Dr. Andreas Eenfeldt is 6’7” tall – a fact I’ve managed to work into two pre-cruise roasts, as well as the speech I gave on this year’s cruise. You can always spot the man in a crowd, unless he happens to be standing amidst an NBA team.

You might expect that his brother is equally tall, or that he’ll have a son someday who’s as tall or taller. But that expectation would be wrong. His brother (an affable guy I’ve met on two cruises) is 6’3” – still tall, but four inches shorter than Andreas. That’s because Andreas is an anomaly. He’s proof of the statistical principle that given enough chances, chance happens. And what happened in his case was probably a genetic quirk that produced some extra human growth hormone.

In his book How Not To Be Wrong: The Power of Mathematical Thinking, math professor Jordan Ellenberg explains how anomalies can produce study results that are statistically significant, but nonetheless due entirely to chance. I recounted those points in a previous post.

In a later chapter, Ellenberg describes how we can fooled by the flip-side of given enough chances, chance happens: regression to the mean. After chance happens, things tend to return to normal.

Ellenberg begins the chapter (titled The Triumph of Mediocrity) by recounting the work of a professor of statistics named Horace Secrist. After examining data on hundreds of businesses in the 1920s, Secrist wrote an influential paper with a rather startling conclusion: the competitive forces of American capitalism lead to mediocrity in business. Secrist’s evidence was that when businesses produced record-high profits one year, they tended to produce average profits in subsequent years. Likewise, firms that produced record-low profits tended to show higher profits in subsequent years. Therefore, something about capitalism must produce middle-of-the-road mediocrity, Secrist concluded.

But as Ellenberg explains, what Secrist’s data demonstrates isn’t an inherent flaw in competitive capitalism, but the simple fact that anomalies tend to be followed by a regression to the mean. A business can have a bang-up year for any number of reasons: a disaster that affects the competition, a temporary surge in demand, etc. When the temporary conditions that produced the record profits go away, so do the record profits.  It’s business as usual again.  That doesn’t mean the company’s management drifted into mediocrity.

Or on the subject of height:

People drawn from the tallest segment of the population are almost certain to be taller than their genetic predisposition would suggest. They are born with good genes, but they also got a boost from the environment and chance. Their children will share their genes, but there’s no reason the external factors will once again conspire to boost their height over and above what heredity accounts for. And so on average, they’ll be taller than the average person, but not quite so tall as their beanpole parents. That’s what causes regression to the mean: not a mysterious mediocrity-loving force, but the simple workings of heredity mixed with chance.

I don’t know how Dr. Eenfeldt would feel about being called a beanpole, but you get the idea. It’s not likely that he’ll produce a son who is 6’7” or taller.

Ellenberg points out that regression to the mean is also common in sports. An NFL running back has a record-setting year, scores a huge contract as a result, then never puts up those stratospheric numbers again. Grumbling fans assume that with all those guaranteed millions in the bank, the running back lost his desire or work ethic. In fact, it’s probably just a case of regression to the mean. The running back was never going to have another season like the record-breaking one, fat contract or not.

As a personal example that doesn’t involve fat contracts or money of any kind, I saw regression to the mean during the past five days, when I played several rounds of disc golf against my buddy Jimmy Moore. Last year I kept our scores in a spreadsheet. My average at the end of that week was 6 under par on the dot.  (It’s an easy course, by the way.)

In one game yesterday, I tossed a 10 under par … four strokes better than average, and my lowest score ever when playing against Jimmy. The next game (a mere 90 minutes later), I finished just two under par … four strokes worse than average.  When I tabulated all my scores for the five days we played, my average was … wait for it … 5.8 under par. I have good games and not-so-good games, but I always seem to drift back to right around 6 under par.

So what does all this have to do with diet and health? Plenty. Here’s an example from Ellenberg:

Almost any condition in life that involves random fluctuations in time is potentially subject to the regression effect. Did you try a new apricot-and-cream-cheese diet and find you lost three pounds? Think back to the moment you decided to slim down. More than likely it was a moment at which the normal up-and-down of your weight had you at the top of your usual range, because those are the kinds of moments when you look down at the scale, or just at your midsection, and say Jeez, I’ve gotta something. But you might well have lost three pounds, apricots or no apricots, when you trended back toward your normal weight.

