Author: slimemoldtimemold

Review of Krinn’s Self-Experiment Where She Lost Weight Taking High Doses of Potassium

slimemoldtimemold, , , , , , 10 Comments

Krinn is a reader who participated in our Low-Dose Potassium Community Trial. She lost 6 lbs taking low doses of potassium, and liked it enough that she decided to keep going, along with a new exercise habit to help support the weight loss. And she started trying higher doses of potassium, eventually ramping up to around 10,000 mg of potassium a day. 

This is a lot — way more than the average person gets from their diet, and a lot more than people added in the original potassium trial.

Krinn writes, “I decided to stabilize at about 10,000mg per day … because that’s about how much potassium people were getting during the SMTM potato diet community trial. … Aiming for that amount also meant that it would be easier to compare my results to something that worked decently well and to ask questions like ‘is there something special about whole potatoes, or is it mostly the potassium?’ If it’s mostly the potassium, you’d expect my results to be closer to the full-potato-diet results than to the low-dose-potassium results — which is what happened.” 

Indeed, she lost quite a bit of weight. Here’s the chart of her weight change so far: 

Having just passed six months on potassium, Krinn has published the current state of her results, along with her protocol, data, thoughts, and comments, in a tumblr post titled An Ad-Hoc, Informally-Specified, Bug-Ridden, Single-Subject Study Of Weight Loss Via Potassium Supplementation And Exercise Without Dieting (henceforth, AAHISBRSSSOWLVPSAEWD). Here’s an archive link if tumblr doesn’t work for you.

Krinn’s report is excellent — nuanced, detailed, and clearly written. She covers almost every aspect of her self-experiment better than we could, so we won’t try to restate her points. We recommend that you read the report for yourself. Instead we will focus on the few small areas where we can add some speculation or additional context.

(Krinn’s full report is also reproduced in an appendix below, because tumblr posts do not always have the best longevity and we figured it might be good for the report to exist in two places.)

Again, you may want to read what Krinn wrote before you take a look at our comments. But we will restate this part: while this seems to be working for Krinn, it’s not clear that high doses of potassium are safe for everyone, and they almost certainly are not safe if you have kidney disease or related diseases like diabetes. Do not try supplementing doses this high without consulting your physician, and absolutely do not try it if you have kidney problems or any conditions that might compromise your kidney function.

Ok, here are our thoughts: 

Optimal Weight Loss Brine

Originally, we argued that high doses of potassium alone might be responsible for weight loss on the potato diet. After all, eating nothing but potatoes does give you heroic doses of potassium.

In retrospect, that seems a little naïve. Sure, it could be just the potassium. But biology tends to be a bit more complicated than that. 

This insight was sparked in large part by Salt, Sugar, Water, Zinc: How Scientists Learned to Treat the 20th Century’s Biggest Killer of Children, an excellent piece by Matt Reynolds on the history of oral rehydration solution (ORS) for the treatment of cholera. ORS is very simple to make, but discovering the right formula was strangely difficult. 

At risk of oversimplifying (read the original piece), people knew that cholera patients needed electrolytes, but feeding them an electrolyte solution didn’t seem to help.

Through a series of coincidences, people eventually discovered that adding glucose to the electrolyte solution sometimes made the treatment work. But this didn’t immediately lead to a cure, because if you put too much glucose and salt in the solution, it made patients worse instead.

After more confusion, they discovered that sugar and sodium ions are absorbed together in the gut through a sodium-glucose cotransport protein, but you need the right concentration or it will dehydrate the patient instead, which often kills them. The solution was simple, but getting there was hard.

Getting sodium into someone’s body isn’t simple — you need to include glucose in your rehydration formula, and even then, you need to get the right ratios. This makes us suspicious that something similar might be the case for potassium.

Even if high doses of potassium are required for curing obesity, it seems pretty likely that potassium by itself isn’t the whole engine. So now we are looking for some other set of factors, probably other switches that are triggered when you eat ~100% potatoes, that might also be needed to make the gears of weight loss mesh. 

Magnesium

The most likely candidate at this point seems to be magnesium.

Potassium and magnesium serve many complementary roles in our biology, and the two minerals are often prescribed together. We spoke to a physician about this, and he pointed out that for patients with low potassium (hypoK), if you don’t have enough magnesium (hypoMg), you’ll be hypoK forever unless you fix the hypoMg first, because of “some renal excretion thing I think” (his words). See also this paper, which says: “magnesium replacement is often necessary before hypokalemia and potassium depletion can be satisfactorily corrected with potassium supplements.”

Electrolyte mixes, like LMNT and Snake Juice, are sometimes used for weight loss, and these mixes usually contain some amount of magnesium. Assuming that, by a process of natural selection (it’s a electrolyte-powder-eat-electrolyte-powder world out there), they have run across something like the right electrolyte ratios to cause weight loss, this also suggests that magnesium might be involved.

Like potassium, most people are not getting enough magnesium, at least per the official recommendations. According to the NIH, you’re supposed to get 300-400 mg of magnesium per day. And potatoes are not only high in potassium, they are also pretty high in magnesium. Each potato contains about 40-50 mg of magnesium. Someone on the potato diet would be getting about 800-1,000 mg of magnesium per day.

If potassium and magnesium together are the cause of this weight-loss effect we’ve discovered, this would explain why potatoes are such a reliable way to cause weight loss, and why they’re often more effective than supplementing straight potassium.

All the above are just reasons for the hunch, but we also want to note that this hunch is supported by Krinn’s case study.

In her report, Krinn writes: 

My go-to snacks are cashews, pistachios, cherries, and granola bars.

Cashews and pistachios are particularly high in magnesium, providing 260 mg per 100 g and 110 mg per 100 g, respectively. We don’t know exactly how much cashew Krinn is consuming, but it’s likely that it’s giving her a respectable amount of extra magnesium.

We discussed this with Krinn over twitter DMs, where she said, “cashews are one of my go-to snack foods, so whatever amount I’m getting, I would be extremely surprised if I was getting something less than a healthy amount of magnesium in my diet.”

She also notes that she tends to consume the magnesium and the potassium relatively close to one another.

…another way to look at it is that “eating cashews” and “drinking gatorade+potassium” are never _that_ far apart

since the potassium stuff is spaced out 4–6 hours apart across the day, and I eat cashews at least once a day, that puts a ceiling on how far apart those two things could possibly be

We have no idea if it’s helpful or necessary to take potassium and magnesium at the same time, but it’s worth noting that Krinn tends to leave little daylight between them.

In short, we’ve suspected for a while that potassium might only cause weight loss, or might cause it more effectively, when combined with reasonable doses of magnesium. The potato diet would get this “for free”, since potatoes contain high amounts of both. This hunch predicts that people who lose weight by supplementing straight potassium will also likely be getting high doses of magnesium from some source, either from supplements or their diet. Krinn’s case matches that prediction.

We’ve also recently been corresponding with a participant from the half-tato diet, who told us that straight potassium seems to have some kind of effect for her, and who mentioned that she has been supplementing 266 mg magnesium a day as magnesium glycinate capsules. Not conclusive, but another hit for the prediction. 

Stearic Acid

Another possible connection is with stearic acid, a fatty acid found in “meat, poultry, fish, eggs, dairy products, and foods prepared with fats; beef tallow, lard, butterfat, cocoa butter, and shea butter are rich fat sources of stearic acid.”

If you’ve heard of this before, it’s likely from Fire in a Bottle (FIAB), a website/program/theory which argues that a diet high in stearic acid can cause weight loss. This is sometimes called The Croissant Diet (TCD), presumably in the hopes of confusing readers — you do not actually eat nothing but croissants. In fact, you don’t have to eat any croissants at all. But you do ideally eat lots of foods high in stearic acid, sometimes supplementing with additional stearic acid, and some people seem to lose weight when they do this.

We find the evidence for stearic acid to be pretty thin (though see FIAB for the other side). And there’s no theoretical reason to suspect that stearic acid influences potassium uptake or anything. But there are just a few hints, so we figured we might mention them here. 

One is from our half-tato diet. Most people lost only a small amount of weight on the half-tato diet, but one person (participant ​​25348806) lost 17 lbs in four weeks. This person gave us detailed notes about the rest of her diet, and this part jumped out in particular: 

…I also have dairy – at least one glass of milk a day (either raw whole milk or 2% or whole conventional) – and a small amount of juice or lemonade.  Some mornings I may have full fat yogurt with collagen and stearic acid (see fireinabottle.net) but not all mornings.  I have some extra potassium as well as other supplements.

For comparison, here’s what Krinn has to say about her fat intake: 

I use a generous hand when measuring out olive oil. I believe that if you need either milk taste or milk fat, you shouldn’t half-ass it, so when I need milk taste or milk fat, I rely on whole milk and heavy cream. Fats, generally, taste good. … Once in a while, dark chocolate, usually with the nuts and fruit.

Milk fat and chocolate (via cocoa butter) are both high in stearic acid. You’ll also notice that Krinn usually takes the chocolate with her cashews.

And remember that participant from the half-tato diet we mentioned at the end of last section? Based on these hints, we asked her if she also consumes a lot of stearic acid, and she told us she eats a whole lot of dairy fat, and chocolate “EVERY DAY” (her emphasis). 

This is certainly suggestive, but what doesn’t fit is the potato diet. Potatoes are high in both potassium and magnesium, so it would make sense if high doses of potassium and magnesium conspire to create the potato weight loss effect. But potatoes contain very little fat and approximately 0% stearic acid. The idea that you might need stearic acid to cause the potato effect is rather inconsistent with the potato diet, since you do not get appreciable amounts of stearic acid from potatoes.

That said, there are hints that people who are on half-tato, or who are supplementing potassium directly, do benefit from stearic acid. At the very least, we’ve noticed that some of the biggest success stories are people who have been getting decent amounts of stearic acid in their diet. Maybe stearic acid helps when you are getting less enormous doses of these minerals?

It’s always possible there’s some unknown connection — maybe the potato diet only works for people who already have sufficient reserves of stearic acid in their body. Or maybe some people need stearic acid for the effect to kick in and others don’t, for genetic reasons. Or maybe other fatty acids can substitute in a pinch, but stearic acid happens to be slightly better than average. But at this point, it definitely fits less well than magnesium. 

This Age Needs Heroes

We love everything that Krinn did here. She participated in a community trial, decided to keep going, and spun it into a self-experiment. She came up with her own design and attacked her questions in her own unique way. She did something interesting and she wrote it down so that all ingenious people could be informed thereof and consider the results for themselves.

More people should do what Krinn did, and get involved in the business of conducting science. Anyone else who wants to do anything else remotely like this should feel free to reach out, we’d be happy to help. We’re in the process of writing a whole series about how to conduct self-experiments, which may be a good starting point. Consider this your invitation.

Here’s a reproduction of Krinn’s full report as it appears in her tumblr post:

An Ad-Hoc, Informally-Specified, Bug-Ridden, Single-Subject Study Of Weight Loss Via Potassium Supplementation And Exercise Without Dieting

Here’s the short version: I lost 30 pounds in 6 months by chugging a bunch of potassium salt and exercising a lot. My subjective experience is that cranking my potassium intake way up made it possible to do a lot more exercise than I had been doing without also eating a lot more. Exercising more without also eating more led to weight loss (as one would hope!). I did not diet: I ate as I had been doing and as it pleased me to do. Do with the raw data as you please.

Losing weight this way is unusual and worth paying attention to because many things about increases and decreases in weight and obesity are very poorly understood. Many people would like their personal weight and obesity levels to be different, so anything that improves our collective understanding of how to make that happen is valuable. However, losing weight this way is an experiment: it’s not necessarily safe to do what I did! Part of why I did it was to find out what would happen, and if you have any kind of existing kidney problems then you definitely should not do what I did. Note to other transfemmes: if you’re taking spiro, that counts as a kidney problem.

I also don’t want to overstate the significance of this experiment: what I’ve been up to in the last 6 months amounts to a single data point that happens to also be 1,100 spreadsheet cells. It’s a data point that is highly suggestive, sure, but it would be extremely ambitious to say that it proves anything beyond “this worked for me” and perhaps “it’s not impossible for this to work”. I am writing about it because as far as I know, this particular experiment is something that nobody else has tried, and, again, anything that improves our collective understanding here is valuable.

The long version comes next: how I came to be doing this experiment, what I did in the experiment, what I plan to do next, and finally what I think about it all. The really long version is the ongoing conversation that this post is part of, starting with A Chemical Hunger, which is a book-length literature review about the 1980s–present global increase in obesity prevalence, also the posts about single-subject research where the same authors discuss the limits of what can be learned from experiences like mine, also the Experimental Fat Loss guy and his wide variety of diet-only experiments, also some critics who disagree.

