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.

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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:

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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. The next outcome is lower HbA1c, which is the opposite of what it should do if it’s harmful. Then, if we switch to the plausibly-causal household-level analysis, only three outcomes remain significant, but they’re at least directionally consistent: lower HDL (bad), lower GGT (good, marginally significant), and lower uric acid (good).

This is a bizarre set of relationships that only gets worse when you look at dose responses. For some reason, it looks like the effects are nonlinear. For HDL, for example, going from the bottom quintile of ultraprocessed food consumption through the next two quintiles seems to not matter to any meaningful degree. It’s only when you get into the top two quintiles that things get bad.

If we change up our dose-response analysis and look at all-cause mortality—the hardest of hard outcomes—then the results become even less clear. It actually looks like a little ultraprocessed food consumption is related to more death, and a lot of consumption is related to no elevation in death risk whatsoever!

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 male, and even the age of the study participant. 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 an almost mirror-image of whole food intake (r = -0.95).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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There’s a widespread belief online that, around the creation of modern welfare states, wealthier people went from being fatter than poor people to being skinnier. There has been inversion, but it doesn’t seem timed with the advent of welfare states. More likely, it’s tied to national development and when countries become sufficiently rich, the poor become fatter even without welfare states. There’s a clear case for this belief.

First, as countries get richer, they tend to get fatter.1

Second, we can see that, in the countries with good data, the poor become fatter than the rich before welfare states in some cases, and before welfare states become significant in some of the rest.

Third, in the United States, we have excellent data on this based on nationally-representative cohorts from 1961 onwards. In that time period, the low-income versus high-income obesity delta has been fairly constant, contrary to the prediction one might make if continuous exposure to welfare states drove poor people to become fat.2

Before the era of representative cohort studies, there are various data points like estimates at the time of the draft for WWII, or estimates from Union Army veterans decades after the Civil War, and if we want to go even less population-representative, we can look at a comparison of legislators from New York and prisoners, and in that whole period, we see that the high-income are fatter than the low-income.3

In modern America, what does this relationship even look like? It’s actually not quite ‘poor people are fat.’ In fact, this shows up overall and it has since the Midtown Manhattan study in 1954, but it only does so consistently in women. It did in men for a few decades, but nowadays, poor and rich men have similar obesity rates!

One question is whether this is just due to a race confound, where Blacks and Hispanics tend towards both higher obesity rates and lower being poorer. If you read the above chart carefully, you’ll realize this can’t be the case in general, and could only be the case within a given sex, and it is! Take a look:

Among men, middle-class Whites are fattest, and both poor and rich are similarly fat. Among Blacks, the gradient is strongly reversed from expectations, and among Hispanics, it’s more moderately reversed. Among women, however, being skinny is status-associated, and this relationship is strong within each racial group, so the overall result where poor people seem to be fat is basically just driven by women.

I’ve gotten this question a lot: How can this be? How can countries get fatter with growing national wealth, when wealthier people tend to be skinnier?

There’s a simple answer: Individual and ecological correlations have different causes!

At the level of countries, national wealth enables more people to get fat if they want to. Your wealth is more determined by national wealth than by anything you can do on your own. At the individual level, people who have good personal habits tend to go further and earn more in life, and overlapping habits help people to avoid undesirable obesity, which is also something that makes it harder to get rich in its own right.4

The studies I referenced or consulted were Bodenhorn (2010), Carson (2009) and (2013), Costa (1998)5, Linares and Su (2005), Moore, Stunkard and Srole (1962), Goldblatt, Moore and Stunkard (1965), the NHES I (1960-62), the NHANES I (1971-75), II (1976-80), III (1988-94), and Continuous (1999-2023), CenSoc WWII Army Enlistment v1.1, Society of Actuaries (1959) and (1980) for the Build and Blood Pressure Study (1959) and Build Study (1979), Actuarial Society of America Medico-Actuarial Mortality Investigation (1912), Staub et al. (2010), Schoch, Staub and Pfister (2012), Staub et al. (2016), Quanjer and Thompson (2021), Kark and Rasmussen (2005), Lissner et al. (2019), Marmot et al. (1991), Sobal and Stunkard (1989), McLaren (2007), Jaacks et al. (2019), Singh et al. (2011), Lehmann et al. (2016), de Saint Pol (2009), Sánchez-García et al. (2021), Bann et al. (2018), Perkins et al. (2018), Templin et al. (2019), Kuehl and Vuegelers (2008), Staub et al. (2011), Komlos and Brabec (2010), and ICPSR 2877.