Okay, so maybe a loss of a few pounds that would have happened anyway fools people into continuing with a wacky diet for awhile. That’s not a big concern in my opinion. The real concern is when regression to the mean fools doctors and researchers, or gives researchers a chance to fool doctors and the public at large.

Ellenberg doesn’t get into the subject, but Chris Masterjohn did in an article from 2011. You can read the entire article if you want the details, but here’s the main point: regression to the mean can exaggerate the reported efficacy of drugs and other treatments.

Suppose researchers are screening subjects for a trial on a cholesterol-lowering drug. Part of the screening process is a lipid panel. Because of the principle that given enough chances, chance happens, some people are going to have a spike in cholesterol on the day of the screening. So they’re now labeled as people with high cholesterol and enrolled in the study. When they’re screened again later, their cholesterol is lower – which it would have been anyway because of regression to the mean. But the lower number is attributed entirely to the drug.

As Masterjohn points out, if it’s a large enough study and the subjects were properly randomized, the regression-to-the-mean effect should be roughly equal in both groups, because both groups would include equal numbers of people whose first screening occurred on a day when their cholesterol was spiking. But smaller studies and studies in which subjects weren’t carefully randomized are particularly prone to the regression-to-the-mean effect. To quote from Masterjohn’s article:

And thus we see that many published research findings are false. Some of these false findings exist because we would inevitably expect by the laws of probability for a small handful of well conducted, thoroughly reported, and appropriately interpreted studies to uncover apparent truths that are really false simply by random chance. This emphasizes the need to look at the totality of the data. Some will be false because of regression to the mean. This emphasizes the need to critically evaluate the data in each study.

The New Yorker ran an article in 2010 about the “decline effect” — the tendency of significant results reported in scientific experiments to end up less significant or even insignificant in practice or in further experiments:

All sorts of well-established, multiply confirmed findings have started to look increasingly uncertain. It’s as if our facts were losing their truth: claims that have been enshrined in textbooks are suddenly unprovable. This phenomenon doesn’t yet have an official name, but it’s occurring across a wide range of fields, from psychology to ecology. In the field of medicine, the phenomenon seems extremely widespread, affecting not only antipsychotics but also therapies ranging from cardiac stents to Vitamin E and antidepressants.

The article partly blames the usual suspects, such as data manipulation and publication bias:

John Ioannidis, an epidemiologist at Stanford University, argues that such distortions are a serious issue in biomedical research. “These exaggerations are why the decline has become so common,” he says. “It’d be really great if the initial studies gave us an accurate summary of things. But they don’t. And so what happens is we waste a lot of money treating millions of patients and doing lots of follow-up studies on other themes based on results that are misleading.”

In 2005, Ioannidis published an article in the Journal of the American Medical Association that looked at the forty-nine most cited clinical-research studies in three major medical journals. Forty-five of these studies reported positive results, suggesting that the intervention being tested was effective. Because most of these studies were randomized controlled trials—the “gold standard” of medical evidence—they tended to have a significant impact on clinical practice, and led to the spread of treatments such as hormone replacement therapy for menopausal women and daily low-dose aspirin to prevent heart attacks and strokes. Nevertheless, the data Ioannidis found were disturbing: of the thirty-four claims that had been subject to replication, forty-one per cent had either been directly contradicted or had their effect sizes significantly downgraded.

I’m sure Ioannidis is correct about the intentional distortions.  But the article also mentions apparently honest scientists who were surprised when they couldn’t reproduce their own results, using the same methods.  Makes me wonder if some of the decline effect is simply the result of regression to the mean.

Either way, the lesson is clear:  a lot of drugs are approved based on results that don’t hold up over time.  So when your doctor prescribes the latest wonder drug, keep in mind it may not be such a wonder drug after all.  As the New Yorker article says, in the field of medicine, the phenomenon [the decline effect] seems extremely widespread.

