Archive for the “Good Science” Category

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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My college physics professor once gave a lecture to a humanities class on the need for scientific literacy. At one point, he told us, “No matter what field you plan to go into, learn math. Math is how you know when you’re being lied to.”

I recently finished a book that makes the same point. How Not To Be Wrong: The Power of Mathematical Thinking was written by a math professor named Jordan Ellenberg who does a nice job of explaining mathematical concepts without causing the reader’s eyes to glaze over.

I liked the book, but before you run out and buy a copy, I should mention that much of the material it covers seems unrelated to the title. Yes, it’s interesting to learn how some MIT students crunched numbers and devised a plan to guarantee themselves payouts from the Massachusetts lottery under certain conditions, but the chapter won’t teach you how not to be wrong … unless you’re designing a lottery, that is.

That being said, there are several sections that are relevant for people interested in the health sciences. Rather than write one very long post about those sections, I figured I’d cover one or two topics in a short series of posts. So let’s start with a topic near and dear to my heart …

Statistically Significant

As I mentioned in my Science For Smart People speech, when most people say an event or a fact is significant, they mean it’s important or meaningful. But in the world of scientific studies, significant simply means that based on tried and true statistical formulas, the result is not likely due to chance. It’s important not to confuse the two meanings.

In science, significance is expressed as a p-value, which Ellenberg explains in the book. If the p-value is .10, there’s a 10% chance the results were due to chance. For the results of a study to be called statistically significant, the p-value must be .05 or smaller. But again, significant doesn’t necessarily mean important.

Given a large enough sample size and enough data to crunch, scientists could say, for example, that cigar smokers have a higher rate of mouth cancer and that the difference is significant. But if the “significant” difference is one additional case of mouth cancer for every 250,000 people, most of us wouldn’t consider that meaningful or important. The actual odds of developing mouth cancer have barely changed at all.

Ellenberg makes the same point about the meaning of significant, then tags on some additional warnings for readers who don’t want to be bamboozled by media reports on the latest something-will-kill-you or something-will-save-you study. One of those warnings falls into the scientists are freakin’ liars category:

And all this assumes the scientists in question are playing fair. But that doesn’t always happen…. If you run your analysis and get a p-value of .06, you’re supposed to conclude that your results are statistically insignificant. But it takes a lot of mental strength to stuff years of work in the file drawer. After all, don’t the numbers for that one subject look a little screwy? Probably an outlier, maybe try deleting that line of the spreadsheet. Did we control for age? Did we control for the weather? Did we control for age and the weather? Give yourself license to tweak and shade the statistical tests you carry out on your results, and you can often get that .06 down to a .04.

Now imagine the numbers you’re crunching are for what was supposed to be a breakthrough drug and there are millions of dollars at stake. You get the idea.

But here’s what I consider the most important (and significant) point Ellenberg makes in the chapter: If the p-value is .05, that means the odds are only 1-in-20 that those impressive results were due to chance, right? Right … which means given enough chances, I could end up with impressive results that are significant, but still due solely to chance.

Ellenberg asks us to imagine 20 scientists running independent experiments to determine if eating a particular color of jelly bean causes outbreaks of acne. In 19 of the experiments, the color of the jelly beans consumed makes no difference. But in one of the 20 experiments, the subjects who ate green jelly beans had more outbreaks of acne – and those results are significant, because the statistical odds of them being due to chance are just 5%.

The 19 scientists who found no difference grumble, light a cigar, toss back a scotch, stuff their papers in their desk drawers, and go write their next grant proposal. The one scientist who found a significant difference proudly publishes his results … and a day later, there are media headlines trumpeting the now-established “fact” that green jelly beans cause acne.

The significant result was due to chance. But as Ellenberg points out, given enough chances, chance happens. That’s why the significant results of many studies don’t hold up and can’t be replicated.

So (and this is me talking, not Ellenberg) … now let’s think about how science is conducted for Big Pharma. Drug companies aren’t required to publish all their results, so they don’t. They aren’t required to share the raw data with other scientists, so they don’t. A good friend of mine has a brother who worked in Big Pharma and admitted to my friend, “We just keep running studies until we get the results we want.”