While you’re here, go check out my article on whether GLP-1s can end the obesity epidemic!

1

Relative to European countries and the United States, the slope of this relationship tends to be a little shallower for Asian countries and a little steeper for Hispanic and African countries. You can sort-of see that here:

These charts used GDP, which overstates the development of tax havens. It would be preferable to use Actual Individual Consumption (AIC) to get around this issue, but we don’t have good continuous measurements of AIC, so we’ll have to settle for the Gross National Income (GNI). Using GNI, we get this replication of the first chart:

And the GNI version of the second chart looks like this:

As a final note, the median figure in the first plot is not an accurate depiction of international tendencies because it’s differentially reweighted along the GDP continuum. A similar issue applies to the regional aggregates in the second chart, but it’s less of an issue there because those countries tend to be more similar substantively and in terms of their ranges on both variables.

April 4, 2026 Update: Several people have asked me if this relationship holds within the United States between different racial groups. It likely does. With GNI, the plot looks like this:

With GDP, the shape for the U.S. data points is basically unchanged, except the graph is more compressed due to high GDP/lower GNI outliers, and the shape of the Median line differs, as above. So, for visibility, just take the GNI plot. Now, if you look at that plot, you’ll notice that the slopes do not appear to differ, but the intercepts do. This is even clearer if we go back as far as nationally-representative sampling allows, with the NHES I dataset:

Unfortunately, the large-scale, nationally-representative datasets started in 1961, meaning that they happened after substantial development. As I recorded on Twitter/X, the slope is sharpest in early development and it appears to considerably flatten out at higher levels.

The data from 1961 is largely after the expected point where the slopes should differ the most. And think about this: in the distant past, when resources were much more limited, the world was much poorer, life was more physically arduous, etc., and it was difficult for most people to be fat, different races’ BMIs should have been more similar—and indeed, the obesity epidemic has involved increasing variance.

To get at whether the slopes really differ within the U.S., we need older data. We do have some in sources like this, where the Black-White BMI gap in the 19th century held stable for a long time and was very small, but that data mostly concerns males, and the modern Black-White BMI difference mostly concerns females. Luckily, we do have female data!

The problem with the female data from the 19th century is that it’s a bit selective: it concerns prisoners. With male data, we have soldiers, laborers, photographs, and prisoners, and the estimates all converge on the conclusion that there was little Black-White BMI gap in the past, and with female data, we have photographs, prisoners, and not much else, but, reassuringly, these too converge on the idea that BMIs were similar in the past. More importantly, in the modern day, female prisons tend to recapitulate the Black-White BMI gap seen in the general population. In Nowotny, Rogers and Boardman’s analysis of the 2004 Survey of Inmates in State and Federal Correctional Facilities compared to the NHANES data we’ve been using, they found the following Black and White female obesity rates:

• Non-incarcerated: 26.5% for White, 45% for Black (+18.5pp; +69.8%)

• Incarcerated: 22.9% for White, 40.9% for Black (+18pp; +78.6%)

• Difference between contrasts: -0.5 (-6.6 to 5.6, p = 0.87; 0.97, 0.70 to 1.37 in relative terms)

So, with some assumptions about the relative constancy of representativeness in prisons, we can get to the conclusion that the slopes did vary predictable in the U.S., in a way that explains the vast majority of the modern intercept difference.

Unfortunately, we can’t repeat this with other groups because the data just doesn’t exist. But it remains a priori likely that there were major slopes differences within the U.S. for each group, which we can see in said groups having different intercepts today, and the past being a place where the range of BMIs was relatively constrained within and between races.

