N=1: Why the Gender Gap in Chronic Illness? 

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research
N=1: Hidden Variables and Superstition


Many chronic illnesses are much more common in women than in men. IBS is about 2-2.5 times more common in women than in men; migraines are about 2-3 times more common; chronic fatigue is about 4 times more common. 

This is pretty weird, and more than a little mysterious. And it’s doubly weird that the ratio is pretty similar — each of these examples is about 3 times more common in women than in men.

Normally this gender gap, if it is addressed at all, is written off as a biochemical difference (e.g. here). But another possibility is that gender is just a proxy for body size (e.g. here). If some chronic illnesses are caused by exposure to irritants, heavy metals, or other contaminants, smaller people will generally have more of a response to the same level of exposure, and women on average are smaller than men.

If this is the case, it should be possible to detect if gender is a proxy for body size in some chronic illnesses. If body size is what really matters and gender is just a proxy, larger-than-average women will be underrepresented and smaller-than-average men will be overrepresented. Basically, once you know someone’s height and weight (and maybe % body fat), their gender shouldn’t give you any further information about their likelihood of getting sick.


We can show this with some simulations.

Here’s a simulation of 10,000 men and 10,000 women. The men have an average height of 69 inches with a standard deviation of 3 inches, and the women have an average height of 64 inches with a standard deviation of 3 inches. 

Let’s start by seeing what things look like if the greater prevalence of women is the result of something like hormone levels, and body size has nothing to do with it. In this case, the men all have a 1% chance of getting the illness, and the women have a 3% chance. Height doesn’t factor in at all. So when you look at the distribution of heights of men and women in the group of people with the chronic illness, it looks something like this:

As you can see, three times as many women have the illness as men do, but otherwise the distributions are quite generic. These are basically just subsets of the distributions for each gender. They should be normally distributed and should generally look similar to one another, except that there are more women than men and the two groups have different average heights.

Now in comparison, we can consider what the data would look like if gender is just acting as a proxy for height, and there are more women with chronic illness only because they are shorter on average. 

Here’s another simulation of 10,000 men and 10,000 women, with the same distributions for height. Without getting into the exact model,[1] this is what it looks like if height is the only thing that determines if you get sick, and shorter people are much more likely to get sick: 

Again we see that there are about three times more women than men, even though this time, gender doesn’t have a direct effect. In this simulation, height is the only thing influencing who gets the illness, but the difference in average height is enough to make it so that there are three times as many women as men. 

While it’s not clear from just eyeballing the distributions, there are signs in the data that height is driving this difference. For example, about 1% of women are 70 inches or taller in the height-based simulation (compared to about 2.2% in general) and about 9% of men are 63 inches or shorter (compared to about 2.2% in general). This seems like a clear sign that height is the actual thing that determines who gets sick.


Since we don’t know what the real-world dynamics would look like, it’s not clear what you would see in real-world data. It could just be that people with the chronic illness would be shorter on average than people without — American women are about 64 inches tall on average, so it would be interesting if the average height on a chronic illness subreddit was just 61 inches (though you might want to account for age and ethnicity). If the effect was strong or nonlinear enough, there might be a noticeable skew in the data instead. Or you might see the underrepresentation of larger-than-average women and overrepresentation of smaller-than-average men that we describe above.

You could conceivably detect this kind of difference with normal survey methods, as long as you got a large enough sample size. To our mind, evidence that height (or possibly weight, you would want to collect both) explains why women are much more likely to have a chronic illness would be evidence that the chronic illness in question is caused by some kind of contaminant, since other causes shouldn’t be so sensitive to body size. If anyone wants to help collect this data for their community, please contact us.

[1]: The probability of a simulated person getting sick was proportional to 82 inches minus their height in inches, cubed. That is to say, in this model someone who is 56 inches tall was 17,576 times more likely to get sick than someone who is 81 inches tall. These numbers mean nothing, we pulled them out of our ass.

N=1: Hidden Variables and Superstition

Previously in this series:
N=1: Introduction
N=1: Single-Subject Research


Our psychology is focused on behavior. We focus on behavior because we want to figure out what actions we can take to influence the world around us. But a focus on our actions can also make us superstitious. 

The classic example is from a BF Skinner study, where he put a bunch of pigeons in a box and dropped in food at random intervals. Instead of realizing that the food drops were random, the pigeons assumed that they were somehow responsible and tried to figure out what they had done to make the food appear. 

Whatever they were doing at the time the food dropped, they tried again. A pigeon who had just turned counterclockwise when the food arrived would turn counterclockwise again and again. When more food eventually did arrive, the counterclockwise-turning was validated. “The experiment might be said to demonstrate a sort of superstition,” wrote Skinner. “The bird behaves as if there were a causal relation between its behavior and the presentation of food, although such a relation is lacking.” [1]

Compare this to a rat confronted with a set of buttons, trying to figure out which of the buttons give food and which give painful electric shocks. Unlike the pigeons, the rat is faced with a deterministic system where her actions lead directly to reward and punishment, so her focus on behavior is justified and leads to a correct understanding of the system. The pigeon is faced with a random system where his actions have nothing to do with the arrival of food, so his focus on behavior is pointless and leads only to superstition and confusion.

so cute though!


We worry this is a common problem in chronic illness. Let’s say that Mary develops chronic fatigue syndrome (CFS). She is proactive and wants to solve the problem, so she comes up with a plan of 26 different treatments, which we’ll call A, B, C, D, and so on. Maybe A is “cut out dairy”, B is “walk 20 minutes every day”, etc. but the specific plans don’t really matter. She starts implementing each plan for two weeks, first plan A, then plan B, etc. 

But Mary is working from the wrong assumption. She thinks her chronic fatigue comes from something she’s doing or not doing. In short, she thinks it comes from her behavior. This is a common assumption because our psychology is focused on behavior — we look for things we are doing right or doing wrong. But what really happened is that last month she bought a bag of rice that was grown in a field that was contaminated with cadmium, and developed low-level cadmium poisoning, which is entirely responsible for her chronic fatigue. Cutting out dairy or walking to the corner store won’t do a thing, because the cadmium is the only cause of her illness. None of the interventions she has planned will help. 

But the cadmium is slowly being cleared from her system by natural means at the same time as she works her way through the 26 treatments. What happens is this: Mary reaches treatment L (“take omega-3 supplements”) just as the cadmium in her system drops below critical levels, and Mary is immediately “cured”. 

Since her symptoms stop almost immediately after starting treatment L, Mary assumes that the omega-3 supplements are what cured her, and continues taking them indefinitely. In reality, the omega-3 supplements do nothing for her — as long as her cadmium levels are low, she doesn’t have CFS, and if she ever gets exposed to high enough levels of cadmium again, her chronic fatigue will come right back.


What Mary should do is she should run a self-experiment with the omega-3 supplements. She should randomly assign some weeks to be on omega-3 supplementation, and some weeks to be off. If she did this, she would quickly find that the omega-3 makes no difference to her chronic fatigue.

It’s understandable why she doesn’t try this — she is worried that if she stops taking the omega-3, her chronic fatigue will come back, and she doesn’t want to risk it. Also, we suspect she wants a world that makes mechanical sense (“I just needed to take more omega-3”) rather than a world where she randomly gets sick and there’s nothing she can do to stop it. It’s hard to blame her for that.

This is how the focus falls on behavior and misses hidden variables. By “behavior”, we mean actions that are directly under people’s control. Eating more or less of something, getting up earlier or later, trying more or different kinds of exercise, and so on. By “hidden variables” we mean essentially any variable you wouldn’t normally think of, especially one not connected to your actions. For example, heavy metals in your drinking water, additives in your food, viruses you contracted from your friends, air pollution from forest fires hundreds of miles away, mold in your ceiling, or things you’re exposed to at work. 

Most of these hidden variables can be influenced by your actions, but they’re not the kinds of behaviors that come to mind. You can always quit your job, but for most people, that doesn’t come to mind as a possible treatment for their illness. You can cut out spinach or dairy, because “eat less dairy” is psychologically simple — but “consume less sulfites” isn’t a clear action for most people because “foods with sulfites” isn’t a category to most people. They may not always know which foods contain sulfites, and they may not know what sulfites are. 

Does this look like the face of mercy?


This is what chronic illness looks like for Mary as an individual. At the group level, things look somewhat different. 

If a chronic disease is caused by a hidden variable (like cadmium randomly being in some foods but not others), you should see something like this: People get sick for apparently no reason. They all try many different treatments, and most treatments don’t seem to work for anyone. Sometimes a treatment will seem to work for a bit, but then it will unexpectedly stop working. Whenever you feel like you start to get a firm grip on things, all the rules you learn go out the window. However, there are many individual stories of trying some new treatment and suddenly being cured. Unfortunately, the cures in all of these stories are entirely different treatments, and the cures that work for one person never seem to work for anyone else.

And this does sound like what we see in many chronic illnesses, which makes us suspect that some chronic illnesses are being caused by a hidden variable. It could be contamination in food, water, or air, like our hypothetical Mary’s experience with cadmium. But it could also be any other unexpected variable that doesn’t have to do with personal behavior. For example, it could be the result of a virus, or an allergy to something in your household, or a curse put on you by the local witch. When taken as a group, chronic illness communities look exactly how we would expect them to look if the illnesses were caused by some hidden variable, and that makes us suspect that they are caused by some hidden variable.

Naturally this makes us wonder if there is any way to figure out what these hidden variables might be, assuming you believe they exist. The fact that they are hidden does make it inherently tricky, but we have a couple of ideas, here they are.


Your chronic illness may be triggered by something in your environment (your home, work, local food, local water, etc.). To test this, you can change as much of your environment as possible all at the same time, for example by taking an extended trip to Nepal. 

If you start feeling better or your symptoms disappear, this strongly suggests that something in your home environment is causing your illness. If you don’t feel any better, it suggests that your symptoms 1) aren’t caused by your environment, 2) are caused by elements of your environment that you brought with you (e.g. your clothes, your shampoo), or 3) are caused by elements of your environment that are common to both your home and Nepal (car exhaust?).

Your chronic illness might also be triggered by something you eat. To test this, you can change as much of your diet as possible all at the same time, for example by trying the potato diet, where you eat essentially nothing but potatoes. The potato diet is good because potatoes are simple, contain no additives, and are more or less nutritionally complete. Many people can survive happily on nothing but potatoes, salt, water, and hot sauce for up to four weeks (we have good data on this!). 

If your symptoms disappear or get better, this strongly suggests that either you had some deficiency that the potato diet fixed, or something in your normal diet is causing your illness. If your illness is just as bad as ever, it suggests that either your symptoms aren’t caused by your diet, or are caused by elements of your diet that are also in the potato diet.

Neither of these approaches will tell you what is causing your illness, but both have the potential to narrow things down enormously. If you go to Finland for a month and your migraines stop three days in and don’t come back until you get home, that’s pretty clear evidence that something at home is causing your migraines. You don’t know if it’s your laundry detergent, your well water, or something at your job, but you can take steps to narrow it down further, and you can stop worrying about your diet so much. 

Similarly, if you try the potato diet for a month and your executive function issues disappear, you can stop worrying about fumes from your boiler and can try to figure out what part of your diet is giving you brainfog.

Even a null result is informative. If you go on the potato diet for a month and your migraines carry on as normal, that’s a pretty clear sign that it’s not something in your diet, and you should look elsewhere. 

Some people find this approach surprising, because scientific investigation usually involves isolating a small number of variables and putting them under tight control. This works fine when you have a small number of variables to start with, or you know which variables you’re interested in. But in the search for hidden variables, there are a nearly infinite number of things that could be the cause of your illness. We need a technique that lets us rule out lots of theories at once, so doing these big splits can be extremely productive.

There’s a classic genre of logic puzzles often called balance puzzles. In these puzzles you have several coins, one of which is lighter than all the others, and you have to use a balance scale to find the light coin in the smallest number of weighings. The way you solve these problems is by splitting the coins into groups and comparing the groups directly. If you split the coins into two groups and the group on the right weighs less than the group on the left, the light coin must be in that group.


Consider a version of this puzzle where there is an illuminated lightbulb and a row of 1,000 switches. You want to find the switch that controls the lightbulb, but you don’t know which it is. You could go down the row of switches and try them one by one, but this would probably take you several hundred steps. On the other hand, if you have some kind of opportunity to flip a bunch of switches at once, that can narrow things down really quickly. 

Let’s say that half the switches are red and half are blue, and you can flip all the switches of a single color at once. If you flip all the red switches and the light goes off, then the master switch must be red. If you flip all the red switches and the light stays on, then the master switch must be blue. Either way, you now have only 500 switches to try. 

This is the same situation we’re in with chronic illness, except that there are something like 1,000,000 switches on the wall, and in some cases the lightbulb might be controlled by complicated interactions between multiple switches. It still makes sense to toggle big groups of switches all at once when you can, because that can narrow things down drastically.

One limitation of this approach is that it’s only really good at finding triggers. If you’re suffering from an iron deficiency, big eliminations probably won’t help find that.

The other limitation of this approach is that it’s not always clear how long you have to eliminate things for. Do you need to eliminate the mystery trigger for a week? A month? Longer? Ideally we could send you to Nepal for a year, or put you on the potato diet for a year, but in reality this won’t be practical for most people.

If Mary is getting poisoned by cadmium, and it takes two months to clear all the cadmium from her system, then going on a restrictive diet for only one month won’t help. But the problem is, she can’t know this in advance. How is she supposed to know about the clearance rate when she doesn’t even know what’s poisoning her?

So we’re stuck with an asymmetry. If one of these eliminations helps you, that narrows things down quite a bit. But if they don’t help you, then it’s more ambiguous. Maybe the half-life of whatever is making you sick is just too long. Still, it seems like it would be worth trying.


Another possibility is to just try various things and see what works. This is grasping in the dark, but we can still do a lot to cover our bases.

For example, there are a finite number of vitamins. “Vitamin deficiency” is a plausible type of hidden variable, so you could just cycle through all the vitamins and see if any of them happen to be an immediate cure. It seems unlikely that you will get this kind of miracle result, but vitamins are pretty safe so the risk is very low, and you could at least check “vitamin deficiency” off your list.

Doesn’t happen very often, but you know, sometimes it does

Similarly, there are a finite number of elements. Some of them, like iron and potassium, are necessary for human health. You could try supplementing these and see if that treats your illness. Other elements, like lead and mercury, are known to be bad for your health. You could try getting blood and/or urine tests, or testing your local water supply, and see if you have higher exposure to any of these known toxins. 

Again these are all shots in the dark, but they’re all plausible variables that could be affecting your health. If it turns out your blood mercury levels are way higher than normal, that would be good to know.

You could also try to hit your illness with some generic treatments, basically anything where the name starts with the prefix “anti-”. If you can convince your doctor, you could maybe get them to put you on a broad-spectrum antibiotic (in case your chronic illness is bacterial), antiviral (in case your chronic illness is… etc.), antifungal, anti-inflammatory, or antihistamine. This is a little more risky, but there’s some chance your chronic illness might be fungal and this is one of the only ways you would ever find out. 

This broad-spectrum approach will generally be better for finding deficiencies, but in some cases it might also help identify triggers.


Haha, but no, seriously. Donating blood is easy, safe, and it’s a nice thing to do for your community. You might save a life. And if there’s something nasty building up in your blood, you might be able to get rid of some of it. There’s already some evidence that donating blood can reduce your serum PFAS levels. Maybe it can clear some other things from your system.

Again, this is a pretty blind approach. It probably won’t work for most people. It may not work for anyone at all. But if you donate blood and your symptoms immediately get better, that would be pretty interesting, right? 


In the beginning, we’ll be taking shots in the dark. People will try dozens of things with little or no success. This will be quite frustrating.

But the hope is that eventually, we will start to get our bearings. If a couple people with chronic fatigue find that they have high levels of cadmium in their blood, then other people with chronic fatigue will want to check their cadmium levels before trying other interventions. Conversely, if a couple dozen people with chronic fatigue check for cadmium and find nothing, checking for cadmium should be moved lower down on the list for chronic fatigue. 

Over time, some people will get very skilled at organizing and interpreting this kind of research. And with enough people trying things and going over the data together, tricky bugs quickly become shallow

Depending on the success of this approach, you can even imagine this being somewhat formalized. Someone could make a centralized list of things to try or to have tested, and people could report what they had tried and whether it worked out. Tests that seem to be helpful could be moved up in the rankings so people could know to try them first; tests that don’t seem to help people could be moved down and left for the last ditch attempt.

Will this work at all? Who knows, but it seems like it’s worth trying. And at the very least, we may be able to rule out some hypotheses. After all, if we can establish that it’s none of the things on this list… then what the hell is it? 