Given enough chances, chance happens. And if that p-value of .06 is the best we could get after multiple chances, well, perhaps a little tweaking here and there …

That’s why I don’t trust the results of studies funded by drug companies. Ellenberg doesn’t come out and say as much directly, but he does mention that industry-funded studies often can’t be successfully replicated.

Math is how you know – or at least have reason to suspect – you’re being lied to.

More mathematical-thinking examples from the book in future posts.


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Well, Fat Heads, Tom had a long trip back from the Low Carb Cruise and I’m sure is preparing a great report, so that means I get to stay in the chair for another day. Wheee!

Okay, by going long I’m not referring to this guest-blogging stint. And I don’t mean like a long run. I mean thinking about a long, long time. Like evolutionary time and how it works into some of my recent (and not so recent) reading material and my model of reality.

Studying different ideas on how we got here and what it means and how everything relates seems to occupy quit a bit of my daydreaming time these days. Things like nutrition and economics and how we developed as runners are all part of the same model. They fit together. They explain things in an internally consistent manner.

So I thought I’d share a few more books that have added to this model. The first I read a few years ago and it dealt directly with some evolutionary ideas. The other two don’t explore evolutionary models directly, but with some of the modern fallout of not considering these realities.

Here’s the first one:

News flash — it’s highly likely, in spite of it being politically perilous to say so, that men and women are different, and in ways that are and will remain significant.

Sorry to just spring that on you.

But instead of just anecdotally compiling differences, Barbara and Allan Pease traveled the world talking to researchers studying the brain and evolutionary biology to illuminate, sometimes hilariously, just how and why we got that way.

Again, in deference to the politically correct times we live in, several of their sources insisted on some level of anonymity to avoid the wrath of the elite and jeopardy to their funding.

For example, men and women have different visual processing. Men tend to see out distances and be more “tunnel-visioned” — required for hunting on the savannah; whereas women tend to have better peripheral vision, which is important to detecting threats to her offspring. They also tend to read expressions and body language better (i.e., “intuition”) for the same reason. On the humorous side, they give an example of a couple leaving a party where the woman is asking “oh my God, did you see the looks those two women were giving each other!?!” with the natural male response of “huh?”

Auditory processing is different, also, with men having more ability than women to detect the direction a sound is coming from, while women are more attenuated to voices and inflections.

They spend some time talking about sexuality and how it can be affected by the levels of testosterone and estrogen the fetus is subjected to in the womb. This seemed related to the whole epigenetics field that is getting more attention, where it’s not just a matter of what chromosome pairs you have, but also how other factors affect expression of those genes. So sexuality, affected by both in utero and environmental factors, becomes not just an either/or proposition, but more of a “spectrum,” as, for instance, Autism is now understood. Or in other words, maybe Monsanto and Big Soy created Caitlyn Jenner!

It was one of those books that you don’t necessarily think they’ve proved the point on everything they talk about, but they all made you think. Agree or not, the writing is very entertaining and at times outright hilarious.

I hadn’t thought directly about the book for a long time, but when I read the next two books — really more something of a matched set — they seemed to tie back to this idea of fundamental differences that we ignore at our own peril:

These are both by the same author – Dr. Leonard Sax. He actually wrote the Boys Adrift book first, then followed up with the “Girls on the Edge.” Sax doesn’t go way back into the evolutionary model of differences between boys and girls, but makes a strong case that they do indeed exist and the fact that progressive society insists on denying their existence are harming children of both sexes.

He talks about how the kindergarten experience that Tom and I had, and perhaps many of you, no longer exists. For us, that meant at age 5 we were spending half a day finger painting, gluing things together, having stories read, maybe working on some letter recognition, counting, and playing outside on the playground.

Today’s kindergarten is the equivalent of our old first or even second grade — all day long, heavily focused on reading and academics, without nearly the amount of physical activity, so another priority is “sitting still.”