Maybe you’ve seen the headlines: “Harvard can’t figure out why ice cream eaters are healthier”; “The best nutritionists can’t explain it: eating ice cream helps your heart!”; “Studies show a mysterious health benefit to ice cream. Scientists don’t want to talk about it.” If you haven’t, consider yourself blessed, because you were saved from the experience of reading something very obviously wrong.

The idea that ice cream—a food item we can all agree is sugary, fatty, and delicious—is healthy for your heart is principally derived from an offhanded comment in a dissertation. Quote:

In our study [of the HPFS, NHS, and NHSII datasets], ice-cream intake was inversely associated with CVD risk among diabetes patients. This result was unexpected given its considerable saturated fat content (6.7 g per 100g) and sugar (21.2g of per 100g) of which, the majority is added sugar (>60%) and the rest is comprised of naturally occurring lactose. Nonetheless, in a previous study conducted among HPFS, the NHS, and the NHSII participants free of disease at baseline, ice-cream intake was also associated with a lower T2D risk. This result was in agreement with the those from two additional cohort studies, and in a meta-analysis it was estimated that consuming 10g of ice-cream per day vs. 0 g was associated with a 19% lower T2D risk. On the other hand, Ice-cream [sic] intake was not associated with risk of total stroke or stroke subtypes.

What are the results? Ice cream was marginally associated with cardiovascular disease (OR = 0.82, p = 0.047), whereas it was nonsignificantly related to coronary heart disease (p = 0.067) and to stroke (p = 0.37) risk.

These are not very impressive results; they’re also not even consistent within the paper. For example, at baseline the study found no relationship between ice cream intake and cardiovascular disease and the relationship only became marginally significant after adding a bundle of controls, including for total dairy product intake. It also wasn’t related at all to cardiovascular disease, coronary heart disease, or stroke risk among diabetics; it wasn’t related to cardiovascular disease among people who had self-reported HbA1c levels; it was related to 33% lower risk among those who reported insulin or other diabetes medication use; the main effect on cardiovascular disease risk became nonsignificant when an 8-year lag was added; and, among ice cream’s correlates including dairy (r = 0.17), low-fat dairy (-0.06), cheese (0.11), cream (0.10), fermented dairy (0.07), it was only modestly correlated with high-fat dairy intake (0.46), not correlated with sherbet consumption (-0.02), or consumption of skim and whole milk (0.11 and 0.001), with a modest correlation with yogurt consumption (0.32).

The pattern of results in this paper is seemingly not robust despite The Atlantic’s portrayal, and, frankly, it doesn’t seem that meaningful given that the authors’ analysis didn’t go very deep: they didn’t use many controls or a sophisticated control strategy; they didn’t attempt to deal with measurement error; and they didn’t use any designs to figure out if the association was driven by any sort of confounding. It was standard nutritional epidemiology, with all of its defects.

As summarized by John Ioannidis about a decade ago: nutritional epidemiology’s “effects” typically represent the impacts of the cumulative biases present in the research rather than the impacts of eating particular foods. What really matters is the crud factor.

Empirical Reanalysis

To look into the ice cream → health hypothesis, I opened up the complete NHANES data and went through progressively more sophisticated models, from basic regressions through doubly-robust estimation, all the way to using family fixed-effects. I wanted to see if the ice cream associations held up, and if they also came with absurd consequences. To make a long paper short, (1) they don’t, and (2) it did.

There are a lot of analyses in the paper, but here I’ll focus on just a few.

Firstly, in Panel A, you’ll notice one set of analyses. The OLS results are the standard cross-sectional correlations, where ice cream seems to relate to things like lower inflammation, higher HDL, lower blood pressure, and so on. But after adding just a few covariates to account for basic confounding, these effects are generally reduced and in the cases of blood pressure, HbA1c, and eGFR, the signs of the effects flip.

When we use very extensive matching on more than 100 different variables, the only thing that survives is that ice cream is associated with being slightly fatter. When we switch over to a plausibly causal analysis—comparing people in the same home—we get a similar picture, where the person who eats more ice cream is a bit fatter, but they’re also a bit less likely to be depressed. But in any case, all of these effects are tiny: certainly too small to matter, but significant because of my large sample size.

