Research Translation Podcast

This week we saw something new: A president of the United States behind a podium, and flanked by the heads of the FDA, the Department of Health, and the NIH, and they were all doing research translation.

Well that’s my job, dammit. So today we’ll do a prelude and prep session for our next episode, which will be a review and discussion of the six studies that underpinned that press conference. We’ll translate those studies so readers and listeners can decide for themselves.

This week, however, we need to lay a little groundwork. So we’ll talk about an observational study just published in a major journal, a study that examined radiation doses in children due to medical imaging (primarily CAT scans) and their association with cancers like leukemia in the decade that follows.

This is a blockbuster article, bound to become a standard bearer, the paper that governments and medical societies and other organizations will cite when they craft recommendations for how to approach medical imaging in children.

But like all observational studies, this excellent study has critical attributes that should make us wonder whether CAT scans and cancers have virtually anything to do with each other. Which is why it’s a perfect foundation for next week’s episode.

Tune in to the podcast, and get your weekly dose of Research Translation.

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Son, I’ve been avoiding this conversation. But it’s important. It will be difficult and I realize you don’t want to hear it, least of all from me. But there are things you have to know. Please try not to think of this as personal. It’s not about your thoughts or feelings. It’s about the future, how to make decisions, and how to understand our bodies and how they interact with the world. So, here goes.

In March of 2020 the Covid pandemic hit hard. The virus was new, leading governments and companies around the world to work furiously on developing Covid vaccines. The most promising and effective were mRNA vaccines from Pfizer and Moderna, which completed clinical trials of 70,000 people by November of the same year. This was a jaw-dropping achievement, and both vaccines reduced infection rates by over 90%.

The vaccines were quickly made available to everyone, and by June of 2021 more than half of the US was vaccinated. But there were many holdouts. People worried about the safety of a new technology pushed by government, and rushed to market by companies with long histories of unethical, profiteering behaviors. Then, in July, the Delta variant of Covid tore through the US, infecting vaccinated and unvaccinated people alike.

So. . . did the vaccines work?

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The Delta variant extinguished any dreams of herd immunity, showing the vaccines didn’t protect well against Covid infection. Even so, they could still work. After all, humans deal with seasonal influenza and countless other viruses. What made Covid awful was its fatality rate. If the vaccine prevents the severest cases, most importantly deaths, Covid could become just another seasonal virus.

So. . . did the vaccine at least do that?

The two big mRNA randomized trials ran just 6 months each, which is frustratingly short. (Placebo participants were then given vaccine shots, so we don’t have trial data to know long term effects, good or bad). At 6 months, however, deaths were equal between groups: 31 vaccine, 30 placebo. And most of the deaths (29 vs 25) weren’t related to Covid.

For the Pfizer trial, which reported 15 vs 14 deaths, FDA documents actually show 21 vs 17. But this includes the ‘crossover phase’ which came after 6 months. Some people therefore like to say deaths were “23% higher” in the vaccine group. While that’s numerically correct, it’s misleading since a) there was no statistical difference, and b) after 6 months most people in both groups were vaccinated. For the Moderna trial, deaths were 16 vs 16.

These numbers don’t tell us much. Okay, yes, the vaccine trials didn’t find a life-saving benefit. But that could be because there weren’t enough deaths due to Covid (just 2 vaccine vs 5 placebo) among participants. Or maybe the vaccine doesn’t save lives overall. Maybe it prevents some Covid deaths but causes others, for instance due to sudden cardiac arrest or heart problems (the National Academy of Science looked, and couldn’t say yes or no). We may never know, both because the studies were cut off at 6 months and because Pfizer and Moderna, even today, refuse to make their data public.

In any case, we should admit the obvious: It feels weird that, in trials done during the peak of Covid fatalities, people who got vaccinated died as often as people who got a placebo. That’s either an unfortunate and misleading anomaly (i.e. the trials were too small), or it’s an important signal of failure lost in the noise of politics and opinion.

Meanwhile, many other studies have concluded the mRNA vaccines save lives. Sadly, they’re all hopelessly shitty. Excuse my French, but let us be honest and blunt: So-called ‘real-world’ data is neither real nor data. It is, at best, the plural of anecdote. The ragtag collection of studies making this claim all suffer from healthy user bias (in which people who choose to be vaccinated are predictably much healthier at baseline than people who choose not to get vaccinated, and therefore die less) and are easily challenged. This includes a devastating, if barely noticed, takedown in the pages of the New England Journal of Medicine.

But, alas, even a naked moment of research verité doesn’t prove that ‘real-world’ study findings were wrong. It just means their data can’t answer the question. And in fact, on an ecological scale it’s likely the vaccines did save millions of lives. The timing of the pandemic’s last gasps—as vaccines phased in—and the statistically powerful finding that people who died of Covid were overwhelmingly unvaccinated, are both strong circumstantial arguments.

But that is what they are, and we should be honest about it. We should be bashful when saying the vaccine ‘works’, and clear about the uncertainty. Particularly when data from the trials show roughly 1 in every 1,000 people experiences a serious harm from the vaccine, like appendicitis, myocarditis, gall bladder problems, and more.

Therefore we must accept that it isn’t crazy, wrong, or conspiratorial to worry the vaccines may not save lives, or could be more harmful than helpful for some. Particularly now, when most people have natural immunity, which is as good as vaccine immunity.