“A pigeon is brought to a stable state of hunger by reducing it to 75 percent of its weight when well fed. It is put into an experimental cage for a few minutes each day. A food hopper attached to the cage may be swung into place so that the pigeon can eat from it. A solenoid and a timing relay hold the hopper in place for five sec. at each reinforcement.

If a clock is now arranged to present the food hopper at regular intervals with no reference whatsoever to the bird’s behavior, operant conditioning usually takes place. In six out of eight cases the resulting responses were so clearly defined that two observers could agree perfectly in counting instances. One bird was conditioned to turn counter-clockwise about the cage, making two or three turns between reinforcements. Another repeatedly thrust its head into one of the upper corners of the cage. A third developed a ‘tossing’ response, as if placing its head beneath an invisible bar and lifting it repeatedly. Two birds developed a pendulum motion of the head and body, in which the head was extended forward and swung from right to left with a sharp movement followed by a somewhat slower return. The body generally followed the movement and a few steps might be taken when it was extensive. Another bird was conditioned to make incomplete pecking or brushing movements directed toward but not touching the floor. None of these responses appeared in any noticeable strength during adaptation to the cage or until the food hopper was periodically presented. In the remaining two cases, conditioned responses were not clearly marked.

The conditioning process is usually obvious. The bird happens to be executing some response as the hopper appears; as a result it tends to repeat this response. If the interval before the next presentation is not so great that extinction takes place, a second ‘contingency’ is probable. This strengthens the response still further and subsequent reinforcement becomes more probable. It is true that some responses go unreinforced and some reinforcements appear when the response has not just been made, but the net result is the development of a considerable state of strength.”

Links for January 2023

@MegaDarren on twitter: “Just learned that Dutch scientists left a hamster wheel outside in 2014 and saw that tons of wild mice used it just for fun as well as frogs and slugs? All the creatures of the forest wanted a turn?? Absolutely phenomenal” @EvilCactus comments: “I hear the Rocky theme play in my mind every time I look at that photo of the snail. Might print it out and use it as a motivational poster.” Original paper is here.

Phytomining for lithium: “…the team also studied which plants could accumulate lithium from the soil at high concentrations. Starting with 34 candidates, the scientists eventually whittled the list down to just three: cabbage, rapeseed and sunflower.” The original paper appears to be Induced Plant Accumulation of Lithium. From the abstract: “The question we sought to answer was, can any of the plant species investigated accumulate Li at levels high enough to justify using them to agro-mine Li. Results show maximum accumulated levels of >4000 mg/kg Li in some species.” 

Joseph Rotblat reflects on why he left the Manhattan Project in 1944.

N = 1 study on improving finger strength, with wild results (h/t Applied Divinity Studies). Warning: don’t try this at home if you don’t have lots of climbing experience.

​​​​Ray Bradbury on the Mysteries of the Universe

Teen Vogue profile of Goddard College

Unusual proposal: Status Microtransaction Paradigm of Psychology. Probably not the grand paradigm we’re looking for, but we like how fresh it is. 

“It seemed absolutely crazy. The idea that an Iowa housewife, equipped with the cutting-edge medical tool known as Google Images, would make a medical discovery about a pro athlete who sees doctors and athletic trainers as part of her job?” Doesn’t seem crazy to us! Extremely condescending tone aside, this is an interesting read. The most recent update we were able to find is this GoFundMe from October 2022.

“Wild how little is known about the genes at the top of the [list of genes with the strongest effects on obesity] (UBR2, GPR75); the Wikipedia pages for these genes are ~5 lines”

​​Great investigative work by Ivan Vendrov on twitter:  

The famous “36 questions that lead to love”… don’t. The NYT and everyone else reported a different set of questions from the same authors, modified to be less romantic! The original set of *40* questions wasn’t online, but I emailed the authors and got a copy. 

In particular we want to emphasize the moral Ivan draws from this story, which seems to us entirely correct: 

I continue to be amazed at the incredibly high returns to “just check the original source”. Thanks to @alexeyguzey, @slatestarcodex, @ArtirKel and the o.g. Noam Chomsky for hammering this lesson over and over again until it stuck with me.

weird medieval guys on twitter: “a lot of people in medieval england paid their rent in eels. if you live in england, you can use this map of real, documented eel rents to possibly find out how many eels your home town was worth….thank me later!” Our English readers are encouraged to find out for themselves the answer to this pressing question: “were your ancestors getting scammed out of their precious eels by greedy landlords?” Eel Value Tax fixes this.

And for those of you not on the rainy isles, you may be as surprised as us to hear of this daring UK jewelry heist from 2015. “It was reported that the burglars had entered the premises through a lift shaft, then drilled through the 50 cm (20 in) thick vault walls with a Hilti DD350 industrial power drill. … video showed people nicknamed by the newspaper as ‘Mr Ginger, Mr Strong, Mr Montana, The Gent, The Tall Man and The Old Man’.”

Argument: Because the borders of an empire can’t be more than one month of travel away from the capital, Earth will keep the moon colony but Mars will become independent. Important implications for Gundam fanatics.

Benzene as another candidate for the cause of the obesity epidemic? Low Grade Benzene Exposure Induces Metabolic Dysbalance and Hypothalamic Inflammation in Mice (h/t @sparrowhawkcap on twitter)

Soviet leadership reportedly had nightmares about nuclear war

Stanisław Leszczyński: Wins the throne of Poland in a civil war, loses it, another civil war happens, wins back the throne, yet another civil war, loses it again, ends up as Duke of Lorraine, dies when his silk robe catches fire when he falls asleep by the fireplace. Still somehow the longest-living Polish king. 

Before Scooby-Doo there was The Famous Five, a series about four children and their dog Timmy who go on adventures or solve mysteries. Apparently this series was wildly popular at the time, but we’ve never heard of it. The five are led by George, who “gets cross when anyone calls her by her birth name” and “[asks] that her name be prefixed with Master instead of Miss.”

Rodney Brooks makes predictions and scores predictions from past years about three topics: 1) self driving cars, 2) artificial intelligence, machine learning, and robotics, and 3) space travel. His conclusion: “In the last couple of years I have started to think that I too, reacted to all the hype, and was overly optimistic in some of my predictions. My current belief is that things will go, overall, even slower than I thought five years ago.” Only time will tell, but take a look at this updated view from 2018 if you need an antidote to the hype around some of these subjects.

Tamara and Tess are running a self-experiment on remissions they experienced in their ME/CFS (Myalgic Encephalomyelitis/Chronic Fatigue Syndrome) symptoms after taking antibiotics. The first phase of the project is fully funded but they are still accepting donations.

60 Minutes covers semaglutide, gives some mainstream attention to the fact that doctors don’t understand obesity, and to the idea (widely accepted in the research world) that diet and exercise aren’t the end-all be-all of treatment, that willpower isn’t the issue. This is the one you can show to your mom (figuratively or literally). 

Is director James Cameron the greatest living anthropologist? 

Unusual claim that foods containing more potassium are also better at sating hunger (h/t InquilineKea on twitter). Not sure about where these figures come from, and they make a number of strange claims, but we’re sharing this link just in case.

Michael Nielsen on discovery fiction. We like this idea a lot, someone should write a set of textbooks that are all discovery fiction (there are a few similar books already). We might do this at some point, but in the meantime if you’re interested in writing or funding such a project, let us know, maybe we can put people in touch.

SMTM Potato Diet Community Trial: 6 Month Followup

Most diets help people lose a little weight. But once you go off the diet, the weight usually comes right back.

But what about the potato diet? In our recent community trial, people lost an average of 10.6 pounds over only four weeks on the potato diet, and the weight loss was very reliable. Of the people who finished four weeks on the diet, all but one of them lost weight, and a few people lost more than 20 pounds.

Most diets are not nearly this effective. The potato diet seems unusually good at causing weight loss. Could it also be unusually good at maintaining weight loss after people stop eating potatoes? 

There are some signs that it might. The potato diet was partially inspired by several case studies, and the case studies suggest that the weight you lose on the potato diet stays off, at least for a while. We focus on three case studies in particular:

Chris Voigt lost 21 lbs on a 60-day potato diet back in 2010. It’s not clear if he gained that back or not — this article from 2018 doesn’t mention it either way. He looks pretty lean in photos, but then again, he was pretty lean to begin with.

Andrew Taylor did an all-potato diet for a full year and lost 117 pounds. This was 7 years ago and he seems to have kept most of the weight off since then. Of course, Andrew did the potato diet for a full year, and was pretty strict about it, so his experience might not generalize to people who did the potato diet for only four weeks. 

And of course, Penn Jillette, of Penn & Teller fame, lost over 100 lbs on a diet that started with a two-week period of nothing but potatoes. This was way back in 2014, and despite only doing potatoes for two weeks, he seems to have kept most of the weight off as well.

In these cases, especially the last two, it seems like the potatoes have somehow reset these people’s lipostats, the system in the brain that keeps you at a particular weight. Their lipostats used to be really high for some reason; then they did a potato diet; now their lipostat seems to be defending a set point about 100 pounds lower. 

The good news is that we now have a larger sample to work with, so maybe we can finally get at some of these questions. It has been about 6 months since the close of the SMTM Potato Diet Community Trial, and this is the 6-month followup analysis.


We sent an email on January 1st, 2023 to everyone who had participated in the Potato Diet Community Trial, asking people to fill out a short 6-month followup survey.

In this survey, we asked them for:

  • Their potato diet participant ID, so we could connect their responses to the original results
  • Their current weight
  • How much potato they continued to eat post-study
  • If they participated in the SMTM potassium trial
  • And any general comments

We gave people approximately two weeks to fill out this survey. Then on January 14th, we downloaded the data.

There were a total of 53 responses by this point.

The majority (51 of them) were people who we analyzed in the original trial.

Of these, 32 were people who made it the full 4 weeks in the original trial. This happens to be exactly half of the 64 who originally made it to 4 weeks.

When we did the original analysis of the potato diet, there were still a few people who were in the middle of their four weeks of the diet, so we didn’t analyze their data at the time. Two of those people responded to this followup survey. They were not in the original analysis, but they did both complete four weeks, so we are going to include them in this analysis. 

So in total we have 34 people who completed 4 weeks on the potato diet and then reported back at the 6-month check-in. This is our main group of interest.

One person (participant 24235303) reported being 136.4 lbs at the 6-month followup, but he was 222.2 lbs at the end of the potato diet, so this would mean he had lost 85.8 more pounds over the intervening 6 months. Because this seems unlikely, and because his comment was, “my weight drifted back up over a few months”, we assumed this was a typo. We followed up by email and he confirmed that he meant to type 236.4 lbs, so we corrected this number for the analysis. 

Participant 63746180 reported being pregnant (congratulations!) so we are excluding her data from this analysis as her weight may not be representative. 

Participant 65402765 mentioned that they “started semaglutide around the same time as potato diet”. Semaglutide (sold under brand names like Ozempic and Wegovy) is an anti-obesity medication, so while this participant did lose 13.4 lbs in this 6-month period, we also excluded their data from the analysis. 

Because of these exclusions, the final sample size for the rest of the post is 32 people.

All new data and materials are available on the OSF.


On average, people gained back most of the weight they lost. This subset of people lost an average of 11.1 pounds from Day 1 to Day 28, and from Day 28 to the 6-month followup there was on average 10.3 lbs of weight re-gain.

People are on average down 0.71 lbs from their starting weight on Day 1 of the original study, but this is not significantly different from zero. On average, people are pretty much back to baseline.

In aggregate, it looks like a pretty strict reversion to the mean — people lost a little more than 10 lbs over 4 weeks on the potato diet, and gained back almost all of that weight over the next 6 months. 

This is still a relatively successful weight loss intervention — you do a diet for just one month and it takes about 6 months to gain back the weight you lost. This suggests that if you were willing to do a week or two of potato diet every 3 months, you could probably keep your weight down indefinitely.

But just looking at the averages conceals a pretty drastic spread. When we plot the results, we can see that 6 months later, most people are back near baseline, maybe slightly under baseline on average. But some people are down almost 20 or 30 lbs, some people are up more than 10 lbs, and one person is up almost 30 lbs! 

That central cluster is what gives us the average. Most people gained weight in the 6 months after the end of the potato diet, and ended up on average slightly under baseline. 

Four people kept losing weight (one of them isn’t obvious in the plot, they were near the top of the pack at Day 28 and are near the bottom of the pack at the 6-month check-in), and three of those people ended up down more than 15 lbs over 6 months. Those three are the clear outliers below the main group at 6 months.

Five people gained back way more (10+ lbs) than they lost. These are the five dots way above the main group at 6 months, including that one dot that is up at nearly 30+ lbs. 

It may be hopeless to try to figure out what is different about these eight or so people, given the small sample size, but let’s try.


Since there are so few outliers, let’s start by looking at them one-by-one.

Participants ​​99065049, 82575860, 66459072, 10157137, and 77742719 all ended up more than 10 lbs heavier than their baseline on Day 1 of the potato diet. 

Participant ​​99065049 is the outlier, having lost 6.3 lbs in the trial and gained back 34.5 lbs since then, for a total gain of 28.2 lbs since Day 1. We wanted to double-check this result, so we reached out to this participant over email and he confirmed that it was not a typo.

This group didn’t say much about themselves in the comments. Only two of them left responses at all. Participant 10157137 said: 

After the potato diet my cholesterol had improved, but post diet it shot back up again 😔

Participant 82575860 said:

Would appreciate a follow up post on the best potato-based recipes that were sent in 

Participants 20943794, 19289471, and 35182564 lost the most weight. All of them lost more than 5 lbs on the potato diet, and kept losing weight after that. Their total weight loss by 6 months was 19.3 lbs, 23.2 lbs, and 28.7 lbs, respectively. 

Participant 35182564, who lost the most weight, said:

Weight is incredibly stable, although I eat normal, just like before the potato diet. This was a great success.

Participant 20943794 offered the most detail, saying: 

After the potato diet ended, I started a pretty traditional CICO diet using the Noom app. Roughly speaking, I lost 10 lbs on the potato diet, and another 10 on the CICO diet. 

Before the potato diet, I tried calorie counting and various high-protein, low carbohydrate diets, and have never had this kind of sustained success. (E.g., I’ve lost 20 – 30 lbs before, but I didn’t maintain that weight for more than a month or so). 

In addition to the potato diet, there are some other confounding factors: 

1. Whey protein has figured heavily in all my previous diet regimens, but I obviously didn’t take any during the potato diet, and even after it ended, I drastically cut back how much protein powder I consumed (because of the lithium hypothesis) 

2. Because of covid and it’s after-effects, I eat out far less frequently than I ever did before. Since January 2020, I’ve eaten restaurant food (whether dine-in or take-out) only about a dozen times (most of that was on a business trip in October 2022). Before that, I’d say I ate restaurant food on average once per week

Moving on from the comments, we can see if any of the other variables offer us insight.

The potato diet included people from all weight brackets, and maybe that’s what is causing this confusing pattern. For example, maybe all the outliers who gained weight over baseline are people who were slightly underweight when they started the potato diet, and who have gone up to a healthy weight 6 months later. Maybe all the outliers who lost extra weight were very heavy people whose lipostats were easier to reset. 

But when we plot the results by BMI bracket, we see basically no pattern: 

Another possibility is that this reflects whether or not people kept eating potatoes after the trial was over. After all, you can eat potatoes without being on the potato diet, and many people do. Perhaps the people who kept losing weight are the people who stuck with the potato diet, even if only casually, for the long-term. And maybe the people who gained extra weight grew disgusted with potatoes and stopped eating them entirely. 

The good news is that we collected this very variable. But again, when we plot it, we see no such thing: 

The person who lost the most weight ate “way less potatoes than [they] used to”. The people who gained the most weight are all in the middle. No clear pattern here.

That said, if you plot this variable WITHOUT the outliers, you see basically what we would expect — people who kept eating more potatoes are mostly still below their original weight, people who didn’t change their potato intake are back to baseline, and people who are eating way less potato than they used to are slightly above baseline. 

Finally, here’s a breakdown by country. Most participants are Americans but take a look: 

American Holidays

Most of our participants are Americans, and in the span between the start of July and the end of December there’s a major American holiday period that famously involves a lot of eating: the period from Thanksgiving to New Year’s.  

Obligatory Rockwell

As a result, at the 6-month followup our participants were asked to weigh themselves just after a period of especially serious and far-ranging eating. Quite possibly they were being asked to weigh themselves at the heaviest they would be all year.

So in some ways, the particular timing of how this all worked out is a rather conservative test of the potato diet. The weight loss from the potato diet does not seem to survive the holiday period, but it might last somewhat better across any other 6-month span.

A number of our participants commented on this as well. Let’s take a look: 

(57875769) For about the first month after doing the trial my weight continued to trend downward although much more slowly. Then it slowly started creeping back up. Most of the weight came back during the holidays (it’s a little unfortunate that the six month follow up landed right after Thanksgiving, Christmas, and New Years!).