The ‘Ice Cream Good’ Narrative

The idea that ice cream is a health food and nutrition researchers can’t explain that fact is contrary to evidence. Ice cream doesn’t look particularly good for you if you examine the association with even a little rigor.

This fact runs counter to theorists like Ray Peat and to the public’s demand for counterintuitive findings. And, if the narrative that’s sprung up around this idea is true—that researchers at elite institutions couldn’t explain this finding—then it indicts a lot of academia, because ‘Ice Cream Good’ is obviously false and it was never on strong ground to begin with.

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

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Given all of that, why are people worried about seed oils? Not for any good reasons. Most of the anti-seed oil hype has to do with feelings that they’re unnatural, that they’re prepared in bad or disgusting ways, and so on—not on the basis of any meaningful, clinically acceptable evidence. The best arguments are based on mechanistic reasoning, but mechanistic reasoning sets up a game of ‘he said, she said’ because proponents of replacing saturated fat with mono- and polyunsaturated fats, seed oils, whatever, also have their own technical, mechanistic arguments and the bulk of the causal evidence.

The usual outcome when the trials and genetic evidence are pointed to is that opponents of seed oils will make some sort of excuse. They seem fully capable of always finding a way to say that seed oil intake was mismeasured or some obscure control variable was needed and not present. These excuses reek; I have never seen one that’s principled or which seed oil opponents will reliably stick to. In one moment, a study will be deficient because it used the wrong measure, and in the next, when that measure seems to correlate with something bad, they’ll accept it once more.

Let’s establish a baseline. Everyone says ‘correlation isn’t causation’, so let’s just check what the correlations between seed oil intake and various health variables are.

For this analysis, we’ll be using the CDC’s nationally-representative National Health and Nutrition Examination Survey (NHANES) dataset. For all analyses, we’ll be controlling for age with natural cubic splines with four degrees of freedom, survey weights,1 normalized weights for logistic regression,2 and robust standard errors for the weighted least squares regressions.3

To measure seed oil intake, I will be using dietary and plasma linoleic acid and, to supplement, I’ll also be using dietary and plasma n-6:n-3 ratios. All of these has been used and apparently been suitable for seed oil bashing by opponents of seed oils. Thus, they must be valid generally, and the only objection will have to do with the measurements in the NHANES. But, the measurements in the NHANES have also been used to note increasing seed oil intake and to attack seed oils, so again, opponents will have to denounce their evidence if they want to attack these results.

Seed oils aren’t even correlated with bad outcomes. Nor is canola oil intake or the n-6:n-3 ratio. But saturated fat is, in case you were wondering. Here’s a revealing big-picture graph: people who got the largest share of their diets from linoleic acid have the lowest risk of dying over just over 30 years of follow-up.

Cross-sectionally, linoleic acid was associated with lower inflammation, cholesterol, and triglycerides, with more worrying positive associations with HbA1c, which are the same or worse for saturated fat.

The bad associations for linoleic acid are driven by dietary confounding—more linoleic acid just means eating more. This is easily corrected, just switch to plasma measures of linoleic acid. With those, the bad associations fall away, the good associations remain, and saturated fat looks worse. Adding in propensity score-matching, most associations diminish; going further with doubly-robust estimation, they decline even more, but some signals remain. Here’s the summary:

After all’s said and done, in the matching analyses, linoleic acid is associated with lower inflammation, higher HDL, lower HbA1c, and not a lot else. And if you’re not satisfied by that, well, there are several supplementary analyses I ran, which include just looking at reported seed oil intake effects directly. For those, there’s not a lot there either!

I also went further and tested the mechanistic claims made by seed oil critics. I found that linoleic acid was not associated with arachidonic acid; it didn’t relate to higher atherogenic particle risk; it didn’t seem related to thyroid suppression or autoimmunity; there was no evidence of vitamin E depletion or oxidative damage; even cooking oils specifically didn’t seem to matter; and there was bupkes for clotting.

Consistent with the causal evidence and the best observational evidence from humans—including, for example, a massive analysis of the Nurses’ Health Study/II/Health Professionals Follow-up Study, the latest meta-analysis of prospective cohort studies, the National Institutes of Health AARP Diet and Health Study, and an analysis of the U.K. Biobank—seed oils not only don’t seem to be harmful, they appear to be healthful. This analysis is not causally informative, but there’s not even any confounded evidence for harm here. The cliché is that correlation isn’t causation, but seed oils don’t even have correlation-level evidence that they’re bad.

