The biggest limitation of an N=1 experiment is external validity. If you run enough trials on yourself, you can show that some intervention does or doesn’t have an effect on you to basically any degree of certainty that you want. But this will never provide much evidence that the same intervention will have the same effect, or any effect, on anyone else.
People are all human and have roughly the same human biology, it’s true. In the higher animals, decapitation is more or less guaranteed to be lethal; people generally like eating sugar and hate eating asphalt. But once you move beyond the fundamentals of biology, most other bets quickly are off.
An unspoken assumption of the self-experiment discussion (including our posts on the subject) is that there are exactly two kinds of research — self-experiments, and large trials. These occupy the sample size slices of N = 1 and N ≥ 30, respectively. The self-experiment and case study are assumed to be a single subject; and with few exceptions, most people don’t trust a survey or RCT with anything less than 30 participants.
But there are two problems with this perspective. The first is that this is a false dichotomy. There isn’t a point where N = 1 turns into N = small, and there’s no sample size where you go from having a collection of case studies to having a trial. Going from N = 29 to N = 30 does nothing in particular, and there is no other threshold that stands out as being at all distinct (except N = 0 to N = 1, of course). A bigger sample size always means more information and better external validity, with no discontinuity.
The second problem is that if N = 1 is at all good (and we think that it is), then N of small has to be better.
Anything that is good with an N of 1 will be better with an N of 2-10. With N of small, you get more data, more quickly. One person doing random daily trials over the course of a week will create 7 data points. Three people doing random daily trials over the course of a week will create 21 data points. Small-group analysis is a little more complicated, but the data can be handled by a standard linear mixed model (here’s an example that involves dragons).
With N of small, you get more diversity of participants and more diversity of responses, quickly drawing the fangs from the problem of external validity. You will be able to get some sense of whether the intervention works differently for different people. If you have five participants, it will be easy to see if they are all responding the exact same way, if they are responding somewhat differently, or if some of them are having huge responses while others feel nothing at all.
The only question is one of cost. Because while the biggest limitation of N = 1 is external validity, the biggest benefit is that it’s cheap in important ways. With N = 1, you don’t need anyone’s permission to start your study — you can just go do it. You don’t pay any coordination costs, costs which are easy to miss up front but can be quite a drag if you’re not careful. These factors help make self-experiments cheap.
But we think scaling up is usually worth it — or at least, once you have some promising N = 1, scaling to N of small usually makes sense. It’s the logical next step. And since there’s no real distinction between a single case study, a small collection of case studies, and a trial of 100 people, it’s also the logical next step on the path towards an RCT or other large trial.
So while this series has focused on true N = 1 self-experiments, the real wins for the future may be in N = 2-10 studies where people grab a couple of friends and run a self-experiment together. Remember kids, friendship is the most powerful force in the universe.
And it’s not at all unprecedented, since this is how we approached our community trials; we looked at a couple of case studies, and then used N of small to do the pilot testing.
For the potato diet, we started with case studies like Andrew Taylor and Penn Jilette; we recruited some friends to try nothing but potatoes for several days; and one of the SMTM authors tried the all-potato diet for a couple weeks.
For the potassium trial, two SMTM hive mind members tried the low-dose potassium protocol for a couple of weeks and lost weight without any negative side effects. Then we got a couple of friends to try it for just a couple of days to make sure that there weren’t any side effects for them either.
For the half-tato diet, we didn’t explicitly organize things this way, but we looked at three very similar case studies that, taken together, are essentially an N = 3 pilot of the half-tato diet protocol. No idea if the half-tato effect will generalize beyond Nicky Case and M, but the fact that it generalizes between them is pretty interesting. We also happened to know about a couple of other friends who had also tried versions of the half-tato diet with good results.
We think that in all of these cases, N of small was much more convincing than N = 1 would have been. With two people, it’s much less likely that the effect is a fluke. Even if it works for one person and not for the other, that’s still evidence that we shouldn’t expect the effect to be entirely consistent; we should expect more ambiguity. And for something where the risks are unclear, like with potassium, two people going through without any side-effects is much more reassuring than one.
This is the two-months-left reminder for entries to our MYSTERY CONTEST. There are already two entries, and you still have two months to write and submit yours!
Speaking of mysteries: Jeff Wood’s story of diagnosing his ME/CFS as a mechanical problem with the craniocervical junction, the place where your skull connects to the first two vertebrae (h/t JG in the comments on N=1: Symptom vs. Syndrome). He found a treatment that worked for him, and as far as we’ve heard, he is still in remission. Most interesting for the simple, obvious diagnostic test; if you have ME/CFS symptoms, try wearing a neck brace or just pull up on your head and see if your symptoms get better. See also the CCI + Tethered cord series from Jennifer Brea.
Still speaking of mysteries: “Paranasal sinuses are a group of four paired air-filled spaces that surround the nasal cavity… Their role is disputed and no function has been confirmed.” Also, why do they (reportedly) generate nitric oxide? The Wikipedia talk page on this one is also amusing. “more details of structure please. they are just empty pockets of air? how does the air get there? are they lined with tissue or Moo Hog are they just bone? hoopenings does each have? how do they becom e ‘pressurized’? etc etc-” writes User:Omegatron in 2005. Maybe the sinuses are well-understood by experts, but in that case, the Wikipedia page itself is a mystery.
No longer speaking of mysteries: We made a tumblr, in case the bird site dies or becomes unusable.
The Ineluctable Smell of Beer — Part 1 in a fascinating series about the rise of healthcare costs (h/t Krinn). Really about the costs and reasons for “coordinative communication”. Kind of argues that bureaucracy is a symptom of bad things rather than the cause of them? You normally look at a dysfunctional, bureaucratic system and assume, “the bureaucracy caused the dysfunction”. But: “maybe it should take us aback that our health care system incurs such extreme coordinative communications costs, that paying all those people to handle it is actually more cost effective than not.”
The Atlantic: Could Ice Cream Possibly Be Good for You? (or here to avoid the paywall). “The dissertation explained that he’d hardly been the first to observe the shimmer of a health halo around ice cream. Several prior studies, he suggested, had come across a similar effect. Eager to learn more, I reached out to Ardisson Korat for an interview—I emailed him four times—but never heard back. … Inevitably, my curiosity took on a different shade: Why wouldn’t a young scientist want to talk with me about his research? Just how much deeper could this bizarre ice-cream thing go?” lol
Tyler Ransom did a N=1, T=1166 self-experiment where he lost 15 lbs in four months.
The institution builders of the Civil War embodied a type of excellence that foreign observers of their era described as characteristically American. … But less than a century after the Civil War, American life did become dominated by centralized and professionally managed bureaucracies. The two world wars only served to entrench this way of life in business and politics. The population, in response, became increasingly conditioned to lobbying for centralized decisions instead of self-organizing. Those who introduced managerial bureaucracy to American life understood the “great strength” bureaucratic tools would grant them. But these tools destroyed the conditions that made them so adept at institution building in the first place. The first instinct of the nineteenth-century American was to ask, “How can we make this happen?” Those raised inside the bureaucratic maze have been trained to ask a different question: “how do I get management to take my side?”
Peter has a bad reaction to melons. Every time he eats melon, he gets sick right away, and he often throws up.
We can say that Peter’s reaction to melon has low latency. When it happens, it happens right away. No waiting about.
Mark also has a bad reaction to melons. But because of a complex series of biochemical interactions, when Mark eats melon, he doesn’t get sick right away. He gets sick about three days (72 hours) later, when he suddenly starts to feel very ill, and then often throws up.
We can say that Mark’s reaction to melon has high latency. It happens, but it always takes a long time to kick in.
Peter and Mark have basically the same reaction to melon. Both have the same symptoms — nausea, sickness, and vomiting. Both reactions happen for sure every time — they are both equally reliable. The only thing that’s different is the latency.
b. Different and the Same
Though their reactions are nearly identical, Peter and Mark end up with very different experiences of their sensitivity.
Peter quickly learns that melon is a trigger. After all, he gets sick right away. He just makes sure to avoid melon and goes about his life with no additional air of mystery.
Mark, on the other hand, is plagued with random, crippling nausea. He sometimes gets sick, and it always seems to be for no reason. This is because it’s hard to remember what you were eating exactly 72 hours ago (for example, take a moment to try to remember what YOU were eating 72 hours ago). So for Mark, the connection is very obscure. He may never figure it out.
Both of these relationships would become equally obvious in a self-experiment. As long as you were tracking melon consumption and looking for relationships over a long enough time frame, you would see that Peter gets sick right after every dose of melon, and Mark gets sick exactly 72 hours after every dose of melon.
Perfect 100% reliability would make this pretty obvious once you noticed it. You don’t need a huge sample size to pick up on a relationship that is 100% reliable, which is why Peter quits melons after getting sick just a few times.
The big difference is whether the relationship jumps out at you or not. Low-latency relationships are obvious; the close proximity of cause and effect highlights the correct hypothesis and draws immediate attention to the relationship, where it can quickly be confirmed. Peter can just eat more melon and immediately get corroborating evidence if he wants to confirm his theory. The relationship is intuitive; you know it when you see it.
c. Cause and Effect
High-latency relationships are much harder to spot, even if they are equally reliable. The separation of cause and effect means that the connection may never come to mind.
To even be able to pick up on this in a self-experiment, you would have to know in advance that you should be tracking how much melon you are eating. And this is the hard part. The hard part is not demonstrating the relationship. At 100% reliability, that’s easy. The hard part is picking up on what to track.
This is somewhat in contrast to our normal concerns in research. Normally we worry about sample size and the quality of our measures. But Mark doesn’t need a big sample size. He doesn’t need any measures other than “got sick” and “ate melon”. All he needs is to consider melon as a possible cause of his nausea, and to consider looking for relationships with a latency of at least 72 hours. Easier said than done.
d. Reliability in Real-World Relationships
Of course, most real-world relationships are not 100% reliable. Few things work every time. But it’s concerning how a little latency can hide an otherwise blatant relationship, and it makes us wonder how many connections we all miss because of relatively small delays in onset.
Zero latency (eat melon, immediately puke) is easy to figure out. These relationships become obvious after just a few trials.
In comparison, 72-hour latency is very hard to figure out. Most people are not looking for relationships with such a long delay, and even if you were, you would be hard pressed to figure out the cause.
You can’t just keep a food journal and look 72 hours back — you don’t know how long the latency is, so you don’t know how far back to look! And if the latency varies at all (e.g. always between 60-80 hours later), it gets even harder.
This makes us wonder how much latency we can handle before connections stop being obvious. It may not take much. Coffee -> heartburn with an hour delay, that seems pretty doable. We think you would figure that one out pretty quickly. But with a four hour delay? Eight hours? Twelve? This would be much more difficult. It would start to look more like, “heartburn around dinnertime / going to bed, especially on weekdays”. That sounds hard to puzzle out.
Latency also makes it harder to get a big sample size. With a latency of less than 5 minutes, Peter can easily do eight trials (eat some melon and face the consequences) in a single day. Mark can’t do that. He has to wait 72 hours to get the results from his first trial, except it’s worse than that, because he doesn’t know how long he has to wait for the results to come in.
If he wants to make sure not to cross the streams, he needs to devote three whole days (though again, he doesn’t actually know in advance how much time he has to dedicate) to each trial, so he needs 3 * 8 = 24 days to do the same number of “eat melon and find out” trials that Peter can easily do in an afternoon, if he’s willing to get sick that much in a single day.
Jo has a bad reaction to one of the additives in her office’s tiny cups of dairy creamer (henceforth: “creamer”). Every time she uses one of the tiny cups, she gets very tired about 30 minutes later. Fortunately, Jo’s kidneys happen to handle the additive really well, and two hours after she takes the creamer, she has cleared all of the additive out of her system, and stops feeling unusually tired.
We can say that the additive has a short half-lifein Jo’s system, and that the symptoms (fatigue) have a short half-life as well. They don’t stick around for long, things quickly go back to baseline.
Lily works in the same office and has the exact same reaction to the same additive in the office’s tiny cups of dairy creamer. Every time she uses one of the tiny cups, she gets very tired about 30 minutes later. But through a random accident of biology, Lily’s body doesn’t clear the additive from her system nearly as quickly as Jo’s does. The additive sticks around for a long time, and Lily keeps feeling tired all week. If she takes some creamer on a Monday, she’s just getting over it on Sunday afternoon.
We can say that the additive has a long half-lifein Lily’s system, and that the symptoms (fatigue) have a long half-life as well. They stick around for a long-ass time, and it takes forever for her to feel normal again.
b. Puzzling it Out
Much like a long latency, a long half-life makes this problem much harder to puzzle out, even when the two cases are otherwise identical.
Jo has it easy. If she comes to suspect the creamer, she has a lot of options. She can try taking creamer some mornings and not other mornings. She can try taking the creamer at different times of day and seeing if the fatigue also kicks in at different times. She can even take the creamer multiple times in the same day. Since the symptoms clear out after just two hours, she’s quickly back to baseline and is ready for another trial. If she wants to compare different brands of creamer to see if there’s a difference, she can get a pretty good sample size in a weekend. It’s easy for her to collect lots of data.