And if you’re getting ready to pick your team for a fantasy football league, I wouldn’t count on the players who had record-setting seasons in 2014 to repeat that performance.


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Interesting items from my inbox and elsewhere …

We’re under-statinated!

Yup, according to this article about a Harvard study, even more people should be on statins:

A new study from Harvard T.H. Chan School of Public Health researchers has found that it would be cost-effective to treat 48-67% of all adults aged 40-75 in the U.S. with cholesterol-lowering statins. By expanding the current recommended treatment guidelines and boosting the percentage of adults taking statins, an additional 161,560 cardiovascular-related events could be averted, according to the researchers.

Well, why the heck stop at 67 percent? The way these guidelines keep expanding the definition of “at risk,” you’ll soon be considered at risk for a heart attack the day you’re born.  Best start adding statins to baby formula just to be sure.  I’m reminded of something Dr. Malcom Kendrick wrote in his terrific book Doctoring Data:

The boundaries that define illness have narrowed inexorably. When I first graduated from medical school in 1981, a high cholesterol level was anything above 7.5 mmol/L. Over the years, this level has fallen and fallen to the point where a ‘healthy’ level is now 5.0 mmol/L. I suspect it will soon be 4.0 mmol/L. Anything above this figure, and you have an increased risk of heart disease – allegedly. Considering that over 85% of the adult population in the western world has a cholesterol level higher than 5.0 mmol/L this is a quite amazing concept. I will admit that I have never been that brilliant at statistics. However, it seems to me that attempting to claim that more than 80% of people are at high risk of heart disease stretches the concept of ‘average’ to the breaking point – and well beyond.

Back to the article about the Harvard study:

“We found that the new guidelines represent good value for money spent on healthcare, and that more lenient treatment thresholds might be justifiable on cost-effectiveness grounds even accounting for side-effects such as diabetes and myalgia,” said Ankur Pandya, assistant professor of health decision science at Harvard Chan School and lead author of the study.

Yeah, what’s a little muscle pain, memory loss or diabetes when you might reduce your risk of a heart attack by teensy-weensy percentage?

They also found that the optimal treatment threshold was particularly sensitive to patient preferences for taking a pill daily, which suggests that the decision to initiate statins for primary CVD prevention should be made jointly by patients and physicians.

When your physician sits down with you to make that joint decision, I suggest you give the answer I gave when a doctor suggested a statin for my (ahem) “elevated” cholesterol:

“I wouldn’t take a statin unless you held a gun to my head and I was convinced you’d pull the trigger.”

Fat makes you feel full … and makes you fat … and … say what?

Pronouncements by nutritionists often make me want to bang my head on my desk. Others just leaving me scratching my head in wonder. A reader sent me a link to an article about avocadoes which includes this gem from a nutritionist:

As with many other fruits, avocados’ primary risks are related to overconsumption. “Consuming too many avocados may lead to weight gain because of the fat content, even though it is an unsaturated fat,” said Flores. “It can also lead to nutritional deficiencies, since fat is digested slower and leaves you feeling fuller longer than [do] other nutrients.”

Go ahead, try to wrap your head around that one. I double-dog dare ya. In just two sentences we learned that 1) fat makes you feel full longer than other nutrients, but 2) fat also makes you fat. So I guess the key to weight loss is to eat foods that don’t make you feel full. Oh, and 3) feeling full leads to nutrient deficiencies.

Uh … uh … because you stop eating before you eat enough to get your nutrients? But then you gain weight?

I’m starting to think every time a nutritionist leaves a crowded room, the average IQ goes up by at least 10 points.

Soy sorry about the soybean oil.

Somebody get Paul Newman on the phone and convince him to change the formula for those Newman’s Own salad dressings. A new study reported in an online article suggests soybean oil induces weight gain:

Sugar has been blasted in recent years for its link to obesity and a slew of health problems, but now experts say the food world has a new problem child: Soybean oil.