(89852176) After ending the full potato diet about 10 pounds below my typical weight, I returned rather quickly to my baseline (spurred on by eating at family vacation) and stayed there for several months. I ended the year roughly 5 pounds higher than baseline, all of which were gained in the second half of December with “typical” USA holiday (over-)eating.

(63187175) Gained about 5 pounds over the holidays, I was closer to 235 at the beginning of December

(50913144) I stayed at the lower weight for a few months, it only started creeping back up at pre-potato-trial rates in the last 6 weeks or so.  I am probably going to do another round of potato intervention, i don’t like the potassium and it doesn’t seem to help me much. 

(15106191) This measurement is being taken just after the holidays. This is higher than my pre-potato weight but I don’t blame the potatoes, its normal for me to weigh about this much more in January than I did in June

This is also somewhat supported by Nicky Case’s followup survey, which she conducted separately (with our peer review) and ran before the holidays. On October 30th, 2022, she put out a survey on the potato diet, asking people about their current status. She only got 9 responses, but found that most people were still below baseline and had kept most of the weight off.

If we expand our plot using her data, we can see that some people were down quite a bit more in late October / early November than they were at our 6-month check in.  

Some people, however, mentioned gaining the weight back more quickly: 

(25547207) It took about a two months to gain all my weight back. My strength training had to cease 2 weeks in for the remainder of the study, and my large lifts dropped about 10%. It took about 1 month to recover my original strength and I was making gains before fully recovering my weight.

(72706884) I gained back all the weight within 3 months


The potato diet causes very consistent weight loss. But whatever makes the potato diet work doesn’t permanently change your set point. The first thing we see is that most people gain back the weight they lost over time, and on average, it looks like they are back close to their original weight about six months later. 

Unless it did permanently change the lipostat for those three people for some reason. Because the second thing we see is striking individual differences. A small number of people ended up weighing 10+ pounds more or less than they did when they signed up for the trial, and it’s not clear why. 

Maybe they had unusual life circumstances that happened to make them lose or gain more weight over those six months. Maybe they are just random outliers. Or maybe they are more/less sensitive to potatoes for some reason, more sensitive to whatever the active ingredients are. Something something cybernetic attractor states.

There’s a chance that the outliers who kept losing weight are just noise, or that they would have lost weight anyways for some other reason and just happened to sign up for the potato diet at the right time. But there’s also the chance that there is something different about these four participants. If we could figure out what that difference is, maybe we could create lasting weight loss for everyone. For example, are these four people the only four vegans in this sample? We didn’t think to ask this question, but if they were, that would be very interesting. A potential extension then would be to do a much larger potato diet study (1000+ participants) and keep special track of the people who kept losing weight after the trial ended. 

Still, the potato diet is a relatively successful weight loss intervention, since one month of dieting gives consistent results that tend to stick around for about six months. And given the significant individual differences we see, it seems that for some people the effects are more lasting. While we don’t know why this happens for some people and not for others, there’s a small chance that you’ll end up being one of these outliers, and you’ll keep losing weight after the potato diet is over.

We will probably still do the 1-year followup to keep up with these outlier participants, and to see if overall average weight remains below the original baseline or not. But in general, it seems like the conclusion is that 4 weeks of potato diet will make you lose weight, and six months later most people will be back around baseline.

N=1: Single-Subject Research

Previously in this series:
N=1: Introduction


Single-subject or single-case research designs date back to at least the 1980s — though as you can see from the sparse Wikipedia page, they haven’t gotten that much attention. While the idea of single-subject research is good, the execution tends to be crap. 

The simplest single-subject design is the “ABA” design, where you have a few control days (“A”), a few days on the treatment (“B”), and then you go back to the control (“A”). See for example this figure from the University of Connecticut:

In this case, we can see that “frequency of disruptions” (from a target student) is high in the first baseline A block, goes down for a while in the B block (“praise”), and goes back up when they switch back to A.

The ABA design is better than nothing. It’s better than a case study, because at least there’s some experimental control. But it just doesn’t give us all that much information. They have 15 days of data, sure, but only three phases. This is really more like a sample size of three (not that a sample size of 15 is all that much more compelling).

Some sources might recommend the more advanced ABAB approach…

…but ABAB isn’t all that convincing either. Is that a change in the periods of “positive attention”, or just a random walk? It’s pretty hard to tell.

The ABA approach is what Seth Roberts used to argue that his “butter mind” protocol was effective, i.e. that eating more butter made him smarter. Take a look at this ABA graph from one of his self-experiments:

Again, this is vague at best. Yes, it took him longer to do arithmetic problems on 30 g/day butter than on 60 g/day butter. But the change is not very distinct, the three periods clearly overlap, it’s not clear if the periods were randomly determined, etc. And most of all, the sample size is just not very large — we can do better than drawing strong conclusions from a mere three intervals.

You shouldn’t totally discount these simple designs. Starting here can be fine (see for example Allan Neuringer’s self-experiments). Simplicity is important, and if you’re doing early stage exploratory work, going with a study design that’s easy has obvious benefits. If you don’t see any difference with this simple approach, you can move on to something else. If you do see a difference in the ABA, and you want to demonstrate that difference in a more convincing way, then you can expand it into a true within-subjects study. 

There’s nothing wrong with Seth’s butter ABA — it’s just that it looks like the start of something, rather than a conclusion. If he wanted to really convince people, he should have spun that off into a longer self-experiment. 

Within-Subjects Approach

Emily is a woman in her late 20s who gets migraines on a regular basis. She usually gets hit with a migraine in the middle of the afternoon, and they happen almost every day. Emily has been tracking her migraines for a long time, and has determined that each day there’s an 80% chance that a migraine will descend on her at about 2:00 pm, ruining the rest of her afternoon.

Emily has recently noticed that if she takes 400 mg magnesium in the morning, it’s much less likely she’ll have a migraine that afternoon. This isn’t a sure thing — she still gets migraines on days when she takes the magnesium — but it seems like it’s less than 80%.

One way for Emily to get some support for this hypothesis would be for her to run a simple AB self-experiment. She could take no magnesium for two weeks, then take 400 mg magnesium every morning for two weeks, and see if it makes a difference for her migraines. If she gets migraines 80% of the time in weeks where she’s taking no magnesium and only 40% of the time in weeks where she’s taking 400 mg magnesium, that seems like evidence that the magnesium is helping. 

And it is evidence, but it’s on the weak side. Depending on how you slice it, the effective sample size here is two — just one fortnight with magnesium, and one fortnight without. You shouldn’t draw strong conclusions here for the same reason that you shouldn’t draw strong conclusions from a study with one person in the experimental group and one person in the control group. There’s just not that much evidence. 

With small sample sizes, there are too many alternative explanations, it’s too easy to fool yourself. Maybe she started taking magnesium in the springtime, and the increased daylight is the real reason her migraines improved. Or she just started a new job two months ago, and it took her two months to stop grinding her teeth, which happened to align with the switch over to magnesium. Or her detergent manufacturer switched to a new supplier for fragrances. Or maybe her mailman’s cat, which she’s allergic to, ran away last week. It could be almost anything.

To account for these problems, Emily can just go ahead and get a larger sample size. She can use a random number generator to randomly assign days to either take magnesium or not take magnesium, and then follow that random assignment.

With the addition of these two small steps, she can now use a normal within-subjects experimental approach. Let’s imagine she finds that there’s an 80% chance of developing a migraine on days when she takes no magnesium, and a 30% chance of developing a migraine on days when she takes 400 mg magnesium. She can demonstrate this difference to an arbitrary level of precision, just by running the trial for more days.

Emily’s data might look something like this. The data shown here is not quite enough to reach statistical significance (χ2 gives p = .051) but it’s looking pretty good for 30% chance of migraines with magnesium and 80% chance without.

Even if the difference is much more subtle — perhaps a 75% chance of a migraine with 400 mg magnesium and 80% without — with enough days, she can still show to an arbitrary level of confidence that the magnesium has the observed effect FOR HER, however small that effect might be. 

This wouldn’t provide any evidence that magnesium will work for anyone ELSE’s migraines. But even if it doesn’t generalize at all to anyone else, Emily can get as much evidence as she wants that it’s really doing something for her. And that still helps the community, because it shows that the treatment works at least sometimes, for some people. 

This is different from a traditional case study. Even though it’s looking at just one person, it uses experimental techniques. Compared to a traditional experiment, you sacrifice external validity (will this generalize to anyone other than this one person?) but you still get the same level of statistical rigor and you can still clearly infer causality. 

And with this design, you should be able to use standard within-subjects statistical approaches. A sample size of just one is unusual even for a within-subjects study, but not entirely unheard of.

This approach is under-used in the internet research community (though Scott Alexander did one here and Gwern did one with LSD). Lots of people are online sharing tips and tricks on things they think might help their reflux/migraines/IBS/heart palpitations/executive function/etc. This is good, but it’s hard to know which recommendations are solid and which are just random chance. 

If you run a within-subject self-experiment, you can do something incredible for your community. It may not help everyone, but you can demonstrate whether it works for you. Publish your null results too — if you suspect that caffeine triggers your reflux, but under close inspection the hypothesis falls apart, report that shit!

We should emphasize that N = 1 studies falsify a very specific kind of null hypothesis: that an intervention cannot work. If the intervention works for you, that just shows that the intervention can work.

It might not work for anyone else, and with N = 1 you have a much higher chance that one person will do something idiosyncratic that makes it look like the intervention was successful, when in fact it was the idiosyncratic thing doing all the work. For example, maybe on days you take magnesium, you always take it with a big glass of lemonade. It turns out the lemonade is what’s helping your symptoms, not the magnesium, and this wouldn’t be apparent from the data, because the lemonade and the magnesium are confounded.

If you want to go above and beyond, you can get a couple friends and do an even more compelling test with only a few people. As long as you all do multiple trials, your effective sample size from a statistical standpoint can still be arbitrarily large. With more people, we have greater certainty that there isn’t something weird confounded with the experimental variable (but never 100%). Every chronic illness subreddit should be generating research pods of 2-10 people, and testing the treatments they think are worth investigating. 


Even with better randomization, however, these designs still have a lot of limitations.

For a start, they’re limited by the speed of your research cycle. For example, repeated-measures studies won’t work very well for studying obesity, because people tend to lose and gain weight pretty slowly. It may take months to lose and then regain weight, so it’s hard to study weight gain with this method. If you have to randomize periods of months, it will take you a full year to get a sample size of 12. In comparison, headaches would be easier to study, since they come and go daily or even hourly, and you could randomize your treatment on much shorter timescales.

Worse, for some treatments we don’t know what the appropriate timescales will be. Let’s return to Emily and her migraines. If magnesium works on the order of weeks rather than days, she will have to randomly assign weeks rather than days, which means it will take seven times longer to reach an equivalent sample size. But how can she know in advance whether to use periods of days or of weeks?

If a cure is too powerful, or has long-lasting effects, that actually makes it harder to study. If magnesium cures Emily’s migraines for a month, she’ll have to wait a month between randomization cycles, and it will take her years to get a decent sample size.

Similarly, this kind of protocol may not be able to detect more complicated relationships. If Emily’s body builds up a reservoir of extra magnesium over time, this may be difficult to model and might throw off the clarity of the experiment. Or if her body gets more aggressive about clearing the excess magnesium from her system, the magnesium will have less effect over time, and will have even less effect on trial runs where she has multiple magnesium blocks one after another. These designs have a lot to offer, but they’re not going to get us very far in the face of genuinely complex problems.

Another downside of this approach is that Emily has already found a treatment she likes. Probably she would like to take magnesium every day and get as few migraines as possible, but to do this within-subjects self-experiment, she has to try going off the magnesium multiple times over the course of several months, to make sure it really works. We think it’s often worth it to know for sure, but it isn’t easy to stop a treatment that seems to be working.

Finally, the big limitation is that you can only use this approach if you’ve already identified a treatment that you suspect might work for you. If you’re sick and you don’t have any leads, this method can’t help you figure out what to try. It’s only good for testing or confirming hypotheses — it can’t give you any new ones, can’t narrow down a list of cures or triggers out of the huge number of things that might possibly be making you sick.

Low-Dose Potassium at 60 Days

In the SMTM Low-Dose Potassium Community Trial, people took some potassium and lost some weight. Specifically, they took an average of about 1900 mg of potassium per day and lost an average of 0.85 lbs over 29 days

That’s not much weight loss, but it’s also not a very big supplemental dose of potassium, and the weight loss is significantly different from zero. People who took higher doses of potassium lost more weight, as did people who weighed more to begin with.

But what about past that first span of 29 days? Some people kept going with the protocol, taking potassium up to 60 days. Today we report their data.

30+ Days Results

We took a snapshot of all participants’ data on January 5, 2023. This was more than a month after we collected the data from the first 29 days, so everyone had the opportunity to reach 60 days by this point if they wanted to. This new snapshot is available on the OSF.

All the sample sizes in this case are too small to be statistically significant with the potential effect sizes involved, so we don’t report any statistical tests in this post. 

We cleaned these raw data and are going to look at the data from Day 1 on the protocol to Day 60. Some people may have kept going past Day 60, but we aren’t going to look at that right now. 

Here are the overall trajectories for the people who reported at least one day’s weight beyond day 29. The vertical red line indicates day 29, so all data points beyond that are past the span of the original trial. 

Overall the trend seems to continue. One person ended up down more than 15 lbs, but that’s not at all representative. 

People lost weight on average, but we already knew that. In this case we are most interested in whether they kept losing weight past the official end of the trial, so here are those same data zeroed from their weight on Day 29:

We see that in this span, people also lost weight on average, though the average weight loss was not very large. The average weight change past day 29 is negative, -0.37 lbs with all data.

See that spike up to more than 10 lbs? As you may have guessed, those are the days immediately following Thanksgiving. The participant reported that this was their “heaviest weight in 9 years”, but as you can see they lost all that excess weight very quickly. 

These plots can make it hard to see what has happened for each individual, so let’s now break things down and just show their last reported weights, again relative to their weight on Day 29. 

Here’s a plot of each person’s last reported day, and their reported weight change as of that day.

You can see that there are roughly two groups — most people either made it just a few days past Day 29, or made it up to very close to day 60.

We can take a special look at that second group, people who made it to Day 60 or nearly did so. Here’s everyone who made it past 50 days, broken out by just the landmark measurements — their weight on Day 1, on Day 29 at the official end of the trial, and on the last day they reported.

And here are those same data as a table:

On average, these people lost a decent bit (2.7 lbs) in the first span of the trial, and less in the second span (1.0 lbs). But this obscures a lot of individual stories that are more extreme in one way or another, like participant 42293886, who gained 3 lbs in the first leg but lost 4.6 lbs going to day 60, for a total change of 1.6 lbs. (This participant told us, “Not going to go off potassium any time soon I suspect.  Making a little effort to lose weight, and it’s showing a small amount of success.”)

Also notable is that the only two people who had net weight gain by 50+ days are people who had already gained weight by day 29.


Probably the people who kept going past Day 29 were the ones who were most motivated, or who had seen the best results up to that point, so there may be some selection bias.

While none of this is super compelling, people who kept going did on average keep losing weight. They didn’t stick right where they were on Day 29 and they didn’t regress back to the mean. It’s a small amount more evidence in favor of the idea that supplemental potassium might cause weight loss, another tiny pebble on the scale.

In a practical sense, we still recommend that anyone who wants to lose weight should go on the potato or half-tato diet. It’s much more reliable, and more delicious.  

N=1: Introduction


The history of science is old, but most research methods are not. 

People have been doing research for thousands of years. But many of the methods we consider standard today — including questionnaires, blind and double-blind research, and the idea of control groups — were all invented after 1700. The first randomized controlled trial in medicine wasn’t published until 1948, and the term “evidence-based medicine” didn’t find its way into print until 1992

Until recently, all research methods looked more or less like this: A demonstration to the Royal Society by Waller’s pet bulldog ‘Jimmie’

Research methods are still very new, probably we can sit down and invent some more. 

This is good news, because right now there are many problems that we have no idea how to solve. One area of particular mystery is human health. Doctors can do a lot for you with surgery, vaccines, and antibiotics, but outside of these interventions there remain many ailments that totally stump the system. 

A weird part of the postmodern experience is that many people feel kinda bad all the time, even if they aren’t “sick”. If you go to the doctor and you’re like “I’m feeling kinda bad”, they don’t know how to help you.

Being “actually sick” doesn’t get you much further. If anything it’s worse. Lots of people have mystery chronic illnesses, but when you go to doctors with one of these problems, they mostly just shrug at you. 


