Research Translation Podcast

David Newman
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Translating Medical and Health Research For All researchtranslation.substack.com

  1. 1d ago

    The Failure of Cardiology's Plumbing Theory

    “Sandy,” I called over my shoulder. “Back in two!” I pressed the metal square on the ER’s back wall and strode into the hall toward the lounge, where lunch awaited. Behind me I heard a grunt. A well-dressed man lay face down, a pool of blood spreading from his midsection. He wore a hospital bracelet. Sticking my foot between the closing doors, I called back into the ER. “Sandy, check that—crash cart to the hall, Code Blue!” The man, I soon learned, had just undergone coronary angiography, ordered by his cardiologist to investigate the cause of a fainting spell. Good news? They found no blockages. Bad news? His femoral artery, punctured for the procedure, reopened on the way out. Once we controlled the bleeding and stabilized him, I asked his cardiologist about the angiography, since fainting spells are virtually never caused by coronary occlusions. “You never know,” he said. “Sometimes we find something we can open up!” RT is 100% reader-supported. Become a paid subscriber so I can keep it going, please! For more than half a century, cardiology has been organized around a simple, intuitive story: Coronary arteries get blocked, blood flow drops, heart muscle starves, people get sick and die. Fix the blockage, restore flow, save a life. Easy peasy! Plumbing problem, plumbing solution. And it has shaped an industry, from angiograms to stents to bypass surgery. But the story is mostly a fairytale, other than one specific scenario. That scenario is the acute, large-vessel blockage—in other words, a big old-fashioned heart attack. When a fresh clot suddenly blocks an artery, opening the artery can save lives. It is one of the great triumphs of modern medicine, and it is real. Almost everywhere else, however, the plumbing theory has failed. For decades, coronary stents were deployed with confidence and satisfaction. Tight lesion? Open it. Abnormal stress? Stent it. The assumption—better flow equals better outcomes—was seldom questioned. Then, trials tested the assumption. Trial after trial showed the same thing: For people not actively in the throes of a heart attack, stenting doesn’t extend lives. Or prevent future heart attacks. Or strokes. And while it seemed to reduce symptoms like chest pain, even that turned out to be wrong. When stenting was compared to fake stenting (artery secretly not opened, no stent placed) the improvement in symptoms was exactly the same. Incredibly, despite this evidence, most of the 1.2 million stents placed annually in the U.S. are still done for non-heart attack cases. Equally disturbing, coronary artery bypass grafting surgery (CABG), the grafting of clean vessels from other parts of the body to bypass clogged heart arteries, is the world’s most common major heart operation, with about 400,000 annually in the U.S. But despite an air of heroism and fancy gizmos—saws! operating rooms! bypass machines!—this procedure, too, is on shaky ground. There are a handful of controlled trials from the 1970s-1990s and most found no mortality benefit. This was despite comparing bypass to nonsurgical treatments that were much less effective than those routinely used today. In more recent data, three of four major trials again found no mortality benefit. Which just means the justification for the great majority of bypass surgeries rests not on proof, but on an abiding faith in the plumbing theory. In reality, coronary disease is not primarily a problem of pipes slowly closing. It is a diffuse, partly inflammatory, biologically active process. Heart attacks, the great danger of coronary disease, occur not due to gradual closure but due to plaque rupture. This sudden, as-yet unpredictable event occurs not in lesions that are tight, but in lesions that are unstable. Which is why the great majority of heart attacks arise from arteries that were never clogged enough to stent. This also explains why therapies that do work for heart disease, like blood pressure control and smoking cessation and diet, don’t fit the plumbing metaphor. They don’t fix pipes. They quiet inflammation and stabilize plaques. They change the ecosystem, not the tunnel. Which brings us back to the man bleeding in the hallway. The angiogram nearly took his life. More than one person in fifty having the procedure suffers a major complication—including kidney injury, stroke, heart attack, or death. In the United States, that means more than 10,000 people each year severely harmed, permanently disabled, or killed. So why was he in the cath lab to begin with, undertaking all that risk? Getting a coronary angiogram for a fainting spell is like getting a CAT scan of the foot for a headache. But to a cardiology brain hijacked by the notion of plumbing and roto-rooters, every problem looks like a pipe that needs checking. The plumbing model flatters our visual instincts, rewards intervention, and turns complicated biology into a target that can be dilated, bypassed, and billed. Most of all, it feels like doing something. “Sometimes,” the cardiologist said, “we find something we can open up!” That sentence contains the entire plumbing theory. But finding something we can open is not the same as finding something we should. Get full access to Research Translation at researchtranslation.substack.com/subscribe

    The Failure of Cardiology's Plumbing Theory

  2. Jul 20

    Is Your Doctor A Cholesterol Bully?