I end with a quote:

Seed oils people do what people often do when attempting to build up a losing case. They rely on a broad panoply of weak, circumstantial, points—mechanistic research in rodents, bs studies—along with plenty of winks and nudges, to give off a strong impression that their argument is correct beyond a doubt. It’s then left to others to show that each one of their points is specious, one by one, and that what they’ve built up is, in reality, a false impression based on a lot of garbage/irrelevant studies and unjustified assumptions. Even if some of the peculiarities they manage to find can’t fully be ruled out, the case overall is far less impressive than [they’ve] made it out to seem.

Replication Files, a Preprint, Sensitivity Analyses, and Q&A

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I just saw this post from Jonathan Haidt claiming that a ‘powerful’ study showed that adolescents who increased social media use had lower cognitive performance compared to adolescents who didn’t increase their social media use.

Naturally, I opened up the paper, read it, and noticed that the model was not a causally informative one. This struck me as odd because the authors definitely had access to the ability to do within-person analyses—to use each person as their own control—to obtain a causally informative result, and they could’ve also used siblings as controls for one another. They could’ve even done both for thoroughness!

Thankfully, I have colleagues with access to this data. So, I drafted some code and pinged a model specification over to them to retest this result immediately. Now, I’ve produced those results for you. Before I show you that, let’s compare what the authors did to what I was able to do within a few minutes.

Nagata et al.’s Model: They used group-based trajectory modeling, where they classified 7,500 participants in the Adolescent Brain Cognitive Development (ABCD) cohort into three latent social media use trajectories across baseline, Year 1, and Year 2 (no/very low, low increasing, high increasing). They then regressed Year 2 cognitive scores on trajectory group dummies, controlling for baseline cognition, age, sex, race/ethnicity, household income, parent education, ADHD and depressive symptoms, non-social media screen time, and site.

Their estimand is the average adjusted difference in Year 2 cognition between trajectory groups—a between-person quantity that is conditional on observables. They can only identify a causal effect with their analysis if the covariate set captures all relevant confounders. Any unmeasured variable that correlates with both social media use and cognition—genetics, parenting quality, household cognitive stimulation, neighborhood and school quality, etc.— will bias the estimate.

My Models:

1. Family fixed-effects: These absorb all between-family variation by demeaning within families. The estimand is the within-family association: among siblings in the same household, does the one who uses more social media perform worse? This controls nonparametrically for everything shared within families—parental genetics, socioeconomic status, parenting style, neighborhood, school district, etc.—whether measured or unmeasured. Identification requires only that siblings’ social media use differs for reasons unrelated to their sibling-specific cognitive endowments.1

2. Person fixed-effects: These absorb all stable individual characteristics by demeaning within person across waves. The estimand is the within-person association: does an individual’s own increase in social media use predict their own cognitive change? This controls for everything stable about the person—their genetics, their personality, their baseline ability, their family background, etc. Identification requires that the timing of social media uptake is unrelated to time-varying individual shocks to cognitive endowments.

3. Panel family fixed-effects: These involve stacking both waves and demeaning within families across all person-wave observations. This is the most efficient within-family estimator because each sibling contributes two observations, increasing the variation available for identification and reducing standard errors by 9-20% relative to the cross-sectional family fixed-effects result.

4. I also ran negative control analyses—showing that their specification produces significant effects on outcomes that cannot possibly be affected—, I computed the minimum detectable effects across methods, I checked for nonlinearities, and I tested covariate sensitivity.

First thing’s first, I replicated their results. I was able to do that. They are not frauds.

Next, I extended their results with a within-family analysis and a within-person analysis which knocked out their results and showed that they were not robust.

I also showed that the difference between my results and their results was not due to power, because I had more than enough power to detect smaller effects than theirs.

