The Origin of Disease

In 2022, the world conducted an inadvertent experiment on millions of people. As GLP-1s exploded in popularity for weight loss, unexpected side effects started to emerge. Patients' disease markers were getting better across many different unrelated conditions.

Within days of starting treatment, before patients experienced any weight loss, their diabetes started improving. Within weeks, their blood pressure started dropping. Fatty liver disease and kidney function were seeing improvements. And longitudinally they were seeing massive risk reduction for diseases like Alzheimer's, and Parkinson's.

And it was showing up behaviorally too. People were drinking less, gambling less, eating less, and reporting a feeling of calm, describing it like an alarm bell that had been blaring for decades finally being turned off.

By 2026, GLP-1s were in clinical trials for dozens to hundreds of diseases across nearly every medical, behavioral, and psychological specialty: heart disease, liver disease, kidney disease, Parkinson's, Alzheimer's, addiction, even cancer. Like dominoes, one disease after another was responding to this single molecule, hinting at the common upstream cause our siloed approach to human bodies structurally couldn’t see.

According to medicine’s current understanding, these were supposed to be completely unrelated diseases. And yet one peptide was touching them all. Suggesting that understanding what this drug was doing, and what chronic disease actually is, would require stepping back from the individual specialties and reading their research as one body of evidence about one interconnected body.

To understand why this went against our current understanding, let’s go back to 1847, to a Vienna maternity ward where women were dying at alarming rates. Pregnant women would come to the hospital to deliver their babies, and then within days they would develop what was at the time called childbed fever. It would start with fever and pain, and then quite quickly lead to death. Everyone knew this was happening, but no one knew why or what to do about it. A young doctor at the time named Ignaz Semmelweis was working in a hospital that had two maternity wards, one staffed by doctors and one by midwives. And he noticed that in the doctor’s ward, 1 in 10 of the women who came in for care was dying, and that in the ward staffed by midwives only about 1 in 30 was dying. The doctor’s ward had better resources, more training and better equipment, so why were their patients dying at three times the rate? Semmelweis became obsessed with figuring out the answer. What were the midwives doing that the doctors weren’t? Or, and this was probably a harder question to ask, what were the doctors doing that was killing their patients?

One day one of the doctors from the ward, one of Semmelweis’s good friends, got sick and died. While he was performing an autopsy with one of his students, their hand slipped, and they cut him with their scalpel. The doctor then developed the same symptoms as the women with “childbed fever”, and the same rapid decline leading to his death. And from this Semmelweis made the connection that these doctors were performing autopsies in the morning, and then they were delivering women’s babies in the afternoon. Maybe they were carrying something from the dead, to the living.

Semmelweis came up with the idea to wash their hands with chlorinated lime before surgery, to see if that would stop them from transferring childbed fever to the patients. The doctors on his ward implemented his protocol, and almost overnight, the death rates fell from 1 in 10 to 1 in 100. But this didn’t fit with how the medical institution of the day thought about things, and so no one would take him seriously. And without institutional backing, individual doctors had no reason to believe him over their training. Which makes sense, why would they believe this one guy with strange data they had no framework for understanding? In 1847, telling doctors to wash their hands between the morgue and the delivery room would be like telling modern doctors they need to spin in circles three times before surgery; it seemed absurd. They had been trained for decades in established protocols.

But Semmelweis knew he was right, so he did what any human would in his position. He escalated. The more people ignored him the more desperate he got. He started writing angry letters, accusing doctors of killing their patients. He just couldn't understand why they wouldn't believe what his data clearly showed, why they wouldn’t just open their eyes and LOOK. And eventually, sick of hearing his wild rants, they drove him from Vienna. Years later, his own colleagues tricked him into visiting what he thought was a spa, but they were actually committing him to an asylum. When he tried to leave he got beaten by the guards so badly that he died two weeks later, of an infection on his hand no less. He was 47 years old.

By this time, in the mid-1800s, physicians had catalogued a huge number of diseases, each with its own set of symptoms and perceived causes. But like with childbed fever, their theories were condition specific, they weren’t stepping back and looking at what they all said when seen as a whole, and without that unifying understanding they also didn’t have any coherent way to prevent or treat these diseases. For patients with festering wounds, for example, a surgeon could perform the same operation on two patients, with the same surgical techniques and pre- and post-op care, and one would heal perfectly, while the other would develop angry red streaks, fever, delirium, and death. They called this outcome "hospital gangrene" or "surgical fever" and had theories to explain it like bad hospital air and the patient's ‘constitution’. They even went so far as to blame the weather.

Cholera outbreaks would come on seemingly out of nowhere, killing thousands of people. During the 1854 London outbreak, some entire neighborhoods were leveled by the disease, and others were unaffected. The belief at the time was that it was the miasma that was causing it, you could smell it, the foul air coming out of the cesspits in the Thames. Except some people living in the worst air stayed healthy, and removing patients from the miasma didn't always help.

Each disease had its own unique theory, and its own experts and treatments. Wound infection experts debated whether pus was healthy or dangerous. Cholera specialists mapped outbreaks and argued about miasma versus contagion. For tuberculosis, doctors prescribed rest cures in various climates, debating which location worked best. Medical knowledge was fragmented, with everyone focused on their specific illness. No one thought to connect them all, they seemed to be different diseases in different locations with seemingly different causes.

Then in 1857 a chemist named Louis Pasteur was hired by the French wine industry. Their wine kept spoiling, unpredictably turning to vinegar and ruining entire batches at once. Pasteur looked at the wine under a microscope, and he saw what at the time were called animalcules, tiny organisms living in the wine. He noticed he was seeing one type of animalcule (organism) in the good wine and a different type in the spoiled wine. Maybe, he thought, specific types of microorganisms were causing specific types of fermentation.

Now microorganisms weren't a new concept, people had been observing them under microscopes for almost two centuries, but the thinking at the time was that these tiny life forms spontaneously generated from decay. When mold suddenly appeared on your food, or maggots in your meat, they seemed to arise from nothing; one day it wasn't there, and the next it was. Pasteur was the first to show that life doesn't just come from nothing. Through what is famously known as the swan neck experiment, he showed that broth only spoiled when airborne particles could reach it, proving these organisms were deposited from the air, not spontaneously generated. Once he understood this, he was able to explain why some batches of wine were going bad and others weren’t, and he was able to come up with a system so that they could prevent it from continuing to happen. If the winemakers heated the wine, that would kill off the bad organisms, which would then prevent them from multiplying and ruining their wine.

Pasteur was so successful with the wine problem that in 1865 he was asked to help in another French industry that was experiencing a similar problem. Whole crops of silkworms were dying off in large numbers, again, seemingly at random, and causing massive loss for the industry. And as you can probably guess, it turned out to be exactly the same story. Pasteur found that microorganisms were infecting the silkworms, causing disease, and that the disease passed from sick silkworms to healthy ones through the spread of these microorganisms, which led to entire colonies being wiped out almost at once. The same thing that was spoiling wine was killing living creatures.

This is when Pasteur started connecting the dots. If the same thing, microorganisms, was spoiling wine and killing silkworms, passing from one to the next through the air or physical contact, what else could it be doing that we aren’t seeing? Was it possible that this was affecting humans too? That all those various medical conditions like cholera and tuberculosis and childbed fever were also caused by these same microorganisms?

Pasteur had stumbled onto a discovery that had the potential to explain all of those disparate illnesses. He figured if these microorganisms could infect and kill silkworms, maybe they were doing that in humans too. For Pasteur’s hypothesis to overturn centuries of thinking, he would need some way to prove that microorganisms were causing disease. Luckily for humanity, it was around this same time, in the mid 1800s, that the way we thought about proof and what counted as evidence was about to change.

Defining proof

For most of human history, "knowledge" came from a combination of observation and, even more importantly (then and now), authority. For example if Aristotle said something, it was automatically considered true. If you had enough authority, you could observe something, and if you had a good enough explanation for what you were seeing, your educated guess could become accepted truth. The idea of taking those observations and somehow testing them to prove what you thought you were seeing just didn’t really exist. But in the 1600s this system where guesses became truth started to change. Francis Bacon famously argued that you shouldn’t just be able to reason your way to ‘truth’, you should have to test it. He thought you should observe it, conduct an experiment, gather data from that experiment, and then you could draw conclusions. And he wasn’t the only one. René Descartes contributed the idea of systematic doubt, which is that we should question everything, and break each problem down into its component parts, drilling down into smaller and smaller parts and then separating and analyzing them carefully. And by the late 1600s and into the 1700s, scientists had largely adopted this framework, and Newton's work on gravity and motion showed the world how powerful this new approach could be.

This new method of gaining scientific validation, what is now known as the scientific method, worked beautifully well for things like physics and math. Things that behaved predictably, didn’t change or adapt, and could easily be broken down into their component parts and measured or tested in a lab. Planets reliably orbit around the sun on the same path, chemical elements combine in consistent ratios, and gases follow precise laws as they relate to pressure, volume, and temperature. Through this new method of testing, sciences like physics and chemistry were transformed from philosophical speculation into rigorous sciences. And through this transformation, a new era of scientific discovery had begun.

Medicine, however, lagged behind. By the mid-1800s, physicians were still relying largely on the old ‘scientific’ methods of tradition, authority, and clinical observation. They would watch patients, catalogue their symptoms, and try treatments to see how they worked, but they weren't designing controlled experiments to test their theories about what caused disease. Which meant that medicine at this time was still largely made up of a lot of guessing, very few effective treatments, and minimal clear answers.

The framework is born

Which brings us to 1876, to a makeshift laboratory in a small German town, where a doctor named Robert Koch was trying to figure out what was killing farmers’ livestock. Anthrax, a bacterial disease that had been killing off livestock for centuries, was devastating cattle and sheep across the region. While some animals would get sick and die, others in the same field would remain healthy, and no one had any idea why. Now Koch, who was extremely methodical and driven, was determined to figure this out. Building on the work of Pasteur, he knew where to look for the answer, and he spent months isolating bacteria from the diseased animals, growing them in pure cultures, and studying them under a microscope. And what he did during these months of meticulous work was something no one else had ever been able to do before in medicine; he created a systematic way to prove that a specific external invader, a microorganism, caused a specific infection.

His system of proof, which became known as Koch's postulates, is still taught in medical schools today, and consists of four steps: First, find the organism in sick patients, by examining their blood, tissues, or other samples under a microscope. Second, isolate it and grow it in pure culture in the lab. Third, introduce it to a healthy animal and reproduce the disease. Fourth, isolate the same organism again from the newly diseased animal and see if it matches the one you originally found. So in short: find the unique organism, isolate and grow it, put it into a new animal, then pull it out and isolate again. If you could do all four steps, you had proof, not just speculation or observation or theory, but proof, that one specific microorganism caused one infectious disease. It was repeatable and testable. Koch had figured out how to apply the scientific method of the hard sciences to the messy field of biology, and now medicine finally had a way to prove causation.

Once Koch had successfully used his new system to find the specific bacterium that was causing Anthrax in the diseased animals, he moved on to studying disease in humans. He was able to use his new system to find the specific bacterium that caused tuberculosis, which was the leading killer of the time. Then he found the bacterium that caused cholera. For every new discovery he used his postulates, and his method proved effective over and over again. This became the first domino in a series of changes that would lead to what we know of as “evidence based” medicine today. Koch had systematized medical research, and this framework: one cause, one disease, isolated in a laboratory, tested systematically, became THE way to think about disease.

And it worked. Spectacularly. Within decades, scientists had identified the bacteria causing diphtheria, typhoid, tetanus, pneumonia, and the plague. These diseases that had killed millions for millennia suddenly had identifiable causes, and even cures. Antibiotics arrived. Vaccines proliferated. Infant mortality plummeted and life expectancy soared. Medical authority and prestige was on the rise, and it was hard earned; the results from this new way of thinking about disease were incredible. Medicine had officially grown up, from bloodletting and humors, to saving lives on a massive scale. The framework was touted and celebrated, and it deserved it.

Stuck in neutral

The framework was succeeding wildly, but with each victory, through these successes and saved lives, it had started becoming the only way people were allowed to think about and conceptualize disease. If you couldn't isolate a pathogen in a laboratory, culture it, and reproduce the disease in a controlled experiment, then what you were describing wasn't considered a real disease. Medical education was transformed around this model and research funding started flowing only to studies that fit it. Pharmaceutical companies built empires around it. The infrastructure of modern medicine, now an over 5 trillion dollar behemoth, was built on Koch's postulates.

And all the while, as we celebrated these victories and organized our understanding around this new system, chronic disease was becoming the dominant health problem of the developed world. And the framework that had worked so brilliantly for infectious disease just didn't quite work for these new diseases.

Heart disease. Cancer. Diabetes. Autoimmune disease. Alzheimer's. These conditions didn't seem to fit the model we had built medical research and treatment around. Because in an interconnected body, with a condition that affects multiple systems, there is no single pathogen to isolate, no simple cause-and-effect. Researchers were able to find associations, risk factors, and genetic predispositions, but nothing that could cleanly explain what they were seeing. There was nothing in these diseases that worked the way anthrax or tuberculosis had worked. And we did what institutional science always does when something doesn’t fit an established framework; instead of questioning whether the framework fit these diseases, or whether perhaps these diseases were something else entirely, we doubled down. There MUST be a specific cause: a gene, a protein, a pathway. It had worked so well before, we just haven't found it yet. We must just need more funding, more studies, and more decades of searching.

We threw money and resources at the problem for decades, making little forward progress, but sticking rigidly to the model. And while researchers kept searching for the single cause for each disease, they ran into two main problems. For the big diseases - cancer, Parkinson's, Alzheimer's, heart disease - the high profile diseases that brought in the funding, they did find things: genetic mutations, protein aggregations, plaques, inflammatory markers. With billions of dollars and decades of research, they could finally point to specific markers. But knowing what was broken wasn’t leading to cures. Cancer treatments remained brutal, often killing healthy cells along with diseased ones. Parkinson's and Alzheimer's medications might slow progression slightly, sometimes helping with symptoms for a while, but they couldn't stop or reverse the damage. Heart disease treatments managed symptoms but didn't address why the disease developed in the first place and didn't actually reverse anything either. The framework had delivered on its promise - it found what was broken. But knowing what was broken wasn’t the same as knowing what broke it. And it couldn't deliver what patients actually needed: healing.

And then there were all the other patients. The ones with Chronic Fatigue Syndrome, Fibromyalgia. IBS. Chronic pain. These conditions received little funding due to the stigma that they weren’t ‘real’. The framework's definition of 'real disease' had become completely untethered from patient experience or even biomarkers, the objective physiological measurements that medicine uses to confirm disease and develop treatments. As an example, when The Rome Foundation, the organization that sets IBS diagnostic criteria, updated them in 2016, they didn't use the biomarkers that had been found - which by then had an Area Under the Curve (AUC) of 0.89 which is a standard metric for evaluating diagnostic tests. Which, if it were used, puts these into the good to excellent range for diagnostic testing, and rivals that of the diagnostic accuracy of any of the ‘big’ diseases. Instead, they surveyed about 1665 people from the general population, excluding people who had been diagnosed with IBS, found the 90th percentile of symptom frequency, and declared that the threshold for disease. The top ten percent of bowel issues in this swath of healthy people became the new definition of IBS. And subjective pain became the primary marker, despite clear biomarker evidence. So anyone with severe, constant, debilitating symptoms that weren't specifically pain - daily bloating, distention, and abnormal bowel function - no longer qualified for diagnosis or treatment.

The change cut IBS prevalence in half overnight - not because fewer people were sick, but because they no longer met the criteria. They had defined disease by population statistics rather than by whether people were actually sick, and this change got ratified by the same committee that proposed it. No real research necessary. The pattern holds across the other dismissed disorders too, for example Fibromyalgia has biomarkers with around 85% diagnostic accuracy, and Chronic Fatigue Syndrome’s biomarkers diagnose the disease with a whopping 96% accuracy. And without the legitimacy that would come from people paying attention to these biomarkers, funding dried up, and without funding, treatments were never developed. Millions of patients suffered for decades with conditions that had documented biomarkers but no available treatments, leaving millions of patients in limbo.

The framework had a label for these cases: "functional disorders." Not real disease, just the body malfunctioning ‘without a clear structural cause’. Because no one was talking about these biomarkers, and without acknowledging it had clear biomarkers, by definition it wasn't a disease the framework could recognize. Medical training even has a term for the patients with these conditions: 'heartsink patients'. They are taught in medical school about this subset of patients that keep coming back with symptoms but no findings, and who ultimately become a drain on doctors time and energy. The term ‘heartsink’ was coined to capture how the doctors felt about these patients, as in, the patients who make doctors' hearts sink when they see their name on the schedule. And it makes sense why they felt this way. Doctors often go into medicine to help people, and the framework they are working within has no tools to help these people. It's human nature to feel overwhelmed when you know you're going to be confronted with an unsolvable problem, especially when it comes in the form of a person sitting in front of you who's clearly struggling. So without any available tools, these patients were shuffled between specialists, each finding nothing wrong in their domain, until the labs they took based on outdated knowledge came back unremarkable and they were eventually referred to psychiatry, told their symptoms were psychosomatic, and that they needed to manage their stress.

And right now in 2026 over 40 percent of the population has one of these dismissed diseases. That’s more than 2 in 5 people who have real physical symptoms that are affecting their lives, being told their condition isn’t serious, or isn’t real. When COVID swept the nation it alone left over 400 million people “mysteriously” sick with long COVID, a disease we societally called psychosomatic. If it were one patient, or ten, out of the world population of billions, ok, maybe you can assume it’s something going on on their end. But when it’s over three billion people worldwide, reason dictates it’s something the framework is missing, not individual failings of 40% of the population.

And so the patients in both groups were still sick. And the framework, having been built entirely around finding specific, visible causes, had no real solution for either group. Neither the “real” diseases where finding the mechanism didn't lead to cures, nor the “functional” ones, with biomarkers medicine wasn’t looking for. And nobody seemed to be asking the more fundamental question. Our bodies regenerate continuously at the cellular level. The gut lining replaces itself every few days, skin every few weeks, blood cells every few months, most of the body every 7-10 years. Bodies are constantly trying to heal. For a disease to be chronic, something has to be actively maintaining it against this constant effort. Chronic diseases are so named because there is explicitly no cure, remission is the best you can hope for and it means we know this can come back at any time. And by the 21st century, chronic diseases were causing 75% of all deaths worldwide. The framework was visibly failing.

The failures should have prompted questions; the pattern was clear in the data. The framework could identify mechanisms but not produce cures. Millions of patients suffered with no identifiable pathology. We were clearly missing something fundamental, the way we had missed microorganisms for centuries, but this time by being too focused on the microscopic to see the pattern at a different scale.

You would think it would be clear the same pattern was repeating again, but we don't seem to learn from history the way we think we do. In the 1830s, "heroic medicine" was the dominant medical framework. Physicians believed that illness stemmed from imbalances in bodily fluids that needed to be expelled, so they would bleed patients repeatedly, or give them mercury to induce violent diarrhea, all in an attempt to purge the disease. And it was actively killing patients. People could tell the official approach was wrong, so alternatives proliferated: herbal remedies, water cures, dietary reform, the 1800s version of the wellness industrial complex today. Some of these helped some. Some didn’t. None had the actual mechanism, they just knew the establishment was failing them. It took germ theory to finally explain what was actually happening, but that same victory created its own blindspot: the assumption that all disease must work like infectious disease. Find the pathogen, kill the pathogen. Heroic medicine had the wrong framework for infectious disease. Germ theory has the wrong framework for chronic disease. And because every generation thinks they’re the ones at the finish line of science, no one can see that they're repeating the same pattern.

Institutionalization

By the mid-20th century, enormous infrastructure had been built on that framework. Medical schools restructured their entire curricula around it - going from two-year apprenticeships to up to fifteen years of rigorous training in identifying pathogens, understanding molecular mechanisms, and finding specific causes. Licensing boards tested whether doctors could think within the framework, and research institutions organized themselves into departments studying specific diseases with specific mechanisms. The money followed the framework too. Pharmaceutical companies built trillion-dollar empires on the principle: one drug, one target, one disease. A pill for every ill, each designed to hit a specific molecular pathway. Research grants flowed to projects that fit the model - isolate a mechanism, develop an intervention, then test it in controlled trials. Entire careers were built on expertise within the framework. Questioning it threatened livelihoods, institutions and industries.

Then there was the public health imperative. Vaccines work. Antibiotics work. When medicine needed people to trust doctors - to get their shots, take their medications, and follow medical advice - "trust the science" became essential messaging. Any hint of uncertainty could be exploited by quacks peddling snake oil, or people spreading dangerous misinformation. Medicine had to project confidence, certainty and authority. To say "we don't actually understand chronic disease very well" would undermine the credibility needed to get people to accept the treatments that DID work. It became a propaganda of sorts, but one with a virtuous goal: make sure people keep taking these life-saving drugs.

So this framework, that had succeeded spectacularly for infectious disease, became the only legitimate way to think about all disease, and anyone who suggested otherwise faced dismissal. Researchers who could see the gaps and proposed methods that didn’t follow these strict guidelines struggled to get funding. Doctors who understood the limits of the framework risked being labeled unscientific, and were stuck following standard protocols even when they could see they weren’t helping. Patients who insisted their suffering was real despite normal test results were told it was psychosomatic, that they needed to see a psychiatrist, that perhaps they were catastrophizing their symptoms.