    A friend of mine has a cholesterol of 258. Recently, when his new doctor saw this for the first time, she flipped. In addition to scolding him for not being on drugs, visibly shaken, she threatened to send him to the ER. For what, exactly, I cannot say. Emergency cholesterol-ectomy? Waterboarding? I found the story amazing, and emblematic of our cholesterol obsession, in which the molecule matters far more than the human. Later that evening NBC’s national news hammered the point home, gushing about a “gamechanger” new cholesterol pill, a “breakthrough,” “more effective than statins.” Let us clarify what they mean by effective. The new drug is enlicitide, approved by the FDA last week. I wrote about it in February when the first major trial results were published. Like other PCSK9 drugs, it lowered cholesterol dramatically—more ‘effectively’ than statins. But incredibly, it did not even pretend to reduce heart problems or deaths. It simply lowered cholesterol. Yay? This is objectively deranged, and perfectly highlights just how massive the chasm has become between the conventional wisdom on cholesterol and biological reality. So, before we go any further, let’s ground ourselves in a few basic, irrefutable facts. First, according to the American Heart Association, ‘elevated cholesterol’ means an LDL of 130 or higher (my friend’s is 160). And it may be useful to know that elevated cholesterol is not a risk factor for heart disease or death. Ya heard? Last year the biggest, best ever study of cardiac risk showed that calling cholesterol a risk factor is, and has always been, a mistake. With over 2 million participants in dozens of countries, some followed for nearly 50 years, the study found that an LDL >130 is associated with LIVING LONGER and FEWER HEART PROBLEMS. Facts. See figure below, and note that when the bars extend to the right of the vertical line, it means more CV disease (panel A) and higher mortality (panel B). Which is true for all of the risk factors EXCEPT FOR CHOLESTEROL, whose bars fall left of the lines. In contrast, high blood pressure, diabetes, obesity, and smoking were confirmed as true, and often powerful, risk factors, particularly diabetes and smoking. I am not making this up. Check my work. Though yes, I concede: The AHA, medical establishment, and mass media have all pretended not to notice the study (or any of its many progenitors). Importantly, a true understanding of these numbers requires nuance. In reality, about 1% of the population has familial hypercholesterolemia, with LDL levels routinely over 200. Particularly above 300, there is a meaningful association with heart disease and death. But including these people in the calculations pulls the overall average higher. Therefore it is true that for some people with LDL >130 (mostly way higher) cholesterol is a genuine risk factor. But incredibly, that also means the risk of ‘elevated cholesterol’ for people like my friend is even farther to the left—even more protective—than the study’s numbers suggest. All of which helps explain a finding that often shocks people: A careful reading of the data shows that, other than for people with known heart and vascular disease, lowering cholesterol does not extend lives and comes with very real dangers. Indeed, cholesterol reduction has been famously unsafe with some drugs. The fibrate and trapib medications, for instance, both reduced cholesterol—but increased deaths. Even statins, which are comparatively safe, can have serious and life-affecting dangers. This is why even the AHA, the world’s most cholesterol-obsessed group, only recommends lowering cholesterol when the baseline risk of a heart problem is high enough (specifically, 7.5%) that it outpaces the risks of the drug. When my friend and I did what his doctor should have, checked his risk on the AHA calculator, it was 4%. Plugging this into the RT statin checker, which personalizes benefits and harms, we saw that he is far more likely to be harmed than helped by statins. Even using the AHA’s happiest (read: b******t) numbers, statins are 4 to 15 times more likely to give him diabetes or painful muscle damage than to help him. And no matter how we slice the data, the drugs will not extend his life. But his doctor never checked. She simply saw an ‘elevated’ cholesterol—in a thin, athletic, otherwise healthy person—and lost her s**t. So whether it’s threatening healthy people with the ER, or news anchors gushing over a drug that lowers a number but doesn't extend a life, this is how modern medicine thinks about cholesterol. Molecules matter more than humans. Until that changes, the real breakthrough will never come from a new pill. It will only come from being informed, and using that information to stand up to cholesterol bullies. Get full access to Research Translation at researchtranslation.substack.com/subscribe