How I came to be doing this

At the tail end of 2022, I noticed both that my BMI had hit 30 and that I had become very unhappy about my weight. There’s a specific photo where I didn’t realize until I saw the photo that my belly was hanging out over my waistband and it’s vividly unpleasant in my memory. Around the same time, I happened to find the potassium-supplementation community trial that the Slime Mold Time Mold folks were running. The value proposition was “this will be easy, cheap, and safe, but also it might not actually work,” and that sounded good to me, so I signed up for it and took a modest amount of potassium all through December and January. It kinda-sorta worked: I lost 6 pounds. Not nothing, but “it kinda-sorta worked” is the most one can really say about losing 6 pounds in 60 days.

The low-dose potassium delivered on all of what the SMTM folks promised, though. It was easy, cheap, and safe. So I kept doing it and, since I was already doing the potassium, decided that I should get an exercise habit going. I am a big believer in the idea that it’s a tremendous amount easier to go from doing Something to doing More Something than to go from doing Nothing to doing Something. The low-dose potassium got me through the first step: once I was doing Something about my weight, it was relatively easy to do More Something. When the community trial ended in early February, I didn’t have to worry about messing up its results by departing from the trial’s instructions, so I started taking more potassium and building my own experiment. I also kept in touch with the SMTM authors, who were very encouraging. 🐯💕

By late March I had brought myself up to daily amounts of potassium and exertion that seemed good to me, and I stuck with those. This is the first time in my life I’ve focused on trying to lose weight, and I was not fully prepared for how demoralizing it is that the weight change from day N to day N+1 sometimes seems perversely unrelated to what you were doing on day N. Fortunately I have experience with spreadsheets, so I put together a tracker for myself that focused on the trailing-week average of my daily weight and exercise measurements as well as long-term graphs. Three months of data were enough to put together a chart whose trendline said very, very clearly, “what you are doing is working — keep it up!” With any kind of long-term project it’s very important to create and sustain sources of feedback. All else being equal, the longer it takes before you can get a read on “is this going well or poorly?”, the worse it will go.

I decided that my goal would be to get my BMI from 30 (the lower limit of “obese”) to under 25 (the upper limit of “normal”). Happily, the math is very easy there: for my height, a BMI of 25 rounds off to 200lbs. I further decided that I was willing to spend all of 2023 working on this. That decision is why I’m writing this post now: halfway through a project is a natural time to pause and take stock.

What I did

By the end of March, my regimen was firmly settled and I kept at it through the end of June without further tinkering. The daily goals I settled on were 10,000mg of potassium and 1,200kcal of exertion. That amount of exercise worked out to be 90 to 100 minutes per day. For contrast, in 2022 my average amount of exercise per day was 15 minutes and my average exertion was 500kcal.

I used my smartwatch’s exertion number (“how many calories are you using above the amount you need to burn to be alive at all?”) and gradually walked up my daily goal, settling at 1,200kcal/day partially because it was working and partially because one hour of watch face equaling 100kcal was helpful for being able to read “how close to my goal am I?” without thinking hard about it. Most of the exercise was treadmill time, usually a brisk walk or light jog. Over the months I also did some running, some bicycling, and some hiking, but treadmill time was the reliable, unremarkable, do-this-every-day core of my exercise regimen. It took a while to ramp up to that amount of exertion and there were definitely days when I stumbled, for good reasons and bad. However, in general I hit the exertion goal and in particular had it absolutely dialed from early March to mid-April.

It was easier to be totally rigorous about the potassium-intake goal — it helped that that part only took a few minutes per day, instead of 90+ minutes! I used potassium chloride powder (whatever came up first on an Amazon search since all KCl should be alike) mixed with regular Gatorade (i.e. not the sugar-free kind) to make it taste okay (I recommend blue Gatorade, it’s the closest to appealing when kaliated — the yellow lemon-lime was meh and the fruit punch red was awful). I added two heaping teaspoons of KCl powder to a 20oz. bottle of Gatorade and drank that. KCl is about 52% potassium and a heaping teaspoon of it is about 6500mg, so I rounded up a smidge and called that 6600-and-a-bit milligrams of potassium per bottle. On Thursdays and Sundays I drank 2 full bottles and on other days 1.5 bottles. I recorded this as 10,000mg of potassium on regular days and 13,500mg on Thursdays and Sundays.

Is 10,000mg of potassium a lot? It’s a lot more than average! The SMTM potassium trial post contextualizes it helpfully:

For a long time, the recommended daily value for adults (technically, the “Adequate Intake”) was 4,700 mg of potassium per day. But most people don’t get anywhere near this amount. 

In every CDC NHANES dataset from 1999 to 2018, median potassium intake hovers around 2,400 mg/day, and mean intake around 2,600 mg/day. In this report from 2004, the National Academy of Medicine found that “most American women … consume no more than half of the recommended amount of potassium, and men’s intake is only moderately higher.” Per this paper, only 0.3% of American women were getting the recommended amount. Similarly low levels of intake are also observed in Europe, Mexico, China, etc. 

But in 2019, the National Academies of Sciences, Engineering, and Medicine changed the recommended / adequate intake to 2,600 mg/day for women and 3,400 mg/day for men. They say that the change is “due, in part, to the expansion of the DRI model in which consideration of chronic disease risk reduction was separate from consideration of adequacy,” but we can’t help but wonder if they changed it because it was embarrassing to have less than 5% of the population getting the recommended amount.

In any case, recommended potassium intake is something like 2,500 to 5,000 mg per day for adults, and many people don’t get enough.

Potatoes are exceptionally high in potassium. A single potato contains somewhere between 600 and 1000 mg of potassium, depending on which source you look at. They are the 6th highest in potassium on this list of high-potassium foods from the NIH, and 9th on this old list from the USDA. If you do the math, this means that someone on the potato diet, eating 2,000 kcal of potatoes a day, gets at least 11,000 mg of potassium per day, more than twice the old recommended intake.

This explanation is most of why I decided to stabilize at about 10,000mg per day: because that’s about how much potassium people were getting during the SMTM potato diet community trial. Because that community trial involved around 200 people, it was unlikely that there would be any truly heinous health effects from knocking back that much potassium, especially together with the anecdotal evidence that inspired the trial. Aiming for that amount also meant that it would be easier to compare my results to something that worked decently well and to ask questions like “is there something special about whole potatoes, or is it mostly the potassium?” If it’s mostly the potassium, you’d expect my results to be closer to the full-potato-diet results than to the low-dose-potassium results — which is what happened.

I measured those results in a very basic way: ordinary bathroom scale, first thing in the morning, every day. Considering how much noise there is in weight measurement, there’s just no advantage to measuring it more often. I kept the circumstances of the weigh-in simple and stable, trusting that that was good enough. I also measured exertion in two other forms — step count and exercise minutes — but that was mostly for my personal curiosity because both are basically downstream of exertion as such. Similarly, I tracked my sleep but didn’t expect that to matter a whole lot.

While I was affirmatively not dieting, I want to make sure to talk about my food habits because I could be missing something that’s easy for others to see as unusual but seems totally ordinary to me. My meals are heavy on pasta, rice, bread, and granola. I work diligently to get enough dietary fiber. I eat some meat but not a lot (eating a pound of meat in a week would be above average for me), and I enjoy coffee but not a tremendous amount of it since usually I make Chemex-style coffee and having a bunch of that in a day would be too time-intensive. My go-to snacks are cashews, pistachios, cherries, and granola bars. Like most people, I should eat more dark leafy greens than I do. I use a generous hand when measuring out olive oil. I believe that if you need either milk taste or milk fat, you shouldn’t half-ass it, so when I need milk taste or milk fat, I rely on whole milk and heavy cream. Fats, generally, taste good. I eat more whole food and food I personally cook than I eat packaged and processed food, and I only infrequently eat restaurant food (weekly pizza night, maybe twice a month other than that). I really like sour candies but basically stopped eating them last autumn after some very patient coaxing from my dentist. Once in a while, dark chocolate, usually with the nuts and fruit.

I ate as I had been doing: I ate the food I felt like eating and ate as much of it as I felt like eating. If I felt like eating more or less, I did that. Since I wanted to keep the exercise habit going regardless of whether or not I lost weight, it was very important to me to not make the exercise any more difficult than it had to be. Going hungry would definitely make it more difficult, so I avoided doing that. One way in which I’m very sure my experience generalizes is, it’s much easier to persuade people to try “add this supplement to what you’re already eating” than to get them to try “replace all of your current food with potatoes,” especially when talking about long-term or indefinite-duration changes.

What I plan to do next

I’ll be thrilled if I can recapture something like the 7-week March/April streak I had going. Most days in this period (44 out of 49) were PB days (i.e. a day where my trailing-week-average weight was the lowest it had been since the start of the year) and no two consecutive days in this period were non-PB days (i.e. if a day wasn’t a PB day, both the day before and the day after were PB days). I was losing almost 2lbs per week and exercising a lot and I felt great. However, my intuition is that that was the honeymoon period of going from mostly-sedentary to exercising regularly, and that I should expect further progress to be more difficult, to be like the less impressive results I got in May and June.

Still, the thing as a whole has definitely been successful enough that I’m going to keep at it until the end of the year, re-evaluating again in December (and maybe when I hit my weight-loss goal, which should happen around halfway between now and then). Since I’m using January 1st as my anchor date for the start of the experiment, it lines up nicely with the calendar if I just keep going all year and see what happens. Besides, I only need 6 months more to generate a year of data, while someone going from a cold start would need a whole year.

Given that I have a setup that is working pretty well, I’m reluctant to tinker with it. I might add one more high-potassium day in addition to Thursdays and Sundays, and I might start tracking some extra data — even though I’m not trying to change them, recording my food habits seems like the most helpful additional thing I could record.

If I develop health problems I’m gonna pull the ripcord (and post about it). There are already too many shitty fake weight loss regimens in the world that fuck up the health of people who try them, we do not need more.

What I think about it

Since I’m the one doing this experiment, I get to be excited about how it’s working out for me personally, which is to say, very well indeed. Right now it seems pretty certain that I’ll be able to reach my goal of losing ~50lbs in a long-term-sustainable way and just as importantly, getting myself to a much better baseline state of physical fitness. I feel pretty great about that part!

The experiment is not just for me, though: the reason it’s an experiment rather than just “I’m trying to lose weight” is that I am keeping track of things carefully such that other people could carry out the same steps I did and get results similar to or different from mine and ideally everyone eventually comes to pretty firm conclusions about whether this — losing weight via potassium and exercise without dieting — works or not. My chugging potassium and Gatorade for six months to a year is the very beginning of that process, and I expect that the difficult parts of the process will be carried out by people with more expertise and resources than me.

I also expect that I have not tumbled to the One Weird Trick for weight loss that everyone else just overlooked. As someone with plenty of programming experience, I have a hearty suspicion towards “well, it worked on MY setup” stories. One obvious alternate explanation for my successful weight loss is “well yeah, you doubled your exertion and kept your food intake the same, of course you lost weight” — but I don’t find that explanation satisfying. To start with, if it were that easy, people would do it more often. There are a tremendous number of people who would like to lose weight and a tremendous marketplace of devices, services, and professionals to help them use exercise for that purpose, and yet in a 20-year NCHS study, average exercise rose without obesity falling. It’s also very, very easy to find fat people who exercise plenty — you will find them more or less anywhere you find lots of people exercising, as well as in places like sumo stables. A member of my family has taken up powerlifting in the last year, making him both fitter and heavier by quite a bit.

Additionally, there’s studies like Keating 2017 concluding that short-term exercise intervention doesn’t do enough to matter, or like the Wu 2009 work concluding that exerise-and-dieting isn’t meaningfully better than just dieting over periods of 6+ months, and then there’s the STRRIDE study, Slentz 2004, concluding that jogging 20 miles a week can get people to lose about 7 pounds over 8 months. The STRRIDE study caught my eye because it’s pretty similar to what I did: they took obese mostly-sedentary folks, had them exercise more, and forbade them from eating less. However, once you do the math the results are much less similar: the average STRRIDE participant did around half the exercise I’ve done for at most a fifth of the weight loss (i.e. around 1lb/month vs. around 5lbs/month and around 3mi/day vs. 7mi/day). If someone else told me “Krinn, your naïve just-hit-the-treadmill exercise regimen is 2.5x as effective as an exercise regimen supervised & measured by professionals,” I would want them to provide some compelling evidence for that.

If you tell someone you want to lose weight and would like their advice, it is overwhelmingly likely that the advice will involve exercising more. Everyone has heard this advice. And yet, as Michael Hobbes observes in a searing piece for Highline, “many ‘failed’ obesity interventions are successful eat-healthier-and-exercise-more interventions” that simply didn’t result in weight loss. Even if we as a society choose to believe “more exercise always leads to weight loss, most people just fuck up at it,” that immediately confronts us with the important question, why do they fuck up at it? and its equally urgent sibling, what can we learn from those who succeed at it to give a hand up to those who have not yet succeeded?

I find the SMTM authors’ metaphor for this helpful:

[exercising more and eating less] is not an explanation any more than “the bullet” is a good explanation for “who killed the mayor?” Something about the potato diet lowered people’s lipostat set point, which reduced their appetite, which yes made them eat fewer calories, which was part of what led them to lose weight. Yes, “fewer kcal/day” is somewhere in the causal chain. No, it is not an explanation.

