Santa is Coming to Town and You Better Beware of His Gifts: Fat Gain, Muscle Loss and Increased Mortality Rates.

Image 1: The "modern" image of the Coke-drinking Santa. Do you really believe he is one of the good guys?

Finally, December 24th is there! The day we have all been waiting for, to get together with friends and relatives and wait for the portly, joyous, white-bearded man in the red coat to deliver "his" gifts. Interestingly enough, the word "gift" in German designates "poison" and while those of you who have been following the SuppVersity news earlier this week may now be speculating that this could in one way or another be related to the millions of iPhones Santa is going to be dropping down the chimneys in the night to come (cf. Mobile Contraception), it seems unlikely that the electromagnetic radiation from the gadgetry could explain the statistically significant +4.65% increase in cardiac and a + 4.99% increase in non-cardiac deaths during the holiday season. After all, the data based on which David P. Phillips, Jason R. Jarvinen, Ian S. Abramson, and Rosalie R. Phillips conclude that "the Christmas/New Year’s holidays are a risk factor for cardiac and noncardiac mortality" is from the pre-iPhone era (Phillips. 2004).

Is Santa not the good guy, the Coca Cola ads made us believe?

So, if its not the radiation, what else could it be? Could it be Santa Claus himself? Is he haunting us, just as his robotic counterfeit in the distant future of the year 2999, where an evil Santa robot is after the blood of the protagonists of Matt Groening's and David X. Cohens TV series Futurama? Or is it a result of the consumption of too many of the Coca Cola bottles Santa is supposed to have in his bag?

Image 2: One really has to marvel at how the soft drink producers dissolve the enormous amount of sugar on the right in the small amount of dark brew on the left.

Did you know that the Coca Cola company alone sells 1.6billion (!) servings of Coke per day? With 27g of sugar per serving, this equals 43,000 metric tons of pure sugar. The average American, who consumes an average of 150 to 170 pounds of sugar each year, would have to live into his/her 558th year of age to eat or drink her way through this sugar mountain. And while I have no doubt that there actually are people out there who would do that withing 100 years, I am not quite sure which of the ailments of our sweet convenience society would strike him / her first and put a spoke in the sweet-o-holic's plans: diabetes, cancer, heart failure or stroke? What would you say?

Phillipps et al. who report in a follow-up study based on the same dataset from the holiday periods between July 1, 1973, and June 30, 2001 that there was an "excess of 42,325 deaths from natural causes above and beyond the normal winter increase" (Phillips. 2010), exclude the possibility that the increased mortality rate was simply a result of the bad weather conditions and related respiratory diseases:

Respiratory diseases. Respiratory diseases increase during winter, and patients weakened by respiratory diseases can die from cardiac diseases. The respiratory hypothesis is undermined by 2 considerations: (1) People dying from cardiac diseases with respiratory disease listed as a secondary cause of death produce a smaller holiday peak than do people dying from cardiac diseases alone: 3.51% versus 3.77%. (2) Interaction between cardiac and respiratory diseases cannot easily explain the twin mortality spikes on Christmas and New Year’s.

So, in view of the latest headlines related to "holiday weight gain" here at the SuppVersity and elsewhere on the web, the next best plausible explanation (which would in fact come back to the "Coca Cola < > Santa Connection" ;-) would be gluttony, right?

Holiday weight gain: Distinguishing fact from fiction

Before we jump to any premature conclusions, here, let's initially have a closer look at how much body weight Santa actually has in his bag for you.I mean, the perceived weight gain is enormous, right? Well, science is however not about perceptions and feelings and it should thusly not really surprise you that, according to a US study which was published in the prestigious New England Journal of Medicine (Yanowski. 2000), the "average" American (in this study represented by 195 US adults with a mean age of 39 +/-12 years) gains no more than 0.37kg, or, expressed in terms of the mean weight of the study participants, 0.5% during the holiday period from from mid-November to early or mid-January.