Alistair Kitchen began having stomach pain. It started out small, but over time grew to “an intensity of pain I didn’t know my body was capable of producing, a literally blinding sensation that shut down every sense in my body except the sensations of my stomach.” He says:

So, four years of this. In the third year, after an endoscopy and a series of scans had cleared me for anything “serious”, the advice given to me was, essentially, this:

Look, some people just have trouble with their stomachs. When they have trouble and we don’t know what is causing it, we just call it IBS. So you have IBS. Watch out for foods that might trigger you, and good luck.

Or take for example the experience of Elisabeth von Nostrand

I had a lot of conversations like the following:

Me (over 20 pages of medical history and 30 minutes of conversation): I can’t digest protein or fiber, when I try it feels like something died inside me. 

Them: Oh that’s no good, you need to eat so much protein and vitamins

Me: Yes! Exactly! That’s why I made an appointment with you, an expensive doctor I had to drive very far to get to. I’m so excited you see the problem and for the solution you’re definitely about to propose.

Them: What if you took a slab of protein and chewed it and swallowed it. But like a lot of that.

Me: Then I’d feel like something died inside me, and would still fail to absorb the nutrients which is the actual thing we want me to get from food.

Them: I can’t help you if you’re not willing to help yourself.

It’s an uncomfortably common story.

Having faced this system, many people end up taking their health into their own hands. This makes a lot of sense and we fully endorse it. But most people have no more success on their own than they do with doctors (though at least they’re not being condescended to). 

It seems like the average outcome is that you end up living with your mystery illness (or even just your mystery sense-of-mild-feeling-bad-all-the-time) for years. It either never goes away, or randomly goes away some day for no apparent reason.


We suspect that people do about as well on their own as they do with doctors because *no one* knows how to study individual issues. This is because our civilization has done a good job developing population-level research techniques, but a crummy job so far coming up with individual-level research techniques.

Our society has devoted a lot of time to doing research on large groups. We’ve come up with lots of ways of running studies on large samples, and lots of ways of thinking about it. We’d bet that 99% of the studies you’ve ever read are studies on groups.

In comparison, doing research on individuals is a very understudied and (dare we say) cutting-edge form of research. Scientists mostly haven’t developed techniques for it, because almost by definition, it isn’t the kind of thing they study. 

Possibly this is because doctors and researchers are more interested in population-level issues. After all, they are usually tasked with solving public health crises, tasked with curing common diseases, things that might affect millions of people. But individuals care more about, well, individuals.

Possibly this is because we started by focusing on the most common illnesses and are only now getting around to the rare ones. Common illnesses are best studied by looking at large groups, so we developed those techniques first, and are only now running up against their limitations.

Possibly it is all a question of computational power. The history of statistics is tainted, because statistics was invented before computers, and was designed within the limits of what a person can reasonably calculate by hand. Even up to the 1990s, consumer machines would take weeks to crunch the kinds of models that today you can run in 15 minutes on your phone. But now we can do more, and maybe that means we can do new things, things that weren’t possible before.

In any case, it must be possible to come up with protocols for such a thing. 

“Jimmy with Electrodes”

Individual-level research comes with certain advantages. The problem with population-level techniques is that the same treatment will always work better for some people than for others. If you give two people the same drug, it might work great for one of them and not work at all for the other, and your statistical modeling needs to take that into account. Individual-level research doesn’t have to worry about that! You are just looking at one person.

A technical way of describing this is that individual-level research always has high internal validity — the research question is “does this treatment protocol work for this person” and you always get a straight answer to that question. This comes at the cost of external validity — you have basically no idea whether your findings will generalize to any other person. That’s an ok tradeoff, because you are already choosing to study an individual, and because population-level techniques have questionable external validity to begin with.

We may also be able to use population-level techniques to guide individual-level research, and individual-level techniques to guide population-level research; there may be many ways in which they are complementary.

Individual-level techniques won’t be limited to studying chronic illness — you could also use them, for example, to make healthy people feel amazing more often, which would be pretty cool. People who already feel amazing all the time, you’re on your own. 

But chronic illness is a good place to start, because these illnesses are a drain on the lives of millions of people who are motivated to figure out a treatment, and population-level medicine isn’t cutting it.

This is a problem we have been mulling over and that we will be writing about over the next couple months. To start with, here are some simple distinctions that seem like they might come in handy: 

Testing vs. Finding Variables

When studying an individual, there are two main situations.

One situation is where you think you know some variables that are involved with your illness, and you want to test them. For example, you may suspect that caffeine makes you feel nauseous. You want to confirm this hypothesis or rule it out. You might also want to demonstrate to a high degree of certainty that caffeine is really a trigger for you, so you can write about it on the internet and other people with random nausea can benefit from your example, possibly by trying it for themselves.

The other situation is where you have no idea what is causing your illness. When you have no clear leads, you want techniques that can help you find variables that might plausibly be involved. This is a much harder problem, but it’s also much more important, because many people are chronically sick and have no idea what is causing their illness. 

This sucks, and it’s very tricky because there are approximately infinity variables in the world. But probably we can do better than “try variables at random” (even if some level of luck is inevitably involved), and we should see if we can come up with techniques for this situation.

Triggers vs. Deficiencies

Sometimes a chronic illness is caused by getting too much of something, like an allergen. These are generally known as triggers. Sometimes a chronic illness is caused by not getting enough of something, like a vitamin. We call these deficiencies

This distinction seems like it might be helpful, because the techniques for finding triggers may be very different from the techniques for finding deficiencies. (More on this in future posts.)

We should also keep in mind that chronic illnesses can be more complicated than simply getting too much or too little of something. Some chronic illnesses aren’t caused by any external variable — there’s always the possibility that you have a brain tumor or something, in which case there may be no triggers or deficiencies involved.

Ruling In vs. Ruling Out

Sometimes we will be in the fortunate position where we can get a lot of evidence that X causes Y. If we can conclusively pin things down and show that dairy causes your chronic nausea, that’s great. Now you can keep yourself from feeling sick all the time, and that’s enormously valuable. 

But sometimes we won’t be able to get evidence in favor of anything — we will only be able to disprove things. It’s important to remember that this is valuable too. Maybe you suspect that your nausea might be caused by dairy, caffeine, alcohol, or fatty foods. If you go through them one by one and rule them all out — nope it’s not dairy, it’s not caffeine, not alcohol, not fatty foods — that’s still good to know. 

It will be disappointing that you continue to feel nauseous all the time and you don’t know why. It may feel like a step backwards, because you’ve ruled out all your best guesses. But it’s still enormous progress. Disproving a hypothesis is valuable, and at least now you’ll be able to enjoy your milkshakes, Irish coffees, and beer-battered onion rings without fear.

The best thing research can offer you is a cure, but the second-best thing it can offer is some peace of mind.


Companions the creator seeks, not corpses, not herds and believers. Fellow creators the creator seeks—those who grave new values on new tablets. Companions the creator seeks, and fellow harvesters; for everything about him is ripe for the harvest.

— Friedrich Nietzsche, “Thus Spoke Zarathustra”

There’s a long tradition in the history of medicine where people figured out the cause of an industrial disease by noticing that one profession had a much higher rate of the disease than everyone else. For example, in Victorian and Edwardian England, chimney sweeps had a rate of scrotal cancer more than 200 times higher than workers who weren’t exposed to tar on the job. No, we are not making this up.

Now it’s your turn to do something similar. Your mission, should you choose to accept it, is to write a review of the mysteries on a topic and send it to us at slimemoldtimemold[at]gmail[dot]com by July 1st 2023.

Pick a topic, and write about the mysterious aspects of that topic, like we did for the mysteries of obesity in Part I of A Chemical Hunger. We mostly expect you to review topics from “hard science” areas like medicine, biology, chemistry, and neuroscience, but we are open to reviews of mysteries from social science, economics, political science, or the humanities. If you feel you can make a strong case for some mysteries and why they are mysterious, that’s good by us.

You can include Normal Mysteries, things that are unexplained but that most people know about and don’t seem all that confusing. For example, IBS and migraines are about 2-3x more common in women than in men. Everyone kind of knows this, so it’s not all that weird, but no one can really explain it, so it is still a mystery. The first three mysteries we reviewed about the obesity epidemic were all pretty normal. 

You should also review Weird Mysteries, things that most people aren’t aware of and/or that seem like they totally don’t make sense, things that fly in the face of our understanding. The rest of the mysteries we reviewed about the obesity epidemic were pretty weird, like how lab animals and wild animals are also getting more obese. What’s up with that? 

Our hot tip is that the simplest form of mystery is just unexplained or unexpected variation. A good example is how obesity rates vary by altitude — low-altitude counties in the United States have much higher obesity rates than high-altitude countries do. This is not predicted by most theories of obesity, and many people found this very surprising.

An unexpected LACK of variation can also be a mystery. For obesity, it feels intuitive that people who eat different kinds of diets should weigh different amounts, but diet consistently seems to make very little difference. From the perspective of the mainstream understanding of obesity, this is pretty mysterious.

How do you know that you’ve found a good mystery? It’s an emotion, a feeling that starts in your gut, not unlike IBS (which, hey now that we think about it, is pretty mysterious). Start with something that you just can’t wrap your stomach around. We’re looking for a confusion that started rumbling in your tummy when you were a student who kept asking the same basic questions and couldn’t get a straight answer, a confusion that has just kept grumbling away right there next to your esophagus ever since — now that’s a mystery. The best mysteries will be assumptions where everyone else thinks everything is fine, but you have a nagging suspicion that something is wrong.

Please focus on the mysteries of your chosen subject — DO NOT include a theory. If you feel you need to provide context, you can discuss popular theories and how your mysteries support or undermine them (like we did in Part II). But no arguing for a theory or introducing a theory of your own. 

This is a mystery contest, not a theory contest. Your mystery review is the hook; if you do a great job reviewing some mysteries and win the contest, everyone will be excited to hear about your theory. Then you can put it on your own blog and get a lot of readers. If people think you have a promising direction, maybe you can get funding to study it further. 

Software engineers who have just lost their jobs; grad students on strike; academics who are fed up with the paywall curtain; couples who have just retired at 35; founders whose last venture was recently acquired; billionaire playboys with too much time on their hands; anyone who is looking to make a pivot to research — this is the contest for you. You don’t need a lot of research chops to look at something and tell that it’s weird; anyone can pick out mysteries by noticing when things don’t add up, when things are unexplained, or when experts all disagree on the best explanation. 

If anything, outsiders and newbies have an advantage. If your career doesn’t rely on pretending to understand, it’s easier to spot things that don’t make any sense.

Don’t do this though

Contest Format

We have recruited some judges to help us evaluate the mysteries: Adam Mastroianni, Lars Doucet, Applied Divinity Studies, Tony Kulesa, and possibly some other judges TBA. We will consult with these judges and will choose around 5-10 finalists, which will be published on the blog. Then readers will vote for the best. First place will get at least $2000, second place $1000, third place $500, though we might increase those numbers later on.

Use your expertise. The best entries will probably be about things YOU are already familiar with, things where you know about the mysteries the rest of us haven’t noticed yet. 

All forms of media are welcome! We like to write really long stuff, and sometimes we just post our correspondence. But if you like to boil ‘em instead of mash ‘em (or stick them in a stew!), that’s cool too. Podcasts, videos, slideshows, semaphore code, etc. are all welcome. All written finalists will be published on the blog. Finalists in other formats (e.g. videos, podcasts) will be linked to. The language shared by the judges is English, so we prefer materials that suit the conventions of English speakers.

You must submit your entry under a pseudonym. This helps people discuss you and your work without having to say, “the guy or lady perhaps or person or team who wrote the SMTM mystery contest entry on pancreatic cancer”. Instead they can say, “blorpShark’s wonderful mysteries of pancreatic cancer review”, which is much nicer. 

Pseudonyms also keep famous people from having an advantage. For this reason, if you already go by a well-known pseudonym on the internet, please choose a new pseudonym for this contest. 

Team submissions are strongly encouraged (friendship is the most powerful force in the universe), and we encourage you to pick a band name. Go to your nearest band name generator and pick the stupidest name it generates. For solo entries, we recommend a rap name generator, like Post Malone did

After the contest is over, if you want to connect your pseudonym to your other name(s), please feel free to do so. If you do not provide a pseudonym, one will be provided for you. 

If you submit a non-written entry, please send it to us in a form that is as anonymous as possible. For example, you might send a podcast entry as an audio file, or a video essay as a video file. Don’t mention your name in the recording, etc.

Please submit written entries by putting them in a Google doc and sharing that doc with us. We will try to preserve your formatting as best we can if we publish your entry as a finalist, but no promises. If you want to make sure your formatting appears as intended, use simple formatting (e.g. bold, italics, and images). The more complicated your formatting is, the more likely we are to make an error in copying it over. 

Please don’t put your name or any hints about your identity in the Google doc itself. If you do, we may remove that information or disqualify your entry.

Please make sure that the Google doc is unlocked and that we can read it and share it with the other judges. Go to the “Share” button in the upper right, and on the bottom of the popup click on where it says “restricted” and change to “anyone with the link”. If you send us a document we can’t read, we will probably disqualify you.

Frankly we reserve the right to disqualify entries for any reason, or no reason at all. 

If you win, we will send you your prize money in the form of an envelope stuffed with cash, or something else if we agree that it’s more convenient. 

Your due date is July 1st, 2023. If you have any questions, ask in the comments so other people who have the same questions can see. You can also email us or ask us questions on twitter. Good luck!

Links for December 2022

We commented on this in an earlier links post, now Aella goes off on the same point: ​​You don’t need a perfectly random sample for useful data, jfc. She’s right by the way, as is Scott Alexander. See also Eigenrobot on twitter: “the biggest problem in both statistical practice and criticism of statistical practice is braindead insistence on following form rather than consideration of whether adherence to form is sufficient to produce the desired insight or necessary to produce any insight respectively”

The Genealogy of Chinese Cybernetics

how and why to be ladylike (for women with autism) — contains dune quotes as promised 

Bad words get more differenter over time, especially adjectives: The word for good is similar in English (“good”) German (“gut”) and Faroese (“góðan”) But the word for bad is “bad” in English, “schlecht” in German, and “illur” in Faroese. In new research, we show that this is a broader pattern that we call “valence-dependent mutation”

@selentelechia on twitter has been trying an “old 4chan iodine theory on fast food salt cravings”, which is about as crazy as it sounds, but she’s experienced some pretty good outcomes: “I’ve been doing this for two days and my hands aren’t cold anymore, nor are they taking half an hour to warm up after coming inside … I don’t feel a constant low level impulse to lie down”. Seems interesting.

Collin Lysford uses the example of IQ to point out how weird patterns of association plus noise can look like pretty generic correlations.

“Many cell lines that are widely used for biomedical research have been overgrown by other, more aggressive cells,” begins Wikipedia’s list of contaminated cell lines. “For example, supposed thyroid lines were actually melanoma cells, supposed prostate tissue was actually bladder cancer, and supposed normal uterine cultures were actually breast cancer … Estimates based on screening of leukemia-lymphoma cell lines suggest that about 15% of these cell lines are not representative of what they are usually assumed to be. … Contaminated cell lines have been extensively used in research without knowledge of their true character. For example, most if not all research on the endothelium ECV-304 or the megakaryocyte DAMI cell lines has in reality been conducted on bladder carcinoma and erythroleukemia cells, respectively.” (h/t @MasterTimBlais)

Chat GPT: Weirdly good at correcting OCR errors in historical texts. Good at condensing mind-numbing academic research into something you can actually read, just ask it to “rewrite this obtuse paper as a children’s book”. Ok at riddles until you give it a riddle with no right answer, in which case it confidently comes up with a completely nonsensical explanation. In this example, it can’t even count:

At-home caffeine analysis by a coffee YouTuber, with some surprising findings (h/t “Rachel” on twitter) 

@goblinodds asks twitter, “do both of your eyes see the same colors or is one’s input cooler-toned than the other?”, finds that 20.9% report different temperature input from different eyes. Uh???

Missed opportunity: You could have owned CHARLES DICKENS’ PICKLE FORK, for the low low price of $6,120!

“Many researchers have conjectured that the humankind is simulated along with the rest of the physical universe – a Simulation Hypothesis. In this paper, we do not evaluate evidence for or against such claim, but instead ask a computer science question, namely: Can we hack the simulation?” Science Banana draws particular attention to Table 1:

Doing science online – A view on science blogging from back in 2009. And from the same author very recently: Exploratory notes: Community as the unit of scientific contribution 

“I think of the spider whom, sitting like the iris inside a lacy eye, tugs and flexes and tightens its grip on different strings, creating an interrogative experience with web and with world. Scientists have likened this behavior to the activity of a brain itself, sifting through and reacting to stimuli. Each tug is a question, each returning vibration a reply. … extended cognition researcher Hilton Japyassú has shown that cutting a part of the silk dramatically shifted and disoriented the behavior of the spider, and seemed to imitate the effects of a lobotomy. This begs the question. Where is the spider’s mind? Is it inside the spider’s actual brain? Is it in its spinnerets or legs? Is it in the web itself?”