When a framework becomes unfalsifiable

In 1984 in the affluent resort town of Incline Village, Nevada, on the shores of Lake Tahoe, dozens of residents, teachers, professionals, active community members, suddenly fell ill with debilitating fatigue. They experienced cognitive problems and flu-like symptoms that wouldn't resolve. Two local physicians, Daniel Peterson and Paul Cheney, saw this sweep over their patient population, and meticulously documented the outbreak, trying to figure out what was causing it. They ran tests and tracked patterns, following every medical protocol they knew.

They reported their findings to the CDC, and investigators were sent to examine the outbreak. When they arrived and found that none of what had been documented fit their framework, they drew the only conclusion the framework allowed: mass hysteria. They even started calling it the "Yuppie flu." The patients - many of whom were successful, educated, and previously healthy - were dismissed as stressed, anxious, and perhaps subconsciously seeking attention. And the two doctors who were trying to figure this out, Peterson and Cheney, were ridiculed for taking it seriously, and for continuing to research what would later be recognized as Chronic Fatigue Syndrome.

And Incline Village wasn't an isolated incident. For decades prior, similar cluster outbreaks had been documented - at hospitals, schools, even an entire symphony orchestra. From 1984 to 1992, an unprecedented wave of these clusters was reported across North America. And the CDC systematically dismissed them as mass hysteria. And because of this systematic dismissal, around 1992, the reporting just...stops. The illness didn't disappear, Chronic Fatigue Syndrome (ME/CFS) continued in sporadic individual cases, but the cluster outbreak pattern that had been documented for over 50 years suddenly vanishes from the medical literature. When institutions declare something illegitimate, people stop reporting it. These patients' suffering was real, but because the CDC declared it hysteria, they lacked the funding needed to find the markers that the framework would take seriously.

Even discoveries that followed the framework's rules perfectly could be rejected if they challenged the wrong beliefs. In 1982, there were two Australian researchers, Barry Marshall and Robin Warren, who discovered that stomach ulcers, long attributed to stress and excess acid, were actually caused by the bacterium Helicobacter pylori, otherwise known as H. Pylori. They followed Koch’s postulates and the framework to the letter, isolating the organism from the diseased tissue, and culturing it. And then Marshall, in an act of desperation to prove his point after repeatedly being ignored despite evidence of successfully using his new information to treat his patients, drank a culture of H. pylori and gave himself acute gastritis. Which he then cured with antibiotics, demonstrating causation. This was Koch's postulates executed flawlessly: a clear bacterial cause for a disease. Exactly what the framework was designed to identify.

But it was rejected anyway. For years. In 1983, their abstract was one of only 11 rejected out of 67 submissions to the Australian Gastroenterology Society meeting. Their paper linking the bacteria to ulcers faced extensive delays at The Lancet, one of medicine’s most esteemed journals, ostensibly due to "difficulties finding reviewers." At conferences, the microbiologists in attendance were intrigued enough to start research projects, but gastroenterologists routinely dismissed the theory as "preposterous." Then Marshall discovered something that when he submitted his first paper to a US journal, it was rejected - not because the science was flawed, but because senior gastroenterologists had made a policy decision that the theory was "too new and radical" and they wouldn't accept papers on it. So they made this decision despite the solid science, because it seemed too new.

The prevailing wisdom at the time was that bacteria couldn't survive in stomach acid, that ulcers were a stress disease, and that the real treatment was antacids and acid-suppressing drugs. And it’s important to note that Tagamet and Zantac were among the world's biggest-selling prescription drugs at the time, and generated billions in pharmaceutical sales. Marshall called it fighting the "Acid Mafia." It would take a decade before the medical community integrated his findings into treatment protocols, and it would be twenty-three years before he received his Nobel Prize for the discovery.

Even when researchers follow the rules perfectly, even when individual doctors or researchers see the truth clearly, if it challenges the framework, they're dismissed until the evidence becomes undeniable - and sometimes not even then.

Chronic fatigue syndrome didn’t fit the framework so it was dismissed. H. Pylori didn’t fit in with current understanding and established knowledge so it was rejected. The framework had become unfalsifiable. If it found a mechanism, that proved it worked. If it didn't find a mechanism, that just meant we needed to look harder, develop better tests, search at a different molecular level, or dismiss it as not real. If you look through the medical literature, it’s full of documented findings that don't fit, labelled "paradoxes," or noted as "surprising" or "novel," saying "mechanisms remain unclear". And then the papers just... stop. They don't follow the implications. They add the anomaly to the list and move on. There is no observation, no pattern of failure, that can challenge the fundamental assumption: all disease must work the way infectious disease works. And this is how it’s always worked throughout history. When a new idea or framework explains existing anomalies better than the current model, it faces resistance proportional not to the weakness of its evidence, but to how fundamentally it goes against established thinking. We demand the new explanation be perfect in every scenario, while giving the old framework infinite grace. Semmelweis had the data. Marshall followed Koch's postulates perfectly. The evidence didn't matter, established thinking is more powerful than proof.

And then, in 2022, came the drug the framework couldn't explain

Trial after trial shows GLP1s improving myriad different diseases, behavioral disorders, and psychiatric conditions. The results are being published rapid fire in medical journals, and new uses are getting approved by the FDA.

In a study of over a million patients with type 2 diabetes, taking GLP1s was associated with up to 70% lower risk of getting Alzheimer's over a 3 year period.

A different study almost 50,000 people showed about 48% risk reduction for dementia.

A meta-analysis of over 450,000 people found a 32% lower chance of getting Parkinson’s disease compared to controls.

Participants in a study of alcohol use reported drinking significantly less on GLP1s, and smoking fewer cigarettes. People are reporting less gambling, shopping, binge eating, and drug use.

It’s being described as feeling like an alarm that’s been blaring has been turned off, someone said, “all of a sudden it was like some part of my brain that was always there just went quiet”; patients are describing feeling calm for the first time in their lives. Someone said of gambling, “the thought of gambling seemed so obviously stupid.”

They are seeing large risk reduction in depression, anxiety, and other psychiatric conditions.

And yet because the framework says each disease is separate, no one is stepping back to see why they seem to all be changing from one single upstream intervention.

The blind men and the elephant

Every single piece of data, every mysterious GLP1 finding, points to one overarching mechanism, a physiological state that precedes all disease expression, that medical research has been documenting in pieces but never connecting, because we've been looking at the pieces instead of the whole.

You've probably heard the parable of the blind men touching different parts of an elephant, each convinced they understand what they're touching. One feels the trunk and thinks it's a snake. One feels the leg and thinks it's a tree. One feels the tail and thinks it's a rope.

Drilling down into the details, specializing in narrow fields of science and medicine, is a really important piece of how science should work. But institutional science has become exclusively that, so specialized and siloed that no one is allowed to step back. You are only allowed to study and comment on your specific slice of the pie, which means no one has the expertise required to allow them to step back and see the full picture across multiple fields, or multiple body systems. But in order to effectively study an interconnected organism like the human body we should be stepping back, seeing a pattern, then drilling down to see if our observations hold, then stepping back, then drilling down. Without the stepping back piece we are missing half of the process and the whole of the puzzle.

Medicine has been meticulously documenting every piece of the elephant for over a century, specialty by specialty, the framework stopping us from ever stepping back to see what we're actually looking at.

So let's do that now. Let's back up so we can see the elephant that's been hiding in plain sight.

We'll start with one clue in particular - something that's been measured in hundreds of thousands of clinical trials, documented since the 1950s, and seen as noise to be subtracted away.

In 1950 a physician named Stewart Wolf conducted a (morally questionable) experiment. A woman had come into his office because she couldn’t stop vomiting. She was pregnant, had lost a lot of weight, couldn’t keep any of her food down, and was getting really dehydrated. The condition she was presenting with is called hyperemesis gravidarum, and Wolf told her he had just the thing for her; he had a powerful new drug that would make all of her symptoms go away.

Then he handed her ipecac.

If you don’t know what ipecac is, it’s not an anti-vomiting medication. In fact, it’s the exact opposite; ipecac is what you give to someone if they have ingested poison and you need them to immediately and violently clear the contents of their stomach. It was so touted and so trusted at this time that parents were encouraged to always have a bottle on hand in their medicine cabinet in case their children ate something they shouldn’t. And this is what Dr. Wolf handed to this woman who couldn’t stop vomiting, this drug that should have amplified her symptoms even more. He gave ipecac to his vomiting patient, told her it would cure her, and then just waited and watched and documented what happened.

And what did actually happen? Well, her nausea disappeared, and her vomiting…stopped. Completely. And Wolf documented it all. He measured her body’s response, like her gastric contractions and the other physical mechanisms of nausea and vomiting, and he saw all of it reverse. He had given her a drug that was widely used, and that predictably caused violent vomiting, and its functional mechanism had been not just overridden but reversed by her expectation.

Wolf published his findings in the Journal of Clinical Investigation. He had demonstrated that belief could override pharmacology; that the body's expectation could reverse a very well proven chemical mechanism. And this wasn't the first time Wolf had documented remarkable observations from a single patient, he liked to push on the established thinking and see if the framework held, and it wasn't the first time his colleagues dismissed him for it. When he'd presented earlier findings on stress and gastric function to the Gastroenterological Society? They…laughed. One case, they said. How could anyone draw conclusions from a single patient?

They had a point, I mean, one case truly isn’t proof of anything. But proof is not the same as intriguing evidence. It was just one case, but it was a case with measurable, documented, physiological changes. This should have at least sparked curiosity. It should have prompted the question: has anyone else seen this? Does this happen with other patients, other conditions? But instead, it was dismissed as an anomaly. An interesting outlier at best. Nothing worth investigating further.

Don’t get me wrong, caution is important, because human lives are at stake. You can’t just rush new findings into treatment; caution is not only justified but necessary. But caution about treatment is not the same as incuriosity about novel findings. In the case of Wolf’s finding for example, he wasn’t proposing a new therapy be implemented immediately, he had just documented something he had measured that had already happened: expectation mediated physiological changes that reversed the expected pharmacological results. Understanding how and when belief could override chemistry could be tested ethically and safely. If anything the stakes at hand should have made investigating the claims more urgent not less, because human lives are involved, and this if true could have far reaching implications.

Five years after Wolf’s findings, in 1955, Henry Beecher, an anesthesiologist at Massachusetts General Hospital, looked at 15 clinical trials spanning different conditions and different treatments to assess the placebo response that Wolf had demonstrated, only this time at scale. His study spanned 1,082 patients, and found that 35% of these patients experienced “satisfactory relief” from placebo alone. This was no longer an isolated case, or an anomaly, it was a pattern across hundreds of patients and multiple conditions, and Beecher published his findings in a paper titled “The Powerful Placebo” in The Journal of the American Medical Association otherwise known as JAMA, which is one of the most prestigious journals in medicine. The phenomenon was clearly real, measurable, and reproducible. So what did medicine do with this discovery?

Beecher himself framed the placebo response as something that needed to be measured and subtracted away, so that the ‘real’ drug effects could be properly evaluated. Because he came in under the model of find the cause, use pharmaceuticals to treat the disease, his focus wasn't on understanding how belief could produce physiological changes, it was on eliminating this messy confound from the data so that the ‘real’ science could show through.

When placebo trials became law

Then in 1962 Thalidomide, a drug that was being prescribed to pregnant women for morning sickness, caused over 10,000 birth defects worldwide. In the US alone, roughly 20,000 patients had received the drug in unregulated clinical trials, and because there weren’t rigorous laws around drug trials, they had missed the effects it had on developing fetuses. And because of this large scale and visible tragedy, there was enough public outrage that congress responded by creating new requirements for drug testing. Pharmaceutical companies would now have to prove both safety and efficacy before they would be allowed to enter the market. This became the beginning of what we now know of as randomized controlled trials, and through this, placebo controls became law.

By the 1970s, beating placebo became the gold standard of proving efficacy. Every drug seeking approval had to demonstrate that it worked better than placebo alone. Which also meant that every study was actively measuring placebo effects, and rigorously recording their outcomes. Medicine needed placebo controls to prove drugs worked, and in doing so, it accidentally documented, with expert precision, over seventy years, in hundreds of thousands of trials, the very mechanism it was trying to eliminate. For seventy years, medicine measured placebo in nearly every clinical trial ever conducted, with millions of patients all showing the same thing: placebo produces measurable physiological changes. And for seventy years, we asked only one question: how do we make it disappear?

In 2001, the Cochrane Collaboration appeared to have the answer. Hróbjartsson and Gøtzsche published a systematic review analyzing 130 placebo-controlled trials across 40 different clinical conditions, and their conclusion was definitive: placebo effects were weak, clinically insignificant, and possibly even nonexistent. The paper appeared in the New England Journal of Medicine, another of medicine's most prestigious journals, and medicine ate it up. Finally, rigorous analysis had confirmed what many suspected: placebo was mostly myth. The emperor had no clothes. The paper then became the most cited work on placebo ever published, it functioned as the definitive reference that shut down conversations about whether placebo effects were real.

Now to anyone examining the analysis, it was pretty clear that Hróbjartsson and Gøtzsche's methodology wasn't scientifically sound; that they came in with a hypothesis and read the data in a way that enabled them to show what they expected to see. They had 130 trials showing placebo effects across different conditions, and some conditions showed consistently strong effects. But Hróbjartsson and Gøtzsche didn't report it that way, what they did was average everything together. Parkinson’s with smoking cessation trials with nausea with schizophrenia with asthma with infertility. All 40 conditions pooled and averaged into a single analysis, measured against an arbitrary marker of “significance” decided on by the writers themselves, and through this pooling and averaging, the real effects disappeared into the noise.

This would be like averaging the effectiveness of antibiotics across bacterial infections, viral infections, broken bones, and depression, then concluding antibiotics don't work because the average effect was small. No drug would survive that analysis.

When they wrote up their results, Hróbjartsson and Gøtzsche emphasized outcomes where placebo showed no effect while minimizing the outcomes where it did show effects. They did acknowledge that placebo might have "possible small benefits" for pain and subjective outcomes, but then spent most of the paper explaining why even those effects probably weren't real, that they were probably just reporting bias, or possibly regression to the mean. Their conclusion wasn't that placebo works for some conditions but not others; it was that placebo effects don't exist in any clinically meaningful way at all.

And then there was the circular exclusion criterion buried in their methodology. They excluded trials where "the alleged placebo had a clinical effect not associated with placebo", which meant they excluded cases where placebo worked too well, reasoning these placebos must have contained some active ingredient or in some other way represented something that wasn’t truly placebo. Any particularly strong placebo effect was, by their definition, not a placebo effect, and so they just…quietly removed it from their data set. When other researchers reanalyzed the same data, they reached opposite conclusions. Wampold and colleagues found the effect sizes were essentially identical to what Hróbjartsson and Gøtzsche had calculated; the difference wasn't in the numbers but in the interpretation. An effect size of 0.28 could be called "small and clinically insignificant" or "robust and meaningful" depending on your framing. For context, that effect size, the one dismissed as meaningless, exceeds many accepted medical interventions.

But the reanalysis came too late. Because their conclusion matched what everyone expected to see, no one poked too hard into the particulars. For two decades, the Cochrane paper shut down investigation. Placebo was myth. The data had spoken.

Placebo research?

Then in 2005, Harald Walach and his colleagues decided to look at placebo data differently. They decided to step back and look at placebo across all of medicine instead of study by study. They analyzed over a hundred clinical trials and found something medicine had been documenting but never noticed: when patients improved significantly on a drug, they also improved significantly on placebo. When a drug showed weak effects, placebo showed weak effects too. The two moved together, trial after trial, with a correlation of 0.78, meaning roughly 60% of the treatment outcomes could be explained by what was happening in both groups. Think about what that means: in a trial where 40% of patients improve on the drug and 35% improve on placebo, medicine celebrates the 5-point difference. The drug works! But 87.5% of what patients actually experienced, the relief they felt, the symptoms that resolved, was happening in both groups. Something other than the drug's pharmacology was producing most of the improvement. This pattern held across a hundred trials. The thing that determined whether a trial would show strong effects or weak effects wasn't primarily the drug.

Decades of data showing that most improvement in clinical trials occurs in both groups, pointing to something massive and obvious, and the response was pretty much nonexistent. This didn’t fit into what was being funded, what the framework expected to see, or how we had been taught to think about medicine as a whole, so nothing much changed.

There were a small number of researchers looking at placebo effects. The world's first and only research center dedicated to understanding placebo mechanisms was established at Harvard in 2011. And in 2014, some researchers formed the Society for Interdisciplinary Placebo Studies, an international association that holds a tiny conference every two years. These teams made and are making real discoveries; they’ve documented endogenous opioid release in response to placebo, showed dopamine production in Parkinson's patients given fake treatments, and found that conditioning and expectation could produce measurable physiological changes. They proved Wolf and Beecher had been documenting something real. Yet despite seventy years of consistent findings and growing evidence of physiological mechanisms, because there was no place for this research in our current framework, the research remained fragmentary.

Placebo sits in every single trial, is one of the best documented phenomenons in medicine, and we still don’t seem to understand it much at all. In order to see why that is, let’s look at the numbers. As of the time of this publication the NIH, which funds the majority of publicly funded medical research in the United States, spends roughly \$47 billion annually across all research. Cancer research gets \$7.3 billion. Alzheimer's gets \$3.8 billion. Diabetes gets \$1.1 billion. The NIH tracks spending across more than 300 research categories, from rare genetic disorders to common chronic diseases, publishing detailed breakdowns of where every dollar goes. So where does placebo research rank on that list?

It doesn't.

Placebo isn't a category. Rare genetic disorders affecting a few thousand people have dedicated funding streams. Diseases with clear treatments and established protocols get billions. But the mechanism documented in hundreds of thousands of trials, affecting millions of patients, explaining 60% of treatment outcomes? Placebo funding isn’t even tracked, indicating it receives essentially no dedicated funding.

Because of this lack of funding, and the lack of investigation into findings that don’t match what we think we know, no one has thought to ask certain questions, to step back and look at the whole of the data and ask what placebo is actually telling us about how our bodies work.

Stepping back

So let’s take the mountain of data and step back and see what it’s telling us. Why don’t we start with blood pressure. When doctors measure blood pressure in the office there is a very large documented placebo effect. But when they strap a blood pressure monitor on you and send you home for the day, there is virtually none. Why, when they are measuring blood pressure in the same patient in both cases, does placebo present so differently? Cardiology literature has little to say about this mystery. Most of what you will see if you look for cardiology's explanation about this is just that home monitoring is more accurate than in the office, so that is the gold standard when precise measurement is needed. When it does try to explain it it invokes "white coat syndrome" most often, which is the effect of feeling nervous in the office which causes your blood pressure to rise. But this doesn’t explain it at all, because the placebo effects they're showing in the office cause your blood pressure to LOWER, not rise. Despite decades of research and billions in funding, the explanation for why these two vital measurements vary so much doesn't account for what is actually happening. Cardiology receives \$3 billion annually from the NIH alone, which accounts only about a third of all medical research and development funding, meaning there is probably around \$9 billion being spent after you factor in money coming from pharmaceutical companies. And this basic discrepancy hasn’t been explained, because blood pressure research is studied in cardiology and placebo research is studied…nowhere.

And blood pressure isn’t the only paradox. When measuring air flow out of the lungs, doctors conduct two main tests regularly, FEV1 and Peak expiratory flow (PEF). They test these two things in the same patients at the same time, and the placebo effects are opposite each other. One shows placebo, the other actually goes down. Respiratory medicine handles it the same way cardiology does, recommends one as more reliable than the other, and moves on.

Medicine has been documenting these discrepancies for decades: measurements that should show the same thing but don’t, conditions that respond differently to placebo for no clear reason, and patterns that are noted but never explained.

The prevailing assumption is that placebo only works on subjective measures like pain and nausea, things patients report feeling. Despite seventy years of data showing otherwise, that's what researchers, physicians, and the general public believe. Placebo is psychological. It's about perception, not physiology. So why don’t we test that assumption, pull some levers and see what we find; look at measurements that can't be explained by belief or perception alone; objective physiological changes that happen whether a patient is aware of them or not.

Coming back to blood pressure, what does the data actually show? When patients are given sugar pills and told it will decrease their blood pressure, in study after study the data shows that their blood pressure drops, a lot. By an average of 5-7 mmHg systolic, sometimes more. These are the same measurements doctors use to do things like diagnose hypertension and prescribe medications. And though this is pretty astonishing, blood pressure can be kind of hard to conceptualize, you can’t see it and what does 5-7 mmHg really mean? Could it be a measurement error?

So why don’t we look at something a little more visible. When you think of Parkinson’s you probably think of the shaking that defines it in our perception of the disease. It’s measurable and even visible to the naked eye. Parkinson’s rest tremor is called a “rest” tremor because it happens when you’re at rest, and stops happening when you voluntarily engage your muscles. And the way medicine has found to treat this tremor is by increasing the dopamine in your brain using a drug called Levodopa, which gets converted to dopamine once it’s in your body. This drug reduces the shaking in rest tremor and is the first line of defense against this tremor in patients.

