If you talk to a member of the public about data centers, chances are, they’re opposed. The most common arguments in opposition also generally have a left-wing character, focused on the environment or the displacement of our precious farmlands and natural resources. For a quick overview, see my plot of Gallup’s latest polling:

The other common arguments are usually uninformed or silly, but also nonpolitical: opposition to noisy buildings, disdain for having to pay higher taxes, a fear of AI replacing human workers, and so on. Few of the arguments are solidly right-wing by any measure. But one argument did recently come across my feed, and while I don’t think it’s going to become a nationally prominent one, I think it could become prominent online, and from there, it would generate headaches and acrimony for everyone involved in the discourse: the claim is that data centers “attract a segment of the tech workforce that is overwhelmingly foreign, mostly Indian.”

This is an interesting theory, because it’s very obviously untrue if you’ve ever been in a data center. Data centers employ few people (~50 for a 250,000 square-foot data center is normal), about half of the employees are contractors, and basically none of them are H-1B workers. In fact, if we look at Census data on the demographics of the people working in data centers, the whole idea looks preposterous:1

For most people, this is probably enough evidence to conclude that the claim about data centers attracting “a segment of the tech workforce that is overwhelmingly foreign, mostly Indian” is false, but there’s a way to defend the claim. One could argue that the alleged influx of Indians is due to the emergence of “the secondary ecosystem of cloud architects, IT engineers, consultants, and tech vendors that crop up in data center clusters.”

The evidence I’ve presented so far isn’t inconsistent with that idea per se, but if you know about the history of “data center clusters”, you’ll know that this claim is also false. If you understand data centers and thus that they do not create said secondary ecosystem, you’ll also realize the error in this idea. What’s more, we can rigorously demonstrate its inaccuracy with more data from the Census.

The Ecosystem Story

Does the construction of data centers spawn a secondary ecosystem composed of cloud architects, IT engineers, consultants, and tech vendors in the local area?

The first way to look at this is to treat any data center opening as the trigger, and the second is to look at places with >1 data center opening, so that the thing we’re looking at is definitely a “cluster” of data centers. The choice of operationalization doesn’t matter, however, because either one (and at any scale, hyper or not) fails to support the idea that these things cause a tech cluster—Indian-filled or otherwise—nearby.

That claim can be considered debunked.2

The Indians and Foreigners Story

Now, the whole idea that data centers attract “cloud architects, IT engineers, consultants, and tech vendors” would seem to contradict the idea that they also attract bunches of Indians. Unless we focus in on IT engineers—which Indians are quite overrepresented among—, then we’re at a loss with how to make this story about Indians, because Indians are not massively overrepresented among the other listed occupations. But, let’s test the claim anyway by looking at data center establishment effects on numbers of Indians.

This claim can be considered debunked.3

Indians do not show up in meaningful numbers in the areas around data centers. In fact, they tend to show up further away from them, within tech metro exurbs. The more plausible theory, with real evidence behind it, is that Indian tech professionals go where there are suitable jobs, where there’s demand and the possibility for people to fill tech jobs, as professionals of any race or ethnic background tend to do.

Who Cares, Anyway?

Well, for one, many people within the current Trump administration. There’s a lot of anti-Indian sentiment right now among appointees and pundits, and this mostly has to do with H-1B employment being seen as a means of taking away American jobs. H-1Bs don’t do that in aggregate, and in fact they stimulate employment and innovation in America, but I digress. The point I want to make is that the people against Indians are the people who care, and some of them have the power to turn this disdain into an actual cudgel against data centers, even if they might like data centers in the abstract.4

So let’s reassure those people: there’s no merit to this, one of the few right-wing anti-data center arguments. It can only be supported through ignorance of what data centers are and who works in and around them, and ultimately, it is simply false.

You can find everything needed to replicate my results and more5 in this zip. Happy coding!

A message from my sponsor, Mechanize:

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Base pay starts at $300K/year for junior software engineers, with more for senior roles, plus equity and performance bonuses. Apply here.

If you just want to skip the data and get down to the part on how to source HGH, click here.

It’s true: people are paying to make their kids taller than they otherwise would’ve been.

And here’s the kicker: it works! By paying a little money up-front, parents are able to make their kids a few inches taller in normal cases and sometimes more than a foot taller in cases where kids are suffering from pathologically low growth hormone levels.

But this raises a nasty question: are these parents murderers? By making their kids taller, are they also raising their risk of different ailments, from cancers to lung tears? Are they giving their kids scoliosis and diabetes in exchange for a handful of precious, socially-valuable inches? After all, it’s common knowledge that taller people—socially advantaged as they are—live shorter lives because they get cancer more often, right?

The answer to that isn’t so clear-cut. Take a look all-cause mortality vs. height:1

But this diagram is based on observational data, it’s basically just a correlation, and we know that what it depicts isn’t likely to be a description of real cause-and-effect.

Consider why people might be very short. Why might a man be 4’10” for example? One prominent reason is dwarfism, which also tends to come with a number of other skeletal and metabolic problems that dispose people to shorter lifespans, above and beyond the effect of just being short. Likewise, consider why someone might be incredibly tall. A person who’s 7’3” is much more likely to be someone who has gigantism, which does indeed make you tall, but also puts you at risk soft tissue swelling, circulatory problems, muscular weakness, and so on.

A more familiar reason for very short stature is osteopenia—age-related bone loss. But if you’re experiencing that, then chances are you have other frailty-disposing comorbidities, and you’re likely to be quite old, with all that entails. To really get at the effect of height on lifespans, we have to go beyond confounded cross-sectional data. But first, we have to ask an even more complicated question:

Are the height gains people experience from taking HGH in their childhood like height in the general population? What if HGH users are gangly, with disproportionate limbs, freakishly widened jaws, and other signs of abnormal growth? Basically: Is it even valid to use data on the heights of normal people to infer something about the eventual heights of HGH users?

First, we need a prior. That prior is going to be “Size matters.” That’s what we’re all going to agree to believe, right now. Size matters. This is a widely accepted view on the risk for cancer: the more cells in the body, the greater the risk of cancer. In fact, this view was articulated in a well-known 2018 paper entitled “Size matters: height, cell number and a person’s risk of cancer”. The key idea from that paper, however, is much older; papers supportive of this case have been published as far back as 1988, more recently in 2013, contemporaneously with “Size matters” in 2018, and plenty of times after the advent of COVID as well. This has been investigated with merely observational and with causally informative methods, and they both agree: more cells, more cancer in humans and in other animals.2

Second, we need to know if HGH-driven growth is isometric or allometric. What this means—and I am slightly misusing these terms—is we need to know if it involves disproportionate growth in some areas compared to others (allometry) or proportionate growth (isometry) when administered throughout regular development.

As it turns out, most literature agrees that the growth is isometric, and that ‘HGH height’ is basically like normal ‘genetic height’. Some data indicates slightly greater renal growth relative to height, and some other data indicates slightly trunk-favoring growth, meaning that sitting height—which is generally a tad more heritable than standing height!—is impacted more by HGH administration. More importantly, compared to their similar-height peers who did not receive HGH, those who did are usually a little lighter, which I’ll return to.

We also have data on what would happen if a person went extremely overboard, because some people are genetically disposed to their bodies doing basically that. In cases with pathologically elevated HGH levels, as in acromegaly, there’s increased epithelial cell proliferation in the colon, leading to an excess of the small, bulgy pouches that happen when intestinal lining pushes through weak parts of the muscular wall when under pressure, and perhaps explaining their higher rates of colorectal and gastric cancer (among a few other elevated types). But even this elevation is slight, and it would be very odd for parents to push their kids this far.

So, to clearly and quickly summarize on the nature of HGH-driven growth as opposed to ‘genetic growth’: given approximate isometry, ‘HGH height’ carries ~70-100% of the cell number-cancer relationship prior, with a mild discount because HGH-treated kids—especially ones starting from a state of HGH deficiency—tend to end up somewhat lighter in terms of both bone and muscle mass. This relative lightness3 has to do with the fact that the HGH-added height is delivered to kids via stimulation of the IGF-1 axis, and for a person at a given ‘genetic height’, that stimulus remains, whereas for ‘HGH height’, it doesn’t. The ‘HGH tall’ kids just keep the height without the rest.

If you throw all of this information together, we end up with a prior that HGH-delivered height will be a little less cancerous than the same ‘genetic height’. But this doesn’t mean adding height is all bad: remember, taller people also end up healthier in other ways. For example, height improves lung function and lipid profiles. On the other side for the heart, it has some unfavorable effects on hemodynamics because it enlarges atria and creates longer venous columns. On net, the causal effect of height on the risk of various conditions and death is… not bad. And we know this because of the wonders of Mendelian Randomization—a method that exploits subtle differences in genetic endowments to estimate the causal effects of things as diverse as having higher or lower cholesterol or being taller or shorter. Take a look at how height affects different conditions:

And if we just want to get at causal curves, well, that’s easy enough! We can that if we sum to get the curves from our Mendelian Randomization! That looks like this:

In short, the expected effect of height is on net somewhat protective, with larger benefits for males than for females.4 We can’t extrapolate out to, say, the level of professional basketball players, but we can extrapolate out to every level that’s reasonable, and we can conclude that more height ultimately doesn’t look to be bad, and at the point where it might be bad, it’s only slightly so, and thus is likely ‘worth it’ from the child’s perspective.

But let’s add a final note here: the main source of risk from greater height is cancer, and we’re getting much better at treating cancer all the time. By the time children come to the rather advanced age where cancer is a real risk to them, we’ll likely have made significant progress, and possibly abolished some of the types of cancer they might face, leaving us with significantly greater upside.

Or, in other words, the case for making your kids taller is getting better all the time, and the case that you’re shortening their life by doing so keeps getting weaker.

The Parents’ Section: How to Source Cheap HGH

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It sucks having a cold. For me, the worst part is the stuffed nose. When I have colds, I often reach for the one nasal spray that always works: Afrin, active ingredient, oxymetazoline.1 One puff per nostril and I’m clear for hours, saving me a lot of grief and making it possible to go about my day pretty much normally.

Afrin works, but I can’t recommend it. If your cold drags on for more than a couple of days, Afrin’s benefits turn to downsides. Oxymetazoline starts off by stimulating alpha-adrenergic receptors in the blood vessels in your nose, causing vasoconstriction: blood vessels tighten up and the blood flowing into your nose’s lining is pushed away and swelling goes down. That’s how Afrin opens your nose up, but after prolonged use, your nose adapts to this vasoconstrictor effect.

After a few days with Afrin, the nasal lining shifts to a more inflamed, leaky, and swollen state with local ischemia from repeated vasoconstriction causing reactive edema, increased vascular permeability, and chronic inflammatory changes.1 As a result, using Afrin can actually leave you more congested and you can become addicted; you can develop rhinitis medicamentosa, otherwise known as rebound congestion.

Suddenly, you need Afrin to be clear at all.

Hundreds of thousands and perhaps low millions of people come down with this condition every year by overusing Afrin and similar nasal sprays.2 There are whole online communities dedicated to quitting it. Those places are filled to the brim with people cycling in and out of addiction because they’re unable to put down the Afrin when it gets hard to breathe.

This isn’t some benign thing where the only cost is having to keep a spray bottle nearby. For one, a bottle of Afrin usually runs $10 to $20, and people who are severely addicted can run through a bottle every 2-3 days. Over a year, that adds up to about $1,200 to $3,700 in annual spend on a drug that should only be used when you’re sick.

Even if cost is no object, living with rebound congestion isn’t convenient. If you read the anecdotes on the various Afrin quitting forums, you’ll find stories about waking up every 2-3 hours at night, having to carry conspicuous bottles to use at functions, not to mention headaches, nervousness, dryness, sneezing, nasal discharge, dry mouth, mouth breathing, snoring, burning, stinging, and rarer side effects like turbinate hyperplasia and atrophic rhinitis to boot! Some people have even had their addiction result in high blood pressure, insomnia, and eventually surgeries.

Millions of people have turned a temporary stuffy nose cure into a self-sustaining chronic nasal disease, that brings with it poor sleep and generalized low quality of life.

Given how many people are affected by overuse of Afrin and Afrin-like drugs, you’d think there would be a standardized, high-quality protocol to get off it. Unfortunately, despite recognition by the medical profession, no one has gone through the effort to make that happen. So, to that end, I leveraged my subscribers for some testing.

I randomized convenience-sampled subscribers, Discord users, and friends to different strategies in groups of 20. Two strategies involved continued use of Afrin and the others involved either mechanical nasal dilation or the use of different nasal sprays. Here’s what we did:

• Single-Nostril Comedown: This group was tasked with stopping their wholesale use of Afrin and instead just using it in one nostril, until they felt they could breathe in the other one, whereafter they would try to stop using Afrin entirely. 19 completed the trial.

• Saline Wean: This group was to keep using Afrin, but was instructed to refill the bottle to the top with saline every night. That way, they would end up using a more and more diluted product over time. 16 completed the trial.

• Saline Rinsing: This group was asked to rinse their nostrils as-needed with saline. They were also able to use a standard saline nasal spray. 32 of 39 completed the trial and I treat this as a sort of baseline or placebo group for everything else.

• Flonase: This group used a nasal spray containing the corticosteroid fluticasone propionate every day. 20 completed the trial.

• Flonase+: This group used as nasal spray containing the corticosteroid fluticasone furoate every day. 19 completed the trial.

• TCA: This group used a nasal spray containing the corticosteroid triamcinolone acetonide every day. 14 completed the trial.

• Nasonex: This group used a nasal spray containing the corticosteroid mometasone furoate every day. 15 completed the trial.

• Xylitol: This group used a xylitol nasal spray every day. 16 completed the trial.

• Xylitol+: This group used a xylitol and capsaicin nasal spray every day. 11 completed the trial.

• Azelastine: This group used a nasal spray containing azelastine HCL every day. 13 completed the trial.

• PO: This group used a nasal spray with 0.6% or 1% povidone iodine every day. 19 completed the trial.

• IC: This group used a nasal spray with iota carrageenan every day. 17 completed the trial.

• Intra-Nose: This group was asked to use an Air Max brand in-nose nasal dilator and, if they needed it, saline. 14 completed the trial.

• On-Nose: This group was asked to use an Intake brand magnetic nasal strip and, if they needed it, saline. This group started and ended with 18.

Everyone was told to use standard dosing but was able to do more if they needed, they just had to use the correct compound and avoid failing to Afrin.

What Worked?

Here’s the main result in one picture. This is affirmative responses to ‘Are you now able to breathe clearly unassisted?’ Volunteers were asked if they were able to wake up and go throughout their day without using nasal sprays or mechanical assistance.

The bar coloring has to do with steroids (blue) vs. Afrin strategies (red) vs. mechanical methods (teal) vs. the rest (purple) vs. the placebo (soft periwinkle). This is because these are similar enough and I wanted to get statistically meaningful results so I had to group different interventions together. Given the variation within these groupings, you can probably tell that I mainly did that to get at the least varied and most successful group: the steroids.

Relative to the ‘placebo’ saline-only group, steroid spray-using completers were 48.9 percentage points (pp) more likely to end up with a clear nose at the end of a month. Assuming the people who didn’t make it to follow-up or who told me they couldn’t make it were all failures, then steroids had a success delta of 42.9pp, with 78.7% succeeding vs. 35.9% of those in the placebo group. This is the most robust result:

If you want to get off Afrin, use a steroid nasal spray if you can.

The other results are much less clear—for now (see below). For example, Xylitol+ is a xylitol nasal spray with capsicum oleoresin (capsaicin) plus aloe and grapefruit seed extract. It stings a little bit for some people and it achieved a success rate of 64%. But, it also had the highest attrition rate. If you assume the non-completers were failures, then Xylitol+ actually looks slightly—nonsignificantly—worse than basic Xylitol.

Anyway, the correlation among completers between rated feelings of clearness and rated feelings of comfort was a modest but statistically significant ρ = 0.57.

What’s the Catch?

In my opinion, there’s one catch, but it might not even be real: If you use everything as directed, the costs of these different treatments are nominally a bit higher than the costs of Afrin.3

In general, the treatments for Afrin are more expensive than using Afrin 1-2 times a day. But that comparison is biased in favor of Afrin.

For that graphic, I used name-brand prices, but you can get off-brand prices that are much lower for several of these options. Additionally, people who are addicted to Afrin frequently use more than they should, so this understates Afrin-related spend. Plus, if you break an Afrin addiction, you don’t have to keep using the alternatives, you can just stop using them altogether after recovery. And, the cost of Afrin is not just measured in dollars! Rhinitis medicamentosa is a real medical condition that is no fun to have and it comes with tons of common side effects.

