Overloaded

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 medicine’s standard current screening tools combined: blood pressure, cholesterol, smoking history, and family history of disease. It alone gives us a more complete picture of future health outcomes than all of these expensive tests combined, we've known about it for over thirty years, and I bet at this point you can guess what medicine is doing with it. Pretty much nothing.

The assessment we are talking about here is called the Adverse Childhood Experience survey, or ACEs for short. You may have even heard of it. It is so predictive of disease that if a patient has a score of six or more out of ten, vs a patient who scores a zero, this test accurately predicts a difference in life expectancy of twenty years. Twenty years.

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 we think of as healthcare today enroll an average of 65 patients and last an average of 6-12 weeks, with larger phase 3 trials capping 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. To be sure this wasn’t only relevant and applicable to this homogenous population, there have 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.

So here we have over a hundred studies over a 30 year timespan, studying hundreds of thousands of patients, tracking a direct correlation between childhood distress and disease. Our first, pretty robust, data point that pathostasis comes before disease. And what is medicine doing with this information? Not much. The researchers who discovered this thought it would be a landmark finding. Something that would rewrite the textbooks. But as we are pretty well aware by now, medicine isn’t wont to do that. Especially when it doesn’t fit with their current understanding of find the pathogen or mutation, kill the pathogen or mutation. But we already know that medicine wants biomarkers, and that they have trouble acting on things they can’t physiologically see with their own eyes. So let’s look at a parallel set of research that was being done over this same 30 year timespan that gave them just that. Biomarkers showing these same exact findings.

Allostatic load

The term ‘allostasis’ was coined in 1988 by Sterling and Eyer. They saw 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 pretty revolutionary finding at the time. Of course we know that what they were seeing was pathostasis in action - the same genetics producing disease or health depending on environmental load.

Then in 1993 researchers Bruce McEwen and Eliot Stellar took the concept a bit further and came up with the term ‘allostatic load’, which they defined as multi-systemic “wear and tear” on the brain and body, experienced when repeated allostatic responses enact ‘stress’ on the body. McEwen saw the brain as the central coordinator of the body's stress response, meaning that the brain interprets the demands on the body, and coordinates system-wide responses. The way they approached researching this was to isolate biological parameters that represented the way the hypothalamic-pituitary-adrenal (HPA) axis was functioning, which included the sympathetic nervous system, the cardiovascular system, and metabolic processes. These biomarkers were comprised of what they called primary mediators, aka the stress hormones cortisol and catecholamines (part of the level 0 pathostasis chemicals), and what they called secondary mediators (which fall into our level 1 and 2 categories) which included things like metabolic markers (blood pressure, cholesterol, glucose, waist-hip ratio) and immune/inflammatory markers (IL-6, CRP). They found that under conditions of cumulative strain, these hormones become dysregulated and that this dysregulation then starts an inter-connected 'domino effect' on biological systems that collectively collapse as individual biomarkers topple towards disease. So just to summarize, they were measuring these chemicals and their physiological effects on the body through a set of measurable biomarkers, and the findings were clear and impactful.

In one study they tracked 738 adults over 5 years, and tested 12 allostatic biomarkers - cortisol, DHEA-S, CRP, cholesterol markers, glucose metabolism, kidney function, and body composition. 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. They weren't measuring people who were already sick - they were measuring these markers in people who seemed fine by medical standards, and then accurately forecasting their disease trajectory years before symptoms appeared. And what's remarkable is how consistent these findings are across populations. Many studies have been conducted globally showing the same patterns. The relationship held across age groups and cultural contexts. Higher allostatic load meant worse health outcomes, period. And they documented this through a specific biological pathway with biomarkers and physiological documentation.

To date, thousands of articles, 2,465 as of the time of this publication, informed by the allostatic load model have expanded stress science theory, research, and clinical perspectives. But they only took it so far. They can see that cumulative stress ‘contributes’ to disease broadly. What they don’t do is connect the dots and see that it’s actually the upstream driver of all of it. But in all fairness, they got really close. The problem is that the way even allostatic load researchers frame it is: "reduce external stressors, improve coping skills, practice stress management". Exactly what we know doesn’t actually work most of the time. But despite this limited interpretation of the data, this is still monumentally important, and predicts and explains disease far better than anything medicine is currently pointing to as a reason or a cause and yet…this whole body of research is mostly not taught in med school. If you happen to be studying one of a handful of fields, you may get some education in it (public health and epidemiology, health disparities and social determinants, psychoneuroimmunology, or some specialized psychiatry training) but barring a specialty in those fields, which most doctors actually treating patients do not have, you can go through all 11-15 years of med school hearing very little or nothing at all about allostatic load research and what it means about disease and health. So they have thirty years of research showing that allostatic load levels predict who gets sick and who stays healthy better than the biomarkers doctors actually use. And it's treated as a niche topic for public health researchers.

So there's our answer to the central objection we mentioned at the end of the last chapter as to whether disease comes first, or pathostasis does. ACEs research shows childhood adversity predicts adult disease decades later - before any disease develops. Allostatic load research shows the biomarkers are elevated years before symptoms appear. These are huge bodies of research and they have very clearly shown that pathostasis comes first, documented and measurable, with disease following predictably behind.