Figure 1: Percentage of normal weight, overweight and obese subjects with "major weight gain", as defined in absolute or relative terms (data adapted from Yanowski. 2000)

And while the average weight gain hardly is something to speak of, there are two other particularly intriguing findings of this study I do want to draw your attention to. The first one relates to the the data in figure 1. As you can see, the number of overweight subjects among those study participants with major weight gain (as defined as >3% of the initial weight) is particularly high. While only 7.9% of the normal-weight (American normal weight ;-) subjects gained more than 3% of their initial body weight 11.1% of the already overweight subjects did. Interestingly, the number of obese subjects was slightly smaller (7.5%). The latter is yet a physical necessity as there simply is a phyiscal limit to the amount of weight you can gain in a given period of time and 3% for a person with BMI>30 is obviously way more than 3% for someone who is only "overweight" (25 < BMI < 30).

The real problem is: The weight does not magically disappear

The real culprit is however that the weight people gain during last weeks of the year "is not reversed during
the spring and summer months", so that he researchers' concern that

[t]he 0.48-kg weight gain of the subjects in this study between September or October and February or March might not appear to be  clinically important and could easily go unnoticed by both the subjects and health care providers [and that] the cumulative effects of yearly weight gain during the fall and winter are likely to contribute to the substantial increase in body weight that frequently occurs during adulthood.

A 2006 by Hull may not only provide a hypothetical explanation for the non-reversibility of the (minor) weight gain (Hull. 2006), it also provides some insights into the true fallacy of "holiday weight gain": The minor increase in total body weight goes at the expense of concomittant increases in body fat and reductions in lean tissue mass.

Figure 2: Relative changes in anthroprometric measures over the holiday season; left axis - overweight / normal weight, right axis + figures - all (data adapted from Hull. 2006)

In the 82 college students from the Hull study, this fat promoting, muscle reducing "recompositioning" effect of the holiday season (Thanksgiving to New Year) was even so pronounced that the study participants actually lost -0.1kg of their total body weight. This was unfortunately a direct result of a +0.8 increase in fat mass and a -0.4kg decrease in lean mass. And what's more, the effect on fat mass was again more pronounced in those subjects, who were already obese.

Beyond candy, coke & co: Five additional reasons why Christmas is potentially deadly

In spite of the fact that these highly unfavorable changes in body composition are certainly not beneficial for anyone's overall health, it stands out of question that their effects would be cumulative and can thusly hardly explain the empirically validated increased mortality risk during the holiday season. In a 2004 comment on the aforementioned paper by Phillips et. al., Robert A. Kloner thusly proposes five additional hypotheses which could explain the potentially fatal side effects of the holiday season (Kloner. 2004):

Image 3: If you do not want to be treated by "beginners" and unexperienced hospital personnel you'd better not get sick over the holidays; and in case you do, please make sure to "postpone your death" in order not to ruin everyone's holidays ;-)

1. Inappropriate delay in seeking medical attention - best way out: don't wait until all the presents have been wrapped out, when aunt Mary chokes over her food
2. Reduced levels of healthcare staffing or fewer staff members who are familiar with individual patients during holiday on-call schedules - best way out: better avoid getting sick in the first place if you do not want to be treated by the SCRUBS staff
3. Increased emotional stress - just ignore your nephew when he starts crying because he did not get the Nintendo Wii he wrote on his wish list
4. Decreased our of daylight - make sure to get as much of the little light there is during prolonged walks with the whole family (may also help cool down any raised tempers ;-)
5. "Postponement of death" - tell your 127 year old uncle that he has been waiting so long now that it would be very inappropriate to die now and ruin everyones' Christmas celebrations

Well, I guess, now that you know about all the terrible things that could happen and the best ways to avoid them, it is about time to wish you, your family, friends and loved ones a happy (death-free) holiday season! And in case you need a break from the festivities, there is no Christmas break, here it at the SuppVersity ;-)

"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

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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).