Lily has it really hard. If she comes to suspect the creamer, she is in a real bind, and most of the traps are invisible. If she tries taking the creamer some mornings and not other mornings, her results will be a mess, because as soon as she takes it one morning, she is fatigued all week. It will look like the creamer has no effect at all, since on days when she doesn’t take the creamer, she is still fatigued from any creamer she took in any of the previous seven days. A day-by-day self-experiment would show no effect, even though this is totally the wrong conclusion.
To detect any effect, Lily needs to test things in blocks of weeks, instead of blocks of days or hours. Each Monday, either take the creamer or not, and see how tired she is that week. But you can see how hard it would be for her to figure out this design — how is she supposed to know in advance that she needs to study this problem in blocks of a full week? She has a lot less flexibility; you might say that her research situation is much less forgiving.
Even if Lily does pin down the right research design, it still takes her much longer to get the same amount of data. Randomly assigning creamer or no creamer each morning, Jo can get 28 data points in four weeks, which is enough data to detect a strong relationship if there is one. Meanwhile, in four weeks Lily would get only four datapoints, not enough to be at all convincing.
If the relationship is weaker (e.g. only a 50% chance of becoming fatigued), things are even worse. Jo can get a sample size of 100 or 200 days if she has to; it would be a pain, but she could make it happen. But for Lily to get a sample size of 100 weeks would take two years.
c. Thought it Worked for a While 🙂
Lots of people try something, feel like it works great, and then later when they do a more rigorous self-experiment or just keep trying it, they feel that the effect wears off. Must have just been excitement over trying a new thing.
For example, back in early 2020 Scott Alexander put out a report describing his experience with Sleep Support, a new (at the time) product by Nootropics Depot. His sleep quality isn’t great, so he decided to give this new supplement a shot, and reported miraculous results:
The first night I took it, I woke up naturally at 9 the next morning, with no desire to go back to sleep. This has never happened before. It shocked me. And the next morning, the same thing happened. I started recommending the supplement to all my friends, some of whom also reported good results.
“I decided the next step was to do a randomized controlled trial,” he says. To make a long story short, the RCT found no difference at all in any measure of sleep quality. “My conclusion is that the effect I thought that I observed – a consistent change of two hours in my otherwise stable wake-up time – wasn’t real. This shocked me. What’s going on?”
Scott chalks this up to the placebo effect, which is certainly possible. But another possibility is that Sleep Support did work great at first but was no longer detectable (for whatever reason) by the time he set up the RCT. Obviously if this is true, it would be hard to study; but it does perfectly match Scott’s experience, which is otherwise (as he says) shocking and somewhat confusing.
If you have any experience with chronic illness or biohacking or anything similar, then you know that “thought it worked for a while” is a very common story. When this happens, the assumption is usually that you were fooling yourself the first time around. But consider:
Vitamin C cures scurvy, so if you have scurvy, the first few doses of vitamin C are great! But after that, vitamin C has basically no effect, because you no longer have scurvy. You have been cured. Looking at this data (huge increases in wellbeing on the first few days, but after that, nothing), the research team concludes that the original reports were somehow mistaken.
No! It’s just that the vitamin C helped and then it had done all it could! It had a huge effect! That effect was just all up front!
This exact scenario should pop up all over the place. If you are iron deficient, the first few doses of iron will have some effect. After that, they will have no effect. If you are B12 deficient, the first few doses of B12 will have some effect. After that, they will have no effect. Et cetera.
This is because the body is able to keep reserves of all of these substances. As long as you’ve been getting enough vitamin C, you can go for 4 weeks without any vitamin C at all before you start getting scurvy (in reality it usually takes more like 3 months, because most people don’t go entirely cold turkey on vitamin C). Same goes for iron and B12 — your body is able to keep reserves of these substances, so as long as you get enough, you should be set for a while.
To put this back in the terms of this essay, we would say that these positive effects have a long half-life. Positive effects with a long-half life face exactly the same issues as negative effects with a long-half life — you have to make sure you take the half-life into account when designing a study, and use long enough study periods, otherwise your data will be confused and misleading.
This same point applies to a lot of treatments, actually. Assuming you have an infection, antibiotics will show a big effect up front and then nothing after that. But we don’t take this to mean that antibiotics have no effect, oops we thought it worked for a while, guess we were wrong.
This isn’t a problem for things with no reservoir. For example, as far as we can gather, zinc isn’t really stored in the body long-term. So most effects of zinc will (probably) have a short half-life. If you need more zinc, you can just take it on a given day and see the effects.
Supplementing anything with a large reservoir (or other positive effect with a long half-life) may not be suitable for a self-experiment, because it will show a strong effect in the first few days and no effect after that. Aggregated over 30 days or whatever, this will look like no effect or a weak effect. Clearly this is the wrong interpretation.
And the longer you run the self-experiment for, the smaller the effect will appear! If you do a 10-day self-experiment with antibiotics, and they have an effect on the first two days, then you will find that this looks like 2/10 days show an effect, which will probably average out to a small effect. But if you kept going for 100 days, you would see that 2/100 days show an effect, which will average out to basically no effect at all.
This is the opposite of our normal assumption about sample sizes, that a larger sample size will always get us a more meaningful, accurate estimate. This assumption simply isn’t true if we’re dealing with a treatment that has a long half-life.
So consider the half-life of positive effects too.
Broadly speaking, triggers have some delay in the onset of their symptoms, and those symptoms stick around for some span of time.
Having a high latency or a long half-life makes a relationship much harder to notice, and harder to study. Having both, it gets even worse.
Perhaps Bob is allergic to dairy, or whatever. It gives him hives, but with a latency of two days, and they persist for four days. Bob will be walking around with random hives, and not much hope of finding out why.
He might come to suspect the true cause if he happens to cut out dairy for a while and the hives go away for good. But if someone challenged him on this — or if Bob, being a good scientist, decided he wanted to run a self-experiment to demonstrate the hive-causing effect — he would be hard pressed to get convincing formal evidence.
Bob wouldn’t know in advance to look for a latency of two days and persistence of four days. If he did something reasonable, like randomly assign each day as dairy or non-dairy, the results would look like zero effect. On most days when he took no dairy, he would have hives anyways, because of the long half-life. On most days when he did take dairy, he would also have hives, because they stick around so long. The few “no hive” days would be in the random periods where he hadn’t had any dairy several days ago; but those days might well be days when he was assigned to drink dairy. So it would look like a wash, even though it’s actually a very reliable relationship.
Bob would have to do something that seems totally unreasonable, like structure the trial in 6-day segments to account for these delays. If he did this right, the 2-day wait and 4-day stay would become entirely obvious. But how is he supposed to know in advance that he has to use this totally weird study design?
People like to argue about whether obesity is a disease. Does it require treatment, or is it more of a social problem? But obesity isn’t a disease. It’s clearly a symptom.
Think about it like this. Fatigue is a symptom, and it’s a symptom of many things. Fatigue can be a symptom of everyday decisions — you can be fatigued because you stayed up until 3 AM last night playing Octodad: Dadliest Catch. It can be a symptom of substances, like alcohol or Benadryl. It can be a symptom of conditions, like anemia or concussion. And fatigue can be a symptom of diseases, like mononucleosis, Parkinson’s, or lupus.
Similarly, a person can be obese for a number of different reasons. Obesity is a symptom of many different conditions. You can be obese because of a brain injury. You can be obese because of a thyroid issue. You can be obese because you’re taking a drug like haloperidol or olanzapine. And while there’s still a lot of dispute over the source of the global obesity epidemic, you can be obese because of whatever cause(s) are causing that.
Things get confusing when you try to treat a symptom like a disease.
Think about fatigue. If your friend is tired from playing video games until the wee hours of the morning, the correct treatment is for them to play video games while pretending to fill out spreadsheets at work, like a normal person. If they’re fatigued from drinking merlot or taking Benadryl, the only real option is to have them wait until the drug wears off (or take an upper, but that’s not really recommended). If they’re anemic, then they need to get more iron. Et cetera.
Similarly, we don’t know how to treat the general obesity we see in the obesity epidemic. But we do have treatments for obesity caused by thyroid disorders or brain tumors. And we shouldn’t be shocked if treatments that work for obesity caused by thyroid disorders don’t work for the obesity caused by brain tumors, or don’t work for the widespread obesity we see today.
Because a symptom can have many different causes, just looking at the symptom won’t always tell you the cause. And if you don’t know the cause, then you may not know the right treatment, because you don’t know the etiology; you don’t know how the cause connects to the symptom, at what points you can intervene, and what kinds of interventions might be helpful.
This is pretty bad — even when there’s a finite list of possible causes, it’s hard to look at a symptom and figure out which of its causes are responsible.
Many chronic illness symptoms are nonspecific. Per Wikipedia:
Nonspecific symptoms are very general and thus can be associated with a wide range of conditions. In other words, they are not specific to (not particular to) any one condition. Most signs and symptoms are at least somewhat nonspecific, as only pathognomonic ones are highly specific. But certain nonspecific signs and symptoms are especially nonspecific and especially common. They are also known as constitutional symptoms when they affect the sense of well-being. They include unexplained weight loss, headache, pain, fatigue, loss of appetite, night sweats, and malaise.
This means that people who are diagnosed with the same chronic illness could have similar experiences, similar symptoms, with entirely different causes. If you have headache/pain/fatigue, you might reasonably assume that someone else with headache/pain/fatigue has the same illness, and that it was caused by the same thing. You might assume that the same treatments will work for both of you, that your illness would have the same cure.
But headache/pain/fatigue are all nonspecific — they can all be caused by a zillion [sic] different things. So someone who shares your exact symptoms may have the exact same experience but for totally different reasons. If this is the case, the treatments that work for one of you may not help at all for the other.
(Even worse, palliative treatments will tend to work for both of you, since they treat the symptoms directly, and this will make the two conditions seem even more similar. But curative treatments that work for one of you won’t work for the other, since your conditions have different root causes.)
Let’s consider migraines. Migraines can definitely be caused by hormones. Some people have migraines only during certain parts of their period (about 7-14% of women, according to Wikipedia), or only when pregnant. Migraines can also be caused, or at least partially caused, by triggers like stress or certain foods.
But there are also people who get random mystery migraines on a regular basis, with no apparent trigger. Presumably these are caused by something, but it’s not something obvious like stress or hormonal cycles or being pregnant. So clearly migraines are a symptom, not a disease — they can be caused by several different things.
All this to say that finding the “cause” of migraines may be the wrong framing. There may be no more single cause of migraines than there is a single cause of car accidents. Some accidents happen because the driver wasn’t paying attention (and many people think of this as prototypical). But some accidents happen because the road is icy. Some accidents happen because the driver had a seizure and lost control of the car. Some accidents happen because the vengeful spouse of the man you killed in El Paso 15 years ago has finally tracked you down and cut your brake lines.
There is no single cause of car accidents. They are more like a symptom. All car accidents look much the same — broken glass, tire marks, people yelling. Most car accidents have similar proximal causes — unless it was an intentional ramming, it happened because someone lost control of their vehicle. But despite these apparent similarities, car accidents can have wildly different original causes. They happened for different reasons.
Consider chronic fatigue syndrome (CFS). Most people assume that CFS is a disease, and that everyone with CFS has it for the same reason, that there is a single cause. But maybe CFS is more like a symptom (obviously “syndrome” is literally in the name). If so, the search for the “cause” of CFS is a mug’s game, since it is caused by many different things. If you go around assuming there is one cause of CFS, one etiology, you are going to end up very confused.
Or consider irritable bowel syndrome (IBS). Most people seem to be aware that IBS is not really a single diagnosis, and probably is a term used to describe all sorts of different, unrelated things. E.g. “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.” Even so, the label kind of implies that there is a similarity of some sort, and suggests that maybe there will be some similarity of treatment and of cure. But this may be misleading.
If nothing else, the shared label means that all these people are likely to end up in the same groups or the same communities “for people with IBS”. If someone makes a post like “this treatment cured my IBS”, you can be sure other people will respond with, “well it didn’t cure *my* IBS”. This is guaranteed to be the source of a lot of confusion.
We think that most unsolved chronic illnesses are probably like this — most of them are probably different diseases with different causes that happen to look very similar.
Compare it to the anthropic principle if you like — diseases that present in a consistent way and have a single cause are easy to figure out, so they tend to be cured and don’t tend to be on the list of unsolved chronic illnesses. But diseases where a number of very different causes present very similarly will be quite hard to figure out, and are likely to remain mysterious for a long time. So things that are unsolved and have been unsolved for a while are more likely to have multiple causes.
(Though even simple illnesses with precise single causes, like scurvy, can be devilishly difficult to figure out, so take this argument with a grain of salt.)
Single-subject (aka N=1) research can be really powerful. But when it comes to cases like this, you have to be very careful. Even if you do a very rigorous single-subject experiment, and provide strong evidence that some treatment works for you, you’ve only really provided evidence that it works FOR YOU. It may not work for anyone else.
If the treatment that works for you doesn’t work for most other people with your diagnosis, that’s actually somewhat informative. We can see why some people would find it discouraging, but it suggests that the illness you have “in common” is actually two different illnesses, or at least two substantially different presentations. That means it gets us one step closer, a small step but a step even so, to figuring out what is going on with your illness, and maybe getting a cure or treatment for everyone.