Since I’ve been doing this for six months, I feel pretty certain that the potassium is doing something positive for me and I’m entirely willing to put in another six months to find out what happens for me. Finding out whether that generalizes is beyond my power: all I can do is explain what worked for me, one middle-aged Seattle housewife, and hope that it’s useful to people who are in a position to do serious work about it.

One kind of serious work that’s available is the very cool analytic techniques that other people in this conversation have used while looking at their data. If you are the kind of person to get elbows-deep in R or Matlab, feel free to grab my day-by-day measurements for that (I release this data under Creative Commons’ CC0 if that’s relevant to you). I’m not going to do that, though, partially because it’s been a long time since I last used R but mostly because of the thing I said earlier about my whole experiment basically being one data point. If you have a data series, then yeah, get in there with some numeric interrogation, but if you only have one data point, that data point is what it is and statistical analysis can’t really add to it. All I can claim here is that this is a new data point: people going about their everyday lives do not spontaneously increase their potassium intake severalfold and the background work from the SMTM potato diet and potassium community trials tell me that no-one’s run a study looking directly at what happens if you do increase your potassium intake that much.

Do you want to increase your potassium intake that much? If you do, I have to re-emphasize the potassium community trial‘s safety warning: if you have existing kidney problems, do not try this. Also I’m gonna deploy the boldface again to make sure I get this across to other trans women: on this topic, taking spiro counts as a kidney problem! I am not a doctor and I’m extremely not your doctor, you should talk to your actual doctor if you have any kind of potential kidney issues and even if you’re in good health and want to try chugging a bunch of potassium, you should titrate up gradually the way the SMTM writeup suggests (which is also the way I did).

In addition to a general spirit of responsibility, those warnings are important because otherwise just telling you that this is easy would sound like a recommendation. Did I mention that the experiment was easy? Easy easy. Piss easy. Lemon squeezy, etc. Of course building an exercise habit wasn’t easy, but the potassium part didn’t make it easier or harder, and the potassium part itself was pretty trivial. Mix this powder into Gatorade a couple times per day, drink it, done.

That said, if you do want to try this, godspeed and please write down how it goes for you. I recommend building positive reinforcement into whatever you use to track it; my personal spreadsheet for this is adorned with color-coding and happy emoji. I also recommend at least thinking about the following questions, whether you’re going to do this, evaluate the results of this, or both.

  • How safe is it, in general rather than for me particularly, to chug this much potassium? This is the big one: “just mix potassium salt into Gatorade and drink it a few times a day” is so incredibly easy that even if the effect size is small, it could benefit a huge number of people, but of course it doesn’t benefit them if it’s not actually safe to do that.
  • Does this replicate? If it’s not safe it matters a lot less whether it replicates, so the safety question comes first, but if it is safe, then one would immediately want to find out whether it works for 1% of people, 10% of people, or 50% of people.
  • How much do other mineral nutrients, particularly sodium and magnesium, matter for this? Maybe they need to be combined in some specific way, as this Twitter thread suggests.
  • Do sex hormone levels matter? I’m a trans woman and I’ve been having problems with access to HRT in this timeframe. Given how many things in one’s body testosterone and estrogen affect, and given that previous obesity research has shown differences based on hormone profiles, that’s definitely something to keep an eye on. Also because spironolactone in particular messes with renal function and potassium metabolism, I expect that it affects this. Digression: spironolactone is total bullshit as an anti-androgen of first resort. It sucks and I hate it and I should have switched to other anti-androgens even sooner than I did. If you’re using spironolactone as an anti-androgen because it was the first thing your doctor tried for that, you really should try something else and see if that works.
  • I steadfastly avoided dieting. I like my existing diet just fine, and that’s why I preferred the “what if I just chug a bunch of potassium” plan. All else being equal, I’d rather try things that let me eat what I like than things that require throwing my relationship with food into upheaval. But of course you wonder, what would happen if you did combine dieting and exercise and potassium? The ExFatLoss guy has been busy trying a lot of diet-only interventions and he’s got a lot of interesting results. I am not the person to try it, but it’s one of the obvious things to try, so I hope someone does try it.
  • How does this interact with the munchies? If you decide to try what I tried and you, like me, enjoy living somewhere where marijuana is legal, I think you should look at whether the potassium changes how you experience marijuana-induced hunger/overeating. One of the things I found very striking about the matter is that it was possible for me to chug enough potassium that the marijuana-induced hunger was drastically reduced. I expected the opposite since the potassium was causing me to eat less (relative to exertion) at other times. However, I have very strong habits about marijuana (exactly twice a week, edibles only, same amount every time) and I’m not willing to change them for this, so who knows how this aspect will work out for others. Definitely something to keep an eye on, though. Even if I wasn’t losing weight, the potassium reduces marijuana-induced overeating enough that I’d probably keep going with it just for that effect.

Conclusion

I spent 6 months trying to lose weight with lots of potassium and exercise but without dieting. So far I have succeeded. Unless something disastrous comes up, I’m going to keep trying it for at least another 6 months and going to keep recording what I’m doing. I’m particularly curious to see where I’ll plateau, since I assume at some point I’ll start getting really hungry and/or tired instead of accidentally starving. I hope that my experience and the data I’ve recorded from it, are useful to people who are looking into questions about obesity and weight. Please feel free to use my data and my writeup (this post) for that. If you want to try doing as I’ve done, good luck and stay safe: this has worked for me but it is still experimental, it might be unsafe and/or fail to work for you.

Links for June 2023

slimemoldtimemold2 Comments

Submissions to the SMTM Mysteries Contest close July 1st! We’ll take a few weeks to look over all entries and share them with the judges, and will start putting out finalists after that. 

N of 2: Identical Twins Hugo and Ross Turner did the same workout for three months, but one worked out for 20 minutes and the other worked out for 40 minutes. There was almost no difference in their results.   

Disentangling the Dark and Bright Side of Constructs with a Bright and Dark Side — a somewhat in-the-weeds post about factor analysis, but relevant if you’re interested in issues of representation and ontology.

The US is getting its first new nuclear reactor in 40 years

In some cases, symptoms “clinically indistinguishable from schizophrenia” are actually caused by lupus. Probably a good example of how diseases with identical symptoms may not have identical causes / etiologies.

In 2009, Grimes “load[ed] a homemade houseboat with chickens, a sewing machine and 20 pounds of potatoes and (briefly) sail[ed] it down the Mississippi while being tailed by Minneapolis park police” (via @caseydarnell_)

@granawkins on twitter recently hacked together long-time dream project eloeverything.co, a site where you compare two things over and over and pick your favorite, so that all things can be given an ELO rating (the ranking algorithm from chess). There have been more than 500k votes as of this writing. The leaderboard is especially fun. 

Simon Sarris: Do children today have useful childhoods?

N=1: Bite the Bullet

slimemoldtimemold, , 5 Comments

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research
N=1: Hidden Variables and Superstition
N=1: Why the Gender Gap in Chronic Illness? 
N=1: Symptom vs. Syndrome
N=1: Latency and Half-Life
N=1: n of Small
N=1: Dr. Garcia’s Queasy Irradiated Rats

When it comes to chronic illnesses, most people try to find ways to avoid the pain. This is because pain bad, and no pain, good. 

But we worry that avoiding pain is good in the short term but bad in the long term; penny wise and pound foolish. 

If you’re worried that pizza makes you bloated, it’s good common sense to try to avoid pizza. But it’s bad science. Past a certain point, avoiding pizza tells you nothing. To learn something more, you have to bite the bullet.

Better to wait for a day when you feel great, no bloating at all, and then intentionally go and eat pizza, and see what happens. Or each afternoon you feel good, flip a coin, and eat pizza when it comes up heads. Do this a couple of times. If you do this, you should be able to see if pizza is really a reliable trigger for your bloating.

There are two reasons to do this. The first is — what is it about pizza that makes you bloated? If you can show that pizza is a trigger, you can start doing empirical splits. Buy the same pizza and flip a coin. If it’s heads, scrape off the cheese and tomato and just eat the bread. If it’s tails, toss the bread and just eat the cheese and tomato scraped off into a bowl. Which makes you bloated? If it’s the cheese and tomato, separate them and do the same thing.

The second is that a lot of the time, we suspect you’ll find that pizza (or your personal equivalent) is not a trigger at all. It’s all too easy to think you see a pattern where there’s none to be found, and we tend to see food triggers even when food doesn’t matter at all. You could have been enjoying pizza this whole time. That seems worth knowing.

More on Macros

slimemoldtimemold, , 4 Comments

ExFatLoss recently put out an essay called RIP Macros, where he expresses skepticism about the common three-macronutrient paradigm, saying:

I suspect more and more that the idea of “macros” is just as useless [as CICO], unless you subdivide each of the macronutrients so much further as to dilute the concept completely.

Carbs, fat and protein are definitely real, and they’re definitely a useful lens through which to view some problems. If you get too much protein, you really can kill yourself by rabbit starvation.

But this doesn’t mean that the macros are a useful lens for every problem related to nutrition. For example, we know they have basically no bearing at all on scurvy. So they may not be a good way to understand other issues, like obesity. 

ExFatLoss makes a number of good points and we encourage you to read his essay. Here is a bit of extra commentary:

Ontology

First of all, ExFatLoss is doing the right kind of work here. In the 1980s, when obesity started looking like a major problem, macros probably seemed like a promising angle. But when you’ve spent 40 years attacking a problem from the same angle with no success, maybe it’s time to find a new angle. This is the kind of ontological remodeling that you need to crack tough problems.

If you commit to a lens too quickly, it’s easier to get locked in on assumptions that might turn out to be wrong. Related to this, we like the caution ExFatLoss shows in his naming conventions:

I changed the name because I wanted it to be more descriptive of what it was, not the proposed mechanism (lack of protein) – after all, I am still not sure that’s the causal factor.

But I also chose the “ex” (for experiment) part because it conveys uncertainty, and that the diet is in flux. It’ll evolve, hypotheses will be disproven. … In a sense, it’s almost like a serial number for an experiment, and I’ve added a few new serial numbers since: ex150deli, ex150sardines, ex150choctruffle, ex225.

…

Maybe we’ll figure out what exactly makes ex150 tick, and then we can nail down a more descriptive name. Until then, I’m hesitant, because it would be speculation and I’d rather have a serial number than a name that’s just flat out wrong.

Macros are probably just too “big”. Dividing all food into three categories is pretty broad strokes, and it won’t be surprising if it turns out that these strokes are too broad to be helpful. We can equally say that dividing all matter up into four elements didn’t work very well, and chemistry progressed much better once people got a handle on the fact that there was more than one kind of earth, that there are various airs, etc. 

Phase of matter was the system of the world at one point, but today we don’t think so much about the solid/liquid/gas distinction — we no longer think of oxygen as a fundamentally different species of thing from copper. They’re not a type of air and a type of earth, they’re both elements, elements that happen to be in different phases at room temperature.

And without getting into it too much, we’ll note that reading about how macros were discovered did not inspire much confidence in them as categories.

History

The other reason we don’t think obesity has anything to do with macros is because of history. 

People ate all kinds of diets throughout history, including all sorts of “bad” diets. People tried every combo of macros, and never got obese.

Some cultures ate high-fat diets. Some ate low-fat diets. Some ate lots of carbs. Others ate almost no carbs. You name it, some culture probably tried it. 

On top of this, people were subjected to all kinds of voyages, expeditions, crop failures, sieges, economic shocks, and migrations. When you’re under siege, you eat whatever happens to be in the city, so people besieged in different places ended up eating different weird diets just to stay alive.

These various shocks gave them all kinds of dietary diseases. Scurvy is famously associated with the age of sail, but also struck on the crusades. Beriberi is often found in prisons. And the ancient Romans discovered protein poisoning while sieging Intercatia around 150 B.C.:

Their soldiers were sick from watching and want of sleep, and because of the unaccustomed food which the country afforded. They had no wine, no salt, no vinegar, no oil, but lived on wheat and barley, and quantities of venison and rabbits’ flesh boiled without salt, which caused dysentery, from which many died.

The point is that throughout time and space, people have chosen or been subjected to almost every strange diet imaginable. These did give them all kinds of weird illnesses — we know that eating the wrong combination of things can make you sick in various ways. But as far as we can tell, these weird diets never made them obese.

This makes it unlikely that obesity can be caused by an imbalance in macros. If there were some ratio of fat / carbs / protein that could make you obese, someone would have noticed in the last 3000 years, because someone at some point would have been eating that ratio. History has provided a pretty thorough search of diet-space (not totally exhaustive, but covering a lot of ground) and has discovered lots of ways that a bad diet can fuck you up. But none of those ways was obesity. 