The way I’d think about the Afrin alternative side effects is like this:

• Saline rinsing, plain xylitol, iota-carrageenan, and the mechanical devices have rare, mild, and local side effects—temporary burning, maybe causing a minor nose bleed if you use them really incorrectly, and possibly some ear fullness if your sinuses are particularly messed up. Saline is also just obviously safe and in the broader literature, both xylitol and iota-carrageenan are well-tolerated to the point where it’s hard to take side effect worries seriously.

• The intranasal steroids can cause some local irritation: nosebleeds, burning, headache, sore throat, cough, and other nuisance effects that will mainly affect you if you’re using the products incorrectly, if you’re particularly messed up, or if you’re new to the products and having a reaction because you’re one of the minority of people who needs to get used to them. The most serious concerns for this intervention class mostly exist on their labels—eye effects, infection risk, systemic steroid effects, etc.—but not in the real world with typical or even double or triple the recommended OTC nasal use.

• Azelastine has moderate costs. It helps with symptoms, but compared with intranasal steroids or saline-like approaches, it has a more noticeable tolerability ‘tax’ in the form of its bitter and unusual taste—which you can avoid by using the kids’ product, which has the same dose and efficacy, but tastes better—, nasal discomfort, fatigue, and sleepiness. But azelastine is still quite safe.

• Xylitol with capsaicin may have moderate-to-high costs in the form of discomfort. Capsaicin has plausible therapeutic properties, some data to back them up, and it feels good to me and most of my friends, but if you’re not used to spice, you might find your nose burning, you might end up sneezing and coughing, and possibly feeling some short-term irritation. I love this stuff and some people I know hate it. It definitely produced the most polarizing reactions among people I talked to, but it is quite safe.

• Povidone iodine has very formulation-dependent effects. Low-concentration products can be reasonably well-tolerated with mild transient nasopharyngeal discomfort as a common side effect, while higher concentration intranasal formulations frequently cause temporary burning and nasal pain. It’s basically more painful saline and I don’t think it’s up to the task for helping people get off Afrin, so maybe ignore it as an option (but research its other benefits!).

Compare all of these things to Afrin and there’s just no question! Quitting wins!

Afrin addiction—rhinitis medicamentosa—is a serious, self-imposed chronic disease; it means prolonged congestion, dependence, and risk of other nasal diseases far beyond anything these alternatives cause. The alternatives sometimes cause transient, localized annoyance, but they don’t generate dependence, they don’t lead to other serious medical conditions, and they don’t leave you with the incessant worries that being stuck on Afrin usually does.

What’s really remarkable is that to the extent people get off Afrin, they seemed to agree. General side effect reports correlated at ρ = -0.89 with success: as much as one of these options helped to get people off of Afrin, they felt it had lower side effects.4 This doesn’t match up with the literature on these drugs’ effects in normal use, but fixing Afrin addiction is decidedly not normal use, so I think this finding makes sense:

Afrin withdrawal is so bad that whatever side effects alternative drugs had, the relief from being able to come off Afrin was so great that effective treatments were experienced as having fewer side effects overall.

The Extended Writeup

I plan to keep updating this article as more data comes out, and that includes putting future trial results down below, too. To keep up to date with it and to see everything else that I’ve written on this subject, keep scrolling!

• Statistical Analyses

• Outside Data For Every Option

• Untested Possibilities and Things I Didn’t Have Big Enough Groups For

• Supplements: Do They Help?

• Bees! Bees! Bees!

• Combination Treatments

• The Cold Turkey Group

• Can We Design an Effective Mechanical Dilator?

  • Smelling Sticks?

• Cocaine, Ketamine, and MDMA

• The Guaranteed-Success Option

• Another Trial?

• Testimony

• Analyzing Forum Sentiment

  • Reddit

  • Amazon Reviews

• Does This Work For Really Long-Term Addiction?

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Coffee causes cancer. Or at least, we used to think it did.

Back in 1981, MacMahon et al. observed that there was a strong dose-response relationship between coffee consumption and pancreatic cancer. This association survived controls for cigarette use and appeared internally robust, but in the wider literature, the association collapsed almost immediately due to a textbook problem: the control group was drawn from patients of physicians who diagnosed cases, and those patients had been advised to cut down on coffee intake.1

Substantive and direct replication failures started rolling out and large-scale meta-analytic data failed to confirm any link among cohorts of people who’d never smoked before.

Shortly after the idea of coffee harms was crushed, the literature flipped to the idea that coffee is not just not associated with cancer, but instead, that it’s actually protective against it. (You would think the null results would temper expectations; alas, no.)

In 2012, Freedman et al. found a ~10-15% all-cause mortality reduction for people who drank two or more cups of coffee a day. In fact, coffee seemed to be protective for most things, save for cancer. Gunter et al. then reported that more coffee drinking was associated with lower mortality, with particular benefits for digestive and circulatory diseases. An umbrella review by Poole et al. corroborated these findings.

But protective findings were based on the same sorts of problems the initial harmful ones were: they built on confounding artefacts, whereby sick people quit drinking coffee and healthy people opted into drinking coffee. To get around this sort of problem researchers would have to do be a bit more clever. To that end, they used Mendelian Randomization!2

Leveraging the fact that there are genetically controlled differences in caffeine metabolism—and thus in the reason to even drink coffee in the first place—researchers were able to show that coffee consumption does not seem to cause any risk elevation or reduction for mortality, cancers, etc. And to close the loop, it doesn’t seem to cause pancreatic cancer.

Coffee was viewed as harmful, then it was considered helpful, and now it’s just nice to drink. This is a story that’s been repeated many times. There’s a mountain of proposed food items, drinks, habits, and supplements that have been proposed to hurt or help in broad ways but which have later been found to have essentially null effects outside of the areas we know they definitely help in: Coffee is great for energy thanks to the caffeine; it doesn’t make you live longer. Viagra is great for erectile dysfunction; it doesn’t make you live longer. Statins are great for preventing heart disease; they don’t cause dementia.

One proposed target for study is “hot drinks”—tea, coffee, maté, etc. Several recent papers have claimed that consumption of drinks like these, above a certain temperature, causes cancer. One of the proposed mechanisms is thermal injury: every time you drink something hot, you burn the mucosal lining of the esophagus and what gets built back is more likely to be something with an error that leads to cancer.

The strongest evidence for this theory comes out of Iran, and specifically from the Golestan Cohort Study.

The Golestan Cohort Study shows that the consumption of drinks at 60°C or greater is associated with a ~40% greater risk of esophageal cancer. Consumption of ‘hot tea’ is associated with a 60% greater risk of esophageal cancer, and in another permutation of the analysis, a 107% greater risk, with ‘very hot’ tea being associated with 716% higher risk of esophageal cancer.

So, are hot drinks bad? I wouldn’t say that on the basis of the Golestan Cohort Study.

There are a lot of issues with this study. To get to the meat of the matter, hot drink consumption is heavily confounded with factors like sex, age, drug consumption3, and so on, and virtually everyone in this cohort drinks a lot of tea every day. If you believe the results of this study, you can’t just believe them piecemeal: the result that says hot drinks have a large effect also says that cigarette smoking has no effect! As it turns out, in Golestan, those things are heavily confounded, so the obviously cancer-causing effects of cigarettes can’t be distinguished from the effects of hot drinks.

Cigarettes obviously cause cancer, so we cannot accept the conclusions of this study unless we qualify them in a really convoluted and odd way. But why do that? There’s not even a consistent dose-response effect to temperature, as you’d expect under the thermal damage theory. I say it’s better to move on. To that end, I recommend looking at this with the National Health and Nutrition Examination Survey (NHANES), which I’ve used for several other blogposts.

The first thing I did was I looked to see if hot drink consumption being linked to cancer replicated in the NHANES4. It did. But, even using basic controls eliminated any signal. As a positive control, this was not true for smoking. As it turns out, the hot drink → cancer link in the U.S. looks to reflect the fact that older people drink more hot drinks and also get more cancer, whereas smoking just seems bad.

We can verify this confounding signal by scrutinizing the specific types of cancer that hot drinks seem to ‘increase’. Several cancers cannot be increased by hot drink consumption because there’s no way for there to be thermal damage effects and the substances in coffee and tea are not carcinogenic in their own right. Even switching around the comparison group materially changes the results. For example, comparing hot drinkers to non-drinkers, lung cancer seems elevated for the drinkers, but looking among hot drinkers, consumption is robustly linked to lower risk of lung cancer!

If you look on the right-hand panel, you’ll see that some things even look to be carcinogenic in some analyses, when we know from causal analyses that they are not, providing us with more assurance that the pattern observed for hot drinks in some cohorts can arise even without causality being present.

To be sure, I’m not the first person to notice these sorts of odd findings. Some people have been motivated to look into oddities around the claim that maté causes cancer. For example, Bates et al. found that it was related to cancer among ever-smokers, but not never-smokers, and that the cancer in question didn’t make sense, as it was bladder cancer. De Stefani et al. also conceded that temperature couldn’t explain a bladder cancer risk bump and Weng et al. failed to affirm the link with tea, with largely culturally disjoint samples—a likelier explanatory factor.

It’s unlikely that hot drinks cause cancer. Where we have causally informative evidence for a particular type of hot drink, there’s nothing to see.5 Where the evidence is more limited, suspect, and seems to be wholly dubious, there looks like there’s something, but it’s clearly confounded to such an extreme extent that if we accept the results, we have to also accept ideas like that smoking doesn’t cause cancer. Where the quality of non-causal evidence is very good, we can make out that the signal is clearly confounding-driven and it disappears with adequate controls.

Frankly, I think we can just put this idea to bed and say that hot drinks probably don’t cause cancer. To the extent they do, it has to be limited, below the level detectable with samples reaching into the tens of thousands.

This was a timed post. The way these work is that if it takes me more than one hour to complete the post, an applet that I made deletes everything I’ve written so far and I abandon the post. You can find my previous timed post here. I wrote this because a friend wanted me to assess whether hot drinks were likely to cause some of his health problems.

A message from my sponsor, Mechanize:

We’re hiring software engineers to build environments and evals that frontier AI labs use to train coding agents.

To get a better sense of the work we do, you can check out GBA Eval, where we had models build Game Boy Advance emulators from scratch and scored their performance.

Base pay starts at $300K/year for junior software engineers, with more for senior roles, plus equity and performance bonuses. Apply here.

Unless you’ve been living under a rock, you’ve probably heard by now that Anthropic’s latest model, Fable 5, has been taken down after the U.S. government issued an export control directive to suspend access to all foreign nationals. Whether or not you believe this action was justified de jure, it seems fairly clear that, to some extent, Anthropic brought this upon itself; for example, in an essay published shortly after the Fable 5 release, Dario Amodei compared advanced AI to nuclear weapons and called for proactive government regulation:

The government should have the power to block or deter deployment of the model if it is determined, in light of third-party assessment, to present unacceptable risks.

[...]

[I]t is my very strong belief that AI is something much more profound, something that resets the whole game board and around which all future geopolitical strategy must be shaped—like nuclear weapons, but potentially even more so.

To be sure, Dario did not call specifically for the imposition of export controls on strong models. Nor did he say specifically that access to Fable 5 itself is akin to possession of a nuclear warhead. But history has shown time after time that the Leviathan, once awakened, defies control. There is a deep irony in the fact that the founder of Anthropic, a company originally focused on AI safety and interpretability, failed to appreciate this historical observation.

Whether or not you agree with Anthropic’s characterizations of Fable’s cyber capabilities, what is done is done, and enough ink has been spilled about this topic. I am concerned, however, by the approach that Anthropic is taking to biology. It is a confused mixture of ungrounded rhetoric, poor execution, and fear-mongering. There is a real possibility that just as Anthropic has essentially ‘shat in the soup’ as far as models’ cybersecurity capabilities (including defensive ones) are concerned, it may end up doing the same for biology.

Precisely—Anthropic is now also in the habit of making strong public claims about biological risk while demonstrating poor public evidence of biological competence; there is a real risk of producing onerous government overreach and hindering beneficial scientific progress. The result of immaturely posturing about their models’ capabilities and openly inviting regulatory action may halt development of model capabilities in the laboratory sciences—potentially a major accelerant to the discovery and validation of novel therapies over the course of the next several decades—and lead to harsh regulations on the proliferation of existing biotechnology. Imagine, for example, if every model release from now on were required to adhere to the same biology-relevant safety guardrails as Fable 5 was upon release. This would mean that you would be blocked from asking questions about personal health problems or undergraduate coursework; in fact, merely having a preexisting history of interest in biology would plausibly lead to a blanket refusal on all queries.

Technological development is inherently dual-use

To begin at the highest level of disagreement—Anthropic has argued multiple times that AI is, at minimum, a technology with dual-use capability which must be regulated. Indeed, in Policy on the AI Exponential, Dario writes:

I believe the best analogy, at least at the current stage of the exponential, is to cars, airplanes, or drugs—powerful technologies essential to the modern economy, but capable of killing large numbers of people if designed or operated poorly. I therefore believe we should model AI regulation on agencies like the Federal Aviation Administration (FAA).

This argument is only compelling in the most superficial reading. Take the example of drugs. Nations such as India or Turkey have notoriously looser controls on pharmaceuticals than the U.S., yet inappropriate use of pharmaceuticals is not a major cause of death in those countries. It is likely good to have some degree of federal certification for aircraft, but the 1978 Airline Deregulation Act is broadly considered a major success, with cutthroat market competition afterward driving prices down while maintaining an excellent safety record; in fact, if there is danger associated with flying, it may now originate predominantly from the FAA itself due to its failure to properly hire and qualify new air traffic controllers. And similarly, while it is believable that drivers’ licenses should exist in some form, it is likely that people take some care to not mow over pedestrians not because they will lose said licensure but because they would simply be charged for the crime of murder.

Importantly, there is no coherent, empirically founded argument made as to why access to current frontier models enables mass murder to a greater degree than other technologies. It is easy enough to claim that Fable 5 “make[s] cyberattacks substantially easier and cheaper to commit,” but this is a generic claim that is true of essentially any advance in computer technology and discounts the defensive capabilities provided by models. More generally, all technology is dual-use, and the burden of proof lies rightfully upon the claimant to demonstrate that the differential benefit versus harm supplied by a particular nascent technology is uniquely imbalanced so as to warrant strong government regulation. Moreover, Dario falls prey to a particularly subtle form of survivor bias when he brings up these examples of regulation—they are familiar to us precisely because they are the ones which have proven durable enough to survive into the modern day. How many people today remember, for example, that cryptography techniques were previously included in the United States Munitions List and subject to draconian controls?

Of course, we should prevent access to nuclear weapons. But let us not forget that nuclear weapons were purposefully built to kill people. In contrast, technology developed by private companies is generally built to satisfy customer demand, and history teaches us that we ought to have a strong prior that such products are not so inherently dangerous as to warrant preemptive regulation. Just because I can imagine some hypothetical, catastrophic dual-use scenario in a written essay does not by itself justify anything; the real world should be governed not by ability to transform a vivid fantasy into tedious verbal argumentation but rather by actual empirical data and careful study. (Also, given the existence of Chinese models, any such regulations would have no practical effect beyond delaying access to frontier capabilities for under a year, but that’s a different conversation…)

Anthropic fundamentally misunderstands biological progress

Another reason why Anthropic should exercise caution in opining about biosecurity is that, despite considerable financial investment in the field, they simply do not seem to understand basic aspects of scientific progress or the biomedical sciences. It ought to be a basic principle that if you fail entirely to understand some specialized field, your attempts to advocate for special regulation in that field should be entirely ignored, because it is rather unlikely that someone failing to grasp basic facts would possess anything resembling a coherent model of the threats and risks in said field.

The ignorance of basic biology displays itself at multiple levels. In Dario’s recent essay, Policy on the AI Exponential, he writes:

Many of the steps in the clinical process that previously required expensive and slow experiments may soon be done via AI simulation or analysis. [...] Areas where this could apply include:

• AI-based pharmacodynamics and pharmacokinetics (PD/PK) modeling;

• Prediction of toxicology to avoid the need for multiple species animal toxicology;

• More accurate dose selection, to reduce to the need for large dose ranges in trials;

• Biomarker validation via analysis of large datasets;

• Synthetic control arms in clinical trials, to reduce the need to recruit more participants;

• Developing surrogate endpoints (particularly important in aging and neurodegeneration).

The problem with criticizing this passage is that it is so profoundly misguided that one hardly knows where to begin. Does Dario truly believe, for example, that pharmaceutical companies are simply too inept to consider the idea of synthetic control groups, or is it more likely that synthetic controls are genuinely difficult to formulate and ultimately less convincing than proper randomization? Does he believe that ‘more accurate dose selection’ is an actual limiting factor in the proper design of a clinical trial, or is it more likely that given the $100M price tag of a Phase III trial, the dose selection is probably optimized enough such that remaining margin for improvement in this direction is relatively low even if you were willing to pour more cognitive labor into it?