So when we look at the clinical trials for drugs that reduce Parkinson’s shaking by increasing dopamine in the brain, and then look at what happens in the placebo arm of the trial where patients receive a sugar pill, we see their shaking measurably decrease by 25-45%. With some patients showing over 70% improvements. And this is pretty substantial evidence on its own, but what’s more striking is seeing that what is happening in the brain to produce these changes is the exact same thing happening in the active drug arm of the trial. The patients' brains are creating more dopamine. The studies show measurable increases in dopamine production in these patients receiving the placebo, creating improvements that, for some patients, rival any drug currently on the market.

We could go through more examples but I think the picture is becoming clear. Placebo is not just perceptual, it is acting on real physiological and biochemical activity in the body, and since placebo is required for most every clinical trial, there is a lot of data that shows just this.

Now to be crystal clear on what we’re talking about here, we need to also look at that same dataset and see if there are things that don’t show placebo effects. Because surely if it acted on every single thing the prevailing assumption wouldn’t be that it was merely psychological. And this is what the data shows as well. Not everything shows a placebo effect, in fact if you look at the two most studied functions from each of the major body systems and assess the amount of placebo effects on each one, only 10 of 22 of those functions show high placebo effects. Things like bone mineral density and fracture healing, ovulation and wound healing, all show low to no placebo effects reliably across the medical testing landscape. So what do the things with high placebo effects have in common? What do the things with low placebo effect have in common?

Let’s think about what the high-responders have in common first. Heart rate. Blood pressure in the clinic. Breathing patterns. Gastric motility. Pain perception. Tremor at rest. These aren't things you consciously control, they happen automatically. So what about the non-responders: bone density. Wound healing. Blood pressure over 24 hours. Red blood cell production. Ovulation. These are also automatic, so it's not about voluntary versus involuntary. The difference is something else.

So what else does the heart rate paradox have to show us. Your heart beat can change pretty quickly. If you were to stand up right now your heart rate would increase within seconds. And then if you take a few deep breaths, it will slow down momentarily. Compare that to bone density: changes in your bones are happening over weeks or months, there's no moment-to-moment adjustment. So speed seems like it could be part of it, but maybe not quite the whole picture.

What about pain perception? That responds to placebo. But so does nausea. Yet pain signals travel through nerves at hundreds of miles per hour, while nausea involves chemical signals in the gut that move much slower. Different speeds, but both show placebo effects. So it's not just speed…what else? Maybe if we look at how these systems work: heart rate changes through direct nerve signals but bone density changes through hormones. Different types of control; direct neural signaling versus hormonal messengers. And look, pain and nausea both work through nerves. Heart rate, breathing, digestion, all controlled by nerve fibers. The things that respond to placebo all share this: direct neural control, real-time regulation, moment-to-moment adjustments based on your body's needs and your brain's perception.

But wait, muscle strength is also neural. When you flex your bicep, nerve signals travel from your brain to your muscle. That's direct neural control too. Yet muscle strength shows essentially no placebo response. So it's not just about having neural control, maybe it's about what KIND of neural control?

Voluntary muscle movement, the kind you control consciously when you decide to lift your arm, is mediated by your somatic nervous system. Heart rate, breathing rhythm, digestion, blood pressure regulation, these are all mediated by your autonomic nervous system (ANS). Somatic function you control consciously, and autonomic is automatic, meaning it keeps things running in the background and can adjust based on your body’s needs and your brain’s perception of threat or safety.

So let's look back to those apparent paradoxes we were looking at earlier and see if this helps explain what we were seeing. Clinical blood pressure, like when your doctor puts that cuff on you in the office, shows large placebo effects. And ambulatory, which is the kind you wear home for a day, shows virtually none. So are these being mediated by different systems? It turns out yes, they are. Clinical, namely short-term, blood pressure is controlled by baroreceptor reflexes, the moment-to-moment adjustments your body makes in response to context and perception. ANS regulated. Whereas ambulatory blood pressure is controlled by renal mechanisms that operate over hours and days, and these aren't under ANS control.

What about the lung function example? FEV1 measures how much air you can expel in one second: think of it like a balloon that's really filled up versus one that's only half filled up. If you unplug the end and let air come out, which one will expel more air? The fuller one, right? Well it turns out that the smooth muscles involved in this involuntary process of pulling more or less air into your lungs are ANS mediated, which tracks with our theory. Whereas peak flow measures the maximum speed of airflow, how hard you can push air out using your voluntary muscles. That's not ANS mediated, it's somatic. So yeah, this explains them.

So what we are seeing is that the functions that respond to placebo all seem to be controlled by the ANS. And when you look at the data, it bears this out with remarkable accuracy. Those 22 functions we talked about earlier? They span every major body system and all match perfectly to this framework. This pattern holds across all 22 functions. The likelihood of that happening by chance is less than one in four million.

For seventy years, we’ve been measuring placebo in hundreds of thousands of trials, documenting which conditions responded and which didn't. The pattern was there the entire time, we just never asked the right question. In fact we barely asked any questions at all. And right now, trial by trial, the same thing is happening with GLP1s; we’re documenting what each condition does in response to the drug, without stepping back to see what it all says together.

The ANS mediation we discovered is just one piece of the picture, sitting in the data, largely unexamined. And on its own it almost raises more questions than it answers. So next let's look at some of the diseases we have been spending time and money on, billions of dollars and decades of research. Let’s start with cancer; arguably one of the most investigated diseases in history. Let's see what it can teach us about disease that we have been missing.

Your body produces new cancer cells every day. Hundreds of billions of them. And every day, your body finds them and eliminates them.

Now to be more specific we're talking about cells that could become cancer if left unchecked: such as cells that are damaged, that divided incorrectly, or that got infected by pathogens. These early could-become-cancer cells are being produced by the billions every day our whole lives, and every day they are being cleared by our bodies. Billions of them. Every single day of your life. But when our bodies stop efficiently clearing these cells, they can start to accumulate. So then we have a lot more cells than we should have in a given area, and a lot of them have mutations and shouldn’t still be there. And now these accumulated cells are competing with each other, for what are now limited resources and space. When this starts happening, natural selection begins. Survival of the fittest: cell edition. Once this competition begins, the cells that can reproduce fastest, grab the now limited resources the most efficiently, or create environments that are harder for the other cells to tolerate, those are the ones that survive.

Because our cells reproduce so quickly, this natural selection process plays out at evolutionarily eye-watering speeds. Each time a cell mutates and doesn't get cleared, it’s made it past another round of selection, which means it's slightly better at surviving than the last version. Think about how antibiotics work. I’m sure you know you’re supposed to finish the entire course of antibiotics even if you start feeling better. Because if you stop early, the bacteria that survived the first doses are the strongest and most resistant ones, which means they’re the ones most likely to survive future rounds of antibiotics. The same thing happens with these cells. Each round of mutations that survives clearing is a little more resistant to your body's defenses. String enough of those mutations together, and that becomes what we call cancer. This is why the places with the highest cell turnover - the gut, the skin, the blood - have the highest cancer rates. More divisions means more mutations means more raw material for selection. Places with little turnover, like the heart, see almost no cancer.

This is all just basic natural selection, and mapped out this way it seems pretty obvious. But then, why does anyone get cancer in the first place, if our bodies are normally so good at clearing these aberrant mutation cells? And since this is clearly a clearing issue leading to natural selection on a cellular level, why are we looking at the cells themselves as the culprits? Why aren’t we instead asking what caused this normally efficient clearing system to stop working?

Let's look back through the history of cancer research to see if we can figure out why it isn't being viewed through this lens.

In 1923 a researcher named Otto Warburg was studying cancer in his lab in Berlin, and he saw that cancer cells were using a process called glycolysis, which produced 2 units of energy instead of the usual 36 from the same amount of fuel. The understanding at the time was that cells only behaved like this to compensate when there wasn't enough oxygen, but the cancer cells he was observing were doing it with more than enough oxygen to go around. Why would they be doing something so seemingly wasteful?

Seeing this he thought he'd found THE answer to what cancer was. Here were cells that had switched to a completely different way of making energy; something normal cells never did unless they were oxygen-starved, and he reasoned that this must be the defining change that transformed normal cells into cancer cells. And he wasn’t alone in this belief. His discovery was so momentous, and he was so celebrated for it, that the Rockefeller Foundation built him his own building specifically designed to his requirements: forty rooms, no offices or conference rooms, just labs and a library. This is where Warburg was doing his work, in Berlin, as a Jewish and almost certainly gay (though this was never publicly discussed) man, when in 1941, the Nazi regime came and dismissed him from his position. But then within weeks Hitler himself, during WWII, personally intervened and reinstated him. Hitler's mother had died of cancer, and he was so convinced Warburg was going to cure it, he personally let this gay Jewish man continue his work.

But unfortunately Warburg never did end up delivering the cure. Because while he'd discovered something groundbreaking, many cancer cells are shifted to this emergency metabolism, but he had the causation backwards. This wasn't how cells become cancer; it was what a hint as to what was causing cancer.

Then in 1992 a pediatric geneticist named Gregg Semenza discovered something called HIF-1, which is basically this master regulator in the cells. But the assumption at the time was still that generally speaking, HIF-1 sent that signal only when oxygen was low. Then throughout the 1990s and 2000s, researchers started finding that sometimes HIF-1 was activating even when oxygen levels were perfectly normal. Inflammation could trigger it. Stress hormones could trigger it. Reactive oxygen species from overworked mitochondria could trigger it. Meaning in addition to low oxygen, we now knew this glycolysis, or Warburg effect, could happen when the body was under various types of physiological stress.

Now let's jump back to the 1970s. When Macfarlane Burnet and Lewis Thomas formalized the concept that the immune system must be patrolling for and ridding the body of the lifelong accumulation of genetic changes. They posited that this was an evolutionary necessity (which, as we established above, it is).

But then in 1974, Osias Stutman at Memorial Sloan Kettering seemingly disproved this theory. He was actually trying to prove that immune surveillance was preventing cancer, by testing whether mice without immunity would develop more cancer than mice with it. But he ended up finding that the immune-deficient mice got cancer at the same rate as normal mice. And this discovery became a landmark paper, and the pace of research in understanding tumour/immune interactions slowed significantly in its wake. For many scientists, this closed the case: immune surveillance was wrong. And after this finding, Stutman would go on to become chair of the Department of Immunology at MSKCC and was an esteemed figure in the field.

But decades later, a researcher named Robert Schreiber saw a problem with Stutman's experiment. He figured out that Stutman's "immune-deficient" mice weren't actually immune-deficient. The mice in his study still had NK cells and some T cells, both of which contributed pretty significantly to immunity, so Schreiber redid the experiment with truly immune deficient mice. And the result? His immune deficient mice developed cancer much more readily than those with regular immunity. In the early 2000s, Schreiber was invited to present his findings at Memorial Sloan Kettering. "I'm sitting there getting ready for my talk and this elder gentleman walks in and sits in the front row." he recalls. The elderly man was Stutman - the man whose work he was disproving. "The first hand up was Stutman's," Schreiber recalled. "I'm just holding onto the lectern going, 'Oh, here it comes, he's gonna just lay into me.' And Stutman got up in front of everyone and said, 'You know, it is remarkable what you can do now at the turn of the century that we couldn't do in the 1970s.'"

Immune surveillance was hypothesized in the early '70s, seemingly disproven in '74, and then definitively reproven in the 2000s. And once they figured out that immune surveillance was in fact real, they took it a step further and documented that healthy people's immune systems could and were successfully clearing precancerous cells all the time. This was when the concept of your immune system patrolling your body, looking for damaged or abnormal cells, and eliminating them, became established science. We knew it worked. We could even see it working. But even with this knowledge, we didn't seem to be asking why it sometimes... stopped.

Don’t kill me flags

In 2002, in the wake of immune surveillance being reproven, researchers discovered that tumor cells were expressing a protein called PD-L1 on their surface. PD-L1 serves as a signal to the immune system patrollers - think of it like a flag you hang outside your door that says "don't kill me! I'm one of you!.” Healthy cells hang these flags so the immune system doesn’t accidentally kill them when it’s mounting a response, so when things like infection or inflammation are happening and the immune system is mounting a defense, cells are more likely to hang these flags. Tumor cells have been discovered to use these flags, which is why they are able to survive even with the immune system patrolling, which makes sense given what we talked about earlier - natural selection favors cells that can avoid being killed. If you are hanging these flags, your chances of surviving immune clearance go way up, so of course the tumor cells that survived are ones that still use this signal. But the medical literature framed this discovery of tumor cells using these flags as: tumors were actively suppressing the immune system, evading detection through the use of these flags. The medical literature was full of this kind of framing: tumors 'co-opt' immune checkpoints, tumors 'evade' immunosurveillance, tumors use 'key mechanisms' to escape immune destruction. The lens through which we are looking has consequences for how we interpret what we are seeing, and because of germ theory's success and how it shaped our understanding of disease, we've been interpreting our own cells as if they were invaders.

Once they discovered this, the solution seemed obvious: block those signals. If tumors were ‘actively’ suppressing immunity, just remove that suppression and unleash the immune system. So they tried it, and it worked. When researchers developed drugs that blocked PD-1 (the receptor the immune system uses to read the PD-L1 signal), tumors shrank, and some patients with previously fatal cancers who would have otherwise died, lived.

In 2010, a clinical trial of a new treatment for patients with advanced skin cancer (melanoma) using the knowledge of these flags produced results at a level that medicine had never seen before. In several of the patients in the trial, signs of their cancer disappeared completely after treatment. These were people with metastatic disease, meaning their cancer was spreading to other body systems, who without treatment would have died within months. The drug that emerged from this research, ipilimumab, was very quickly approved by the FDA, in 2011. It was the first new treatment for advanced melanoma in over a decade, and following the wake of ipilimumab, the field exploded. Allison and Honjo, the people who developed this method, won the Nobel Prize in 2018. The word "revolution" appeared in paper after paper.

But there was a pretty big side effect to this treatment: autoimmunity. A whole lot of it. Patients being treated with this new method developed things like inflammatory colitis, hepatitis, thyroiditis, and dermatitis. Their immune system, without the usual signals telling it not to, had started attacking healthy tissue throughout their bodies. Some of these autoimmune reactions were severe enough that patients had to stop the cancer treatment. Because PD-L1 isn't something ‘invading’ cells do, it's how ALL cells protect themselves during immune activity. Block it everywhere, and healthy tissue loses its protection too. When your immune system is attacking an infection or responding to injury, inflamed but regular tissue puts out the PD-L1 flags to prevent collateral damage—to say "yes, I know there's activity here, but don't attack ME, I'm just responding to the problem." Macrophages do it. Epithelial cells do it. Normal inflamed tissue does it all the time.

Tumor cells hanging the PD-L1 flag in response to attacking T cells isn't evasion or a clever strategy, it's just... what cells do when they're in an inflammatory environment. It's the normal cellular response.

So this discovery was more evidence that tumor growth is just natural selection playing out. When immune clearance fails, cells accumulate and compete. The ones that happen to retain normal protective signals survive. We had discovered that some tumor cells happened to retain or upregulate this normal protective signal, and the cells that did had survived immune surveillance. The ones that lost it got cleared. This proved that the immune system CAN clear tumors - the capacity exists, it's just being held back. When they removed the safety mechanisms with checkpoint inhibitors, tumors disappeared. But patients developed autoimmunity. It was now clear that immune capacity matters, we just weren’t yet looking at cancer as a failure of our normal immune function.

Why does cancer spread

Then in 2015, researchers found that epithelial cells, which make up most of the cells that become cancer, have a built-in migration program called 'unjamming.' During wound healing, these cells shift from a solid-like 'jammed' state to a fluid-like 'unjammed' state, allowing them to migrate collectively to repair tissue. More specifically, these cells unjam when their neighbor goes missing. So to conceptualize this, let's think about a sheet of honeycomb for a minute. If you imagine all those little pockets as cells, you can see how they each have neighbors on every side. What triggers unjamming is if you were to cut a line in that sheet, all of the cells along that cut that had a neighbor a second ago and now don't would get the signal, "I lost my neighbor, we need to repair this gap that was just created." And what is particularly important here is that these cells move in sheets or clusters to repair these gaps, not individually. This is important because we know from the research that single rogue cancer cells rarely cause cancer to spread in the body; it’s almost always clusters of traveling cancer cells that are the catalyst for spread.

So let’s look at this one a bit more closely. Researchers have shown that cancer metastasis (when cancer spreads from its original location to other places in the body) involves cell clusters spreading to places they shouldn't be. They’ve shown most deadly cancers are epithelial. And a decade ago researchers figured out that epithelial cells naturally migrated using this unjamming program, and that this program creates moving clusters or sheets. And ten years later, because of how we silo our research and go deep without ever stepping back, we haven’t yet connected the dots.

And what this means is that cancer metastasis isn't some sophisticated evolutionary strategy that tumor cells developed. It's epithelial cells doing epithelial cell things; activating their normal wound-healing migration program. If there's tissue damage around growing tumors, that will create wound signals. Normal cells in the area respond appropriately, migrate a bit, repair, and stop. But tumor cells, which are just epithelial cells that survived selection for rapid growth when clearing failed, they also get these wound signals. They unjam and migrate too, but if they've lost the normal controls through mutation, without the normal stopping signals, they go places they shouldn't.

So if unjamming is how epithelial cells migrate (it is), and cancer cells are primarily epithelial cells (they are), and unjamming is triggered by edge creation (it is)... then what would we expect to see? And what would that mean for how we do things? The huge implication here is that surgery creates massive wound signals, it is literally creating wounds. And the data bears this out. Modern studies show right after surgery, within 12-18 months, there is a sharp peak of metastasis, which suggests a triggering event, not gradual progression. Radiation damages tissue, and even chemotherapy causes tissue damage that can cause these edges needed for unjamming to take place. So our front line treatments for cancer are likely triggering the migration program in cells that have already been selected for proliferation. We've known about unjamming for nearly ten years, but because institutional science goes deep and not wide, which prevents us from connecting what we’re seeing, we're still using treatments that create the exact signals that trigger metastasis.

But maybe the biggest potential implication of all is biopsy. Biopsy is a front line diagnostic procedure. See anything out of the ordinary on your scans? Biopsy the potential mass to see if it's cancer. And it turns out, someone has already looked into this. And they found more lymph node involvement, and specifically macrometastases (clusters), not individual displaced cells. In 2004 Hansen conducted a study of over 600 women who had breast cancer to see if there was a correlation with biopsy and metastasis, and she found that women who had needle biopsies were 50% more likely to have cancer in their sentinel lymph nodes than women who had their tumors surgically removed without prior biopsy. And not just cancer cells but macrometastases which means groups of cells clumped together, which is exactly what unjamming triggers. And cancer cells in your lymph nodes are categorically bad. Lymph node involvement is one of the primary criteria that determines how far along your cancer is and how aggressive your treatment should be, which means the biopsy may be increasing not just spread but also the intensity of subsequent treatment.

Another study by Moore in 2004 conducted at Memorial Sloan Kettering showed the direct correlation: no biopsy (1.2%) → FNA (small needle) (3.0%) → core needle (bigger needle) (3.8%) → surgical biopsy (4.6%). With a p=0.002 which basically means the chances of this happening by chance are extremely unlikely. So what we are clearly seeing is: more manipulation = more metastasis. Which means we should be urgently doing more studies looking at our practices around cancer screening. Miller et al. did a 25-year randomized controlled trial of nearly 90,000 women that showed women randomized to screening vs no screening for breast cancer had the exact same death rates. So it’s not a given that all this screening is providing more benefit than harm.

One researcher who saw cancer differently

There was one researcher who did see it differently. Robert Gatenby, a radiologist at Moffitt Cancer Center, had been thinking about cancer through an evolutionary lens since the 1990s. He remembered being taught in medical school that "cancers grow because cancer cells grow faster than normal cells", which he knew was wrong on multiple levels. Cancer cells don't grow faster because a mutation flipped a switch. They grow faster because, in a competitive environment where damaged cells are accumulating instead of being cleared, fast proliferation is a winning strategy. We're seeing the result of selection, not the cause of the problem.

In 2009, Gatenby published a paper proposing that we stop trying to eradicate all cancer cells in the way we have always done, and instead try using treatments strategically; use evolution against itself. He called it "adaptive therapy." He figured that maximum-dose chemotherapy kills sensitive cells, creating space and resources for resistant cells to proliferate. That's why cancer so often comes back resistant—we're selecting for resistance with each treatment. But what if we cycled treatment on and off, maintaining a population of sensitive cells to suppress resistant ones?

In 2017, Gatenby published results from a 11-patient prostate cancer trial. He was able to demonstrate that using this approach could double patient survival time, while cutting drug use in half. The paper appeared in Nature Communications, which is one of the most prestigious journals in science. Other centers started testing variations of his protocols, and he got follow-up funding for larger trials. By 2022, five years after publishing those initial results, multiple adaptive therapy trials were ongoing or planned in different cancers. But in 2025, sixteen years after his original concept paper, eight years after proving he could double survival time, adaptive therapy was still described as "promising" and "gaining traction."

So what we’re seeing here is that cancer isn't that mysterious, it’s really just normal epithelial cells doing two normal epithelial cell things - proliferating and migrating - without normal supervision. When normal immune clearance fails or depletes, natural selection favors the fastest replicators. When wound signals trigger migration, these selected cells spread chaotically. Every 'hallmark of cancer' is just normal cell behavior without normal regulation. When immune clearance fails, natural selection happens at the cellular level. The winners become what we call cancer.

This understanding predicts cancer should develop specifically in epithelial tissues (which can unjam and migrate) where clearance has failed. Cancer distribution data confirms this: malignancies cluster in epithelial tissues (lung, GI tract, breast), while benign tumors that don't metastasize occur in tissues without this migration program.