Great bloggers are rare, weird, and not team players. Showing our biases here, but we actually think that this is an argument for teams of bloggers, like yours trulies. For one person to be a great blogger they may indeed need to be obsessed about writing all the time & very widely read & interested in just about everything & willing to work for relatively low wages, but if your blogging team is two people, you only need to have that combination of traits between the two of you. If you can make a blogging collective of four people, you only need one person who has each of those traits! Maybe it’s a crazy scheme but we’re the ones with the hive mind over here.

If you ask ChatGPT to behave like a Linux terminal and start feeding it Linux commands, it will invent an entire fictional machine, complete with an entirely hallucinated internet that exists only inside ChatGPT’s language model. If you look in the letters folder, you can (sometimes) find John Doe’s resume.

horrifying-pdf-experiments/master/breakout.pdf (h/t @andy_matuschak)

People Took Some Potassium and Lost Some Weight

In November 2021, we finished our series A Chemical Hunger, where we argue that the obesity epidemic is the result of environmental contaminants, and that one of those contaminants might be lithium. We hadn’t really expected anyone to read it. But we were wrong — tens of thousands of people have now read the series, and to date the twitter thread giving an overview of the series has more than 2 million views. 

In April 2022, we announced the Potato Diet Community Trial. We expected that the potato diet would be really hard to stick to and people would only lose a little weight, if any. But we were wrong — people said the potato diet was easy, enjoyable, and on average, people lost 10.6 lbs over 4 weeks.

Potatoes are really high in potassium, so we wondered if potassium could be the active ingredient causing the weight loss in the potato diet. We decided to try a self-experiment where we took small amounts of potassium salt every day, but it seemed unlikely that such tiny doses could have any effect. But we were wrong — we each lost about 5 pounds over four weeks. One of us kept going and lost 12 lbs over 60 days. 

In October 2022, we announced the low-dose potassium community trial (twitter thread here). Even with the results of our self-experiment, it still seemed unlikely that such tiny doses of potassium would do anything for people on average. 

Now, you are reading the post with analysis and results from that study.


  1. Motivation
  2. Variables
  3. Protocol
  4. Participants
  5. Weight Loss
  6. Effects Other than Weight Loss
  7. Interpretation
  8. Future Studies
  9. Conclusion

1. Motivation

The goal of this study is to see if the large doses of potassium found in potatoes could plausibly be the reason why people lose weight on the potato diet. 

The doses of potassium in this study are small in comparison to the potato diet, only a few thousand milligrams per day. This is much less potassium than people got on the potato diet, so we don’t expect the effect to be large in any practical sense. In fact, we expect that if there is an effect at these doses, it will be quite small, probably a loss of only a few pounds on average. We are just looking to try to see if there is any effect at all.

Potato diet estimate per the USDA’s estimate for potassium in 2000 calories of potatoes

We are studying potassium because it is a major variable from the potato diet that we can easily look at in isolation, not because we think potassium will be a great or a practical treatment for obesity on its own. 

We don’t expect everyone to lose weight on this protocol, or for it to be sustainable in the long term. We just want to know if potassium could be the reason why people lose weight on the potato diet, something that we currently have almost no information about. If it looks plausible, that tells us something about why the potato diet works; and then we can consider, ok wait a minute, why would potassium cause weight loss at all? But more speculation on these points after we look at the results.

Raw data, the analysis script, and study materials are available on the OSF. The dataset is very rich and there’s a good chance that we haven’t found everything there is to find. So if you are statistically inclined, after you’ve finished reading this post we encourage you to download the data and have a look for yourself. If you find anything interesting, or even if you’re just able to confirm our findings, you should write up your analysis on your own blog and let us know about it! Science is a game, please play!

If you recreate these analyses at home, your results may be slightly different than ours because three participants asked that their data not be shared publicly.

Whether or not you like what we’ve done here, we encourage you not to take our word for it. Download the data and materials, perform your own analysis, share your criticisms, run your own study. If you think you can do a better job, maybe you are right! Show us how it’s done.

2. Variables

We collected variables at three points.

First, we collected demographic variables at signup. The variables we collected at this point were:

  • chromosomal sex 
  • reported hormone profile (so we can distinguish trans participants with less ambiguity) 
  • age in years
  • profession
  • race/ethnicity (from a limited number of options)
  • local postal code
  • current country of residence
  • whether they had done any sort of potato diet in the last year

In response to this last question, the majority told us they had not done any potato diet in the last year, but 40 told us they had done some kind of potato diet on their own, and 7 said they took part in our Potato Diet Community Trial.

After signup, we had people track a number of variables about their health and their diet (and how much potassium they were taking) over the course of the study, on a spreadsheet we provided. You can view a version of that spreadsheet here.

The main variables collected on this sheet were: 

  • weight (in the morning)
  • potassium doses (up to four doses a day)
  • variables for whether or not participants consumed meat, eggs, dairy, leafy greens, and tomato products each day (just a 1 for “ate it today” and a 0 for “didn’t eat it today”), because we suspect these foods may be high in lithium (though we’re not sure)

We also included fields for several bonus variables, which were optional but encouraged. These variables were:

  • calorie intake
  • waist circumference (which a couple people asked for after the potato diet)
  • sodium intake
  • energy, mood, and ease of study (all on 7-point scales)
  • systolic and diastolic blood pressure
  • total Cholesterol, as well as LDL and HDL cholesterol
  • triglycerides
  • resting heart rate
  • fasting blood glucose
  • body temperature
  • estimated hours of sleep the night prior
  • sleep quality the night prior 
  • fidgeting (on a 1-7 scale)
  • estimated minutes of exercise
  • (and several fields for notes) 

After we took a look at the data, we realized we had a few questions about aspects of the study that we hadn’t really measured. For example, some people mentioned that they hated the potassium while other people mentioned finding it delicious. But most people didn’t mention this aspect at all, so it would be hard to conduct any analysis related to how much people enjoyed the potassium.

So finally, on December 3rd, we sent a followup survey asking about some of these remaining questions. Five days later, there were 105 responses. We downloaded these responses and added them to the dataset.

The variables we collected at this point were:

  • what potassium compound they had primarily consumed
  • what form they had taken it in (e.g. salt vs. capsule vs. tablet)
  • what brand of potassium they had primarily consumed
  • what delivery methods they had used (e.g. in food vs. in a drink)
  • change in their appetite
  • how much they enjoyed the potassium at the beginning of the trial
  • how much they enjoyed the potassium at the end of the trial
  • whether they felt leaner or chubbier subjectively
  • whether they were intentionally exercising or eating more or less during the trial
  • whether they were on some other diet or routine when they started the potassium trial
  • and a free-response question asking if there was anything else we should know

For more detail, see the copies of the materials available on the OSF

3. Protocol

As a reminder, the main study protocol was: 

  • Start with two doses of 330 mg potassium (1/8 tsp Nu-Salt) on the first day.
  • If you feel fine, try three or four doses of 330 mg potassium (1/8 tsp Nu-Salt) on subsequent days.
  • If you’re feeling fine after 4-7 days, try one dose of 660 mg potassium (1/4 tsp Nu-Salt).
  • If you still feel good, keep increasing your dose by small increments. For example, if you are on two doses of 660 mg (1/4 tsp Nu-Salt) a day, you might increase that to three doses of 660 mg, or one dose of 660 mg and one dose of 1300 mg (1/2 tsp Nu-Salt). If a higher dose makes you feel bad, try returning to the dose you were on before and maintain that.
  • Try slowly increasing to two doses of 1300 mg (1/2 tsp Nu-Salt) a day. Only go beyond that if you are feeling totally fine. 
  • You should calibrate based on your own experience — different people will have different needs and different limits. For example, we’d expect someone who weighs 300 pounds would be able to tolerate higher doses than someone who weighs 150 pounds.
  • If you feel weird / bad / tired / brainfog and you can’t tell why, try:
    • eating something;
    • drinking some water; 
    • getting some sodium; 
    • and see if any of those help. It may be easy to end up needing food / water / salt and not notice.
    • If you still feel weird, try dropping to a lower dose or taking 1-2 days off.
  • If at any point you feel sick or have symptoms of hyperkalemia, stop immediately and seek medical attention.

Participants were asked to record their weight every morning, and they were asked to record data up to the weight measurement on the morning of day 29 regardless of whether they stuck to the protocol. That way even if someone found the potassium intolerable, we could still use their data.

4. Participants

A total of 305 people submitted the initial form.

Of those, 15 people filled out the signup 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 290 people in the study.

Of the 290 people who were enrolled, 57 never entered any data on their spreadsheet, leaving 233 people who entered at least one day of weight data.

The most common outcome in this group was to make it the full 29 days, but the majority of the 233 people who entered data on day 1 stopped entering weight data before day 29. Here’s the distribution of days completed (as measured by last weight entry) from that group:

As shown above, 104 people entered weights on both the first day and on day 29. This was the criteria we specified in advance for the group we would focus on for the main analysis. Specifically, we said: 

Anyone who records data for 29 days is clearly taking the study seriously, even if they weren’t able to stick to the potassium supplements the whole time. … Based on this, our main analysis will focus on participants who provide 4 weeks of data. If you provide a weight measurement for the morning of day 1 and the morning of day 29, so we can calculate your weight before and after, and you took at least one dose of potassium, we will do our best to include you in the analysis.

5. Weight Loss

The main outcome of interest is weight change by 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:

On average, people lost weight. The mean is -0.89 lbs, or an average loss of 0.89 pounds over 29 days. With a sample size of 104, this is significantly different from zero in a one-sample t-test, p = .014, and the 95% confidence interval for average weight change is [-1.59, -0.19] pounds. 

However, this obscures the data of several people who made it to the end of the study, but who mistakenly didn’t report a measurement on day 29. If we look at the data of everyone who reported a weight on day 28, this is the histogram: 

This has a mean of -0.85 lbs and a larger sample size, and is also significant, p = .016.

The same thing is true if we look at everyone’s weight at day 27 — the average weight loss is 0.86 lbs and this is significant, p = .016. The exact cutoff doesn’t matter, which indicates that the result is robust

People who dropped out before reaching the end of the four weeks also seem to have lost weight on average. You can see that the majority of people who stopped before day 21 are below zero (the horizontal line), indicating they lost some weight over the time they spent on the trial:

In fact, if you look at the weight change from EVERYONE who reported at least two weight measurements (i.e. not including those people who only reported weight for day 1, who literally could not have seen weight change), people still lost 0.79 lbs on average. Here’s the histogram:

Because of the much larger sample size, this is still significant. In fact the p-value is quite a bit lower (p = .0002) and the 95% CI is noticeably narrower, [-1.20, -0.38] pounds.

The average weight loss here is smaller, but remember that about half of these people did not make it the full four weeks! In fact, this analysis includes 26 people who didn’t even make it 7 days. 

Looking over the course of the study as a whole, it appears that people slowly lose weight over time, with no apparent changes in the trend: 

Of interest here is that the 95% CI excludes zero for the first time on day 7, and that day 25 is the point of greatest average weight change.

Looking at individual trajectories is a right mess, but here’s the plot anyways:


On average it looks like people lose about 0.8 lbs over four weeks on this protocol. This isn’t much weight loss, but it’s statistically distinguishable from nothing.

But obviously some people do lose more weight, sometimes a lot more. Three people lost more than 10 lbs. It’s clear that there is a lot of variation around the small average weight loss. Can we figure out what caused any of this variation?

Well for one thing, some people did not have much weight to lose to begin with. Here’s weight change on day 29 compared to starting BMI:

As you can see, people who started with higher BMIs lost more weight. This correlation is significant, r = -0.269, p = .006, and is exactly what we would expect. People who have a BMI of 22 don’t have much weight to lose, so we should expect to see very little weight loss from them, perhaps no weight loss at all. Meanwhile people with higher starting BMIs have more to lose. It’s interesting to see that the person with the highest starting BMI also lost the most weight. 

Many lean people participated in this study, and most seem to have signed up because they wanted to contribute to the research even if they were unlikely to lose weight. This isn’t an experiment, but some of them do provide a sort of baseline response. “I am happy with both weights,” said one participant, “and wasn’t expecting or hoping for a big weight loss number. I thought of myself as somewhat of a ‘control group.’”

If this were a “normal” study, and we were “normal” researchers, we probably would have restricted signups so that only people with a starting BMI of 30 or higher (technically obese) could sign up for the study. 

If we had done that, here’s what the analysis would look like. Unsurprisingly, this group lost more weight on average: 

The average weight loss for participants who started the trial with a BMI of 30 or above was 1.83 lbs, and again this is significant, p = .031.

Another thing that might matter is what country people are from. This is especially interesting from the perspective of the contamination hypothesis, because we suspect some countries have more contaminants than others. We tried doing a “USA vs. all other countries” analysis, but that was not significant, p = .341. There also doesn’t seem to be a clear effect of what continent people are on, but we can still plot these data:

Nothing groundbreaking here, but we do want to note that we see much less variation in Europe than in North America.

But of course, the main thing we should expect to make a difference in the results of the potassium trial is the amount of potassium! 

In this study, everyone was on the same protocol, but some people took much more potassium than others. People were asked to start with two doses of 330 mg on the first day and slowly work up to two doses of 1300 mg a day, but they were asked to drop to a lower dose if a higher dose made them feel bad, and to only go beyond two doses of 1300 mg per day if they were feeling totally fine. We also asked people never to go above 1300 mg in a single dose or 5200 mg in a day.

Given this protocol, it’s natural that some people ended up on higher doses than others. Here’s the distribution of average daily doses for people who made it the full four weeks:

As you can see, there is considerable variation. 

With this information, we can compare the amount of potassium people were taking to the amount of weight they lost. When we do, we see a clear relationship, where people who took more potassium lost more weight on average: 

This relationship is statistically significant, r = -0.276, p = .005. This is not an experimental result, since we didn’t assign people to different doses, so we shouldn’t assume it’s causal. There are certainly alternative explanations. For example, there may be weird selection issues. People who chose to take more potassium could have been the people who were like “I feel fine, I’ll take more” or people who were like “It’s not working, I’ll take more” or people who were like “I’m losing a little bit of weight, so I’ll take more and lose more”. But this result is also consistent with what we would expect if potassium supplementation was causing the weight loss.

Let’s stop a minute and take a closer look. The regression line here is y = -0.0011x + 1.3110. Essentially what this means is that the model says that on average you would gain 1.3110 lbs if you supplemented no potassium at all for 29 days, but you lose 0.0011 lbs for every mg per day you supplement above that baseline. 

For example, someone consuming 2000 mg per day would lose 2.2 lbs more than baseline; since baseline is 1.3 lbs gained, we would expect them to lose about 0.9 lbs on average over 29 days. 

The potato diet gives exceptionally high doses of potassium. Sources differ on exact numbers, but the USDA says that a medium potato has about 900 mg of potassium and about 160 calories, so 2000 calories of potatoes a day would give a daily dose of about 11,000 mg potassium.

Plugging that dose into the linear equation above, the predicted weight loss on the potato diet (i.e. on a dose of 11,000 mg/day) would be:

> (-0.0011 * 11000) + 1.3110

> -10.789

It’s hard to get any closer than that — the observed weight loss on the potato diet was 10.6 lbs on average. That’s why we titled the report, LOSE 10.6 POUNDS in FOUR WEEKS with this ONE WEIRD TRICK Discovered by Local Slime Hive Mind! Doctors GRUDGINGLY RESPECT Them, Hope to Become Friends

Realistically, the fact that the linear equation in this case lines up with the potato diet so well is just an amusing coincidence. The 95% confidence interval on the slope is [-0.0019 to -0.0003], so model fits for 11,000 mg/day include anything from 19.6 lbs to 2.0 lbs lost.

But you have to agree, it is amusing.

This is in fact moderate support for the idea that potassium is the only active ingredient in the potato diet. We say moderate because it’s certainly not conclusive, but it would be hard for the data to be any more consistent with that interpretation.

Another interesting comparison can be found in the relationship between weight loss and total potassium taken over the course of 29 days:

This relationship is also significant (r = -0.209, p = .033), though it’s somewhat smaller than the relationship between weight loss and daily average potassium. This may mean that taking a consistent dose is more important than the amount of potassium you take overall, though the confidence intervals of the two correlations clearly overlap, so don’t conclude too much from this difference.

Other than starting BMI and potassium dosage, we can’t really tell why some people lost more weight than others. Sex, reported hormone profile, age, ethnicity, previous experience with the potato diet — none of them seem to matter.