The problem is, girls’ brains at age five are generally ready to begin reading. The reading capacity of a five year old boy’s brain is about the equivalent of a three year old girl’s. You couldn’t design a better model to frustrate young boys, convince them that they’re dumb, and to begin hating school. Oh, and not being able to sit still is now a medically treated condition. i.e., “ADHD.”

[One interesting piece of research Sax cites in one of the books was on the psychology school classic where you give a bunch of kids toys and the girls end up playing with mostly dolls and some trucks, where the boys play almost exclusively with the trucks. Then you debate on whether it's some innate preference or environmental/social conditioning. You know which answer you're supposed to get, right?

Researchers did the same experiment with monkeys. Guess what -- the female monkeys played mostly with the dolls and some of the trucks, and the male monkeys played almost exclusively with the trucks. The theory is that it goes to the different visual processing between males and females in primates.]

Sax has other areas where each sex is being led astray. For boys, in addition to the feminization of school, they are also constantly exposed via the popularity and ubiquity of video games to levels of depraved behavior (i.e., rape and murder of innocents are rewarded in the games) unheard of even in hard-core porn in my generation, and medication for ADHD as already mentioned.

For girls, they are now sexualized way before they have reached emotional maturity, subjected to a 24/7 cyberbubble that stunts the growth of a real identity, and obsessions — whether being thin, the “brain,” the athlete, etc.

As a common issue for both boys and girls, Sax hits a resounding bulls-eye with “Environmental Toxins.” Unfortunately, he hits the bulls-eye on the wrong target.

Or at least, hits a single target to the exclusion of much bigger, more sound targets. I think most Fat Heads will be naturally open to the idea that the things we’re putting into, on, and around our bodies is having continuing bad effects on our collective health. Not just in terms of the sundry diseases of civilization, but also in the realm of epigenetics. There’s soy and its estrogen-mimicking havoc, gluten, the ungodly amounts of sugar in the SAD, etc. Sax points out that girls are entering puberty months and even years earlier than just a couple of decades ago. Men’s sperm counts are lower, boys’ bone are more brittle, and research indicates that exposure to environmental estrogens makes females less female and males less male (again, see Ms. Jenner).

Sax seems to have a near singular focus as to the source of all these evils as — plastic bottles. As in BPA. He has the studies and information to support his point, but I couldn’t help but think, “really? How about picking up a copy of Wheat Belly or something?” Is America’s problem, and our children’s health really more endangered by a plastic bottle of soda than by the rest of the crap on the school lunch program menu? So I’m with him on the environmental toxin idea — that we are literally playing roulette with our ancient genetic code — but think he’s missing a large part of the picture.

Other than that one issue, I thought both books were good reads, along with some very good suggestions for interested parents and grandparents who want to raise kids into healthy, productive adults. That makes their value higher than adding a few more interesting ideas to inform my model of the universe. I already bought a copy of “Girls on the Edge” for our daughter as the granddaughters are 5 and 7.

Hmmm. Looks like I could have also meant the length of this post when I said going long.

Ah well, I really enjoyed getting to man The Big Chair again for awhile. Hope I gave you couple of things to think about or add to your summer reading list. If nothing else, you got another great recipe from The Oldest Son out of the deal!


The Older Brother


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Dr. William Davis has a PBS special based on his book Wheat Belly Total Health that’s currently airing in several U.S. cities.  Here’s a list of times and stations.


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Hi, Fatheads!

Anyone else notice there’s been an uptick in mainstream media reporting related to the gut microbiome?

It’s even crept into my local paper, which picked up an AP article relating how artificial sweeteners could possibly tie to diabetes via its effect on said gut:

A preliminary study done mostly in mice suggests that artificial sweeteners may set the stage for diabetes in some people.

The study authors said they can’t make dietary recommendations but that their results should inspire more research into the topic.

Basically, the study suggests that artificial sweeteners alter the makeup of normal, beneficial bacteria in the gut. That appears to hamper how the body handles sugar in the diet, a situation that can lead to developing diabetes.

The results, from researchers in Israel, were released Wednesday by the journal Nature.