  3. Jul 14

    Tamiflu, and the Fatal Mistake of Doing Something

    As a long time critic of Tamiflu and its brethren, I have often heard defenders make the following last ditch plea to justify its use: “What about people in the ICU with the flu?? Without it they could die!.” The unspoken insanity behind this claim is a powerful, almost gravity-like bias in medical culture: the urge to intervene. At the bedside of a critically ill patient, doing something simply feels better. But in medicine there is no free lunch. Sometimes, doing something is a grave mistake. At a conference last month, researchers presented unpublished results from the first ever trial of Tamiflu for people critically ill due to influenza. Despite the absence of trial data, major guidelines (CDC, WHO, IDSA) have for years strongly recommended the drug in such patients. But Tamiflu in the ICU, we learned, has been a decades-long massacre. The trial was abruptly halted when a safety committee saw, after enrolling 652 people, that Tamiflu increased deaths by more than 40%. Based on these preliminary data, it appears that guideline-based ICU care has probably killed hundreds of thousands of people. To grasp the absurdity of ever having used Tamiflu in the ICU, we first have to look at how poorly the drug performs in the best of circumstances. As the Cochrane group famously found after a years-long battle to unearth the drug makers’ hidden data, Tamiflu was a bad drug to begin with. When the data were in, Tamiflu and its class didn’t reduce hospitalizations, or pneumonias, or critical illness—the marketing claims that led governments to stockpile the drug, and millions of doctors to prescribe it. The sole ‘benefit’ found in trials was a symptom reduction of less than one day (out of seven)—but even that was available only when the drug was started in the first 48 hours of illness. This is why Tamiflu’s only FDA approved use in treating flu is for “uncomplicated illness with symptoms no more than 2 days.” That should make its use very rare, since most people don’t seek medical care for a flu-like illness unless it becomes severe, or long-lasting. Which means only the hypochondriac who panics at every tickle in their throat can reap the awesome one-day reward. And only if they have easy access to their (poor) doctor. Unfortunately for the hypochondriac, even that dubious benefit is basically erased by side effects like nausea and vomiting. Therefore in the exquisitely rare, very best case scenario, Tamiflu means trading a few hours of sniffles for a day of hugging the toilet. Extending this silliness to the ICU was always nonsense. If it barely works for mild outpatients in a strict 48-hour window, why on earth would it save the life of someone on a ventilator who is virtually always days or weeks into their illness? And should we really toss yet another chemical into their body, then cross our fingers? It was an insane proposition from the start. So how did guideline writers justify it? With observational studies. The most famous, most cited paper is a review combining many observational studies. It is also a twisted, misleading, and smug spin job that was funded—shocker—by Roche, the maker of Tamiflu. But guideline committees fell for it. Even more shocking? Seven of the 18 IDSA guideline committee members disclosed financial ties to the makers of either Tamiflu, or drugs in its class. For a quick primer on how observational studies misrepresented Tamiflu, see the Paxlovid version of this eerily similar story. In short, they’re hopelessly contaminated by biases, most prominently the healthy user bias. Which means observational studies don’t show people getting healthier because of the drug, they show people getting the drug because they’re healthier. This is because in the real world the decision to give a drug is not random, therefore people who receive it are fundamentally different from those who don’t, in ways a spreadsheet can never fully capture. Observational studies of drug effectiveness are designed to find a difference and almost always do—one that favors the drug. This is why using observational data to craft recommendations is a reliable path to both bad care, and ignominious reversal—because most drugs don’t work for most conditions, but most observational studies will find they do. Crucially, the critics of Tamiflu are not visionaries. We have simply done an honest reading of the evidence, using elementary scientific principles, common sense, and the discipline to resist a bias to ‘do something’. To the IDSA and others who agitated for Tamiflu in the ICU, let this be a lesson: We should have waited to see randomized trial data before unleashing a drug on our sickest and most vulnerable. Instead, we let a bias to ‘do something’ override the fundamentals of evidence, and of science. The cost of that mistake is a body count we are just beginning to comprehend. Get full access to Research Translation at researchtranslation.substack.com/subscribe

    Tamiflu, and the Fatal Mistake of Doing Something

  4. Jul 7

    Is Moderate Alcohol Consumption Dangerous?