Therefore the decision to be vaccinated can be personal, not communal. For most people under the age of 30 the flu is more of a life-threat than Covid, and the Covid vaccines are way more likely to be harmful than helpful. For those 30 to 60 it’s close, but Covid may be worse than flu as their age rises. And over 60, Covid definitely is the bigger threat.

Which explains why, for instance, most European countries only recommend Covid vaccination for the elderly—a reasonable, not insane, approach.

Does the vaccine work? It depends. It probably won’t keep you from getting Covid, and if you’ve had the infection it’s likely redundant. But if you haven’t, or you’re getting along in years, it’s possible—though not definite—that it could reduce your chance of dying from Covid.

So. . . son: Be honest and be true, and respect people. It’s their body, it’s their choice.

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Media, Substackers, and authorities are screaming bloody murder about the difference between new Covid vaccine guidelines posted by the American Academy of Pediatrics and guidelines from the CDC. For kids under two the AAP recommends the shot. The CDC says not so fast: discuss it with a doctor.

Who’s right? Guidelines are written by panels that review the data, then make judgment calls. The only way to know if you agree is to understand the data. And to do that, we must first revisit a fated moment in Covid history.

For the July 4th weekend in 2021, I was on Cape Cod with my kids. We were supposed to be safe from the virus. In the preceding months more than half the US population had been urgently vaccinated, and in a town full of LGBTQ+ people, artists, and self-styled intellectuals, it seemed almost certain the vaccination rate was way more than half. But the Delta variant hit Provincetown like a tornado, and vaccinated people everywhere got sick, including me. I briefly lost my sense of smell for the first time. But the world lost something else, permanently: the dream of herd immunity.

Here’s why. The MMR vaccine prevents about 97% of measles infections, which is high enough that if 90% or more in a community get the shot, the measles virus has no inroad. While rare ‘breakthrough’ infections may occur, outbreaks are impossible. For the virus, every road is a dead end. That’s herd immunity.

With a prevention rate of 95% in Covid vaccine trials, herd immunity seemed tantalizingly close. Then Provincetown happened. The Cape Cod outbreak signaled to the world that the virus would constantly mutate, and even vaccinated people would routinely be infected. In a single jubilant holiday weekend, the dream of herd immunity came and went like so many fireworks.

But there was some good news: vaccinated people rarely died. Covid vaccination, it seemed, made death or serious illness extremely unlikely. Which is critical for decoding the vaccine’s benefits, and thus the guidelines.

Now, to the data.

The best data in kids comes from a vaccine trial of 4,526 children, a little less than half of whom were between 6 months and two years old. The actual focus of the trial was whether the kids developed Covid antibodies, which they mostly did, but importantly the researchers also reported the vaccine prevented 76% of Covid cases.

Unfortunately, this estimate was based on just 4 illnesses in the vaccine group and 8 in the placebo group, or 1% vs 5%, respectively. That is weak data, and a useful reminder: even in a 2021 trial from the heart of the pandemic, kids get sick with Covid less than adults and experience less severe illness.

But remember what we said. The benefit of the vaccine is not in preventing illness—vaccinated kids will still get sick—it’s prevention of serious illness. Which raises an obvious question: how often do young kids become seriously ill with Covid? In other words, how common is the problem the vaccine aims to prevent?

It's rare. The CDC estimates 1 in every 2000 young children in the U.S. was hospitalized with Covid in 2025. While that is a surprisingly large number (suggesting about 3,500 children hospitalized), it’s also seven times lower than the corresponding number for adults. Moreover, less than half of the hospital stays were for "severe Covid", in which a child received ICU care, oxygen, or other Covid treatments. Indeed, more than half the kids were home in less than two days, suggesting the admission was, at worst, precautionary. But, in theory, vaccination could prevent 76% of those hospitalizations by preventing the initial infection, which would mean 1 in every 2,632 vaccinated kids in the U.S. avoids hospitalization because of the vaccine.

Next we have to quantify the vaccine’s harms, but the data is even sparser. The Pfizer trial of young children reports “severe reactions” in 0.3% (1 in 333), but these were fevers, irritability, and loss of appetite, usually for a few days or less. It takes larger datasets, like the 67,000-person original Pfizer and Moderna trials, to find major harms. The best report on harms from those two trials found roughly 1 in 800 people experienced serious illness due to the vaccines. Unfortunately, current data can’t tell us whether children are more or less harmed than adults, so we’re left to apply adult harm data to kids.

In aggregate, then, a best-case estimate for the vaccine suggests about 0.038% (1/2,632) could benefit by avoiding hospitalization, while 0.125% (1 in 800) would be harmed by a life-threatening or serious illness. Based on these numbers harms are three times more common than benefits.

There you have it! Sort of.

Now take a moment to consider how many judgment calls it took to generate these calculations. To estimate benefits I selected the most flattering possible study, extrapolated tiny numbers to the rest of the world (4 vs 8 kids!), and then accepted a dubious, almost certainly inflated, CDC estimate of hospitalization rates. For harms I ignored all transient ‘reactions’, and was forced to apply adult data to children.

Clearly, there’s room for judgment. It becomes easier to see how two expert panels might differ. If you believe vaccines are under-appreciated and very safe for kids (read: if you’re the AAP), you might conclude the serious harms from the vaccine are likely much less common in kids than in adults. Which could shift the recommendation because even a tiny benefit, or potential benefit, would be bigger and more common than a serious harm.