We asked people to report how often they ate meat, eggs, dairy, leafy greens, and tomato products, and while there are sometimes vague trends, none of these variables are ever significantly associated with weight loss. On the other hand, we should note that these were measured in a very rough fashion (just “did you eat it or didn’t you” for each day), so the variables aren’t sensitive enough to detect anything less than a very strong effect.

We also tried looking at all these variables while controlling for starting BMI and daily average dose, but there still don’t seem to be any associations with these variables and weight loss (though it’s possible we’re missing something.)

Similarly, we looked at the variables from the followup survey, but with the exception of one appetite result we will report below, we didn’t find any associations with these variables and weight loss. Even if there were relationships, we probably wouldn’t find them in these data, because there wasn’t much variation in these variables — most participants took potassium in about the same ways and (per our request) didn’t change their diet or exercise during the trial.

Ease of Weight Loss

So much for absolute weight loss. But what about relative weight loss? Were there signs that the potassium made it easier to lose weight? 

Indeed there were, at least in the self-report data. Some people mentioned being surprised at how easy it was to lose weight, and some people mentioned that they were surprised they didn’t gain weight given how poorly they were eating:

(77174810) First of all – holy shit! It’s amazing how well this worked and it’s also surprising that it’s never really been studied before! Thank you for the analysis and thought that you put into this. For this trial, I basically just ate whatever I felt like, went to a football tailgate party nearly every weekend with lots of beer and foods you would not associate with dieting… and still lost nearly 10 lbs! I plan to continue on for at least another couple months so feel free to follow up later if you want to.

I have tried every diet/exercise and variation of CICO, atkins, keto, IF, etc., etc., etc. to try and lose weight. To no one’s surprise, nothing really worked for long and the weight always came back. At the end of 2020 I was over 275. It took me three months of busting my ass to lose 20 pounds and as soon as I started eating “normally” again, I slowly started putting weight back on.

(23881640) I started a quick calorie-restricted diet before the holidays (got to fit into those festive pants!), and I’m combining counting macros, counting calories, AND adding 1 tsp of potassium chloride a day to my water, and the weight is coming off. It’s making the calorie restriction much more bearable. I can tell I’m technically hungry, but adherence is so much easier doing it this way. (I lost 20 pounds before by counting macros, and that was hard.) 

(60114890) Trial was very easy. Lost 5.5 lbs.

I definitely attempted to run a calorie deficit. So, this was a deliberate weight loss attempt. I’ve lost the same 5-15 lbs. maybe six times over the last 30 years. This was the first time it wasn’t really painful and didn’t require a lot of discipline. It’s also the fastest rate of weight loss I’ve experienced (1.5-2 lbs/week as opposed to 0.75-1.0 lbs/week). Very very easy. Why? Mostly appetite suppression. Historically I have been able to run 500 kcal deficits with a lot of effort. I was able to run 750-1000 kcal deficits with almost no effort. Real appetite suppression kicked in after second week, at levels of about 1800mg additional potassium. It was ridiculous—yesterday I ate 1300 kcal and burned 2600 kcal and wasn’t really hungry.

…for my purposes,  I don’t really care if its placebo. My appetite was substantially suppressed. It was easy to run a 750kcal deficit. I’m going to stay on the diet until I’m at target weight of 185lbs, which would be total loss of 13.6 lbs. Feels very doable.

This wasn’t a universal experience, but we think these reports were interesting. 

It seems possible that for most people, small doses of potassium aren’t enough to cause weight loss by themselves, even if they affect your appetite (see below). But they might still be helpful because they enhance other weight-loss approaches.

At this point we would like to draw your attention to the beverages known as “ketoade” and “snake juice”. 

Ketoade is a term for home-made electrolyte drinks people sometimes take as part of the ketogenic diet. These almost always include potassium, usually in the form of Morton® Lite Salt™, a half-and-half mixture of potassium chloride and table salt. Since it’s all homebrew, recipes differ widely, but some people are clearly getting several thousand milligrams of potassium a day from their ketoade.

It’s possible that the keto diet works but is hard to stick to, and that ketoade has become popular because it makes weight loss on a restrictive diet much faster and easier. It’s also possible that the keto diet doesn’t cause weight loss at all, and that most successes on the keto diet actually come from people who are taking large amounts of potassium “on the side” as ketoade. 

Snake juice is a term for (you guessed it) home-made electrolyte drinks people sometimes take as part of various weight loss strategies, including intermittent fasting, keto, and something called the… snake diet. As far as we can tell, no snakes were harmed in the making of this diet — it appears to refer to how snakes go a long time between meals, since it’s a weight loss strategy about going a long time between meals. 

Anyways, snake juice involves drinking a concoction that gives you several thousand milligrams of potassium every day. See this helpful instructional video to learn more. It opens with a man yelling “hey FATTY, behold!” at you, so you just know it is a trustworthy and authoritative source. 

In any case, most participants in the potassium trial were essentially drinking ketoade / snake juice / whatever you want to call it: potassium salt and sodium salt mixed in some beverage, often with a little bit of flavoring. And while the effect size was small, on average it seemed to cause weight loss, even without keto or fasting or anything else.

The results of this study suggest that the ketogenic diet community, and this community of “snake people”, have correctly developed a folk wisdom tradition of taking large doses of potassium to amplify their weight loss routines. If so, that is pretty wild, and it speaks well of the value of folk wisdom in solving people’s real problems.

It’s especially interesting that their theories of obesity don’t seem to point at potassium at all. These people don’t think that potassium is the active ingredient here, and they don’t have any idea why potassium might help them lose weight, but they have figured out that they should take it. That’s pretty impressive.

The inverse is true as well. The fact that internet people have settled on potassium salt as part of their folk weight loss routines supports our finding that straight potassium causes weight loss.

6. Effects Other Than Weight Loss

People mentioned a wide variety of effects, but most effects were only mentioned once or twice. One person said that the potassium made their tinnitus worse, but there doesn’t seem to be any sign of this generalizing to other participants.

We did let people report some bonus variables, but most of these variables didn’t get many responses, so we often didn’t end up with a big enough sample size to analyze. For example, only one person reported their total cholesterol on day 29, and no one reported HDL cholesterol, LDL cholesterol, or triglycerides on day 29. So we won’t be taking a closer look at any of those.

Even so, a few things did come through. Here are the effects that people mentioned more than a couple times in the self-report data, or where there were enough measurements to make taking a look worthwhile: 


The most commonly mentioned effect of potassium was reduced appetite.

(36100230) I found that my appetite was dulled a bit — My mind focused on food a bit less, I snacked less between meals, and ate slightly smaller servings. I found this started to wane a little bit towards the end of the month — not entirely, but I found myself more likely to feel hungry between meals.

(58007117) Taking the potassium was very easy (with the exception of the few times I put nu-salt into pill casings and took it that way – this caused stomach pain, which I did not experience when just taking it dissolved in liquid). My overall impression is that potassium acts as a mild appetite suppressant.

(11538897) I didn’t think of food while doing the trial. At the lower doses, my hunger was affected but my appetite was not. At the higher doses, both were affected. … There was a huge difference in my general desire for food if I took the supplement in the morning before eating. If I took my first dose with food, I would be thinking about food sooner (though I wouldn’t say it was even hunger, just craving). When I took only the supplement and then went to work, it was almost always that I wouldn’t think of food until after work.

(77174810) I settled on 3 doses of ~990mg (3/8 teaspoon) a day at 0730, 1130, 1600. I felt like this kept hunger at the lowest level overall and was easy to stick with. I found that if I took the supplement when I was already hungry, I’d eat more overall. So I take the dose an hour or so before I’d normally eat a meal. 

(19620767) Finished the trial. It was weird, I lost a pound the first day, then nothing for a week, then 4 more pounds, then nothing. My appetite was pretty suppressed the whole time, but due to injury and illness I wasn’t really able to exercise beyond going on walks and doing my PT, I also ate an unusually large amount of junk food for life reasons (depression, birthday cake, etc) without gaining any weight.

(18556224) The potassium didn’t magically decrease the calories I took in — I had to consciously restrict them, or have circumstances dictate that — but it did suppress my hunger, i.e. four weeks I was as hungry during the day (mostly not at all) no matter how much food I had eaten.

I haven’t decided whether the weird feeling that potassium gives me is better or worse than the hunger I’d otherwise experience, since I’ve gotten fairly good at handling that.

I haven’t noticed any cravings during the trial, which is good because that is often a problem for me — not craving things carby things, but craving certain foods I eat anyway (butter, cheese) so that I eat more calories than needed, even though I’m not really hungry for anything, just seeking pleasure.

(49045265) I did notice an reduced appetite. There was only one day during the study I was hungry.

(60114890) I definitely attempted to run a calorie deficit. So, this was a deliberate weight loss attempt. I’ve lost the same 5-15 lbs. maybe six times over the last 30 years. This was the first time it wasn’t really painful and didn’t require a lot of discipline. It’s also the fastest rate of weight loss I’ve experienced (1.5-2 lbs/week as opposed to 0.75-1.0 lbs/week). Very very easy. Why? Mostly appetite suppression. Historically I have been able to run 500 kcal deficits with a lot of effort. I was able to run 750-1000 kcal deficits with almost no effort. Real appetite suppression kicked in after second week, at levels of about 1800mg additional potassium. It was ridiculous—yesterday I ate 1300 kcal and burned 2600 kcal and wasn’t really hungry. …for my purposes, I don’t really care if its placebo. My appetite was substantially suppressed. It was easy to run a 750kcal deficit.

(06769604) My appetite was clearly suppressed, especially in the morning. The issue seemed to be that it would come roaring back in the afternoon and I’d be quite hungry.

This was true even for many people who didn’t lose weight, or who lost only negligible amounts. But it wasn’t universal, and some people explicitly mentioned that there was no change in their appetite. 

We found this interesting, so we included a question about appetite changes in the followup survey. In these data, the majority of people reported no change to their appetite, but about a third reported decreased appetite, and six people reported greatly decreased appetite. Only one person reported any amount of increased appetite.

And you probably won’t be surprised to see that reduction in appetite was associated with weight loss: 

When we treated this self-report measure as a continuous variable on a 1-5 scale, the relationship was significant, r = 0.295, p = .011. But you’ll also notice that many people who did not lose any weight still reported a reduced appetite, suggesting the potassium had some effect for them, just not enough to cause weight loss. 

You might think that potassium caused weight loss because it reduced appetite, which caused people to eat less, which caused weight loss. That may be the case, and several people did mention that they were running a calorie deficit. But we also included a field for people to track their calories if they wanted to, and while only 22 people provided complete data, the correlation in that data is nonsignificant and pretty flat, r = -0.100, p = .659. 

You’ll also notice that it’s trending in the “wrong direction”, where people who reported eating more also lost more weight.

We don’t think it’s helpful to conclude that potassium is “just an appetite suppressant”. Clearly it is an appetite suppressant, but like, um, why? Why would it do this? Everything has a mechanism. What is the mechanism for this?

We think potassium reduces appetite because it turns down your lipostat. As we said with the potato diet,

[Reduced appetite] 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.

Also not shown: increased body temperature, reduced fat storage, etc.

But even if we accept that potassium turns down your lipostat, you still have to ask, why does it do THAT? What is the mechanism that makes potassium turn down your lipostat’s set point? Well, more discussion in a minute.


Some people mentioned noticeable improvements to their sleep.

(24646801) Regarding sleep, in the month or two prior to the study, I had started to wake semi-regularly (5-6 nights/week & 1-2 times per night) to use the toilet. This tapered off rather quickly during the trial and with few exceptions has not returned. I don’t know enough medically to explain why this would be, but it’s definitely an improvement to my sleep, and I would continue the trial indefinitely to retain this result.

(81847724) Sleep is highly subjective but overall I think my sleep quality improved during the experiment, generally sleeping longer without waking up in the middle of the night.

(87352273) Sleep was the most pleasant surprise. I have issues with insomnia, so I tend to stay awake until 2-3 am when I get really sleepy so I don’t end up just lying awake in bed getting frustrated. With ~2000 mg of potassium as well as magnesium before bed, I found myself naturally getting sleepy and falling asleep around midnight every night without much effort or thinking about it.

We included some bonus variables about sleep in the spreadsheet, but the results are inconclusive. 

Sleep quality did go up by 0.2 points, but that was not significant (p = .480). 

Hours slept went down somewhat, which is interesting, but that change also was not significant (p = .296). 

We should note that most people did not report either sleep variable, so the sample size in both of these cases is less than 40. It looks like potassium may improve your sleep a little and/or may help you sleep less, but this isn’t well-supported and even if there is an effect, the effect is probably small.

This is interesting given that Gwern, who is notorious for his attention to detail, did a self-experiment with potassium citrate and “confirmed large neg⁣a⁣tive effects on my sleep”, with a large apparent effect (d = 1.1). Possible differences may come from the fact that Gwern was originally taking potassium in the evening rather than in the morning, and when he tested this he found a difference; was taking about 4000 mg a day, much higher than most people in this trial; and that he was taking potassium citrate, while most people on this trial were taking potassium chloride. (Also Gwern may just be built different.)


We didn’t find any effect of fidgeting (if anything, people fidgeted less over time), but there were a few self-reports of intense or manic energy. 

(87352273) I had really noticeably elevated energy at first, and pretty regularly had the urge to walk or exercise just to burn off some of the nervous energy. The intensity leveled off after the first week or so, but energy overall stayed higher than usual.

(84130320) I had a huge rush of energy, like borderline hypomanic, and I ended up pulling a chest muscle doing pushups because I felt like I was 10 years younger (note to others: you are not actually 10 years younger, do not suddenly do a bunch of pushups). So that sucked.

(93059017) I had so much energy after work that I just needed to walk and I walked an extra mile home.

The participant who lost the most weight (81847724) was also notable for this report:

My mood and energy have been nothing short of fantastic. On a normal day pre-trial, I’d rate my average mood and energy levels in the 4/5 area on the 1-7 scale. Somewhere during week 2 of the trial, I really noticed how elevated I felt in my mood all day long and generally my energy levels were high regardless of the amount of sleep.

However, this increased energy did not seem to be widespread, and some people specifically mentioned not feeling any more energetic. 

Looking at the self-report question we included about energy (though FWIW, a sample size of only 29), people’s energy improved by 0.54 points on a 7-point scale, but this was not significant (p = .126).

Surprisingly, Stimulants

A couple people noted stimulant-like effects, and strangely, some also mentioned a kind of stimulant reduction or substitution effect.

(36100230) I felt a little more focused after taking the potassium. A few times I wanted to get some caffeine, and took potassium instead, and no longer needed the caffeine.

(72706884) My caffeine intake decreased substantially during the early part of the diet. I typically intake 100-250mg of caffeine daily. This was reduced to 30-60mg every other day during the first 2 weeks. I found supplementing with a 200mg caffeine pill helpful and used one daily during weeks 3 and 4.

(64983306) While taking potassium, I also experienced heightened concentration abilities, as if I was taking ritalin/adderall. This feeling would last for 2-3 hours after taking a dose of potassium.

We can corroborate this with our own experience. Caffeine seemed to have less of an effect for us while on the potassium, and weirdly, seems to have less of an effect still! Not sure what’s up with that.

Blood Pressure

Only seven people reported their blood pressure readings on day 29, so there wasn’t enough data to do a proper analysis. 

However, most of them saw their blood pressure go down, so we figured we should go into some detail anyways.

In the seven cases that reported their BP on both day 1 and day 29, people saw their blood pressure go from: 

  • 120/81 to 113/77
  • 114/64 to 116/63
  • 121/91 to 114/78.5
  • 123/90 to 123/80
  • 131/78 to 130/85
  • 111/75 to 99/82
  • 121/78 to 126/81

On average, systolic BP went down by 2.9 points, with a maximum of 12 points down; and diastolic BP went down by 1.5 points, with a maximum of 12.5 points down.

Again, these differences are not significant. But with the very small number of people reporting BP, the sample size isn’t large enough to reach statistical significance. Most of these people also had relatively low blood pressure to begin with, so it’s not clear what kind of change you might see if you had hypertension.


People were split on the potassium. Many people found it distasteful, and some people hated it.

(50612600) this is way too disgusting to drink

unbelievable it’s sold as a food product

(79606462) it truly does taste horrible, even dissolved in 12 oz water

Unsurprisingly, many of these people chose to end the trial early, and we can’t blame them.