If you end up with Treatment A that works for 20% of people with your condition, and Treatment B that works for 50%, and there’s basically no overlap, you’re off to a great start. You can start looking for anything that the Treatment A people have in common that’s never found in the Treatment B group, and vice-versa. If you find something (“holy cow, everyone who liked Treatment A has Irish heritage!”), you can start directing people to try the treatment that’s most likely to work for them.
Even if you find nothing in common within the groups, you’re still in good shape. There are only two treatments, and we know that Treatment B works for more people. Newcomers can start by trying B, and if that doesn’t work, they can try A next. If neither work, then they are in the other 30% with no discovered treatment. But it’s still progress in general, and you can start putting your efforts towards finding treatments C, D, E, etc.
It may be tempting to jump ahead and start looking for differences now, before we have treatments that distinguish between various groups, and there is some merit in this idea. If we find that half of people with IBS tend to have bloating with no reflux, and the other half tend to have reflux with no bloating (or whatever), that’s a pretty interesting sign, and will probably end up being useful.
But this approach doesn’t usually seem to work. Probably this is because clustering by symptoms isn’t useful; or when it is useful, it will already be obvious. Different causes can present with identical symptoms, as we’ve been discussing. But IDENTICAL causes can also sometimes present with DIFFERENT symptoms! There’s no royal road, no way to cut this knot for sure. You just have to be careful.
The real enemy here is the confusion (lit. fusion together of different things; “(transitive) To mix thoroughly; to confound; to disorder.”). Talking about “having CFS” or “having IBS” is handy, but when it comes to diagnostics, more detail is better. You may be surprised to discover that someone with the same diagnosis as you has almost nothing else in common. And even when you have every symptom in common, don’t confuse this for a common cause. Your friend may also have migraines, but don’t be shocked when the thing that worked for you doesn’t work for her.
Remember that car crashes all have similar presentation. In true diagnostic fashion, they usually show three or more of the following symptoms: broken glass, injured driver(s), skid marks, bent fenders, police on scene, plastic debris on the road, etc. Take two Geico and call me in the morning.
If you only did an analysis of symptoms, you might think that all car crashes have the same cause. An analysis of symptoms would suggest just one group. But we know that’s not the case — car crashes can happen for many different reasons, and even car crashes with very different causes will usually have very similar symptoms.
Maybe if you are a genius detective and you know just what to look for, you can tell them apart — maybe a car crash caused by a seizure will show signs of uncontrolled driving well before the point of impact, while a car crash caused by excessive speed will have longer, straighter skid marks on the blacktop. But you certainly won’t be able to discover the different causes of car crashes by going down a checklist of “was there broken glass?”, “were there skidmarks?”, “were the drivers injured?”, etc.
If you add in criteria like “how long were the skidmarks?” you might get closer. But you’d have to understand the causes well enough to add that question in the first place.
: If you know of any examples of looking at a disease, looking for patterns in its symptoms, and finding that it is really two diseases (or something similar), we’d be interested to hear about that, since we can’t think of any examples where this approach has worked.
ExFatLoss on measurement: a “Regular, Boring Scale” is the best way to measure body weight for weight loss. “All other methods are either less precise, more prone to user error, or too impractical/expensive to do daily. It doesn’t matter which scale you buy. Just get any $20 model from Amazon. No smart/body fat sensing required. All scales are imprecise. Still good enough. Yea, yea, the scale isn’t perfect. Most scales aren’t that accurate. … But everything else I’ve seen is worse. And I’ve tried a lot.”
The Fosbury Flop Changed Athletes’ Bodies – “the coach can impart important principles, but my sense of great coaches is that they also allow — or encourage — some freedom for the learner to experiment and find a personal solution within the bounds of the task. … I think there’s an analogy for coaches or mentors or bosses of any kind: we should all think about how we can use our influence essentially to underwrite smart risk-taking and experimentation, even within the confines of a well-defined goal.”
Most descriptions of sumptuary laws (historical laws that, among other things, limit who can wear what) focus on how these laws keep common people from imitating those of higher status. If we pass a law that only dukes are allowed to wear cloth-of-gold, then rich merchants can’t pretend to be as important as a duke just because they can afford it. But we have this long-standing suspicion that sumptuary laws also kept rich people from pretending to be lower status than they really are. Think of it as a combination of the king wanting to know who is flush enough to tax heavily, and enforcing noblesse oblige, with everyone being able to tell who is fortunate enough to have an especial obligation to the less fortunate. Anyways: You Can’t Even Tell Who’s Rich Anymore
“The spread of cases across the country and the fact it has been predominantly affecting schoolgirls, with fewer boys and adults falling ill, were central to his conclusion, he said. The nature of the symptoms and the fact most patients quickly recovered were also key, he said.”
In the original potato diet study, we asked people to try to eat nothing but potatoes. This worked pretty well — people lost 10.6 lbs on average over just four weeks.
But we also told them, “perfect adherence isn’t necessary. If you can’t get potatoes, eat something else rather than go hungry, and pick up the potatoes again when you can.”
People took this to heart. We asked people to track how often they broke the diet, and almost everyone took at least one cheat day.
Five people said they stuck to the diet 100%, but everyone else said they broke the diet at least once. Most people cheated only a few times, but as you can see from this histogram, a substantial minority cheated more than half the time:
Taking these cheat days didn’t seem to matter much. Almost everyone lost weight, even if they cheated a lot:
In general, the more often people cheated, the less weight they lost. But even the people who cheated the most still lost around 5 lbs.
Realistically, our original potato diet study was really more like a 90% potato diet. People took quite a few cheat days, and it mostly didn’t seem to matter. Makes you wonder how low we can push that percent and still have it work — after all, the original weight loss effect was ginormous.
This is one reason why today we are announcing a 50% potato diet study. We’re looking for people to volunteer to get about 50% of their calories per day from potatoes for at least four weeks, and to share their data so we can do an analysis. You can sign up below.
The other reason we’re doing this study is a number of extremely interesting case studies.
Case Study: Joey No Floors Freshwater
The earliest case study comes from Joey “No Floors” Freshwater, who shared his story on twitter. He did a version of the potato diet consisting of “1-1.5lbs of potatoes a day when I could”. This comes out to about a 20% potato diet, and it turns out the 20% potato diet works quite well, at least for Joey.
Sadly Joey is no longer on twitter, but we do still have the screenshots:
Nicky Case Study: Nicky Case
The second case study comes from Nicky Case. Nicky participated in the original potato diet study and lost more than 10 lbs over four weeks, without much difficulty. This is kind of striking because Nicky was pretty lean to begin with.
After the potato diet ended, her weight slowly climbed back up. So 50 days after the end of the potato diet, she started a half-tato diet (“at least ONE meal per day is potato”). On the half-tato diet, she lost weight at about half the rate she did on the potato diet, and described it as “TRIVIALLY EASY to do”. Here’s the figure:
This is very encouraging. Nicky tried both the potato diet and the half-tato diet for more than 40 days each, and the direct comparison makes it pretty clear that the half-tato diet caused about half as much weight loss, at least for her.
M tried a version of the potato diet he calls “potatoes by default”. He describes this approach like so:
If I didn’t have anything better to eat, I’d eat potatoes. This meant that if I had plans for lunch or dinner, I would eat whatever it was I would’ve normally eaten ad libitum, and I tried actively to prevent the diet from materially interfering with my lifestyle (I drank alcohol socially as I normally would’ve, I participated in all the meals I normally would’ve participated in with friends, I tried arbitrary new dishes at restaurants, etc.). … In practice, “potatoes by default” meant I was eating potatoes for roughly 1/3 of my meals, mostly for lunch when I was working from home during the week or on weekends, since I usually had dinner plans of some kind.
This relatively potato-light approach caused surprisingly rapid weight loss. M describes it like so: “I think my main reaction to the data was that it was kind of insane? I was eating potatoes a third of the time and literally whatever else I wanted the rest of the time, and losing weight almost as quickly as the full potato diet.”
Here’s the figure. The chart on the right is just a zoomed-in version of the chart on the left, the vertical red line is when he began the potato diet, and the gray bars are when he was traveling and ate no potatoes:
The orange dots in this plot follow the daily averages for the full-tato diet we did. You can see that they are very similar to the blue dots, which are M’s data. When M says that he was losing weight almost as quickly as the full potato diet, he wasn’t joking. While the half-tato diet worked about 50% as well for Nicky, “potatoes by default” seemed to work much better than 50% for M.
You’ll also notice that M kept on “potatoes by default” for much longer than 30 days, and while the weight loss seems to slow a bit near the end, he keeps losing weight for basically the whole period covered in the plot. He loses more than 10% of his body weight over about three months! And he wasn’t even getting that many calories from potatoes — only like 30%!
That’s why we are running a half-tato diet community trial. Let’s take a look at the design!
Half-Tato Diet Protocol
The half-tato diet is very flexible. As long as you are getting around 50% of your calories each day from potatoes, you’re on target.
Here are three ways of doing half-tato:
❖ True Half-Tato: Try to get 50% of your calories from potatoes each day, however you want.
❖ Potatoes-by-Default: This is M’s plan, and it worked well for him. Basically, if you don’t have any other plans for a meal, eat only potatoes (a little cooking oil and spices/hot sauce are ok, but nothing substantial). Otherwise, if you are seeing friends or going on a date or anything else, eat as you normally would. If you choose this plan, consider taking a close look at M’s email to us where he describes his protocol in more detail.
❖ Potato Meal: Have one meal a day be nothing but potatoes (with basic spices, etc.). For other meals, eat as normal. This is basically what Nicky Case tried for her half-tato diet. She describes it as “½ the weight-loss effect, but it was *much* easier than Full-Tato. Trivially easy, even.”
On the signup sheet (linked below), we will ask you to indicate which approach you are planning to follow. You don’t have to stick with the approach you choose, but it will be good to know which approaches are most popular, and if there happens to be a big difference between these approaches for some reason, maybe we’ll be able to pick up on it.
When you’re not eating potatoes, please eat as you normally would. The goal is to see how the diet works when you otherwise eat, exercise, and live as normal, so try not to change too much.
We do, however, have two small suggestions.
In the original potato diet study, we asked people to try to avoid dairy. But now we are not so worried about it. For the half-tato diet, please feel free to continue eating dairy if you want. We will just ask you to track the number of servings of dairy you eat each day on your data sheet. That way, on the off chance that dairy does make a huge difference, we may be able to detect it.
The second has to do with tomato products, especially ketchup. We reached out to the case studies we mentioned above, and most of them told us that they didn’t have ketchup with their potatoes, or didn’t have it very often, so “no ketchup” may be important for the half-tato diet to work. You may want to avoid tomato products and not have ketchup with your potatoes, but it’s really up to you.
Like with dairy, we will just ask you to track the number of servings of tomato products you eat each day on your data sheet. That way, if tomatoes stop the potato effect for some reason, we may be able to detect it.
To sum this up:
Get around 50% of your calories from potatoes each day, using whatever method (one potato-only meal a day, potatoes-by-default, etc.) you like.
Start with whole, raw potatoes when you can, consider cooking them in a way that keeps them as whole as possible.
Otherwise, eat as you normally would. Don’t consciously eat better, but also don’t consciously eat worse.
On the spreadsheet we share with you (below), track your weight, approximate percent potato for each day, your energy, mood, and the ease of the study, as described on the sheet.
Track servings of dairy just in case, don’t bother avoiding it if you don’t want to.
Track servings of tomato products, just so we can see if there’s a difference. Maybe consider avoiding them, especially if you’re not losing weight.
Track any bonus variables you’re willing/interested to track.
On the first day of half-tato, start eating potatoes as per the approach you chose above (e.g. potatoes-by-default). As long as you are feeling ok, keep trying to stick with it. The effect sometimes takes a couple days to become clear; there’s lots of variation between different people; you may lose a little weight one day and gain weight the next; don’t worry if the effect takes a little while to show up.
If you start feeling bad or weird, try one of these helpful hints:
Eating a potato (or something else). Hunger feels different on the potato diet and you may not realize that you are hungry. Yes, really.
Eating a different kind of potato. Different varieties of potatoes may seem like they’re all pretty much the same, but they can really be quite different, and if you’re eating a lot of potatoes, these differences become much easier to notice. You will almost certainly want to eat more than one kind of potato.
Peeling your potatoes. Eating less peel / no peel seems to help some people with digestive and energy issues, especially after a few days on the diet.
Eating more salt. Potatoes are naturally low in sodium and you may not be getting enough. They’re also high in potassium, which can throw off your electrolyte balance if you don’t get enough sodium to match it.
If you try these things and still feel bad or weird, take a day or two off the half-tato diet and just mark down on your sheet that 0% of your food (or whatever) for those days was from potatoes.
If you start feeling really bad, or you otherwise can’t make the half-tato work for you, just stop the trial early. We don’t want anything bad to happen to you. Just send us an email to close out the trial as normal (see below).
In our previous community trials, we didn’t include a control group. This is because we expected the effect sizes to be ginormous. People don’t, generally speaking, spontaneously drop 10 lbs in four weeks, so it’s clear the weight loss on the potato diet is “real” without the need for a control group.