I then looked at whether their methods would suggest that social media use causes impossible outcomes: racial admixture, polygenic scores, and cognitive ability measures from earlier in time; and implausible outcomes: brain volumes. They sometimes could, but not within families!2

And to top all this off, I checked for nonlinearities in the forms of quadratic terms and categorical bins for 0, >0-0.5 hours, 0.5-1 hours, and >1 hours of social media use. Nothing was significant save for one outcome that showed a non-monotonic pattern that was inconsistent with a dose-response relationship.

And how long did it take me to find that their results didn’t hold up? Minutes.

It was trivially easy to specify and run these models and to output the results. It took practically no additional effort to go from the uninformative models used by Nagata et al. to using an actually informative model. Metallic laws, anyone?

Social Science is Always Like This

There are a lot of things that are deeply wrong with social science but, I’ll argue, none is so bad as the abject laziness with which researchers approach every subject. It took me mere minutes to correct a result that looked, to me, to be patently absurd. And let’s be frank: it is absurd. Social media does not make people less intelligent and anyone telling you it does is lying.

When you measure well, concussions don’t even lower intelligence and years of education don’t raise it. With those facts in mind, it should seem patently ridiculous to think that selecting into another hour a day of Twitter is going to make kids dull.

Nagata et al. didn’t get the right result because they’re at least one of the following: ignorant, lazy, biased, or publication farmers. They might not have known that they could test their results—ignorance. They might not have wanted to fit proper, informative models even though they knew they could—sloth. They might have wanted to make social media look bad and not cared that people could see they didn’t run appropriate models—bias. Or they might be looking to overturn their own results in a future publication, with this one intended to pad their publication and citation numbers—farming.3

Whatever their reason, Nagata et al. aren’t atypical (and I don’t want to insult them; I think calling them ignorant or lazy is descriptive and correct). Consider the issue of measurement invariance. This is a critical thing in fields where objective measurements like height and weight aren’t being used and the measurements are instead of hypothetical quantities like group perceptions, neuroticism, intelligence, or what-have-you. To compare groups or individuals across times, you have to establish that those measurements are comparable over time, and despite this possibility being routinely available to researchers, almost no one tests it, and when they do test it, they tend to test it incorrectly. Yet, if you can’t compare measurements over time or between populations, any differences might be due to that incomparability rather than due to variation in the target trait. So, why don’t researchers assess measurement invariance?

Well, they’re lazy. They’re so lazy that they rarely ever do the required robustness and sensitivity analyses to assure that their results are as they are, or to let them triangulate in on something causally informative.

The field of economics has made a lot of strides in this regard. The typical economics paper in a modern, high-tier journal is littered with tables full of robustness tests4, and these allow economists say that their results hold up. In fact, they’ve gotten so good at this that I like to cite something called Henderson’s Law: “When you read an econometrics study done after 2005, the probability that the researcher has failed to take account of an objection that a non-economist will think of is close to zero.”5

But we’re not yet at the point where the rest of the social sciences—including genetics and medicine—can confidently say the same. In fact, it’s more typical for there to be glaring errors that even laymen could spot.

I don’t know how to fix this. I imagine any fix has to be internal to a field. In economics, this was self-led based on an outcry to “Take the Con out of Econometrics”, but it was also possible because economists tend to be smart. The people in other fields tend to be considerably less intelligent and, especially, less capable at or interested in doing mathematics-heavy work, even if they’ve got plenty in terms of raw smarts. If I had to place a wager, I’d say the average sociologist, social scientist, or even medical doctor cannot write even simple proofs.6 What hope do they have to notice something like that a method such as Non-Linear Mendelian Randomization is just not workable?

Happy Note

In 2019, I gave a conference presentation that documented that methodological innovations precede theoretical improvements rather than vice-versa in the domains of differential psychology and analytical chemistry. I think those conclusions held up well, and they present the possibility that social science doesn’t have to be this way.

The fix I have in mind is to have methodologists steward their fields. The people who have a deep understanding of and desire to improve on and implement different methods are the real heroes of science, and to the extent they’re empowered, they can force compliance. Consider the issue of ‘small sample sizes’. Once people started pointing out that many samples used in the social sciences were too small to answer their questions after Ioannidis’ Why Most Published Research Findings Are False and its’ knock-on work, it became a rallying notion. Suddenly, a study could be dismissed out of hand based on a sample being too small, and lots of bad work became unpublishable!