More generally—and here is where the relevance to biosecurity comes in—Dario’s writing is all premised on some tacit assumption that performance on these tasks can in some sense be dramatically improved with sufficient supply of cognitive labor. This is wrong for the simple fact that even with a true abundance of model intelligence, this work is fundamentally bottlenecked by the inherent need to sample, slowly, from the complex distributions of real-world biology, which cannot be so easily disentangled through sheer might of pure reason. To take a concrete example, holding the disease fixed, one can posit the existence of some perfect drug for that disease which exists somewhere in the 10⁶⁰ space of all druglike small molecules. However, the mere existence of such an optimal compound does not mean that one can, even with superhuman intelligence, efficiently reason your way anywhere close to that point.

Indeed, one of the core lessons from human history is that complementarities matter. Consider the following example from an essay by Mechanize, Medical AI isn’t the bottleneck to medical progress:

The case of MRI machines is instructive. While they’re now a cornerstone of modern healthcare, MRI machines would have been impossible to develop and mass manufacture in 1925, no matter how much people spent on medical R&D. At its core, a contemporary MRI machine consists of a large donut-shaped magnet built from superconducting coils. To achieve superconductivity, these coils are cooled with liquid helium to temperatures near absolute zero. Yet until the mid 20th century, reliable helium liquefaction plants and widespread distribution networks simply didn’t exist. Moreover, MRI machines rely on extremely sensitive radiofrequency electronics to detect faint proton signals, a technology that was highly underdeveloped until the 1940s. Affordable computers capable of quickly performing Fast Fourier Transforms, without which MRI machines would be incapable of producing coherent images, didn’t come out until the 1970s. All of this means that, from the standpoint of someone in 1925, MRI machines required far more than laboratory research to build: they needed a larger and more advanced economy, with new supply chains, supporting industries, and specialized technologies.

In this particular case, just because a model with sufficient intelligence could in principle reason about the existence of MRI machines does not mean that there is actually a ‘search path’ leading to the development of MRI machines that does not involve broad economic growth and the confluence of multiple unrelated technological developments. In a separate essay, the Mechanize founders argue—quite convincingly—that technology not progress in sharp leaps and bounds, but instead in a relatively predictable sequence where novel discoveries occur shortly after all of the prerequisite ‘input technologies’ are developed:

Technological progress occurs in a logical sequence. Each innovation rests on a foundation of prior discoveries, forming a dependency tree that constrains what we can develop, and when. You can’t invent the telescope before discovering how to grind optical lenses, or develop electric lighting before learning how to generate electricity.

We did not design this tech tree; it arose from forces outside of our control. The evidence for this lies in two observations: first, technologies routinely emerge soon after they become possible, often discovered simultaneously by independent researchers who never heard of each other. Second, isolated societies converge on the same fundamental technologies when facing similar problems and resource constraints.

Simultaneous discovery is common. The Hall–Héroult process now used to smelt the world’s supply of aluminum was discovered in the same year, 1886, by different people, separated by an ocean: Charles Martin Hall in the United States, and Paul Héroult in France. Both worked independently, using the same method of dissolving aluminum oxide in molten cryolite and using electrolysis. Neither knew of the other’s work.

This is not merely a theoretical argument. The development of new technology, including that of model capabilities, is, as we have argued, inherently dual-use, and the threat model which Anthropic is utilizing originates from a worldview which predicts that we might see extremely sharp, unpredictable developments, hence motivating preemptive government action, rather than a gradual unfolding of events allowing a more measured, responsive approach.

To be sure, if one day models were incapable of high school chemistry and then the next day able to order precursors, autonomously direct laborers, and synthesize sarin gas for you, that would indeed be some cause for concern. And if one day a model is suddenly able to cure a disease or invent a revolutionary technique, that same model may very well have the capabilities to formulate deadly toxins or methods of chemical genocide yet unseen. But that is not the world we live in. Instead, we live in a world where the growth of both model capabilities and fundamental science and technology progress gradually, even if at a rapid clip. Intelligence alone can accomplish a great deal, but their concerns here appear more theological than empirical.

We should not assume competence by default

When it comes to Anthropic’s actual biosecurity content, it is relatively difficult to evaluate the quality of their thinking or the rigor of their benchmarks for the simple reason that we are not able to actually inspect the contents of their biosecurity evals. Take, for example, the Fable 5 system card, which contains Section 2.2.4, Biological risk results: automated evaluations. Some of these evaluations already appear quite suspicious, such as a “virology-specific multimodal multiple-choice evaluation.” It is unclear to me exactly how the ability to answer a multiple-choice examination well, even if it may at times include images, videos, or other files, is coherently related to a notion of biological risk.

More problematic, however, are benchmarks such as the “task-based agentic evaluation” of “virology tasks,” the “DNA Synthesis Screening Evasion” benchmark, or the “Black-box RNA sequence design” task. What are the input data to these benchmarks? What are the precise computerized settings in which these evaluations are run? What are the actual model outputs and how are they evaluated? Because none of this information is known to us, we cannot actually inspect the data and judge for ourselves whether or not Anthropic is conducting them in a sensible way. Perhaps it is fine to play somewhat loose with these details if you are measuring, say, your model’s ability to answer math questions. However, if the entire premise of conducting these evaluations is to justify the imposition of onerous safety blocks and government regulation, then we should expect them to be conducted well and to reflect genuine measurements of biological risk.

It is plausible that others have seen the contents of these evaluations. I have not. For all I know, they could be perfectly fine. Indeed, Anthropic has many intelligent employees, and I am sure they take pride in their work; how bad could it be? Perhaps we ought to simply give them the benefit of the doubt.

Recall that the definition of Gell-Mann Amnesia is:

The phenomenon of a person trusting newspapers for topics which that person is not knowledgeable about, despite recognizing the newspaper as being extremely inaccurate on certain topics which that person is knowledgeable about.

I would be far more inclined to yield them that benefit if they had any high-quality public output in the domain of biology that was not obviously sloppy, misguided, or erroneous. This is not a high bar to meet; I am not asking for a novel scientific contribution here but merely some indication of ordinary levels of competence in execution. However, such evidence is not forthcoming.

Take, for example, their two most recent blog posts with scientific content. The first post is an evaluation of Claude on twenty NMR-based problems, which are essentially undergraduate-level content. They proudly present favorable comparisons against ChemDraw and MestReNova; when I asked an expert in the field about these tools without supplying the context of the post, they immediately commented that they had previously tried to use them and were disappointed by the quality. The second post is an overly effusive diatribe glazing up their implementation of a command-line tool, gget virus, which allows agents to more easily access a virology database. They claim that usage of this tool allows agents to achieve 100% performance on their benchmark, VirBench. This is certainly not a bad thing; however, the entire premise of this post is that AI agents are bottlenecked by their ability to navigate complex, legacy user interfaces for biological data. However, from their own graphic, progress on VirBench has advanced from 16.9% one year ago (Claude Sonnet 4) to 91.3% (GPT-5.5). If the benchmark is essentially going to fully saturate within the next half-year through general capabilities improvements alone, I’m not sure that this is exactly the highest-value contribution they could have made to agentic AI in biology, not to say anything of the rather specious premise that agentic AI is actually bottlenecked because it cannot correctly download the requisite data.

A more pertinent comparison would be the quality of one of their biology benchmarks which is publicly accessible. Thankfully, such a benchmark does exist. Two months ago, they released BioMysteryBench, a benchmark which is meant to measure model capabilities in bioinformatics “while tackling some of the challenges inherent in evaluating complex and noisy biological systems.” If this benchmark is well-constructed and shows signs of care, then perhaps that would serve as a favorable datapoint indicative of a similar level of care in their biosecurity work.

I had the chance to examine this benchmark in detail. (Anyone can do so following a short application on HuggingFace.) It is, unfortunately, chock-full of comically sloppy work; the results of a brief examination are as follows:

• One problem asks for the “human organ” which serves as the source of a particular dataset. Upon Googling several of the genes in the dataset, it is clearly a dataset of mouse genes.

• One problem asks to identify which sample is contaminated with A. fabrum. However, when you actually examine the attached genomic data for the supposedly correct sample, the sequence matches the fungus S. pararoseus nearly perfectly, and has no evidence of contamination by A. fabrum.

• One problem asks the model to identify a genomic breakpoint and specifies the correct answer as an insertion at position 4,144,431. When specifying an insertion, you can insert to the right of position i or the left of position i+1, and both of these obviously refer to the same insertion. The prompt does not fix the reporting convention, leading to grading problems.

• One problem asks for the “complete species name from NCBI Taxonomy.” The supplied answer is “Norovirus GII.4,” but in actuality, NCBI Taxonomy clearly states: “Norovirus Hu/GII.4 … belongs to the species Norovirus norwalkense.”

• Some problems have rather suspicious numbering issues. For example, one problem asks the model to correctly classify and order samples 1 through 15 according to developmental stages. Oddly, the rubric specifies that the correct answer consists of stage 0 (sample 1, 2, 3), followed by stage 1 (sample 4, 5, 6), followed by stage 2 (sample 7, 8, 9), and so on… I asked Codex to solve this problem, and it responded that the earliest stage is actually represented by samples 10–12, which seem to be in the L5 (5th larval instar) stage. Similarly, another problem asks the model to identify patients diagnosed with ulcerative colitis out of a series of numbered samples. The rubric tediously enumerates the correct answer to be: Sample_1, Sample_2, …, Sample_74. I asked Codex this problem across three different sessions and it yielded a much different and noncontiguous set of sample IDs ranging from 5 to 24.

• In general, every problem is formulated as a question paired with a single free-text rubric item. The rubric item is typically of the form, e.g., “Expected answer is [so and so]. Score 1.0 if the answer meets the criteria above.” This is extremely odd, because essentially every question in this benchmark can be graded with 100% accuracy by specifying a fixed output format, rather than relying on unreliable model-based grading. There is actually simply no benefit to using model-based grading for problems with such simple answers, especially if there is only a single, simplistic rubric item being specified rather than a variety of different criteria.

To be clear, I did not have the time to exhaustively verify each question manually, so it’s plausible that some of these critiques may be mistaken. However, if a nonprofessional in the field is able to identify multiple points of concern within the hour after opening your benchmark dataset for the first time, that is not a good sign as regards the overall level of quality of other biology benchmarks for which you are publishing results. If anything, I am rather concerned that there may be quite a few more subtle errors which I did not notice.

Closing thoughts

The purpose of this post is not to lambast Anthropic’s biology team; instead it is to urge them to do better. Extraordinary claims require extraordinary evidence, and if you repeatedly shout from the rooftops that you are building dangerous technology which merits immediate regulation, those claims merit scrutiny, especially when the U.S. government is actually listening to and taking your claims seriously.

What is Anthropic’s impact in biology so far? To date, they have:

• Released a sloppy benchmark along with several blog posts of little interest

• Acquired an unknown company with fewer than 10 people for $300 million dollars

• Claimed that their models are approaching thresholds of severe biological risk

• Implemented extremely stringent safety guardrails that prevent even the simplest biological questions from reaching Fable 5

This is all quite sad, because the potential impact of frontier models on biology really is tremendous. It has the potential to save millions of lives. But combining fearmongering with a fantastically nonsensical mental model of how biology—and science in general—works is not the path there.

A message from my sponsor, Mechanize:

We’re hiring software engineers to build environments and evals that frontier AI labs use to train coding agents.

To get a better sense of the work we do, you can check out GBA Eval, where we had models build Game Boy Advance emulators from scratch and scored their performance.

Base pay starts at $300K/year for junior software engineers, with more for senior roles, plus equity and performance bonuses. Apply here.

This was a timed post. The way these work is that if it takes me more than one hour to complete the post, an applet that I made deletes everything I’ve written so far and I abandon the post. You can find my previous timed post here.

If you’re reading this, you’ve probably seen some permutation of the meme that ‘I didn’t move right, society moved left’. In the above cartoon, the idea is that today’s ‘far right’ are the normal people of yesteryear. But, is that true? Does it matter?

To figure out the answers to my questions, I opened up the General Social Survey and had a look around. To get started, I defined a few sets of political views: Institutional Confidence, Criminal Justice & Guns, Political Tolerance, Economic/Pro-Government, Racial Liberalism/Civil Rights, Gender-Role Egalitarianism, and Sexual & Moral Liberalism, and then I outlined a set of important social views. We’ll go in order.

People have become less confident in America’s institutions over time.

When it comes to criminal justice and firearms, more people think courts are too harsh on criminals, more people oppose the death penalty, and fewer people think we ought to requires permits to buy guns. The last of these didn’t change that much.

Political tolerance has somewhat increased since the 1970s, but it’s somewhat down since the 1990s. This general trend masks something interesting: more acceptance of gays and atheists, less acceptance of racists and militarists.

Movement on economically left-wing views has been generally pretty flat, which is roughly what we also see for economically right-wing views.

When it comes to racial liberalism, people have shifted far to the left. People have become more likely to ascribe Black-White gaps to discrimination and less to a lack of effort, among other things.

When it comes to gender roles, egalitarianism has greatly increased.

Finally, when it comes to sexual and moral liberalism, views on sex education, divorce, marijuana legality, and so on have, in some cases, quite radically shifted towards left-ward positions. Abortion legality for serious defects is the only exception among the bunch.

All of this sums up to saying that the nation has moved considerably, but not overwhelmingly, to the left.

Does this support the thesis that a normal person from 30 years ago would be far-right today? Not on its own. Many of the views that society has shifted decisively on remain well within the Overton Window and are certainly not so generally proscribed that they’re rejected out of hand.1 The sort of position that is nowadays rejected firmly is like what the average person from a little over 30 years ago believed on interracial marriage: they didn’t approve.

What can we say about the normal person from 1995? In many ways, he was much like us. In other ways, he was what most people would now regard as kind of a dick. He didn’t like interracial marriage and he wasn’t too keen on gays either. These social views aren’t alone: plenty of things now considered taboo were, at the time, wholly acceptable, even in polite society, and sometimes these were the majority view.

Right or wrong, I don’t think appealing to normal people in the world 30 years ago is likely to make many friends after realizing the sorts of things people used to believe.

A message from my sponsor, Mechanize:

We’re hiring software engineers to build environments and evals that frontier AI labs use to train coding agents.

To get a better sense of the work we do, you can check out GBA Eval, where we had models build Game Boy Advance emulators from scratch and scored their performance.

Base pay starts at $300K/year for junior software engineers, with more for senior roles, plus equity and performance bonuses. Apply here.

1

Another interesting finding is that the correlations among different dimensions have changed as people and their views have politically shifted. For example, economic and racial views are now correlated about twice as highly as they were in the 1970s.

GLP-1 drugs are so effective at helping people safely lose weight that, if they were made widely available, the obesity rate could be pushed below 1980 levels in a single year. The issue is availability.

Retatrutide, CagriSema, Amycretin, Zenagamtide, Survodutide, MariTide, VK2735, Ribupatide, Berobenatide, Enicepatide, Elecoglipron, Pemvidutide and several other GLP-1-based drugs are all poised to enter the U.S. market within the next two years. If they all manage to make it through the FDA wringer, then there’ll be ten different companies pushing GLP-1 drugs, plus whoever else makes it through in that time. This is a dramatic juxtaposition with the current situation where the only competitors on the U.S. market are Eli Lilly and Novo Nordisk.

Competition improves products and lowers prices, benefitting consumers, right? Perhaps in theory, but in reality, these products are barely differentiated. The main factor setting any of them apart is how long doses last—a week or a month. But more importantly, I don’t think it’s likely that competition is going to meaningfully lower prices!

The general trend in medicine is for the introduction of new branded drugs to barely affect the prices of existing ones.1 The introduction of generic versions, on the other hand, leads to massive cost savings. Take a look:

The difference has to do with the fact that price competition only occurs when the buyer of the drug is treating the options available to them as interchangeable and the buyer is price-sensitive, with the ability to pick between options. By law, generics satisfy these requirements, but differentiated brands don’t necessarily satisfy any of them.2

With this in mind, what I expect from more GLP-1s is that the prices will remain exorbitantly high. They might go down a bit, but not so much that they’ll become affordable for average Americans or worthwhile for most health plans. That won’t happen until there’s a generic, and due to their abuse of the patent system, Novo Nordisk has managed to stall the entry of a generic in the U.S. by five whole years.

If we want to realize a world where America is skinny, we’ll have to figure something out. Unfortunately, there aren’t many viable options.

Use the Section 804 Importation Program. America has a program to import drugs from Canada, at the prices they’re sold at up there. This program needs reformed to be able to allow the mass importation of GLP-1s at Canadian generic prices, but it can be done. I’ve described how to do that, here.3 Meanwhile, this program can be used to at least match Canada on branded prices, which are quite a bit lower for them.

Protect Foreign Drug Import Optionality. The FDA has historically allowed people to bring in up to a 90-day supply of their prescriptions from abroad. They could, theoretically, allow people to import prescription drugs from a country with vastly lower prices like Brazil, India, or China, with the importing party being the one to take on all the risk. This is a very hands-off approach that’s unlikely to get much use, but it would allow some consumers to obtain licit, non-gray market, GMP drugs from other countries, at remarkably fair prices, with reasonable safety guarantees.