If you’re a Covid vaccine skeptic, or a pharmaceutical company skeptic, (read: if you’re RFKjr) and you believe Pfizer’s reported benefits and the CDC’s rates of hospitalization are likely exaggerated, you might conclude harms are probably way more common and more serious than any benefits.

But if you explained all that you’d be admitting that opinion has affected your recommendations. Neither the AAP nor the CDC seems prepared to do that.

But they should. Then, the ‘science’ would be evident, and accessible to all, and the judgment would be transparent. And people could use their own values to make decisions about the vaccine.

Imagine that.

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In case you were wondering: After you crack a knuckle it’s about 20 minutes before you can crack it again. This was established by a series of experiments in 1947 at a research hospital in London. According to the researchers, “the minimum time was 17 minutes… but never more than 22.”

Eureka!

This may seem like menial work, but efforts to understand knuckle cracking pepper more than a hundred years of scientific literature, and continued into the 2000s. Then, in 2015, Canadian researchers watched knuckles cracking on MRI and were able to document ‘cavitation’, bubbles that pop open to fill space where a knuckle joint is stretched. Soon after, mathematical work confirmed those tiny bubbles can fully account for the impressive sound of a knuckle crack.

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The modern quest to explain knuckle cracking is a useful reminder that, even today, physicians stand on a tiny island of knowledge, surrounded by a nearly endless sea of unknowns. Until just recently, that sea included knuckle cracking. But the single greatest unknown remains. That, ironically, is the great repository of knowledge itself, the human brain. How it works and what’s happening in it is the holy grail of unknowns.

One reflection of our ignorance about the brain is the DSM-V, a book that defines and classifies mental illness. It is an important and broadly helpful book that codifies a universal language for psychiatry. But name any condition in it, and you’ve spoken a word with absolutely no anatomic or physiologic explanation. Depression? Don’t know where it is, don’t know what it is, don’t know how it happens. Schizophrenia? OCD? Bipolar? Addiction? Same.

This means there is not one pill, procedure, or treatment for mental illness that targets the underlying problem, a fact that makes psychiatry special in modern medicine. The biomedical model—identifying pathology at the anatomic or cellular level, then curing or improving it—simply does not apply.

Which may be one reason why a Nobel Prize was awarded in 1949 for the advent of the frontal lobotomy, a procedure for psychiatric patients in which an ice pick-like instrument was inserted through the forehead or eye socket, into the brain. The tip then pierced, and irreversibly damaged, the frontal lobe. This often led to a permanent loss of personality, lifelong apathy and listlessness. Even some of the procedure’s proponents referred to it as 'soul surgery’.

Today, frontal lobotomy is broadly recognized as unethical, and largely banned. But it arose from the hubris of scientists who believed so firmly in the power of their small island that they ignored the sea around them. Despite knowing nothing of the underlying disease or the inner workings of the brain, they developed a ‘treatment’ designed to permanently alter brain anatomy and physiology. It is a very different approach to medical treatment than the biomedical model, and it is one to avoid.

Incredibly, to this day, the Nobel Prize website defends the 1949 award, saying “at that time there were no other alternatives!” This is flagrantly untrue, and betrays a profound misunderstanding of psychiatric treatment. While antipsychotic drugs were not yet available, there were many alternatives to lobotomy. Options like talk and group therapy, institutionalization, safety measures in periods of danger, assisted living, social support and guidance, and more, are crucial measures that still help psychiatric patients every day, and through periods of crisis.

Yet the Nobel language implies that like lobotomy, drugs are somehow uniquely directed at the underlying problem in psychiatric illness. In reality, they are no more targeted than a rubber room.

To be clear, for many people psychiatric drugs are a godsend, making life livable, safe, and productive. But while they may change thinking and behavior, they don’t even attempt to correct the underlying condition. Like lobotomy, they simply go into the brain and muck around.

Confusion on this point is intentional. For decades pharmaceutical campaigns used the misleading phrase ‘chemical imbalance’ to describe mental illnesses, as if this is a part of any known pathology. It is not.

But that did not stop the makers of dopamine blocking drugs from advancing the theory that ‘overactive’ dopamine causes schizophrenia. Nor did it stop advocates of the ‘monoamine theory’, who suggested neurotransmitter imbalances caused mood disorders and monoamine drugs could fix them. And then came the greatest bamboozle of all, the idea that serotonin deficiency causes depression, which made serotonin drugs among the most prescribed in the world.

But the joke’s on us. Today, the serotonin theory is thoroughly debunked, but serotonin drug sales continue to soar. The illustration below, from the current website of a high profile research lab, is typical. It is based entirely on an advertising campaign, and has no basis in scientific fact.

Obviously, serotonin drugs don’t target any known disease. And yet they are commonly used not just for depression, but for anxiety, OCD, phobias, bulimia, premature ejaculation, PMS, fibromyalgia, irritable bowel syndrome, sleep, pain, and more.

Why else would they be prescribed for such a mind-boggling array of conditions? As one of my instructors in med school used to say: a drug that works for everything, doesn’t work for anything.

Again, some psychiatric drugs can be life saving (not the serotonin ones—mostly lithium and the antipsychotics), and there are many who need them to function. But if we’re being careful with our terminology ‘psychiatric drugs’ are not psychiatric. Like lobotomy, they simply have effects in the brain. Some may be desirable, others may not.

To be fair, using a pill to treat a symptom, rather than cure a disease, is common. Ibuprofen for a headache is one example. But when people and their families agree to long term medicines for mental health, they and their families often believe the pills are specific to their illness, or at least based on knowledge of the underlying problem. That belief is buttressed by white coats, framed degrees on the wall, and misleading illustrations like the one above.