· · ·

So now that we’ve stepped back, and seen that everything we have discovered about cancer points to one upstream failure: inhibited immune clearance, the next obvious question is why? What is inhibiting it? Let’s look at that next.

We see autoimmunity as the immune system gone rogue. Why is this person's immune system attacking their own body? The research focuses on understanding the mechanisms of attack: which tissues are targeted, which inflammatory pathways are activated, what's different about these attacking immune cells. And the treatment follows this research, attempting to turn the immune response off in targeted ways so it stops attacking the body.

But every healthy body has cells that attack its own cells, called autoreactive immune cells, which serve useful purposes like clearing damaged cells and debris. Which means that what's happening in autoimmunity is similar to what we saw in cancer, in that it's a normal cellular process running without its normal controls. In healthy people this process is kept in check by regulatory T cells (Tregs) that suppress them before they can cause damage in healthy tissue. But in autoimmunity, a few things happen simultaneously: Treg production drops meaning there are fewer overall Tregs, and the remaining Tregs function less efficiently, meaning they are less able to regulate the way they’re meant to. And there is something called IL-2, which feeds both Tregs and autoreactive immune cells, which are the ones that always exist to attack your own tissue. And when Tregs are diminished, there's more IL-2 to go around and so the autoreactive cells get a lot more of it, which effectively ramps them up. You end up with a situation that's kind of like removing the police while simultaneously arming everyone in a crowd. The autoreactive cells that are normally suppressed can now attack freely and have more fuel to do so. Which means the question we should be asking is: why does the monitoring fail like we saw in cancer?

So this is what’s happening in autoimmune diseases, and yet we seem surprised that autoimmune diseases cluster together. About 25% of people with one autoimmune condition develop at least one more - and for some conditions, the risk of developing a second is 4 to 10+ times higher than the general population. But when you understand what we just laid out, that autoimmune disease comes from systemic monitoring failure plus autoreactive activation, of course you'd see multiple autoimmune conditions develop. If your entire immune monitoring system is compromised while autoreactive cells are ramped up throughout your body, why would it only affect one tissue type? Or occur in one isolated spot in the body? Ibuprofen doesn’t just target inflammation in your hand, or your gut, it’s system wide because that's how the body works. The mystery isn't why they cluster - it's why we expected them not to.

We have identified between 80 and 100 'different' autoimmune diseases, which as a whole affect 1 in 10 people. We keep discovering new ones every year, adding to the list like we're cataloging species. Hashimoto's thyroiditis. Graves' disease. Type 1 diabetes. Rheumatoid arthritis. Lupus. Sjögren's syndrome. Each one gets its own specialist, its own treatment protocol, its own research funding. As if each one is a separate phenomenon requiring separate explanation. It's like discovering that water can make cotton wet, and wool wet, and silk wet, and declaring you've found 100 different wetting diseases instead of recognizing that water makes things wet.

And autoimmune clustering is just the beginning of a much bigger pattern; disease clustering extends far beyond autoimmune disease. We use the term "comorbidity" when diseases occur together - for example if you have both diabetes and heart disease, or depression and Alzheimer's. We think of "comorbidity" as separate diseases that just happen to coincide. But the clustering is far too consistent and predictable to be explained by chance alone.

In fact, over half of US adults have multiple diagnosed health conditions. It's not unusual to see patients with 5-7 conditions, and older adults having 5+ conditions means an average of 50 prescriptions, 14 different doctors, and 37 visits per year. And they’re clustering in predictable patterns. Depression with stroke, chronic pain, and Alzheimer's. Diabetes with heart disease, depression, and kidney disease. Asthma with cardiovascular disease, COPD, and depression. The more conditions you have, the more likely you are to develop more. When you look at the actual patterns, what you see is that certain diseases cluster in specific constellations. Alzheimer's, congestive heart failure, COPD, diabetes, and cancer cluster together at rates far beyond what chance would predict. About 80% of people with Parkinson's disease will develop dementia during the course of the disease. If you have depression you are about 40% more likely to experience a stroke. The list goes on and on, across every body system you could name.

And neurodegeneration clusters the same way. Alzheimer's and Parkinson's share so much pathology that at autopsy, about 60% of Alzheimer's patients have Lewy bodies - the "hallmark" of Parkinson's. MS patients have about twice the risk of developing Alzheimer's disease. The boundaries between these "different” diseases blur the moment you look closely. We draw boundaries between them, but the bodies keep showing all of them bleeding into each other.

When researchers analyzed 10 million patients to look at the pattern of comorbidities, they identified very well-defined disease clusters. Metabolic syndrome clusters together: obesity, high cholesterol, hypertension, diabetes. Autoimmune and inflammatory conditions cluster together. Mental health disorders cluster with chronic pain. Cardiovascular diseases cluster together. The pattern is there, clear and consistent, across millions of people. We see the pattern, but since we treat each disease as its own discrete entity with its own specialist, we never step back far enough to see what's creating the cluster.

Disease state

So let's do that now. If we look across all of medical research, and then step back so we can see the full picture, it becomes clear that there's a specific physiological state that precedes all chronic disease. Medicine has identified some of its markers: high blood sugar, hypertension, inflammation, and obesity, and noticed these appear upstream of hundreds of conditions. But we stopped there, treating these markers as causes rather than symptoms. We never asked what was upstream of those markers. What could explain why they were happening in the first place?

Let’s think about a volcano for a minute. A volcano erupts and it creates a lava flow and ash. We aren't studying the lava at the base of the volcano as different from the lava 2 miles away, in different departments. We aren't studying the ash and wondering what could produce this weird substance without connecting it back to the volcano. We all intuitively understand that would be ludicrous. We look first to the volcano. (the body in this case: it’s the thing that's producing all of these disparate results.) What caused the eruption? What was happening over time that caused the pressure to build up the way it did. The way a volcano explosion affects the surrounding landscape depends on the features of that landscape, but no one is studying how it affects trees vs how it affects lakes vs how it affects grass as though those are different things. It's all: what was in the blast radius when the volcano went off and how do we FIRST stop the lava flow, and THEN clean up the rubble.

But when it comes to disease we’re instead studying how the grass was affected by the lava flow down to the molecular level in one department, and how the lake was affected by the lava in another department. Both are independently seeing lava upstream of their diseases. But they aren’t talking to each other, and they aren’t looking at the volcano. If we were looking at the body as the originator then we would be looking for what was similar in each disease, not looking at them each as their own separate mystery.

When we look at the full picture to see where disease actually begins, maintains itself, and progresses, a clear physiological state emerges. The chemicals being maintained in this state cause Tregs to downregulate and become less effective which leads to autoimmunity. In cancer, this same chemical state suppresses the NK cells and cytotoxic T cells that normally eliminate aberrant cells thus stopping the abundance of cells that leads to the natural selection process that produces cancer. And when we look below at all the rest of our chronic disease categories, we will see it cleanly explains them as well.

From here forward we'll call this upstream disease state pathostasis. Patho meaning disease, stasis meaning steady state. Pathostasis is the disease state medicine has been meticulously documenting piece by piece but has never assembled whole.

So what exactly is pathostasis? It’s a coordinated biological response that has existed in every vertebrate animal for 500 million years, and is one of the most conserved systems in all of evolutionary biology. It's what happens when the animal’s adaptive and useful acute threat response, which is designed to activate briefly then resolve once a threat passes, gets stuck on.

Every value in this table is in our research data, our medical textbooks, our curriculum, we’ve just never been able to look across silos and recognize that this chemical configuration IS the chronic disease state, not a risk factor for it or a contributor to it, but the actual mechanism itself.

While in this state, due to prolonged exposure to these chemical changes that our bodies weren’t evolved to maintain, your clearing systems fail, your immune system dysregulates, inflammatory processes go haywire, and disease develops. And though it seems random: why did person A get cancer while person B got depression? What we can tell from the data is that it isn't as random as it may seem. Let’s think about it mechanistically. Imagine a chain. If a chain were to get rusty and weathered, and someone dropped something heavy on it, is it just going to break where the thing hit it? Or is it going to break at its weakest point - where there's the most rust, where the latch is damaged, where it's been rubbing against something? It can be either or both. Where your system got the blow, and/or where there was already damage or vulnerability. This is what we see happening with the development of disease; it shows up where there's already vulnerability - genetic predisposition, prior damage, existing strain, or it develops where it received the blow, like lung cancer in smokers. And then, once that system starts failing, those failures create more stress on adjacent systems in that same cluster, and the next most vulnerable point in that system breaks.

This is why cardiovascular diseases cluster with kidney disease and stroke risk, for example. Hypertension develops because your blood vessels are constricted due to the elevation of norepinephrine, epinephrine, vasopressin and cortisol from pathostasis, and then the damage from that creates more load on your heart, and heart disease develops. Then that heart damage affects your kidney’s ability to function optimally, and kidney disease develops. The vascular damage affects your brain, and stroke risk increases. It's not four separate diseases. It's progressive failure in an already-compromised vascular system, each failure creating conditions for the next.

The same pattern plays out in metabolic clusters. The chemical cocktail of pathostasis creates the exact conditions for diabetes: increased glucose production, and insulin tolerance/resistance. When this is expressed, this metabolic dysfunction affects the cardiovascular system, and heart disease develops. The inflammatory state from both affects more metabolic processes. The pattern cascades through related systems, all connected through the same metabolic infrastructure that was vulnerable to begin with.

Autoimmune conditions cluster together, which makes sense because if your immunity is already a weak point, then it’s easier for further autoimmune diseases to tack on to the first. Again, these are not separate diseases randomly occurring in the same person; they're progressive failure in an interconnected body system.

This explains everything we find puzzling about comorbidity. It explains why "comorbidity" rates are so absurdly high - they're not actually CO-morbidity, they're the same morbidity expressing in related systems. It explains why treating each disease separately downstream doesn't stop new ones from developing - because you're not addressing the upstream state that's causing the failures.

So let’s look at how this plays out across the spectrum of diseases. To read this tree, you can look at the letters on the chemicals in Level 0, and the numbers on the level 1 and 2 disorder/disease mechanisms, and see how those map to the example diseases below. This tree shows that chronic diseases cascade down from level 0 chemistry. Levels 1 and 2 map out commonly studied cascade pathways medicine has documented but never unified. The disease list is a representative snapshot, not an exhaustive list.

You will notice how 'real' diseases, 'functional' disorders, and 'psychiatric' conditions all trace back to the same Level 0 chemicals? That's because these divisions never existed in biology, only in medical specialization. An emergency room doctor, a psychiatrist, and a rheumatologist could all be treating the same patient for what are defined as three different diseases, chest pain, depression, and arthritis, when in fact they're all looking at different expressions of the same Level 0 chemistry.

So let’s walk through what we're looking at here. At the very top, Level 0 is the chemical picture of the body in pathostasis like we showed in the first chart, and it explains in a clean domino effect, every single chronic disease we know about. Notice how cortisol (A) appears in almost every disease pathway? Or how immune dysfunction (mechanisms 8, 9, 10) shows up everywhere? These are the common threads we missed by studying diseases separately. In levels one and two we are showing byproducts of level zero, downstream cascades that medicine talks a lot about and sees causing a lot of other stuff.

Inflammaging

And we have gotten SO CLOSE. Sort of. Here are a list of the terms medicine has used to connect siloes:

Inflammaging: when they see increased inflammation in aging. Metaflammation: chronic low-grade inflammation from metabolic disease. “Metaflammation is characterized by the same mechanisms underpinning inflammaging.” Was a sentence published in Nature endocrinology about the effects of inflammaging.

Psychoneuroimmunology: the field studying psychology, neurology, and immunology. Neurogastroenterology, Gut-brain axis, Immunometabolism. Cardiovascular-kidney-metabolic syndrome: The American Heart Association proposed this name because heart, kidney, and metabolic disease keep appearing together. And after naming it they almost immediately moved to start including the liver as well. Researchers have started calling Alzheimer’s “type 3 diabetes” because they found insulin resistance in the brain.

Whenever they look across they see the connections, that’s where all these mashups come from. It’s like saying we have a toe-ankle axis, or an esophageal-stomach axis. The body is connected, it was always connected, they just haven’t had the opportunity to step all the way back to study it as such. So let’s walk through what we see when we do.

Diabetes

You can think of diabetes as the poster child for pathostasis. It is literally just: what happens in the body when these chemicals are elevated and stay elevated? Diabetes. Let’s walk through it.

Elevated cortisol (A), epinephrine (C), and glucagon (E) at Level 0, these chemicals tell your body to produce glucose because threat means you need immediate energy. So that gives you chronic hyperglycemia (5).

That excess glucose forces insulin production higher and higher, trying to shuttle all that glucose into cells. But over time, those cells build a resistance (tolerance, because most any receptor that’s being flooded in the body will downregulate its uptake) to insulin's signal.

So now that you've built up a tolerance to insulin, the pancreas has to work overtime trying to overcome that tolerance by producing even more insulin. This chronic overstimulation eventually exhausts the pancreatic beta cells that produce insulin.

When they fail, insulin production drops; that's F at Level 0, depleted insulin, which also leads to weight loss, which should be a hint that weight gain itself is downstream of something else. Early disease causes weight storage (gain), late disease causes weight loss. This insulin drop combined with the ongoing glucose elevation from A, C, and E, plus the inflammation from mechanism 10, you get the full picture of Type 2 diabetes at position 13 in Level 2. It's not a separate disease entity, just pathostasis playing out. The tree shows exactly how A, C, E, F → 5, 10 → 13. Every step is documented in medical literature.

Obesity: symptom not cause

Notice obesity is on the level 2 list, secondary cascades. This means it’s a symptom of disease, as we just saw with diabetes, not the cause. In fact, why don't we look at obesity a little more closely; use obesity as a case study to see how this tree can shake out the truth that we’ve been missing, since it's blamed for so much. We see that a lot of disease happens in people who are 'overweight,' and so we conclude that weight gain causes disease. So how well does it correlate?

27% of thin people have metabolic profiles that look just like what medicine thinks is caused by obesity. They actually label them “metabolically obese normal weight” which is…something. We are so sure weight is the cause that we label people obese who aren’t to make sense of the contradictory data.

And then there are the obese people who are perfectly healthy. 35% of them. They call them “metabolically healthy obese”.

So we’ve got over ¼ of thin people who are sick in the way we attribute to being CAUSED by fat, and ⅓ of obese people NOT being sick in the way we think is caused by fat, and the response is to…label them a different category and continue believing fat causes disease.

We've noticed that belly fat specifically correlates with certain diseases, so we declare belly fat is especially dangerous, rather than asking WHY the body would preferentially store fat around organs during certain conditions. But JUST losing weight doesn’t actually help. In 2004 when Klein et al. removed 28-44% of abdominal fat via liposuction, they found no change in insulin sensitivity, no change in inflammatory markers, and no change in blood pressure, glucose, insulin, or lipid concentrations. And this was about 12% of overall body fat removal. They then tracked these same people for 1.5 to 4 years after the liposuction and these same results persisted. If fat were really causing disease, wouldn't you expect losing it to provide some consistent benefits?

Given that losing fat alone doesn’t seem to change much, and being fat is neither enough nor necessary to cause disease, let's think about what's actually happening at Level 0 and Level 1 when weight gain occurs in pathostasis. You have elevated glucagon (E) and cortisol (A) driving chronic hyperglycemia (5). Your body is producing more glucose than it needs because your threat response thinks you need immediate energy available. That glucose has to go somewhere. Insulin’s job is to get the cells to open the door and let in glucose, so when glucose rises like this, initially, insulin rises as well, to shuttle it into cells. But just like your body needs more and more alcohol to get you drunk the more you drink, the more insulin the system has, the more insulin is needed to perform the same amount of glucose shuttling. So the cells become tolerant (medicine calls this resistance) to insulin's signal, and now you have high glucose and high insulin circulating in your bloodstream. Insulin does two other things as well. It promotes fat creation, and stops fat from breaking down. So when there are chronic high levels of insulin in the blood it makes it very easy to gain weight and very hard to lose it.

And cortisol which is also elevated in pathostasis specifically promotes visceral fat accumulation - fat around your organs. In acute danger having readily accessible energy stores near your vital organs makes evolutionary sense. You might need that energy fast. The problem is that when you’re in pathostasis and these chemicals persist, your body just keeps packing fat into your midsection month after month, year after year. You're stuck in storage mode without an efficient release mechanism.

So where did this idea come from then? If there isn’t really any medical basis for it? It turns out the idea of body fat as the cause of disease came from one guy. The same guy who proposed that eating fatty foods was bad. In the 1950s, a physiologist named Ancel Keys started pushing what he called the diet-heart hypothesis, which posited that eating saturated fat raises cholesterol which he thought caused heart disease. He had access to data from 22 countries and in an effort to prove his idea, he only used data from the 6 that supported his hypothesis. Rather than reexamine his idea once he saw the data didn’t cleanly fit, he just ignored the findings that didn’t match. He was ridiculed by the scientific community for his antiscientific methods. Two epidemiologists even published a devastating critique pointing out that Keys had data from 22 countries but only used 6, and that he was studying a "tenuous association" rather than proof of causality. In it they also showed that if he had included all 22 countries the correlation essentially disappeared. In fact they were even able to show that if you picked a different set of 6 countries you could show the exact opposite effect that he was trying to prove. After being ridiculed he wanted to prove his detractors wrong, so he decided to conduct a study. But he used the same cherry picking methods from his original study, chose 7 countries where the data best fit what he wanted to see, used cohorts that weren’t representative of their countries, used inconsistent measurement methods, and through this research “proved” his hypothesis. This somehow worked and got him onto the board of the American Heart Association, and eventually on the cover of Time magazine in which he promoted "the evils of fat" including both body fat and dietary fat as one unified evil.

Keys was also the person who named BMI Body Mass Index, which later got adopted by the NIH and then used by the world health organization to recognize obesity as an epidemic. BMI actually came from one statistician in the 1830s who was trying to define “the average man”. To determine this he gathered data of Scottish and French military men’s height and weight, and came up with a formula that he felt represented it: weight/height^2. This was just based on his observation that these European military men didn’t tend to get wider as they got taller. So no actual medical or even scientific basis and it wasn’t intended for medical purposes. It was just a curious statistician trying to figure out the mathematical correlations between human physical characteristics. But when Keys renamed it the Body Mass Index, it somehow took hold, and now every doctor's office in America uses it to determine if people are diseased.

Which means many people labeled 'overweight' by these arbitrary standards aren't sick at all. They're just larger than a formula designed around 19th century European military men said they should be. Their blood markers are fine, their energy is fine, their bodies are functioning exactly as they should. We even have a name for it, “medically healthy obese”. Meaning we know fat doesn’t always cause issues. And yet because of this deeply entrenched belief we continue to pathologize normal human variation in body size. Meanwhile, the people who ARE sick, the ones actually in pathostasis, aren't sick because they're storing fat. They're storing fat more than healthy people because they're sick.

So this one guy single handedly vilified body fat, dietary fat, coined BMI leading to it becoming a clinical measurement tool, and convinced everyone cholesterol was bad—all without any actual evidence. And the question isn't really why this one guy thought these wrong things; people think wrong things all the time. The question is how and why we bought in, amplified his wrongness, and protected it for seven decades. And why it's all still largely believed and implemented today across the globe. Semmelweis had the right data and was dismissed because it didn’t fit the framework. Keys had the wrong data and was amplified because it did.

Keys was the co-principle investigator on a study meant to prove his hypothesis. They studied over 9000 people in controlled settings where they could actually measure dietary compliance, from 1968-1973, and they found two key things. One, that lowering saturated fat did in fact reduce cholesterol. And two, that reducing cholesterol didn’t reduce heart death. In fact the participants in the study who had greater reductions in cholesterol had higher risk of death. The inverse of what his hypothesis predicted. So what happened with this study? They just…never published it. It sat in the lead investigator Ivan Frantz’s basement until his son found it in 2011. When asked why they never published it Frantz said “We were just disappointed in the way it came out.” The most rigorous study ever done to test his hypothesis proved it was wrong, and they didn’t like the results so they just sat on it. Frantz’s son helped get it published in 2016, but by then it was all so institutionalized there was really no reversing course. It got published and completely ignored.

What research has shown is that cholesterol is actually good for you and necessary for immune function and brain function, and many other vital processes; every cell membrane requires it. It is so vital that 80% of your cholesterol is produced “in house” in your body. When you are in pathostasis your body produces more of it because it’s so beneficial, but we saw elevated cholesterol in sick people and assumed correlation meant causation. And we have spent decades trying to lower people’s cholesterol. Statins are a front line drug for people with heart disease and statins do actually provide benefits. And we assume this is because of the cholesterol lowering function, but statins also reduce inflammation and improve endothelial function. When we use drugs that ONLY reduce cholesterol, we see no benefits at all on cardiac health. When ezetimibe (a non-statin cholesterol lowering drug) was added to statins, it lowered cholesterol even further but showed no additional mortality benefit. Same with PCSK9 inhibitors, they lower cholesterol dramatically but don't reduce death rates. The drugs that lower cholesterol without the anti-inflammatory effects don't save lives. Statins which have anti-inflammatory effects do. The mechanism we attribute the benefit to isn't actually the mechanism producing the benefit.