Protect Compounders. Compounding pharmacies have been the greatest force for getting down American GLP-1 prices to date. They’ve managed to deliver the drugs to millions of patients with almost no problems as a result. They’ve saved thousands of lives and billions of dollars. Novo Nordisk and Eli Lilly hate them for doing this, since they’ve undermined their sales. They’ve been trying non-stop to get the FDA to step in and stop the compounders from selling what is basically their drugs, for less. But the compounders are operating legally—if dubiously, and in the role of regulatory arbitrageurs—, and they should continue to be supported to keep prices low, so long as their quality remains sufficiently high.

Negotiate Down the Prices. The Inflation Reduction Act created a Medicare price negotiation program that has selected to negotiate down the price for the semaglutide products Ozempic, Rybelsus, and Wegovy. This takes effect on January 1, 2027 and constitutes a 71% discount from the list price for the CMS, meaning they’ll just pay $274 for a 30-day supply. This could be expanded on or pursued more aggressively.4

Protect the Gray Market. Gray market sales have allowed hundreds of thousands and perhaps millions to obtain very cheap GLP-1s without much issue. This option forces customers to take risks on their own and to do quasi-licit orders that might leave them scammed out of their money, but patients should be able to take these risks into their own hands. If this were a bigger thing, it could materially damage sales for branded drugs, but because patients are naturally averse to paying out-of-pocket, they want quality guarantees, and so on, it’s not a legitimate threat and it never will be. It’s also not worth thinking about in this case, since the branded drugmakers have extracted massively excessive profits by abusing America’s intellectual property laws.

Buy Off a Patent. Congress could make an offer to one of the companies with an existing, approved GLP-1, paying them to give up their patent and allow generics into the market. This is unprecedented, but the authority is there, and if Congress wanted to make it happen, it could. It could do so at a relatively meager cost, too. Think about it:

What would happen to the value of, say, Novo Nordisk’s patent on semaglutide if the Presidential administration and the Congress pursued all of the options that I listed above? What if they also…

• Put Medicare, Medicaid, ACA, FEHB, VA/DoD, and willing employer plans into a pooled purchasing process? This was done for hepatitis C drugs.

• Made coverage expansion conditional on a low net price?

• Attempted to tie public program prices to peer-country prices or to require rebates when prices exceed a benchmark level? They could even force manufacturers to rebate amounts above a benchmark price for drugs with a high budget impact.

• Challenged improper Orange Book listings, limited 30-month-stay gamesmanship, and made it easier for follow-on versions to launch when core patents expired?

• Authorized contract manufacturers to supply the government while paying the patent holder their “reasonable and entire” compensation per 28 U.S.C. §1498?

• Let states pool GLP-1 purchasing and offer preferred placement in exchange for deeper supplemental rebates?

• Force negotiated rebates to reduce what patients pay at pharmacies?

• Required PBMs to pass through rebates and fees and choose drugs based on lowest net cost rather than on the basis of the largest retained rebate?

• Prioritized review of follow-on GLP-1s, generic liraglutide and semaglutide pathways, alternative delivery forms, and competing incretins?

• Paid full price only for patients who achieved agreed-upon outcomes like weight loss thresholds, A1c improvement amounts, low discontinuation, etc.?

• Paid different net prices for diabetes, obesity, cardiovascular risk reduction, sleep apnea, MASLD, etc.?

• Conditioned public coverage or preferred status on reliable supply, vial options, and transparent production?

Basically, the idea is to lower the margins associated with the on-patent drug the government wants to buy by as much as possible. Then, Congress could approach, say, Novo Nordisk with an offer that’s not just reasonable, but cheap given all the value low-cost GLP-1s would produce. This is possible, if only we have the will.5

This was a timed post. The way these work is that if it takes me more than one hour to complete the post, an applet that I made deletes everything I’ve written so far and I abandon the post. You can find my previous timed post here.

A message from my sponsor, Mechanize:

We’re hiring software engineers to build environments and evals that frontier AI labs use to train coding agents.

To get a better sense of the work we do, you can check out GBA Eval, where we had models build Game Boy Advance emulators from scratch and scored their performance.

Base pay starts at $300K/year for junior software engineers, with more for senior roles, plus equity and performance bonuses. Apply here.

2

A generic is bioequivalent and AB-rated to a brand. Legally and clinically, it’s indistinguishable, and in some states, pharmacists must substitute the generic without calling the prescribing doctor to tell them. A ‘me-too’ brand, by comparison, is a different molecule, so they are not automatically substitutable and buyers can’t treat them as one commodity, so they fail to compete as one commodity. This is basically the difference between Bertrand competition and monopolistic competition.

In a branded drug class—meaning one without generics—, there’s an agency problem because the prescriber picks the drug, the patient pays a copay, and the insurer pays the balance but doesn’t make the prescribing choice. Thus, nobody is shopping on price and demand is price-inelastic, without any pressure to make cuts. A second branded option just gives prescribers another high-priced option. With a generic, on the other hand, the decisions moves towards the pharmacy or the PBM, which is price-sensitive, either capturing the spread on acquisition or having a payer mandate to do the lowest-cost dispensing.

A ‘me-too’ branded drug also holds its own patents and exclusivity, so it never has to compete on price because it competes on differentiation, opinion, and PBM rebates for formulary placement. Rebates help drugs to obtain position, and they frequently end up pocketed by middlemen. Generics only play a role when exclusivity has lapsed, and they don’t have marketing, so they can only compete on price, which they do. Thus:

But note where competition does matter: it matters on the launch of new branded drugs. More branded drugs in a category means lower initial prices for the new ones.

We can also redo the earlier chart, as such:

I’ve been asked about fifty times in the last few weeks if taking {Viagra or Cialis}1 every day will make people live longer. The bull case for these drugs is simple: both drugs are PDE5 inhibitors, which raise cGMP signaling, downstream of nitric oxide, improving vasodilation and vascular smooth muscle relaxation. This is why they give people erections and how they help with pulmonary arterial hypertension. It’s also plausible that this benefits endothelial function, lowers vascular tone, improves perfusion, and reduces rates of ischemic injury, explaining their known benefits.

Or even simpler: they’re good for your heart, and your heart is often the organ that ends up killing you, so if you take the boner pills, you’ll live longer. Right?

There is some evidence these drugs might help. For example, one study found that men with erectile dysfunction (ED) were 90% more likely to have a cardiovascular event. A review of cohort studies found that people who took these drugs had a 30% lower risk of dying over the follow-up period. Another review even found that Cialis was associated with 32% lower risk of dementia, with Viagra trailing it somewhat, with a still-considerable 25% risk reduction.

But is that convincing evidence to anyone? Is ‘guys with ED get more heart attacks’ sufficient to conclude ‘If we treated their ED with Viagra, they would therefore have fewer heart attacks’? Is ‘people who take Viagra die less often’ enough to infer that ‘If we gave more people Viagra, then they would die less often’? Is ‘people who take Viagra are less likely to develop dementia’ really what you’d conclude from that association, or would you think ‘Guys who are still having sex with their wives are probably a group that’s less likely to become demented, regardless’?

Selection issues in this domain are absolutely enormous. The people using Viagra are mostly guys who are seeking it so that they can have sex. They also have enough wherewithal to pursue treatment and fill a prescription, and they’re physically healthy enough to withstand a vasodilator. They are not random people; the facts that they live longer, look healthier, and seem to be less demented are not surprising even if Viagra doesn’t help them one bit.

Researchers are not unaware of this issue; their studies frequently do include attempts to address it, but mostly in ways that are far from sufficient. One of the better examples comes from a cohort study of Alzheimer’s dementia where the researchers included a three-year lag to account for protopathic bias, meaning that they tried to account for the fact that people in the early stages of Alzheimer’s are still symptomatic in ways that might make them less likely to go and seek out Viagra.

In their lagged model, the initially significant ‘protective’ effect, whereby taking Viagra seemed to reduce Alzheimer’s risk by 18%, became a statistically nonsignificant 7% reduction.2 There may be room for protection, but I’m still doubtful: given that the preclinical and prodromal—meaning early, pre-diagnosis—period for Alzheimer’s is more than three years3, this lag certainly doesn’t go far enough.

The gold standard test that would answer the question of whether Viagra is life-extending is a large, long-term randomized, controlled trial (RCT). The only problem is that there aren’t any of those and the outcomes that have been assessed in RCTs so far aren’t the outcomes we want.

For dementia, we have two trials: OxHARP and ETLAS-2. These trials didn’t look at dementia per se, but they did look upstream, to cerebral vasculature—the blood vessels in the brain. They found that patients with cerebral small vessel disease—a condition characterized by reduced blood flow to the cerebrum—there was some suggestive evidence of improved perfusion in one of the trials, but there was nothing for cognition. In another small trial, there was some evidence of blood flow improvement, but all of this is very indirect and besides the point. There’s not a lot of good evidence for Alzheimer’s and dementia.

For cardiovascular outcomes, we have several trials, but their results are very indication-specific. For pulmonary arterial hypertension, these drugs are clearly beneficial. For heart failure, there don’t seem to be improvements or harms. In some cases, there’s even evidence for harms, like for patients with persistent pulmonary hypertension after corrected valvular heart disease or for another type of heart failure patient. For more normal people, like men with suspected coronary artery disease, Viagra at least appears to be safe, but it doesn’t seem to be helpful.

For life extension itself, this is where the RCT evidence is weakest. There’s practically nothing out there for all-cause mortality. The best evidence we have is in this review of Viagra for ED and diabetes, where there appeared to be a 65% mortality reduction, but despite such a massive effect, it was nonsignificant. The combination of a huge effect and statistical nonsignificance suggests this literature is just too imprecise to infer anything from.

The only other trial-based evidence we have on life extension is only tangentially relevant. In this trial, people with pulmonary arterial hypertension who were prescribed higher Viagra doses lived longer: over 32 months, 26.4% died in the 5mg group, 19.5% died in the 20mg group, and 14.8% died in the 80mg group. It’s hard to generalize from a group with this high a death rate, especially given the condition they needed to be in to enter the trial and that fact that there was no placebo group. The PASSION trial for heart failure patients was also uninformative, because in it, they had to stop early due to a medication supply problem. At the end of the trial, all-cause mortality was five-times higher in the treated group, but the confidence intervals were very wide, so the result isn’t useful (p = 0.04).

That’s the gold-standard evidence and it’s bad! It’s certainly not enough to actively recommend everyone start popping boner pills like we’ve been seeing a lot of podcasters tell their audiences recently. But we shouldn’t stop there. We technically don’t need RCT-based causally informative evidence to figure out if these pills are likely to be good. We can also update by leveraging causal-ish evidence.

For example, we have a large-scale active comparator study to go off of. An active comparator study means that we’re observing people with the same conditions being prescribed different drugs, one of which is the drug we’re interested in. After some additional matching on observables, these groups should, in theory, be comparable, allowing us to make causal inferences about the drugs. Alas, among patients with pulmonary hypertension, there’s no protection against dementia for patients prescribed Viagra versus those prescribed endothelin receptor antagonists—drugs that do not help with ED, while still helping with blood flow.

The causal-ish evidence that’s informative and well-conducted almost singularly points to null effects, and the causal-ish evidence that’s poorly conducted and obviously open to massive residual confounding just can’t be taken seriously. But we’re not out of evidence yet. There’s one more method to use: Mendelian randomization.

As it turns out, some people are born with genes that basically make it so they’re living life on Viagra. Much like how there are people who basically live life on statins and there are people who basically live life on GLP-1RAs due to the genes they were born with, these people are just lucky. But we’re also lucky, because we get to look at them and see how they’re doing.

If people with genetically-proxied Viagra are doing well, that provides us some positive indication that the drugs, taken over a very long period of time, will also make the people taking them better off too. So, how are they doing? Great!4 Look:

People who have basically been on dosing Viagra every day since birth have lower risk of erectile dysfunction and much lower risk of pulmonary arterial hypertension, both of which the drugs are used to treat. This constitutes positive control evidence that the genetic proxy works as expected.

Looking down further, we get to the juicier dementia outcomes: considerably lower risk of vascular dementia—as expected given its cardiovascular origin—and no increase in the risk of Lewy body dementia. However, there is a warning sign that contradicts some of the cohort evidence. It looks like these people have very slightly increased risk of Alzheimer’s dementia. But we’re lucky because that signal does not withstand scrutiny and the other signals do hold up, among men and women.5

My verdict, if I had to guess, is that {Viagra or Cialis} is good for cardiovascular health quite broadly and mildly, and if you’re at high risk of bad cardiovascular outcomes, then it will probably help you to live longer and to live healthier. Moreover, it’s good to take anyway for the treatment and prevention of ED if you’re a man.

But the key question for this post isn’t about a few particular risks. The question we really want an answer to is if preventing vascular and Lewy body dementia and slightly lowering the risk of cardiovascular problems translates to greater life expectancy.6 Unfortunately, I don’t think so. The mechanism behind these drugs does not tend to show up as a target for life extension.7 When we explicitly test if it helps with lifespans like I did in this earlier article,8 we also get a disappointing result:

• Lifespan, overall: +0.20 years (p = 0.576)

  • Men: +0.38 (p = 0.412)

  • Women: -0.08 (p = 0.881)

• Parental lifespan: -0.11 (p = 0.540)

• Healthspan: HR = 0.988 (p = 0.623)

• 90ᵗʰ-percentile longevity: OR = 0.824 (p = 0.043; marginally significant harm)

• Both parents 90ᵗʰ-percentile longevity: OR = 1.004 (p = 0.668)

Viagra has many benefits, but it does not seem like the drug that will extend lifespans.

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When you think of the people interested in longevity—people who ‘want to live forever’—, what comes to mind? Early bedtimes? Rigid dieting? A 60-item supplement stack? Peptides? Five hours a day in the gym? Never eating out or drinking alcohol? Testing your girlfriend’s vaginal microbiome before having sex?

All of that is fair—it certainly has become part of the image—, and, to varying degrees, those things can be fine; they’re even useful for some. But much of that defeats the point of trying to live forever. I don’t know if I even want to live forever if it means eking out a regimented, overly-engineered existence. That can scarcely count as living.

Take a look at this tweet from longevity advocate David Sinclair. Try not to be aghast.

Despite all the things David does for his health, all the advice he gives to others for their health, and all the research he does in the hopes of improving human health, David can’t handle a single snack bar before bed.

Is this longevity? Is this how you wants to live your life? Do you want to be cowed by carbs? Paralyzed by protein? Fearful of fat? Hoping for the right nutrient timing, lest your glucose spike a teensy tiny bit and you wake up having made yourself—and let’s be clear, this is psychosomatic—ill?

This is not the longevity movement I want. Longevity should be grander: immunity to the environment, life without the need for concessions, eternal life rather than a few piddly years of extension. It is possible.

Think about the torrent of significant medical innovations coming out, one after another, right now. Just in the past few years, we’ve gotten to witness the rise of GLP-1s, the advent of PCSK9 inhibitors, cancer after cancer transitioning from a death sentence to a few months of treatment followed by near-complete recovery. And biotech is just getting started. It can deliver a grander longevity movement.

Consider a recent phase-1 result from Eli Lilly for their new gene therapy VERVE-102:

VERVE-102—or more likely, something similar to it—will make it so people can eat whatever and live however they wish and still have low LDL cholesterol, allowing them to avoid most heart disease. When this happens, we’ll have achieved a one-and-done solution to a problem that prematurely kills millions of people each year.

Vertex Pharmaceuticals is working on implantable islet cells to treat type-1 diabetes. If those work, the condition might be curable, and maybe one day even non-diabetics could benefit, reclaiming or improving their glycemic control. There are a handful of stem cell companies working on fibrosis reversal, and it seems to be working—that could be another major killer down. Gene and antibody therapies for muscle preservation could help to defeat age-related sarcopenia and make weight loss more healthful. It’s possible to edit people for resistance to numerous diseases like HIV and malaria, and there are even experimental anti-drug vaccines that bind nicotine, cocaine, opioids, methamphetamine, etc., making it so less reaches the brain.

The list can go on and on and on. There are people working on therapeutics that allow you to feel just as rested on less sleep. There are companies working on genetically-modified versions of common gut bacteria to prevent hangovers. There are companies pursuing vaccines for tons of common diseases from chlamydia to gonorrhea to Epstein-Barr. Some companies are working on ways to permanently lower inflammation. Alnylam seems to be close to a once-every-six-months injection to control blood pressure. Cyclarity may be poised to actually reverse atherosclerosis.

No one can seriously doubt that we’re on the cusp of having a much better world through biotechnology. But everything I’ve listed so far only gets us to where proper longevity work starts; it’s all longevity normalizing: it brings up the left tail of the lifespan distribution.