This worked less well for the potassium trial, but we wanted to get the biggest sample size we could for that study, and we weren’t sure how many signups we would get beforehand. We stand behind the idea that when you’re trying to estimate an effect size, it’s good to get as many people in the experimental condition as possible.
We’re still not going to include a control group, because we don’t think it would be very interesting to recruit half of you to sit around and do nothing for several weeks, and it wouldn’t teach us very much.
But we will do the next-best thing, and that’s to ask you to take a baseline of your weight change without the half-tato diet. For the first two weeks of the study, eat as you normally would, and track your weight over time. Then on the fifteenth day, start the half-tato protocol and get on to eating lots of potatoes. It’s simple.
This lets us use everyone as a control group for themselves, sort of like a crossover design. While this design wouldn’t work for everything, we think it works pretty well for the half-tato diet.
We’d like you to try the half-tato diet for at least four weeks. With the two-week baseline, this is a total commitment of six weeks.
But if you’re willing to go further, we would be really interested to have that data. So for the half-tato diet community trial, we are opening things up and letting people enroll for however long they want.
Credit where credit is due, this part of the design was Nicky Case’s idea. She describes it as a “hey this trial runs for however long you want, and we’ll just report data every month for whoever hasn’t dropped out yet” design, and we think it makes a lot of sense.
This is a bit like what we did with the potassium trial — we asked people to keep going to 60 days if they were willing, some did, and we reported on their data in a second analysis post. We want to do the same thing in this study, except that we’d like to ask you to sign up for longer spans up front, if you’re willing.
We won’t hold you to this. It’s not a commitment. We’d just like to know up front how long you’re planning to sign up for. If you can’t make it that long, that’s fine. Just tell us how long you’re thinking you might try.
(Obviously you can also keep going for longer if you want, don’t let us stop you.)
For example, you can sign up for:
2-week baseline + 4-week half-tato
2-week baseline + 8-week half-tato
2-week baseline + 12-week half-tato
And so on and so forth, all the way up to 2-week baseline + 68-week half-tato. We will take snapshots of the data at relevant intervals and analyze the data up to that point.
Sure, “report every month on whoever hasn’t dropped out yet” has a selection bias. The people who sign up for 52 weeks will not be your average ordinary citizens. In fact, they will be paragons, heroes. But that doesn’t concern us. We still want to see those data.
And if you sign up for 52 weeks but it turns out no one can actually be bothered to do half-tato that long, that’s still useful data. Just think about it. 😉
Ok researchers, time to sign up.
The only prerequisites for signing up are:
You must be 18 or older;
In generally good health, and specifically with no kidney problems;
Willing to do a two-week period of baseline measurements;
Willing to get about 50% of your calories every day from potatoes, as described above, for at least four weeks, and;
Willing to share your data with us.
As usual, you can sign up to lose weight, lower your blood pressure, get more energy, or see one of the other potential effects. But you can also sign up to help advance the state of medical science. This study will tell us something about nutrition, weight loss, and obesity. If the half-tato diet works for most people, it will give us a practical weight-loss intervention that’s much easier than the 100% potato diet.
And beyond that, running a study like this through volunteers on the internet is a small step towards making science faster, smarter, and more democratic. Imagine a future where every time we’re like, “why is no one doing this?”, every time we’re like, “dietary scientists, what the hell?”, we get together and WE do it, and we get an answer. And if we get a half-answer, we iterate on the design and get closer and closer every time.
That seems like a future worth dreaming of. If you sign up, you get us closer to that future. We hope that this is only the beginning of what will be a century full of community-run scientific trials on the internet. Maybe by 2030, the redditors will have found a way to triple your lifespan. But for now we are doing potato.
Eating this much potato may sound a little daunting, but people who have tried it say that it is much easier than they expected, and delicious to boot. Here’s our suggestion: If you are at all interested in trying the half-tato diet, go ahead and sign up and start collecting your data. Collect your baseline measurements for two weeks, then try the first day or two of half-tato and see how it feels. If you hate it and have to stop, we would still love to have that data.
If at any point you get sick or begin having side-effects, stop the diet immediately. We can still use your data up to that point, and we don’t want anything to happen to you.
We are mostly interested in weight loss effects for people who are overweight (BMI 25+) or obese (BMI 30+), but if you are “normal weight” (BMI 20-25) you can also sign up. The original full-tato diet caused weight loss in people of normal weight, and it would be interesting to see if the same thing happens for the half-tato.
And for everyone, please consult with your doctor before trying this or any other weight loss regimen.
If you were part of the original SMTM Potato Diet Community Trial, or the SMTM Low-Dose Potassium Community Trial, please feel free to sign up for this study as well! We know that most people who were part of the Potato Diet Community Trial have returned to their baseline weight in the last 6 months, so the original results shouldn’t interfere. And it will be very interesting to compare your weight loss on the half-tato diet to your weight loss on the full-tato diet. Since we can make direct within-person comparisons, this will give us a much better sense of if the half-tato diet works half as well (or better; or worse) as the full-tato diet.
Anyways, to sign up:
Fill out this google form, where you give us your basic demographics and contact info. You will assign yourself a subject number, which will keep your data anonymous in the future.
We will clone a version of this google sheet and share the clone with you. This will be your personal spreadsheet for recording your data over the course of the diet.
On the first day, weigh yourself in the morning. If you’re a “morning pooper”, measure yourself “after your first void”; if not, don’t worry about it. We don’t care if you wear pajamas or whatever, just keep it consistent. Note down your weight and the other measures (mood, energy, etc.) on the google sheet.
For the first two weeks, eat as normal and continue to track your weight and other variables to provide the baseline. Then when the two weeks of baseline are complete (clearly marked on the data sheet), start eating about 50% potatoes, and continue with the half-tato diet for however long you signed up for (4 weeks or longer).
We prefer that you try to get around 50% of your calories from potatoes for at least four weeks. But imperfect adherence is ok. If you only get 30% of your calories from potatoes one day, or you have to skip a day entirely, that’s all right. Just note it down on your sheet. We’re interested in how the diet works for normal people at home, with all the complications that entails.
When you reach the end of the diet (whether you’re ending the diet early, reaching the span you signed up for, or going beyond it), send us an email with the subject line “[SUBJECT ID] Half-Tato Diet Complete”. This will give us a sense of how the study is proceeding in general and is your opportunity to tell us all about how the study went for you. Please tell us any information that doesn’t easily fit into the spreadsheet — how you felt, what kind of potatoes you used, how you prepared them, before and after pictures (if you want), advice to other people trying this, etc. There’s a chance that the half-tato approach will work for some people and not for others, and if that happens, we’ll dig into these accounts to see if we can figure out why.
Remember that it is ok to end the study early if you need to, for example if you get sick, or if you decide that 12 weeks or whatever is too long of a commitment. It’s also fine to reach 12 weeks and keep going if you’re having a good time. Just make your intentions clear in the comments on your data sheet and send us an email whenever you decide to finish, we’d love to hear from you.
Assuming we get 20 or so people, we will write up our results and publish them on the blog. We would really like to get a couple hundred people, though, since at that point it becomes possible to do more complex statistical analyses. So if you think this is an interesting idea, please tell your friends!
This post is an interview with some guy, writing under the name Exfatloss, who has been conducting a weight loss self-experiment and recently put out a blog post about the results so far.
Exfatloss has tried a lot of different weight loss techniques, including the potato diet, but nothing seemed to work over the long term. Until now, that is. He has invented a diet he calls “ex150” that has caused a surprising amount of weight loss, and which seems to be quite reliable — at least for him.
This interview is lightly edited for clarity, and to make Exfatloss “sound smart and funny” per his request.
Exfatloss: Hey SMTM, I finally wrote up a summary on my crazy diet experiment, now that I’ve lost just over 43lbs in 5 months. It has a weight graph that I hope you find enlightening.
Feedback from an experimental/author/publication/science/whatever perspective highly appreciated!
SMTM: This is very exciting, and it makes us want to drink some heavy cream right away, yum. Several questions:
SMTM: For starters let us make sure we understand the ex150 diet as you describe it. It involves:
Eating just one meal per day, of:
~150 g meat, usually as
ground beef chuck (80% lean / 20% fat) or
~60 g green vegetables, usually as
microwaved frozen vegetables “(okra, spinach, green beans, fajita mix)”
~80 g pasta sauce, usually as
“the sauce is low-everything and mostly water (e.g. most store brand tomato/alfredo pasta sauce)”
I.e. either red or white sauce
As much butter as you want to cook these things in. (“usually about 15g”)
None of these things measured or weighed precisely, i.e. the diet seems quite flexible. “I don’t think the exact number matters much.”
Exfatloss: Initially I just cut a 1lb thing of ground beef into thirds. It’s pretty much exactly 150g that way. I’d say it doesn’t matter much if you do 130g or 170g. That’s what I mean by “exact numbers don’t matter much.” If lack of a kitchen scale is holding you back, don’t worry about it, eyeballing it worked fine for me. Now if you were to eyeball double the amount of meat… I dunno. I’d consider that more “ex300” than “inexact numbers.”
tl;dr, just buy 1lb of meat and cut it into thirds.
Otherwise eating no meals but:
butter and whipped cream, as much as you want, as snacks/desserts
Sometimes with instant coffee powder for flavor or tomato sauce to cut the fat taste
Quite a lot of it, “I go through a lot of cartons of heavy cream, maybe one every 2-4 days.” How big of a carton? 16 oz?
As a result, most calories come from cream.
No-calorie foods like coffee are also ok, including coffee with arbitrary amounts of cream, and including going to Starbucks.
Exfatloss: The heavy cream comes in 32oz. I have 3 of those in my fridge right now. I think it’s about one 32oz carton every other day I go through. I put instant coffee powder in the whipped cream most days for flavor.
SMTM: Also you are currently in the USA right?
Exfatloss: Yes, and have been for this entire weight loss period so far.
SMTM: As we understand the intent behind the design, the butter and whipped cream are there to make it high-fat, the 150 g meat is there to make it a low-but-nonzero protein diet, and the vegetables are there to give some minimum amount of fiber. Does that seem right?
Exfatloss: Vegetables for flavor/texture and minimal fiber, yes.
Pure ground beef tastes like shit. Trust me, I’ve tried it.
Butter/cream are there to provide calories that are not protein/lithium/whatever the factor is. They’re a known-not-fattening source of calories that also happens to cause no bloating and that I deal with super well.
SMTM: Butter and cream are “a known-not-fattening source of calories that also happens to cause no bloating”? Our sense is that most people would assume that butter and cream are fattening and might cause bloating, so the fact that you seem so confident is surprising. Known to whom, how? It’s news to us!
Exfatloss: Well, known to me, at this point 😉 Through trial and error. There are a bunch of people with theories why (low protein, low PUFA, low UFA).. but honestly I have no clue if any of them are right. I just know I lost a bunch of weight eating mostly heavy cream.
I think it’s an important factor of any sustainable diet that you are NOT in a caloric deficit, or it won’t work (Caloric deficit symptoms -> “willpower breakdown” -> quit diet).
SMTM: This also really stands out! It does seem to fit with what we saw on the potato diet. How did you come to this conclusion?
Exfatloss: Decades of experience? Pretty much any time you restrict your intake or increase your expenditure, you can expect to keep it up for 1-3 weeks or maaaybe if you’re really hardcore a bit longer, and then it stops working and you lose “willpower.” That seems like THE ultimate diet experience of everybody who’s ever tried to lose weight. I write about this in my latest post.
Also when people say “deficit” they are super vague and conflate things and that’s why it’s both necessary and impossible to run a deficit to lose weight. Planning on writing about this at one point.
SMTM: What is the pasta sauce there for? You say, “mostly water”, is that also part of the design?
Exfatloss: Flavor and to soak up the fat 🙂 It tastes significantly better with the sauce. Maybe that’s just me. This whole meal is my previous go-to meal for over 3 years, just scaled down. I used to eat 1lb of that stuff per day, now it’s ~170g and I added the cream to make up for the calories.
SMTM: It may not matter, but we’re curious, what method do you use to test whether you’re in ketosis? If you tracked your ketones data it might be interesting to graph or publish it as well.
Exfatloss: Currently using a ketone blood meter (finger prick style). I will say a lot of carnivore peeps are calling my “zero fiber != ketosis” statement BS and I’ve updated that section of the blog post to clarify.
Since ketone blood strips are expensive and annoying I haven’t tracked those in years, since first starting keto 7 years ago. So unfortunately no data to show 😦
Would be cool if CGMs could track more than just blood glucose! I would love to have years worth of ketone levels. Good news is that the next-gen Libre Freestyle CGM will have this! Very excited.