ExFatLoss makes this same point: 

There were of course a near infinite amount of diets people could’ve consumed back [in ancestral times]. All we know is they didn’t add refined flour and seed oils, because they wouldn’t have had those. But there might’ve been carnivorous ancestral peoples, fish-eaters, maybe some near-vegetarians. Some might have lived heavily off dairy. Some ate a lot of muscle meat, others more fat. The paleolithic era lasted over 3 million years and the earth is a big place.

So if obesity is a dietary disease, you’d think that some culture somewhere would have stumbled onto it at some point. As far as we can tell, that’s not the case. Though if someone can find an example of a reliably obese culture from before 1900, we would be very interested to know. 

To us, this is strong evidence against any macronutrient cause of obesity. And in general, we don’t think obesity has to do with ANY nutritional element of food. Vitamin C isn’t a macro, but the random walk of diets through history discovered the related disease (scurvy), and eventually normal science discovered the cure and the underlying compound. If obesity were caused by some micronutrient or something, we think it also would have been stumbled upon in antiquity, and that since then we would have found the missing compound at fault.

The exception might be nutritional elements that were very rare until the late 20th century. If there’s some substance that it was hard to even get 1 mg of before 1940, but most people are eating 200 mg/day of today, it would make sense why no one had gotten fat off that substance until recently. 

This is one point in favor of the seed oil theorists, who usually blame linoleic acid for the obesity epidemic. This compound has always been in foods, but it used to be much harder to get a lot of it. So if too much linoleic acid makes you obese (we don’t think it does, but just by way of example), it would make sense that no one before 1940 would have ever stumbled on this, because almost no one before 1940 was ever exposed to linoleic acid in these quantities. Hence such images:

We said, “we don’t think obesity has to do with any nutritional element of food”. But it might plausibly have something to do with non-nutritional elements of food, like pesticides or other contaminants. Again, if it’s something no one was exposed to before the 20th century, or that no one was exposed to in such modern quantities, then it isn’t ruled out by the relative absence of obesity before the 20th century.

Symmetry

It’s easy to assume the cure and the cause will be symmetric. For example, people who believe that a low-fat diet will cure obesity usually believe that this is because high-fat diets caused obesity. We think that high-fat diets can’t have caused the obesity epidemic, because people in history sometimes ate high-fat diets and didn’t get obese. Similarly, people who believe that a low-carb diet will cure obesity usually believe that this is because high-carb diets caused obesity, etc.

But it could be that something else (FACTOR X) caused obesity, and a low-fat diet happens to cure obesity for reasons totally unrelated to the cause.

This kind of thing is common. Antibiotics cure infections because they kill the bacteria that are making you sick, not because the infection was caused by a penicillin deficiency.

Empirically, it looks like macro-changing diets (e.g. low-fat, low-carbs, etc.) don’t reliably cause weight loss. But it’s possible that some nutritive diet could treat obesity — the potassium trial essentially fits this description, since potassium is a necessary mineral. We just don’t think a nutritive diet could cure obesity because of a matched deficiency. 

Half-Tato Diet Analysis

slimemoldtimemold, , , , , , 13 Comments

So we did this half-tato diet community trial. People signed up for a minimum of six weeks — two weeks of baseline, so we could see how their weight changed when they were eating as normal, and then four weeks where they got around 50% of their calories from potatoes every day.

This was inspired by our original Potato Diet Community Trial, which worked pretty well. In that study, people lost an average of 10.6 lbs over four weeks eating almost nothing but potatoes.

We say “almost nothing but potatoes” because most people took multiple cheat days, and it didn’t seem to make much of a difference. Combined with a couple of case studies, who reported enormous success on a half-tato diet (in particular, M with his potatoes-by-default), this made us wonder if a half-tato diet could be made to work almost as well as a full-tato diet. 

Anyways, let’s look at some results. 

Today’s analysis is based on a snapshot of the data taken on June 1, 2023 (about 10 weeks after the study was launched). This means we have up to 10 weeks of data, specifically 2 weeks of baseline and 8 weeks of half-tato. A few people are still going with the half-tato diet, but we will look at their data later.

The dataset is mostly straightforward, but here’s one note: One or two important measurements were missing for a small number of people. For example, they might have entered a weight for Day 28 and Day 30, but not Day 29 (which is important because Day 29 is the end of the first four weeks). 

When an important measurement like this was found to be missing, we filled it in by making the missing measurement the average of the two values around it. For example, if the weight measurement for Day 29 was missing, we filled it in with the average of the weights on Day 28 and Day 30.

We did all these replacements before doing the analysis, and only a few measurements were interpolated like this.

As usual: raw data, the analysis script, and study materials are available on the OSF. 

Participants

A total of 123 people filled out the signup form. 

Of those, 8 people filled out the form incorrectly in such a way that we couldn’t sign them up (they didn’t enter an email, didn’t enter critical data such as height, etc.). We enrolled the remaining 115 people in the study.

Of the 115 people who were enrolled, 92 entered at least one day of weight data.

For people who entered any data, the most common outcome was to make it the full 2 weeks baseline + 4 weeks half-tato, though people dropped out at various points along the way, and a few people didn’t finish the baseline two weeks. 

Here you can see how many days people completed. In this figure, the vertical line at 0 divides the baseline span (Days -14 to -1) from the half-tato span of up to 8 weeks (Days 1 to 57). 

Let’s summarize that plot. As of the snapshot on June 1st:

  • 92 people entered at least one day of weight data
  • 75 people made it to Day 1, past the baseline period of two weeks
  • 38 people made it to Day 29, the end of the first 4 weeks of half-tato
  • 8 people made it to 8 weeks or further, and some are still going

For this analysis, we will mostly be focusing on weight change up to Day 29, since there’s not much data past that point. 

Weight Change over Baseline

First let’s look at the baseline. Similar to a crossover design, this baseline serves as a kind of control group.

There was very little average weight change in the baseline period, and it was not statistically distinguishable from zero. Here’s the histogram of weight change over baseline, with a black vertical line at 0 lbs (i.e. no weight change over baseline) and a red dashed vertical line at the mean weight change:

The mean weight change over this period was -0.22 lbs, with a 95% CI of -0.70 lbs to 0.27 lbs. This is not statistically distinct from zero. 

The mean suggests an average loss of 0.11 lbs per week on average, or 0.35 per week if we take the lower bound of the confidence interval. 

Of course, it’s also consistent with an average weight GAIN of 0.14 lbs per week if we take the upper bound of the confidence interval.

In previous studies, people have expressed concern about the Hawthorne effect — that when we ask people to measure their weight, they might start losing weight simply because they are aware that their weight is being observed. Looking at the baseline period, we find very little support for this idea, even with a sample size of 75 people. 

Observing your weight for two weeks just doesn’t change it much, and likely doesn’t change it at all. Going forward, we will continue to not worry about the so-called Hawthorne effect. 

(Also, it’s amusing to see that Wikipedia kind of drags this whole idea: “some scholars feel the descriptions are fictitious” and “J. G. Adair warned of gross factual inaccuracy in most secondary publications on the Hawthorne effect and that many studies failed to find it.”)

Here’s a plot of weight change over baseline, including only people who finished the two-week span. As you can see, these look like a bunch of random walks around zero.  

Weight Change at Four Weeks

Our main interest is weight change on the half-tato diet, specifically people’s weight change between the morning of Day 1 and the morning of Day 29. Here’s the histogram of that variable, with a black vertical line at 0 lbs (i.e. no weight change over 29 days) and a red dashed vertical line at the mean weight change:

People lost 1.7 lbs on average over these four weeks, and that loss is significantly different from zero, t(37) = 2.70, p = .010. Another way of putting this is that 27 out of 38 people (71%) lost at least some weight.

By now we’re sure you’ve noticed the extreme outlier, the person who reported losing 17 lbs over four weeks (participant 25348806). This outlier is impressive, and we’ll look at her results in more detail later, but excluding that person doesn’t change the overall results. Without the outlier, average weight loss is 1.3 lbs over four weeks, and that loss remains significantly different from zero, t(36) = 2.66, p = .012.  

We see that weight loss is significantly different from zero. People do seem to lose weight on the half-tato diet. 

But we should also emphasize that they don’t lose much — the effect size here is a disappointment. We had hoped that the half-tato diet might have around half the effect of the full potato diet, but that just didn’t happen. 

Overall, the effect is less than half the effect of the original potato diet. Average weight loss on the potato diet was 10.6 lbs, so half of that would be 5.3 lbs. Instead we see only around 15% of the effect of the full-tato diet. 

We should note that there are some mitigating factors here. In particular, about 30% of participants in the half-tato diet started out as “normal weight” (BMI < 25), compared to only about 15% in the original potato diet. (In the original study, people who were obese or overweight tended to lose more weight, so this means the average weight loss will look smaller when there are fewer obese or overweight participants.)

But weight loss on half-tato is still quite minor, even if you limit the analysis just to overweight (BMI > 25) participants, who lost 1.8 lbs on average, or obese (BMI > 30) participants, who lost 3.1 lbs on average. This is still much less weight loss than on the original potato diet.

Another way to put it is like so: On the original potato diet, 64 people made it 4 weeks. One of those people lost no weight. Everyone else lost more than the AVERAGE weight loss on the half-tato diet. It’s really no contest; full-tato is overwhelmingly more reliable and causes overwhelmingly more weight loss, at least among the people who can make it four weeks on mostly potatoes. 

Frankly, this just emphasizes how successful the original potato diet study was. In fact, on reflection the Potato Diet Community Trial was probably the most successful weight loss study of all time. Are there any other studies that caused weight loss in 98% of people who finished the study, and caused an average of 10.6 lbs of weight loss over just four weeks? Not that we know of. 

Trajectory

As we mentioned, there’s one extreme outlier who lost 17 lbs over four weeks. You may also have noticed a less-extreme outlier who lost 9 lbs, who happens to be someone who participated in the original Potato Diet Community Trial and saw a lot of weight loss there as well, losing 19 lbs. Both of them stand out quite clearly in a plot of people’s weight loss trajectories:

Having seen some reports like this one, we wondered if there might be a yo-yo effect on the half-tato diet, where in the beginning people lose weight no problem, but at some point the potato effect stops working and their weight heads back to baseline. That seems like a reasonable way to interpret this plot: 

From: Results of the 50%-potato diet experiment

But overall, this doesn’t seem to be the case. In general, half-tato weight loss over four weeks seems small but constant: 

Weight Change at Eight Weeks

We also have a tiny bit of data on people’s weight loss taking the half-tato diet out to eight weeks. Here’s the plot: 

The average weight loss at eight weeks is 3.6 lbs, though you can see that one person has lost more than 10 lbs. With only eight individuals, this is too few people to do a statistical analysis. But it does suggest that longer spans on the half-tato diet may be effective.

Note that the extreme outlier does not appear in this group — that person only sent us data up to Day 29.

Here’s the whole span from everyone who finished baseline (minus our main outlier), showing all data points from the start of baseline to the end of eight weeks: 

What Things Correlate with Weight Loss

There’s not much variation in people’s weight loss over these four weeks, but some people did lose more weight than others. This makes us wonder if there are any variables that might be correlated with weight loss.

Take the analyses below with a grain of salt. They’re very exploratory. The sample size is small. We’re not correcting for multiple comparisons. And of course, all these correlations are correlational.

As you well know, correlation does not imply causation — but as XKCD reminds us, “it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’.” Correlations can still be suggestive, and if any of the correlations we find are real, we should eventually be able to demonstrate the same relationships experimentally. So let’s take a look and see if anything stands out.

BMI

Our first surprise is that BMI doesn’t seem to have much to do with weight loss.

The correlation between weight loss and starting BMI is relatively small, and is not statistically significant, r(36) = -0.29, p = .078.

Protocol

We let people sign up for three different protocols for the half-tato diet, three different ways you could try to get about 50% of your calories from potatoes. People ended up about evenly split between the three approaches:

Here is a plot of weight loss by each of the protocols:

As you can see, there are no huge differences in weight loss between the three protocols, though Potatoes-By-Default includes the outlier who lost the most weight.

Percent Potato

We asked people to estimate what percent of their total calories they were getting from potatoes each day, and some people reported getting a much higher percent potato than others. Since some people were doing about 50% potato, and others were doing only about 10%, you might suspect that the diet caused more weight loss for people getting more potato. 

This is much more muddy than we expected. Getting closer to 50% of your calories from potatoes does seem to maybe cause more weight loss, but if so, it’s not super clear. The correlation is quite small and not significant, r(36) = -0.28, p = .084, and weaker if you exclude the major outlier, r(35) = -0.24, p = 0.147.

It’s hard to imagine that percent potato doesn’t matter at all, and we do see that the three people who lost the most weight were all getting close to 50% potato. This suggests that for best results, you should try to get around 50% potato on average. But there isn’t a clear correlation overall. 

Dairy

In the original Potato Diet Community Trial, we asked people to avoid dairy entirely. This time around, we decided to just ask people to track how many servings of dairy they got each day. This lets us look for any correlation between dairy consumption and weight loss on half-tato. 

There may be a bit of a trend where more dairy is related to less weight loss, but the person who lost the most weight ate plenty of dairy, and the overall correlation is not significant, r(36) = 0.15, p = .355.