For safety, island dwellers must respect the sea. To avoid disasters like bloodletting and frontal lobotomy we must recognize and acknowledge what we do not know.

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This week I've opted for a slightly longer format, which I sometimes like to do—we'll be back to normal next time. Enjoy.

On Friday December 13th, 1799, President George Washington was in a chipper mood. He sat by the fire talking politics with Martha and Tobias Lear, his secretary. At age 67, two years after his presidency, Washington was still America’s most popular public figure. Robustly healthy, he had spent the afternoon on his Mount Vernon estate marking trees through a snowfall. Now, however, he mentioned a sore throat that had been dogging him for days. Lear noted the President was hoarse, and suggested medicine. Forever the stoic soldier, Washington declined. “Let it go as it came," he said.

Among medical historians the infection that ended Washington’s life the next day is a subject of debate. All agree, however, on the nature of his care. The physicians who worked furiously at his side were highly trained, deeply concerned—and fully culpable.

In the early morning hours Martha woke to find the President in pain and short of breath. Washington insisted she wait til light to summon help, lest she be exposed to the night air. At sunrise Martha notified Lear, and messengers were dispatched for James Craik, a local physician and dear friend. While awaiting Craik’s arrival Washington summoned the overseer of the estate, Albin Rawlins, to perform a bleeding. Bloodletting, to remove toxins and restore balance, was routine in such illnesses. Rawlins incised a vein in the President’s inner forearm, and 14 ounces of blood flowed. Martha, suspicious of bloodletting, voiced her discomfort. But Washington encouraged it. "Don't be afraid. The orifice is not large enough. More, more."

Dr. James Craik, who fought at Washington’s side in the Revolutionary War, arrived late in the morning. Craik was a former Physician General, similar to today’s Surgeon General, and attended the University of Edinburgh in Scotland, widely considered the world’s most respected training ground. The President had recently convinced Craik to move his practice nearby, and he arrived to find Washington with severe throat swelling and a fever. Craik began treatment with a blister of Cantharides on the President’s neck. The powdered beetle preparation was intended to purge toxins by raising a blister on the skin, which it did.

Washington’s condition, however, worsened. Craik performed the morning’s second bleeding. This time he drained 20 ounces, again from the General’s arm.

As noonday passed the President’s breathing became labored. Attempts to swallow, including a concoction of molasses, vinegar, and butter, resulted in violent coughing spasms. The President’s throat appeared to be closing. Craik administered doses of calomel and tartar rectally. Calomel, a mercury-based pesticide, functioned as a purgative to stimulate bowel evacuation. Tartar, another common pesticide, produced vomiting. Both were believed to remove toxins. The president moved his bowels and retched. But he did not improve.

Concerned, Craik bled him again, for 20 more ounces.

In the late afternoon Dr.'s Elisha Cullen Dick and Gustavus Brown arrived to find Washington gravely ill and barely able to speak. Dick was a prominent young doctor who had earned his degree from the University of Pennsylvania. Dr. Brown, a lifelong friend of the President and Surgeon General of the Continental Army in the Revolution, had also trained at Edinburgh. After conferring, the physicians reached a consensus and acted.

They spilled 40 more ounces of the president's blood, for a total of 94. So far.

The first president of the United States, a young country’s most beloved figure, was slipping away. The three physicians conferred again and quickened their pace. Blisters on the arms, inhaled vapors, cataplasms (a form of compress) for the skin, more purgatives. Nothing was helping, and Washington’s throat was now almost fully occluded.

The doctors were frantic, but the President was not. According to Tobias Lear, the great general asked them to cease their efforts. “I thank you for your attentions, but I pray you take no more troubles about me” he whispered. “Let me go off quietly.”

Dr. Dick, the youngest and brashest of the three, suggested they perforate Washington’s trachea. It was a last resort and Dick understood the implications, offering to take responsibility for any untoward outcome. Surgically opening an airway—tracheostomy—was new. Twenty-five years later the French surgeon Armand Trousseau would use the technique to save children suffocating from diphtheria, allowing them to survive an infection whose only fatal mechanism was airway closure. Dr. Dick felt asphyxiation was the President’s greatest threat. Still, tracheostomy was experimental and new. The older physicians would not assent.

Instead, they offered a final effort: 32 more ounces of presidential blood.

Just after 10pm the President spoke his final words, “‘Tis well”, and checked his own pulse. As one witness recalled “With surprising self-possession he prepared to die. Composing his form at length and folding his arms on his bosom, without a sigh, without a groan, the Father of his Country died. “

**********

To the modern mind, the events of Washington’s death are unfathomable. Basic principles of medicine and common sense seem grievously violated. But all three of Washington’s physicians trained at leading universities, and had the purest of intentions. Two were close friends and all were aware of the President’s god-like status. Yet incredibly, they took a gallon of his blood. Even for a man of the President’s stature (over 6 feet, more than 200 lbs.) this was nearly two thirds his total blood volume.

Could it be the importance of human blood was unknown in 1799? More than a century earlier William Harvey elucidated the mechanics of a beating heart, and the blood it propels. And despite a confused understanding, blood was known to be necessary for survival. Two of Washington’s doctors were battlefield surgeons who had witnessed, and attempted to stanch, mortal blood loss. What, then, explains the quasi-assassination?