    Last month the Journal of Studies on Alcohol and Drugs (‘JSAD’) published a large data review comparing mortality rates in light-to-moderate drinkers with mortality among people who don’t drink alcohol. The NY Times and others gleefully picked up the story, trumpeting the most provocative finding: Up to 1 in 25 moderate drinkers will die because of their alcohol intake. SAD! As always, the study deserves a close look. For my part, I did not know about the SAD journal, so this was a welcome chance for me to see what kind of SAD research it’s offering. It turns out the SAD paper is a review of earlier studies. No new data is offered. The researchers selected their data from earlier studies of “conditions with established causal relationships to alcohol.” Hmmm. There are only a few such causes of death that are ‘established’. For instance, deaths due to alcoholic liver failure, and fatal drunk driving accidents. Both can comfortably be attributed to alcohol. In contrast, as I’ve written before and shown extensively, on careful inspection almost every cancer that researchers have often claimed is caused by alcohol turns out to have, at best, a fatuous statistical association with alcohol. Indeed, not one of those associations can plausibly be described as causal (much less ‘established’) by any honest expert of epidemiology or research translation. This mis-attribution of cancer deaths isn’t a new problem. In 2024 the Surgeon General launched a campaign making frightening claims about the dangers of moderate alcohol intake and cancer, only to be embarrassed a few days later when a comprehensive review by the National Academy of Sciences—written by real researchers, carefully and honestly examining the data—found the opposite. Having sifted the alcohol data myself, I can confirm these findings. I can also testify to the hidden agenda driving many public health claims on alcohol: In 2009, while I was teaching a class in research translation at Columbia University, a prominent public health official asserted that “There is no level of alcohol consumption that can be considered safe.” When my students and I emailed him to ask about the claim, he basically admitted that he knew this was untrue, but hoped scaring people might reduce drunk driving. SAD? Back to the SAD study: There is a wonderful and informative figure in the paper that, to my eye, answers a question the headlines never asked: What exactly are these ‘alcohol deaths’? The figure above shows accumulating causes of death in different color bands, on a graph that plots number of deaths against alcohol consumption. First, note that most deaths ‘caused by alcohol’ in men are due to injuries. Hmmm. How might we understand this finding in the context of the evening drink that most headline readers are now second-guessing? For instance, did my father’s longstanding tradition of an evening martini put him at risk of a tragic refrigerator incident? If so, I never saw any close calls. On the other hand, I suppose I can understand how, for people who find high speed driving and forestry irresistible after their martini, moderate drinking may represent a serious threat. Fortunately, my father doesn’t have that problem. After ‘injuries’, the next most important cause of death due to alcohol in the Figure is liver cirrhosis, for both men and women. Cirrhosis deaths rise with alcohol intake, as one would expect. But remarkably, the risk begins to appear in the Figure at one drink per week. Obviously, that suggests a problem in the data: reporting bias. These data are based on survey studies, and—shocker—people aren’t always honest about their drinking. Which seems pret-ty obvious when a study claims that a drink a week can cause fatal liver disease. Because that isn’t how liver disease works. In a recent review more narrowly focused on cirrhosis risk, alcohol consumption was not even statistically associated with cirrhosis until respondents reported at least two drinks per day. It took five drinks a day for the numbers to look consistent and causal. But do we even need studies to know that claim was bunk? The notion that a drink a week can kill someone due to liver failure doesn’t pass the whiff test. Instead, it says more about the reliability of the data than it says about alcohol. SAD! Finally, the figure makes it clear that if we remove injuries (don’t play with chainsaws) and remove liver failure (your ‘one’ drink shouldn’t be a Venti) all of the remaining risk was due to cancer. Here, once again, the problem is real—but it is relegated to severe alcoholics. I’ve been through these data over and over and I invite you to check my work. In short, esophageal and liver cancers occur more often in very heavy drinkers, but in moderate drinkers the numbers are barely different. For most other cancers the numbers are similar for moderate- and non-drinkers. There are even a few cancers (kidney, thyroid, lymphoma) mathematically less common in drinkers than non-drinkers. Which brings us to an interesting finding: Check out the red areas on the graphs. Cardiovascular deaths, the most common cause of death, were LOWER with moderate alcohol intake. HAPPY! Unfortunately, none of the associations found in the underlying studies represents a cause-and-effect relationship. Even the ones we like. There is neither an important protective effect nor a mortality risk. Whether the category is cardiovascular death or cancer, the small differences found between moderate- and non-drinkers aren’t because of drinking habits. They are because people who do and don’t drink are different to begin with: genetically, environmentally, culturally, socio-economically, geographically, and otherwise. So they have different rates of cancer. Is moderate alcohol intake a threat to your health? The NY Times wants you to think so, as do the public health experts who wrote the study. But most readers who see the headline will naturally picture damage done by alcohol itself. Yet the majority of colored territory in the figure is for injuries—drunk driving, falls, drownings, violence, and other behavioral consequences that can be associated with alcohol use. Apparently the greatest danger from moderate drinking isn’t liver failure. It’s an irresistible urge to climb ladders, operate chainsaws, and drive into trees. Meanwhile, non-injury deaths in this dataset are almost entirely due to severe alcoholism. Headlines like “1 in 25 killed by moderate drinking” sound frightening because they invite you to picture something the study never actually observed: ordinary people dying because they enjoy a glass of wine with dinner. That’s not what this paper found. Instead, it bundled alcoholism, injuries, and weak associations into one emotionally charged statistic, then attached the words ‘moderate alcohol intake’. That’s how SAD research, and SAD translation works. It isn’t built on outright fabrication. Instead, it’s built on selecting words that lead people to picture something the data never actually showed. Get full access to Research Translation at researchtranslation.substack.com/subscribe