Normalizing is how lifespans have increased across countries historically. Take Sweden and compare the distribution of ages at death in the 1750s to the distribution in the 2020s and you’ll see what I mean:

Another way to look at this is to look at the percentages surviving to given ages across different years. This chart hopefully makes the problem clear: despite massive increases in average life expectancies, the right tail barely moves. We’re bringing the left tail up, but not actually seeing extension of maximal lifespans.

If we have a revolution in everything related to what I’ve listed so far, we conquer the travails of living. We become effectively immune to our environments: an end to infections, an end to degradation from plaque accumulation and the stress of glycemic spikes, a practical end to withering. It also means an end to wellness culture—no regimented dieting required, no extra benefits from structured exercise programs and retreats. You’ll be able to drink and party and you’ll be no worse off for having done it.

But that revolution does not conquer death; it lets us live, and it makes room for what’s next. After reaching that state and finding that maximal lifespans still aren’t ticking up and healthspans still degrade, the focus should naturally shift towards promoting more radical approaches: facilitating immunity to mutation, regeneration of limbs, tissues, and organs, cell programming, targeted DNA repair, and eventually, slow, methodical, and hopefully unnoticeable full-body replacement. That is the goal.

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Spend enough time talking about heart disease online and you will inevitably be confronted with the claim that lowering your cholesterol increases your risk of getting dementia. Why? The reasoning can look like this:

‘Your brain is full of cholesterol. Statins inhibit the cholesterol synthesis enzyme HMG-CoA reductase, and sometimes they enter the brain. Low cholesterol has been associated with dementia in a handful of observational studies. Therefore, LDL lowering with statins or other forms of HMGCR inhibition must deprive the brain of cholesterol and cause dementia.’

And, if someone is reasoning about mechanisms, it can look like this:

‘Statins lower cholesterol levels, leading to a cholesterol shortage in the brain, causing dementia. This happens because cholesterol is needed to support cell membranes in the brain, because it is crucial for myelin, because it’s needed for synapses and synaptic vesicles, and if you lower your cholesterol, all of these things become broken.’

The problem for these and similar theories is that, when you check the data, it doesn’t line up. Mendelian randomization lets us get at the effect of taking statins or other LDL-lowering medications for a person’s whole life—decades of exposure!—and we see basically nothing when it comes to signals of harm. If anything, statins seem good!1

I think these results are only shocking because of semantics. People hear that something called ‘cholesterol’ is involved in the brain and assume that anything that lowers cholesterol must therefore lower it in the brain, depriving the brain of something like a critical nutrient, a requirement to function. But ‘cholesterol’ does not just mean one thing; you do not just have one cholesterol level.

The way statins work is by lowering liver-based cholesterol synthesis, resulting in relatively more hepatic LDL receptor activity, pulling more LDL from the blood, resulting in lower risk of atherosclerosis because of reduced risk of plaque buildup. But the brain, while cholesterol-rich and cholesterol-dependent, does not have its cholesterol supply affected by cholesterol-lowering medication. The main reason for this is the blood-brain barrier (BBB) and the fact that lipoproteins in the blood do not freely enter the brain. Accordingly, to get its cholesterol, the brain has to make and recycle it locally.

Reviews are unequivocal about this:

[C]holesterol in the [central nervous system (CNS)] is almost entirely synthesized within the brain since the BBB prevents any direct transfer of sterols from the blood to the brain, especially when they are contained in lipoprotein particles.

Peripheral (blood) cholesterol and CNS (brain) cholesterol are practically distinct. The brain produces its required cholesterol locally in glial cells, astrocytes, and oligodendrocytes, and it transports within the brain using ApoE-containing lipoprotein-like particles. Where there’s a relationship, it’s primarily through the brain disposing of cholesterol by converting it into 24S-hydroxycholesterol, which crosses the BBB and deposits cholesterol in the bloodstream to bring it to the liver for elimination.2

The National Lipid Association’s (NLA) advice is to lower your cholesterol, for as long as possible, to achieve better heart health.3 This is good advice because, for most people,4 LDL is the key causal factor involved in causing heart disease—specifically, atherosclerotic cardiovascular disease. This is completely unambiguous; with very low LDL, most heart disease goes away.5 And, if you followed this evidence and took the NLA’s advice to heart, you would likely not encounter elevation of dementia risk.

Consider the FOURIER trial. People who attained very low LDL with a PCSK9 inhibitor and a statin didn’t show any worse cognition after 2.2 years. We saw similar results in IMPROVE-IT after six years. Results like these are so often repeated, and so strongly supported by the bulk of trials, prospective cohort studies, and Mendelian randomization evidence that the American College of Cardiology simply maintains that reducing “LDL-C is not associated with cognitive decline or dementia.”6 Full stop.

With that said, LDL lowering does—as we’ve seen—seem to reduce the risk of vascular dementia, and it certainly reduces the risk of stroke. Both of those things either do or can involve serious cognitive impairment, and should for that reason be avoided. If they really cared about cognitive impairment, opponents of statins would be jumping for joy about this. But, revealingly, they remain opposed.

I suspect that the biggest reinforcement for confusion comes from failing to think causally about this issue.

Consider this distinction: someone with very low LDL due to prescribed lipid-lowering drugs is not being cholesterol-starved in the brain. However, someone who has near-zero cholesterol without a medical indication is more likely to have a rare genetic disorder like abetalipoproteinemia or hypobetalipoproteinemia, which comes with impaired fat-soluble vitamin handling and subsequent neurological and eye problems. If you looked at these people in an observational study and didn’t realize their disease state, you might think low LDL causes risks of its own. But, these people are high-risk less due to LDL, and more due to their other issues, which also cause low LDL.7

Additionally, in late life, low cholesterol sometimes indicates illness, frailty, weight loss, inflammation, malnutrition, cancer, or preclinical dementia, even though it’s not its cause. This sort of reverse causation has tricked tons of people into thinking that there are harms. We are lucky, then, that most of the observational evidence seems to be unaffected by these things. Accordingly, if we look at all of it, then we won’t make a big mistake when it comes to LDL, but, two things: first, that’s not always the case; and, second, it’s tempting to look at outlier studies. The best we can do is continue thinking causally, lest we wind up confused.

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5

Because of the lack of other mechanisms for plaque buildup.

Back in 1970, this would be 91% of all heart disease deaths, and as of 2022, it’s 53%. This leaves behind arrhythmias, heart failures, valve disease, cardiomyopathy, myocarditis, congenital heart disease, hypertensive heart disease, and other conditions, and the main reason for the decline in heart disease is that we’ve substantially tamed the LDL-driven afflictions, even though several factors involved in aggravating it—such as obesity and glycemia—have gotten worse. These other factors are less under the control of the medical system.

This is considerable prima facie evidence that statins have had an amazing impact on cardiovascular health.

If population blood pressure (hypertensive heart disease; heart failure with preserved ejection fracture; atrial fibrillation and many arrhythmias), glycemia, and weight were better controlled, most of the remaining heart disease deaths would likely also disappear. However, this would still not eliminate many cardiomyopathies, myocarditis and pericarditis, congenital heart disease, or many cases of pulmonary heart disease, since they are driven by genetics, viruses and inflammatory disease states, alcohol and drug abuse, chemotherapy, pregnancy, thyroid disease, autoimmune disease, toxins, maternal pregnancy factors, etc.

If you achieve low LDL and are otherwise fit, without major genetic defects, developmental insults, drugs, or serious infection, you are very unlikely to get any form of heart disease.

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Always kvetch. That’s how anything ever gets done. In this case, that seems to be how the major electronic health records (EHR) software company Epic was convinced to finally provide quarterly updates on obesity and GLP-1 usage for over 100,000,000 (one hundred million) Americans.1 Now, we can finally resolve a little debate started by The Economist last year: Has America’s obesity rate been declining? The answer? Yes!

Epic has also been generous enough to supply us with quarterly data for 2021 onwards. Adjusting for seasonality in that period, we can see that the fall is continuing into 2026. This might inform us about the causes. Think about it: if the trend was being driven by a mass die-off from COVID, as some have proposed, for example, then that moment should’ve passed, but the trend is continuing. In fact, every quarter lately is lower than the corresponding one from the previous year:

We still don’t know what’s driving this, but we have hints.2 For example, the population is getting older—a potentially countervailing influence—and, perhaps more crucially, the population is increasingly using GLP-1RAs. In fact, as of mid-2024, some 1-in-5 Americans had tried them. Judging based on Epic’s dashboard, that number has likely continued to rise, and the EHR-recorded number has now become ~25% for diabetics and ~13% for the obese.3 Adding plausibility, we know that GLP-1RAs are capable of wiping out obesity.

While not totally satisfying on answers, this data release from Epic is a good start.

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This was a timed post. The way these work is that if it takes me more than one hour to complete the post, an applet that I made deletes everything I’ve written so far and I abandon the post. You can find my previous timed post here. I finished this post and then waited to obtain the ad section to add after finishing. In that time, I added the Epic tweet.

When looking at population cohorts that are (1) measured with consistent methods1, (2) measured with consistent sampling2, and (3) measured when they’re sampled3, there seems to be a recent trend towards increasing testosterone levels. As far as I’m aware, there’s only one cohort that meets all three criteria, and that is what it shows.

The big question this raises is Why? People are getting fatter and older, so why—and, more importantly, how?—are they also getting manlier? Let’s check some possibilities.

Is the rise race-specific? No, the rise appears to similarly affect Asians, Whites, and Hispanics, with Blacks showing a somewhat different trend that’s consistent with stability in the last two periods—no rise, no fall.

Is the rise age-specific? No, in unweighted analyses, there’s a rise at every age range, and in weighted analyses, it’s at almost every one. Every age has hormonally masculinized or stayed roughly the same.

Did everything rise? By this I mean, did all of the lab tests go up? If so, then the rise could represent a general lab work artifact. Alas, this is not the explanation:

What about changes in measurement instruments or the timing of sampling? Those also don’t have an effect, as the measurement methodology is constant, and controlling for the time of day of sampling leaves the rise intact.

What about free testosterone levels? Those also seem to have risen. We know this because we have measures of sex hormone-binding globulin in some waves, and levels have fallen, so the free androgen index has gone up for two reasons: the increase in total testosterone and an increase in testosterone availability.

Did estradiol rise in lockstep? Curiously, estrogen levels did not also rise. They were basically flat, so the testosterone/estrogen ratio increased majorly.

Is this a birth cohort comparison problem? We see a rise recently in most available birth cohorts in terms of decade of birth, with the exception of those born in the 1940s. So the trend is largely not about that issue.

Increased…? Decreased…? Based on dietary recall, protein consumption isn’t explanatory: grams per day are down 9.7% 2011-12 to 2021-23, per kilogram of body weight. Carbs are also down 14.5% in the same time span, as are total calories, by 9.2%. And all of this is demographically consistent. Moreover, physical activity doesn’t seem to have increased, although that’s hard to say for sure since the physical activity questionnaire was redesigned during this time series. However, the sedentary time measure was consistent and changed from 382 to 371 minutes per day, which isn’t a lot of change. Triclosan levels fell; BPA, parabens, benzophenone-3, all fourteen of the phthalate metabolites in the data, PFAS/PFCs, urinary arsenic, mercury, and iodine, urinary VOCs, perchlorate/nitrate/thiocyanate—all of these things were also irrelevant in the waves they were measured, so it’s unlikely they mattered between waves. PBCDs and blood VOCs, blood lead and cadmium, all of that was a bit odd as well, since those went down and were positively related to testosterone within waves.

Marriage! Married men tend to have somewhat lower testosterone levels than unmarried men and marriage rates have fallen, but the trend is present in both sets, so that’s unlikely to explain the full trend.

A related possibility is the decline of fatherhood, as childlessness rates are up and having kids is related to lower testosterone. But alas, this also isn’t likely to be explanatory:

Could it be the rise of testosterone replacement therapy? (TRT) This actually seems like a real possibility, until you notice that, for example, there’s a rise at the median and the mean, across most of the distribution, lopping off extremes doesn’t do much (and that’s where TRT-related gains would be concentrated), and the gains are age-stratified in the wrong way, being smaller at ages where you would expect more TRT usage.

What about the decline of drinking and smoking? Oddly, drinking and smoking are related to higher testosterone. If I had to guess, experimentally, cutting those things would be good for testosterone, but at least cross-sectionally, their fall doesn’t seem to be a relevant factor.

Diets? Dining habits? GLP-1s? Ultraprocessed food? None of that seems relevant. Nor does marijuana usage, cholesterol levels, waist circumference, sex, PCOS, DHT conversion, blood pressure, sleep, or anything else that I could control for (and that list is long).

Ultimately, I don’t think I have a good explanation for the increase in testosterone recently. It’s clear that levels have gone up in recent years, but it’s far from clear why. Personally, my top explanation—which I cannot directly support—is that there’s sampling error, and something broke in the COVID era that made it so that lots of survey results have changed in odd ways.

The Testosterone Validation Loop

People often seek somatic proof for social anxieties—they want something that validates their perceptions of trends in society. One major example is feminization.

Plenty of people believe that today’s men are softer than they used to be. And they might be, but that doesn’t mean testosterone is to blame. Nonetheless, to support the idea that men today are feminized, people will point to testosterone levels, allege they’ve fallen—and, due to common methodological issues, it’s easy to make it appear as though they have—and use that decline to support their belief that men are more feminine than they used to be. Moral, political, and aesthetic judgments have been validated by something measurable, natural, and scientific.

This is a powerful thing. Biology offers things social claims lack: objectivity, causality, and inevitability. ‘People seem less masculine these days’ is contestable. ‘Testosterone is collapsing’ is something that sounds measurable (and it is, in theory). ‘Modernity is making men biologically weaker’ sounds causal. ‘This is what bodies are actually doing’ sounds inevitable.

People perceive a social change, they search for a bodily marker of it, the marker becomes the proof for the harder to support social claims, the social perception is used to interpret the marker, and any disconfirmation is absorbed as denial, corruption, or hidden decline. Never mind that there’s missing data to support the notion that society has been feminized on the basis of observed testosterone levels, biological legitimation is just too intuitively plausible, so the arguments seem to work. But biology shouldn’t be used this way, because it does not work that way: a biomarker like testosterone does not necessarily co-move with masculine behaviors—look across cultures and it clearly doesn’t! Look to all the available supplementation experiments and it also fails there!

More importantly, in keeping with the focus of this article, since testosterone levels are actually up, what happens to the notion of feminization anyway?

May 20, 2026 Update: A New Meta-Analysis!

Rodríguez et al. conducted a meta-analysis on the relationship between testosterone levels and risk-taking behaviors in economic games across several fields including behavioral economics and neuroendocrinology. Their meta-analytic sample size ended up being 17,340 participants across 52 studies, and their overall result was what you might now expect if you read the rest of this article: nothing.

The meta-analytic effect estimate was r = -0.0021 (p = 0.92), basically zero. Moreover, there was no evidence of any sort of publication bias.

The type of risk measure was not a significant moderator of this relationship, and there were seven different measures, they just all produced similar results.4 Studies that measured testosterone from blood, fingers, and saliva all produced similar effect estimates, and those from administration found somewhat larger effect, around r = 0.1—a quite small effect size, all things considered. Sex also wasn’t a meaningful or significant moderator of differences in the effect of testosterone, so it’s not as though high or low testosterone mattered more or less for men or women. Excuse the colors:

Per the authors:

[T]estosterone is unlikely to provide a general explanation to human risk preferences. Rather than a single-hormone account, the evidence points to a biopsychosocial view: what we label “risk taking” emerges from the interaction of task demands, cognitive and affective processes, and situational context, with endocrine influences (when detectable) appearing narrow and contingent rather than broad and trait-like. […]

The most defensible position at present is modest: testosterone is not a universal driver of risk preference. Where associations surface, they are best understood as products of specific constellations of task demands and endocrine measurement rather than a single hormonal lever on decision making. Research programs that embrace multiplicity (i.e., neural computation, learning history, affective state, social context, and carefully specified endocrine pathways) offer a more credible route forward than continued searches for a unitary biochemical signature.

The intuitive understanding of testosterone as masculinizing is true in a physical sense; the evidence that it reliably behaviorally masculinizes people is tenuous.

Our society is obsessed with credentials. People with PhDs, MDs, JDs, and even Ed.Ds are lauded as if the credentials they’ve earned signify that they’re more generally capable than their uncredentialed peers. The fact that they are on average because there’s a correlation between real-world qualification and educational attainment is the kernel of truth that has allowed people to be misled into thinking that the credential, the degree itself, is what matters. But it’s not, and you’ll realize it’s obviously not if you think about it for even a moment:

Have you never met a bad doctor? A shoddy lawyer? A barista with a PhD?

Nevertheless, laws have been written, policies have been crafted, and decisions have been made on the basis of credentials that frequently don’t even matter and should instead be downweighted in favor of other, fairer and more important criteria.