How about that. Not that this is new — the whole Resistant Starch thing triggered a lot of interest around here in the gut — the “second brain,” as one researcher called it — awhile ago.

It had been on the radar for quite awhile. I remember seeing a year or two ago research talking about how there where over 150 distinct species of this microbiome community that lives on and inside us, but aren’t related to us — i.e., don’t have any of our DNA.  They have 100 times the number of genes we have, and weigh at least a couple of pounds. They drive all kinds of chemical and physiological processes in us, but have been largely unstudied.

Like I said, not new. What is new is that it’s news.

I didn’t think the general media would be reporting on this stuff for years. I mean, you’re just starting to see LCHF get regular respectable mentions, and now even saturated fat is getting better press, but that’s been a decades-long haul.

Within days of seeing the artificial sweetener/diabetes story,  I also saw a couple of other “gut” articles in Yahoo’s new links. One was from Forbes on the same idea, but this time specifically targeting diet sodas as culprits through the same mechanism of altering the gut balance. Then, another linking through to the Huffington Post(!) regarding food allergies:

Mice that were raised in a sterile environment or given antibiotics early in life lacked a common gut bacteria that appears to prevent food allergies, US researchers said Monday.

The bacterium, called Clostridia, appears to minimize the likelihood that rodents will become allergic to peanuts, and researchers would like to find out if it does the same in people.

In the meantime, they found that supplementing rodents with probiotics containing Clostridia later in life could reverse the allergy, according to the report in the Proceedings of the National Academy of Sciences…

The precise cause of food allergies is unknown, but some studies suggest that changes in diet, hygiene and use of antimicrobial soap and disinfecting products may lead to changes in the bacteria of the gastrointestinal tract that leave people more susceptible.

I’m not sure what I found more amazing; that the HuffPo would cover something accurately, or that I would read something they printed.

To be clear, many of these studies were looking at mice, and we know that is far from a “gold standard.” I didn’t perform Tom’s normal exercise of pulling up and dissecting the source articles.

First of all, that’s not in my wheelhouse. But mainly, I’m not interested specifically in the research, per se — it’s the fact that it’s seeped into the regular press, and is providing answers to some questions many people seem to be seeking better answers to. Like, “how come all of these kids seem to be allergic to everything these days?”

I also find it interesting in that these are reporting findings that aren’t in line with the current medical establishment zeitgeist. The reports indicate the answer may be in less medicine, less sterile environments, less industrial foodstuffs.

I really didn’t expect to see anything about the gut microbiome until Merk or Monsanto or someone figured out a way to patent a couple of them, then that’s all we’d hear about.

I think it’s possible that the things Tom talked about in his Vox Populi speech — why people just don’t believe the “experts” in medicine, nutrition, etc. and are looking to the “wisdom of crowds” — are starting to guide the questions that get asked, and the stories that get covered.  A couple of years ago, the only answer to food allergies was testing, avoiding, and a prescription. All of your reported options resided in the medical establishment, because those were the only people who got asked.

Now, it’s looking more like the press and regular folks are starting to clue in that there’s other options. Like, keep little Johnny away from the Pink Stuff unless it’s major, and let him go outside and eat some dirt.

Just your grandma told you. See, it was science after all.


Well, Tom should be wrapping up the big parts of the book by now so Chareva can start doing her part.  Sorry you got stuck with me for an extra week, but it should pay off in the end. The Wife and I are going down to their farm next week, so maybe I’ll get a sneak preview. At least I’ll get to try this “disc golf” thing.

Thanks for putting up with me. See you in the comments.


The Older Brother


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Isn’t evolution great?

I don’t mean the monkey-to-mankind stuff.  I got tired of that debate years ago.  I’m talking about the kind of evolution you can observe.  Specifically, how folks in the low-carb,  paleo, LCHF, etc., etc. camps have evolved back to potatoes!

Yes, the lowly tuber is back in the rotation, and I’m happy about it.  Honestly, I was okay with not eating them, and still like the recipes with cauliflower, but The Wife had really missed them, and as we all know, “when momma ain’t happy, ain’t nobody happy.”