That said, the relationship is slightly stronger if we exclude the outlier, though still not significant, r(35) = 0.29, p = .078.

Tomato 

We were also concerned that tomato products might interfere with potato-based weight loss. So just like dairy, we asked people to track how many servings of tomato products they had each day. Here’s the scatterplot:  

Surprisingly, this relationship is significant, even with such a small sample. The overall correlation is r(36) = 0.37, p = .021, and it remains significant if you remove the extreme outlier, r(35) = 0.36, p = .031. 

You can see that the two outliers, people who lost the most weight, almost entirely avoided tomato products on the diet. Also interesting is that the person who gained the most on the diet happens to be the person who ate the most servings of tomato products. 

This is correlational, not corrected for multiple comparisons, etc., but it does provide more support for our suspicion that tomatoes interfere with the potato weight loss effect. This would be great to experimentally confirm at some point, and it should be relatively easy to test — just assign some people on a potato diet to use ketchup, and others to eat their potatoes bareback, i.e. no ketchup. In the meantime if you are trying to lose weight using potatoes, we certainly encourage you to avoid ketchup.

Cooking Method

We’ve previously mentioned that boiling or soaking potatoes removes a lot of their potassium. So we’re curious to see if people who boiled their potatoes lost less weight than people who baked, roasted, fried, or otherwise kept their potatoes for the most part whole and un-leached. 

Most people didn’t leave detailed notes on how they prepared their taters, but the people who did leave notes often mentioned either boiling them or using frozen potato products, which are generally pre-boiled / blanched / parboiled. 

This might explain why the half-tato diet did not cause much weight loss on average — if we’re right, and the weight loss is caused by potassium (or anything else in the potatoes that is leached out on boiling/blanching/soaking; who knows, maybe iodine), then many people were consuming less effective potatoes.

There aren’t enough reports to bother hand-coding preparation method or doing an analysis, but here are some examples:

(42475044) Most of my potato meals were a 50/50 mix of roasted yellow potatoes (partially peel 1 inch cubes, lightly oil, 375 convection for 45 minutes), and store-bought frozen french fries (whatever seemed to have the least oil) cooked in the air fryer with no additional oil. 

(63062664) My protocol was mostly whole boiled potatoes pan-fried in ~15g of butter or a small glug of rapeseed or olive oil. Usually ~1kg for breakfast + lunch.

(78152385) I ate mainly russet or golden potatoes, baked or roasted, and I didn’t eat the skins of the russet because last time I did that it gave me the worst stomach cramps I’ve ever had. I also ate a lot of Alexia french fries with sea salt, and some sweet potatoes.

(80975703) I always ate potatoes I had boiled in batches and kept in the fridge. My favourites were red potatoes, half peeled, but I also had yellow or white potatoes, fully peeled. Always with a bit of olive oil and salt and spices, chopped up and reheated in a pan on the stove.

(28228309) I had visions of making home-made latkes or really fine hash browns. I just didn’t make time. While I know we are supposed to start with whole potatoes, I’m sure glad I found frozen potato patties at the store, or there’s no way I could’ve even approximated the quantity of potato I needed. I put my toaster to 6 (nearly the highest setting) and toast them twice, and they’re great, and I could do it for breakfast on work days.

(30834698) I do not like skin on the potatoes; I can eat it, but I do not like the taste or how it makes me feel; I prefer them without skin, so I mostly eat them like that; usually just boiled with a pinch of salt, sometimes in the oven, sometimes with a drop of olive oil; sometimes with some harissa; the easiest and tastiest for me was boiled with salt, then peel the skin and eat them

(72618178) In general I was making homemade oven-baked ‘fries’ (thinly sliced par-boiled potato). I would often give in and allow myself ketchup or spicy mayo. I also went through some phases of doing homemade gnocchi, mashed potato, and faux-dauphinoise (thinly sliced, stacked, oven-baked potatoes with veg stock and a bit of butter).

As you can see, many people boiled their potatoes or used frozen potato products that were likely boiled in some way before freezing. But to be fair, this does not describe everyone. Some people did report mostly baking or roasting:

(58681391) I usually baked an entire 5 lb. bag of gold potatoes at 350 for 1.5 hours, for roughly three servings. I didn’t use oil when baking but would sometimes refry the baked potatoes into hash browns with about 1 tsp of avocado oil.

(70030447) My main method for eating potatoes, as I work from home, was to chuck a few russets in the oven for an hour after coating them in salt and pepper, then once they’re done I would cut them into two halves and eat those entirely. I found olive oil a hassle, and putting salt and pepper on the insides after they’re done was also too much hassle for me to want to bother doing everyday. Maybe I’d do that if I cooked them some other way.

Despite eating baked or roasted potatoes, neither of these people lost weight. The first saw no change at all, and the second gained 4 lbs. This is enough to show that baking or roasting is not enough to ensure weight loss. 

But there may be other reasons these two didn’t lose any weight. 58681391 ate a lot of tomato and dairy, and got only about 38% of their calories from potatoes. 70030447 ate an unusually large amount of dairy (third most out of everyone) and got only about 20% calories from potatoes.

In any case, we still suspect that starting with whole, raw potatoes, and not boiling, soaking, or blanching them, might be important for causing potato weight loss. We didn’t make people roast or bake their potatoes in the original potato diet study, but maybe with +90% potato, it doesn’t matter.

It might have been an oversight not to ask people to roast or bake their potatoes for the half-tato protocol. If you’re trying it for yourself, probably don’t boil them or live off of frozen french fries.

Regression Analysis

To wrap up these correlational analyses, we fit some regression models to try to predict weight change from multiple factors at once. In all these models, we excluded the outlier who lost 17 lbs, participant ​​25348806, because we wanted to try to understand things that might have impacted weight change for the average participant, who did not lose so much weight. 

One especially strong model included total dairy consumption (p = .007), total tomato consumption (p = .003), and their interaction (dairy * tomato; p = .035). This interaction had a negative sign, suggesting that tomatoes and dairy are slightly less than the sum of their effects. All three terms were significant predictors of weight change, and the model explained 23.7% of the adjusted variance in people’s weight change. 

This was a much better fit than we expected, especially given the small sample size, and it provides more support for the idea that tomato and dairy consumption for some reason inhibit the potato weight loss effect. Note that this is TOTAL dairy and tomato consumption over four weeks, not average daily consumption, which provided a weaker fit.

This was not the best model we found, however. When you dummy-code the three potato protocols, and put them in a model with total tomato consumption and the two-way interactions, many terms are significant (for example, True Half-Tato condition * tomato sum is significant, p = .0004) and the model explains 37% of the variance in weight loss. We literally are not sure what to think of that, and are not sure how to interpret this result.

In any case, these are very simple models. It will be hard to squeeze more information out of just 37 observations, but if you have experience with more complex forms of statistical modeling, we encourage you to download the data and see if you can make more sense of it than we can. 

Potatosis

Some people liked getting half of their daily calories from potatoes:

(23555212) This was cool! I have a newfound appreciation for potatoes.

Other people did not:

(28228309) Oh happy day. No more forcing myself to eat bland potatoes. 

(81471891) Not super happy with my mindset about this diet. It’s currently “I *have* to eat 1 kg of potatoes per day!”, and feels a bit forced.

This is kind of striking compared to the absolutely rave reviews we got about the 100% potato diet, where most people said that they loved it. You’d think that eating 100% potatoes would be a bigger ask and a bigger pain than eating just 50% potatoes, but apparently not. 

This makes us wonder if most people in this study never went into “potato mode”. In the original potato diet study, we found that after a day or two of eating potatoes, most people’s appetites waned, they didn’t want anything aside from potatoes, and they began to steadily lose weight. This seemed like a separate “mode” the body can be in, that both caused weight loss and made it easy to eat nothing but potatoes without major discomfort.

If something about the half-tato diet keeps people from going potato mode — the percent potato wasn’t high enough, the potatoes were prepared wrong, ketchup is a potato inhibitor, etc. — that would explain why people didn’t lose much weight, and why many people found it difficult to stick with even a mere 50% potatoes. 

This is corroborated by a comment from one person who was also a participant in the original potato diet study, and says that they found half-tato very different:  

(42475044) Overall this didn’t work anywhere near as well for me as the full potato. My weight over the last 8 weeks has largely stayed the same, whereas on the full-tato I lost 9 pounds in 3 weeks. I could definitely feel that the potatoes were helping me not gain weight, but I think my non-potato calorie intake was just too high for the potatoes to compensate for. On the full-tato diet I was able to eat as much as I wanted and still lose weight, but that doesn’t seem feasible for me on half-tato.

That said, at least one person on the half-tato diet did report signs that sound a lot like potato mode:

(21268204) Sweating at night, which I never do otherwise. Appetite low… Get full really fast even when eating non-potatoes … 2nd day in a row that it didn’t occur to me to eat until 4pm … Have not been hungry at all the last few days. The calories I did get were because I forced myself to sit down, mostly, with some potatoes

This participant lost only one pound over the first four weeks, but kept going and lost 3.5 lbs over eight weeks. 

All this suggests that there might be a right and a wrong way to do half-tato. If you do it wrong, basically nothing happens, maybe you lose a little weight on average. But if you do it right, you go into potato mode, much like on the full-tato diet, and you start losing weight very quickly.

Let’s assume for the moment that there is such a secret magic switch (or set of switches) that can make half-tato cause rapid weight loss, and try to figure out what it is. If there is such a switch, then almost everyone on the full potato diet tripped it. All the case studies (like M) managed to trip it. The major weight-loss outlier in this study, and maybe some of the less major outliers, seem to have tripped it. Maybe they were doing something right that puts you in potato mode — so what would that be?

The extreme outlier (​​25348806) in this study give us a fairly detailed report of how she approached half-tato, saying:

I signed up for a spreadsheet for 52 weeks.  I’m doing the diet and have had great success … Am female with 100 or so lbs to lose (now 30 down).

I first lost about 15 lbs doing a very loose version of potato by default after first reading your blog pre half tato experiment and have since lost another 15 beginning April 22 with starting half tato in earnest.  I steam peeled yukon gold in batches in the Instant pot for 12-15 minutes at high/manual (depends on size, I try to get bigger but often its just medium available).  Right out of the instant pot I add white vinegar which helps preserve color and appearance and tastes great later (more subtle than adding vinegar at mealtime) before cooling and fridge.  I started eating a mix of cold and hot depending on if microwave is available (sometimes with mustard) but now I’ve settled into just hot (2 min microwave) with mainly salt.  I try to have this 2-3 meals out of the day (2 medium or 1 big 1 smallish per meal).  One of the 2 potato meals I may add one of:  poached egg yolks; calf liver lightly sauted in butter (plus lingonberries and/or honey); or cooked ground beef (with 21 gun salute seasoning from trader joes and sometimes full fat sour cream), and possibly pepper or cholula sauce (rare), occasional oysters (fresh or canned).  I don’t add ketchup (except once – when I went out and had beef fat fries at a steakhouse bar which did not seem to stall).  I really enjoy the potatoes and look forward to them.  I am not hungry but feel satisfied.  I also have dairy – at least one glass of milk a day (either raw whole milk or 2% or whole conventional) – and a small amount of juice or lemonade.  Some mornings I may have full fat yogurt with collagen and stearic acid (see fireinabottle.net) but not all mornings.  I have some extra potassium as well as other supplements.

We love the level of detail, but it’s hard to know which of these elements are required to enter potato mode, if any of them are. But there are some features that this outlier and all the half-tato case studies (M, Nicky, and Joey “No Floors” Freshwater) share:

  • Nicky had a bit of ketchup, but everyone else either never or almost never had ketchup with their potatoes. 
  • None of them avoided dairy
  • All of them mention eating meat and eggs
  • All of them used butter and/or oil
  • None of them ate boiled potatoes; their potatoes were generally steamed, air fried, microwaved, or baked 

To us, this further supports the idea that at least part of the secret switch is eating not-boiled whole potatoes and mostly avoiding ketchup and tomato products. Dairy doesn’t seem to matter much, or at least it didn’t stop these people, and neither do various fats, meat, or eggs. Of course, it’s difficult to tell if there might be some ADDITIONAL element that they are all getting right. Are they all getting lots of magnesium or something? Hard to say. 

Just in case it helps, here’s a closer look at the other people who lost relatively large amounts of weight on the half-tato diet: 

Participant 26130773 lost the second-most over four weeks on half-tato, a total of 9 lbs. Overall he ate a good potato percentage, reporting 40%-60% most days, though on some days he only got 20%. 

This participant left almost no notes and didn’t report his dairy or tomato intake, which makes it hard to figure out what he might have been doing right. But one thing that jumps out is that it’s clear he was eating lots of eggs. Here are his notes from the first three days of the diet:

5 eggs, potatoes for lunch (350 cal eggs. If I do 2 yokes 3 whites, 190 cal) Protein shake (120) for snack Turkey b patty, salad (600?) 