  5. Jul 2

    Alzheimer's and Amyloid, A Summary

    What you see below is not grape soda. It is urine from a human being—Purple Urinary Bag Syndrome, as it’s called, is quite something to behold. But it is brief, and happens when bacteria interact with the plastic of a urinary bag. Treating the underlying infection (typically mild, if there is one) is all that’s required. The color is a distraction. At the Royal Psychiatric Clinic in Munich, Germany, in 1906 a similar distraction was discovered by an enterprising young neurologist named Alois Alzheimer. On slides from the brain of a woman who died of early-onset dementia, Dr. Alzheimer saw plaques that looked deeply abnormal, crying out for explanation. Surely something so striking must be the cause, he thought. But that is not a question that can be answered by gestalt, as the German doctor might have said. It is a question for wissenschäft — for science. In part 1 we reviewed two autopsy studies from the 1990s, the first real scientific test of Dr. Alzheimer’s hypothesis that amyloid plaques cause dementia. His Amyloid Hypothesis failed: both found no association between amyloid burden and dementia. In Part 2 we saw landmark studies from the early 2000s that again disproved the Hypothesis. In a mouse model of Alzheimer’s the plaques appeared—but only after dementia developed. Karl Popper, the father of modern scientific reasoning, would have called these findings ‘black swans’—each one potent enough by itself to discard the Amyloid Hypothesis. Popper argued that the surest path to truth is not proving hypotheses, but trying to disprove them. His touchstone example was black swans, clear proof that “All swans are white” is a hypothesis to be discarded. Despite repeated black swans for the Amyloid Hypothesis, in 2005 amyloid devotées pressed forward with a novel vaccine for people with early Alzheimer’s. The vaccine successfully cleared amyloid from the brain—and their dementia progressed, unchecked. It was the most irrefutable black swan yet. By every standard of scientific reason, the Amyloid Hypothesis was dead. Some amyloid researchers pivoted, perhaps desperately, hoping to find invisible precursors that might salvage a role for amyloid. This also did not end well: In Part 3 we reviewed the precursor ‘star-56’, presented in a 2006 paper. Its ‘discovery’ turned out to be, arguably, the greatest fraud in the history of modern neuroscience. We also saw that while amyloid’s star fell to earth, the clinical enterprise became increasingly invested in amyloid plaques. Imaging companies engineered PET scans to visualize them, chemistry companies developed blood tests to detect them, and pharmaceutical companies created dozens of drugs to remove them. Which led to something remarkable—though eerily familiar. Instead of asking whether removing amyloid helps people, the field gradually began asking whether removing amyloid… removes amyloid. The endpoint changed. The question was no longer whether people improved, it was whether amyloid improved. In Part 4 we therefore saw industry trials designed to find differences between their drug and a placebo that people could not perceive. Amyloid was removed. People didn’t get better. Then, in Part 5, we saw the final capitulation. Medical journals called amyloid drugs ‘disease-modifying’. PET scans and blood assays that were actually for amyloid became, in the lingo of the establishment, “tests for Alzheimer’s.” Alzheimer’s Disease, in other words, became Amyloid Disease. How could this happen? History is our guide—we have seen this movie before. Doctors and scientists, like all of us, are irresistibly attracted to elegant explanations. One lesion, one pathway, one cure. This plaque causes Alzheimer’s. This cholesterol causes heart disease. This tear causes knee problems. Human biology, however, is virtually never that simple. Which is why the history of medicine is littered with visually striking pathology that rises to mythical, unimpeachable status. Doctors, industries, and systems inevitably stake careers—and commerce—on finding and fixing these culprits. Remove amyloid! Open arteries! Repair cartilage! Lower cholesterol! It then takes generations to wind down the mythology. Why does this pattern repeat, and redound? Because humans and their systems are easily captivated by stories and pictures—in other words, by gestalt. In some domains, like art, this works in our favor. But in pursuit of scientific truth it does not. The purpose of wissenschäft, of science as described by Popper, is to force us, repeatedly and sometimes painfully, to abandon the theories that fail—the path to truth. Scientific, and thus human, progress depends on finding black swans, and even welcoming them. Sadly, after more than a century of amyloid research, including hundreds of billions in grants, laboratories, scanners, blood tests, and drugs, people with Alzheimer’s suffer much as they always have. Not because science failed—because we failed to follow it. Instead of abandoning a theory that failed, we abandoned scientific method. And in doing so, we abandoned the patient. Because disease is not what appears under a microscope. Disease is what happens to people. Get full access to Research Translation at researchtranslation.substack.com/subscribe