One of the most common criticisms you will hear about new theories or ideas is that 'correlation doesn't equal causation' and yet here we've built an entire treatment paradigm around mistaking a symptom for a cause. We saw a correlation - obesity occurs alongside disease - and declared it causal, with no mechanistic explanation for why having more adipose tissue would cause such diverse conditions. Having more fat cells doesn't cause diabetes any more than having a runny nose causes a cold. They're both just observable symptoms of an upstream process.

Cancer

We already walked through cancer in detail. Now look at how it maps: the immune suppression (8) and impaired clearance (17) we spent a chapter on trace straight back to Level 0 — elevated cortisol (A), cytokines (N), histamine (M). The Warburg metabolism shows up at mechanism 7, but as we discussed, it's a marker of stressed cells, not a driver. The real pathway is A, C, N, H → 8, 10, 17. Everything we discovered in chapter three is right here in the tree.

Neurodegenerative diseases

Remember how we saw neurodegeneration clustering earlier - Alzheimer's and Parkinson's sharing so much pathology that half of Alzheimer's patients have Lewy bodies at autopsy, MS patients developing Alzheimer's at 2-4x the normal rate?

Let’s step back like we’ve been talking about and see if there are any clues to follow. If we step back and look at what the research shows across all of these conditions, the same thing comes first in every single neurodegenerative condition: reduced blood flow (hypoperfusion). Hypoperfusion precedes neurodegeneration in Alzheimer's, Parkinson's, ALS, Huntington's, MS, and frontotemporal dementia.

The pathostatic chemicals we've been tracking - norepinephrine, epinephrine, vasopressin, cortisol - all cause vasoconstriction at mechanism 1. That's their job during acute threat: shunt blood toward muscles and away from non-essential functions. But when these chemicals stay elevated chronically, the vasoconstriction becomes chronic too. And the brain regions that get hit first are the ones supplied by small terminal vessels, the last stops on the vascular supply chain, or the areas with the highest metabolic demands and the least backup blood supply.

And we already know hypoperfusion causes dementia. That's what vascular dementia IS - cognitive decline from reduced blood flow. They diagnose it all the time. They even have a category called 'mixed dementia' for when they can't cleanly separate what's vascular from what's Alzheimer's, because the boundaries blur so often. So they know hypoperfusion causes cognitive decline. They know Alzheimer's shows hypoperfusion before plaques appear. Each specialty documented hypoperfusion in their own disease, published it in their own journals, and never looked across.

We think the neurons are dead, it’s right in the name “neurodegeneration. But these diseases are full of things that shouldn't be possible if the standard model is right. Lucid periods in Alzheimer's patients who suddenly recognize their family after months of confusion. Good days and bad days across all these conditions. Stress making symptoms worse. Exercise helping. The dramatic placebo responses in Parkinson's where patients reduced tremors by 70%.

If the neurons are dead, none of this should happen. Dead cells don't come back because you had a good day or believed in a sugar pill. But we don’t have another explanation, so these observations get filed away as anomalies rather than clues.

We have been trying to clear amyloid plaques and Lewy bodies, treating these protein aggregates as the cause of neurodegeneration. But patients can have extensive plaques with no cognitive symptoms, and severe dementia with minimal plaques. The proteins aren't causing the disease. They're accumulating because the clearing systems we talked about in chapter 3 are failing, for the same reason they fail everywhere else in pathostasis: the body's resources are being redirected toward survival, not maintenance.

Hypoperfusion is just one clue. One that you can see when you step back and see what these related diseases have in common. And this is where we need the regular medical model to step in and drill down into this idea. If it comes up empty we step back and look again. But this requires us to take new ideas seriously. Vasoconstriction has been studied…a bit. But it’s hard to get funding when something doesn’t fit what people already think. We have spent decades and tens of billions of dollars trying to clear amyloid plaques, the prevailing theory for Alzheimer's, which has produced exactly zero effective treatments. Meanwhile vasoconstriction has received almost no funding and has therefore barely been studied.

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Everything we just walked through has been meticulously documented in pieces for over a century. Every mechanism. The elevated hormones, the physiological effects, the cascading failures, all of it measured and published in thousands of studies across every medical specialty. The pieces are all there, we just hadn’t before stepped back far enough to see it's all connected.

But as we established above, correlation isn’t causation. So let’s look next to see what’s been documented about what comes first. Disease, or pathostasis.

In the 1990s, researchers accidentally discovered that asking ten questions about a patient's childhood could predict disease better than anything else medicine routinely measures. It costs next to nothing to administer, takes about three minutes, and patients would only need to take it one time ever, as their answers, once they are over 18, would never change. This simple assessment predicts lifelong disease probability as well as or better than all of our standard current screening tools combined: blood pressure, cholesterol, smoking history, and family history of disease.

The ACEs survey consists of ten questions about your childhood before the age of 18, and you answer yes that happened to me, or no it didn’t happen to me. That’s it. So if you answered “yes that happened to me” to 6 or more of the questions, your life expectancy is on average 20 years less than your peers who answered no to all of the questions.

The original study that figured out how predictive this was tracked 17,421 people over decades of their life. For contrast, most clinical trials that dictate our care across the entire medical model of healthcare today enroll an average of 65 patients and last an average of 6-12 weeks, with larger phase 3 trials capped at around 3,000. The original study focussed on a low-risk population of mostly white, middle class, college educated, insured people. They asked the participants these ten simple questions about their childhood, and then they tracked their health outcomes for decades. They found that about 60% of these people reported at least one yes on their assessment, and one out of eight people reported 4 or more yeses. And there was a direct dosage response, meaning for each additional number you answered yes to, your health outcomes were predictably worse. There have since been hundreds of studies in the 30 years since this original study, and through that expanded research those same results have been replicated across many different populations.

Now let’s look at a parallel set of research that was being done over this same 30 year timespan that shows actual pathostatic chemistry showing up in the body and how that correlates with disease.

Allostatic load

The term ‘allostasis’ was coined in 1988 by Sterling and Eyer. They saw something that challenged the framework of the time, noticing that black Americans had far higher rates of hypertension than genetically similar populations in West Africa. They had basically the same genetics, but existed in different environments, and they were showing vastly different disease rates. The belief into the 1980s was that your genes and constitution (namely if you were weak or hardy) determined whether you got disease and what diseases you got. This allostasis work proved that our physiological responses weren't fixed, but were actually adaptive to context, which was a revolutionary finding at the time. They were seeing pathostasis in action - the same genetics producing disease or health depending on pathostatic load: how much pathostatic chemistry you are carrying and for how long.

Then in 1993 researchers Bruce McEwen and Eliot Stellar took this concept further. They measured the same chemicals we've been looking at: cortisol, catecholamines, inflammatory markers, metabolic markers, and tracked what happened to people over time. They called the cumulative burden of these chemicals 'allostatic load.' And what they found was that these chemicals become dysregulated and then cascade through connected systems exactly the way our tree predicts.

In one study they tracked 738 adults over 5 years, and tested 12 allostatic biomarkers. In this study they found that for every single additional biomarker that was out of range at baseline, people had 35% higher odds of developing type 2 diabetes, 21% higher odds of cardiovascular disease, and 15-24% higher odds of physical impairment - even though they seemed healthy by regular medical measures when originally tested.

Once they had this data they divided participants into three groups: those with 0-3 biomarkers out of range (healthy), 4-5 out of range (moderate load), and 6 or more out of range (high load). Compared to the healthy group, people with just 4-5 dysregulated markers had nearly triple the odds of developing diabetes over the next 5 years (2.78 times higher). Those with 6 or more dysregulated markers had more than double the odds of cardiovascular disease (2.32 times), and double to triple the odds of physical impairment. These were people who seemed fine by medical standards, and this model could accurately forecast their disease trajectory years before symptoms appeared. And these findings are consistent across populations. Many studies have been conducted globally showing the same patterns. The relationship holds across age groups and cultural contexts. Higher allostatic load means worse health outcomes.

To date, thousands of articles, 2,465 as of the time of this publication, and over 30 years of data, predicts and explains disease far better than anything we are currently pointing to as a reason or a cause and yet this whole body of research is mostly not taught in medical school. And that is likely in large part due to the fact that the researchers framed the solution as ‘reduce stress and improve coping’, which we all know doesn’t actually do a whole lot. They see this as something that amplifies disease, not as something that causes it. But what this research does very well is show that tracking pathostasic chemicals and markers BEFORE disease arrives predicts disease better than anything else we’re measuring. Just like ACES do.

Correlation vs causation

Other than obesity one of the main theories we have about where diseases come from is genetics, so let’s look at that next.

In 1993 they discovered that Huntington's disease is caused by a specific genetic mutation - a CAG repeat expansion in the huntingtin gene. Medicine identified this by studying families where Huntington's ran across generations, found the mutation in affected individuals, and concluded the mutation causes the disease with near-complete penetrance, meaning they were saying that pretty much everyone with the gene will get the disease. But the thing is, they only tested people who already had Huntington's disease, or who had family members with Huntington's disease. They found the mutation in people with the disease, confirmed it was inherited, and declared it fully penetrant. Which is the definition of selection bias. But because of this seemingly full penetrance, Huntington’s disease became the poster child for genetic causation.

But when they finally did screen the general population 23 years later in a 2016 study of over 7,000 people, they found that approximately 1 in 400 individuals carry the expanded CAG repeat associated with Huntington's disease. That's 250 per 100,000 people. But Huntington's prevalence is only about 5-13 per 100,000. Which means roughly 95-98% of people with 'the Huntington's gene' never develop Huntington's disease. For context, Huntington's expression depends on how many times the CAG sequence repeats - anywhere from 36 to 41+ repeats is considered the "disease range," with higher numbers associated with more severe symptoms and earlier onset. When they finally looked at the general population instead of just symptomatic families, they found that having the mutation doesn't actually mean you'll develop the disease. Up to 86% of people with 36 repeats - the low end of the range - never got the disease in their lifetime. They found 10 people aged 67-95 with 36-39 repeats who showed no signs of Huntington's. Even at 40-41 repeats, traditionally called the "full penetrance" range where the disease should be inevitable, they documented asymptomatic carriers. The mutation is far more common than the disease. Most carriers never get sick.

So as rigorous research standards make very clear, if you only test people who have the disease or are in families where it appears, and you find a genetic mutation in those people, you can't conclude that everyone with that mutation gets the disease. That is correlation, not causation, which means you can only conclude that everyone with the disease has the mutation - which is a very different claim. But despite this, the belief that genetics are determinant is still pretty strongly believed, even though we know it’s not true. And this same flaw runs through genetic research on most chronic diseases. They study sick people, find genetic variants that are more common in sick populations than healthy ones, and declare those variants cause disease. But they're not screening everyone with those variants to see how many never get sick. They're not investigating what determines expression versus non-expression. They're looking only where they expect to find disease, then concluding what they find is universal.

And then there is something called the Huntington’s phenocopy. Which is people who present as having Huntington’s disease who don’t have the gene at all. The response has been to look for other genes that might explain it, because by this point genetics is the explanation, so when the gene isn’t there the only reasonable explanation seems to be, it must be another gene we haven’t found yet. The current estimate is that about 20% of suspected Huntington’s cases are phenocopies, which is one in five people. Which means that having the gene only gives you a small chance of getting the disease, and you can actually get the disease without the gene present at all.

If even the most "genetic" disease in medicine depends on something beyond the gene to express, and the gene doesn’t always cause it, then genetics isn't the cause, it’s a vulnerability. It's where the chain breaks.

· · ·

So now we’ve walked through a lot of medical diagnosis, but what about psychiatric ones? How did disorders of the brain end up being somehow separate from disorders of the body anyway, being that they all stem from the same animal?

It turns out the answer isn't medical or scientific, it's historical and institutional. So let’s look at how this divide happened, and why it persists despite all evidence to the contrary.

Back in the 1600s, in the time of Descartes, the prevailing belief was that the body and soul were one thing. Diseases were considered spiritual or moral failings. And because of this, the body couldn't be studied as just a body and diseases weren't something to be just scientifically understood, they were to be understood spiritually as well. And remember that back in those days theology and law were far more intertwined than they are today. The church dictated what was and wasn’t allowed. Which meant that people weren’t allowed to study disease and physiology outside of the orthodoxical framework of the time.

To fully illustrate the authoritative reach of the church, and what happened when you spoke out against their beliefs, let's look quickly at Galileo. In 1633, Galileo claimed that the earth moved around the sun, which was contrary to the belief at the time that the earth was at the center of everything. And because of this assertion that went against their beliefs, he was condemned by the Catholic Church and was sentenced to house arrest for the rest of his life. He died there nine years later at the age of 77. And René Descartes, who had just finished writing a book defending the very same idea, seeing the church's reaction, immediately withdrew his book from publication.

So when Descartes published his next book in 1641, Meditations on First Philosophy, he was very careful to draw a line. The soul, he argued, was immaterial - and at that time, 'soul' meant everything we would now call 'mind': thought, consciousness, emotion, the inner life. That domain belonged to the Church and religion. But the body was something else entirely: a physical machine that operated according to mechanical laws, and could be studied by science without threatening religious authority. This let him carve out space for scientific inquiry while appearing to leave the Church's domain intact. He even sought the Church's approval by dedicating the book to theologians. While some theologians were suspicious, the conceptual divide had been introduced: body and mind were separate substances, operating by different rules, belonging to different authorities. When they eventually did ban his works in 1663, it was too late; the separation had already started formalizing on the institutional level, and was too hard to reverse. And the split he created to protect science from the Church became the framework that would shape science for the next four hundred years.

At that time medicine, finally free to conduct business as they saw fit without church oversight, understandably ran with it. Over the next two centuries, what started as a philosophical framework to enable scientific study, transformed into a complete institutional separation between "diseases of the mind" and "diseases of the body," as though they were fundamentally different categories requiring entirely different systems of care.

The separation evolved gradually through a series of institutional decisions that seemed perfectly practical and reasonable at the time. In the early 1800s, as cities grew and industrialization accelerated, European societies had to figure out what to do with people who were acting strangely, who couldn't work, or were disruptive or distressed. The answer, arrived at more or less independently across multiple countries, was to create specialized institutions to house them. Britain passed the County Asylums Act in 1808, leading to the first public asylum opening in Nottinghamshire in 1812, and similar institutions began sprouting up across Europe and America.

So now there were these asylums filled with sick patients who needed care, including needing doctors who could attend to them, but regular physicians didn't want to work there. The work didn’t feel 'medical' in the way medicine was coming to understand itself in the germ theory era. There were no clear diseases to identify, or pathogens to kill, and there were no surgical procedures to perfect. So to fill this clear gap, a new specialty emerged, initially called "alienists" - doctors who specialized in treating people whose minds had become "alienated" from reason. And by the late 1800s, this specialty had evolved into what we now call psychiatry. Because these asylums were pretty much completely separate from regular medical hospitals, the psychiatric doctors who worked in them were separately developing their own theories, and treatments, and even their own professional organizations, deepening the structural divide.

In 1871, the Association of Medical Superintendents of American Institutions for the Insane, essentially the early psychiatric professional organization, was invited by the American Medical Association to merge together into one umbrella organization, but they declined. Their meetings were focussed solely on the care of the insane, and they didn't feel like regular doctors were very interested in that, so why should they merge and muddy the waters with people who were so separately focussed? It seemed better to stay separated so they could each focus on their domains. And so asylum psychiatry was developed in complete isolation from the rest of medicine. They weren't attending the same conferences, reading the same journals, or training in the same institutions.

By 1894, the separation had become so complete that American neurologist Silas Weir Mitchell stood before a gathering of asylum physicians to tell them that they had isolated themselves from the rest of medicine, their medical records were inadequate, and their research was nonexistent. Because the diseases didn’t look the way medicine had defined what diseases should look like, and it wasn’t being studied the way medicine had decided disease should be studied, the divide became a hierarchy system where medicine saw itself as superior.

And once these institutional structures were in place, they became self-reinforcing. Medical schools developed separate training tracks - you either studied to be a psychiatrist or you studied to be a regular doctor. Insurance systems created separate coverage categories and different reimbursement structures. Research funding got divided into mental health research and medical research. Hospitals were either psychiatric hospitals or general hospitals, but rarely both. Professional journals specialized in either psychiatry or medicine. Even the language diverged - psychiatrists talked about mental illness, affect, and behavior, while other doctors talked about physiology, organs, and biomarkers.

But in order to be taken seriously enough to get funding and insurance reimbursement, psychiatry had to create a diagnostic system similar to medicines. The solution, eventually, was the Diagnostic and Statistical Manual of Mental Disorders, the DSM. First published in 1952, and now in its fifth edition, the DSM has become psychiatry's defining document, the authoritative guide to what counts as a mental disorder and what doesn't.

If you crack open the tome that is the DSM and really look at the diagnosis laid out for you inside, you will see that it's not made up of clearly defined diseases, but rather a collection of different expressions of distress or dysfunction, that have been grouped together and labeled. Imagine a deck of cards spread out on your table. What the DSM did is they took the cards of disease expression, and noticed common clustering patterns. So sleep issues, trouble concentrating, avoiding public places, etc, and noticed that some of these clustered together in people often enough that they gave them an overarching name. They basically picked up 5 cards and called it a disorder. Then they threw those cards back in the pile so they'd be available to the next diagnosis they were creating that might include them as well. Not because the biology suggested these clusters had clear boundaries, but because the system they were working within required them to give them concrete definitions. You need discrete diagnoses to bill insurance. You need specific disease categories to get FDA approval for drugs. You need defined patient populations to design research studies. You need clear diagnostic criteria to train psychiatrists in a standardized way. The DSM gave psychiatry all of that. But in so doing it created the same siloed problem we see in medicine, each disease or condition a discrete entity rather than a connected web of downstream shared symptoms.

Look at Major Depressive Disorder and Generalized Anxiety Disorder, two of the most commonly diagnosed conditions in psychiatry. To meet criteria for Major Depressive Disorder, you need five out of nine of their symptoms cards, and for Generalized Anxiety Disorder you need three out of six of the cards. But both include fatigue, sleep disturbances, and difficulty concentrating. So you could be diagnosed with Generalized Anxiety Disorder with just those three, but not depression because you would need two additional cards to get to play. So where exactly does one disorder end and the other begin? If someone has four symptoms from the Major Depressive Disorder list, they have nothing - no diagnosis, no treatment, no insurance coverage. But if they have five symptoms, suddenly they have a disease. The cutoff is arbitrary.

PTSD symptoms overlap massively with panic disorder, depression, and generalized anxiety disorder. Fibromyalgia, classified as a physical condition (sort of, as we discussed previously) has symptoms that overlap substantially with depression and anxiety. Chronic fatigue syndrome has a lot of overlapping symptoms with depression. Parkinson's patients commonly experience depression, anxiety, and fatigue - often years before the tremor appears. Alzheimer's patients frequently develop depression and sleep disturbances before memory problems. Heart disease patients show high rates of depression and anxiety. "Comorbidity" rates in psychiatry are so absurdly high (it's not unusual for someone to meet criteria for three, four, or five different DSM diagnoses) because we've taken one continuous phenomenon and sliced it into multiple overlapping categories.

Psychiatry also tried to find biological explanations for their DSM categories. This gave us the 'chemical imbalance' theory of mental illness - the idea that depression was caused by low serotonin, that anxiety was a GABA problem, that schizophrenia was about dopamine. Except it didn't work. Decades of research have now shown that SSRIs don't outperform placebo in mild to moderate depression, and it only looks like it works a bit better in severe depression because people with severe depression don’t respond to placebo very well so the placebo arm is lower making the active arm look more effective. A landmark meta-analysis that included unpublished trial data by Kirsch in 2008 found that the difference between antidepressants and placebo was clinically negligible. The simple chemical imbalance theory was wrong.

What happened was they noticed serotonin seemed to improve people’s moods, so they started testing depressed people to see what their serotonin levels looked like. What they actually saw was that in some depressed people serotonin was elevated and in some people it was depleted, which baffled them, but if you look back at Table 1 in chapter 4, you will see that initially when we enter into acute threat our serotonin levels elevate. Over time as we get stuck in pathostasis, serotonin drops and that’s when you see chronically low levels. So researchers were testing people at different stages of pathostasis, not understanding what they were seeing, and still thought the answer was to further disrupt an already dysregulated system.

What SSRIs actually do is inhibit reabsorption of serotonin. So normally serotonin is released, it floats across the gap between neurons, it binds to receptors on the receiving neuron, and that binding becomes a signal. Then normally it gets pulled back into the original neuron to be reused.

By blocking reuptake, the serotonin stays in the synapse longer, so it has more opportunities to keep binding to those receptors, and keep signaling. By staying in the gap longer it keeps hitting the receptor over and over instead of being cleared away.

Think of it like a text message. Normally you send it and it gets read one time. What SSRIs do is more like the message keeps re-appearing in someone's inbox, so they keep reading it again and again. More signal events from the same serotonin. But just like with every other time we try to adjust the chemistry of the body artificially over the long term, the brain adapts. If you're getting bombarded with serotonin signaling, the receiving neuron goes "okay this is too much" and starts downregulating its receptors. Pulling them back. Becoming less sensitive. This is the same tolerance we talked about with insulin in diabetes. So you end up with more serotonin floating around than you started with, but this amount becomes the new normal, and the receptors adapt and now you also have fewer receptors to receive it and/or less responsive receptors.