This was by no means perfect; the complaint is often taken too far because the people brandishing the excuse to dismiss a given paper usually do so without the knowledge of what a small sample actually means. Nevertheless, this was progress. Suddenly, tons of papers that would very likely be bad died in the cradle and the number of false-positives was probably comparatively meager. And this shows us the way!

The way is this: increase the salience of methods and reduce the friction to using them. Make software packages that easily implement new methods. This will result in a proliferation of careless results—as has happened with the literature on Mendelian Randomization—but it will nevertheless improve the average quality above simpler methods and lacking checks. We also need to increase the salience of the need for assurances. Authors should be compelled by editors, reviewers, and their coauthors to check twice if they’re going to cut once; it costs effectively nothing to host a large supplement containing robustness tests, so journals should heavily encourage them.

Am I being too optimistic about this? Almost certainly. I live to be disappointed.

P.S. Did you know that the New York Times wrote a slanderous and obviously error-riddled article kvetching about me having access to this data? If not, you can go check that out here!

P.P.S. Here’s my replication code. Unfortunately, I can’t provide the data; you’ll have to apply for access to that yourself.

Have you ever heard about “HAES”? It stands for “Healthy at Every Size” and it’s the idea that people can be healthy regardless of how fat they might be.1 This idea might sound surprising given what we know about how obesity affects health.2 And yet, it has garnered a degree of intellectual cachet. In fact, it’s inspired some meaningful empirical research into the condition of being both fat and healthy, a condition known as metabolically healthy obesity, or MHO.

MHO is a poorly-defined idea.

A review of the MHO concept by the nutritionists Smith, Mittendorfer and Klein (SMK) noted that there “is no universally accepted standard for defining MHO, and more than 30 different definitions have been used in different studies”. Most studies define MHO based on being obese while having some small number of the symptoms of metabolic syndrome.3 That sounds fair enough: MHO is basically defined as having some signals of looking healthy and being obese. But is this even necessary? How prevalent is MHO versus its antithesis, metabolically unhealthy obesity, MUO? In the CDC’s nationally-representative NHANES dataset, the answer is ‘fairly uncommon.’

8.8 to 13.9% of each obesity category qualified as MHO in the latest sampling year based on a fairly strict definition I found had been used in this dataset by Wang et al. Because more of the obese are Class I (i.e., BMIs of 30-34.9) than are in the higher BMI classes, the majority (about two-thirds) of those who are MHO are borderline obese, and with more extreme obesity, MHO is rarer relative to MUO. This is consistent with the findings of the SMK review:

In general, MHO is more common in women than in men, in younger than in older adults, in people with BMIs less than 35 kg/m² than in people with BMIs of 35 kg/m² or higher, and in people of European ancestry than in those from Africa, South America, and South Asia (Indian ancestry).

Notice the line “MHO is more common… in younger than in older adults”. This is crucial. MHO is unlikely to be a stable condition. I tried to find every study that examined how people converted between MHO and MUO and found a lot of them, and they generally agreed: the more time that passes, the fewer people maintain MHO.

Though I’m not confident in the appropriateness of this analysis, it is possible to take the results from these different studies, with all their differences in definition noted, and to make a projection about how stable MHO can be. If we do that, it looks like 8-9/10 cases of MHO become MUO by 20 years in. That’s a theoretically dubious estimate, but I imagine something like it is not that far off the mark. Majority conversion in that time is highly believable.

MHO is defined as obesity with a reduced but not zero metabolic downsides typically associated with obesity, and… it’s not very protective.4 A few select quotes from SMK should suffice:

The risk of developing T2D is much lower in those with MHO than MUO, but is still about 4-fold greater than in those who are MHL [i.e., metabolically healthy and lean], and is directly related to the number of metabolic abnormalities at baseline….

The risk of CVD events (new-onset angina, fatal and nonfatal myocardial infarction, sudden cardiac death, fatal and nonfatal heart failure, and peripheral vascular disease) is lower in people with MHO than in those with MUO, but is still higher in people with MHO than in those who are MHL….