    Alzheimer's and Amyloid, A Summary

  6. Jun 26

    Alzheimer's and Amyloid 5: The Big Mistake

    In part 2 of this series I described a trial in which researchers developed a novel vaccine for Alzheimer’s patients, designed to rid the brain of amyloid. The vaccine worked, clearing amyloid—but their dementia progressed, unabated. This was, in other words, a full debunking of any notion that amyloid plaques are the cause of Alzheimer’s and that removing them can cure the disease. Interestingly, in a 2008 follow-up report on the study, a curious turn of phrase caught my eye: Do you see it? Did you feel the tectonic shift? In a single sentence the authors both tout their success with “disease modification,” and concede that removing amyloid didn’t help people. In other words, the ‘disease’ was not memory loss or crippling cognitive decline. It was amyloid. Which they ‘modified’. The operation was a success—but the patient died. RT is fully reader-supported. To keep doing it, I need help. Please consider paying for a subscription. Lest you believe this kind of language is isolated or antique, this week’s Journal of the American Medical Association, the worlds most circulated medical journal, includes an article about Alzheimer’s, with a review of the history of amyloid targeting. In it the author, an NIH neuroscientist, describes the anti-amyloid drug Aduhelm as “the first ever disease-modifying drug,” while acknowledging that it failed to help people. He also describes the two recent anti-amyloid drugs as beneficial, and ends with a breathless look to the future of amyloid research. Why should this language matter? Here is why: Incredibly, this 2024 NY Times article describes a study of a novel blood test for brain amyloid. Not for dementia. Or cognitive decline. Or human suffering. In other words, not for Alzheimer’s Disease—not as it is experienced by people—but for amyloid. Yet, the headline calls it ‘Alzheimer’s’. This is where decades of amyloid-centered thinking inevitably led. It began with the identification of brain amyloid, moved to the invention of highly specialized PET scans to see amyloid, and culminated in a blood test whose only purpose is to detect fragments of the amyloid seen on PET scans. The new blood tests are therefore not tests for Alzheimer’s disease. They are tests for amyloid, which they detect readily. And that is why they constantly miss Alzheimer’s when it is truly present, while labeling people with Alzheimer’s who don’t have it. (For the numbers, check out my 2024 piece). The test, in other words, fails miserably to identify Alzheimer’s Disease, because amyloid and Alzheimer’s are not the same thing. The distinction may seem trivial, but it represents one of the most consequential shifts in modern medicine. For more than a century, Alzheimer’s was a human condition: progressive loss of memory, reasoning, language, independence, and ultimately self. It was a disease experienced by patients and families. Amyloid was just one proposed explanation. But when it repeatedly failed, something happened: Rather than abandon the explanation, the field redefined the disease. The question quietly changed from ‘Does this person have Alzheimer’s dementia?’ to ‘Does this person have amyloid?’ Everything else followed naturally. PET scans no longer needed to identify dementia—just amyloid. Blood tests no longer needed to predict who would lose their memory, they only needed to detect amyloid. Drugs no longer needed to help people, they had to remove amyloid. The microscope, the scanner, and the laboratory, all quietly replaced the patient. This is the Big Mistake, the same mistake that medicine repeatedly makes. In cholesterol, LDL gradually became more important than survival, or quality of life. In orthopedics, MRI abnormalities became more important than pain, or function. And now, in Alzheimer’s, amyloid became more important than dementia. People want to live longer and better, while doctors aim to seek pathology and diagnose disease. Doctors, and by extension the medical establishment, fail most tragically when they forget to put people first. Disease is not what appears on a PET scan, or an MRI, or in a cholesterol test, or under a microscope, or in a test tube. Disease is what happens to people. When medicine forgets that distinction, it treats pathology, not patients. And that is how Alzheimer’s gradually ceased to mean dementia, and came to mean amyloid. That is the Big Mistake. Next week we’ll talk about how intelligent, compassionate physicians can be swept up by pathology what it all means, and how you can avoid the traps that have been laid by a multi-industry machine carefully crafted to profit from the Big Mistake. Get full access to Research Translation at researchtranslation.substack.com/subscribe