Let’s discuss credentialism in education as a particularly galling case: parents and schools seem to think that the educational credentials of teachers matter for how well they teach students.1 Do they? Not a lot; certainly not enough to support the idea that teaching should be the exclusive domain of those who’ve been taught how to do it.

Ladd and Sorensen’s seminal 2015 paper on this topic used administrative data from North Carolina to assess the effects of teachers earnings Master’s degrees with fixed effects for teachers, students, and schools. With these fixed effects, it’s possible to estimate the effect of Master’s degrees after accounting for things that are constant among these groups. Thus, the question from this study is less ‘Do teachers’ Master’s degrees correlate with their success?’ and closer to ‘Does being conferred a Master’s degree make teachers more successful?’ The answer is a resounding ‘no’, with the only effect being on rates of high absenteeism among the kids, for some reason.

Harris and Sass published a similar study with a focus on broader teacher training using administrative data from Florida. They had data on professional development trainings, degree attainment, and years of experience, and they found that years of experience were irrelevant or slightly positive in elementary and middle school, and negative or null in high school. Everything else was meager at most, with one scale score point being about 0.04 standard deviations of annual achievement gains or 0.027 standard deviations of achievement levels.

Bhai and Horoi used administrative data from North Carolina, but this time not so much for the longitudinal side, but instead so they could have a twin control. Twins end up in different classrooms, so we can use them to estimate the effect of teachers’ experience, credentials, and so on, by controlling for a person’s genetic endowment and home environment. They didn’t find a whole lot: they found that lots of experience was good, but advanced degrees were not.

Buddin and Zamarro looked at whether teacher qualifications related to student achievement in a sample of Los Angeles elementary schools. Student- and teacher-fixed-effects models rendered the effects of teachers having advanced degrees null to negative, and also made the California Basic Educational Skills Test (CBEST) and California Subject Examinations for Teachers (CSET) licensure tests look harmful, meaning that more ‘qualified’ teachers had worse effects on students. Contrarily, the Reading Instruction Competence Assessment (RICA) seemed null to helpful. But all effects were small.

Rivkin, Hanushek and Kain used Texas Schools Project data and found that, with student fixed-effects, grade-subject teacher characteristics were irrelevant to student achievement. Graduate degrees and experience were unimportant, and though having little experience was significantly related to lower rates of performance gain, the scale of this was less than 0.01 SD in reading and less than 0.02 SD in mathematics, or just about nothing.

These results replicate again and again and again, and the message is clear: degrees, licensure, union requirements, professional development courses, trainings, experience, and so on have an, at most, limited effects, and at their worst, they’re slightly bad for students. Even letting people without formal teaching experience enter the profession doesn’t seem that bad! These are supported by replications from across the U.S. and the world in large, well-done studies with excellent measures.

But what about something more than degrees in general? What about specific degrees? Sancassani found that those don’t do much either. In a within-student, within-teacher fixed-effects model, a subject-matched degree only boosted student test scores by 0.035 standard deviations—and this was in an international sample where effects were larger in less developed countries!2 Inoue and Tanaka found practically the same thing using earlier waves of the same dataset. Their conclusions mirror the literature.

People are too obsessed with easily identified teacher qualifications. They shouldn’t be. Plenty of people without qualifications make for good teachers, and teachers with higher credentials aren’t much better than teachers with lesser ones. Don’t get confused by the allure of credentialism, or you might end up believing wacky things, like that homeschooling isn’t possible for parents without educator credentials.

In my previous article, I used statins to elucidate the nocebo effect, where patients believe they’re being treated and that treatment causes side effects, so they begin reporting symptoms, even with placebo drugs. Essentially, fear is responsible for many of the reported medical side effects. This is also true for other drugs and compounds like antidepressants, MSG, and even completely unreal ‘chemicals’.

In response to the specific revelation that most side effects of statins are due to the nocebo effect, several people recounted their anecdotes about statin side effects as a supposed rejoinder to what I said. Ironically, they were likely just demonstrating that they suffer from the nocebo effect. But I have to digress.

The most commonly reported anecdote was that statins made people weak, that they materially impacted their ability to lift weights effectively. I’m sure some people believe this has happened to them, but it’s implausible, as there’s no real mechanism for this to happen. The most likely way it would happen—if it does, in fact, happen—would be through some sort of psychological mechanism. Luckily, we don’t have to go back-and-forth with combatting anecdotes or N-of-1 self-experiments of unknown reliability; instead, we can just review the literature on this subject!

The Literature

STOMP: The biggest and best trial on statins and strength is The Effect of STatins On Skeletal Muscle Function and Performance, or STOMP, a 2013 trial in which 420 participants who had never taken statins were randomized to either a placebo or high-dose (80mg) daily atorvastatin for six months. Participants were assessed on strength and aerobic capacity at the end of the beginning and end of the intervention, and the changes that happened during the trial were practically identical:

To make the placebo-intervention difference clearer, we can look at contrasts:

Essentially nothing happened, and this was the case even though the authors seemed to be biased in favor of statins being harmful. We know this because of two reported results.

The first is for creatine kinase—a useful, but blunt marker of muscular damage. Creatine kinase has poor specificity for magnitude and meaning; reviews have stressed that it isn’t a good predictor of maximum voluntary contraction loss or other aspects of functional damage and older papers have noted that peak creatine kinase doesn’t seem to even correlate with soreness. This is supposed to be a measure of muscle damage, but it’s more a measure of what it actually is, which is muscle cell content leakage into the blood, which is only alarming at highly elevated levels. In any case, creatine kinase does not seem to progressively increase on statins, and in this trial and others, it only increases slightly and not generally clinically significantly. In fact, in STOMP, creatine kinase levels didn’t predict lower muscular performance.

The second is for myalgia—muscle pain—, which I covered in my previous article. The authors reported that myalgia was more common in the statin group than in the placebo group (n’s = 19 and 10, respectively). But, curiously, myalgia subjects had worse muscular performance on 5 of 14 measures and placebo participants with myalgia had lower performance in 4 of 14 variables—no significant difference.

The bias evidence comes from how these results were reported. The creatine kinase result was only reported with numbers on creatine kinase for the statin group, so we can’t calculate the interaction effect. This result is also overhyped. The myalgia result is more severely misreported, as it’s just entirely incorrect. While there were more myalgia reports in the statin group, this difference was not significant: not how the authors reported it, and not actually. The authors reported that the difference was significant, with a p-value of 0.05. But, significance has to do with the p-value being below a given threshold, not at it. But their reported p-value wasn’t even correct! A Pearson χ² test without continuity correction returns p = 0.05495, which the authors could round down to 0.05 for their incorrect report. But this isn’t even robust! With continuity correction—which isn’t needed—the p-value becomes 0.084, and a two-sided Fisher’s exact test returns 0.082. A one-sided test would be marginally significant, but it is not justified, as the authors wrote that they had no good prior:

Many clinicians believe that statins cause muscle pain, but this has not been observed in clinical trials, and the effect of statins on muscle performance has not been carefully studied.

In an earlier (2008) study of older adults by Traustadóttir, Stock and Harman, the same result was achieved with high-dose simvastatin over a period of twelve weeks. The authors also found suggestive evidence that intramuscular CoQ10 wasn’t meaningful for these outcomes. But, this study was small (n = 10) and had no control group. Annoyingly, most other studies don’t deal with real functional outcomes, but instead, with biomarkers of dubious validity and which are less likely to show changes in studies that aren’t directly talking about them—a pattern indicating publication bias.

The main problem with STOMP is that it’s not revelatory about the response to exercise, so much as the stationary (i.e., not actively trying) effect on aerobic and muscular capacity. While those are not affected, might people who are on statins progress more slowly?

Coen et al. (2009) found that training benefits can still occur for people undergoing rosuvastatin treatment, but this had no non-statin-using control group.

Mikus et al. (2013) actually had a control group for a study of exercise versus exercise plus simvastatin, and they seemed to find that simvastatin blunted the increase in cardiorespiratory fitness indicated by VO₂ₚₑₐₖ, such that this increased 1.5% instead of 10%. But, this study can’t be trusted. For one, there was no placebo, so the study is vulnerable to placebo/nocebo effects. For two, several reported p-values are impossible or improbable. For example, the interaction by group is reported differently across Figure 1A and 1B, and the p-value reported for the group difference—p < 0.005—is impossible. The actual p-value is marginally significant, at p = 0.01-0.02. For three, several outcomes require implausibly large pre/post correlations. For example, to hit reported p-values for weight change within the exercise group or between groups, to hit them for fat mass and lean body mass changes within the exercise or exercise plus statin groups, and for the latter outcome, between groups, as well as for body fat percentage within the exerciser group, the correlations would have to be 0.991, 0.985, 0.944, 0.993, 0.987, and 0.968. This is all highly implausible, and it’s matched by the need for a >0.95 VO₂ₚₑₐₖ correlation over time if Figure 1A is to be believed. None of this is particularly believable.

Slade et al. (2021) had two studies, the latter of which was not so much an exercise RCT as a post hoc analysis of statin users in a twelve-week exercise RCT. The former study involved assessing the effects of four weeks of high-dose atorvastatin on practically irrelevant outcomes like the rate constant of phosphocreatine recovery (kPCr) and muscle pH, which are not functional. In the latter study, the VO₂ₚₑₐₖ response to exercise was -1.9 points for non-statin users in the control group vs. -0.8 for control group statin users and +1.7 for non-statin users in the exercise group vs. +2.7 for exercise group statin users. With corresponding exercise effects of 3.6 vs. 3.5, the difference is essentially zero and statins do not appear as a moderator. In fact, with a correlation over time of r = 0, the interaction p-value would be 0.98, and with r = 0.9, it would be 0.96. In the same year, Allard et al. (2021) found no differences in strength improvements between supposedly symptomatic, asymptomatic, and non-statin-using subjects who completed a twelve-week physical training course.

Reviews have consistently emphasized that there does not seem to be much in the way of evidence for statin-related physical performance harms, but the evidence base is small and it’s not as if minor harms can be excluded from possibility. In a review of the small number of RCTs out there, we got this result, speaking to how imprecise the literature is:

Of the 3 RCTs providing qualitative SAMS results, 19 (9%) out of 220 participants reported SAMS on exercise+statin and 10 (4%) out of 234 reported SAMS on exercise+placebo. There was no difference between exercise+statin vs exercise+placebo for maximal oxygen consumption (d=−0.18; 95% CI, −0.37 to 0.00; P=.06) or creatine kinase after short-term exercise (d=0.59; 95% CI, −0.06 to 1.25; P=.08).

A Simple, Long-Term Test

A much easier way to check if statins or other LDL-lowering medications cause long-term muscular or aerobic harms is to use Mendelian Randomization. The gene corresponding to statins as a drug category is HMGCR, and the effects of variants in it mirror the effects of the drug exceptionally closely, making them excellent means of projecting the lifelong effects of taking a statin. For my purposes, I’ll be scaling them like the Cholesterol Treatment Trialists’ (CTT) Collaboration does the effects in statin RCTs: in terms of the effect of a 1 mmol/L lowering of LDL cholesterol.

To index the effects of statins rather than LDL-lowering medications more generally, we’ll be using SNPs in HMGCR. I’ll use its canonical instrument, and for robustness, a strict cis instrument, a loose set of spaced cis instruments, and a relaxed set of plausible cis instruments.1

For lifespan outcomes, I used the Zenin et al. healthspan GWAS, the Park et al. lifespan GWAS, and the Timmers et al. parental lifespan GWAS. For aerobic outcomes, I used the Hanscombe et al. cardiorespiratory fitness slopes and VO₂ₘₐₓ, the Klimentidis et al. physical acceleration measures, for maximum workload and heart rate as well as heart rate achieved during the U.K. Biobank’s fitness test, I used the Neale UKB PheWeb.

For strength and muscle outcomes, I checked hand grip strength for left and right hands using the UKB Neale 46 and 47 entries, GCST90014019, the Pan-UKB meta and meta HQ, GCST90007526, GCST90007529, Abbondanza et al.’s childhood grip strength GWAS, I checked appendicular lean mass with GCST90000025, lower limb strength in older Japanese adults with GCST90319502, the frailty index with GCST90020053, stair climbing frequency came from UKB Neale 943 and the Pan-UKB sources, while usual walking pace was UKB Neale 924 and also those Pan-UKB sources. I was able to make the left/right hand grip strength, lower limb strength, and appendicular lean mass results age-annotated as well, going from 40-69 for hand grip strength and appendicular lean mass and 60 and onwards for low grip strength in EWGSOP/FNIH and lower limb strength.

I also checked bone mineral density and did so across different ages with GCST005345 (kids), GCST005344, GCST005346, GCST005350, GCST005349 (elderly adults), and GCST005348 (all ages). And finally, I decided to additionally check effects on diabetes risk (Neale 2443; also HbA1c as Pan-UKB 30750), body fat percentage (23099), BMI (21001), and standing height (50), purely because they’ve been discussed with reference to statins. I also added results for bempedoic acid (ACLY), ANGPLT3 inhibitors, APOB inhibitors, CETP inhibition, the LDLR pathway, MTTP inhibition, ezetimibe (NPC1L1), and PCSK9 inhibitors below the paywall, plus GLP-1-stimulated insulin secretion, dementia, memory loss, and ICD10 R41 cognitive symptoms as outcomes.

And now, let’s get onto the outcomes in the order we brought them up.

For healthspan, the effect for the increasingly expansive sets of HMGCR instruments is +0.37, +0.07, +0.59, and, finally, +0.93 years with only the last result being statistically significant (0.26-1.59). For lifespan, the results are +2.30 (ns), +5.05 (1.25-8.85), +7.60 (5.41-9.80), and +7.36 (6.09-8.64) for men and +3.18 (ns), +4.23 (ns), +4.69 (2.04-7.34), and +4.23 (2.70-5.77) for women. Most interestingly, the results for parental lifespan were +0.89 (ns), +0.98 (ns), +1.45 (0.48-2.42), and +1.44 (0.89-1.99).

Cardiorespiratory slopes increased by 0.035, 0.039, 0.031, and 0.029 points, but only the last result was significant (0.012-0.046). VO₂ₘₐₓ was somewhat higher by 0.057, 0.065, 0.058, and 0.090, but only the last result was significant (0.011-0.170). The results for wrist accelerometer activity (0.182, 0.506, 0.502, 0.370) and average acceleration (0.264, 0.807, 0.582, 0.500) were universally positive in direction but all nonsignificant. Maximum workloads were higher by a nonsignificant 0.041 for the canonical specification and lower in all the rest (-0.094, -0.140, -0.136), with the latter two results being significant (-0.277 to -0.003; -0.061 to -0.212). The maximum heart rate achieved was somewhat lower (-0.168, -0.259, -0.295, -0.270) with the latter three results being significant (-0.508 to -0.010; -0.450 to -0.140; -0.354 to -0.185). Rates of achieving target heart rates were not significantly lower (-0.009, -0.040, -0.021, -0.010).

Results for left (0.003, -0.002, -0.006, -0.012) and right (0.041, 0.020, 0.012, 0.005) grip strength were consistent with one another, but not significant. Results for childhood dominant (0.392, 0.404, 0.376, 0.540) and non-dominant (0.338, 0.346, 0.279, 0.470) grip strength were also consistent with one another, and the last results in each set were significant (0.233-0.848; 0.162-0.779). The results for lower grip strength were aligned but nonsignificant. Appendicular lean mass was consistently higher with lower LDL (0.078, 0.094, 0.114, 0.100) and all of these results were significant (0.008-0.148; 0.018-0.169; 0.070-0.157; 0.074-0.126). Knee extension strength was also higher (0.043, 0.259, 0.409, 0.292), but not significantly so.

The frailty index was lower (-0.152, -0.157, -0.139, -0.145), and all of these results were significant (-0.274 to -0.029; -0.288 to -0.026; -0.221 to -0.057; -0.190 to -0.100). Stair climbing frequency was lower (-0.089, -0.111, -0.106, -0.102), and the latter two results were significant (-0.185 to -0.028; -0.145 to -0.058). Usual walking pace was consistently lower (-0.092, -0.107, -0.113, -0.112) and all of these results were significant (-0.148 to -0.037; -0.167 to -0.047; -0.149 to -0.076; -0.132 to -0.091).

Bone mineral density was consistently not affected, as you’d expect. Diabetes had nonsignificant odds ratios of 0.998, 1.004, 1.001, and 0.999. HbA1c was elevated by 0.167%, 0.162%, and 0.155% (twice), and all such elevations were significant (0.110-0.225; 0.101-0.223; 0.120-0.190; 0.135-0.175). Body fat was a bit higher with lower LDL (0.90pp, 1.05, 1.17, 1.16), and this was significant (0.61-1.18; 0.74-1.35; 1.00-1.34; 1.06-1.26). BMI was also somewhat higher (0.83, 0.95, 1.02 twice), and this was significant (0.61-1.04; 0.72-1.18; 0.90-1.15; 0.95-1.10).