Palatability and Variability
SMTM: In your post you talk a bit about hypotheses, including this one:
Palatability/brain hack: there is a lot of science out there around the brain’s ways of dealing with food, food reward, and metabolism. Stephan Guyenet’s The Hungry Brain is maybe the best summary, I think. I’ll admit I haven’t read the book, but I listened to a few podcasts where he talks about the ideas, and I think the ex150 diet fits his hypothesis. The idea is that hyper-palatable food that is very energy-rich causes us to overeat in terms of energy. The ex150 diet has 1 hyper-palatable meal every day, but it is very small. The remaining calories come from a bland mono-food that’s hard to overeat (heavy cream). Maybe this tricks the brain into not overeating the cream, yet never feeling more than 24h away from a hyper-palatable meal to release lots of dopamine or other happy food reward signals? I think that even if this might not be the main causal factor, it sure helps make the diet sustainable. I’m never more than 24h away from the most delicious meal I could imagine, and I can eat unrestricted amounts of “dessert” (=whipped cream w/ instant coffee powder).
This mostly seems like evidence against the palatability hypothesis to us, though it might be interesting to ask Guyenet what he thinks. But to us it seems like you are eating delicious foods and getting a lot of food reward. If as much heavy cream and butter as you want plus “the most delicious meal I could imagine” counts as “low palatability”, then the term is so meaningless that it should be tossed out.
Exfatloss: I do personally think that “palatability” (and “satiety”) are meaningless the way they’re often used, even by Guyenet. I heard him on a podcast where he basically said (paraphrasing) “Science has found that humans tend to be caused by their brain to overeat foods that have high palatability.” Wait, isn’t that the definition of palatability? Very roundabout way of saying “Food that tastes good tastes good.” 😀
SMTM: Yeah that has always seemed kind of circular to us.
Exfatloss: I’ve @mentioned Guyenet on Twitter, but he didn’t reply (maybe cause I’m a nobody lol). Maybe I’ll ask him again when I have street cred lol. I do think he’s a good representative/explainer of “The Science” on this because he’s got a good grasp of various ideas out there and has been in full-contact debates with Taubes etc. and was able to hold his own. I respect him. I sometimes feel like citizen-scientism is bordering on anti-science and someone knee-deep in science like him is able to check that tendency. That said, in a fight, my money is on Taubes.
SMTM: We like Guyenet and he’s interacted with us a tiny bit, maybe we can help get his attention. We understand that he’s reluctant to engage with weird randos on the internet but we’d be curious to see what he thinks of this.
Exfatloss: Regarding the dichotomy here: I think the meat/vegetables/sauce meal has near infinite palatability, I’ve literally eaten a pound of this before scaling it down for ex150. So if this diet was “eat as many of these tiny meals a day as you want” I’d eat 15 of them. But I can only have 1. The cream/butter on the other hand has extremely low “palatability” in the sense that it’s very hard to overeat.
How do you know you’ve had too much cream? You’ll fricking know. It comes from one second to the next, where the thought of another sip almost makes you gag. Total on-off switch for me, whereas I can literally eat carbs until I puke and not be satiated (ask me how I know. College, man!)
So if “palatability” means something like “able to overeat” then cream is not it, because it’s very self-limiting. Potatoes and dry chicken breast are also very self-limiting, but they’re in fact so limiting that I got into a massive deficit, got caloric deficit symptoms, and had to quit the diet (plus all the fiber made me feel bloated and gross).
SMTM: Your results look a little more like the variability hypothesis, though. We interpret this as a version of (or closely related to) the palatability hypothesis, where the problem is not tasty foods per se, but eating a variety of foods that are tasty in different ways. We think this theory is poorly-supported but ex150 is definitely a low-variety diet, mostly consisting of the same 5 or so foods eaten every day, however delicious they might be.
It’s clear that ex150 is a low-variety diet, close to a mono diet. But it also seems like variety or mono-ness aren’t the active ingredients here because you did other low-variety and/or mono diets and they didn’t work for you at all. If low-variety or mono diets worked for you, then you would have lost weight on the other low-variety/mono diets you tried — on the carnivore diet, on the “eating only at In’n’out burger” diet, and on the potato diet.
Exfatloss: Yea I do think there’s something to “variety -> overeating.” I do think mono-foods “work” in being self-limiting. My hypothesis here is basically that ex150 manages to hit the goldilocks zone – 1 hyper-palatable meal per day and the rest is a self-limiting mono-food, but it’s not so mono that you get into a massive deficit that makes the diet unsustainable (like potatoes did for me).
On my first week of Potato I was doing only boiled potatoes sans everything, and I couldn’t get down more than 600kcal per day. I’d force myself to go to the fridge, grab a boiled potato, take a bite. It’s not even that the bit tasted bad – but after 1 bite, I was almost gagging. I just couldn’t take a second bite.
So clearly too self-limiting to be sustainable. 200ml of heavy cream has almost 700kcal just on its own and you don’t have to boil it, doesn’t come with all the bloating fiber, digests super easily. Plus you can put instant coffee in it 🙂
SMTM: This isn’t our sense of how the potato diet works in general, since it seems like the tato makes you LESS INTERESTED in other foods.
This seems especially clear in people who have tried half-tato diets. They let themselves eat other foods, but eating a big chunk of potatoes on a regular basis seems to lower your appetite for everything else. For example see M’s experience on the half-tato diet. He says, “maybe two or three weeks in, for the first time in a really long time, I did not have the urge to finish off leftover food at dinner.” Or Joey No Floors Freshwater, who said, “The difference is now I get full and stop eating. I leave food on the plate, which is new for me. I leave texmex on the plate y’all. Its wild.” So the potato diet doesn’t seem to work on just the self-limiting aspects of potatoes because people are less interested in eating other foods too.
That might be one more reason to do a larger half-tato diet study, to see if this generalizes.
Exfatloss: Hm, that sure wasn’t my experience. For me, it was just an inefficient fast plus the worst bloat in years.
I only did full-tater, maybe half-tater would’ve worked better for me? Not sure why it would work so awesome for some and so badly for me.
One theory I have for that is the goldilocks satiety idea. If you eat a mono food and it’s not satiating enough, you’ll overeat and gain weight. If it’s too satiating, you won’t be able to eat enough to meet energy expenditure and you’ll begin getting caloric deprivation symptoms and will eventually land in caloric bankruptcy (see my post).
Not sure why exactly potatoes don’t hit goldilocks zone for me but do for others. Maybe I have a higher energy expenditure or do worse on potatoes?
Re-reading the section of those people who had potato-success, I do think there’s something to “fix satiety.” Maybe the common thread is that these diets somehow fix satiety in people whose satiety signals are broken.
Of course that just moves the question to “how did the satiety signals break and what fixes them?”
SMTM: To us this looks like evidence against both palatability and variability in your case, and some evidence against them in general, especially if it turns out that ex150 works for other people.
Exfatloss: As those hypotheses are commonly understood, yea. But maybe the Goldilocks Palatability thing? 🤷
I.e. it’s not that “lowest palatability/variety” is optimal, but “modest palatability/variety?”
SMTM: Some other theoretical explanations do come to mind. You’re probably eating close to zero calories from seed oils, so while we don’t find seed oils to be a very plausible theory of obesity, we want to at least note that this result is very consistent with that theory.
Exfatloss: Yea, I’ve actually kind of added that as a new hypothesis. I found this insane s/saturatedfat subreddit after posting my article. People there were like “Duh, of COURSE you would lose tons of fat by eating saturated fat!” There’s this guy Fire in a Bottle (twitter/youtube) who has this whole theory how modern meat (even beef) is full of TCCD (I forget what it stands for but it’s BAD cause CHEMICALS) and PUFAs etc. and that’s what’s making us fat.
I will say I did Paleo for 3+ years before Keto, and Keto for 7 years before this, so it’s not like I was chugging seed oils. But I was eating TONS of US commercial grown beef. So if it’s in that..
It would be cool to design experiments to kind of disentangle the various hypotheses, although I’m not sure exactly how. I suppose a month of 2lbs/day of grass-fed beef? 🤷 Maybe ask the gurus who recommend those theories to design an experiment? They’d know.
SMTM: We also notice that “lots of pure fat” does sound kind of like the Shangri-La Diet, so there might be some connection there. A chemical engineer we work with has repeatedly emphasized that while lithium probably accumulates in many foods, it shouldn’t end up in oils because it’s not fat-soluble. Maybe this is connected to why high-fat diets sometimes work? This isn’t limited to lithium, it’s just generally a note that if obesity is caused by a contaminant, there’s some reason to think that the contaminant doesn’t accumulate in fats.
Exfatloss: Interesting, yea. I remember reading about Shangri La years ago. It does seem very similar. Question is why, I guess: is it “appetite suppression” and what does that even mean – ensuring energy balance is met or some psychological thing?
Have you sent a bunch of meat/milk/cream/potatoes to the lithium lab yet? That would shine some light on it, I imagine.
SMTM: We’re working on it! 😉
SMTM: The fact that you didn’t lose any weight on the potato diet and “HATED how bland it was” seems really interesting, especially given that most people on the potato diet said they loved it and many talked about how delicious they found the potatoes, even after 30 days. And for the most part, obese participants had the most success on the potato diet, so it’s interesting that you found it boring.
It kind of suggests there might be at least two kinds of obesity, one that responds to the potato diet and one that responds to ex150. If something like that were the case, it would be pretty easy to demonstrate experimentally.
Exfatloss: I’ve long suspected that obesity is a “slightly complicated problem.” My analogy is a broken down car.
You drive by a car broken down by the side of the road.
You say: “Have you tried putting gas in the tank?”
“Yes, still doesn’t drive.” says the guy
“I’m pretty sure putting gas in cars works, my friend broke down once and he put gas in the tank and then it worked again.”
“Doesn’t work for me,” says the guy in the car.
“I think you’re just not putting enough gas in the tank,” say you.
That’s basically the state of our discourse on obesity, when even much simpler things like cars can break down for a handful of reasons. Maybe spark plugs. Flat tire. Crankshaft. Hell, I’ve had a cylinder blow up on me because the timing belt skipped a beat and one of the cylinders fired out of order.
If there were 4 causes of obesity and 4 different diets to fix them, we would currently conclude that there is no solution and we don’t know and nothing works better than anything else, because we insist on averaging everything out.
On average, putting gas in broken down cars’ tanks doesn’t work. But sometimes it does. When lack of gas is the problem.
SMTM: Yes! We’ve been working on a post about this. People spend a lot of time saying obesity is a disease when clearly it is a symptom that can be caused by all kinds of things. So while there could be just one epidemic, there could equally be several.
There could even be several epidemics for just one reason. If you roll a bunch of cars over a cliff, many of them will break when they hit the bottom and won’t be able to start. But they might all end up broken in different ways, even if the ultimate cause is “was rolled off of a cliff”.
Exfatloss: Yea “disease” has always seemed wrong to me. I get that people want to take the moral stigma out of it, but “disease” sounds like your immune system will clear it out after 2 weeks or it’s a viral infection or something.
If anything, I’d call it a “condition.”
SMTM: Your experience actually seems like some evidence against the contamination hypothesis and in favor of some kind of deficiency hypothesis. Let’s say that obesity is caused by a deficiency in either X or Y. Potatoes contain X and heavy cream contains Y. If you are X-deficient but have good Y levels, then the potato diet will cure your obesity but cream will taste gross because your body is trying to avoid overloading on Y. If you are Y-deficient but have good X levels, then ex150 will cure your obesity but potatoes will taste gross because your body is trying to avoid overloading on X.
Exfatloss: I don’t know if I agree, it could easily be consistent with contamination. Maybe potatoes and fats are both very low in contaminants, but some people do super well on fiber and starch whereas others do better on fat. I’m sure people with dairy intolerance will hate my diet, but I used to chug a quart of milk for breakfast as a kid and I can dairy all day. Others apparently love potatoes all day. Personally I do best on “close to zero but not quite zero” fiber.
I think the “feels good on X/Y diet” and “contamination” theories can exist side by side and explain this.
SMTM: Would you be at all interested in running an ex150 community trial, maybe recruiting specifically from people who also found the potato diet bland/difficult? You could start by just getting a couple of other people to try it as case studies, since there’s a rather blurry line between “2-3 case studies” and “community trial of 10-20 people”. If the effect is as strong for other people as it is for you, you wouldn’t need a very big sample size to produce convincing results.
Exfatloss: Yea, definitely. In fact I’m trying to recruit some of my friends 🙂 One insisted on switching to the diet at the same time as doing CrossFit for the first time, because apparently isolating variables is for losers…
If you pointed a couple people my way I’d be happy to set up some kind of study. People can contact me on twitter or the blog, or can email me at firstname.lastname@example.org
SMTM: Absolutely! People will see it in the blog post and we’ll share about it on twitter, we’ll encourage people to email you there.
Exfatloss: Sounds good!
If it’s just a handful of people I think I’d be more comfortable managing it. I’m not sure I’d be up for hundreds of people like you had on Potato because I’ve never done that type of thing before. That seems to require infrastructure.
SMTM: Yes, and it’s good to start with more case studies before scaling it up to dozens of people. Better to make sure it generalizes and we can re-create it so we don’t waste everyone’s time.
Exfatloss: Yea for sure. Maybe I’m just somehow a crazy sat fat outlier who can’t deal with potatoes 🙂
SMTM: You mention,
For example, could you make the diet work eating only at common fast food restaurants? Using only prepared deli meats? What about cheese? Is it really just about the amount of protein, or does it matter what kind of protein? Eggs?