Of course this won't be the only objection. The implications of pathostasis are too far-reaching, too disruptive, to accept without scrutiny. So let's take a minute and address some of the other most likely objections and see how they hold up. And let’s look at how those same objections would hold up if the mirror were turned on medicine itself as well, and see which theory has better answers.

"This is too reductionist - disease is too complex for one cause"

Darwin didn't need a separate explanation for each individual species, because he had the unifying mechanism. Natural selection acting on variation explained all of it. The complexity wasn't in having multiple causes, it was in how one principle expressed across different contexts, different environments, different timescales.

Pathostasis: One upstream cause expressing through interconnected systems in context-dependent ways. Individual diseases emerge based on genetic vulnerabilities, prior damage, environmental factors, which systems are under most strain. The complexity is in the expression, not the cause.

Medicine: Medicine doesn't have "complex explanations" - they have hundreds of incomplete explanations that don't connect to each other. When they see patterns they can't explain, they call it "comorbidity" or a “paradox” or “multifactorial” and treat each disease separately anyway. That's not accounting for complexity - that's missing the pattern.

"You're just saying stress causes disease - we already know stress is bad"
/ “Not all sick people report stress or trauma”

The word "stress" has become so vague it means everything and nothing. Humans are built to experience a lot of stress on a daily basis. We evolved over millions of years to be hunter gatherers, and that includes dealing with a lot of very stressful things like finding food that may or may not be available, watching for and avoiding predators, and existing without consistent shelter. That kind of stress, acute stress, causes our body to go into sympathetic activation, and then once our brain signals to our autonomic nervous system that the threat has passed, it shifts us back into our normal homeostatic physiological state. The confusion comes from conflating "stress" - the vague cultural term we use for feeling overwhelmed - with the specific physiological state pathostasis describes.

Pathostasis isn't "feeling stressed." It's a precisely defined physiological state with measurable chemical changes, documented cascades, and predictable disease outcomes. It's the state your body enters when the stress response activates and then fails to deactivate, remaining locked in a configuration designed for short-term survival but destructive when sustained.

Medicine acknowledges stress is "a factor" in disease, along with diet, lifestyle, and genetics, but they treat it as a vague psychosocial factor rather than the primary physiological driver, because they are thinking about it in terms of having too much work to do, or not taking enough breaks. In fact they have no clear causal explanation for any chronic illnesses; just risk factors and associations.

Pathostasis isn't making claims about your emotional state or life events. It’s describing a measurable physiological configuration. We’ll discuss in Section Four what actually creates and maintains pathostasis.

The point here is simple: medicine can't explain what causes most (any?) chronic diseases. Pathostasis provides a direct causal link pointing to a distinct physiological state that clearly maps directly and causally to every chronic disease we have documented medically.

"If this were true, everyone under chronic stress would be sick"

This is like saying "if smoking caused cancer, every smoker would have cancer." Causation doesn't require 100% occurrence, it requires that the mechanism reliably produces the outcome at the population level, which pathostasis cleanly does.

As we mentioned above, being “stressed” in the colloquial sense, and being in pathostasis are not the same thing. We were meant to be able to handle everyday stress. Not everyone who is stressed gets into pathostasis, what matters is degree and duration. If your spouse dies and you're already carrying years of pathostatic load, that additional stress might be when cancer develops. If you were healthy with low load and your spouse dies and you're deeply stressed for six months, you likely won't get sick - your system can handle acute stress, even prolonged acute stress, because that's what it was designed for. Pathostasis isn't about having a hard year. It's about a system that got stuck and stayed stuck.

Medicine also can't explain why some people get diseases and others don't. They invoke "genetics," "environment," "lifestyle," "bad luck" - a constellation of factors with no unifying principle for why disease develops when it does.

"You haven't proven causation - this is just correlation"

We just walked through ACEs research tracking childhood adversity predicting disease decades later, and allostatic load research showing elevated biomarkers years before symptoms appear. The direction of causation has been documented across hundreds of studies and hundreds of thousands of people. This isn't correlation - it's tracking cause before effect.

Medicine on the other hand uses correlation and treats it as causation all the time. They find associations between genes and disease, between biomarkers and disease, between lifestyle factors and disease. They catalog what's broken without explaining what broke it. That’s like looking at a runny nose and claiming it’s the cause of colds. When they claim obesity causes diabetes or that amyloid plaques cause Alzheimer's, they're pointing to correlation and calling it causation - exactly what this framework will be accused of doing.

"What about genetic diseases like Huntington's?"

This is worth examining closely because it reveals something important about how medicine conducts research and draws conclusions.

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, for the record, the definition of selection bias.

And when researchers 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 5-10 per 100,000. Which means roughly 96-98% 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.

Pathostasis acknowledges genetic variants matter - they determine your vulnerabilities, where disease shows up first when the cascade runs. But genes aren't destiny. They're weak points that only matter when the system comes under the kind of sustained stress that pathostasis describes. The genetic research medicine loves to cite as proof of biological causation is riddled with this same selective sampling problem. Millions of people carry the same "disease genes" and never get sick, and millions more get sick without the genes, and medicine just shrugs and says disease is "multifactorial and complex."