On the other hand…

(02689028) does liking kcl salt too much count as anything important

(84130320) My experience overall was actually very pleasant. I didn’t think the taste of the KCl was nearly as bad as advertised. To me, it tasted like salt, if salt were perishable and had spent a little bit too long in the refrigerator. Putting it in sparkling water was fairly good, I could tell it was weirdly salty (especially once I got up to 1300mg/dose) but if I just chugged a little, like half a glass, and then topped it back up it was legitimately delicious. If I did a schorle (fruit juice mixed with sparkling water) instead I could barely taste it. … when I felt really bad and backed off of the potassium per the instructions, I craved potassium. Like I really wanted to eat bananas and was like “boy I could really go for some sparkling water with KCl in it.” It was super strange.

(23578149) I went from finding Nu-Sal revolting (even mixed 2:1 with salt) to finding it pleasant.

But one thing is for sure: it really makes you pee.

(7619655) Have you ever eaten a really salty meal, like pizza or Chinese food, and then felt really thirsty afterwards? That’s how the potassium made me feel a lot of the time. It was drink, pee, drink, pee, drink, pee all day. If I didn’t keep up on the drinking, I would get parched lips and a headache. It was hard to keep this up, so I skipped a bunch of days towards the end.

(74537321) I found I had to pee a lot more often depending on how much water I was drinking. I tried to drink a lot of water throughout the day so I could get the most out of my bowel movements, but one issue was I just had to go pee a lot more. It felt like I would drink a cup of water, and then 20 mins later have to pee like I hadn’t gone all day. 🙂 I would say I had 1 to 2 liters of water per day in addition to meal time drinks (milk, juice, diet soda).

We found these self-reports interesting (also hilarious) so in the followup survey, we explicitly asked people how much they enjoyed the potassium. Because some people mentioned that their opinion of the potassium changed over time, we asked them how they felt about it at both the beginning and at the end of the trial: 

In the beginning, most people found it unpleasant or disgusting (though you will notice there is still one “very delicious” rating!), but:

By the end, a majority found it either neutral or pleasant, though many people still found it super gross.

You might expect that potassium enjoyment would be related to weight loss, but we didn’t find much evidence for that. We didn’t notice any statistically significant relationships with weight loss, though looking at the plots does seem suggestive:

So it’s possible that people who enjoy potassium salts are more likely to lose weight by eating them, but if so, the effect is probably too small to detect in this study. 

7. Interpretation

The lithium hypothesis is the only theory of obesity that predicts that straight potassium might help people lose weight. It’s not a very strong prediction,​​ we simply noticed that lithium and potassium are both monovalent cations, and that they appear to have some interaction in the brain, where the lipostat is located. But other theories wouldn’t predict a relationship between potassium and weight loss at all.

We first introduced the lithium hypothesis in Part VII of our series A Chemical Hunger, expanding on the idea in Interlude G, Interlude H, and Interlude I. In Part X, the conclusion to the series, we speculated that if obesity is caused by lithium exposure, potassium might be an effective treatment: 

Lithium … is an alkali metal ion that appears to affect the brain. Other alkali metal ions like sodium and potassium also play an important role in the brain, and there’s evidence that these ions may compete with each other, or at least interact, in interesting ways (see also here, here, and here). If lithium causes obesity, it may do so by messing with sodium or potassium signaling (or maybe calcium) in the brain, so changing the amount of these ions you consume, or their ratios, might help stop it. 

However, the results of this study are not conclusive evidence in favor of the lithium hypothesis, and it benefits us to explore some alternative explanations. 

Prosaic explanations like “potassium caused people to lose water weight” would seem to be ruled out by the fact that many people’s appetites got noticeably weaker, and the fact that some people mentioned that they had never lost weight so quickly or easily before. Same thing for placebo.

So the two classes of likely alternatives are that either it’s something confounded with the potassium dose (i.e. when you take more potassium, you also do more X), or that potassium causes weight loss for some other reason than its relationship to lithium.


A natural starting point is to consider whether obesity could be just another disease of deficiency, one you develop if you don’t get enough potassium. Scurvy is the disease that happens when you don’t get enough vitamin C, beriberi is the disease that happens when you don’t get enough vitamin B1, could obesity be as simple as a potassium deficiency? 

Unfortunately we think that is not the case. Diseases of deficiency are easy to identify because they regularly crop up in situations where people eat a limited diet for a long time. Both beriberi and scurvy, for example, were common among sailors on long voyages. 

Obesity does not really fit this profile. People today may not be getting enough potassium, but if obesity were a disease of deficiency, you would expect to see it showing up in historical records of cities under long sieges, sailors on long voyages, explorers in the Antarctic, and so on.

We see two distant ways to reconcile this idea, however. The first would be if potassium deficiency causes obesity, but only over the very long term. For example, maybe you only develop obesity if you eat a low-potassium diet for 10 years. This would be unusual and we think it is unlikely, but it’s consistent with the data.

The other is if obesity occurs in the rare cases when people both have a potassium deficiency AND have lots of access to calories. Sailors, explorers, and other people who tend to get diseases of deficiency usually are not eating that well in general. Maybe obesity is only triggered when you’re not getting enough potassium, but you can otherwise eat as much as you want. We think this also seems unlikely, but again, we can’t rule it out. 

Hydration / Clearance

People drank a lot more water on the potassium trial because the potassium salt made them thirsty, and they had to pee a lot. People also drank a lot of water on the potato diet, for similar reasons. Is it possible that both diets cause weight loss because they encourage you to drink huge amounts of water, and that water flushes your system (or something)?

This seems pretty unlikely to us, though it is consistent with all the evidence. If someone wants to try the super-hydration community trial, where you try to drink 5 liters a day or something (don’t use that number we made it up, figure out what is actually safe), that would be fairly interesting. We don’t expect it would cause comparable weight loss, in part because we think someone would have noticed by now if staying hydrated was enough to cure obesity. But it sure would be interesting if it did! 


Potassium and sodium balance each other in biological functions. To regulate the increased amount of potassium they were consuming, we encouraged people to consume more sodium as well, and they may also have naturally craved more sodium as they ate more potassium.

As a result, people on the potassium trial may have been getting more sodium than normal. For similar reasons, people on the potato diet may have been getting more sodium than normal. So one kind of weird possibility is that sodium is what’s causing the weight loss here, not potassium.

We did have this in mind from the start, so one of the bonus variables for the study was estimated daily sodium intake.

Unfortunately, out of the 233 people who entered data, only 20 people tracked their sodium, so we don’t have much evidence. But what evidence we do have doesn’t support this interpretation. People who consumed more sodium actually ended up with higher weights at the end (r = 0.101), though the relationship is not significant (p = .670).

In general we do not expect that sodium is responsible for the weight loss observed in this study, nor would we encourage anyone to try a high-sodium diet. But again, we can’t really rule it out. 

Other Biology

Is it possible that potassium increases the clearance of something other than lithium? Just making more urine will increase the clearance of some things! Or could it treat obesity in some other way? 

It seems likely, but we can’t really be much more specific than that. Potassium has approximately one zillion roles in biology, so for example if obesity is caused by anything to do with “hormone secretion and action”, which seems like a pretty broad category, potassium could potentially be a treatment. This seems like a question for someone who knows more about biochemistry than we do.

8. Future Studies

There are a number of studies that could be run to get more information. We might run some of them ourselves in the future. For now, here they are as brief sketches. 

Experimental Extensions 

We know that one of the biggest criticisms we’re going to get on this study is about the lack of blinding and lack of a control group. Everyone in this study took potassium, and everyone knew exactly what they were taking. 

Let’s imagine what a control group might look like. It’s well-established that people get heavier as they get older, so over the course of 29 days, people who do nothing should on average end up weighing slightly more by the end. We’re pretty sure that a straight control group would have lost about 0 lbs and maybe would have gained some small fraction of a pound over the course of the study — if you gain 2 lbs a year, that’s about 0.17 lbs a month.

But it’s true that people in this study were paying more attention to their weight and to their diet, and it’s possible if they were taking packets of some other white powder that wasn’t potassium, they would lose weight for some other reason. It’s possible that there’s some level of placebo. 

That’s fine, because this study was never intended to be the final word. It’s the first study, not the last. 

While the hierarchy of evidence is very important, a meta-analysis of multiple randomized controlled trials doesn’t just happen overnight. With this study, we’ve shown that it’s plausible that potassium by itself could lead to weight loss. There wasn’t evidence for that before.

For example, this comment from the extremely measured thread by Agaricus 

But now that we have this evidence, it might be worth investing more time and energy in a more controlled or more complex study. 

We wouldn’t want to do a straight control group where people do nothing, because that would reduce our effective sample size and it would be boring for participants. Fortunately, there are designs that can help with both problems. Here are two ideas:

First of all, we could run a crossover trial. In this case, the study would run for two months. One half of the participants would be assigned to take potassium for the first month and then take no potassium for the second month. The other half of the participants would be assigned to take no potassium for the first month, and yes potassium for the second month. This allows both groups to serve as controls without reducing our sample size.

Another idea would be to run a dose-dependent experiment. The design might look something like this: one half of the participants would be assigned to a protocol that involves them working up to a dose of 2000 mg of potassium a day. The other half of the participants would be assigned to a protocol that involves them working up to a dose of 4000 mg of potassium a day. (You could also do a dose-dependent experiment with more conditions — some people assigned to 1000 mg a day, some to 2000 mg/day, some to 3000 mg/day, etc.) If potassium is the active ingredient, you should see more weight loss in the group(s) assigned to the higher dose(s). 

Comparing different doses allows us to have a control group without having to have a “no treatment” group that spends the month doing nothing. Both groups are providing valuable data, and we still control for the effect of the intervention. It isn’t blinded, but this design guards against placebo effects because it would be hard for the people in the 4000 mg/day group to arrange to lose more weight than the 2000 mg/day group. 

The main issue in both cases is statistical power. You might need very large sample sizes to detect these differences, and no one should run one of these studies without conducting a very careful power analysis. But, the designs are theoretically sound.

Other Diet(s) High in Potassium

Potatoes are very high in potassium, but they are not the only food that is very high in potassium. Other foods that are very high in potassium include lima beans, swiss chard, spinach, bamboo shoots, butternut squash, kohlrabi, portabella mushrooms, white beans, bok choy, and many others (though avocado and banana are maybe overrated as sources of potassium!). 

If the potato diet causes weight loss because it’s high in potassium, a non-potato diet that is high in potassium might also cause weight loss. So one thing you could do is arrange a trial of some other high-potassium diet and see if that also caused weight loss.

This isn’t a sure thing, however. Other foods do contain potassium, but it’s possible that the potassium is different in these other foods — less bioavailable, released more slowly, part of a different compound, etc. So we don’t think this would be a very strong test of the theory, because it introduces so many new variables. 

In addition, we want to note that many of the items on the list of high-potassium foods are foods that we suspect might be high in lithium. In particular, there’s evidence that lithium accumulates in leafy greens, sprouts, and maybe in gourds, which matches most of the foods on the list above. If the potato diet works because it’s high in potassium AND low in lithium, these other high-potassium foods might not have any effect at all.

If we had to pick just one high-K food to test, we would probably pick coconut water. It’s a liquid, so the potassium is probably more available than average. It’s relatively high in potassium, with about 600 mg per cup. It’s easy to find and requires no preparation. And (as far as we know at least) coconut water isn’t swimming with lithium. So if people wanted to try getting 2000+ mg per day of potassium from coconut water, that would be pretty interesting.

Low-Potassium Potato Diet

In the course of designing this study, we came across a set of practices used to remove potassium from potatoes. Some people with serious kidney disease have to avoid consuming too much potassium, and these techniques were developed so they could enjoy potatoes safely. Potassium removal is usually accomplished by slicing or dicing the potatoes in small pieces to increase surface area, and then soaking (before and/or after cooking) or boiling them in water to leach out the potassium (e.g.: link, link). Some sources claim that this can remove more than 50% or even up to 70% of the potassium in potatoes.

We could test these techniques by preparing some potatoes with these methods and sending the potatoes (and the water they were soaked/boiled in, which should contain the removed potassium) to a lab for analysis. If the sliced/boiled/soaked potatoes had much less potassium than potatoes that were baked or roasted or something, that would suggest that these techniques remove potassium as advertised.

We could then use this information to do another test of the weight-loss powers of potassium, by running an experiment with a modified form of the potato diet.

One group would be assigned to eat a potato diet with potatoes prepared in a way that preserves as much potassium as possible (probably baked), and the other group would be assigned to eat a potato diet with potatoes prepared in a way that removes as much potassium as possible (probably boiled and then soaked and then fried). If the preserves-potassium group lost a lot more weight on their potato diet than the removes-potassium group, that would be further strong evidence that potassium is the main active ingredient in the potato diet. 

This prediction matches the following tidbit from M’s experience with the potatoes-by-default diet, which makes it seem somewhat more more plausible: “I seemed to be able to eat much more when the potatoes were sliced/grated (e.g. Swiss rosti, Chinese tudousi) than when they were closer to whole potatoes (i.e. diced, potato wedges, etc.). I’m not sure why.”

Some people think that the potato diet works because it is a mono diet. It cuts out most other foods, so there’s very little variety, and some people (e.g. here) think that food variety is part of what makes people gain weight. But if soaking all the potassium out of potatoes made for a much smaller effect, that would mean there was a big difference in weight loss between two otherwise-identical mono diets, which would be hard for food variety to explain.

Potato Diet with Urine Test

One plausible hypothesis is that potassium helps clear lithium from your brain, and this is why it causes weight loss. 

If this were the case, most of the lithium that is cleared from the brain should end up in your urine (urinary lithium seems to be a good proxy for levels in the body in general). It should be possible to test people’s urine for a while to establish a baseline, and then start them on the potato diet and see what happens. The level of potassium in their urine should increase dramatically, since there is so much potassium in potatoes. It would be interesting to see if the level of lithium in their urine increased as well. 

If urinary potassium levels were correlated with weight loss, that would be more evidence that potassium is the active ingredient (though they might not be correlated, since urinary potassium levels are part of a control system). If urinary potassium levels were correlated with urinary lithium levels, that would be more evidence that potassium is forcing lithium out of your brain (or some other reservoir). And if urinary lithium levels were correlated with weight loss (or frankly, even if they just went up when you started the potato diet), that would be strong evidence in favor of the idea that lithium is the cause of the obesity epidemic.

This could be the smoking gun for the lithium hypothesis, which makes it a pretty attractive idea. The problem is that we don’t have any experience running studies with urine samples, so we’re not sure how to design this study or how to run it. We’re also not sure whether it’s possible to run it over the internet, or if we would have to get a bunch of people together in person. If you do have experience in running studies with urine samples, and you’re interested in helping, please contact us.

However, even this study might not be conclusive. It’s possible that potassium counteracts the effects of lithium but doesn’t increase the rate of clearance. For example, potassium might compete with lithium in the brain without forcing it out. It might reduce lithium absorption in the small intestine. It might keep lithium from leaching out of your bones. It might do something else. (Lithium pharmacodynamics remain poorly understood.) So while it’s plausible that potassium increases lithium clearance, we aren’t confident that’s how things work. 

9. Conclusion

We ran this study because we suspected that potassium might be the active ingredient in the potato diet, that the high levels of potassium found in potatoes might be why a diet high in potatoes causes weight loss. These results support that interpretation. 

The weight loss observed in this trial was small on average, but the doses of potassium were intentionally very low. There’s evidence that the relationship between weight loss and potassium consumption is dose-dependent, such that people who took larger doses lost more weight on average. Regression modeling suggests that someone who was consuming a dose of potassium equal to the amount provided by the potato diet would lose a similar amount of weight as people lost on the potato diet. 

These results are not decisive. Indeed, no results ever are. However, given the small doses involved, the results could not be more strongly consistent with the potassium hypothesis. 

Potassium supplementation is scientifically valuable because it’s relatively controlled. But it’s not very practical, because it’s not clear if large doses of straight potassium salt are safe for most people, and because many people find potassium salt really gross. We strongly recommend that anyone who wants to lose weight should do the potato diet instead. The potato diet gives a much higher effective dose of potassium while probably being a lot safer, and may have other benefits. 

The all-potato diet is a relatively big commitment (though much easier than most people expect), so you may prefer to try the half-tato diet instead. This involves getting about 50% of your calories from potatoes and, based on the available case studies, seems to be more than 50% as effective and much less annoying. We plan to study it more soon.

If for some reason the potato diet doesn’t work, we would recommend you try to find some other way to eat a diet that’s exceptionally high in potassium. 

If none of these things work for you, then you can try direct potassium supplementation, though you should consult with your doctor, definitely not do it if you have diabetes or kidney disease of any kind, and limit yourself to no more than 5000 mg a day.

We probably will not follow up on this study at 6 months and 1 year, since the average weight loss was so small. It seems unlikely that 0.89 lbs of weight loss will be statistically detectable several months later.

However, several people reported that they are planning to stay on the potassium longer-term, so we may have more results soon from the people who reach 60 days on low-dose potassium. 

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.