5 eggs w 2 yolks, few bites turkey (225) Protein shake (120) Soup w meatballs (500) 

5 eggs w 2 yolks (190) Protein shake (120) Normal dinner cheat (900) 2 drinks

Participant 56896462 lost the third-most over four weeks on half-tato, a total of 6 lbs. He had a very good potato percentage, 40% or 50% almost every day. He ate some dairy and some tomato, about 2 servings of dairy a day and 1 of tomato, on average. He also left very few notes, though we notice that he is in Italy.

Conclusions

The half-tato diet causes some weight loss in most people, but for most people, it is much less than half as effective as the full potato diet. If you really want to lose weight, probably go for the full potato diet instead, and try to get as close to 100% of your calories from potatoes as you can.

However, a small number of people do lose a lot of weight on the half-tato diet. This suggests that there might be some way to go into “potato mode” while on half-tato, if you do it right. If we could find out how to make this happen reliably, that would be pretty neat.

Our guess is that it involves some combination of:

  • Baking, steaming, microwaving, or roasting whole potatoes instead of boiling them or using pre-boiled frozen potato products
  • Avoiding tomato products, especially ketchup
  • Getting enough of something else, possibly something found in eggs, meat, or dairy.

We should note that this list is largely based on circumstantial and/or correlational evidence. We do worry that ketchup might be a potato-blocker, but the evidence is not yet all that strong. That makes all of these guesses good subjects for future experiments.

You could design a large trial to answer these questions — randomly assign 100 people to do half-tato with ketchup and 100 people to do half-tato without — but you might need a very large sample size to be able to detect a difference. And while we’d love to see more community trials, it may not be practical to do multiple trials of several hundred people each, one after the other, to try to chase down whether each of these things makes a difference. That seems like it would take forever and be a lot of work.

So instead, another option would be for individuals to test these guesses as a self-experiment, which could provide very strong evidence, and might be able to provide it quickly. 

For example, let’s say that Gary is a fellow who is happily losing 2 lbs a week on the full-tato or half-tato diet. Whatever makes potato mode happen, Gary has found it, even if he doesn’t know what he’s doing right.

Now Gary can test individual switches to see if they turn potato mode off. For example, he can randomly assign some weeks to be ketchup weeks, where he always has ketchup with his potatoes, and other weeks to be no-ketchup weeks, where he religiously avoids ketchup and all other tomato-based foods. 

If Gary’s weight loss always stalls on ketchup weeks, but continues humming along on no-ketchup weeks, that’s a pretty clear sign that avoiding ketchup is one of the switches to make the half-tato diet work. If the randomization makes no difference, that’s a pretty clear sign that ketchup doesn’t matter, at least not for him.

You can imagine a similar design for anything else. Gary could randomly assign some weeks to try only boiled potatoes, and other weeks to try only baked potatoes, and see if it makes any difference. 

We doubt things will be this simple — it’s quite possible that one brand of ketchup kills the potato effect, while another brand has no impact — but we won’t know until someone has tried. It might take several weeks to pick up a clear signal, but anyone who is able to get a potato diet working for them can test any of these switches out for themselves. 

All we ask is that if you try something like this, please publish your results online, regardless of how it turns out. We’re very curious to know what will happen!

Closing Notes

Some people have gone for more than eight weeks on half-tato, and we plan to analyze their results at some point in the future. It will be a small sample size, but we are excited to have some more case studies. So stay tuned. 

If you are interested in doing an N=1 experiment about these ideas and want our help designing a protocol, please feel free to contact us. 

If you would like to be notified of future stupid studies, or if you want to keep up with our work in general, you can subscribe to the blog by email (below), or follow us on twitter.

And if you feel like reading this post has added a couple of dollars’ worth of value to your life, or if you have lost weight as the result of our research and you think it improves the quality of your life by more than one dollar a month, consider donating $1 a month on Patreon. 

Thanks for going on this journey with us.

Sincerely, 
Your friendly neighborhood mad scientists,
SLIME MOLD TIME MOLD

N=1: Dr. Garcia’s Queasy Irradiated Rats

slimemoldtimemold, , , 7 Comments

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research
N=1: Hidden Variables and Superstition
N=1: Why the Gender Gap in Chronic Illness? 
N=1: Symptom vs. Syndrome
N=1: Latency and Half-Life
N=1: n of Small

I. 

In the old days, psychology was dominated by the school of behaviorism.

Behaviorism taught that mental states like thoughts and feelings are unworthy of study, and possibly don’t exist. 

Behaviorists also thought that animals are born without anything at all in their brains, that the mind at birth is a blank slate, and that everything an animal learns to do comes from pure stimulus-response learning built up over time. Turns out, this is wrong.

At some point in the 1950s, a guy named John Garcia was irradiating Sprague-Dawley rats for his job at the U.S. Naval Radiological Defense Lab, like you do, when he noticed something weird. The rats who had been exposed to low levels of gamma radiation were eating and drinking less than usual, and groups that had been exposed to radiation the most times ate and drank the least. 

Garcia thought that the rats might be learning to associate their food and water with the nausea from radiation exposure. After all, rats have no concept of ionizing radiation, so from their point of view, they were going about their day as normal when they suddenly started feeling nauseous for no clear reason. They might reasonably wonder if it was something they ate. In particular, he noticed that the rats wouldn’t drink out of the plastic bottles they were used to, but were happy to drink out of unfamiliar glass bottles. Garcia thought that maybe the plastic bottles gave the water a particular taste that the rats had learned to avoid. 

So in a series of experiments, Garcia tried exposing rats to different kinds of stimuli to see what they would learn. He discovered two surprises that called the whole behaviorist concept into question. 

First, he discovered that if a rat was exposed to radiation (making it nauseous) after encountering a new food, it would quickly learn to reject the food, even if the radiation came hours later. 

This contradicted the understanding at the time of how conditioning worked — behaviorists thought that you had to present the unconditioned stimulus (nausea) immediately after the conditioned stimulus (the new food), or the animal wouldn’t learn to associate the two. But Garcia found that learning could occur even if the rat got sick well after eating a new food. 

Rats would instantly associate nausea with whatever food they had most recently eaten, and had no problem doing so. If he made them sick after giving them Cheetos, they would learn to reject Cheetos forever. But the rats simply could not learn to associate their nausea with any other kind of stimulus. It didn’t matter if the stimulus was bright lights, or an annoying buzzer. No matter how many times Garcia flashed lights at them, the rats never learned to associate their nausea with the lights.

Everyone knows it’s mice that like cheetos, anyways

On the flipside, when he gave the rats electric shocks instead of exposing them to radiation, they would learn to be afraid of the lights and sounds. But no matter how many times he shocked them after eating, the rats would never learn to associate food or water with getting shocked.

This was another big pie in the face of behaviorism. Learning was supposed to be purely stimulus-response, and you were supposed to be able to teach an animal to do just about anything by pairing a behavior with the right reward or punishment. But Garcia’s rats seemed to be hard-wired to associate nausea (from radiation) with what they ate or drank, and similarly hard-wired to associate pain (from electric shocks) with what they saw or heard, and not to associate these things with anything else.

This was confusing to the behaviorists, but makes perfect sense if you think about evolution for even one second. In the real world, rats become nauseous when they eat spoiled food, so it’s important for a rat to associate nausea with things they recently ate. Any rat that doesn’t learn this will be dead, so eventually all rats are born prepared to make these food-nausea associations. Even though Garcia’s rats had been born in a laboratory and had never eaten a bit of ham left out in the sun for too long, they still came with an overwhelming bias to associate a feeling of nausea with whatever they most recently ate.

Similarly, pain is associated with sights and sounds, like the sight of an owl or the sound of a fox; or specific locations, like parts of the forest where predators are common. So rats are born ready to associate pain with things like weird noises or flashing lights. The idea that pain might be related to food, on the other hand, never crosses their minds. 

As you may have guessed, these predispositions aren’t limited to rats. In his review of John Bradshaw’s book on domes⁣tic cat psychology, Cat Sense, Gwern mentions that cats have a similar tendency to associate food with nausea: 

…[cats’] lack of trainability apparently has an exception, Bradshaw states: food can trigger learning of powerful associations even hours after consumption. This would make sense as an anti-bad-food defense, but unfortunately, this is yet another maladaptation in the modern context: “…this mechanism occasionally has unexpected consequences: a cat that succumbs to a virus may then go off its regular food even after it has recovered, because it has incorrectly associated the illness with the meal that happened to precede it.”

More generally this is called conditioned taste aversion, and it occurs in most mammals — though maybe not vampire bats, since they eat only one thing that never spoils, and being put off their food would be a guaranteed death sentence. 

(Some researchers did a version of Garcia’s study where they compared vampire bats with closely related species of bats that eat more than one thing, and while the other bats learned to avoid new flavors that were paired with nausea, vampire bats didn’t learn to associate new flavors with nausea when they were fed different kinds of flavored blood. Just imagine being that researcher on a first date; “Oh, what do I do at work? Yeah, I’m the guy who injects vampire bats with a 1% weight/volume lithium chloride solution to make them nauseous, it’s not much but it’s a living!”)

II.

Humans are also mammals, so we might have the same tendency. Maybe when we feel nauseous, or sick, or even just kind of weird, we assume it’s something we ate or drank. 

Wikipedia thinks this is the case, claiming, “even something as obvious as riding a roller coaster (causing nausea) after eating the sushi will influence the development of taste aversion to sushi,” but doesn’t offer any citations. We suppose you could run this study on your own with a few sushi meals and a season’s pass to INSERT LOCAL THEME PARK.

People often suspect that their chronic illnesses have food triggers, different kinds of food or drink that will bring on an attack or generally make them feel like crap. But if our brains are hard-wired to pick out food-based explanations for feeling ill, maybe we tend to latch onto the idea of some food trigger causing our illness, even when food has nothing to do with it. 

When our ancestors felt nauseous, it was usually because they had eaten the wrong kind of frog, so we come with a strong bias towards assuming that a random feeling of sickness is connected to something we ate. We don’t assume it has anything to do with the awesome glowing rocks we found in that sweet cave.

Such a cool rock, right? Oh hold on I have to lie down I feel terrible, must have been the goat’s milk I had for lunch

This worked well up until 3000 BC, but since then humans have discovered and invented lots of new things that can make you sick, most of which are not foods.

In general this should make us more skeptical of food triggers (and food-related triggers like packaging), especially if your chronic complaint is anything related to nausea, anything that feels like an illness, or anything digestive.

Food can still make you sick, and there are for sure some real food triggers out there. But the lesson here is that your instincts will tell you that your random sickness is caused by what you ate, even if it’s actually caused by something completely different. If you were one of Dr. Garcia’s rats, you would never have guessed that you were being hit with gamma radiation. You’d be all like, “it must be some chemical in those nasty plastic bottles.”

Links for May 2023

slimemoldtimemold2 Comments

One month left in the mysteries contest! Get your submissions in by July 1st. Good luck! 🙂 

ExFatLoss: The Slightly Complicated Theory of Obesity 

In a sense, nutrition science has maneuvered itself into a corner here. Due to the religious insistence on randomized controlled trials and using a large number of people for studies, it’s pretty much impossible to find any solutions unless they apply to everybody.

If we insisted on the same methods [for] car mechanics, we would have to declare that there is no solution for cars stranded on the side of the road.

After all, we did a large study: we took a sample of 10,000 cars stranded on the side of the road, and we attempted all popular ways of fixing them. We put gas into them, we pumped up their tires, we topped off the oil, and we checked for any engine errors.

Yet not a single one of these repairs made more than 15% of the cars run again!

Clearly, cars cannot be repaired. That’s just science. Gasoline in, gasoline out!

Also from ExFatLoss: Looking for ex150 trial volunteers. ExFatLoss is trying to expand his self-experiment into an n of small study. We encourage you to consider signing up, especially if you tried some version of the potato diet and that didn’t work for you — the potato diet also didn’t work for ExFatLoss, so maybe there’s a common factor.

Old but good piece from The Atlantic: Roller Coasters Could Help People Pass Kidney Stones, though apparently only some rollercoasters work. Specifically, Big Thunder Mountain works pretty well but Space Mountain and Aerosmith’s Rock ‘n’ Roller Coaster don’t work at all. As usual, tumblr provides excellent commentary:

Land Ownership Makes No Sense — new WIRED piece on Georgism / land value tax, by friend of the blog Uri Bram. “Under Georgism, you would pay the same tax for your home as for an equivalent vacant lot in the same location.” Very nice framing!

A Simple Exercise to Strengthen the Lower Esophageal Sphincter and Eliminate Gastroesophageal Reflux: An Autobiographical Case Report (h/t Andrew Quist on twitter). This guy was struggling with gastroesophageal reflux and, after trying and becoming dissatisfied with some traditional treatments (“even after several refinements, the bed wedge remained intolerable”), came up with a form of resistance training for his lower esophageal sphincter, i.e. tried eating with his head below his stomach. It took several months but this intervention seems like it worked for him: “A 24-hour pH and manometry test was done, which yielded completely normal results. I then discontinued the use of the bed wedge and now have no symptoms that I can attribute to gastroesophageal reflux.” Not something we are going to focus on in the near future, but this seems like a good candidate for an n of small study, or even a full community trial. In particular, it’s nice because the intervention is very low-risk. Eating with your head below your stomach should be pretty harmless. If you have GERD or are otherwise plugged into the GERD community, you should consider running a study, we’d be happy to advise! 