Experiential delusion and ideologic gumption.

For Hippocrates the delusion of experience plagued the practice of medicine, which he called 'the Art'. In his best-known aphorism Hippocrates wrote, “Life is short, the Art is long, opportunity fleeting, experience delusive, judgment difficult.” With bloodletting, the delusion was powerful. Even in the 18th century, while bloodletting harmed the chance of survival (as controlled experiments later showed) most people still recovered soon after, creating the illusion of effectiveness.

And Dr.’s Craik, Brown, and Dick were accomplished bleeders. With years of practice and thousands of anecdotes, all three were fully deluded. Of course, none subjected their knowledge to rigorous testing. None compared notes with physicians who did not use bleeding. And none conducted—or had likely ever heard of—a randomized trial.

Experiential delusion can be all-consuming. But a second, equally powerful trap was in play at Mount Vernon. In 1799 the Humoral Theory of illness was the prevailing ideology at top medical schools and universities. A vestige of the ancient Greeks' four humors (blood, phlegm, yellow bile, black bile) the theory centered on toxins and a belief in 'balance'. To heal, doctors were taught to restore balance by removing toxins. Dr. Craik began with a blister of Cantharides which created a skin eruption, a visual sign that evil toxins were erupting forth. The tartar and calomel, designed to wreak intestinal havoc by inducing diarrhea and vomiting, provided two more routes of egress for toxins. Indeed, all the methods used on the president—cataplasms, vapors, purgatives, suppositories, blisters, salves, and bleedings—were for purging toxins.

Washington’s doctors stuck to the ideological script they were given. Neither their experiences in war, nor recent papers challenging bloodletting, not even their friend’s death spiral, could make them deviate from their faith. When bloodletting made Washington worse, they doubled down. Even for honest, rational men, ideologic gumption overwhelmed both the sciences and the senses.

Experiential delusion eschews scientific method and handicaps reasoning, teaching the opposite of truth. Ideologic gumption consecrates the delusion, closing the mind's eye, and setting a fixed, inalterable path. Together, the traps are a prison of wrongheadedness.

Which leads to an inevitable question: If the most experienced, best trained, brightest physicians of the day, using the most advanced theories and therapies, hastened the death of their friend, what of today? In the science-soaked 21st century are we fooled by experiential delusion? Do we suffer from ideologic gumption?

You bet.

A hundred years from now scientists will shake their heads at us. Our theories will seem primitive and our methods feeble. And for many of our errors the evidence is here, now, staring us in the face.

Today's cardiologic version of the Humoral Theory, for instance, is the Lipid Hypothesis, suggesting cholesterol is the primary cause of heart disease. First debunked by findings from the Framingham study in 1987, the Lipid Hypothesis was birthed, ironically, by earlier results in the same study. In 1961, preliminary papers from the project explored cholesterol as a potential risk factor for early death. But over time the already tenuous connection waned further, even as risk factors like high blood pressure, smoking, and diabetes grew more robust. By 30 years of follow-up low cholesterol was associated with early death, not high. Other than men under 50 with exceedingly high levels (e.g. those caused by rare conditions like familial hypercholesterolemia) 'high' cholesterol was meaningless.

Many modern, arguably more rigorous studies have confirmed the final Framingham findings, and they often include figures like the one below, plotting mortality against cholesterol levels.

The solid pink line is mortality, plotted against LDL cholesterol in US adults between 1999 and 2014. The thin black line (at 1.0 on the y-axis) represents the expected mortality rate. Keeping in mind that high cholesterol is defined as an LDL over 130, note that mortality does not begin to rise until the number reaches 200 or more—a level rarely found in adults. Conversely, when LDL dips below 120 mortality starts rising and takes a sharp upward turn at <90, with a death risk far outpacing anything on the high end of the LDL curve.

If Martians came to earth today and saw these data they would find it utterly baffling, and existentially suicidal, for humans to routinely take drugs that aim to place cholesterol levels at the steepest, most dangerous part of the death curve. They would be equally confused by any notion of attempting to lower cholesterol, other than in people with rare, ultra-high LDL levels.

With millions prescribed statins, and the ongoing pursuit of ever lower cholesterol, we are witnessing experiential delusion and ideologic gumption in full flower. Drugs satisfy and delight by lowering cholesterol (think: draining blood)—and most who take them survive. The delusion grows with every prescription, and the ideological thirst is quenched as cholesterol levels drop.

The two logic traps have led to thinking on cholesterol that is hard to understand as anything other than delusional. This week's New England Journal of Medicine includes a paper suggesting children should be on life-long, preventive statin drugs—despite no study ever showing cholesterol reduction in children is even safe, much less effective for preventing heart problems.

----------

In the days following Washington's death, Dr. Brown sent a letter to his friend James Craik. Among other regrets, of young Dr. Dick, Brown confessed "I have often thought if we had acted according to his suggestion when he said, 'He needs all his strength—bleeding will diminish it,' and taken no more blood from him, our good friend might have been alive now."

As the Lipid Hypothesis continues to unravel, might cardiologists engage in the kind of soul-searching in Brown’s letters? When, as one truth-teller recently asked, will the cholesterol reckoning begin?

Most likely, never. As cholesterol's impotence becomes increasingly clear it is far more likely the demand side of the supply curve will change. If so, the Lipid Hypothesis will die a slow, quiet death. Educated patients will refuse the drugs, media will question the diets, and pharmaceutical companies will shift their gaze in search of new, shinier targets. The conventional wisdom will adjust, and disordered thinking will resume, most likely with a new, equally delusional focus.

