberg JA. The obesity paradox in the US population. Am J Clin Nutr. 2013 Jun;97(6):1195-200.

    Is Most of Our Science False? A Radio Lesson (12PM EST) on Biases, False Positives and Reading Between the Lines of Scientific Studies

    Just in case you got nothing else to do: Tune in live and listen to me @ Carl Lenore's Super Human Radio to learn whether or not Mr Ioannidis from the Tufts University School of Medicine in Boston is right and ...

    Most Published Research Findings Are False!?
    Update: Episode steht zum Download bereit!

    Listen live to SHR @ 12:00PM ET
    For those interested in the original study, here is the link. Ah, and don't worry! I will try my best to make it more palatable ;-)

    Update: Episode steht zum Download bereit!