  7. Jun 17

    Alzheimer's and Amyloid 4: The Long Mistake

    Over the first three parts in this series we followed the rise and fall of the Amyloid Hypothesis. The plaques failed. The idea of toxic precursors failed. The most influential oligomer paper in the field was ultimately retracted. By any ordinary standard of scientific reasoning, this should have marked the end of amyloid as a central explanation for Alzheimer’s Disease. Instead, something remarkable happened: As the scientific case for amyloid weakened, the clinical investment in amyloid accelerated. Drug companies continued developing anti-amyloid therapies. Regulators continued evaluating them. Researchers continued measuring amyloid. Billions of dollars flowed into a treatment strategy increasingly disconnected from the evidence. The result was the modern anti-amyloid drugs. What follows is two short essays I wrote about the clinical trials that won FDA approval for two of them. In both cases drug makers quite literally designed the trials to prove that the drugs DON’T help people with Alzheimer’s, apparently with full confidence that they could spin the results to suggest the opposite. It is among the most brazen examples of FDA game theory, and snake oil sales, that I can recall in decades of reviewing FDA trials. New Alzheimer’s Drugs Are Bringing Back the Wrong Memories Apr 03, 2024 I remember when, in 1999, the FDA approved Vioxx, a pain reliever that went on to kill an estimated 50,000 Americans. According to whistleblowers the FDA not only had the data to prevent the tragedy, they obstructed the investigation. I also remember the inspiring story of Frances Kelsea, an FDA physician who in 1960 refused to approve thalidomide despite approval in dozens of other countries, and industry pressure. The drug caused over 10,000 birth defects worldwide, but just 17 in the US where it was never approved. Now, in 2024, the FDA is on the verge of approving a third Alzheimer’s Disease drug. The first was Aduhelm in 2021, the second was Leqembi, in 2023. Donanemab, the third, is under review. The three are extremely similar. They’re all monoclonal antibodies, they all reduce amyloid plaques in the brain, and they all don’t work. In the 1980s researchers discovered amyloid plaques were associated with Alzheimer’s, spawning the ‘amyloid hypothesis’. If the plaques cause cognitive decline, it was hoped, removing them may slow or even reverse Alzheimer’s. Unfortunately, the theory has been dashed. First, studies show many with Alzheimer’s have no brain amyloid. Second, scientific reviews show at least 72 anti-amyloid agents have been researched, and none have worked. This includes nine monoclonal antibodies, a class that has failed so miserably and consistently a 2021 meta-analysis announced “the time has come to divert therapeutic efforts away from AAB [amyloid] immunotherapy.” Abject failure is certainly the case for Aduhelm. Two FDA studies were halted early for futility, and an expert panel voted 9-1 against approval (the FDA approved it anyway). Meanwhile, Leqembi ‘slowed cognitive decline’ by just 0.45 points on an 18-point scale, less than half the 1-point minimum deemed meaningful. Finally, the donanemab trial used a 144-point scale and reported a 3-point edge, well below the 9-point minimum determined by the company’s own research. These failures highlight a crucial distinction: statistical versus clinical differences. Studies often find a ‘statistical’—meaning mathematically identifiable—difference between groups. But research is a human task and thus inherently biased, so trial results commonly lean toward the drug. Particularly in large trials (the Alzheimer’s trials enrolled thousands) this often leads to a ‘statistical’ difference. But a clinical difference affects people’s lives. Which is why there are thousands of studies examining and meticulously defining the ‘minimal clinically important difference’ for scales like those in the Alzheimer’s trials. Those studies tell us the ‘differences’ in the antibody drug trials were so small that people with the disease, their families, and their doctors, would literally never notice them. In other words, they’re not real differences. Another issue may also be confusing starry-eyed loyalists. The drugs removed amyloid plaques quite effectively—a finding that closes the door on the amyloid hypothesis. Vanquishing amyloid plaques didn’t help people with Alzheimer’s. The chicanery of parading differences well below the thresholds for true benefit is happening now for a reason: We’re heading into a perfect storm of potential profit for AD drugs. Nearly 7 million in the US have symptomatic Alzheimer’s, with an expected doubling by 2050. This will likely balloon with the use of new, flawed blood tests that severely over-diagnose Alzheimer’s. And the drug, given through IV infusions, costs nearly $30K per year (not including infusion costs, facility fees, and other charges). A final tidbit: The drugs cause brain swelling, headaches, confusion, and occasionally death—in huge numbers. Donanemab, the latest, caused 24% of people’s brains to swell, while another 9% suffered infusion reactions. That’s 1 in 3 people seriously harmed by the drug, and zero helped. What’s worse, as noted in these pages, one can always expect the harms to be greater and the benefits smaller than reported in trials. As the FDA wrestles with the amyloid drugs, it seems natural to remember the agency’s heroic performance on harmful drugs of the past. But an effective Alzheimer’s treatment would bring back different memories. Why the Alzheimer’s Drugs Won’t Make Your Burger Better Feb 04, 2025 I like a burger. With cheese. And lettuce and tomato and maybe onion and ketchup or sauce (house choice). I prefer it rare. Really rare. Like, true blue. This last part is the downfall of many a burger joint where, fearing liability, kitchens won’t make a rare burger. To me, besides corrupting its taste, this means dubious beef. Sushi, steak tartare, and raw bar items are much riskier than anything cooked, but they’re still common menu items. A kitchen that won’t make a rare burger doesn’t trust its sourcing and prep for burger meat. Sad! But crucially, rare burgers aren’t just hard to find, they’re also tricky to make because they must—MUST—be seared on the outside and lightly cooked on the inside. This delicate contrast, a pillowy but juicy inside with a firm outer layer, is the mouth-watering soul of a great burger. So for home burgering, I got a meat thermometer. To my surprise, the first time I used it the reading was 122.68°. Impressive precision! But this number was, as the kids say, TMI (not to mention sus). Because while my gifted palate is a wonder of epicurean virtuosity, I cannot tell a 1° difference in temperature, much less 0.01°. Those decimal points are not helping me. I can tell rare from medium-rare (I think). That’s about 10° different. Which means somewhere between 1° and 10°, for me, the difference becomes perceivable and thus potentially meaningful. If I was bored and curious I might experiment to find the threshold, and in research parlance we would call it the ‘clinically significant’ difference. This principle, also known as the minimal clinically important difference (or ‘MCID’), defines the smallest improvement patients and doctors are able to perceive, a starting point for judging whether treatments provide a meaningful benefit. Curiously, however, all three FDA-approved Alzheimer’s drugs were tested in studies that are the equivalent of a 2-decimal point thermometer. Each trial enrolled way more people than needed to find improvements in dementia. Why? Because the more participants, the smaller a difference a study can find. But my thermometer, for instance, can detect differences that are much too tiny to matter. And so can studies. In research this is called over-powering, but in trials it’s very unusual. After all, genuinely helpful drug effects, even lowly MCIDs, aren’t tiny. So why would researchers go looking for tiny differences? Who even has the money and resources to enroll way too many people in a randomized trial?? Oh. Wait. As discussed in these pages before, a jarring attribute of the Alzheimer’s drugs is that trials proved they do not make a perceivable difference. One hapless employee at Eli Lilly even spent years studying (and writing at least seven reports) to establish the MCID for cognitive impairment. He found it was at least 5 points on a 144-point scale for mild disease and 9 points for early Alzheimer’s. But his company’s drug Kisunla produced scores within 3 points of a placebo—in other words, indistinguishable from placebo. Meanwhile Biogen and Eisai’s drug Leqembi was within 0.45 points of placebo on an 18-point scale—also less than half the established MCID of 1 point. How could this happen? Were the studies purposefully over-powered? Or did they stumble upon tiny differences while seeking meaningful ones? In the Kisunla report Eli Lilly says they aimed to find a 3-point difference (!!!). The authors calculated that 1,000 participants would give them a 95% chance, or ‘power’, to find it. So they enrolled 1,000 people. Then they enrolled 736 more. What about Leqembi? The FDA approval trial also, amazingly, targeted 0.4 points better than placebo (!!!). This, they found, required 1,566 participants—and they enrolled nearly 1,800. This is over-over-powering. The original plans were over-powered, and then each study ADDED extra enrollments. These are confessions, hidden in plain sight in the methods sections of the papers, and they mean the companies knew before the trials started that their drugs don’t help. So they targeted statistical—not perceivable—differences, then convinced the FDA to approve the drugs.1 Bonus question: How could the companies feel confident the studies would generate a statistical