After getting clued in to Richard Nikoley’s (of the Free the Animal blog) new thinking on resistant starch, I had two thoughts:

1) That’s really interesting, and seems to fit with the paleo/evolutionary model; and

2) How am I going to tell The Wife?

I’m kidding. Some. She really had been a good sport, and went above and beyond the call of duty experimenting with cauliflower, turnips, rutabagas et. al.  But she missed them more than I did.   I wasn’t sure how she was going to take it when I told her the whole “no potatoes” thing for the last couple of years had all been a big misunderstanding.

I just knew I didn’t want to be in the room alone with her at the time. Fortunately, The Oldest Son happened by and asked how she’d taken the news that potatoes were actually okay. Right in front of her, before I’d said anything.  She took it really well.

So they’re back, and we’ve been enjoying them in moderation.  Like this:

Those are Wasabi/Horseradish mashed potatoes under that grilled, sesame-seed crusted tuna, with the bacon-wrapped asparagus as a sidekick.

Tuesday was one of our pastured chickens that The Oldest Son & I had processed, with sides of peas and “Bourbon Bacon Whipped Sweet Potatoes with Brown Butter and Crispy Sage.” The sides looked like so:

[Foodie alert: Not a very good picture -- the sweet potatoes had a much better presentation besides being delicious. Have to say, we didn't get much out of the sage. That's the second recipe we've tried with fried sage. From now on, we're putting it in raw or just skipping it.]

Forgot to take a pic while they were plated with the chicken, which was used in the “Chicken with 80 Cloves of Garlic” recipe from the Eades’ book, “The 6-Week Cure for the Middle-Aged Middle” …

That was one tasty bird, and the new thinking on tubers (I know sweet potatoes were already kind of tolerated) really added something. We’ve also taken, as Tom has also mentioned, to baking some potatoes and just keeping them in the fridge.

All of the potatoes recipes are made ahead of time, then refrigerated. We’re interested in the resistant starch process, but the fact is that they taste just as good — and I think maybe better — when reheated, and there’s a real convenience factor being able to prepare some courses ahead of time, so you’re not juggling them at the same time as a rocket-hot charcoal chimney…

… that tuna only goes 30 seconds a side, so it’s nice to be able to focus on the main dish.

Okay, back to the evolution thing. My real point is — how long did it take, given a heretical “new” idea introduced to the sundry LCHF, paleo, etc. communities, for what was really something of a paradigm shift to occur. I know, not everyone is necessarily on board with the tuber stuff yet, and “your mileage may vary” depending on whose N=1 experiment we’re talking about. But seriously, there’s been a pretty abrupt shift in the general model of nutrition and how these venerable starches fit in.

The inconvenient facts Richard raised were, albeit with some perseverance required, gradually looked at and evaluated. When it became reasonably apparent that the current thinking couldn’t account for these facts, the model adjusted. It wasn’t declared a “Tuber Paradox.” Most people didn’t double down and commence name-calling. The model changed.

It evolved. It’s robust. It adapts. It bends. It improves.

Contrast that with the official government line on, well, just about anything. Saturated fat. Statins. Cholesterol. Hearthealthywholegrains. The gut biome (official government line on the gut biome: “the what?”). Farm programs. Subsidies. War. Energy. Bailouts. Raw milk.

Nothing changes. Once a “model” is adopted by a bureaucracy, all of the money and power coalesces around the model, not the pursuit of the knowledge the model was trying to conceptualize.

Government models don’t adapt. They implode. They collapse.

This is the difference between the market, the “wisdom of crowds,” on one side, and on the other various systems of force, which are genetically infected with what F.A. Hayek termed “the fatal conceit.” Eventually, the options are — evolution, or extinction?

I’m going with the fries.

See, I did have something on my mind other than teasing you all with some food pics. If that’s all I wanted to do, I would’ve put in a picture of Sunday’s desert.


Ok, the honey-lavender ice cream wasn’t low-carb or paleo, but it was all real — honey, cream, egg yolks, lavender. Yeah, the praline basket was a total cheat.


The Older Brother


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