Soybean oil, considered a “healthier” alternative to some oils that contain more saturated fat, actually leads to more weight gain than fructose, according to new research on mice that was published in the journal PLOS One.

Okay, how many scientists and health organizations have to announce that saturated fat isn’t actually bad for us before we stop seeing products labeled as “healthier” because they’re low in saturated fat? A hundred? A few thousand? All of them? Anyway …

For their research, scientists divided the mice into four groups and fed them each a different diet that contained 40 percent fat (similar to the average American diet). One diet used coconut oil (which largely consists of saturated fat), another used half coconut oil and half soybean oil (which primarily contains polyunsaturated, or “good” fat). The third and fourth diets had fructose added.

All four diets had the same number of calories, and the mice were fed the same amount of food.

Here’s what researchers discovered: Mice that were on the soybean oil diet gained 12 percent more weight than those that ate a fructose diet, and 25 percent more weight than mice on the coconut oil diet.

The mice on the soybean oil diet also had larger fat deposits in their bodies and fatty livers, and were more likely to have developed diabetes and insulin resistance. Mice on the fructose diet didn’t get off easy, either — they had similar issues, but to a less severe degree.

It’s only a mouse study, so let’s not get too excited. We can’t conclude that the effects on human beings would be the same. But here’s what I find most interesting: the ol’ calories-in/calories-out theory sure didn’t hold up in this study, did it? Yes, these are mice, but we’re told over and over that CICO is A LAW OF PHYSICS. Mice aren’t immune from the laws of physics.

Neither are humans, of course. If you gain weight, you absolutely, positively consumed more calories than you burned. But what this study demonstrated (again) is that the quality of the calories consumed affects the number of calories burned. To repeat a quote from the article:

All four diets had the same number of calories, and the mice were fed the same amount of food.

So only an idiot would believe the mice on the soybean-oil diet gained 25% more weight because of calories alone.

It could also be a matter of calories alone, certified dietitian-nutritionist Jessica Cording tells Yahoo Health. Soybean oil is a fat, and fats contain nine calories per gram, she says. However, carbohydrates such as fructose contain four calories per gram.

Every time a nutritionist leaves a crowded room …

This thing will stop your weight from ballooning?

Up, up and away …. or down, down and in your belly. A balloon is the latest, greatest weapon in the Just Eat Less! battlefront, according to this article:

The FDA has approved a gastric balloon to treat obesity, adding to a fat-busting device arsenal that includes gastric banding and a vagal nerve stimulator.

The ReShape dual balloon system is indicated for obese adults who have a body mass index (BMI) of 30 to 40, and at least one other obesity-related comorbidity such as hypertension, high cholesterol, or diabetes.

It’s placed into the stomach using an endoscope, and once it’s inflated it is meant to diminish obesity by triggering feelings of fullness, “or by other mechanisms that are not yet understood,” according to the FDA press release.

It gives me great confidence in the FDA to hear that they’re approving medical devices whose mechanisms are not yet understood. But I totally understand that “triggering feelings of fullness” method for losing weight. I feel full after my meals. But those meals don’t include sugars or grains (or soybean oil) that induce weight gain.  In fact, I’ve lost weight while eating meals that made me feel full.

So what kind of dramatic weight loss does the up, up and away balloon induce?

In a 326-patient clinical trial, patients on the device lost an average of 14.3 pounds over 6 months, compared with 7.2 pounds for those in the control group.

Hmm, let’s do a little simple math here. The balloon-belly treatment group lost 14.3 pounds, while the control group lost 7.2 pounds. The trial lasted six months. Okay, hang on … subtract, divide … WOW!! That balloon was responsible for an additional weight loss of 1.18 pounds per month!

I think it would do more good if they filled it with helium and gave it a slow leak. Then people could at least sound like the munchkins from the Wizard of Oz when they say, “I walked around with an inflated balloon in my belly all month, and I only lost one extra pound? What the @#$% is the point of that?!”

Rice not nice to teeth?