    Alzheimer's and Amyloid 4: The Long Mistake

  8. Jun 10

    Alzheimer's and Amyloid 3: The Mistake of the Invisible Friend

    Neuroscientists are not famous for being dumb. So when the following timeline unfolded, they should have understood exactly what it meant: First, autopsy studies in 1991 and 1992 found that amyloid plaques—supposedly the cause of Alzheimer’s, as the centerpiece of the Amyloid Hypothesis—did not scale with the disease. Then, in 2001 and 2002, researchers published data showing that an Alzheimer’s mouse model developed dementia—before ever developing plaques. Finally, in 2004 a vaccine successfully removed plaques from the brains of Alzheimer’s patients—yet their dementia continued to spiral, unabated. RT is fully reader-supported. For the price of a cup of coffee once a month from you, I can continue RT—please become a paid subscriber. It is hard to imagine a more obvious demise for the Amyloid Hypothesis. Some researchers, however, particularly those with funding and livelihoods tied to it, concentrated on the possibility that amyloid precursors might still be involved. If true, such a finding could preserve a role for amyloid researchers and their laboratories. But most precursors, detailed above, were well known, well studied, and stubbornly non-toxic. They could not explain the Alzheimer’s hallmarks of damaged tissue, lost synapses, and tangled detritus. Except, perhaps, for one, which remained a mystery. ‘Soluble oligomers’, theoretically the building blocks of fibrils, are exceedingly difficult to isolate because they are transient, tiny, and live in a dissolved state. Few labs had ever found them, much less studied their effects. Which is why it was blockbuster news in 2006 when Sylvain Lesné, at the University of Minnesota lab, pulled off a miracle with the potential to energize (read: fund) amyloid theorists everywhere. Despite the world’s prior work yielding—at best—messy, shaky evidence of oligomers, Lesné reported a clean, concrete, and copious yield of a new oligomer named beta-amyloid-*56, or ‘star-56’. Based on gel photos in Lesné’s paper the oligomer was clearly detectable in a mouse’s brain just days before the onset of dementia. Based entirely on this 2006 paper (since no other lab has ever reproduced the star-56 finding), for 16 years the star shone bright, offering an exciting new offshoot: The Amyloid Precursor Hypothesis. Then, barely in its adolescence, the new star flickered—and died. Matthew Schrag, an MD/PhD neuroscientist at Vanderbilt University, was hired by investors who suspected that an anti-amyloid drug maker’s claims were exaggerated. Schrag dug deep into the Amyloid Hypothesis and its derivatives, and came to Lesné’s study. In 2022, on a geeky post-publication review site called PubPeer, Schrag posted the following images and coolly accused the report of being a fraud. Soon, other super-sleuths jumped in. By the end, multiple forensic investigators and leading researchers flagged over 70 images from Lesné papers as likely to have been tampered with. For those interested in a full anatomy of the fraud, you can find it in Science, where the story broke. On PubPeer you can also read the often cringe-worthy, years-long dialogue between the researchers, Schrag, and others. In 2024 the paper was retracted after the Minnesota group (sans Lesné) finally conceded it had to be. Then, in a real head-scratcher, the senior author Dr. Ashe published a new paper purporting to show that the original claims were all correct. The whole saga, she seemed to suggest, was just a silly misunderstanding. Who cares if you can’t see our friend Snuffleupagus—we can! The response from the research community was a collective eye roll. Dr. Schrag, for one, wasn’t having it. When Ashe touted their new paper in the PubPeer string, Schrag eviscerated each and every claim, concluding that the new version “objectively falls short of reproducing the earlier result.” Awkward. In any case, with the Amyloid Hypothesis dead, the crucial question was whether the Amyloid Precursor Hypothesis was viable, and the best answer is this: It’s been two decades, and no other research group has been able to even confirm the existence of star-56, much less replicate its effects. The few other oligomers that have been isolated suffer the same problem. The cherry on this debunking pie is the story of the BACE and GSI drugs, which inhibit the ‘secretase’ enzymes and therefore reduce virtually every precursor: monomers, oligomers, fibrils, and plaques. In clinical trials comparing them to a placebo, neither drug class improved dementia—they both worsened it. So, um, no. The Amyloid Precursor Hypothesis is no more viable than its fallen father. Next week, we’ll examine the unblushing audacity of anti-amyloid drug makers, who continue to make it clear that for them, amyloid IS the disease. Get full access to Research Translation at researchtranslation.substack.com/subscribe

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