The objections we’ve just gone through were probably the biggest ones, but let’s run through just a few more really quickly:

Why do some people's chronic diseases go into remission?

Pathostasis: Remission and recovery happen when you turn Level 0 down or off, and address the pathostatic conditioning and structural remodeling that develop during the course of disease - we'll discuss both in detail in section four.

Medicine: Views chronic disease as permanent and progressive. They use the word 'remission' rather than 'recovery' or 'cure' because they view chronic disease as something you manage, not something you reverse. Even when someone's diabetes completely resolves or their autoimmune markers normalize, medicine frames it as 'remission' - implying the disease is still lurking and could return at any time. They have no framework for true recovery because they don't understand what caused it in the first place.

Why does disease cluster geographically and socioeconomically?

Pathostasis: Load is higher in environments with poverty, discrimination, instability, pollution, food insecurity. Same mechanism, different exposure levels.

Medicine: Health disparities due to access to care, environmental toxins, diet quality, healthcare literacy. Each disease studied separately for social determinants.

What about diseases that run in families?

Pathostasis: Two things run in families: (1) genetic vulnerabilities determining which systems fail first, (2) pathostatic conditioning transmitted through co-regulation, modeling, and shared environment. Both create familial clustering accounting for but not requiring specific disease genes.

Medicine: There are two distinct ways medicine explains this. One is through genetic mutations like we talked about in Huntington’s disease, where they have actually found a genetic marker and (eventually) studied how often that marker led to the disease expressing (causing disease). And the other is what they call heritability. For example they say schizophrenia has 80% heritability. It’s important we lay out what that actually means, because if you don’t dive into the details it sounds like a pretty strong correlation, as if 80% of the disease is determined by genes, or 80% of people with family history will develop it.

To come up with that number, researchers found twins where at least one had been hospitalized for schizophrenia, and asked them to participate in their survey. Then they determined whether the other twin also had schizophrenia, calculated concordance rates (how many sets of twins BOTH had schizophrenia vs how many had only one twin with it), and compared identical twins (100% genetic similarity) to fraternal twins (50% genetic similarity). They found that only 15% of identical twins were both affected in the largest study - with other studies ranging up to 28% - not the near-100% you'd expect if genes were truly determinative.

Then they used a formula that I will walk through here: 1.) Take the difference between identical and fraternal twin concordance. 2.) Double the number (why? who knows). 3.) Use the doubled number, and apply it to all the twins who don’t have the disease yet, because you assume the same percentage will get it but just haven’t gotten it yet. 4.) Call this new number heritability. By the time you get from "15% of identical twins both have it" - which is what the largest study, covering over 30,000 twin pairs, actually found - to "80% heritable," you've passed through so many layers of statistical transformation and assumption-baking that the final number has almost no intuitive relationship to the original observation.

Actually let’s look a bit more at why they double the number because it’s…well…just see for yourself. So the thinking is that since identical twins share 100% of genes, and fraternal twins share 50%, the difference in concordance must represent that extra 50% of genetic sharing. Double it to get the "full" genetic effect. Except…the difference is already represented here. That's the entire reason you're comparing these two populations in the first place. The whole point of the twin study design is that you're directly observing what happens at 100% genetic sharing versus 50%, that’s what you were measuring. Adding it again is literally doubling the difference.

And the reason they did this strange calculation in the first place was because they couldn’t find any genetic markers at all to explain schizophrenia diagnosis. So if you have 100% of the same genes as someone else, you have at most a 1 in 4 chance of sharing their diagnosis - and possibly as low as 1 in 7. If you think about how many genes you actually share with anyone in your direct or extended family who isn’t your identical twin, that number is a lot less. So this means that having schizophrenia in the family means…not a whole lot other than which system might be vulnerable if you experience sustained pathostatic load, not that you're genetically destined to develop the disease.

⋯

So we’re saying that pathostasis explains everything: cardiovascular disease, metabolic disease, autoimmune conditions, neurodegenerative diseases, cancer. The mechanism is the same, the cascade is documented, the pattern is clear.

And depression, anxiety, schizophrenia - they're right there alongside heart disease and diabetes. According to this framework, Parkinson's and panic disorder are the same mechanism hitting different vulnerable systems. Alzheimer's and depression are both just pathostasis affecting the brain.

Which means the entire structure of modern medicine - the fundamental division between medicine and psychiatry, between "physical" and "mental," between diseases treated by cardiologists and diseases treated by therapists - is built on a false premise.

So how did we end up here? How did medicine decide that symptoms in the heart are fundamentally different from symptoms in the brain, when both are just organs responding to the same upstream cascade? Why is depression sent to psychiatry while chronic pain goes to rheumatology, when they cluster together following the exact same pattern?

It turns out the answer isn't medical, and it’s not the least bit scientific. It's historical and institutional. And understanding how this divide happened, and why it's persisted despite all evidence to the contrary, will help clarify a lot of things.