Does the diet even need to be ketogenic? What happens when you reach a healthy weight, can you back off the diet? Do you cycle it? Is there a maintenance version?
These seem like the most important questions to us. If you switched to 150g eggs + different vegetables + as much olive oil as you want, would it persist? What about other formulations?
What if you stick with the original formulation but slowly add rice until you are no longer in ketosis? You wouldn’t necessarily need to go out of ketosis if you could show a correlation between the rate of weight loss and your ketone levels (though obviously dropping out of ketosis without it affecting the weight loss would be most convincing).
Exfatloss: I’m actually currently on day 5 of ex150deli, which substitutes supermarket deli meat cuts (salami, turkey breast, roast beef..) for the ground beef. I kept the vegetables/sauce the same for science’s sake and let me tell you, they do NOT go well together with sliced deli meats lol. [SMTM note: since finished with success, see here]
Agreed that this would be awesome. It seems there’s gotta be a whole lot of alpha out there in fat loss, and we’re probably nowhere near the efficient frontier. So we should explore the boundaries. What do we actually have to give up to be successful? Why give up more.
SMTM: We also noticed that you say,
What piqued my interest though was that the super-low-protein carnivore diet, while it still kicked me out of ketosis, made me rapidly lose weight, about 10lbs in the 12 days until I ended the experiment early (because I was out of ketosis already, proving the hypothesis).
This story suggests that such a thing is possible.
Exfatloss: See my 2/17/2023 update on the post on the fiber/ketosis issue, several carnivore people claim I’m wrong on this and I concede it’s possible.
SMTM: It also seems like this could just be a cream-maximalist diet, right? Do you know about how many calories you’re getting per day, and how many of them are from cream? Seems like it’s over 50% right?
Exfatloss: 50%? Ha. It’s 85% before I add the cream in the coffee 🙂 I have a macro estimation in this blog post.
SMTM: There would be a darkly comic element if obesity was cured by high doses of cream, but it would also make some sense. What is the one treatment for obesity that no one would ever think to try? “Drink as much heavy cream as you can stand, every day.” We’re confident that most people would never try this (ok maybe some people would on keto), so it would make sense if everyone missed it…
Exfatloss: Ha you should see the r/saturatedfat people.. as I understand it, the claim is literally that eating saturated fat will increase your metabolic rate by insane levels and thus create a massive bottom-up deficit.
SMTM: In general, is there a principle of “if you’ve been looking for a long time and tried everything you can think of and nothing works, the real answer must be something that seems really stupid”? Reminds us of Sherlock Holmes’ “when you have eliminated the impossible, whatever remains, however improbable, must be the truth.”
Exfatloss: I will confess to having read a lot of Sherlock Holmes.
SMTM: Us too!
Superstition and ABA
SMTM: Generally we are concerned about the “superstition” element of self-experiments. If spontaneous remission is a possibility, and you try a long enough list of things, you might randomly spontaneously remiss and it would look like the thing you were trying at the time is the cause:
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 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.
This is basically what happened when you moved back to the US from China.
So we REALLY like how you took a 14-day break from ex150 right in the middle of your self-experiment. If you were randomly losing weight for some other reason, then you should have kept on losing weight during this break. The fact that you gained weight back, and that it closely corresponds to the break (modulo pemmican), seems like strong evidence that, as you say, “it wasn’t some other random factor in the environment causing the fat loss.”
We see the same thing in a smaller way in two other short breaks you take.
Exfatloss: Definitely agreed that it doesn’t prove “what did it” or even anything.
But it disproved a bunch of really likely environmental factors like a) city (walkability? air quality?) b) weather/temperature c) drinking water d) cancer haha.
I think it’s a really easy and pretty good thing to do. If you really know why the light turns on and off, you shouldn’t be afraid to hit the switch a couple of times and see if it works as you thought. That’s kind of the least you can do. If you never turn the light off because you’re afraid it won’t come on again, does that really sound like you understand why it’s on in the first place?
SMTM: In your Q&A section you give this exchange:
Q: You’re just going for walks now.
A: No, fat loss started 2 months before that and the rate hasn’t changed. But yes, I feel so energetic many days on this diet that I started spontaneously wanting to go outside and take long walks. One time I even fell into a light jog! In my experience this is a result of effective fat loss, having “unlocked” the key to utilizing my body fat, not the cause of fat loss. 1,150kcal/day (0.3lb of body fat) would be a long walk to take every day.
This is interesting to us because it suggests your set point is falling faster than your weight is. Compare this experience to how you mentioned that running as exercise just makes you hungrier to compensate for the extra calories you burn. So that suggests that something about this diet changes your set point very quickly, which seems interesting.
Exfatloss: I kind of believe that we’re thinking about “set points” slightly wrong. This is inspired by my understanding of circadian rhythms.
All humans have a “genetically predetermined circadian rhythm.” But it’s not that somewhere in your genes it says “8am EST” or anything. The best analogy I’ve read is that what’s basically encoded is a spring weight. Imagine your circadian clock is powered by a spring, and sunshine pushes down on the spring. Different people have different spring weights. Most people’s weight is such that if they get even a little bit of sunlight during a normal day, their spring is fully compressed and ready to go again. Some people have a very stiff spring, and they need enormous amounts of sun exposure to get it compressed during one day.
If you move even normal people to the north pole or something, even their springs will never compress (in the constant dark) or always be overcompressed (in the constant sunlight). If you put people in a cave, the spring mechanism just completely stops working.
My point being, what if it’s not that we have a “set point” that says “He shall be 210lbs” but instead, the rate of how “calories in” is split up? Similar to the P ratio. This ratio could be influenced by various factors like macro composition, chemicals in the food, sunlight, sleep quality.. some people have a ratio in such a way that pretty much no matter what they do, the calories they eat will be sent to the furnace. Other people will have ratios that require them to take super extreme measures to prevent gaining fat. If you put healthy people on a PUFA-sugar-juice diet and sleep deprive them and feed them tons of lithium, even they will probably gain fat.
For example, maybe I’m just an insulin hyper-responder, and what normal people consider “normal” amounts of carbs or protein makes me obese. And suddenly my ratio has swung from one end of the scale (->90% of calories in go to fat) to the other (->90% calories are sent to the furnace and you will fricking go for a walk every day even in freezing rain just cause you can’t stand sitting still).
Maybe it’s not that this ratio per se is encoded. Point is it could easily be encoded as a flow rate, not as an absolute “set point == 210lbs” value. And you just reach a different equilibrium with your current environment depending on the flow rate/spring rate. Just as you’ll reach a certain “waking set point” in the winter, and a different one in the summer, depending on factors like sun exposure.
SMTM: This is a good argument, but the difference between circadian rhythm and metabolic set point is that while the body doesn’t have access to a direct measure of time (it uses external cues like sunlight), it does have access to internal metrics about obesity. This seems to involve signals like leptin, literal compressive weight on your bones, blood sugar, stomach fullness, etc.
Exfatloss: But those metrics aren’t an objective, comprehensive obesity score like body fat %. It’s different chemical signals. Those signaling pathways can be disrupted or conflated or confused.
In a computer analogy, there isn’t one program in your body that can read the total fat storage value and set the heater/AC accordingly. It’s a bunch of distributed systems sending each other messages in various ways. If something goes wrong with some of the packages, unspecified behavior can set in. The TCP port could be blocked. The pipe could be broken. Your packages might get misrouted by a rogue/broken system in the middle. There might be backpressure in the signaling system that changes the frequency/density of the packages arriving.
Might also be personal. For example, my sensitivity to physical stomach fullness is practically zero. I always assumed that people meant this figuratively. I have literally eaten until I was painfully full and felt zero satiation. I wanted to continue, I just couldn’t, from the pain.
Pro-tip: never go to an all-you-can eat pizza place.
The potato diet wasn’t quite that bad, but it was also really bad.
On the other hand, the whipped cream satiety hits me like a cement truck. One bite fine, second bite good, third bite NO WAY I’M FULL. (These are the last 3 bites, not the first 3 bites, of a whipped heavy cream meal.)
SMTM: How about the hairpin turns when you try going off the diet and back on again? Whatever this diet is doing, your weight seems really responsive! That’s weird, but it kind of matches the results on the potato diet, which also seems to cause abrupt changes in most people’s weight.
Exfatloss: A lot of the hairpin is water retention. I’ve seen as much as +6lbs the day (!) after ending my second ex150 month, and -4lbs after the first day of pemmican.
One confounder with potato for me is actually that I was pretty low-fiber before it, because I hate fiber. So I went from a low-fiber to an all-fiber diet, which would jack up my water retention. So given the above numbers, even if I lost 6lbs on potato, the very first day of increased water retention could negate it.
Btw these water retention effects are plateau effects, which was my criticism of your recent potassium study. But if you do switchbacks, it creates these insane hairpin turns.
I basically disregard the first, really steep weight loss when I go on the diet. Usually it takes at most 5 days to finish the plateau effect and for the “real” fat loss rate to show.
SMTM: We agree that the real point is the meta-framework of experiments, so we’re really interested to hear more about these other things you tried that you list near the beginning of your post (cold showers, no online news, carnivore diet), what can you tell us about those?
To emphasize: the real point is the meta-framework of experiments. Formulate a hypothesis, design a 30-day experiment, test it. I’ve probably done dozens of these over the years.
Here are some examples from the last few years:
30 days of cold showers
90 days of no online news (I thought stress might contribute)
90 days of the carnivore diet
30 days of eating only at In’n’out burger
Doing Starting Strength, a beginner’s powerlifting program
Doing Simple & Sinister, a kettlebell training program
30 days of drinking only distilled water (including for coffee)
Eating only pemmican, a raw meat paste invented by Native Americans
Exfatloss: Ha I’m planning to eventually write a longer post where I detail some of these experiences. [SMTM note: this post has since been written, see here]
Some highlights: cold showers did nothing. No online news (suggested by a friend) showed me that I consume news as entertainment, but that I just replace it with movies/video games when I stop consuming the news. Carnivore diet was super bland and boring (YES steak gets boring!) and I didn’t lose any weight. In’n’out was the best, I love that place! My first low-protein trial was entirely done at In’n’out, as was the low-fiber one. Starting Strength made me fatter. Pemmican was even more unpalatable than potatoes lol, I chewed every bite for 2 minutes. It just tastes like I imagine cow manure tastes like. Ugh. Sad because I really wanted to like it.
SMTM: A lot of science criticism seems really facile. In particular, it seems like lots of people don’t understand measurement, they think that measuring things is both objective and easy. It makes us wonder if these people have just never tried to measure anything for themselves so they don’t realize what is involved (compare: Reality has a surprising amount of detail). So our sense is that trying a lot of failed diets is part of what has made you a careful experimenter. What was your experience of this? Does this have practical implications for training, or for people who want to get into research / self-experimentation? You seem very virtuous to us. What advice would you give to other people who wanted to do self-experiments like this?
Exfatloss: Hm, not sure. I just like experimenting and trying new stuff, I’d probably keep doing it even if I reach my goal weight. Just for fun.
Most experiments have no effect or almost no effect. “It makes no difference” seems the default result.
SMTM: Good insight.
Exfatloss: All diet experiments that somehow rely on you eating less or burning more energy seem to fail very quickly because caloric deficit symptoms set in. I call these diets “inefficient fasts” because you could’ve saved yourself 15 days and gotten too hungry to continue by water fasting, instead of getting too hungry to continue on day 18 of your diet.
Even when you’re really, absolutely, positively sure you identified The Thing, you can be completely wrong. This was my experience after “knowing” through experiment that keto is what made me lose 100lbs. I had literally already written the book 🙂
Lesson in humility. One of the reasons why I’m couching my terms more this time and mostly going off of my experience so far. One of my broader claims this time was the zero-fiber/ketosis thing, and apparently I’ve already been proven wrong. Zing!
I am very critical of Science(tm) as an institution, especially in fat loss and nutrition. It seems that a lot of scientists hide behind mouse models and sophisticated studies so they don’t have to face the fact that nobody actually fricking knows how to lose weight.
Saw a meta-analysis recently that concluded “all diets work well to reduce weight.” Really? Must’ve not heard about this obesity epidemic.
That’s why I love the citizen scientist stuff so much. I think modern ethics boards literally make it illegal to do meaningful diet research.
Exfatloss: Try getting a 85% calories from heavy cream study approved.
SMTM: Finally, can we publish your responses to these questions (and responses to any followup questions) as an interview on our blog? If that sounds good, we’ll produce a version of this email thread, edited for clarity and flow, and go over it with you before publishing.
Exfatloss: Yea, that sounds great! Please edit it to make me sound smart and funny lol. Exfatloss is good as a name. You can mention that I’m a guy just for clarity, as that can make a big difference in fat loss/metabolism I think.
SMTM: How should our readers reach you if they have questions? Comments on your Substack? Email?
…a program of studies designed to assess the health and nutritional status of adults and children in the United States. … [It] began in the early 1960s and has been conducted as a series of surveys focusing on different population groups or health topics. In 1999, the survey became a continuous program that has a changing focus on a variety of health and nutrition measurements to meet emerging needs. The survey examines a nationally representative sample of about 5,000 persons each year. These persons are located in counties across the country, 15 of which are visited each year.