This is the same pattern we see across medicine when they try to artificially replace decreased chemicals. It works for a while - then tolerance develops. The body adapts to the artificial supply, downregulates its own production or sensitivity, and you need more to get the same effect. Eventually the drug stops working, or you become dependent on it just to maintain a baseline.

And this keeps happening despite this clear issue because drug trials are typically conducted over 6-12 weeks. And SSRIs are no exception. The average amount of time people stay on antidepressants is 5 years, with 25% staying on them for 10 years or more, and we really only tested them as a short term bandaid. The fact that drug testing for chronic diseases is so short is worth examining. Think about alcohol tolerance. If you needed to test how drunk someone will get when they drink a specific amount of alcohol every day, and you only test them for 6 weeks, they would probably get pretty drunk off of let’s say 6 beers a day for that 6 weeks. But if someone is drinking a 6 pack every day for 5 years? We all intuitively know that at some point there is likely no effect at all on that person from those beers. This is how most long term medicine works too.

Psychiatry was looking in the brain for something specific to 'mental' illness because that's the organ they'd been assigned. They were trying to find the 'depression chemical' or the 'anxiety receptor' because the institutional framework demanded that mental illnesses be brain diseases, separate from the body.

So psychiatry hasn't been any more successful than the rest of medicine. Just like with diabetes, heart disease, and IBS, psychiatry found some mechanisms, but none of them led to cures. But despite the gaps in their understanding and ability to effectively help these patients, like medicine, psychiatry kept diagnosing, they had to in order to get reimbursed by insurance. Millions of people were given labels - Major Depressive Disorder, Generalized Anxiety Disorder, Panic Disorder - complete with narratives about chronicity, prognosis, and what to expect. And this raises a question we haven't addressed yet: if the treatments don't work better than placebo, what exactly are these diagnoses for? What happens when we give someone one of these diagnoses - medical or psychiatric? Medicine treats diagnosis as though it's a neutral act of observation - we're simply naming what's already there, documenting reality. But what if it's not? What if the act of giving someone a diagnosis is itself an intervention that changes what happens next?

In Chapter 2, we saw that expectation produces biology. When patients believe treatment will help, their bodies respond - blood pressure drops, tremors reduce, pain decreases. Real, measurable physiological changes driven by belief. This expectation effect is so strong it can even override and reverse the expected effects of a drug like ipecac. So given that we have 70 years of data proving just that in trial after trial after trial, what about the other side of that equation? If expectation can produce healing, can it also produce harm? If believing you'll improve creates improvement, does believing you're disordered create disorder? What happens when we hand someone a psychiatric diagnosis - when we tell them 'you have this condition, here's what it means, here's how it will affect you'? Are we describing a pre-existing reality, or are we teaching their nervous system how to organize itself around a new identity, creating the very patterns we claim to be documenting?

In 1979 at just 18 years old, Michael J. Fox dropped out of high school in Canada and moved to Los Angeles to pursue acting. He was so broke that he was literally dumpster diving for food, taking packets of jam from the local IHOP, hiding from his landlord because he couldn't pay rent, and selling his sectional sofa off piece by piece to avoid admitting defeat and going home. He lived like this three years before, in 1982, he got a call from his agent saying he'd landed a supporting role on a show called Family Ties. The show was originally supposed to focus on the parents' lives with the children as side characters, but viewers fell so hard for Michael J. Fox's character Alex P. Keaton that after just four episodes, they restructured the entire show around him. And pretty much overnight, he became one of Hollywood's sweethearts.

Two years later when Robert Zemeckis and Steven Spielberg were casting Marty McFly for Back to the Future, they knew immediately they wanted Fox for the starring role. But the producer of Family Ties, David Goldberg, was worried that if Michael J. Fox got the role in Back to the Future he would leave Family Ties, and he worried the show wouldn't survive if that happened. So he hid the Back to the Future script from him completely. This left Zemeckis and Spielberg with no option but to cast someone else, which they did, but after only a month of shooting with this other actor they realized it wasn't working. The kid they’d gotten to play the part wasn’t fitting the role the way they had envisioned, they couldn’t seem to find a way to make it work, so they doubled down on trying to get Fox. On January 3, 1985, Goldberg finally told him about the script he'd been hiding, and Fox agreed to join on the spot, without even reading it. By January 15th, less than two weeks later, they were already filming.

The agreement was that he could do the role as long as it didn’t stop him from also shooting Family Ties. So for three months straight, he worked on Family Ties from nine in the morning until six at night, then got driven to the Back to the Future set where he'd shoot until 3 in the morning. His Teamster drivers would literally carry him to bed, where he'd get maybe four hours of sleep before starting the whole cycle again. Twenty-hour days, every day, for months. He later wrote that during this period he was "Alex, Marty, and Mike" - and that was two too many. Mike, the actual person, had to disappear.

Back to the Future exploded that July, staying number one for eleven weeks. Fox went from TV star to movie star, and offers started pouring in: Teen Wolf, The Secret of My Success, Bright Lights Big City, Casualties of War. He said yes to all of it. Between 1985 and 1991, while still filming Family Ties, he made ten feature films.

Then in 1991, while filming Doc Hollywood, he noticed his left pinkie twitching. He went to see a neurologist who assured him he'd probably just injured his funny bone. But six months later his entire left hand was trembling, his shoulder was stiff and achy, and another doctor ran tests and diagnosed him with Parkinson's disease. He was 29 years old.

The diagnostic experience

The doctor told him he had "about ten good working years left.” He used words like progressive, degenerative, and incurable. And then he told him "You don't win this, you lose".

After his diagnosis and being cautioned he had "ten good working years left", he hastily signed a three-film contract, appearing in For Love or Money (1993), Life with Mikey (1993), and Greedy (1994). And he also started drinking a lot as he grew increasingly depressed, using the alcohol to "disassociate, to escape my situation". He spent the next seven years in a sort of denial, hiding his condition, popping dopamine pills "as if they were Smarties candies" and washing them down with alcohol, working to time everything perfectly to keep his acting intact.

And…of course he was falling apart. Think about what getting a Parkinson's diagnosis actually looks like in practice. You go into the doctor's office with a small symptom, probably some shaking. They run some tests, and when they come back they tell you: you have an incurable, progressive disease. That means you will have this for the rest of your life, it will keep getting worse, and there’s not a whole lot we can do for you. We have medications to help manage the symptoms, so we will write a prescription for you today, which should help with the shaking at least for a while. Here’s a list of symptoms you can expect:

• Tremor (rhythmic shaking, especially at rest)
• Slowness of movement (bradykinesia)
• Muscle stiffness/rigidity
• Balance problems and falls
• Reduced facial expression (mask-like face)
• Decreased blink rate
• Small handwriting (micrographia)
• Soft or fading voice
• Difficulty turning over in bed
• Less arm swinging while walking
• Difficulty with swallowing
• Drooling
• Loss of smell (often appears years before diagnosis)
• Constipation (can precede motor symptoms)
• REM sleep behavior disorder (acting out dreams)
• Depression and anxiety
• Apathy (lack of motivation)
• Cognitive problems (from mild difficulties to dementia)
• Hallucinations and delusions
• Fatigue/exhaustion
• Low blood pressure when standing (orthostatic hypotension)
• Sexual dysfunction
• Urinary problems
• Pain
• Excessive sweating
• Restless leg syndrome
• Excessive daytime sleepiness
• Visual symptoms

When you notice these symptoms, please let us know, and we can run more tests and decide if we need to adjust your medication. You should talk to your loved ones about this. Here are some resources for you.

We all take the way this is handled medically as a given, but let’s actually think about what just happened. You walked into the doctor’s office with a seemingly small symptom, a tremor, and walked out with a life sentence, from someone you've been conditioned to trust as an authority on your health. And you're already in pathostasis, you have to be, the symptoms prove it. Your body has been stuck in pathostasis long enough that it's starting to break down in visible ways. So at the exact moment when reducing your pathostatic load would be most critical, you just got a massive threat on top of an already overloaded system.

Which begs the question: what does it mean to receive a diagnosis? We treat it as a benign thing. Someone has a condition, you educate them about that condition so that they can manage it more effectively. Within the existing model, this makes perfect sense. You can't work on something you don't know exists.

But pathostasis changes the calculation entirely. Yes, it's reversible - but it's also progressive. Not progressive the way medicine uses the term, not the way Fox's doctor meant it when he said the disease would inevitably worsen until Fox ‘lost’. Pathostasis being progressive just means the longer you spend in it, the more entrenched the dysfunction becomes. The deeper you dig the hole, the more work it takes to climb back out.

Which means early intervention matters a lot. Which for the record is WHY medicine is providing these diagnoses. They want to act fast and try to manage the condition before it gets worse. Within the current understanding, that makes perfect sense.

The nocebo effect

Let’s take a minute to look at what we know about this diagnostic effect.

In January 2022, researchers wanted to know more about what kind of side effects people were experiencing from the COVID-19 vaccination, so they conducted a big systematic review. What they did was compare the results across multiple clinical trials, of what side effects people who received the actual COVID vaccine experienced, versus those who only received a placebo injection (which was just saline, no active ingredients). They found that these placebo patients were experiencing many of the same side effects as the active vaccine group. A whopping 76% of the adverse effects people reported after their first vaccine dose were nocebo effects, which is essentially placebo's evil twin: instead of our ANS creating positive drug effects from expectation, it creates side effects and symptoms, also from expectation. So in this study over three-quarters of the side effects people experienced, the fatigue, headaches, arm soreness, etc., occurred in both groups. The symptoms were real. People genuinely felt terrible. But most of what they experienced wasn't caused by the vaccine itself. It was caused by expecting to feel terrible.

Remember from Chapter 2 what we learned about how expectation works: it creates measurable physiological changes through ANS-mediated systems. Real, documented biological changes driven by what the brain expects to happen, like the dopamine production we saw in Parkinson’s patients. That mechanism doesn't only work in the positive direction. Expectation produces positive effects by shifting ANS function, and negative expectation can produce symptoms and side effects through the exact same pathway.

So outside of these trials, millions of people received COVID vaccines, and many of those millions experienced side effects like exhaustion, headaches and body aches. And most, ¾ of those symptoms, weren't produced by the vaccine's biological mechanism, they were produced by the information they'd received about what to expect. News articles told them to plan sick days and they heard about side effects from friends and family and social media. By the time people went to get their shots, they knew exactly what symptoms they were supposed to develop. And their bodies obliged.

This isn't a new discovery. In 1961, a physician named Walter Kennedy coined the term "nocebo", from the Latin "I shall harm", to describe the negative counterpart of placebo effects. He had connected the dots: if positive expectations could produce healing, then negative expectations should be able to produce harm. He then took his hypothesis and tested it, documenting cases where inert substances produced side effects simply because patients expected them. And armed with this knowledge he warned that this phenomenon was likely contributing to adverse effects across medicine.

But the finding was largely ignored. For decades, the phenomenon he'd identified, that negative expectations could produce real, measurable harm, languished in obscurity.

Which brings us back to: what happens when we give someone a diagnosis?

When we hand someone a diagnosis - whether ‘psychiatric’ like depression or ‘medical’ like Parkinson’s, we're not just describing their current symptoms. We're teaching them what to expect. The patient learns: these are the symptoms I should notice, this is how my disorder will present, this is what my future will look like. And just like the COVID vaccine recipients who knew to expect fatigue and headaches, the brain has now been given a script.

And what’s especially problematic about this effect, given the framework we have just uncovered, is that when it comes to chronic diseases, medicine’s understanding of what they are diagnosing is limited at best. Like in Alzheimer's where they think the patient's neurons are dying despite lucid periods proving they are not, medicine says Parkinson's is caused by the death of dopamine-producing neurons in the substantia nigra. And because dead neurons can't come back, they consider the disease progressive, and irreversible. Yet as we know, Parkinson's patients show dramatic improvements with placebos - producing real dopamine, improving real motor function. And Parkinson’s patients show improvements from relaxation and mindfulness as well. That’s not how it would work if the cells were dead.

ALS is another neurodegenerative disease that’s considered progressive and incurable, but Dr. Richard Bedlack at Duke University has been documenting ALS reversal cases since 2011 and has confirmed 60 reversals to date. These are patients who met full diagnostic criteria including EMG-confirmed denervation, meaning their muscles had visibly stopped working and this could be seen on tests, and then recovered significant or complete motor function. Follow-up EMGs show reinnervation patterns. Dead neurons don't reinnervate. Dormant ones do. But because medicine doesn’t have a template for reversing or healing from chronic disease, they assumed these patients must have been misdiagnosed.

So given that, let’s go back to Michael J. Fox for just a minute. Imagine if instead of being told: this is it forever, your life as you know it is over, you are going to be dependent on the medical institution to manage your symptoms in-so-much as we are able from now on, but expect precipitous decline. Imagine instead if his doctor said something to him like this:

This shaking you are experiencing tells us that your body has been stuck in pathostasis for a while. But this is something we can manage. We caught it early, you’re really young, we know how to help.

You can finish working on the projects you are in the middle of if that’s important to you, all the choices you make will be yours to make, but we strongly recommend taking a break or at minimum hugely reducing your workload. At least for six months to a year so we can try this new protocol to get this under control, and see how your body is responding. You are in a critical window where we can give you these drugs to help your shaking, and they will work for a while, but eventually they will stop working as well, you’ll need more, or different drugs, or they can stop working entirely. The rehabilitation work you need to do to reverse this is easier now before you develop associations in your brain with these symptoms and before the cascade from these chemicals progresses too far. Here is a referral to a rehabilitation program that can teach you how to turn your prefrontal cortical control over the off switch back on, and help you recondition any associations in your brain that have already been made.

If he had been told this, instead of a life sentence, he would have had hope, agency, and a way out. Let’s look next at why his system got stuck.

Even single-celled organisms have threat detection built in. If you put a single bacterium in a dish with poison on one side and food on the other, it will move toward the food and away from the poison. Which makes sense when you realize that aside from reproduction, the ability to avoid threat is the most important survival function any living being has. It is so vital that something very close to our human threat detection system exists in the lamprey, a jawless fish that's been around for 500 million years and is one of our earliest vertebrate ancestors. The pressure to get it right was so intense that evolution perfected it almost immediately, and it has remained largely unchanged since. To put 500 million years into perspective, the first dinosaurs appeared 230 million years ago and went extinct 66 million years ago. This fish predates them by almost 300 million years.

The lamprey's brain is so primitive it barely looks like a brain at all; it looks more like a swollen nerve cord. But when researchers examined this ancient blob of neural tissue, seemingly so different from modern animals, they found the same threat detection architecture that we see in modern humans today. An amygdala-like structure for detecting danger, a periaqueductal gray (PAG) for coordinating response, and a hypothalamus for triggering threat chemistry. This basic setup appears in every vertebrate, and has for half a billion years. And what we see in our world today are the winners of this long evolutionary march toward the present. Anyone whose threat detection system wasn't quite up to snuff? They're not our ancestors, or anyone's ancestors. Their genes got edited out of the evolutionary line.

Half a billion years is a long time not to get an upgrade. Evolution tinkers constantly, unless something is working so well there's nothing to improve. So let's look at how this system actually works, and why evolution never found a reason to change it.

This brain system's whole job is to keep us alive by evading threats, and it does so in a one-two-three punch that happens in the blink of an eye. Which makes evolutionary sense, because the animals who could respond the fastest were the ones that got to live. It really is as simple as that. Part one of this sequence happens in the amygdala, whose job it is to process your surroundings and decide what’s a threat. The way this works is sensory information, what you see, hear, smell etc., gets sent directly to the amygdala before your conscious mind even knows it's there. This takes about 12 milliseconds. The amygdala gets a rough, blurry sketch of what's happening: something big moving fast, a loud sudden noise, a shape that pattern-matches to "predator", and it fires. You jump before you know what scared you. The whole point is speed over accuracy, because for 500 million years, the cost of reacting to a false alarm was low, and the cost of reacting too slowly was death.

Part two of the threat response is the job of the periaqueductal gray, otherwise known as the PAG, which coordinates the body’s actual response. The PAG has two primary modes of response, which you can think of almost like a toggle switch. The brain quickly asks the simple question: will action improve this situation? If the answer is yes, if there's somewhere to run, something to fight, some action that might work, the dorsolateral part of the PAG activates, which we’ll call the mobilizing PAG. Your heart rate spikes, your energy stores are mobilized, and your muscles prepare for action. This is fight or flight, the one everyone's heard of.

But if the answer is no, like if you're trapped, or if the threat is too big, or if there's just nothing you can do, a different part activates. The ventrolateral PAG triggers freeze, shutdown, and/or collapse, which we’ll call the immobilizing PAG. Your heart rate drops, your body starts trying to conserve energy, and in extreme cases, you can dissociate, or go numb. Even fish do this.

And people have been noticing these two opposite patterns for decades. Attachment theory identified anxious attachment and avoidant attachment - which map perfectly onto people whose PAG tends to flip one way versus the other more often based on what their systems learned early on. Polyvagal theory talks about dorsal vagal shutdown and sympathetic activation which are again, these two responses playing out. What both were witnessing was this very clear PAG switch pattern; these patterns are obvious if you watch human behavior. Or really any animal's behavior. Because this toggle between "I can act" and "I can't act" is the oldest decision in the animal playbook.

Part three is the job of the hypothalamus, which you can think of as the pharmacy of the brain. Within seconds, your hypothalamus releases the entire biochemical cascade that shifts your body from homeostasis to survival mode. Your heart rate changes, your blood flow redirects, digestion shuts down, glucose mobilizes. Everything about your body's chemistry reorganizes around one goal: survive this.

And for 500 million years, this system worked perfectly. A threat would appear, the amygdala would detect it, the PAG would coordinate the response, and the hypothalamus would deploy the chemistry to fuel it. The animal would fight, flee, or freeze, and then the threat would pass. And once this happened, the system would reset. The chemistry flooding the body would start to clear, heart rate would return to baseline, digestion would turn back on, and the animal would go back to living normally. The threat response was designed to last minutes, maybe hours in extreme cases, and then it was over. And this is still how it works, in every animal on the planet. The ancient system activates when needed and deactivates when the threat resolves.

Except in one species.

Because around 100,000 years ago, a different switch got flipped. For hundreds of thousands of years, anatomically modern humans existed with brains very similar to ours. They made the same basic stone tools, generation after generation. They lived in small groups, and left little trace of symbolic thought.

Then suddenly, the archaeological record explodes with evidence of radical behavioral change. Cave paintings appear depicting animals, humans, and abstract symbols. People start wearing jewelry made from shells and bones, some transported hundreds of miles from their origin. Burial sites begin including grave goods like tools, ornaments, and food, suggesting belief in an afterlife or at least symbolic thinking about death. Musical instruments appear, complex multi-part tools, the creation of which required planning several steps ahead, trade networks spanning continents, and boats capable of reaching Australia across open ocean.

This wasn't evolution in the way Darwin understood it, with gradual changes over millions of years. Brain size reached modern levels by 300,000 years ago, but brain shape continued evolving, with the frontal and parietal regions expanding into their modern globular form between about 100,000 and 35,000 years ago; evolutionarily lightning fast for something this advanced to come online. Why and how it happened is still an open question.

And it makes sense why this trait got selected for so quickly once we got it. Recursive language alone, our ability to nest ideas within ideas indefinitely, to say not just "danger" but "the man who saw the lion that killed the hunter from the neighboring tribe is afraid to go near the watering hole where it happened", was an immediate survival advantage. The populations with this capability rapidly outcompeted and replaced those without it.

What happened next

With these new cognitive abilities, two things happened that would shape the course of human health. First, we were able to start optimizing our environment for survival; securing food, building shelter, eventually developing agriculture and settlements and cities, each advancement made us safer and more comfortable, but as we'll see in the next chapter, each one also quietly stripped away something our bodies needed. Second, these cognitive abilities gave our brains the power to sustain and amplify threat chemistry once it was running. An animal experiences threat, responds, and resets. But a human can feel the threat response activate and then think about it, which can keep the chemistry running long after the original threat has passed. The ancient system can't tell the difference between a real threat and a thought about a threat. It just responds.

We have the ability to imagine infinite futures and rehash past threatening events, and we keep running all of it through threat detection systems that are still shockingly similar to that of the lamprey. We can now maintain threat activation through our thoughts, which is something no other animal can do.

Rumination

In the 1980s researcher Susan Nolen-Hoeksema, who would later become chair of psychology at Yale, began studying what she called "ruminative thinking.” She defined rumination as repetitive, passive thinking about one's problems, their causes, and their consequences. So basically when your thoughts circle the same worries over and over without resolving anything. Through decades of longitudinal studies following thousands of participants, she and other researchers documented that people who ruminated more developed a whole range of physical health problems at higher rates than those who didn't. The presence of ruminative thoughts reliably predicted future illness even after controlling for current health status.

In 2014, Peggy Zoccola's team at Ohio University brought healthy young women into the lab and had them give a stressful speech. Then they randomly assigned half the women to ruminate about how the speech went, while the other half were distracted with neutral thoughts about sailing ships and grocery stores. The researchers drew blood samples and tracked inflammatory markers, and the women who ruminated showed sustained elevation in C-reactive protein, an inflammatory marker, that continued rising for at least an hour after the speech. While the markers of the women who were distracted returned to baseline.