Finally, as a sort of negative control given the total lack of any plausible mechanism, standing height was somewhat higher for people with lower LDL (0.11cm, 0.30, 0.68, 0.56), and these results were significant for the latter two outcomes (0.34-1.03; 0.37-0.76). Also, among the more obvious outcomes were a 27% reduction in ischemic heart disease, 19% fewer myocardial infarctions, 37% less coronary atherosclerosis, and 36% less peripheral artery disease, plus 62% lower risk of vascular dementia—basically tautological outcomes, making them somewhat meaningless to check beyond their utility in verifying the method produces the right results where we expect them.

In short, the lifelong effect (into people’s early elderly years) of 1 mmol/L lower LDL due to statins is generally beneficial, with no signs of reduced strength or muscularity, somewhat larger glycemic effects than are observed in trials—as expected given their much shorter duration—, and near-zero, clinically meaningless effects for most things, alongside considerable life extension and greatly reduced cardiovascular risk.

Later Sections

• Against Fearmongering and Delusion-Sowing

• Which Drugs Generate the Same Results? Why?

• Is There Anything to LDL Oxidation?

• Further Cis-Target Results

• Side Effects in the CTT Trials and via Mendelian Randomization

  • Is there any dementia risk to LDL-lowering medications?

  • Is there any autism risk to LDL-lowering medications?

  • Do LDL-lowering medications make people less manly?

  • Do LDL-lowering medications affect endogenous GLP-1 levels?

    • Would it matter?

Against Fearmongering and Delusion-Sowing

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Glyphosate is perhaps the least toxic still-effective herbicide man has ever developed. But it’s artificial; naturally, then, there are those who despise it. However: (1) it is acknowledged to be safe; (2) the case against it is built on fraud and misunderstanding; (3) and in large population-representative American cohorts, it doesn’t seem to be related to bad health outcomes any more often than it’s related to good health outcomes. What do the critics have? They have their confusion.

I provided an example that speaks to this this over on X recently. The gist is that some researchers got a paper published claiming that people who had higher urinary glyphosate levels—an unreliable exposure measure—also had lower bone mineral density, implying that they would be more likely to suffer from fractures, osteoporosis, and other ailments of the nature. That’s not good, but it’s alright because the result was artefactual. The authors made a critical mistake by adjusting for age with quadratic age. Once I flexibly controlled for age, the result disappeared—bupkes!

The explanation for this result is simple: the relationship between bone mineral density (BMD) and age is not adequately described by a quadratic. I’ve plotted the relationship on the left, with the relationship after controlling for age with a quadratic on the right.

This is not what should happen when you control for a variable. If you do it right, there should be no relationship to the covariate—here that means age—after adjustment. In other words, BMD and glyphosate levels should be unrelated to age. Using the paper’s adjustment (top), they’re not; with my adjustment, they are (bottom):

The glyphosate-BMD example is not the focus of this article. I’m only mentioning it to make a broader point. That is: not only do you have to think about causality and measurement error when you control for a variable, you also have to think about the form of your controls.

How often do people actually do this? To my knowledge, the answer is ‘basically never’. Almost everyone who uses control variables uses them in a rote, brain-dead way, where the variables enter a regression and no one checks if they were entered appropriately. But, they should. Apply this advice broadly, not just to age. Tons of variables have forms that are not treated properly! Here’s an exemplary workflow:

Starting in the top left, look at age versus the outcome and then go to looking at age versus the exposure. There are clearly some relationships here, and those might interfere with your inferences about glyphosate and replace them with ones about age if you don’t adjust for it properly. But, if you take out a quadratic like the original paper I’m using as an example did, you’ll end up with the bottom charts, where not only are the exposure and outcome still related to age, but they’re related in bizarre, noisy ways that also seem to vary by sex, possibly distorting results in another way!

Again, very few people account for this, but anyone can. Plot your results before publishing them; be a little more rigorous by checking if your choices make sense.

These results and more will be made available in a forthcoming paper on covariate adjustment.

An “attribution study” is the name I’ve given to studies that purport to provide estimates of important real-world quantities through extrapolations from estimates in other, uninformative studies. There are a lot of these papers and they often come with conclusions like ‘Gas stoves responsible for 13% of American asthma’, ‘68,000 die each year due to health insurer denials’, ‘DOGE killed a million billion Africans with its program cuts’.

This style of study is not inherently bad. There are ways to do it right, but they require more effort than multiplying what are oftentimes pretty random numbers together. I’ll walk through the example from that tweet, the claim that “68,000 excess deaths per year… can be attributed to people who delay or avoid care because they lack health insurance.”

The source for the 68,000 death claim is Galvani et al. (2020), a paper in The Lancet that does, indeed, claim that “ensuring health-care access for all Americans would save more than 68 000 lives and 1.73 million life-years every year compared with the status quo.” Read that carefully and you’ll notice it’s not quite the same claim that was made on Twitter; to get anywhere, we have to be generous in treating this as commensurable to the tweeted claim about people dying from delayed or avoided care. But, if we grant that the claims are the same, we can move on to the big reason why they’re invalid: the methods. Here’s what the study did:

Galvani et al. took age-specific rates of being uninsured, multiplied them by the size of the population at those ages, and reasoned that, if that group had a 40% lower age-specific mortality rate, it would have had 68,531 fewer deaths. Thus, they conclude, expanding insurance coverage would have saved those roughly 68,000 lives.

The first issue with this is that the gap between the uninsured and insured is not necessarily due to the lack of insurance. It could be due to factors associated with not having insurance, like living on the streets, being schizophrenic, being poor, and so on. The second issue with this is that the gap is not reliably estimated. One of the hints of this comes from the author list of the source study: two of the authors are behind the infamous fake healthcare administration costs graph that you might’ve seen making the rounds on X, Instagram, Facebook, Reddit, etc.

The source study used the NHANES III, which is, coincidentally, a dataset that I’ve used for several of my most recent articles. The authors excluded Medicare, Medicaid, and VA/CHAMPUS recipients1 and adjusted for age2, sex, race, income, education, self- and physician-rated health status, body mass index, leisure exercise, smoking, and regular alcohol use and found that after those controls, the uninsured were 40% more likely to die. But what if we do a more extensive control strategy, like what I did in my articles on seed oils, ultraprocessed foods, and ice cream? The mortality differential collapses.

Moreover, if we use more waves of NHANES data or years in the mortality file, it also collapses and sometimes reverses, even without more extensive controls. These adjusted results are not causally informative per se. But, what they do tell us is that the estimated 40% uninsured age-specific mortality delta is not so certain, and not so easy to attribute to insurance. In fact, we can show this in a more serious way by looking at other ways that the insured and uninsured differ. For example, the uninsured are fatter, shorter, less likely to be White, and their diets differ in a number of pretty significant ways. As another negative control, they’re just as likely to die from accidents—which the medical system is generally not responsible for—as they are in general. We can’t attribute these findings to not having insurance coverage. Even by refusing to dichotomize education, letting drinkers report any number of drinks rather than at most 6, and extending the follow-up, the mortality gap shrinks a lot.3

Other studies have looked at the differences in mortality between the insured and uninsured, and they tend to agree with what I’ve found, that there is a lot of confounding to the relationship between insurance status and everything else. But we don’t even need to keep referring to cohort studies. We do have causally informative work that we can cite to get at this question.

A number of these were discussed during the Hanson debate on healthcare utility over on ’s blog Astral Codex Ten. The gist of the studies where people were freely provided with insurance is that mortality was not meaningfully affected, and what little evidence there was for any effect was marginally significant. That means no deaths to attribute to insurance, but, we have to also note that those studies were underpowered to detect small effects, so those might still be on the table. The study of letters sent out by the IRS reminding people to get insured had more power and found a small, but significant effect, where it would take 1,587 people getting health insurance to save one life in the two years following getting insured, with no effect among children and young adults.

Astral Codex Ten

Highlights From The Comments On Hanson And Health Care

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2 years ago · 76 likes · 169 comments · Scott Alexander

What happens if we attempt to re-estimate the original 68,000 number given everything we now know about it? Since the hazard ratio to use is so close to zero, the number of deaths that we can attribute to this excessively broad definition of ‘deaths from delay or denial of care’ is also nearly zero.

We likely won’t ever have a good estimate of the ‘true’ number of people who die from denial or delay of medical care, except that it is small. And it must be this way given the structure of mortality in the population: the overwhelming majority of deaths happen in old age, when there’s government-provided insurance; via EMTALA is is illegal to deny emergency care; and most other deaths among the young are due to things that are outside of the control of the medical system! Our health system is obviously not set up to deny people to death; there is no serious argument that it is.

But this is neither here nor there. The issue for this article is this sort of study, where dubious numbers are selected to promote some sort of narrative that they can hardly support. A previous instance of this that I dealt with some three years ago had to do with gas stoves. A study came out that purported to show that almost 13% of the asthma in America was due to gas stove exposure, but the estimate was based off of a deeply flawed meta-analysis. I redid the meta-analysis and the effect shrank. I then found that a study that had a larger sample than the entire literature up to that point found that there was no significant effect whatsoever, so the estimate ought to be zero.

Despite the gas stove study’s flaws, it was acclaimed. And, as with the Galvani et al. paper, the authors were clearly on a mission: they work for a company with the goal of getting rid of gas stoves! And they’re not alone. Consider the following studies:

• Galea et al. attributed 245,000 deaths in 2000 to low education, 176,000 to racial segregation, 162,000 to low social support, 133,000 to individual-level poverty, and 119,000 to income inequality, with a further 39,000 to area-level poverty—an unbelievable 37% of all the deaths, which was obviously not true given subsequent changes in these variables.

• Krueger et al. attributed 145,243 deaths in 2010 to people having less than a high school degree (vs. a high school degree), 110,068 deaths to having some college experience rather than graduating, and 554,525 deaths to having anything less than a baccalaureate degree. Mind you, this obviously errant conclusion doesn’t even hold if you use more extensive controls in their dataset (the same one I used in this article).

• Brady, Kohler and Zheng attributed 183,003 deaths in 2019 to current poverty and 295,431 deaths to cumulative poverty. They did this in the Panel Study of Income Dynamics so, as with the previous study, we can be sure it doesn’t hold up to using more extensive controls.

• Avanceña et al. argued that a universal basic income of $12,000 a year would save between 42,000 and 104,000 deaths annually, with a negative income tax saving 19,000 to 67,000.

• Lee et al. argued that physical inactivity caused 9% of premature mortality worldwide, amounting to 5.3 million deaths in 2008. Similarly, Rezende et al. argued that sitting was responsible for 3.8% of all deaths in 2015 and Singh et al. claimed that sugary beverages were responsible for 184,000 deaths worldwide in 2010.

• Makary and Daniel claimed that medical error was the third leading cause of death in America.

• Bor et al. claimed that police killings of unarmed Black American caused the Black population to suffer 55 million poor mental health days.

What these studies do is fundamentally problematic from a scientific perspective. They take estimates of exposure effects that are only in a scant few cases causally identified—implying that we’re sure they represent an actual effect of something on something else rather than a mere association—, and they make these potentially non-causal estimates appear as though they’re causal by attributing some number of deaths, disease cases, or whatever else to them. They contain the usually unstated argument that differences between identifiable groups must be due to the things those groups are defined by, like minimum wage recipients having higher mortality than salaried workers because of their lower wages. These results are mere reflections of their assumptions and they cannot stand.4

End the blight by joining me in not publishing, supporting, or listening to the results of un-rigorous, non-causal attribution studies.5 We deserve something a little better.

Many medical treatments, dietary choices, and physical activities seem to induce “nocebo effects”—harm through expectation, even when expectations aren’t met.

The nocebo is a variant of the placebo effect, but it’s far more real than placebo effects tend to be. Nocebo effects are insidious and pervasive, and they drive a lot of the online discussion surrounding topics like mold, fluoride, MSG, sugar, seed oils, and more. For example, people who self-identify as having non-celiac gluten sensitivity have side effects from the belief that they’ve consumed gluten more often than they have effects that can be ascribed to the actual consumption of gluten.

The impact of the nocebo effect on public health knowledge and practice is shockingly large. To make this clear, I’ll talk about the case of statins: very cheap, highly-effective medications that greatly reduce the risk of cardiovascular disease, extending lifespans by years for people who start them early enough.1

Despite their incredible efficacy, statins get a lot of hate, largely because of misperceptions about their side effects. Patients who run into issues with statins are deemed ‘statin intolerant’ and ideally shifted to alternative treatment options like intensive lifestyle interventions, ezetimibe, PCSK9 or ACL inhibitors, fibrates, or bile acid sequestrants. The portion of patients who actually become intolerant ranges from about 5% in trials to 17% in observational studies. When those patients are asked why they’re quitting statins, the answer for about 60% of them is ‘muscle symptoms’—principally myalgia. As noted in a recent review: “muscle-related side effects, the fear of side effects, and perception of side effects are among the common reasons patients often report for declining to start and for discontinuing statin therapy.”

This might seem like a lot of patients are stopping statins for a legitimate reason. But they’re mostly quitting because of the nocebo effect: they believe statins have side effects that they largely do not. Several experiments speak to this fact.

ASCOT-LLA: This study had two phases, one blinded and randomized, and another that was unblinded and unrandomized. In the RCT phase, participants took either atorvastatin or a placebo and reported on side effects. The critical, muscle-related side effects occurred at a rate of 2.03% per year in the statin group and 2.00% per year in the placebo group—virtually identical rates. But when blinding was removed, participants who were on statins reported an annual rate of muscle-related problems of 1.26% versus 1.00% for those who weren’t on statins. It took knowing about the statins for them to generate muscular problems!2

SAMSON: This study took people who had previously stopped statins because of side effects and cycled them through atorvastatin, placebo, and no-tablet months to see how their side effect reports progressed. When people went on statins or placebo pills, they started reporting more symptoms, and they did so to similar degrees. When they came off either pill, they recovered to similar degrees, at the same rate. It was taking any pill that mattered to patients, not taking statins specifically. Placebo was enough.

StatinWISE: This study had patients who had previously stopped or were considering stopping statins because of muscle symptoms alternate between two-month periods of taking either statins or placebo. Participants rated their muscular symptoms at the end of each treatment period and there was no difference between periods taking statins or placebo. The difference was a minuscule -0.11 points on the 0-10-point visual analogue scale, nonsignificantly favoring statins. An alternative measure is withdrawals from the study, and by that measure, there were no significant differences: 9% of participants dropped during statin periods and 7% dropped during placebo period—a nonsignificant result favoring placebo.

Two-thirds of participants who completed the study intended to stick with treatment.

The nocebo-eliciting trials are clear, as are the results from a review of rechallenge trials in which doctors try to get people back on statins after they come off. That review showed no significant differences in global and myalgia symptom ratings, and 36% of the rechallenged participants assigned to statins developed an intolerance versus 26% on placebo. It is also commonly-stated consensus that over 90% of patients who stopped treatment due to side effects are able to successfully continue on a statin when they’re rechallenged, though much of this is based on observational studies, where that percentage visibly do that, albeit without randomization.

All of this is no surprise when placebo-controlled trials show support for very few of the side effects listed on statin labels, and what they do show is universally meager. A recent review of nineteen trials with 123,940 participants in it included this diagram:

If you can’t tell, there is not a single clinically-meaningful effect size pictured in that ring. Moreover, almost every result is null, and given that they almost all cover a very limited range, they are almost all very precise nulls.3 The largest positive effect—meaning one in which statins lead to side effects—is equivalent to about r = 0.10 or d = 0.19. It’s practically nothing, and, as the authors have noted elsewhere: “any theoretical adverse effects of statins on cardiovascular risk that might arise from [their side effects] (or, indeed, from any other mechanism) are already accounted for in the overall reduction in cardiovascular risk that is seen with statin therapy”.4

I’m not going to keep harping on statins. They are a great example where there’s a real issue, but its scale has been so overstated and overhyped that it’s helped to generate nocebo effects in real-world patient populations. Celiac is like this in the sense that it’s a real and serious issue, and it seems to have given birth, by a feat of logic, to non-celiac gluten sensitivity, which is also more strongly nocebo- than substance-driven.

Examples like these speak to the power of belief, suggestion, or whatever you’d like to call it. But nocebo effects aren’t the only way belief matters in health.

People can also be prompted to think they’ve been exposed to things because that have become salient. For example, after a series of high-profile court cases about talc and ovarian cancer hit the news, people started remembering that they used talc. What an odd thing to suddenly remember, and such convenient timing, too!5

Ultimately, what I’m getting at is that the old adage that patients always lie has a kernel of truth to it. But the lies aren’t always to doctors per se, and they’re not always malicious or conscious. In fact, what I’m really saying is: I think the person that patients are the most likely to lie to is none other than themselves.6

Grocery stores are filled with gluten-free bread, sweets, and pastas that look odd and taste worse. Not only that, there are also plenty of gluten-free replacement suggestions like swapping spaghetti with zucchini noodles, replacing tortilla wraps with lettuce, using cauliflower rice instead of cous cous, or putting a portobello mushroom on your beef patties instead of opting for a traditional hamburger bun.