The combined data from five large cohort studies that followed participants for an average of 13 years found that people with MHO and no metabolic syndrome components (excluding waist circumference) did not have an increased risk of all-cause mortality compared with the MHL group; however, the risk of all-cause mortality was greater in participants with MHO versus MHL when participants with one abnormal metabolic risk factor (excluding waist circumference) were included in the MHO group.

The people who are MHO are still at a high risk, and because so many of them convert to MUO, the risk is higher than it might appear at a given ‘healthy’ baseline because so few can stay ‘healthy’. And this has a lot to do with fat itself being bad and, like cholesterol, causing damage that accumulates over time.5 We can see this in a variety of ways.

For one, we know that being fat is bad for glycemic parameters—that is, blood sugar. Bad glycemic parameters lead to cumulative damage because they’re constant stressors on numerous areas of the body, and perhaps also because they lead to obesity. In long-term follow-ups, there’s evidence of metabolic memory such that earlier periods of better or worse glycemic control affect complication risks years down the line. Patients that engage in early, intensive glucose control efforts also show reduced rates of microvascular complications, with numerous systemic benefits including, notably, reduced risk of all-cause mortality.

Mendelian Randomization supports the idea that BMI and abdominal adiposity are causally linked to both insulin resistance and type-2 diabetes, but not clearly to fasting glucose itself. This fits with obesity mostly wrecking insulin sensitivity first, with fasting glucose remaining compensated for a while. An individual could appear ‘MHO’ while being on a path to MUO as their insulin sensitivity degrades.

Moreover, one of the things that predicts going from MHO to MUO is visceral fat accumulation—which tends to happen with time spent being MHO and MUO alike—, and visceral fat seems to be itself bad for glycemic parameters. As a sort of natural experiment, people with lipodystrophy also tend to develop severe insulin resistance, hypertriglyceridemia, fatty liver, and diabetes. However, it’s not clear that just removing this fat fixes the issue. Liposuction cases don’t seem to show improvements in insulin action and glycemic control.

For two, obesity is harmful through mechanical load. Having more tissue also predicts, and quite likely causes, higher rates of cancer, in addition to all the other carcinogenic effects of obesity. It directly leads to sleep apnea and hypoventilation; fatty liver and liver fibrosis; osteoarthritis and musculoskeletal damage; venous thrombosis and impaired fibrinolysis; kidney damage; adverse adipokine signaling; chronic low-grade inflammation; and by being fat, you’ll probably be respected less, leading to whatever health harms follow from being socially rejected and disliked more than might be the case if you were skinnier.

If you’re a guy, you should be worried by obesity because it lowers total and free testosterone and raises the estrogen to testosterone balance because adipose tissue expresses aromatase, which converts androgens to estrogens. As a result, obesity can lead to gynecomastia, lower libido, erection problems, worsened fertility, and reduced muscle mass—obesity-related sarcopenia! For women, the worry runs the opposite direction, with obesity being relatively masculinizing, contributing to the worsening of PCOS and hyperandrogenic abnormalities that can impair fertility.

I could go on.

My point is this: Healthy at every size is quasi-mythical. There are people who appear to have relatively normal metabolic parameters while being obese, but they are generally not very obese, just somewhat fat. Moreover, few of the seemingly healthy obese people can sustain whatever metabolic healthfulness they have for very long, and to the extent they can, it’s probably because whatever damage they’re accumulating isn’t being measured or isn’t being used to define that person as metabolically unhealthy even though they’ll soon become metabolically unhealthy by any measure.

If we want people to be as healthy as possible, we’ll not humor the idea that people can be healthy at every size, because, frankly, it’s just not true.

The studies referenced in the charts are ATTICA, ELSA-Brasil, the Nurses’ Health Study, Hwang et al. (2015), Kang et al. (2017), MESA, KERCADRS, the Kadoorie Biobank, Ahvaz, Gilardini et al. (2018), ELSA-England, Kim et al. (2019), TLGS, NWAHS, CPRD, REGICOR, Whitehall II, Achilike et al. (2015), SATSA, Elías-López et al. (2021), Higashioka et al. (2025), CHARLS, and REACTION.