A major meta-analysis published in Psychological Bulletin in 2016 synthesized the research across dozens of studies. The findings consistently showed that rumination and worry were associated with elevated blood pressure, elevated heart rate, elevated cortisol, and reduced heart rate variability. The same physiological patterns that show up in chronic disease. Thinking about a threat was producing the same body chemistry as facing one.

In 1997, Kubzansky and her colleagues published a landmark prospective study where they followed 1,759 men without heart disease for twenty years. They measured their worry levels at the start, and after controlling for cholesterol, blood pressure, smoking, and diabetes, all the standard cardiac risk factors, worry independently predicted who would have heart attacks. Men in the highest worry category had more than double the risk compared to those in the lowest category, and there was a clear dose-response relationship.

Suppression

But repetitive thinking isn't the only way our cognitive abilities keep the chemistry running. There's a second pattern that looks completely different from the outside but produces the same result.In the 1990s, Stanford researcher James Gross started studying what happens in the body when people suppress their emotional responses. He'd show participants films designed to provoke disgust or sadness, and tell some of them to watch the film without showing any reaction - to sit there looking calm no matter what they felt. And he found that the people who suppressed their reactions successfully on the outside, showed an increase in threat activation internally, whether the emotions they were suppressing were positive or negative. And this research has been replicated dozens of times, across cultures, ethnicities, ages and genders over 30+ years of research. Demonstrating that our unique human ability to change our physical response to its internal cues can maintain our threat response physiologically.

The health consequences for people who do this often have been well documented as well, and are what you'd expect given everything we've been tracking. Habitual suppressors show elevated C-reactive protein, the same inflammatory marker that Zoccola found in the ruminators. A one standard deviation increase in habitual suppression is associated with significantly elevated C-reactive protein, even after controlling for demographics, socioeconomic factors, and health behaviors.

And there's a whole population of people doing this as their default mode. Researchers identified a group they call "repressors" - people who score low on anxiety questionnaires but show high physiological arousal when measured. Meaning they report being fine, while their bodies show elevated threat response chemicals. In one study, researchers measured skin conductance and self-reported distress continuously, in real time, while participants watched emotional films. The repressors showed high skin conductance and low self-reported distress simultaneously, which means even in real time, these people thought they were calm as their bodies were physiologically activated.

Repressive coping accounts for up to 50% of certain populations, and the prevalence increases with age. This population shows a two-fold increased risk of death, heart attack, and cardiac events, even after controlling for other variables. And there are demonstrable links to coronary heart disease, hypertension, asthma, and cancer outcomes. Thirty years of research documenting that the people who say "I'm fine" while their chemistry says otherwise are getting sicker at dramatically higher rates.

Rumination and suppression are both correlated with higher pathostatic load.

Structural brain changes

In 2002, researchers at the National Centre for Biological Sciences published a foundational study in the Journal of Neuroscience demonstrating how chronic threat chemistry reshapes neural architecture. Vyas and colleagues subjected rats to chronic immobilization stress and then examined their brains at the cellular level. They found that in the hippocampus, the region that helps regulate memory, emotion, and the threat response itself, chronic threat chemistry caused dendritic atrophy. Meaning the neurons shrank and their branches retracted, and the structures they use to communicate with other neurons withered. These changes were visible under a microscope, and they correlated with impaired function. One of the functions of the hippocampus is to help stop the threat response, putting on the brakes and providing negative feedback to calm things down after a threat has passed. If you shrink the hippocampus, you weaken those brakes.

In the amygdala, the brain's threat detection center, the opposite happened. Neurons sprouted new dendrites, grew new branches, and formed more connections. The amygdala got stronger, more elaborate, and more connected to everything else. So the region responsible for triggering the alarm expanded its capacity while the region responsible for calming things down contracted.

The prefrontal cortex, which is the part of the brain responsible for executive control, and for consciously overriding automatic responses, shows similar changes. This is the part of the brain that can say "I know this feels dangerous but it's actually fine." Chronic threat chemistry causes dendritic retraction here too, weakening the very circuits that allow us to regulate our emotional responses. More recent research has revealed how this happens: microglia, the brain's immune cells, become overactive under chronic threat and start pruning synapses in the prefrontal cortex. A 2023 study showed that threat chemistry elevates complement C3, which tags synapses for elimination, and microglia then engulf them. The brain is literally dismantling its own regulatory capacity.

And this is all expected if you’re stuck in threat, because as we will see later, the brain physically changes based on what it practices, whether that’s piano, learning a language, or being in threat. Which also means these changes persist even after the threat is gone. The brain has been architecturally reorganized to be efficient at maintaining a state of vigilance: stronger threat detection, weaker threat regulation, reduced capacity to override the alarm once it's triggered.

So we now can see that humans can sustain pathostasis once it’s turned on. But this doesn’t explain why it’s already turned on for so many people, and why it seems to be getting worse. With higher and higher rates of disease with each passing year.

So now it’s time to come back to GLP1s and see what they’re telling us that our siloed approach is missing. Because they’re telling us a lot. The INFORM survey of 550 people found 62% had constant food thoughts before semaglutide, dropping to 16% after. A Cleveland Clinic doctor describes it as "a light switch going off, where all of a sudden they have some freedom in their brain and relief from constant signals." Patients use phrases like "the food noise was gone" and "mental quiet."

But no one seems to have a coherent answer to WHY there is so much food noise in a population that is by definition getting enough food. One hypothesis is that processed food isn’t triggering satiety the way whole foods are, but that accounts for an extra 500 calories of consumption per day, not constant food noise. And no one eating whole food optimized diets is reporting their food noise going away. If the processed foods were the culprit, eating whole foods should be reducing food noise too, and no one’s reporting that, so what else could it be? The other explanations are environmental and emotional triggers, hormonal imbalances, restrictive dieting, not eating enough, not sleeping enough, dehydration, lack of mindfulness. But do any of those account for the fact that people are reporting having had constant food noise for years? And if this is such a wide spread phenomenon, that would require believing either: everyone has the same one of these as a cause, OR that ALL of these explanations happened to produce food noise in humans? That not optimizing our inputs of sleep and food and our environment universally created the desire to eat?

Additionally, GLP1s are affecting many many things in addition to weight loss. If it were a food phenomenon, then this list wouldn’t make sense. Let’s look at the current landscape of what is being treated using this medication:

In addition to reduced food noise GLP1s are FDA approved for treating:

Type 2 diabetes Weight loss Cardiovascular disease risk reduction (heart attack, stroke) — and notably, this benefit was shown to be INDEPENDENT of weight loss Chronic kidney disease (in diabetic patients) * MASH / fatty liver disease

There is strong clinical data showing:

Heart failure (European approval, FDA pending)
Alcohol reduction / alcohol use disorder
Knee osteoarthritis pain
Asthma symptom control (60,000 patient study)
Fibromyalgia (pain, fatigue, opioid reduction)
Reduced risk of Alzheimer's (70% reduced risk vs insulin)
Reduced risk of Parkinson's (32% lower in meta-analysis of 450,000 people)
Reduced gambling and compulsive shopping (behavioral)
PCOS improvement
Improved fertility

Early/emerging treatment targets:

IBS pain
CFS/ME (trials underway)
Chronic pain broadly
Cancer risk reduction (some data, also some concerns)
* Sleep apnea

And the explanations for why it’s doing all of this are equally far reaching. Researchers are calling this a 'pipeline in a pill' and scrambling to explain each benefit through the lens of their own specialty. The gut researchers see gut mechanisms. The neuroinflammation researchers see neuroinflammation. The reward researchers see reward pathways. Everyone is attempting their own explanation. But given everything we’ve seen as we’ve walked through all of the medical data, let’s step back and see if there’s anything we can find that would explain it all through a single mechanism rather than 10 separate ones for each downstream effect.

So what are we seeing? We’re seeing something that affects seeking behaviors: food, alcohol, shopping, gambling. And disease progression and outcomes. And also things like mood, sleep, and pain. What could possibly explain all of these disparate seeming expressions?

Mechanism

The primary way that GLP1s work in the brain is this:

Your hypothalamus has two sets of neurons that drive seeking behavior. On one side you have AgRP/NPY neurons which are your need neurons, your hunger neurons. When they fire, they drive seeking behavior. They say: you need something, go find it. On the other side you have POMC neurons which are your satiety neurons. When they fire, they say: you have enough, stop seeking.

What GLP1s do in the brain is signal the POMC neurons, which subsequently quiets the AgRP neurons. And this area of the brain, the need center of the hypothalamus, controls more than just hunger signaling, it controls thirst signaling too. And they’re finding that people on GLP1s aren’t drinking enough water, so much so that they are telling people to set alarms on their phones to remember to drink more water because the need signal has been effectively turned off or down.

So in a population of millions of people that are getting enough food, and in a society where we have more than enough water to drink, why would turning off a signal saying “you need more” stop people from eating when they clearly already had enough to eat? Why were they getting that signal in the first place? Why would it stop people from gambling and shopping and drinking alcohol? What people are describing is that it’s like an alarm that has been going off in their head for as long as they can remember has been finally turning off. What could account for that?

The invisible need

Humans evolved for millions of years to live surrounded by dozens of people. The way apes currently live is a good analogue for the social world our brains expect. 30-80 people who are always available. Which means that when your brain sent a signal telling you to go get social connection, it was pretty much always available. You didn't have to "make time for connection" or "self care", those things were baked into the world we lived in. The ambient social environment was richly connected and the constant social signal and response was like air; it was the fabric of your reality every moment of your life.

In our modern world we see rich social connection as a nice to have. But in 2023 the U.S. Surgeon General put out an advisory saying social connection is "a fundamental human need, [and is] as essential to survival as food [and] water." The advisory went on to say that the impact on death risk "of being socially disconnected is similar to that caused by smoking up to 15 cigarettes a day, and even greater than that associated with obesity and physical inactivity", and calling loneliness and isolation a public health crisis.

A meta analysis that same year published in Nature, one of the most prestigious journals in medicine, looked across 90 studies including over 2 million people, and found that social isolation and loneliness increased overall death rates, and specifically heart disease and cancer related death rates. The effect size rivaling that of smoking.

Harvard neuroscientist Dulac took this idea a step further and actually studied loneliness and social need to find out where it lives in our brains, to see if calling it a basic need was true. Was it closer to an emotion like sadness? Or more akin to something like our need for food or sleep? And what she found was that it does not reside in the part of the brain that controls emotion, but that it was in fact located in the hypothalamus, which is the part of the brain that controls our basic biological human needs like hunger and thirst. It uses the exact same neural architecture. The way she framed it was to think of it like a bean counter. Your brain tracks your social input the same way it tracks your caloric input. When social input drops below threshold, the need neurons fire telling your brain “you need more”, and drive social seeking behavior. When social input reaches satiety, the satiety neurons fire and inhibit the need neurons. The same way it does for hunger or thirst.

And when we have a need signal that is being continuously unmet, the research shows this activates and keeps on the threat response. When you’re chronically hungry your cortisol levels elevate and remain high. In rat studies they’ve shown that even at 75% of the quantity of their daily intake of food their equivalent of cortisol was chronically elevated. And they’ve also studied this for loneliness. When your social needs are unmet, your HPA axis remains chronically activated. Chronically under met needs chronically elevate pathostatic chemicals. Which makes sense given that not getting enough to eat IS an existential threat. And evolutionarily, being socially disconnected was too. It would lead to death. It’s located in the same region of the brain that controls your pathostatic chemicals for a reason: because having a chronically under met need is a physiological threat.

In the 1960s, researchers studied a small Italian-American town called Roseto, Pennsylvania. The residents fried everything in lard, smoked cigars, drank wine regularly, and worked in hazardous slate quarries. By every metric medicine uses to predict heart disease, they should have been dropping dead. But what they found was that they had virtually no heart attacks in men aged 55-64, normally the highest risk group. The only thing that was different about Roseto was its social structure. The town had been settled by immigrants from a single Italian village, and they had recreated that village's tight-knit multigenerational social world almost exactly; three generations in one house, elderly central to daily life, dense interconnected relationships as the background fabric of existence.

Intermittent reinforcement

In the 1930s a researcher named B.F. Skinner was running low on food pellets one weekend and didn't want to go make more. So he set his apparatus to only reward the rats in his experiment sometimes instead of every time, which led to one of the biggest discoveries in learning and behavioral research. Skinner had been studying how rewards shape behavior - when a rat presses a lever, they get food. But when he came back Monday after his lazy weekend adjustment, the rats were pressing the lever like maniacs, far more obsessively than when they got food every single time.

When a behavior is rewarded unpredictably, the brain never knows when the next reward might come, so it keeps trying, the uncertainty itself drives persistence and compulsion. If you never know when the next meal will come it makes sense to keep checking. And it's not just rewards that work this way. The same mechanism applies to anything unpredictable, including threats. When danger appears sometimes but not always, the brain can't afford to let its guard down. It stays vigilant and keeps scanning for the threat, keeping the threat detection system running.

Because we evolved to be in tribes where you had dozens of adults and other children around you all the time, we evolved to have attunement pretty universally available. Which means that children’s bodies expect this. In our modern society where all you have is one or two busy, overwhelmed adults around you most of the time, what you get is textbook intermittent reinforcement of a basic human need. They need connection, they reach out, sometimes it’s there, sometimes it’s not, and sometimes it comes with threat attached. And over time through practice and repetition, those behaviors and feelings get wired right alongside connection.

Pairing threat with connection

In 1975 a researcher named Tronick at UMass in Boston studied what happens when a mother who is normally very responsive to her baby emotionally stays in front of them but goes completely expressionless for 3 minutes. What they saw was that there was a predictable cascade of behaviors that the infants in the study would enact. First, they would solicit reconnection, they would smile, coo, reach, point, anything they could think of to get their mother to reengage with them. When that didn't work they would escalate; they would start being louder, more insistent, exaggerate their expressions etc. When that didn't work they would show distress, they would frown, flail their limbs, show confusion or agitation, and eventually they would start crying. When all of this failed to work they would then withdraw. The baby would go still, start sucking on their fingers or thumbs to self sooth. Their facial expression would look withdrawn and hopeless. And this study has been replicated hundreds of times across many different cultures. And even more telling, they have measured cortisol levels and have seen a distinct elevation starting at phase three. This happens in just three minutes of social withdrawal. Three minutes to go through this entire cycle of distress. And they see these same behavioral patterns in infants of depressed mothers. Those mothers are the still face experiment all the time, and so they see them cycling through these patterns over and over again. The effects of adults looking down at their phones while caring for their infants are likely staggering.

If we next look at a more extreme version of social isolation, children raised in orphanages from birth where they were fed and changed but otherwise ignored, they also have a very clear pattern of dysfunction. These children show stunted brain development, physical growth delays, behavioral problems, autism-like behaviors like rocking, stimming, and hand flapping, and psychological issues like depression, anxiety, disruptive behavior, and adhd. The longer they were in the institution before adoption, the more these patterns stayed stuck throughout their lives. Which shows that the behaviors and responses to social connection or the lack thereof laid down in childhood persist.

Remember the ACEs survey from earlier in the book? With its ten questions that predict disease better than anything medicine routinely measures? Looking at those questions again, every single one is measuring social environment failure. With a dose response curve showing the severity of social starvation during development predicts how deeply pathostatic wiring gets laid down. Being in an environment with other animals, our parents, who are by design of modern life, intermittently available and likely overwhelmed themselves, as our primary social connection, leads to wiring in threat and compulsion alongside connection. Which means that even when we do have people in our lives, that social connection isn’t feeding us the way it could if it weren’t also coded in as a threat. And the cognitive loops we described in the last chapter, rumination and suppression, are how we try to manage this constant threat state internally. If you think about the content of rumination and suppression, it is always based around safety needs and the threat we are experiencing alongside them, and most of the time, in our modern world, that need is social.

The cognitive revolution gave us the ability to think differently than any other animal before us, and we used this ability to optimize for food security, and shelter, and water. For comfort, entertainment, and ease of life. We have objectively never been more safe, more secure, and more comfortable. And we have never been more chronically ill. Each innovation has stripped away more ambient social fabric, replacing it with longer commutes, smaller households, more screens, less unstructured time with other humans. And each generation raised with less social infrastructure raises the next generation with even less capacity to provide it. Zoo animals show the exact same disease and dysfunctional behavioral profile as humans, where wild animals do not. Zoo animals are fed perfectly optimized diets; if this were about the food, zoo animals should be the healthiest animals on the planet.

And this is why we see chronic illness rates on the rise. 60% of US adults have at least one chronic condition and 40% have multiple, with 90% of the annual healthcare spending going to chronic disease. And those numbers are only including the diseases that medicine considers "real". If you included the whole swath of diseases, the actual full picture of chronic suffering, adding things like fibromyalgia and chronic fatigue and IBS, that number gets closer to 80 or 90%. Even in children the rate of chronic disease has nearly doubled from 23% in 1999 to more than 40% today. We see the rates of chronic disease rising across all age groups, and nobody seems to know why.

Why GLP1s work so widely

If you picture a society where most people’s brains are screaming at them “you need more”, and these needs can be confused with each other as evidenced by GLP1 users not drinking enough water, and this need state is triggering pathostatic chemicals what would you expect to see? What do we see societally?

We see widespread compulsive eating. Compulsive drinking. Compulsive shopping. Compulsive scrolling. Workaholism. Political tribalism: seeking belonging and safety through ideological identity. Social media addiction: seeking connection through a channel that can't actually deliver it. Rising rates of every chronic disease. Rising rates of anxiety and depression. Rising rates of addiction across every category.

And then a drug comes along that flips that one switch; it tells the brain “you have enough” and we see people’s compulsions ease, their disease markers improve, and we see things like inflammation, anxiety, and obesity go down. Because GLP1s are doing two things. They are telling us we have enough, we can finally stop seeking. And they are turning off or down our pathostatic chemistry.

Researchers are seeing lower reward seeking and saying GLP1s act on reward circuits; but if you think about it through this lens, if you are starving and you eat food you get a huge reward. If you are full and you eat a meal you get far less reward chemistry. Taking the GLP1s is telling the brain “you have enough” which means the rewards are naturally lower when they consume or do addictive behaviors. These people didn’t have broken reward pathway signaling, they were just never getting the you’re good here signal to turn off the seeking.

Our cognition allowed us to optimize our environment for safety, and to become the most powerful animal on the planet. And in so doing we created a world wide famine that led to ever increasing rates of dysfunction and disease. GLP1s get us part of the way toward addressing the downstream consequences. But remember from the last chapter how when our brains are stuck in a chronic threat state they change structurally? Which means often, when we’ve spent years in a chronic deficit, just turning off the signal isn’t enough. In order to understand the full landscape and what we can do about it, let’s look at what we know about how our brains change, and what we can do about it.

In 1913, Santiago Ramón y Cajal was studying nerves' ability to regenerate. He found that some nerves were able to regenerate, and some were not. Your peripheral nervous system, which is the network of nerves that go from your brain out to the rest of your body like your arms, legs, and heart, can regenerate. When you sever a nerve in your arm, for example, it can grow back and restore its function, at least somewhat. But he found that in your central nervous system, which is your brain and your spinal cord, if those neurons, which are the cells that make up those systems, get damaged, they aren't able to physically regenerate. The connections that were severed stayed severed. Cajal could see this clearly under his microscope: axons physically regrowing in specimen after specimen in peripheral nerves, but never in the brain or spinal cord. However he also understood, and even publicly stated, that the brain must be able to reorganize somehow. Otherwise we wouldn't be able to change our minds about anything. That reorganization just wasn't happening through physical neuronal growth like it does in our arms or lungs.

Cajal didn’t actually like what he’d proven. He didn’t like the idea that our brains couldn’t regenerate into adulthood the way the rest of our nervous system could, so he issued a challenge of sorts: he said "In adult centers the nerve paths are something fixed, ended, immutable. Everything may die, nothing may be regenerated. It is for the science of the future to change, if possible, this harsh decree." Meaning basically he had found that in adult brains, the nerves are fixed, and can die but not be regenerated, and that it was the challenge of future scientists to try to prove him wrong if they could. He was literally challenging the future of medicine to figure out how to overturn his observations, hoping humanity's fate wasn’t that of a fixed brain.

He’d done other research in his career as well, that spoke to the parts of the brain that are changeable. In the 1890s Cajal had introduced a concept he called "neuronal plasticity." He'd documented cases where damaged brains formed new circuits to work around injury. As an example, he'd seen how long axon cells could convert into short axon cells with new collateral branches after trauma, creating alternate pathways that could restore function even when the original connections were destroyed. And in addition to these observable changes he saw happening in the brain, a completely fixed, rigid brain made no logical sense to him. How could learning happen? How could we adapt to new situations? How could therapy or practice improve anything? The brain, he argued, had to have the capacity to reorganize its existing connections even if it couldn't grow entirely new neurons. He'd seen it happen, and he’d documented it. The structure might be fixed, but the function was dynamic.

But for some reason what stuck, what got taught in medical schools for the next three generations, was the first part: that the brain is fixed, ended, immutable. Everything may die, nothing may be regenerated. That became the operating assumption of neurology, and the foundational principle that shaped how doctors thought about brain injury, stroke, developmental disorders, mental illness, and aging. If the brain couldn't change, then damage was permanent. If someone had a stroke, whatever function they lost in the first few months was gone forever. If a child's brain developed abnormally, there was a narrow window for intervention and after that, nothing could be done. If someone's mental faculties declined with age, that was simply the inevitable march toward death. The textbooks said so because Cajal had said so. And Cajal was the father of modern neuroscience, the man who'd won the Nobel Prize for describing the structure of neurons.