Your friendly neighborhood mad scientists,

APPENDIX A: Delivery 

People overwhelmingly took potassium chloride (93.3%), overwhelmingly as a salt (92.3%), and mostly as the brand Nu-Salt (62.9%). The most popular method of delivery was to take it dissolved in water, juice, a sports drink, or some other beverage.

We didn’t detect any differences in weight loss for any of these variables, but given that almost everyone took the same kind of potassium in roughly the same way, we wouldn’t have the statistical power to detect any differences unless they were really huge. So there may be differences, but we wouldn’t expect to see any evidence for them in this data, and indeed we do not. 

However, the delivery method does seem to make a difference in terms of enjoyment. Here is a sample of people’s recommendations: 

(45454797) The metallic taste went away after just a few days and I found the salt to actually taste good with a little apple cider vinegar and water. Gatorade without the sugar! (and easier than pressing lemons all the time)

(40941749) I highly recommend orange fanta if you’re gonna drink your magic potion, and hash browns if you wanna eat it. 

(77174810) Yes, KCl tastes gross/weird/bad. I tried a few different concentrations and mixtures with food (don’t mix with a bite of guacamole – yuck!). What I discovered was that mixing it with Simply Strawberry Lemonade makes it very palatable! I dissolved the KCl and a little sea-salt in about 1 oz of water. Then added about 4-6 oz strawberry lemonade. You could damn near sip it this way! Apple cider was the second best mixer.

(94352426) Higher concentrations were only drinkable to me in carbonated drinks made it okay to drink. For me this was the biggest limiting factor, always having to have carbonated water in home, buying it every time I went grocery shopping those bottles are a lot of extra weight. 

Though there was considerable variation: 

(52533228) By far, the easiest way for me to integrate it into my routine was to add it as a salt substitute in my cooking or meal prep. I could not stand adding it to drinks – the taste was usually awful and harsh. When it was added to food, the flavors mixed well in general and it was much much less noticeable.

(79332762) In terms of taking the potassium, I really disliked it. I would happily take a pill 1-2x per day, but I really dislike the taste of KCL. I tried two approaches to taking it, mustard & lemonade. With mustard it worked ok for low doses (1/8 tsp) but for larger doses it felt like too much salt hitting my stomach at once. With lemonade I don’t want to routinely drink enough lemonade that fully masks the flavor. I also really like lemonade as a treat so making it a daily routine (& making it taste bad) felt weird. I don’t really want to chug powerade/gatorade either.

APPENDIX B: Regulated Success

The body puts in a lot of effort to make sure you don’t get too much potassium. So one thing you might expect to see on this trial is that people start losing weight at first, but as their body acclimates to the extra potassium and their kidneys start filtering it out more aggressively, they stop losing weight and they maybe even gain back the weight that they lost. 

Some people did mention something along these lines. For example, participant 98856740 (who submitted after Dec 1 and whose comments are therefore not in the main dataset): 

I lost 6 pounds in the first week and then didn’t lose any more. In fact I bounced between that low number and about three pounds higher. During that weight loss period, I felt hot, enough to wake me up at night. I’ve heard people describe hot flashes during menopause that way. Once I got to the plateau stage, I no longer felt hot, just normal. I speculate that my metabolism was using heat to lose weight. I have no idea why it stopped. I don’t think there was anything materially different about the early days.

From the data, we’re not sure what to think. On the one hand, there are very few clear reversals. For example, the number of people who dropped 5 lbs at some point but ended up losing no more than one pound by day 29 is two, specifically these two participants: 

But on the other hand, most people hit their minimum weight well before the last week of the study, suggesting that many people hit a plateau early on. Here’s the plot where we highlight each person’s day of minimum weight: 

You can see that some people did hit a relatively low minimum weight early on and then never go down further from there. This may be evidence that some people hit a plateau. 

APPENDIX C: Accounts of Greatest Weight Loss


Well, my time in the experiment has been shocking, to say the least of it. So obviously I’m morbidly obese so I should probably address that right away considering I’ve lost over 12 lbs during this experiment.

In January 2022, I started working with a doctor that specializes in weight loss. I was put on a low-carb, ketogenic diet 6 days per week with 1 day of free eating anything I wanted, and an exercise routine of moderate walking every other day. My starting weight was ~485 lbs. My compliance with the diet and exercise routine was 100% from January until the start of the potassium trial. My starting weight at the beginning of the trial was 476.2 lbs, so I lost approximately 9 lbs during that 9-month time frame.

I DID NOT change my diet or exercise habits during the trial to any appreciable measure. There were a couple of times I mixed up my exercise routine but mostly I stuck to the same 60 minutes on a treadmill every other day. Any changes to the exercise were noted in the sheet.

Overall I think it’s incredible that the simple change of adding potassium seems to be responsible for a sudden change in the rate at which I was able to lose weight. I will be continuing supplementing potassium going forward, this is the single most amount of progress I’ve made in weight loss in a month.

I’m going to try to think of anything I can disclose here to give context to the data.

Potassium was consumed from Nu-salt and mixed with a Gatorade zero powder that also had some potassium (both details recorded on the sheet). I didn’t have any set schedule for the potassium, I simply added it whenever I felt thirsty and acquired water (up to the dose limit for the day)

My diet was a strict ketogenic diet (under 20 grams total carbohydrates per day, gross carbs, not net) for 6 days per week and one day a week of eating anything I wanted. I do not track calories. I don’t track macros other than the number of carbohydrates consumed to stay under 20. The 20-carb limit includes the 2g carb per serving of the Gatorade zero powder I used to mix the nu-salt.

I weighed myself completely naked on an “Ideaworks JB5824 Extra Wide Talking Scale” between 8:30 and 9:00 AM every day, preferably after having a morning bowel movement. If I didn’t have one, I would still record my weight. I made a note on the sheet whether or not I had a bowel movement for that particular day.

My heart rate was tracked using an AmazFit band with the pulse check feature, typically immediately before or immediately after weighing myself in the mornings.

Sleep is highly subjective but overall I think my sleep quality improved during the experiment, generally sleeping longer without waking up in the middle of the night.

My mood and energy have been nothing short of fantastic. On a normal day pre-trial, I’d rate my average mood and energy levels in the 4/5 area on the 1-7 scale. Somewhere during week 2 of the trial, I really noticed how elevated I felt in my mood all day long and generally my energy levels were high regardless of the amount of sleep.

During the first week of the experiment I remembered to measure my waist circumference as per the CDC method but frankly, I forgot to do that, but I have included a final measurement.

A final note about compounding factors: lithium reduction

I first discovered Slime Mold Time Mold through the “A Chemical Hunger” series of blog posts, but in particular, the section covering lithium is what caught my attention for potential causes of obesity. The reason it caught my attention is I was put on lithium to treat a neurological condition that I was diagnosed with (tourette’s syndrome) when I was 7 years old, and I can positively say that was the time when I began to put on weight steadily over years and decades regardless of my diet and exercise habits. I am 36 years old and have been off lithium for over 10 years now, but the lithium article really resonated with me as a potential cause. So I’ve installed activated carbon and reverse osmosis water filtration systems on all of the water taps in my house since the first lithium post in 2021. The filters I’m using the claim to remove “over 90%” of lithium from water. (City of Cincinnati water, Cincinnati, OH)

So I don’t know how entirely relevant all that could be to the data, but all of the water that I was mixing the potassium in was also water being treated for the removal of lithium specifically (although its been approximately a year of running filtered taps and only the addition of the potassium has resulted in dramatic weight loss)

I did not participate in the potato diet trial.

Anyone that wants to supplement potassium with Nu-salt should try mixing it with the Gatorade zero powder, it almost completely covers the taste and made the trial a breeze.

One last thing, I chose to limit the amount of Nu-salt I was consuming at the 1300mg per serving mark just because I didn’t want to go through my supply of Nu-salt and Gatorade zero powder too quickly. I felt entirely fine with the amount I was consuming and believe I could have easily continued in either increasing to higher doses or adding more 1300mg doses throughout the day.

Well, I feel like I’m rambling at this point but if there are any questions please feel free to ask, in the meantime I’m going to continue supplementing with potassium.


First I just wanted to clarify that I have been following a Time Restricted Eating, or Intermittent Fasting plan since Sep 30th, prior to learning about this study. I was excited to join the study since I found your posts on Twitter talking about the potato diet that people have raved about. I’ve been eating my meals between 12pm and 6pm every day and I’m sure it has contributed significantly to my weight loss. I hope this doesn’t skew the study results too much as a result of my eating schedule.

I did focus on keeping my calories under 3000 per day with a target of 2500. I also made an effort to exercise 2 to 3 times per week of 30 mins or more. That being said, I do think the potassium helped me manage my hunger, and specifically I felt like I didn’t need to eat that much during the day to feel full.

I found the study relatively easy to do. I set reminders for each dose through out the day, as well as a reminder for recording my weight and waist measurements and used an app to track those using my smart scale and smart measuring tape, both from Renpho. I discovered that drinking each dose with straight water was the easiest and fastest way to get it down. I tried with other drinks and things, but I just knew going in that it would taste funny, and got it over with quickly each time.

Starting out I didn’t have an 1/8th teaspoon measure, so I just started with 1/4 teaspoon. Being 6’4″ and 300 lbs, I figured I could handle a larger dose to begin with. Then as a result of not paying attention to the instructions very well, I ended up going up pretty quickly in dosage the first two weeks. For side effects, it was noticeable the first few days where I felt some stomach discomfort, and general unease, but it went away after the first week. The only other side effect that I think was related to the potassium, is that I found I had to pee a lot more often depending on how much water I was drinking. I tried to drink a lot of water throughout the day so I could get the most out of my bowel movements, but one issue was I just had to go pee a lot more. It felt like I would drink a cup of water, and then 20 mins later have to pee like I hadn’t gone all day. 🙂 I would say I had 1 to 2 liters of water per day in addition to meal time drinks (milk, juice, diet soda). I’m going to continue my eating and exercise schedule, but will stop taking potassium and just record my stats each day for the next month. I’d like to really see how the weight loss was impacted by the potassium. I’ll keep updating the spreadsheet and see how things go. I’m happy to talk more about my experience or answer any questions as part of any follow-up.


My 4 weeks are done, although I intend to keep taking potassium given the moderate success I experienced. Taking the potassium was very easy (with the exception of the few times I put nu-salt into pill casings and took it that way – this caused stomach pain, which I did not experience when just taking it dissolved in liquid). My overall impression is that potassium acts as a mild appetite suppressant. Thanks for running this trial, I’m looking forward to reading about the compiled results.


Sorry for the delay- I couldn’t load the sheets properly on my phone, but I was keeping track and am just now getting the chance to fill out the last week. Please excuse the order of the train-of-thought below.

I took my last weight the morning of Thanksgiving and proceeded to eat my weight in food. I haven’t been eating fast food lately but the cravings hit me hard (probably from a combination of eating way too much, alcohol, and not supplementing for a couple of days). My plan for now is to finish up leftovers today, grab some fast food over the next couple of days, and probably restart a 30 day period on Wednesday having gained about 5 pounds in a week.

All of my supplementary data (heart rate, sleep, exercise) was from my fitbit.

It was very true that I didn’t think of food while doing the trial. At the lower doses, my hunger was affected but my appetite was not. At the higher doses, both were affected. 

The biggest struggle for me was trying to keep track of my calories. I feel like it negatively impacted my trial because it did affect what I ate, even though I was supposed to eat whatever I wanted. I would eat what I wanted and feel shame/guilt for eating over X amount of calories (arbitrary number from back in my restriction days). The perhaps more interesting way it affected the trial was, once my appetite started being affected by supplementing, I would finish meals that I wouldn’t have because “I had already tracked the calories for it, I should get it,” “how can I track 1/3 of a meal,” etc. For my second attempt at the trial I will not be tracking calories, and hopefully not have the pressure of numbers to affect my eating habits. I understand that it was an optional variable anyways, but hypothetically the change in weight would reflect the appx input anyways.

I did not look into the lithium correlation at all, but if it is important- for meat markers, I only eat white meat. For egg markers, I only eat egg whites. The only thing I noticed that seemed to give me actual hunger pangs was if I drank a significant (about or more than 24 oz in a sitting) diet soda. Of course you can see in my data that alcohol also ruined a couple of days, but that didn’t actually make me feel any more hungry, just more crave-y and less likely to resist eating an entire pizza (apparently).

My work schedule is Fri/Sat nights, Sun-Tues mornings, and a random overtime on either Wed or Thur. Although my Fri shift is the same every week, there is a huge difference between that 3pm-11pm shift and my Tues 530-130 shift in terms of when and what I typically eat (and my sleep schedule). 

There was a huge difference in my general desire for food if I took the supplement in the morning before eating. If I took my first dose with food, I would be thinking about food sooner (though I wouldn’t say it was every hunger, just craving). When I took only the supplement and then went to work, it was almost always that I wouldn’t think of food until after work. If I took a dose without food and then went on my walk, even if I had already eaten that day, I would feel very light-headed.

I’m happy I found out about this trial. I am generally pleased with the outcome, if not the methods I specifically used, and am more excited about starting next week with a little less restriction. I’ll still track in case the data is useful for you, but probably only the weight and doses.


[SMTM’s note: despite the comment below, this participant reported losing 8.6 lbs.]

Thanks for running this trial, it was interesting. My subjective feeling is that the potassium supplementation had no discernable effect on my brain function, hunger/diet, or weight. I’m planning to continue supplementing potassium though because my food diary shows my intake of it was very low and I’m curious whether it might have any longer term effects past just the first 4 weeks.


First of all – holy shit! It’s amazing how well this worked and it’s also surprising that it’s never really been studied before! Thank you for the analysis and thought that you put into this. For this trial, I basically just ate whatever I felt like, went to a football tailgate party nearly every weekend with lots of beer and foods you would not associate with dieting… and still lost nearly 10 lbs! I plan to continue on for at least another couple months so feel free to follow up later if you want to.

Interestingly, I was born and raised in Colorado. I lived there for my first 30 years until 2003 when we moved to the East coast and although I am a bigger person (6’5″/225 in 2003), I was never really “heavy” until maybe 2010 or so. I kept putting on weight as I aged into and past my 30s and I just followed conventional “wisdom” that it was due to getting older. Each year I would have a few extra pounds. 

I have tried every diet/exercise and variation of CICO, atkins, keto, IF, etc., etc., etc. to try and lose weight. To no one’s surprise, nothing really worked for long and the weight always came back. At the end of 2020 I was over 275. It took me three months of busting my ass to lose 20 pounds and as soon as I started eating “normally” again, I slowly started putting weight back on.

Anyway, you may have just solved obesity. I hope you enjoy being billionaires. Don’t forget us little guys that did nothing but participate in your study when you are trying to decide on the color for your private jet (I think dark blue would be nice).

Notes and observations:

Yes, KCl tastes gross/weird/bad. I tried a few different concentrations and mixtures with food (don’t mix with a bite of guacamole – yuck!). What I discovered was that mixing it with Simply Strawberry Lemonade makes it very palatable! I dissolved the KCl and a little sea-salt in about 1 oz of water. Then added about 4-6 oz strawberry lemonade. You could damn near sip it this way! Apple cider was the second best mixer.

I felt thirsty a LOT of the time, especially in the first week or so. I increased my water consumption by over a quart/day for the duration of the study (still ongoing)

On the weekends, I ate poorly (nutrition wise) but still overall was eating way less than I usually did.

I only tried a 1320mg dose once. I didn’t feel great but cannot say for sure that it was that higher dose. I plan to try two higher doses/day for the second month

I settled on 3 doses of ~990mg (3/8 teaspoon) a day at 0730, 1130, 1600. I felt like this kept hunger at the lowest level overall and was easy to stick with.

I found that if I took the supplement when I was already hungry, I’d eat more overall. So I take the dose an hour or so before I’d normally eat a meal. 

I’m very curious about this mechanism for weight loss. Does K+ just act as an appetite suppressant? Or is it more that the lipostat is turned down and that makes you less hungry? If lithium passes through the body fairly rapidly, how long does the effect last on the brain (if that is what is happening)? When I have cut calories in the past, it was an uphill battle to fight hunger. Presumably my lipostat was set too high so I’d be hungry and also not lose weight effectively because my body was not trying to lose weight. Hmm, might make sense… I plan to do this for at least another month if not two. It will be interesting to find out:

Could there be any detrimental long-term effects of taking this much extra K?

If I stopped the supplemental K, will I start to trend back up in weight? How hard will it be to keep the weight off?

How long does the effect last? Will I be (normal) hungry tomorrow if I stop supplements today? 

I intend to experiment with the following after I hit my target: 

Could I take the supplement every other day or once a week as a “maintenance” dose and keep the weight off? Or maybe just a smaller daily dose?
Looking forward to your further analysis and trial results.