Data from the NHANES is publicly available. It includes hundreds of health and nutrition measures for thousands of people, collected in multiple rounds of examination, across several decades.
But NHANES data is very hard to work with for the same reasons. Each two-year period of data (e.g. 1999-2000, 2001-2002, etc.) is split up into several different datasets, which have to be combined for analysis. Comparing measures across multiple years can be quite tricky, because formatting and variable names often change year-to-year, sometimes with no explanation. Variables are often added or removed, and it’s not always clear if a measure used in one year is the same as a similarly-named measure in another year. So while the dataset is extremely rich, its hugeness can make it difficult to work with.
To address these problems, we worked with a data scientist, Elizabeth, to combine the NHANES datasets from 1999 to 2018 into a single package. The dataset we ended up with is certainly imperfect — we didn’t include every measure, we still aren’t sure which variables are duplicates, etc. — but it’s a starting point.
Now, Elizabeth and the SMTM team are going through the dataset to see what it can tell us about the obesity epidemic, to see if there are any new mysteries we can uncover, and in particular to see what it can tell us about the contamination hypothesis of obesity. Analysis was performed only on data from adults (people 18 or over), and not including anyone marked as pregnant. Elizabeth is great, all mistakes are ours not hers, etc.
Here’s our standard disclaimer: all of the analysis you are about to see is correlational. As you well know, correlation does not imply causation — though as XKCD reminds us, “it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’.” Correlation can still provide some evidence, and we can always follow up on anything we find in correlational data later on with more controlled research. We are fishing for weird surprises, like when you cast a net into the sea and pull up a bicycle.
A final reminder: about 75% of the modern variation in BMI is genetic, so any correlations of environmental variables with BMI will probably appear to be quite small. We should expect to see correlations like r = 0.10, not like r = 0.65. But that appearance of smallness is misleading, because we are looking for factors that can explain the 25% of the variance in BMI that isn’t genetic. If we find a factor that accounts for 5% of the total variance in BMI, it’s more like it explains 20% of the remaining mystery, the non-genetic variance (.05/.25 = 0.2), which is what we are really interested in.
We started with the simplest possible analyses. In this case it was as simple as: in the NHANES data, which variables have the biggest correlations with BMI?
The strongest correlations of all, of course, were correlations of BMI with other measures of obesity. Naturally BMI is correlated with things like height and weight, this is not a surprise. So let’s pass over these variables and move on to more interesting findings.
From here, two correlations stand out in particular:
Serum copper levels have the strongest correlation with BMI in the dataset, r = 0.240 (p < .001), and serum copper levels account for about 5.8% of the variance in BMI.
Serum gamma-tocopherol levels are a close second, correlated with BMI at r = 0.230 (p < .001), and serum gamma-tocopherol levels account for about 5.5% of the variance in BMI.
Both of these relationships stand out quite a bit from the herd. The next-strongest correlation with BMI in the NHANES is just r = -0.170 (we’ll look at some of these variables in future posts). This is still significant but it’s definitely a step down — serum copper and serum gamma-tocopherol are, for some reason, pretty clear outliers.
Here’s a plot of the 20 strongest correlations with BMI in the NHANES data that we looked at (not including other measures of obesity). There are plenty of variables correlated with BMI, but serum copper and serum gamma-tocopherol are the only correlations above .20 and really stand out when you plot them graphically. Here they are plotted with absolute value, so we can compare positive and negative correlations without worrying about the sign:
These correlations might seem small, but remember that about 75% of the variation in BMI is genetic. These small correlations are actually quite big in context. For example, the correlation between BMI and age (people generally get heavier as they get older) is only r = 0.072 — all the correlations pictured above are a bit larger than this.
This seems particularly interesting because while these two correlations are potentially compatible with a number of different theories, we don’t think there’s any theory of the obesity epidemic that would actually have predicted these correlations in advance.
Sadly we cannot compare copper and gamma-tocopherol directly since, as you will see in a minute, they cover different years of the dataset. But we can look at them individually, so let’s do that.
The NHANES only collected serum copper in three datasets: 2011-2012, 2013-2014, and 2015-2016. But these provide three mostly independent replications, and the same relationship is found all three times.
Here’s that relationship with both variables log-transformed. The log transformation makes the visualization clearer, but the correlation remains pretty much the same:
And here’s the distribution of serum copper levels in those three datasets:
Serum copper levels are lowest in 2011-2012, the first year we have data for. They might be increasing a bit over time, but really there’s not enough years of data to tell.
Elizabeth did a different kind of visualization, where she took a look at the individual deciles for copper (she split people up into 10 groups by how much copper was in their serum) and found that the relationship is even more striking with this kind of analysis.
The people in the lowest two brackets for serum copper levels have median BMIs of 25, and around 70% of them have BMIs under 30.
In comparison, higher levels get you a higher median and a bigger spread — the top two brackets for serum copper levels have a median BMIs over 30. People in the top 20% for serum copper are on average obese. This is very weird.
Finally, if you put serum copper in a regression model with age, gender, ethnicity, and education, it remains significant and the coefficient suggests that serum copper levels account for about 3.9 points of BMI difference across the normal range of serum copper levels. The model as a whole explains about 11% of the variation in BMI in this dataset.
You might think that this correlation is driven by people with another condition that manifests as high serum copper, like kidney disease. But in fact, around 80% of people in this sample are within the normal range. If we look at just the people in the normal range (62-140 μg/dL), we get almost exactly the same correlation with BMI, r = 0.241. It seems like the BMI / serum copper relationship exists within the normal parameters and the general population.
Copper does a bunch of stuff in the human body, so looking at its biological role does not narrow things down much at all. Per Wikipedia:
Copper is incorporated into a variety of proteins and metalloenzymes which perform essential metabolic functions; the micronutrient is necessary for the proper growth, development, and maintenance of bone, connective tissue, brain, heart, and many other body organs. Similarly to some other divalent ions, copper strongly interacts with lipid membranes and is involved in the formation of red blood cells, the absorption and utilization of iron, the metabolism of cholesterol and glucose, and the synthesis and release of life-sustaining proteins and enzymes. These enzymes in turn produce cellular energy and regulate nerve transmission, blood clotting, and oxygen transport.
One way to interpret this could be that serum copper is not directly related to BMI at all — it could be that there’s something else that makes you obese AND messes up the control system that should keep your serum copper levels in the right range.
This is supported by the fact that the correlation between BMI and dietary copper is negative and nearly zero (r = -0.035). (Though also bear in mind imperfect collecting methods — all the nutrition measures are based on dietary interviews, so the estimates of elements are probably not very accurate.)
We brought this finding to a doctor we know, hoping he could shed some light on the result, but he was also pretty mystified. We also brought it to a biochemist, and she said, “Woah!!” (She also pointed out that copper is kind of an unusual ion because it has a couple of different charge levels.)
With the help of these colleagues, we started looking through the literature, and it turns out there’s already a small body of research out there about serum copper and obesity. We’re not even the first researchers to notice this in the NHANES data — this team of Chinese researchers wrote about serum copper and obesity in the NHANES data in 2017. But most of these papers haven’t gotten much attention.
The oldest source we saw was this paper from 1997, by a Turkish team studying children in Turkey. They found that serum copper concentrations were significantly higher in obese children than in “healthy controls”. They also report a similar relationship for serum zinc. They don’t mention any previous work linking copper to obesity.
This paper from a Tunisian team in 2001 found higher levels of serum copper in a group of obese participants. While they don’t report a correlation, they do mention that people with higher BMIs tend to have higher serum copper levels.
This paper from 2003, by a team from Kuwait University, found that serum copper concentration was associated with BMI (r = 0.220, p < 0.001). That’s really similar to the correlation we see in the NHANES data, r = 0.240. They also report some other relationships between leptin (one of the hormones that regulates fat storage) and various measures, including the serum zinc/copper ratio. It does seem a bit like they’re just picking variables and searching for relationships at random, but it’s still very encouraging to see this weird relationship we found in the NHANES replicated in a Kuwaiti population from almost a decade earlier.
This paper from 2019, by a team at Johns Hopkins, found a “significant positive and element-specific correlation between copper and BMI after controlling for gender, age, and ethnicity. Serum copper also positively correlated with leptin, insulin, and the leptin/BMI ratio.” They actually find a stronger relationship than in the NHANES, r = 0.40.
Finally, this meta-analysis from 2019 looked at 21 previous publications and found that serum copper is generally related to obesity. They speculate that “elevated [copper] in circulation could contribute to oxidative stress disorder that reflects free radical concentrations and antioxidant imbalance” as well as some other mechanisms. That’s nice and all, but it doesn’t really explain why copper levels would be elevated in the first place.
(Of interest to previous discussions, they also found that “in stratified analysis by the detection methods, the results showed that the association of serum Cu and obesity was significantly detected by AAS including flame atomic absorption spectrometry (FAAS) … but not for studies detected by ICP-MS.”)
One thing that stands out here is that many of these papers mention zinc. Copper and zinc are antagonists, meaning they work against each other in the body, so if copper is somehow involved in the obesity epidemic, it would make sense if zinc were involved too.
However, we don’t see much evidence for it in the NHANES data. Serum zinc is not very correlated with serum copper or with BMI, though we noticed that if you put serum zinc and serum copper in a regression model predicting BMI, the interaction is significant (though only p = .004 with n > 5000). The idea that zinc is part of the picture remains interesting but tenuous.
So much for copper.
Γ-Tocopherol (gamma-tocopherol) is a form of vitamin E. Vitamin E is not a single compound, but a family of eight different compounds, four tocopherols and four tocotrienols (and also a secret third form of vitamin E, the synthetic tocofersolan). Gamma is the third letter in the Greek alphabet, and gamma-tocopherol is the third tocopherol in the vitamin E family.
Along with serum copper levels, serum concentration of gamma-tocopherol stands out in the NHANES data for having an unusually strong correlation with BMI, r = 0.230, or r = 0.242 if both variables are log-transformed.
Unfortunately, the gamma-tocopherol data is a little confusing, because NHANES kept changing the names of the serum tocopherol measures and the datasets they were a part of. Just because something is in a public dataset doesn’t mean it’s trivial to organize and make sense of it. If anything, quite the opposite. Let’s quickly disentangle.
In 2001-2002, gamma-tocopherol is in the Vitamin A, Vitamin E & Carotenoids (L06VIT_B) dataset as “g-tocopherol”, and alpha-tocopherol is in this dataset explicitly as “Vitamin E”. Both alpha- and gamma-tocopherol are ALSO in the Vitamin A, Vitamin E, & Carotenoids, Second Exam (VIT_2_B) dataset, but in this dataset they are called “alpha-tocopherol” and “gamma-tocopherol”. These seem to be the same measures taken on a second day of testing, which is kind of neat, but you’d think they could keep the variable names consistent within a single year’s data.
(The NHANES is very rich but, as you might have noticed, rather disorganized. If you 1. have the data / database chops and are interested in cleaning it up, 2. have medical / biochemical / nutritional / etc. expertise and are interested in organizing the measures across years, or 3. are interested in funding a project to clean up and organize this mess so other people can more easily do the sort of thing we’re doing here, let us know and we’ll put you in touch with one another.)
While the naming conventions across these years are very messy, we didn’t see anything to suggest that these variables were measured in any substantially different way in different years. The 1999-2000 dataset seems to be ambiguous, but the others all describe the analysis method as being “high performance liquid chromatography with photodiode array detection”. So we combined all the tocopherol measures across years into single variables that we will use from here on out.
Here’s the overall relationship between BMI and gamma-tocopherol as a scatterplot. As usual, we have log-transformed both variables for a cleaner visualization:
Here it is broken out by year:
As we can see, the relationship seems to be very robust, replicating across five different datasets.
Serum levels of gamma-tocopherol seem to be declining slightly over time. The average was 240.0 µg/dL in 1999-2000 and is only 181.9 µg/dL in 2017-2018. This is certainly one point against the idea that gamma-tocopherol is causing obesity. If it were causing obesity, you would expect gamma-tocopherol levels to increase over time, as obesity has increased.
Here’s the distribution in those years:
Elizabeth used a more advanced technique and found the following:
A 2 variable decision tree for that year sometimes splits up on just Gamma Tocopherol and Iron (only time Iron shows up directly), which is an unexpected level of importance.
Less restricted decision trees put Gamma Tocopherol levels after a split on gender, with a 4 point difference in predicted BMI. Some of the literature suggests that the effect may be gender specific, so that’s interesting.
She’s right: the relationship is stronger for women than it is for men. The correlation between BMI and serum gamma-tocopherol is r = 0.18 for male participants and r = 0.29 for female participants. In a multiple regression, this interaction is clearly significant, p < 0.001.
And like Elizabeth mentions, this is alluded to in some of the existing literature. This paper looking at the British National Diet and Nutrition Survey also finds a relationship between serum gamma-tocopherol and BMI, and highlights it for women in particular. They say:
In older women gamma-tocopherol and gamma-tocopherol:alpha-tocopherol ratios were directly related to indices of obesity. In young men alpha- and gamma-tocopherols were directly correlated with obesity, but gamma-tocopherol:alpha-tocopherol ratio was not.