A Cartography of Encounters:

Let me ask you again to draw your life but now with a slight shift in perspective. Do not draw a line. Draw a map of the encounters you have had with animals, insects, birds, weather systems, microbes that have metamorphically rearranged your matter. Draw a constellation of these encounters. What shape does your life take on when it is no longer articulated by the grammar of human progress?

Why Do So Many Book Covers Look the Same? Blame Getty Images

Strong opening salvo in this year’s ACX book review contest: Your Book Review: Cities And The Wealth Of Nations/The Question Of Separatism. Touches on a number of our interests, viz. Jane Jacobs, Quebecois separatism, balkanization, and cybernetics. Highly recommended! Here’s an excerpt:

Our breathing rate is regulated through a feedback mechanism. Too much carbon dioxide in the blood, or too little oxygen, and the brain stem commands the diaphragm to accelerate breathing. Once the levels are back to normal, the brain stem receives this feedback and slows breathing down again.

Now, Jacobs asks, imagine an impossible creature: ten people, all doing their own thing, but whose breathing is somehow regulated by a single brain stem. The feedback the brain stem receives is a consolidated average of everyone’s carbon dioxide and oxygen levels, and the breathing rate the stem decides on is applied to all ten people, regardless of whether they’re sleeping or playing tennis. 

This, to put it mildly, wouldn’t work.

“Porphyrios (Greek: Πορφύριος) was a large whale that harassed and sank ships in the waters near Constantinople in the sixth century. Active over a period of over fifty years, Porphyrios caused great concern for Byzantine seafarers. Emperor Justinian I (r. 527–565) made it an important matter to capture it, though he could not come up with a way to do so. Porphyrios eventually met its end when it beached itself near the mouth of the Black Sea and was attacked and cut into pieces by a mob of locals.”

Relatedly: Whales are huge. So why don’t they get a ton of cancers? (h/t @JSheltzer)

Some research on how microplastics may impact digestion (h/t @ellegist; we think this is the original paper), though the design is not exactly the most realistic: “The team added nanoplastics to a slurry that contained proportions of protein, fat, carbohydrates, sugar and fibre comparable to the average US diet. The researchers then added heavy cream to boost the fat content. To simulate digestion, they passed this solution through three other liquids containing enzymes and molecules present in the mouth, stomach and small intestine.” Also of interest might be this similar paper, from one of the same authors, on the impact of titanium dioxide on lipid digestion. The in vitro methods are pretty whatever, but sharing these just in case.

The Problematic Myth of Florence Nightingale:

…like most lone-hero narratives, this one is not entirely true: For one thing, Nightingale herself trained with a group of German deaconess nurses, something she could hardly have done if she invented nursing. She did become famous for advocating for nursing as a trained profession, but as she did so, she shrank nursing into a restrictive, exclusionary Victorian corset, constructing a version of nursing that conformed to rigid social mores, one divided by class, race, and gender—a reimagining of nursing palatable to British colonialism.

Tempus Nectit Knitting Clock — “Wilhelmsen’s clock was designed as an art project that showed the passage of time by knitting a stitch every half hour, a row every day. At the end of a year the machine would drop a 365-row scarf from the bottom.”

More evidence of possible meteor deaths from the premodern era: 1490 Ch’ing-yang event

​​Merriam-Webster: “Hey ding-dongs, let’s have a chit-chat about Ablaut reduplication.” We’re happy to report that the dictionary continues to be one of the best poasters [sic] on twitter.

One of our first posts to break containment was a very long essay titled, Higher than the Shoulders of Giants; Or, a Scientist’s History of Drugs. If you read this piece, you’ll be familiar with Vin Mariani, a popular “tonic wine” of the late 19th century, and by “tonic wine” we mean a man named Angelo Mariani put cocaine in wine and then sold it to the feverishly twitching masses. Well, we’re happy to report that Babco Europe brought back Vin Mariani in 2017, and it appears to be still available, though we see that it is “fortified with de-cocainised Peruvian Coca leaf”. Disappointing but not surprising. 

Queen of Pigs

N=1: n of small

slimemoldtimemold, Leave a comment

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research
N=1: Hidden Variables and Superstition
N=1: Why the Gender Gap in Chronic Illness? 
N=1: Symptom vs. Syndrome
N=1: Latency and Half-Life

The biggest limitation of an N=1 experiment is external validity. If you run enough trials on yourself, you can show that some intervention does or doesn’t have an effect on you to basically any degree of certainty that you want. But this will never provide much evidence that the same intervention will have the same effect, or any effect, on anyone else. 

People are all human and have roughly the same human biology, it’s true. In the higher animals, decapitation is more or less guaranteed to be lethal; people generally like eating sugar and hate eating asphalt. But once you move beyond the fundamentals of biology, most other bets quickly are off. 

An unspoken assumption of the self-experiment discussion (including our posts on the subject) is that there are exactly two kinds of research — self-experiments, and large trials. These occupy the sample size slices of N = 1 and N ≥ 30, respectively. The self-experiment and case study are assumed to be a single subject; and with few exceptions, most people don’t trust a survey or RCT with anything less than 30 participants. 

But there are two problems with this perspective. The first is that this is a false dichotomy. There isn’t a point where N = 1 turns into N = small, and there’s no sample size where you go from having a collection of case studies to having a trial. Going from N = 29 to N = 30 does nothing in particular, and there is no other threshold that stands out as being at all distinct (except N = 0 to N = 1, of course). A bigger sample size always means more information and better external validity, with no discontinuity.

The second problem is that if N = 1 is at all good (and we think that it is), then N of small has to be better. 

Anything that is good with an N of 1 will be better with an N of 2-10. With N of small, you get more data, more quickly. One person doing random daily trials over the course of a week will create 7 data points. Three people doing random daily trials over the course of a week will create 21 data points. Small-group analysis is a little more complicated, but the data can be handled by a standard linear mixed model (here’s an example that involves dragons). 

With N of small, you get more diversity of participants and more diversity of responses, quickly drawing the fangs from the problem of external validity. You will be able to get some sense of whether the intervention works differently for different people. If you have five participants, it will be easy to see if they are all responding the exact same way, if they are responding somewhat differently, or if some of them are having huge responses while others feel nothing at all. 

The only question is one of cost. Because while the biggest limitation of N = 1 is external validity, the biggest benefit is that it’s cheap in important ways. With N = 1, you don’t need anyone’s permission to start your study — you can just go do it. You don’t pay any coordination costs, costs which are easy to miss up front but can be quite a drag if you’re not careful. These factors help make self-experiments cheap. 

But we think scaling up is usually worth it — or at least, once you have some promising N = 1, scaling to N of small usually makes sense. It’s the logical next step. And since there’s no real distinction between a single case study, a small collection of case studies, and a trial of 100 people, it’s also the logical next step on the path towards an RCT or other large trial. 

So while this series has focused on true N = 1 self-experiments, the real wins for the future may be in N = 2-10 studies where people grab a couple of friends and run a self-experiment together. Remember kids, friendship is the most powerful force in the universe. 

And it’s not at all unprecedented, since this is how we approached our community trials; we looked at a couple of case studies, and then used N of small to do the pilot testing. 

For the potato diet, we started with case studies like Andrew Taylor and Penn Jilette; we recruited some friends to try nothing but potatoes for several days; and one of the SMTM authors tried the all-potato diet for a couple weeks. 

For the potassium trial, two SMTM hive mind members tried the low-dose potassium protocol for a couple of weeks and lost weight without any negative side effects. Then we got a couple of friends to try it for just a couple of days to make sure that there weren’t any side effects for them either. 

For the half-tato diet, we didn’t explicitly organize things this way, but we looked at three very similar case studies that, taken together, are essentially an N = 3 pilot of the half-tato diet protocol. No idea if the half-tato effect will generalize beyond Nicky Case and M, but the fact that it generalizes between them is pretty interesting. We also happened to know about a couple of other friends who had also tried versions of the half-tato diet with good results. 

We think that in all of these cases, N of small was much more convincing than N = 1 would have been. With two people, it’s much less likely that the effect is a fluke. Even if it works for one person and not for the other, that’s still evidence that we shouldn’t expect the effect to be entirely consistent; we should expect more ambiguity. And for something where the risks are unclear, like with potassium, two people going through without any side-effects is much more reassuring than one. 

Links for April 2023

slimemoldtimemold7 Comments

This is the two-months-left reminder for entries to our MYSTERY CONTEST. There are already two entries, and you still have two months to write and submit yours! 

Speaking of mysteries: Jeff Wood’s story of diagnosing his ME/CFS as a mechanical problem with the craniocervical junction, the place where your skull connects to the first two vertebrae (h/t JG in the comments on N=1: Symptom vs. Syndrome). He found a treatment that worked for him, and as far as we’ve heard, he is still in remission. Most interesting for the simple, obvious diagnostic test; if you have ME/CFS symptoms, try wearing a neck brace or just pull up on your head and see if your symptoms get better. See also the CCI + Tethered cord series from Jennifer Brea. 

Still speaking of mysteries: “Paranasal sinuses are a group of four paired air-filled spaces that surround the nasal cavity… Their role is disputed and no function has been confirmed.” Also, why do they (reportedly) generate nitric oxide? The Wikipedia talk page on this one is also amusing. “more details of structure please. they are just empty pockets of air? how does the air get there? are they lined with tissue or Moo Hog are they just bone? hoopenings does each have? how do they becom e ‘pressurized’? etc etc-” writes User:Omegatron in 2005. Maybe the sinuses are well-understood by experts, but in that case, the Wikipedia page itself is a mystery. 

No longer speaking of mysteries: We made a tumblr, in case the bird site dies or becomes unusable. 

Adam Mastroianni argues that science is a strong-link problem. See also this excellent elaboration on the point, A Model of Quality Control in Strong Link Science, from Maxwell Tabarrok.

Salt, Sugar, Water, Zinc: How Scientists Learned to Treat the 20th Century’s Biggest Killer of Children. Like the story of scurvy, a clear example that eventual cures may look no more than vaguely promising at first, before we figure out the details of how to make them work reliably. Also, a lesson on following up on leads, even if they look weird or dumb or inconsistent at first. It doesn’t have to take 140 years!

The Ineluctable Smell of Beer — Part 1 in a fascinating series about the rise of healthcare costs (h/t Krinn). Really about the costs and reasons for “coordinative communication”. Kind of argues that bureaucracy is a symptom of bad things rather than the cause of them? You normally look at a dysfunctional, bureaucratic system and assume, “the bureaucracy caused the dysfunction”. But: “maybe it should take us aback that our health care system incurs such extreme coordinative communications costs, that paying all those people to handle it is actually more cost effective than not.”

The Atlantic: Could Ice Cream Possibly Be Good for You? (or here to avoid the paywall). “The dissertation explained that he’d hardly been the first to observe the shimmer of a health halo around ice cream. Several prior studies, he suggested, had come across a similar effect. Eager to learn more, I reached out to Ardisson Korat for an interview—I emailed him four times—but never heard back. … Inevitably, my curiosity took on a different shade: Why wouldn’t a young scientist want to talk with me about his research? Just how much deeper could this bizarre ice-cream thing go?” lol

Tyler Ransom did a N=1, T=1166 self-experiment where he lost 15 lbs in four months. 

A School of Strength and Character:

The institution builders of the Civil War embodied a type of excellence that foreign observers of their era described as characteristically American. … But less than a century after the Civil War, American life did become dominated by centralized and professionally managed bureaucracies. The two world wars only served to entrench this way of life in business and politics. The population, in response, became increasingly conditioned to lobbying for centralized decisions instead of self-organizing. Those who introduced managerial bureaucracy to American life understood the “great strength” bureaucratic tools would grant them. But these tools destroyed the conditions that made them so adept at institution building in the first place. The first instinct of the nineteenth-century American was to ask, “How can we make this happen?” Those raised inside the bureaucratic maze have been trained to ask a different question: “how do I get management to take my side?” 

Someone tracked down the original take of the Wilhelm Scream.

Weinersmith on political hobbyism. 

AI and the American Smile: How AI misrepresents culture through a facial expression

On the unexpected joys of Denglisch, Berlinglish & global Englisch: 

The great Milk Diet experiment results are in (h/t anon). Compare for sure to ExFatLoss’s +80% cream diet. Do be careful of excessive calcium intake, drinking this much milk may not be good long-term (though ExFatLoss seems to be doing ok?).