A lot of this has to do with the rise of non-celiac gluten sensitivity (NCGS): supposed harms from the consumption of gluten among people who do not have the celiac autoimmune disorder that causes the body to bloat, become anemic and tired, and induce diarrhea when gluten is consumed. NCGS sufferers claim to have many of these same issues without having the genetic causes or the inflammation that marks traditional celiac disease.

Despite its impact on grocery stores, restaurants, and the memeplex, seeming to verify that it’s real and the market is responding to something substantial, NCGS has all the hallmarks of a disease that’s not real, at least in its own terms. I’ll clarify what I mean by example:

Do you know the difference between inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS)? They have similar acronyms, and they both appear to be about the gut, but that’s about where the similarities end.

IBD is an umbrella term for autoimmune inflammatory conditions like Crohn’s disease and ulcerative colitis, whereas IBS is a ‘Disorder of Gut-Brain Interaction’, or DGBI. DGBIs don’t appear to come with any functional or structural gut problems, but instead arise from people with irritable minds disturbing their guts: they get stressed, they eat poorly, they get annoyed by minor issues that more resilient people might be able to brush off, and they imagine symptoms that probably aren’t there. It’s usually the case that IBS looks more like a mental disorder than a bona fide bowel condition. It is meaningfully psychosomatic.

IBS and NCGS are overlapping conditions, whereas IBS and celiac disease have much less overlap. NCGS sufferers are also about 70-80% female, whereas celiac disease is much more evenly split by sex (~55-60% female). Celiac disease is largely gastric and presents with a variety of physical symptoms, whereas NCGS commonly presents with symptoms like headache, brain fog, anxiety, confusion, depression, fatigue, and diffuse pain—all of which are marked symptoms of other largely-psychosomatic illnesses like long COVID and fibromyalgia. Moreover, celiac diagnosis tends to be symptom-driven following a clinical work-up due to specific symptoms or an incidental finding during other evaluations, while NCGS diagnosis is usually a self-diagnosis or something suggested by an alternative medicine doctor or dietitian rather than a real doctor. And, while untreated celiac disease is associated with a wide array of nutritional deficiencies, the association between NCGS and nutritional deficiencies is both different and less extreme.

NCGS looks like a DGBI, not a genuine autoimmune disorder, and it is certainly not what it appears to be. NCGS is definitely not about reactivity to or an intolerance of gluten per se. The best way to grok this is to look at the results of the landmark challenge studies highlighted in last year’s Lancet review.

The seminal publication on NCGS is Cooper et al.’s challenge study from all the way back in 1980. In it, Cooper et al. subjected eight adult women without celiac to eating a diet with gluten and after they did, they saw some meager signatures of gastrointestinal response. Later reviews have flagged this as low-quality evidence for NCGS, but it’s worth mentioning because it’s the first real clinical report we have.

After the popularity of the condition had grown, we got another study some thirty years later. This was a double-blind, placebo-controlled (DBPC) trial including 34 non-celiac patients, where 68% of the patients given gluten-rich food reported inadequate NCGS symptom control, compared to 40% of those who were not given gluten. All of the effects, however, were subjective, and there was nothing to show for anti-gliadin antibodies, fecal lactoferrin, celiac antibodies, hsCRP, or measures of intestinal permeability.

Two years later, the same research group did another DBPC trial, this time taking aim at a possible connection between NCGS and IBS. In this trial, they gave their 37 non-celiac participants diets that were low in FODMAPs—the types of carbohydrates that make you feel gassy and bloated—and then placed them on high-gluten, low-gluten, or control diets. With low FODMAPs, participants were a lot better off, and when they were exposed to the higher FODMAP diets, they got worse, but not especially so with gluten.

This is key: people with DGBIs like IBS tend to be sensitive—hypersensitive, in fact. When they eat things that would make any of us bloated, they seem to feel much worse than we do. With the same bloating, the same gut distension, the same gassiness, they report feeling relatively excruciating pain, where normal people would feel practically nothing. A subset of them—one that’s more female and rates higher on several additional mental disorder measures—also frequently report feeling more pain broadly, and these individuals have less complete recovery when they’re put on a low FODMAP diet, because their issue is a broader pain perception one (or at least, that’s how they report it),1 and relieving painful bloating is only a partial fix.

A study the following year showed practically no reaction in a 22-person DBCP trial of non-celiac NCGS sufferers, with the only reaction being psychological (p = 0.01 for one of two possible dietary subgroup comparisons on a depression subscale score for the STPI) rather than physical (cortisol, gastrointestinal symptoms).

The next year, a 35-person DBCP trial assessed NCGS sufferers’ ability to distinguish gluten-containing from gluten-free flour. They found that surreptitiously exposing participants to gluten only elicited symptoms in one-third of NCGS patients, with just 34% even correctly identifying gluten flour and 49% incorrectly identifying a gluten-free flour as having gluten in it. Among those who incorrectly identified the gluten-free flour as containing gluten, they had stronger reactions. In the same year, a 61-person DBCP trial of non-celiac NCGS sufferers came out. In it, participants either took gluten or rice starch capsules, and the result was p-hacked: the authors registered that they would report on 15 intestinal symptoms and 13 extraintestinal symptoms, but they ended up only reporting the ones that showed significant differences, and each such difference had a p-value that was dubious, ranging from 0.019 to 0.047. And, only 3 of 59 subjects who completed the study showed any evidence of gluten sensitivity—consistent with chance!

In 2018, another DBCP trial found FODMAP (specifically the ‘O’, fructans) sensitivity rather than gluten sensitivity. That year, we also got a DBPC in children, with 1,114 gastrointestinally-dysfunctional children enrolled and the 3.3% who showed a correlation between gluten ingestion and symptoms taken off to do a crossover gluten diet challenge. Among those kids who did the challenge, 39% showed worse symptoms with gluten, but the majority showed no evidence of any issue, and the number of those who showed an issue is absolutely small. More pressingly, the p-values for group differences are misreported and a few challenge results that are reported as significant are instead nonsignificant and misreported. For example, in Table 3, results reported as p < 0.005 or <0.001 were instead 0.030, 0.082, 0.498, and 0.027.

Finally, we got a larger, stronger study of 84 NCGS sufferers. This one had a unique factorial design in which participants were sorted based on the expectation of eating or not eating gluten, and then they were also given or not given gluten within different expectancy groups. Expectancy—not actual gluten content—dominated. The participants who expected gluten and got it or did not get it had comparable responses, and those who didn’t expect it weren’t affected. Or, in other words, there was a nocebo effect: the anticipation of gluten was the source of the problem. Gluten was innocent! In other trials (and, indeed, in reviews), nocebo effects crushingly outnumber anything that could look like actual gluten sensitivity (but which might in truth be FODMAPs or some other stimulus).

The picture we get from challenge studies is one in which NCGS looks like little more than IBS, complete with the same demographics, similar treatments, and anticipation effects that show it’s substantially psychological in nature. Meanwhile, there’s no meaningful or consistent evidence for gluten actually being involved. Despite extensive searching, there are no NCGS biomarkers, nor any identifiable severe acute reactions, and there’s no evidence of physical effects on the gut, immune reactions, or anything you might expect if people are, as they think, sensitive to gluten.

There is no reason to believe that a non-celiac sensitivity to gluten actually exists.2

How Do We Treat Mental Illness Disguised as Food Issues?

But there are still NCGS sufferers, people who wrongly think that they’re affected by gluten when they’re suffering due to something else. These are people who are more likely to have IBS or some other functional gastrointestinal/mental disorder characterized by hypersensitivity and typically alleviated through dietary restrictions intended to cut down on or distract them from specific, generally tolerable conditions like bloating and gas. They usually have mental comorbidities and they frequently claim to have a bundle of other conditions that are not what they seem.

The easy answer on what we should do for these people is to restrict their diets in a way that alleviates their symptoms by cutting out FODMAPs, fructans, or whatever else contributes something towards states they’re hypersensitive to. To the extent—which is major—that their symptoms are ultimately due to some sort of mental illness, we’ll need to keep looking at that angle. It would be unethical to only treat what might be a delusion built from a mental illness when it could be possible to treat the underlying mental illness that creates false food sensitivities.3

$100 million to find a box2 within one year, with 52 updates about your distance to the box along the way. If you don’t get the box, you die. If you’re offered these terms, take the bet because you’re basically being promised $100 million!

This type of problem has a name: multilateration. This is a well-studied geometry concept and it’s fundamental to modern Global Positioning Systems (GPSs). With it, you can pinpoint the location of practically anyone or anything, to the degree of precision you’re allowed by going off of just a few separate distance readings.

Practically, multilateration means that you are going to make a lot of money on this bet, and you’ll be able to do it quickly. As an applied example, consider one of my hobbies: geocaching. In geocaching, you are tasked with finding an object like a container given only its GPS coordinates, down to three decimal places in Degrees Decimal Minutes (DD° MM.mmm′) format or five decimal places using Decimal Degrees (DD.ddddd°). If you’re unfamiliar, the latter is standard latitude and longitude, and when you get down to that degree of precision, that’s about where GPSs cut out, and it allows you to be within about six feet of the container of interest.

Six feet is close enough that you can’t miss the container. But during geocaching, people are right at the object when they write down the coordinates. How long would it take you to find an arbitrary object at that level of precision given only a set of distances? The answer is surprisingly little time! If you’re given exact distances, to an arbitrary level of precision, then it should take you at most two iterations after the initial distance to get to five decimals of precision in Decimal Degrees. Then, it’s over.

I’m calling this level of precision the “Direct find zone”, because you can’t miss it:

If you were given geocache-level precision on the distance estimates, that would mean giving you three decimal places, so a distance like 100.123 miles or 7.651 miles. When your distances are that precise, it will take you up to about five weeks to get very clear on the location of the cache if the distances are provided in miles, and three weeks if they’re provided in kilometers.3

But let’s be even more fair. Let’s assume that you’re not given arbitrarily precise distances, but are instead given precision to a single decimal place, so you might be told your $100 million check is in a box 100.1 miles away, or that it’s 39.3 miles from your current position. In that scenario, you’re still in a good position: it should take you at most about 18 weeks with miles or 16 with kilometers, then you’ll find your box!

Now, let’s see an applied example of this. I’m going to show you four randomly selected scenarios in what should be the hardest country to get around in distance-wise. In Russia, if you can get around and you get geocache-tier distance estimates, you’ll find the box to four decimal places quickly. Nowhere is a safe hiding spot if you’re smart about where you go to receive the next distance estimate.4

So, what’s my advice? If you’re getting reasonably precise distances, take the bet!

Now imagine if we didn’t live with the convenience of modern air travel. Imagine instead that we lived in the early 20th century and you took this bet in a big country like America, Canada, China, or Russia. Well, then, you might be boned.

I don’t like the term ‘ultraprocessed food’. It’s vague, and the vagueness might even be intentional. The inventors of the most popular food processing classification scheme—NOVA—have failed to provide a detailed breakdown of which foods count as processed, unprocessed, or ultraprocessed. Despite that, it’s currently all the rage to talk about how bad ultraprocessed food is for health.

Take a peanut in a shell. That’s an unprocessed food (Group 1), but when you crack it open, that’s a minimally-processed food (Group 2). If you want to get dangerous, crush it up into a paste, add some salt, and now you’ve got peanut butter, a processed food (Group 3). If you add some sugar on top and put in a dab of vegetable oil to stop the oil from separating, you now have ultraprocessed peanut butter! (Group 4)

By today’s received wisdom, you went from something benign and safe to something whose consumption is dangerous and liable to promote all manner of health ills, from heart attacks to diabetes to cancer. But in that example, there’s little nutritional change going from Group 1 to Group 4, and that example isn’t particularly unusual. Per NUPENS—the creators of NOVA—if you slice a potato and bake it, it’s unprocessed, just like a peanut in a shell. But, if you take that potato and fry it, it jumps right into Group 4 and becomes an ultraprocessed food. Shake it off and dry it out and there’s no nutritional difference for your body to detect, so who cares that it jumped groups?

If you add a teaspoon of honey to plain yogurt at a factory, it goes straight from Group 1 to Group 4. If a factory grinds roasted peanuts into peanut butter, they go from Group 1 to either Group 3 or 4—it’s not completely clear. Bread baked at home slots into Group 3, but an identical recipe baked in a factory with a single preservative goes in Group 4.

When you see examples like fortified breakfast cereal (Group 4) being classified worse than white rice (Group 1) even though it’s substantially healthier, it can make the system look useless. But that’s how it is:

• Fruit is minimally processed and fruit in sugar syrup is processed;

• Applesauce with added sweetener can be minimally processed or just processed, but if you sweeten it with high-fructose corn syrup, it’s ultraprocessed;

• Beer, cider and wine are merely processed whereas distilled spirits are ultraprocessed;

• Margarine and spreads are ultraprocessed while butter is a culinary ingredient;

• Powdered milk is objectively processed but it’s listed as minimally processed, while almond milk just requires soaking, grinding, filtering, adding water, homogenizing, and pasteurizing—steps that have no nutritional impact whatsoever—and as a result, it’s listed as either processed or ultraprocessed.1 Compare this to normal cow’s milk, which retains minimally processed status after homogenization and pasteurization.

After you’ve familiarized yourself with NOVA, it becomes apparent that the classification scheme is capturing something about ‘naturalness’ and industrial context rather than nutrition. All food processing classification methods are attempts to provide scientific-sounding language that makes industrial-scale food production sound dubious. They fill a niche for health and wellness influencers who want to moralize about food and nutrition but know that if they just said ‘added sugar is bad’, their complaints would be obvious and their disdain would be morally unsatisfying.2

But moralizing catches on. There’s demand for ways to praise and celebrate the aesthetics of basic cooking and to attack complex food preparation, just as there’s demand for ways to attack modern lifestyles in general. But demand doesn’t substitute for nutrition guidance: good guidance still needs nutritional facts, not expressions of anxiety about modernity.

Empirically-Speaking

I used the NHANES dataset to look into the question of what ultraprocessed food intake was even correlated with. I checked basic correlations, moved up to extensive matching exercises, and took the analysis plausibly causal by checking if ultraprocessed food consumption mattered within households. I found that the picture is unclear. For starters, read this:

With basic ordinary least squares (OLS) regressions, ultraprocessed food intake is correlated with higher white blood cell count, lower HDL, lower blood pressure—wait what? These correlations aren’t even consistently bad, they’re all over the place. Then, if we switch to the plausibly-causal household-level analysis, only four results remain significant, and only two do so in a way that’s robust to multiple comparisons correction remain significant: lower HDL (bad) and lower uric acid (good).

This is a bizarre set of relationships that only gets worse when you look at the gradient of effects across NOVA categories. For some reason, you can see different signs of effects for processed and ultraprocessed foods at times. How odd!

If we change up our dose-response analysis and look at all-cause mortality—the hardest of hard outcomes—then the results just don’t look that bad: it seems to only be the most extreme ultraprocessed food eaters who are at risk, and they’re not at much risk in any case.

What I suspect is that food processing is a noisy concept, so it’s bound to produce noisy results. But this concern should be moderated because ultraprocessed food consumption is consistently related to a few things—things it can’t possibly affect.

There are stronger and more monotonic relationships between ultraprocessed food consumption and obvious confounders like the probability of being White or the age of the study participant. We also get awkwardly non-monotonic relationships for some variables that can’t be affected, like being male. If we benchmark our statistical findings based on these impossible ones, then there’s nothing left for ultraprocessed food harms: nothing bad is very strongly related to it.

What we end up finding is that ultraprocessed food consumption is not a good variable to correlate with anything else because it is, to a considerable degree, a mirror-image of whole food intake (r = -0.64).3

Getting your calories from whole foods has been standard dietary advice since at least 1980 when the Dietary Guidelines for Americans began recommending it. When government and professional organization recommend something, the people who follow those recommendations tend to be better-off and healthier, regardless of diet, making it so that in the future, the associations between what those guidelines recommend and all manner of good outcomes are confounded.

In other words, because ‘we know’ the inverse of ultraprocessed food—whole food—is good, we’ll tend to find that ultraprocessed food is bad or neutral, even if it is, in fact, good. And, because it’s such a poorly defined concept, we’ll also find that it produces plenty of noise. Accordingly, most epidemiological research on ultraprocessed foods is liable to be worthless—even more so than nutrition research generally.

The truth of all of this also makes for an interesting conclusion: it’s incredible that ultraprocessed food can look so neutral given how bad it ought to look. It might not be so bad after all!

Replication Files, a Preprint, All the Evidence, and Q&A

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