We mentioned this finding to a physician we know. He didn’t have an immediate explanation for why gamma-tocopherol might be related to BMI, but he pointed out that vitamin E is fat-soluble, so it could just be that if you’re overweight, you have more body fat, which can store more of these fat-soluble vitamins. More fat, more gamma-tocopherol stored in your body, and more in your serum.
This is plausible — anything that gets stored in fat will probably show up in higher quantities in the bodies of people with more body fat. And this is a major possible confounder in general for this kind of big correlational analysis, keep it in mind for future posts (in case you’re wondering, copper and other metals in general are not fat-soluble).
But one strike against the idea that fat accumulation is driving the relationship with BMI is that the correlation between BMI and other tocopherols is weaker.
The relationship between BMI and serum alpha-tocopherol is also significant, but the magnitude of the relationship is much smaller.
Here’s the distribution of serum alpha-tocopherol in those years:
We also happen to have one year of data for delta-tocopherol, so why not. It also has a bit of a relationship with BMI, stronger than alpha but weaker than gamma.
Some of the literature suggests that the ratio of gamma- to alpha-tocopherol is important, and yeah, the ratio is indeed correlated with BMI. Again we can plot it. We removed two weird outliers (their ratios are about 5x higher than everyone else’s) to make the plot a little cleaner, but removing them doesn’t make a difference to the overall correlation:
Here’s the distribution for those ratios in these years:
Again we want to note that the average ratio is going down over time. If the gamma-tocopherol:alpha-tocopherol ratio were causing obesity, you’d expect it to go up over time.
While the gamma-tocopherol:alpha-tocopherol ratio is significantly correlated with BMI, this correlation is smaller than the correlation between BMI and gamma-tocopherol by itself. Does the ratio actually add anything to the picture?
Looks like yes. In a multiple regression predicting log BMI, both alpha- and gamma-tocopherol are significant predictors, and so is their interaction, all p-values extremely small. A model consisting of just alpha-tocopherol, gamma-tocopherol, and their interaction predicts about 7% of the variance in log BMI.
However, in this model both alpha- AND gamma-tocopherol have positive coefficients, meaning a higher serum level of either tocopherol predicts higher BMI. And their interaction has a negative coefficient, meaning that they are less than the sum of their parts.
That’s kind of weird. Here’s our best interpretation. Gamma-tocopherol is clearly associated with BMI. But alpha-tocopherol is barely associated with BMI at all, and while the correlation is sometimes significant, it’s much more tenuous. In a multiple regression, the interaction is negative. This means that the association between gamma-tocopherol and BMI is weaker as people have more alpha-tocopherol in their serum, so this is at least consistent with the idea that alpha-tocopherol has a protective effect against bad effects of gamma-tocopherol (more on causal inference in a moment).
If we include the gender effect we mentioned above, all these variables are still significant, all the interactions that were previously significant are still significant, and the alpha-tocopherol x gamma-tocopherol x gender three-way interaction is also significant. This model explains about 8% of the variance in BMI.
Ok, what are we to make of this?
This review from 2001 is a good starting place for anyone interested in reading more about “the bioavailability, metabolism, chemistry, and nonantioxidant activities of γ-tocopherol and epidemiologic data concerning the relation between γ-tocopherol and cardiovascular disease and cancer.” Here are some quotes that we found especially relevant:
γ-Tocopherol is the major form of vitamin E in many plant seeds and in the US diet, but has drawn little attention compared with α-tocopherol, the predominant form of vitamin E in tissues and the primary form in supplements. However, recent studies indicate that γ-tocopherol may be important to human health and that it possesses unique features that distinguish it from α-tocopherol.
γ-Tocopherol is often the most prevalent form of vitamin E in plant seeds and in products derived from them (10). Vegetable oils such as corn, soybean, and sesame, and nuts such as walnuts, pecans, and peanuts are rich sources of γ-tocopherol (10). Because of the widespread use of these plant products, γ-tocopherol represents ≈70% of the vitamin E consumed in the typical US diet (10).
In humans, plasma α-tocopherol concentrations are generally 4–10 times higher than those of γ-tocopherol (13).
We can compare this to some other sources as well. Here’s an old USDA poster on nuts and seeds as sources of both alpha- and gamma-tocopherol. According to this source, “the highest sources of alpha-tocopherol in nuts and seeds are sunflower seeds, almonds/almond butter, hazelnuts, and pine nuts. The highest sources of gamma-tocopherol are black walnuts, sesame seeds, pecans, pistachios, English walnuts, flaxseed, and pumpkin seeds.”
This page from the NIH says, “most vitamin E in American diets is in the form of gamma-tocopherol from soybean, canola, corn, and other vegetable oils and food products.”
α-tocopherol is the main source found in supplements and in the European diet, where the main dietary sources are olive and sunflower oils, while γ-tocopherol is the most common form in the American diet due to a higher intake of soybean and corn oil.
As far as we can tell, animal fats seem to be high in alpha-tocopherol, but we can’t find great sources on this. If you find one that looks reliable, please let us know.
This seems like remarkably good news for the seed oil theorists, who think that the obesity epidemic comes from the fact that we started using lots of new food oils derived from plant seeds, like soybean, corn, and canola oils. We’ve previously looked into seed oils as an explanation and didn’t find much evidence in favor of the idea, but we don’t think it’s totally implausible.
Seed oil theorists usually pin the blame on linoleic acid as the reason seed oils might be making people obese, but maybe they’re wrong. Maybe it’s actually tocopherol ratios. Maybe they picked the right theory for the wrong reasons — it happens. Or maybe gamma-tocopherol is only correlated with BMI because it’s a proxy measure for how much seed oil you’re eating. Causal inference is hard y’all.
We don’t think that gamma-tocopherol causes obesity. If it did, then you would expect gamma-tocopherol levels (or the ratio with alpha-tocopherol) to increase over time. Instead, they have slightly decreased. And just in general, we don’t think it fits well with the shape of the obesity epidemic. There are big differences in obesity rates between different professions, and we don’t think auto mechanics are somehow getting hugely more gamma-tocopherol in their diet than teachers.
More likely, having more body fat makes you retain more gamma-tocopherol for an unrelated reason, possibly because vitamin E is a fat-soluble vitamin.
You could maybe test this by looking at the serum tocopherol levels of a group that was gaining weight for a known reason, for example people who are starting olanzapine, an antipsychotic that often causes weight gain. If they gained weight but their tocopherol levels didn’t change, that would suggest that adiposity doesn’t have a direct effect on tocopherol levels.
Or, the thing that is really causing the obesity epidemic also happens to increase gamma-tocopherol for unrelated reasons. For example, if linoleic acid is the cause of the obesity epidemic, then you would expect people who are obese to have high levels of gamma-tocopherol, because foods that are high in linoleic acid (like soybean oil) also tend to be high in gamma-tocopherol. Or if lithium is the cause of the obesity epidemic, then maybe people who are obese would have high levels of gamma-tocopherol, if the kinds of foods that accumulate lithium (perhaps pumpkin seeds?) also tend to be high in gamma-tocopherol.
But that said, it would be pretty straightforward to try to eat a diet that’s high in alpha-tocopherol and/or low in gamma-tocopherol, and see what happened. If you started losing weight immediately, that would be quite striking, and you could maybe back it up with blood tests to measure your serum tocopherol levels.
So it’s worth considering if anyone has tried a high-alpha / low-gamma tocopherol (HALGT?) diet already.
The ketogenic diet seems like it might sometimes be a HALGT diet, depending on the fats you use. If you focused on olive oil, almonds, and animal fats (for example), you would be getting a lot of alpha-tocopherol and not much gamma-tocopherol. But if you focused on fat from walnuts, peanut butter, and corn oil, you would be getting the opposite. This would kind of fit with the picture of how keto seems to have amazing effects for some people and basically no effect for others.
It would certainly fit with the Shangri-La diet. This is a “diet” that basically just involves taking two tablespoons of olive oil every morning. Seth Roberts, who developed this approach, claimed that this was enough to totally kill his appetite and make him lose weight, and this is backed up by some other anecdotes (here’s one example).
Well, olive oil contains mostly alpha-tocopherol. Taking olive oil every morning would definitely increase your alpha-tocopherol intake, and depending on how much alpha-tocopherol and gamma-tocopherol you’re getting from the rest of your diet, this might be enough to change your ratio or whatever. But someone who was getting a lot of gamma-tocopherol from other sources might just have their gamma-tocopherol wipe out the alpha-tocopherol from the olive oil, and this might explain why the Shangri-La diet works for some people but not for other people.
What about the croissant diet? We’re not sure. This diet involves getting a lot of fat from butter and other animal sources, so if it’s true that these are high in alpha-tocopherol, then the croissant diet might be a HALGT diet. It would also depend on how much gamma-tocopherol is in the other foods from this diet. The same logic would probably apply for many carnivore-style diets.
Ok, how about the potato diet? Potatoes contain a little vitamin E, and most sources strongly hint that this is mostly or entirely alpha-tocopherol. For example, the USDA says that 100g of potato contains 0.01 mg Vitamin E as alpha-tocopherol, and 0 mg of beta-, gamma-, and delta-tocopherol. Similarly, this U Rochester page says that potatoes contain 0.04 mg “Vitamin E (alpha-tocopherol)” per “1 Potato large”.
This is not much vitamin E — the recommended dose is 15 mg a day. So if anything, the potato diet looks more like a vitamin E near-elimination diet. Maybe the elimination is the real factor, and it lets your body re-balance your tocopherol levels? But aside from that elimination, you are getting just a little vitamin E as alpha-tocopherol. And depending on the cooking oil you use, you might be getting more or less. This perspective suggests that the potato diet might work better with high-alpha-tocopherol fats like olive or sunflower oil and would work worse, or possibly not at all, with high-gamma-tocopherol fats like corn or soybean oil. That at least is an empirical prediction (assuming all these measurements of the tocopherol levels in foods are at all accurate).
Even if the HALGT hypothesis is entirely accurate, though, there will always be lots of complications. Most of the variation in BMI between different people remains genetic. Tocopherols are destroyed by exposure to high temperatures, so the way you cook your foods and the methods used to process your cooking oils might make a huge difference. So maybe cold olive oil has 10x the effect of olive oil that was heated and used to cook food. If it isn’t this difference, it’s inevitably something else. So even if the real answer to obesity is as simple as the ratio of your alpha- to gamma-tocopherol, expect it to also be at least this complicated.
So color us still skeptical but, there’s some evidence pointing in this direction.
How to Achieve Long Term Success on the Potato Diet from Critical MAS. We don’t agree with the conclusions, but this is the kind of criticism we’d like to see more of — disagreement on specific theoretical points that we could maybe try to resolve empirically. MAS if you are reading this, we’re curious to know what you think of the half-tato diet.
Declining Sperm Count: Much More Than You Wanted To Know. People ask us all the time about doing a deep dive on sperm count — now Scott at ACX has done it, concludes that the evidence is mixed but maybe favors pesticides as a cause, “I’m pretty split about how concerned to be”. We are glad he did it so we didn’t have to.
Nearly every scientist who has used mice or rats to study depression is familiar with the forced-swim test. The animal is dropped into a tank of water while researchers watch to see how long it tries to stay afloat. In theory, a depressed rodent will give up more quickly than a happy one — an assumption that has guided decades of research on antidepressants and genetic modifications intended to induce depression in lab mice.
But mental-health researchers have become increasingly sceptical in recent years about whether the forced-swim test is a good model for depression in people. It is not clear whether mice stop swimming because they are despondent or because they have learnt that a lab technician will scoop them out of the tank when they stop moving.
Obesity seems like it would be bad for your health and would make you more likely to die from things like heart attacks. But many studies find no effect of BMI on mortality — this is sometimes called the obesity paradox. However, a new paper argues that this can be explained by simple statistical biases. The paper concludes, “the mortality consequences of overweight and obesity have likely been underestimated, especially at older ages.”
But academia … does something different. Like my yoga teacher, it affirms what so many of us wanted to believe about ourselves: that we’re good enough, smart enough, potential-filled enough, to go to grad school. Maybe it started when you wrote a paper you were particularly proud of, and your professor told you, off-handedly, “maybe you should think about grad school.” Maybe someone else in your life — the parent of a friend, someone you nannied for, your parent — told you the same. When my undergrad professor told me as much, it was like someone had unfogged the windshield of my life: oh, yes, there’s the road in front of me! Everyone I met in grad school had some version of this story.
“Another thing I don’t like, not only about Kabul but broadly about life after the fatha, are the new restrictions. In the group, we had a great degree of freedom about where to go, where to stay, and whether to participate in the war.
However, these days, you have to go to the office before 8 AM and stay there till 4 PM. If you don’t go, you’re considered absent, and [the wage for] that day is cut from your salary.”
I took my new revelation to a friend older than I, a game designer who’s been involved with internet technology since the beginning. He too was amazed by my confession, but from a different angle I’d never considered: one of an internet historian. “I think you made the first true meme,” he told me. We looked through lists of the earliest internet memes, and although several preceded The Dancing Baby (like the Hamster Dance), their popularity had remained contained within the internet. The Dancing Baby was the first meme to truly permeate meatspace.
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, 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.
: 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.