The irony is that Cajal was right about both things. We now know that you can't grow new neurons in most of the adult brain. Adult neurogenesis does happen, but only in specific regions like the hippocampus and the olfactory bulb. For the vast majority of your brain, the neurons you have are the neurons you'll die with. Cajal was also right that damaged neurons in the central nervous system don't regenerate their axons the way peripheral nerves do. Cut the connection and it stays cut. But he was equally right about the other part, the part that got discarded. The brain reorganizes its existing connections constantly, throughout our entire lives. Every time you learn something new, every time you practice a skill, every time you change your mind about something, your brain is physically rewiring itself. Not by growing new neurons, but by strengthening some synaptic connections, weakening others, and forming new pathways, throughout our entire lives.

Mapping the brain

It took until the 1980s for someone to actually prove what Cajal had observed about plasticity. Michael Merzenich, a neuroscientist at UC San Francisco, started doing experiments that would finally prove the brain could reorganize itself, though that wasn’t what he set out to do when he started his work. The prevailing assumption at that time was that our brain maps were fixed. You were born with them, they developed in childhood, and then they stayed put for the rest of your life. That's what localizationism said: that specific areas of the brain handle specific functions, these areas were the same in everybody, and they were unchanging. So Merzenich and his colleagues actually only set out to create more detailed maps, essentially documenting with greater precision which exact parts of the brain corresponded to which parts of the body to establish the standard brain organization that everyone assumed was fixed and universal. But as he did this work, he started noticing that when he mapped the same monkey twice, weeks or months apart, the maps were changing. The boundaries had shifted. The territory devoted to one finger might be slightly larger or smaller. It was subtle, but it was there. And the maps varied from one monkey to the next too; they weren't identical copies of a universal template.

So Merzenich went further. When he stimulated all five fingers of a monkey simultaneously, 500 times a day for a month, their brain eventually mapped them as one "finger", a single unified territory instead of five separate ones. Neurons that fired together in time wired together to make one map.

Then Bill Jenkins joined Merzenich's team at UCSF to study how the brain changed when an animal actually acquired a new skill rather than just losing function. They taught a monkey to touch a spinning disk with one finger, training it over and over until it became proficient at the task. Before training, they mapped the sensory cortex for that finger. After training, they mapped it again, and found that the brain territory devoted to that specific fingertip had expanded significantly. Learning the skill had caused the brain to devote more processing power to the finger doing the work. And it wasn't just that the map got bigger, the individual neurons within that expanded territory became more efficient, more finely tuned to the specific task the monkey was practicing.

This represented a complete reversal of everything we had believed for eighty years. The adult brain wasn't fixed. It reorganized based on what you did with it, what you practiced, what you paid attention to. Use a finger more, and its brain territory grows. Stop using a finger, and its territory gets invaded by neighbors. Make two fingers move together, and their brain maps merge. Practice a skill, and the neurons processing that skill become more numerous and more efficient. Merzenich had made one of the most important discoveries in medicine: our brains change based on what we do with them. And almost no one has ever heard his name.

Seeing the changes in humans

Around the same time, the technology finally caught up to allow us to see this happening in living human brains. Functional MRI made it possible to watch which areas of the brain lit up during different tasks, to track how those patterns changed over time, to observe the physical reorganization that Cajal had predicted but couldn't directly measure. Studies on stroke patients showed that the brain could reorganize within weeks of intensive therapy. Research on London taxi drivers revealed that the part of their brain devoted to spatial navigation, the hippocampus, was enlarged compared to bus drivers, and the longer someone had been a taxi driver, the bigger the change. When people learned to juggle, their visual-motor areas reorganized. When musicians practiced, the finger areas of their motor cortex expanded.

By the 1990s, the evidence was overwhelming. The brain wasn't fixed. We now knew that intensive practice causes physical changes in brain structure, that recovery from injury was possible through targeted behavioral interventions. The National Institutes of Health (NIH) sponsored workshops, leading scientists gathered to discuss clinical applications, and papers flooded the journals documenting the therapeutic potential.

And then... not much happened.

It's not that nothing at all changed. Some things did. Stroke rehabilitation shifted toward more intensive task-specific training. Constraint-induced movement therapy emerged, forcing stroke patients to use their affected limbs by restraining the good one, capitalizing on the brain's ability to rewire. Some physical therapy protocols incorporated principles of neuroplasticity. The field of neurorehabilitation experienced what researchers called a "paradigm shift," moving from a focus on compensation, which is teaching people to work around deficits, to a focus on recovery through neuroplasticity. On paper, this looked revolutionary, but in practice, most of these advanced technological interventions remained largely confined to specialized research centers and expensive private clinics.

So why hasn't neuroplasticity-based treatment expanded beyond this narrow window? There's no money in it. Pharmaceutical companies fund the majority of the research which means that research needs to produce monetizable things. Neuroplasticity-based interventions can't be patented, you're just teaching people to use their own brains differently. So the research doesn't get funded, which means the large-scale studies don't get done.

Physical therapy would seem to be an exception to the idea that medicine only funds and thus integrates drug or device or procedure innovation, but it turns out that it’s the exception that proves the rule - it actually emerged from wartime necessity before the current pharmaceutical-dominated research model took hold, and even now, most 'rehabilitation research' funding goes to prosthetics and assistive technology, things that can actually be sold. The behavioral interventions get almost nothing, and have remained largely unchanged because of it.

The implications of neuroplasticity are far broader than the narrow window medicine is using it for. If the brain reorganizes based on what you do with it, based on what you practice and pay attention to, then neuroplasticity is relevant to virtually every condition involving brain function.

Allergy desensitization is actually neuroplasticity in action: retraining the immune response through repeated exposure. But that’s just one thing, and it’s not the first line treatment for allergies either. The first line treatment is to just avoid the thing causing the reaction.

Disease and the changeable brain

So what about chronic diseases? There has been some research. Scientists have documented extensively that the immune system and nervous system learn patterns of disease just as readily as the brain learns to play piano. And once learned, these patterns can spread and entrench themselves in ways that look exactly like what we saw with Merzenich's monkeys.

In 1962, a researcher named Turnbull proposed that maybe asthma was actually a learned response. He had noticed that asthma patients would sometimes have attacks in the complete absence of any allergen. The most famous case was reported back in 1886, when a woman had a full asthmatic attack after seeing an artificial rose, one made of paper and cloth. Her body had learned the pattern so well that the visual cue alone was enough to trigger the entire physiological cascade.

This was dismissed as psychological, something separate from "real" asthma with its documented immune responses and airway inflammation, but researchers kept finding the same pattern. They could condition guinea pigs to have asthma attacks in response to completely neutral stimuli, pairing the stimulus with an allergen until eventually the neutral stimulus alone would trigger the whole cascade: bronchoconstriction, inflammation etc. The body was learning associations between arbitrary cues and immune responses, and wiring them together through repetition until they became automatic. And once wired, these learned responses proved remarkably persistent, showing up even when the animals were just stressed or in new contexts.

Medicine has documented that when someone has repeated asthma attacks, the sensory nerves in their airways become more sensitive, firing at lower thresholds. The brainstem neurons controlling breathing become more reactive. The parasympathetic nerves that trigger airway constriction become hyperresponsive. Chronic inflammation increases the density of nerves in the airways. The more attacks someone has, the more efficiently their nervous system learns to produce them. It's Merzenich's principle playing out in the respiratory system: use it and it grows stronger. Practice makes you better at what you practice, even when what you're practicing is having asthma attacks. This has been documented across body systems. The first seizure someone gets lowers the threshold for the next one. Each seizure makes future seizures easier to trigger. This is so well documented neurologists have a name for it: kindling. After a first heart attack, the risk of a second is dramatically higher. After a first stroke, subsequent strokes become more likely. Once you’ve had one kidney stone your chances of another go up dramatically. Once the body learns a pattern, it gets easier and easier to repeat it the more it gets practiced.

These practiced patterns can and do also generalize out to other related things with similar pathways. In asthma for example kids who start out reactive to one allergen, say cat dander, often become reactive over time to more and more triggers. First cats, then dogs. Then dust mites. Then pollen. Then cold air. Then exercise. Medicine tracks this with molecular precision, measuring exactly which proteins a child reacts to, the list growing longer year by year. They can see that children sensitized to four or more allergens by age two have more than four times the risk of having asthma by age ten compared to kids sensitized to one allergen or none. The pattern spreads through repeated activation of the same underlying inflammatory cascade, each new trigger strengthens and expands the pathway and gives the system more opportunities to practice.

And like we discussed, once you get diagnosed, you're given a list of triggers to watch out for, which helps this process along. You start to pay more attention to those triggers, and your brain sees that attention and vigilance and adds those things to the list. Just like we saw in the placebo and nocebo chapters, we tell our brains what to expect, and they oblige. The diagnosis that was meant to help you manage the condition actually gives your nervous system a comprehensive training program on how to be more efficiently sick.

And this spreading isn’t confined to just airway triggers. Over half of children with severe eczema go on to develop asthma, because both conditions involve barrier dysfunction in epithelial tissues, both involve immune hyperreactivity, both show the same pattern of sensitization spreading over time. Medicine calls this the "atopic march," the progression from eczema in infancy to food allergies in toddlerhood to asthma in childhood. And while it’s framed as a genetic predisposition playing out developmentally, what they're actually documenting is the nervous system learning a pattern of immune overreaction and then applying that learned pattern to different barrier tissues. The broken skin barrier in eczema teaches the immune system to freak out about allergens, and then when those same immune responses show up in the lungs or the gut, the pattern is already established, already practiced, ready to be triggered by an expanding list of stimuli.

The same sensitization pattern shows up in IBS, where an initial infection or inflammatory event can sensitize the gut's nervous system, creating visceral hypersensitivity that persists long after the original trigger has resolved. The nerves fire at lower thresholds, foods that were previously fine become triggers, and patients end up hypervigilant about eating, scanning for symptoms, which reinforces the patterns that keep them stuck.

What all of these conditions share is the mechanism we've been tracking through this chapter: the brain and nervous system learning through repetition, associations forming between triggers and responses, patterns becoming automatic and then spreading to similar contexts, neuroplastic changes that start as temporary sensitivity and become permanent architecture. Medicine has documented every piece of this in isolation, the immune responses in asthma, the central sensitization in IBS, the atopic march from eczema to other allergic conditions, kindling in seizure patients, but they didn't connect them as examples of the same underlying process. They didn't recognize that what they were seeing was maladaptive neuroplasticity, the brain and nervous system learning patterns that made people sicker, using the exact same mechanisms that allow us to learn to swim or paint or any other skill.

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So now we’ve seen that the brain wires in disease the same way it wires in any other skill. For over a century, researchers have been documenting the specific rules that govern how our brains learn. And these rules explain not just how disease gets wired in, but why it's so hard to get out, and ultimately, how we can.

In 1920, an American psychologist named John Watson set out to prove that all human behavior was learned, not inherited. He was convinced that he could mold anyone to do or be anything. And for some reason he chose to start this crusade of proof by terrorizing a nine month old child named Little Albert. Before this experiment little Albert had no fear at all of rats, but when Watson presented him with a white rat, just as baby Albert started reaching for it, he struck a steel bar with a hammer directly behind Albert's head. The baby cried and fell forward, his threat response triggered by the sound. You can actually watch videos of this experiment on YouTube. It’s…disturbing. Watson shows no emotion at all about the child’s distress. Then Watson repeated this experiment seven more times over two sessions. By the end of these seven exposures, Albert would cry and try to crawl away just from seeing the rat, without any noise at all. The fear association to white rats had been wired in.

Five days later, they found that Albert was now also afraid of a white rabbit he'd previously happily played with. Then he cried at a white dog, a fur coat, and even a Santa Claus mask. Watson's own white hair caused Albert to cry when he leaned in close. The fear of the white rat had generalized, causing baby Albert to be afraid of anything that looked similarly white and furry. His brain had taken one learned association and attributed it to anything that seemed remotely similar. Watson had only repeated this experiment seven times, and in that short time he had created a network of fear responses in Little Albert to an entire category of objects. Watson had proven that emotional responses could be created through simple association, and that you could wire fear to literally anything if you paired it with something scary.

Seven pairings. That's all it took to create a fear that spread to everything white and furry. And this is exactly the pattern we saw earlier with the asthma patients whose triggers kept multiplying - first cats, then dogs, then dust, then cold air, then exercise. Or the IBS patients whose safe foods kept shrinking. Medicine notes these expanding lists and thinks, yep, the disease is progressing as we expect it to. But the learning science is clear; the brain learns one association and then generalizes it to anything that seems similar. It's trying to protect you by casting a wide net - and once that net catches something, it becomes part of the pattern.

And like we showed in the last chapter with allergies and food sensitivities getting wired in through associations, they are also wired into threat, and the intermittent nature of them makes that even more sticky. When you're in threat, which by definition we likely are when we’re sick, you’re more hypervigilant. Which can lead to scanning for symptoms, which then trains your nervous system to detect smaller and smaller signals. The same synaptic strengthening that lets you learn a language or memorize facts is now making you better at feeling pain, or gut sensations, or whatever you're monitoring, just like any skill gets honed through practice and attention. Remember the monkeys in Merzenich’s experiment who were learning to touch a spinning disc? The region in their brain for that specific finger exploded, they actually grew more neuronal connections in that part of their brain to enhance their ability to perform that skill. The same thing is happening here. Signals that used to be too weak to reach conscious awareness now register loud and clear. Medicine sees this hypersensitivity and assumes it's causing the condition - but you weren't born with intestines that feel everything, or hypersensitive lungs.

Think about it this way. If you lived somewhere with tigers around every corner, you would get better and better at hearing specific crunching of leaves, or rustling in the bushes as threats. Your body would fire off the threat response earlier at smaller signals. The vigilance is a survival skill, and the sensitivity is a product of the vigilance, not the other way around. Your brain is training itself to feel more by paying so much attention. Which makes sense when it comes to monitoring for a tiger, but can become maladaptive when it’s keeping us in a feedback loop of threat and symptoms.

So now we’ve mapped how we can wire in illness patterns through learning and neuroplasticity. Luckily these same processes that get us sick and stuck can also be used intentionally to get us out. Let’s look at the other side and see how we can condition them out.

Unlearning responses

In the 1970-80s, Mark Bouton at the University of Vermont wanted to know if we could reverse the kind of conditioning that Watson did to Little Albert; if we could extinguish (get rid of) or undo that kind of fear response. So he first taught rats to be afraid of a specific tone by pairing it with a shock, similarly to how Watson paired the white rat with the loud noise. Then he played that same tone repeatedly without the shock until they stopped showing fear.

He showed that when he extinguished a fear completely in one location and then tested the animal in a different room, the fear was still there, as strong as ever. The animal hadn't unlearned that the tone was dangerous; it had only learned that the tone wasn’t dangerous in that specific room. So then he tried testing another assumption: he would extinguish a fear completely, and then he would wait a few weeks without any training or exposure, and do the test again. He found that the fear returned completely, as if the extinction had never happened, even in the learned location. And finally he showed that when he extinguished a fear, and then exposed the animal to mild stress that was completely unrelated to the original fear conditioning, the fear came roaring back. So what he was seeing was that extinction wasn't actually erasing anything, it was just temporarily being suppressed under specific conditions.

By the 1990s, brain imaging technology finally let researchers see what was actually happening during extinction, and it confirmed what Bouton's behavioral experiments suggested. When they looked at the brain activity during extinction, they found that the fear neurons in the amygdala still initially activated when the stimulus appeared. The amygdala was still recognizing "this is the thing that was dangerous," but for responses that have been extinguished, neurons in the prefrontal cortex would quickly activate and inhibit the fear response before it could fully develop into the physiological and behavioral fear reaction. Which makes a lot of sense. If you learn that snakes are dangerous, and then you experience some snakes in the wild that don’t attack you, you don’t want that fear circuit to disappear completely, you just want slightly more nuanced activation. Because some snakes ARE dangerous, and your brain needs to know that.

Contextual learning

If you study for a test when you’re tipsy, you’d be best served by being tipsy while you take the test too. In the 1960s researchers found that when people learned something while they were drunk, they could remember what they learned better when they were drunk again, rather than sober. And when they studied this weird discovery further, it turned out to be true for every state they tested. Information learned while anxious was best recalled during anxiety. Skills practiced while calm were most accessible when calm. Pain states, and mood states, and even specific body positions all created state-dependent memory networks that were most available when those states returned. And this understanding actually helps Bouton's findings make a lot more sense. The rat didn't learn 'the tone is safe now.' It learned 'the tone is safe in this room.' The safety learning was tagged to that specific context, which is why it disappeared the moment you changed the room. If you change the context, you change the learning. Which makes sense evolutionarily. Your body could learn, tigers can’t get me in this cave, but when I go outside all bets are off. We had to be able to make context dependent threat assessments, so we could let our body relax when it was safe, and could stay vigilant when the possibility of threat demanded it.

In the 1980s a researcher named Gordon Bower at Stanford hypnotized subjects to feel either happy or sad, then had them learn lists of words. Later, he tested their recall in either the same mood or the opposite mood. People who learned words while sad remembered them better when sad again. People who learned while happy recalled better when happy. The brain wasn't just storing the information; it was storing the information tagged with the internal state present during learning. This means that if you learn coping skills while feeling calm and safe in a therapist's office, these get neurologically tagged as "calm state" information, making them least accessible precisely when you need them most, during the stressed, symptomatic states where the maladaptive patterns live. It’s why meditation is often just like doing crossword puzzles. You are learning to be calm WHILE you meditate, in those specific circumstances. When you are annoyed with your spouse because they didn’t empty the dishwasher, that meditative state you’ve been practicing isn’t going to come up unless you make it come up. And both of these state dependent findings, mood states and state-dependent learning, provide a lot of context for how illness is self perpetuating. When you're sick, you're learning all your patterns in the sick state. Every symptom, every fear response, every coping attempt gets tagged to that pathostatic state. So being sick becomes the context that triggers being sick - your body learns to be a certain way when it's getting specific stimuli, and the symptoms and physiology that state creates actually create a feedback loop that becomes more and more sticky and thus chronic.

And just to round out how far these associative findings go, let’s look at the research on drug overdose as it relates to location. A researcher named Siegel showed that situation-specific drug tolerance is capable of preventing fatal overdoses. There was one man who received the same morphine dose four times a day for four weeks, always in his dimly lit bedroom, where he was bedridden. But one day he for some reason dragged himself out of his bedroom into the brightly lit living room. When his son gave him the exact same dose in the living room he died from an overdose. His body didn’t mount the tolerance response it had learned, without the context of the room it had learned it in. And if context can determine whether or not a morphine dose is fatal, imagine what it's doing to every other physiological process. Pain researchers discovered that chronic pain patients often felt better in novel environments like hospitals or vacation spots, only to have their pain return the moment they got home. The brain had learned not just "pain" but "pain in my bedroom," "pain at my desk," "pain at 3 PM," creating elaborate networks of association that turned entire environments into symptom triggers.

Two necessary components

In 2004, Phelps and colleagues looked at what was happening in the brain during fear extinction. What they found was that successful updates required two things firing simultaneously: the fear center, where the automatic threat response lives, and the prefrontal region responsible for conscious processing and safety learning. The prefrontal cortex forms the new 'this is safe' memory and actively inhibits the fear response, but only if the fear memory is active at the same time. The strength of connection between these two regions predicted how well the extinction stuck.

And for complex threat responses that have been encoded throughout many instances in your life, this can't just happen once. You need the new integrated experience to happen across contexts just like the fear learning did. Across different moods and times of day and situations, in the actual moments where life activates the old pattern. Each time you meet the activation with both systems online, feeling it while staying present, you're building the new wiring in that specific context. Do it enough times, in enough different situations, and the new pattern starts to become the default.

Just as symptom expression gets encoded into our brain in the same way we learn how to play piano, the same system can also be used to update the threat response feedback loop that is causing learned symptoms and disease expressions.

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We now have the science showing how disease patterns wire in, and the science showing how to update them. Combined with what we've learned about what's driving pathostasis in the first place, for the first time we can see both sides of the problem, and what we can do about it.

Institutional science is structured to go deep not wide, leaving no one inside allowed to step back. But that also means that each piece has been studied meticulously, over decades, with expert precision, by generations of researchers and clinicians. Medicine themselves documented what we just walked through.

The neuroplasticity and associative conditioning that wires in disease and dysfunction can be used intentionally to wire it out. The disease our bodies have been practicing can be unlearned. Medicine is already doing this for allergy desensitization, we just need to expand those same types of practices to other diseases as well.

Through this same work we can learn to extinguish the threat that is wired into connection. We can’t go back to tribal living, there is no way in our modern world to create the conditions that our hypothalamus expects. But we can learn to stop blocking the snacks that are available to us. We may never be able to get a full three course meal, but if we’re actually able to eat the snacks, that can be enough.

And for people whose alarm is too loud, or too entrenched, whose need signal and threat chemistry are blaring, we now have a pharmacological intervention that addresses the upstream cause rather than playing whack a mole with downstream expressions. GLP-1s aren't a cure all, but paired with the deconditioning work, it seems we might be closer than we’ve ever been to being able to actually address chronic disease at the source.

We can’t know what the future holds, but for the first time since germ theory, we seem to be on the precipice of understanding not just what chronic disease does, but what it actually is.