This isn’t from an article; it’s from a book. When I commute to Nashville or spend five hours behind a mower cutting the back pastures, I listen to books. The one I just finished is Helmet For My Pillow, by Robert Leckie. If you saw the terrific HBO series The Pacific, Leckie was one of the marines featured. The audiobook is read by James Badge Dale, the same actor who portrayed Leckie in the series, which is a nice touch. You can listen to part of the book and then watch an episode of the series (as I did last week), and you’re hearing the same character speaking with the same voice.

Anyway, in Helmet For My Pillow, Leckie describes how after a battle, some marines would go prospecting in the mouths of dead Japanese soldiers. Why? Because at the time, Japanese dentists filled cavities with gold – and according to Leckie, some of the Japanese soldiers had a treasure of gold in their mouths. Lots and lots of cavities.

The Japanese weren’t eating lots of sugar in the 1940s – even today, the Japanese consume less than half as much sugar per capita as Americans. But they were certainly eating plenty of white rice in the years before WWII. In fact, on Guadalcanal, the U.S. navy was forced to withdraw for awhile, which left the marines stranded without a food supply. They ended up living on rations captured from the Japanese — which mostly consisted of rice.

So I’m thinking whatever its status as a safe starch, perhaps white rice isn’t so great for keeping a pearly smile.

Good thing I don’t much like the stuff.


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I’m just about out of the woods on the big work project.  I’ve been working long days partly to get ‘er done, and partly to front-load my billable hours so I can work shorter days next week.  Weather permitting, I’ll be spending part of my days next week in the front pastures, playing disc golf with Jimmy Moore.  It’s become an annual tradition — which for some reason we always observe during a July heatwave.

Speaking of Jimmy, The Ketogenic Cookbook: Nutritious Low-Carb, High-Fat Paleo Meals to Heal Your Body (which he wrote with Maria Emmerich) is now available.  I haven’t seen it yet, but I suspect he may have a copy with him when he arrives on Saturday.

As you probably know, I don’t measure ketones or aim for ketosis, but I always enjoy thumbing through new low-carb/keto/paleo cookbooks just because some of the recipes look awesome.  I still enjoy plenty of high-fat meals that would be considered ketogenic from a macronutrient standpoint.

We still have 500 pounds or thereabouts of pork in our downstairs freezer, so I’ll have to see if Jimmy has any especially good recipes for Boston Butt.  If not, there’s always sausage …



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Chareva and I were the guests on a recent episode of the AgriCast Digest podcast show.  We talked about chickens, of course, but also about diet and health, why we decided to move to a small farm, the upcoming kids’ book, etc.

You can listen to the episode here.


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Sometimes that darned working-for-a-living thing gets in the way of more important stuff — like writing blog posts.

I was too swamped with work on Monday to write a post, and I still am.  Chareva’s visiting her parents, so I’m also on chickens-dogs-cat-kids duty for the next few days.

See you next week … I hope.


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A production crew from Korea came to the Fat Head farm on Sunday to interview me and to film us collecting eggs and cooking them up with some sausage. The segments will go into a TV special about the controversy over whether saturated fat and cholesterol cause heart disease. I will, of course, be one of those who says nope, they don’t. They’ll interview several other people who share my opinion (Uffe Ravnskov and Chris Masterjohn among them, if I remember correctly) and, of course, the usual suspects who still promote the artergycloggingsaturatedfat! theory.

I wasn’t sure how specific they’d want me to be as far as citing research, so I took some time over the weekend to poke through my database of articles and studies. Turns out their questions were more general (“Do you worry at all about how much saturated fat you eat?”), but what the heck, since I came across a couple of interesting items in what I now think of as the Cold Case Files, I thought I’d share them.

The first is a study published in – wait for it – the Journal of the American Heart Association. The AHA is, of course, one of the organizations most responsible for scaring people away from saturated fat. Saturated fat raises cholesterol, and high cholesterol causes heart attacks, doncha know. At least that’s been their position ever since Ancel Keys joined the AHA board of directors.