N=1: Latency and Half-Life

slimemoldtimemold, , 8 Comments

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research
N=1: Hidden Variables and Superstition
N=1: Why the Gender Gap in Chronic Illness? 
N=1: Symptom vs. Syndrome


I. Latency

a. Melons

Peter has a bad reaction to melons. Every time he eats melon, he gets sick right away, and he often throws up. 

We can say that Peter’s reaction to melon has low latency. When it happens, it happens right away. No waiting about.

Mark also has a bad reaction to melons. But because of a complex series of biochemical interactions, when Mark eats melon, he doesn’t get sick right away. He gets sick about three days (72 hours) later, when he suddenly starts to feel very ill, and then often throws up.

We can say that Mark’s reaction to melon has high latency. It happens, but it always takes a long time to kick in.

Peter and Mark have basically the same reaction to melon. Both have the same symptoms — nausea, sickness, and vomiting. Both reactions happen for sure every time — they are both equally reliable. The only thing that’s different is the latency.

 

b. Different and the Same

Though their reactions are nearly identical, Peter and Mark end up with very different experiences of their sensitivity. 

Peter quickly learns that melon is a trigger. After all, he gets sick right away. He just makes sure to avoid melon and goes about his life with no additional air of mystery. 

Mark, on the other hand, is plagued with random, crippling nausea. He sometimes gets sick, and it always seems to be for no reason. This is because it’s hard to remember what you were eating exactly 72 hours ago (for example, take a moment to try to remember what YOU were eating 72 hours ago). So for Mark, the connection is very obscure. He may never figure it out.

Both of these relationships would become equally obvious in a self-experiment. As long as you were tracking melon consumption and looking for relationships over a long enough time frame, you would see that Peter gets sick right after every dose of melon, and Mark gets sick exactly 72 hours after every dose of melon. 

Perfect 100% reliability would make this pretty obvious once you noticed it. You don’t need a huge sample size to pick up on a relationship that is 100% reliable, which is why Peter quits melons after getting sick just a few times. 

The big difference is whether the relationship jumps out at you or not. Low-latency relationships are obvious; the close proximity of cause and effect highlights the correct hypothesis and draws immediate attention to the relationship, where it can quickly be confirmed. Peter can just eat more melon and immediately get corroborating evidence if he wants to confirm his theory. The relationship is intuitive; you know it when you see it. 

c. Cause and Effect

High-latency relationships are much harder to spot, even if they are equally reliable. The separation of cause and effect means that the connection may never come to mind. 

To even be able to pick up on this in a self-experiment, you would have to know in advance that you should be tracking how much melon you are eating. And this is the hard part. The hard part is not demonstrating the relationship. At 100% reliability, that’s easy. The hard part is picking up on what to track. 

This is somewhat in contrast to our normal concerns in research. Normally we worry about sample size and the quality of our measures. But Mark doesn’t need a big sample size. He doesn’t need any measures other than “got sick” and “ate melon”. All he needs is to consider melon as a possible cause of his nausea, and to consider looking for relationships with a latency of at least 72 hours. Easier said than done. 

d. Reliability in Real-World Relationships

Of course, most real-world relationships are not 100% reliable. Few things work every time. But it’s concerning how a little latency can hide an otherwise blatant relationship, and it makes us wonder how many connections we all miss because of relatively small delays in onset. 

Zero latency (eat melon, immediately puke) is easy to figure out. These relationships become obvious after just a few trials. 

In comparison, 72-hour latency is very hard to figure out. Most people are not looking for relationships with such a long delay, and even if you were, you would be hard pressed to figure out the cause. 

You can’t just keep a food journal and look 72 hours back — you don’t know how long the latency is, so you don’t know how far back to look! And if the latency varies at all (e.g. always between 60-80 hours later), it gets even harder.

This makes us wonder how much latency we can handle before connections stop being obvious. It may not take much. Coffee -> heartburn with an hour delay, that seems pretty doable. We think you would figure that one out pretty quickly. But with a four hour delay? Eight hours? Twelve? This would be much more difficult. It would start to look more like, “heartburn around dinnertime / going to bed, especially on weekdays”. That sounds hard to puzzle out. 

Latency also makes it harder to get a big sample size. With a latency of less than 5 minutes, Peter can easily do eight trials (eat some melon and face the consequences) in a single day. Mark can’t do that. He has to wait 72 hours to get the results from his first trial, except it’s worse than that, because he doesn’t know how long he has to wait for the results to come in. 

If he wants to make sure not to cross the streams, he needs to devote three whole days (though again, he doesn’t actually know in advance how much time he has to dedicate) to each trial, so he needs 3 * 8 = 24 days to do the same number of “eat melon and find out” trials that Peter can easily do in an afternoon, if he’s willing to get sick that much in a single day.

II. Half-Life

a. Creamer

Jo has a bad reaction to one of the additives in her office’s tiny cups of dairy creamer (henceforth: “creamer”). Every time she uses one of the tiny cups, she gets very tired about 30 minutes later. Fortunately, Jo’s kidneys happen to handle the additive really well, and two hours after she takes the creamer, she has cleared all of the additive out of her system, and stops feeling unusually tired. 

We can say that the additive has a short half-life in Jo’s system, and that the symptoms (fatigue) have a short half-life as well. They don’t stick around for long, things quickly go back to baseline. 

Lily works in the same office and has the exact same reaction to the same additive in the office’s tiny cups of dairy creamer. Every time she uses one of the tiny cups, she gets very tired about 30 minutes later. But through a random accident of biology, Lily’s body doesn’t clear the additive from her system nearly as quickly as Jo’s does. The additive sticks around for a long time, and Lily keeps feeling tired all week. If she takes some creamer on a Monday, she’s just getting over it on Sunday afternoon. 

We can say that the additive has a long half-life in Lily’s system, and that the symptoms (fatigue) have a long half-life as well. They stick around for a long-ass time, and it takes forever for her to feel normal again.

b. Puzzling it Out

Much like a long latency, a long half-life makes this problem much harder to puzzle out, even when the two cases are otherwise identical.

Jo has it easy. If she comes to suspect the creamer, she has a lot of options. She can try taking creamer some mornings and not other mornings. She can try taking the creamer at different times of day and seeing if the fatigue also kicks in at different times. She can even take the creamer multiple times in the same day. Since the symptoms clear out after just two hours, she’s quickly back to baseline and is ready for another trial. If she wants to compare different brands of creamer to see if there’s a difference, she can get a pretty good sample size in a weekend. It’s easy for her to collect lots of data.

Lily has it really hard. If she comes to suspect the creamer, she is in a real bind, and most of the traps are invisible. If she tries taking the creamer some mornings and not other mornings, her results will be a mess, because as soon as she takes it one morning, she is fatigued all week. It will look like the creamer has no effect at all, since on days when she doesn’t take the creamer, she is still fatigued from any creamer she took in any of the previous seven days. A day-by-day self-experiment would show no effect, even though this is totally the wrong conclusion.

To detect any effect, Lily needs to test things in blocks of weeks, instead of blocks of days or hours. Each Monday, either take the creamer or not, and see how tired she is that week. But you can see how hard it would be for her to figure out this design — how is she supposed to know in advance that she needs to study this problem in blocks of a full week? She has a lot less flexibility; you might say that her research situation is much less forgiving. 

Half-and-Half-Life

Even if Lily does pin down the right research design, it still takes her much longer to get the same amount of data. Randomly assigning creamer or no creamer each morning, Jo can get 28 data points in four weeks, which is enough data to detect a strong relationship if there is one. Meanwhile, in four weeks Lily would get only four datapoints, not enough to be at all convincing. 

If the relationship is weaker (e.g. only a 50% chance of becoming fatigued), things are even worse. Jo can get a sample size of 100 or 200 days if she has to; it would be a pain, but she could make it happen. But for Lily to get a sample size of 100 weeks would take two years.

c. Thought it Worked for a While 🙂 

Lots of people try something, feel like it works great, and then later when they do a more rigorous self-experiment or just keep trying it, they feel that the effect wears off. Must have just been excitement over trying a new thing. 

For example, back in early 2020 Scott Alexander put out a report describing his experience with Sleep Support, a new (at the time) product by Nootropics Depot. His sleep quality isn’t great, so he decided to give this new supplement a shot, and reported miraculous results: 

The first night I took it, I woke up naturally at 9 the next morning, with no desire to go back to sleep. This has never happened before. It shocked me. And the next morning, the same thing happened. I started recommending the supplement to all my friends, some of whom also reported good results.

“I decided the next step was to do a randomized controlled trial,” he says. To make a long story short, the RCT found no difference at all in any measure of sleep quality. “My conclusion is that the effect I thought that I observed – a consistent change of two hours in my otherwise stable wake-up time – wasn’t real. This shocked me. What’s going on?”

Scott chalks this up to the placebo effect, which is certainly possible. But another possibility is that Sleep Support did work great at first but was no longer detectable (for whatever reason) by the time he set up the RCT. Obviously if this is true, it would be hard to study; but it does perfectly match Scott’s experience, which is otherwise (as he says) shocking and somewhat confusing.

If you have any experience with chronic illness or biohacking or anything similar, then you know that “thought it worked for a while” is a very common story. When this happens, the assumption is usually that you were fooling yourself the first time around. But consider:

Vitamin C cures scurvy, so if you have scurvy, the first few doses of vitamin C are great! But after that, vitamin C has basically no effect, because you no longer have scurvy. You have been cured. Looking at this data (huge increases in wellbeing on the first few days, but after that, nothing), the research team concludes that the original reports were somehow mistaken. 

No! It’s just that the vitamin C helped and then it had done all it could! It had a huge effect! That effect was just all up front! 

This exact scenario should pop up all over the place. If you are iron deficient, the first few doses of iron will have some effect. After that, they will have no effect. If you are B12 deficient, the first few doses of B12 will have some effect. After that, they will have no effect. Et cetera.

This is because the body is able to keep reserves of all of these substances. As long as you’ve been getting enough vitamin C, you can go for 4 weeks without any vitamin C at all before you start getting scurvy (in reality it usually takes more like 3 months, because most people don’t go entirely cold turkey on vitamin C). Same goes for iron and B12 — your body is able to keep reserves of these substances, so as long as you get enough, you should be set for a while.

To put this back in the terms of this essay, we would say that these positive effects have a long half-life. Positive effects with a long-half life face exactly the same issues as negative effects with a long-half life — you have to make sure you take the half-life into account when designing a study, and use long enough study periods, otherwise your data will be confused and misleading.

This same point applies to a lot of treatments, actually. Assuming you have an infection, antibiotics will show a big effect up front and then nothing after that. But we don’t take this to mean that antibiotics have no effect, oops we thought it worked for a while, guess we were wrong.

This isn’t a problem for things with no reservoir. For example, as far as we can gather, zinc isn’t really stored in the body long-term. So most effects of zinc will (probably) have a short half-life. If you need more zinc, you can just take it on a given day and see the effects.  

Supplementing anything with a large reservoir (or other positive effect with a long half-life) may not be suitable for a self-experiment, because it will show a strong effect in the first few days and no effect after that. Aggregated over 30 days or whatever, this will look like no effect or a weak effect. Clearly this is the wrong interpretation.

And the longer you run the self-experiment for, the smaller the effect will appear! If you do a 10-day self-experiment with antibiotics, and they have an effect on the first two days, then you will find that this looks like 2/10 days show an effect, which will probably average out to a small effect. But if you kept going for 100 days, you would see that 2/100 days show an effect, which will average out to basically no effect at all.

This is the opposite of our normal assumption about sample sizes, that a larger sample size will always get us a more meaningful, accurate estimate. This assumption simply isn’t true if we’re dealing with a treatment that has a long half-life. 

So consider the half-life of positive effects too.

III.

Broadly speaking, triggers have some delay in the onset of their symptoms, and those symptoms stick around for some span of time. 

Having a high latency or a long half-life makes a relationship much harder to notice, and harder to study. Having both, it gets even worse.

Perhaps Bob is allergic to dairy, or whatever. It gives him hives, but with a latency of two days, and they persist for four days. Bob will be walking around with random hives, and not much hope of finding out why. 

He might come to suspect the true cause if he happens to cut out dairy for a while and the hives go away for good. But if someone challenged him on this — or if Bob, being a good scientist, decided he wanted to run a self-experiment to demonstrate the hive-causing effect — he would be hard pressed to get convincing formal evidence. 

Bob wouldn’t know in advance to look for a latency of two days and persistence of four days. If he did something reasonable, like randomly assign each day as dairy or non-dairy, the results would look like zero effect. On most days when he took no dairy, he would have hives anyways, because of the long half-life. On most days when he did take dairy, he would also have hives, because they stick around so long. The few “no hive” days would be in the random periods where he hadn’t had any dairy several days ago; but those days might well be days when he was assigned to drink dairy. So it would look like a wash, even though it’s actually a very reliable relationship. 

Bob would have to do something that seems totally unreasonable, like structure the trial in 6-day segments to account for these delays. If he did this right, the 2-day wait and 4-day stay would become entirely obvious. But how is he supposed to know in advance that he has to use this totally weird study design?