Unfortunately, ideologic gumption and experiential delusion are stubborn traps that rarely lead to mea culpas or recantations. Brown concluded his otherwise reflective note with a defense that reads like the final half-woof of a chastened dog: "But we were governed by the best light we had; we thought we were right, and so we are justified."

Sorry, Dr. Brown. That is not how history sees it.

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I learned years ago that people’s beliefs about medical care are often religious. That is, they’re built on faith, not evidence. That’s not always a bad thing. Faith in the patient-doctor relationship can, quite literally, lead to physical and biological healing. Extensive data proves it.

But using faith in the absence of good data is more often harmful than helpful. One historical example is bloodletting, a treatment used for hundreds of years without data. A more modern example is Paxlovid, a Covid drug that never worked but was hailed and touted based largely on faith and hope. The fact that it’s still used, even after it failed in every relevant trial, is testament to the stickiness of faith.

Last week the New England Journal of Medicine, however, published a faith-busting article, a study I alluded to in May just after it was pre-released online. The faith under siege in the new paper is Cholesterolism. The Cholesterolites are a proud missionary people, with strong traditions of vehemence and certitude.

But do they seek the truth?

We’ll find out, as the findings from this new study sink in. It is the largest ever examination of the classic risk factors for heart disease and death, combining 133 study cohorts from 39 countries around the world, and representing just over 2 million participants. Established in the mid-20th century, the five classic modifiable risk factors are high blood pressure, smoking, high cholesterol, diabetes, and obesity. Each study interviewed and examined participants at the outset and periodically, drawing blood for testing, and following them for years. In many cases the follow-up lasted decades, with one study extending for nearly 50 years.

All of which means there is unlikely to ever be a paper more reliable or more valid for estimating the general importance and impact of the individual classic risk factors for heart disease and death.

Which is why it will be difficult for truth-seeking Cholesterolites to stay in the fold. Below are the two figures from the study that estimate the impact of each individual risk factor, and all of them combined. At the top of each panel is the impact of all five, i.e. of being an obese, diabetic, smoker, with high blood pressure and high cholesterol. Below are the individual risk factors. Impact is measured in years of heart disease added (top panel) and years of life lost (bottom panel).

What is obvious, and jarring, is that only one of the five factors consistently has either zero impact, or even a negative impact. While diabetes and smoking are by far the most powerful, taking 5-6 years of life each, high blood pressure takes roughly two years. Obesity is more equivocal, but at extremes can represent up to three years of life lost.

Meanwhile, high cholesterol stands alone as the only classic ‘risk factor’ on the wrong side of the ledger, appearing to ADD YEARS OF LIFE.

This is, to be frank, no surprise for those versed in cholesterol study data. This is not new. What’s new is its publication in one of the worlds best known journals and its pedigree, arising from the largest and most rigorous dataset in history. These findings cannot be denied or swept under the rug.

Why is it so important? Because for years the medical community has been playing a Cholesterol Charade, pretending not to notice findings that are fundamentally inconsistent with the notion of cholesterol as a risk factor. To find therapies for preventing or treating heart disease, data must first establish an association with heart disease and death. When found, studies can then attempt to treat or modify that risk factor. If it can be modified, studies must then show it saves lives to do so.

That is how high blood pressure became a proven, effective target: Elevated BP was consistently associated with heart disease and death. Studies then showed it could be lowered with medicine, and later studies showed doing so saved lives. First comes the association, then the treatment, then the proof of effect. In the case of cholesterol the first link in the chain has been absent all along.

Which explains why and how lowering cholesterol could never be an effective path to preventing heart disease or prolonging life. And why the statins, and other cholesterol-lowering drugs, have failed so miserably.

The sect of Cholesterolites is doomed to extinction. It may take years or even decades, but history will shake its head at the strange blind spot we developed for cholesterol and its role in heart disease.

****

For a more in depth look at the study, and its context in the history of cholesterol data, listen to today's audio and definitely check out the written or audio version of my prior post, The Cholesterol Charade: How Statins Debunked the Lipid Hypothesis.

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In Louisiana there is a supermarket chain called Winn-Dixie. They sell their own soda brand called ‘Chek’, which lines the shelves with bright, colorful, retro-style labels. Thinking about it makes me thirsty.

On most days Chek sells for about half the price of standard Coca-Cola products, so when it goes on sale I lean in. Hard. This week I brought home a sample (read: 12-pack) of the diet strawberry, grape, and orange flavors. Highly recommend them all, though lemon-lime is still champ.

Predictably, opening the beverage drawer in my fridge has led many a guest to catch their breath. There’s usually a pause, followed by a careful attempt at mitigation. “Wow. Um, your kids must really like soda, huh?”

There’s generational irony in this. When I was young I marveled at the constant supply of Tab in the fridge. For my mother, this pink-canned diet cola (the country’s first) was a needful thing. What an addict, I thought. Now, opening my refrigerator is a time warp.

Health studies exploring the effects of diet soda are legion, and a constant source of clickbait. Google it and you’ll always find a recent, highly trafficked news story reporting on a recent, highly trafficked diet soda study.

This is not new. With my mother’s Tab the big scare was saccharin, and the possibility—nay, near certainty—it causes bladder cancer. Based on rat studies, reports in the 1970s declared saccharin a carcinogen. But a close look shows the risk was often for second generation rats (babies of saccharin-devouring rat-mommies) and doses were a human equivalent of about 1,500 soda cans per day.