But this study is from 1961 – before Keys joined the AHA board. So I find the study’s conclusions rather fascinating. The researchers gathered data on serum cholesterol levels and coronary artery blockage taken from 200 autopsies. Here’s what they found:

The mean atherosclerotic indices, i.e., the amount and severity of atherosclerosis in the aorta and the coronary and cerebral arteries, showed progressive increase with age.

The mean serum total cholesterol concentration rose progressively from the first decade to a maximum level in the fifth decade and subsequently declined.

In other words, cholesterol tends to rise until sometime around age 50, then drop a bit. The buildup of plaque in the coronary arteries, meanwhile, progresses throughout life. The researchers noted those facts because they wanted to avoid a false association:

The mean serum total cholesterol showed a progressive rise from the first to sixth groups of aortic atherosclerosis, but, at the same time, the mean age for each group also increased. Since the amount of atherosclerosis in the aorta increased with age and the serum cholesterol concentration also rose up to the fifth decade, it is important to determine if the significant correlation between the concentration of serum total cholesterol and aortic atherosclerosis is a correlation with severity of atherosclerosis per se or is merely due to the effect of age, or both.

So they compared serum cholesterol and coronary blockage within age groups. The results:

No correlation could be found between the two, indicating that, when the age factor was removed, the positive correlation between aortic atherosclerosis and serum total cholesterol is statistically insignificant.

And later in the same paper:

In the present study, we did not find any significant correlation between the blood serum total cholesterol and atherosclerotic index as a representation of the extent and severity of atherosclerosis for any of the vessels studied. The mean serum total cholesterol concentration in the six groups of aortic atherosclerosis showed a successive rise but, when the age factor was taken into consideration, the correlation between atherosclerosis and serum cholesterol in these same groups was found statistically insignificant.

No significant association once you take age into account.  Doesn’t that just make you want to run out and get a prescription for statins?

In my research database, I also found an abstract from a European Journal of Clinical Nutrition study of diets in the U.K. vs. France. It’s a bit of a silly study, based on dietary recall and all that, but I saved it because of this gem:

There were positive and negative trends in food consumption in each country. UK respondents reported eating more beans and pulses, less cheese, red meat, and processed meats than French respondents. However, on the negative side, they ate less fruit and vegetables, fish and poultry, cereals, and more sweets and chocolates and cakes, pastries, biscuits and puddings.

Hey, way to go, UK! Sure, the Brits reported eating more sweets and biscuits. But by gosh, they also reported eating less meat, processed meat and cheese than the French. I’m pretty sure they also eat less butter than the French. And aren’t foods like meat, cheese and butter the causes of heart disease?  They raise cholesterol levels, ya know.

The study was published in 2000. I happen to have spreadsheets of World Health Organization data on average cholesterol levels and heart-attack deaths from that period. (Some of it’s from 2000, some from 2002.) I plucked the data for the UK and France. I also added data for the Czech Republic, Germany and Russia. Why? Well, the Russians have low average cholesterol, the Czechs have the same average cholesterol as the French, and the Germans have one of the highest average cholesterol levels in the world.

Here are the average cholesterol levels among men, from lowest to highest:

Russia 189
UK: 197
France: 209
Czech Republic: 209
Germany: 220

According to the Cholesterol Kills! theory, the Russians are in great shape as far as heart disease, while the Germans are probably grabbing their chests and dropping like flies.

Here’s a chart I created in Excel to plot cholesterol levels against rates of heart-attack deaths. The blue line is average cholesterol levels among men; the orange line is annual heart-attack deaths per 100,000 men.

Hmmm, things aren’t looking so good for the Russians after all. And German men have fewer fatal heart-attack deaths as a group than men in the UK, despite an average cholesterol level that’s 23 points higher.

The Russian heart-attack rate is so high, including Russia scrunches the chart. So here it is again with Russia removed.

If high cholesterol causes heart disease, those lines should more or less rise together. But they clearly don’t. If anything, they tend to move in opposite directions.

It was fun digging through the Cold Case Files. But I’ll be happy when the entire Cholesterol Kills! theory is a cold case file.


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