That’s a lot of Chek, even for me. And alas, despite an explosion of saccharin use in the 1970s and ‘80s, there never was a bump in bladder cancer. In fact it’s going down.

Today, however, headlines on diet soda claim it can cause diabetes, obesity, heart disease, and giant eyeballs (maybe not the last one). But also. . . DEATH. A commonly cited 2019 study from Europe, for instance, reports a 26% higher mortality among regular diet soda consumers.

Which helps clarify why diet soda is totally safe. A “26% higher risk of death” means a relative risk of 1.26 among diet soda drinkers. To use a favorite comparison, the relative risk of lung cancer for smokers is about 30, just a tad higher than 1.26. And the higher risk of death isn’t 26%, it’s 3,000%.

That is what a causal relationship looks like. Diet soda studies are actually proof of its safety. And, of course, even if they did find meaningful numbers, the studies are hopelessly confounded by the baseline differences between people who do, and don’t, drink diet soda. Comparing their outcomes and blaming the soda is like blaming overalls versus suits for the health differences between farmers and financiers. Dumb.

Finally, I must reiterate Newman’s Law: Studies finding no association are far more predictive, and more likely correct, than studies finding an association. In fact if ANY studies find no relationship, it’s likely there is none. And there lots of studies showing no relationship between diet soda and poor health outcomes. The Law is both mathematically and intuitively obvious. The absence of a statistical finding means either there is no important relationship, or else the confounders on both positive and negative sides of the equation have perfectly balanced each other. Highly improbable.

The lack of association between diet soda and health problems found in many large studies, plus a piddly relative risk of 1.26, basically clears diet soda of wrongdoing. And I’m certainly not the first to say so: Aaron Carroll, who IMHO is pretty much always right about study interpretation, has written on this topic multiple times.

All of which means the dangers of diet soda are, as a famously bad dancer once said, “Fake, fake, fake, fake.” And so is the strawberry flavor. Slurp.

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When I was little, my father read classics to me and my sister. In the evenings after dinner we sandwiched him on my parents’ bed, listening to the schemes of Miss Havisham and the adventures of Jim and Long John Silver.

I knew they were stories, but I also knew they held truth. Years later I became a doctor and a data scientist, and my sister became a brilliant television writer. Irony: she packs more truth into 42 minutes of television fiction than I typically find in a week’s worth of biomedical studies.

My sister knows that when it’s story time, the story comes first. Through the experience and journey of the story we see truths about ourselves and our world. The story comes first, the truth rises from it.

Scientists also love a story, sometimes too much. In reporting their data, scientists can forget that science is the opposite of fiction: the story is supposed to come from the truth, not the other way around. Mark Twain, a world class story teller, famously lamented this problem when he said “There are lies, damn lies, and statistics.”

Practicing good science means presenting data in a manner that is unbiased and transparent. The story then rises from the data. What makes Great Expectations a classic is the opposite—a universal and undeniable truth rises out of the story. Great writers like Dickens give us the story, and we see the truth it tells. Great scientists give us the truth, and we see the story it tells.

This week on the podcast I talk about a paper published in the highly influential British Medical Journal, on the subject of masking to prevent illness. The paper will undoubtedly become a primary source when policy makers, journalists, and others want to cite a scientific source for the assertion that masking works. But in truth the paper is rich with story, and the data are sidelined and obscured in service of a larger narrative. It is an object lesson in anti-science: The story came before the data.

How can we know this? Because we dived the data ourselves. If you haven’t heard it before and you want to know and understand the data on masking, read and listen to Mask Mandates and Sausage, the post and podcast where I briefly walk through every important randomized trial on the topic.

Knowing the data allows us to compare and contrast dueling reviews, and to understand how and why seemingly bright, accomplished scientists can disagree on the meaning of twelve robust clinical trials. The famous Cochrane review says the evidence is limited and does not support masking, while the BMJ review says the evidence proves masking works.

How can both be true? They can’t. Let the data dive begin.

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Perhaps the most important attribute in science and scientists is humility. A scientist must be passionate about finding truth, but dispassionate about judging it.

That’s hard to do, particularly with issues like politics or religion. For instance in a recent NY Times piece titled “As Congress Debates Cutting Medicaid, a Major Study Shows It Saves Lives.”

We are in a complicated and fraught moment of political history. Things are changing and it isn’t clear where we’re headed in the USA—which means big feelings. Really big.

But translating research properly means setting aside big feelings and dispassionately judging what a study can tell us. And one of the worst mistakes one can make in interpreting research is to adopt the hubris of certainty. For instance “… Shows It Saves Lives.”

Reader, may I ask you to search your soul for the type of research data that can provide certainty? In the pages, notes, comments, and discussions of Research Translation we have tackled the issue more than once (perhaps infinitely). What say you? What kind of research offers certainty?

One reasonable answer would be ‘none’. Arguably, virtually any research result can and will be overturned or at least seriously challenged at some point. Another possible answer would be ‘randomized trials’. More pointedly, randomized trials that have been replicated, showing the same results.

So is the ‘Major Study’ a randomized trial replicating previous work? Well no. It isn’t a trial at all. Actually, it’s not even peer-reviewed and would therefore not be considered a published or vetted study in the scientific community. It’s a ‘working paper’ by the nonprofit National Bureau of Economic Research. The NBER describes the paper as “circulated for discussion and comment purposes,” and confirms it is neither peer reviewed nor reviewed by the group’s directors (i.e. Don’t blame us!).

And what exactly does the paper examine? The paper looks at over a decade of mortality data in states that did and did not expand Medicaid from 2010 to 2022. In states that did, they report, fewer people living under the federal poverty level died than in states that didn’t.

From the many discussions of studies in RT, I feel sure my brilliant readers know that since this was not a randomized trial it cannot speak to causation, only association. Therefore this observational paper cannot “show Medicaid saves lives.” Incredibly, the authors of the paper seem illiterate on this fact. Their opening sentence begins “We examine the causal effect of health insurance on mortality… .”

Why can this observational inquiry not claim causation? One reason is people living in states that did and didn’t expand Medicaid were different before 2010, through the next decade, and they’re different now. Mortality trends are affected by car accident rates, Covid deaths, the opiate crisis, obesity rates, violence and crime rates, and a million other things known to vary wildly from community to community and state to state. Claiming Medicaid as the cause for these differences is unscientific and, frankly, daft.

It’s a shame it wasn’t a randomized trial, since there is one previous trial of Medicaid (just one). When Oregon expanded Medicaid in 2008 they had just enough resources to insure half of new applicants, so they randomly assigned Medicaid by lottery, then followed outcomes for two years. Medicaid appeared to reduce personal bankruptcies, increase ER visits, increase diabetes diagnoses, decrease diagnoses of depression, and increase the use of preventive services. Perhaps because it only lasted two years with a relatively small cohort, however, there was no statistical change in physical health or death rates.

Bottom line, there is no ‘Major Study’ and the un-vetted paper the Times relied on did not show Medicaid saves lives. The question of whether insurance saves lives in the U.S. is simply not answerable with study data. Instead, you’ll have to do what the Times did—use your gut to answer that question.

But since you, as a reader and listener of RT, are far more scientific than the Times journalists perhaps you’ll preface your opinion by noting that this is a question of politics and ideology, and the implications of the data are uncertain.

After which, you may feel free to go ahead and indulge your very biggest feelings. Enjoy!

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In Victoria, Australia from 2003 to 2005 thousands of elderly women were enrolled in a study that researchers thought might represent the future of geriatric health. For five years the women were given a single annual dose of Vitamin D, or a placebo. From earlier small studies experts surmised that Vitamin D, which helps bones absorb calcium, would prevent hip and other fractures, potentially reducing suffering, preserving function, and lengthening lives.

When the results were tabulated the researchers found good news, in one sense: the shots consistently and meaningfully raised Vitamin D levels. The bad news? Vitamin D increased fractures by 26%.

The vitamin shot, it turned out, induced falls by causing dizziness. This trial, and a few dozen more, are why the United States Preventive Services Task Force recommends against routine Vitamin D supplementation.

Which was hard to process, for me. I was taught to believe vitamins were the bomb. As a kid I took daily Flintstones and spent countless hours learning about the benefits of vitamins from the backs of cereal boxes. I even knew that Ovaltine, my favorite chocolate milk powder, was “fortified with 12 vitamins and minerals!”

Which, it turns out, is precisely why it’s banned in some countries. Denmark, for instance, figured out that while deficiencies are exceedingly rare in developed settings, vitamin overdoses and misleading claims of benefit are common. Health authorities there look with suspicion at ‘fortified’ products, and typically disallow them.

This stands in stark contrast to the American obsession with vitamins, where the market for vitamin supplements is estimated at around $150 billion (and growing). This is despite huge reviews of randomized trials finding no benefit to either multivitamin combinations or single vitamins. And while the studies aren’t perfect, and don’t cover every possible use, it is safe to say daily vitamins do not, for instance, prevent deaths from cancer or heart disease.

What’s tricky, however, is that many people taking vitamins do so not to stave off death, but to boost energy, or joint health, or eyesight, or hair growth, or weight loss, or anxiety, or muscle strength, or halitosis, or Wordle, or mind-reading, or time travel, or whatever.

And YES, for honing memory one recent trial suggested a possible benefit with multivitamins—but alas, it is weak data showing a tiny, probably meaningless effect, while large reviews of trials have repeatedly failed to find the same benefit. This sequence, where one or two studies finds a small, questionable benefit but large reviews of many others find nothing, is the rule and not the exception.

On topics like mind reading, however, the research is silent and will remain so. Not just because mind reading is tough to study, but because the VITA-MIND® study (I call naming rights), in order to be reliable, would have to be well funded, well designed, and executed by professional scientists. Big Vitamin won’t be sponsoring that trial anytime soon. For snake oil salesmen, touting snake oil with no data is waaaay more lucrative than sponsoring rigorous trials that are likely to end the charade.

In an ideal world the NIH or other public interests would sponsor such trials, offering a reliable source of evidence. In some cases the NIH has done just that, which is why we know vitamins don’t work for cancer and heart disease. But the NIH is not likely to spend millions evaluating whether Vitamin Q improves patience with the in-laws.

In summary, vitamins aren’t vital. At least not as supplements for healthy people in developed settings, where food has more than enough nutrients. People with symptomatic vitamin deficiencies should take vitamins, and women in early pregnancy or trying to get pregnant should take folate. Everything else is proven not to work, or not proven to work—a loophole that generates endless profits. (Ah, the power of placebo).

Which is why vitamin Q, for now, can act like a perfect cure for in-law anxiety.

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