Multiple Sclerosis. Everything you need to know

Let’s imagine viruses and other pathogens are criminals, and lymphocytes are police officers. These cops hunt for criminals constantly, without sleep and without even a lunch break. However, there are heavy restrictions in this world: there is no internet, no cameras, no computers, and not even a telephone—just like back in the 18th century. The only thing that helps the detectives catch bandits is a collection of composite sketches—mugshots assembled from random facial features. The police archive holds a massive catalog of hairstyles, eyebrows, eyes, noses, lips, and chins. A bandit's mugshot is just a completely random combination of these features. It’s not very convenient, but it is highly effective: eventually, every single variation will be generated, and not a single criminal will slip through. This is pretty much exactly how V(D)J recombination works.

Our police officers are quite simple-minded; their memory is like that of a goldfish. To avoid forgetting who they are looking for, they have to carry a printed copy of the mugshot around and constantly look at it. To make matters worse, they are near-sighted and not particularly sharp, meaning they might easily harass a peaceful citizen who just happens to look like a criminal. If the surrounding environment is calm, they will question them and let them go. But if things are tense, they might reach for their weapons. In this model, these police employees are lymphocytes—cells that are 100,000 times smaller than a grain of sand. They are as dumb as a box of rocks; they don't understand anything. All they can do is look at that printed mugshot and compare it to a suspect. You've probably guessed it by now: the mugshot is a receptor.

The state is an absolute police state: there are a trillion officers on the payroll, and about a billion different mugshot variations out there. In the training camp called the Thymus, roughly 90% of the recruits are weeded out immediately. The "suspects" on their mugshots have three ears, two noses, or four rows of teeth—you don’t encounter such freaks in real life, so there's no point even looking for them. The remaining 10% of mugshots actually look like normal human beings, which creates an obvious problem: they are supposed to catch only criminals, while leaving peaceful citizens alone. How is a lymphocyte supposed to know if it's dealing with a peaceful citizen?

It is a dull, monotonous, and painstaking process: the recruits have to walk along a massive wall where the Chief Archivist has pinned up portraits of millions of peaceful citizens. If one of those portraits turns out to be an exact match for a cop's mugshot, the officer silently pulls out his service weapon and shoots himself. There's nothing to be done—he just got unlucky. His receptor turned out to be autoreactive, meaning he would cause nothing but trouble in the city, so there is no place for him in this world. In the training camp called the Bone Marrow, the setup is a bit more forgiving. If a first mugshot is flagged as dangerous to its own people, the struggling recruit is handed a pencil and an eraser—they are allowed to rub out the nose and redraw the chin. However, if the mugshot still matches the face of a peaceful citizen after that, the ending is exactly the same. This is how the mechanism of central tolerance works: only those who have no intention of hunting down peaceful citizens are sent out to active duty. The blueprint of the immune system is brilliantly simple—it destroys everything except the cells of its own organism.

The numbers are astronomical, and the defect rate is worse than at a factory making counterfeit iPhones. It's physically impossible to inspect everyone. Every single day, 50 million lymphocytes leave the young thymus, and a couple of million of them walk out with a mugshot that looks like a peaceful citizen. In other words, every twentieth officer in the immune police force is a "corrupt cop." Just so we are completely clear on this: every single day, a young thymus releases two to three million T-lymphocytes into the bloodstream that are fully capable of attacking their own tissues, and there is absolutely nothing abnormal about that. An autoimmune reaction doesn't start because defective cells are born—it starts when the control system fails.

Don't forget, our police officer is simple and limited. He doesn't even look around—he can only identify a criminal when a scout colleague, a dendritic cell, shows him one. This colleague, by the way, is incredibly rude: she drags absolutely everyone into the station without filtering, completely unable to tell the difference between a peaceful citizen and a street thug. And why should she care? All the police officers who could mistake criminals for peaceful citizens were supposed to shoot themselves back at the training camp anyway. The dendritic cell just chops up pieces of everything she finds—"not my circus, not my monkeys." Once she arrives at the station, she displays thousands of gathered clues to the officers. As you might have guessed, the police station in this scenario is a lymph node—this is usually where the cops and the scouts cross paths.

Remember, one in twenty cops is corrupt. If they started arresting citizens the moment they met the scouts, the city would be wiped out. To prevent this, peripheral tolerance has another safety feature: co-stimulatory receptors. Both participants in the meeting look for additional indicators—chemical confirmations that actual warfare has broken out somewhere. The logic is simple: if there is nothing but peace and love all around, then the suspect isn't really a criminal. We are talking about lymphocytes here—they have no brains, they can't think. The rule is basic: if the criminal isn't bothering anyone, let him be.

If the co-stimulation signals are active, the protocol is straightforward—clone yourself and destroy everything that matches the mugshot. If the signals are silent, the officer has to accept that his mugshot is defective—it must feature a peaceful citizen. He stares at the printout, lights a cigarette, and sinks into deep thought. Now he has two choices: either pull out his weapon and shoot himself right then and there, or become a protector of peaceful citizens—a T-regulatory cell. The fate of the officer is decided entirely by how closely a suspect matches the mugshot and by the chemical signals in the air around them.

If the scout and the cop meet at a moment when both are highly agitated, lymphocyte activation begins. The officer stops going out on patrol, locks himself in the precinct, and begins to multiply by dividing himself, taking about 6 to 8 hours for each round. Every single new copy is equipped with the exact same criminal mugshot—the receptors of the clones are always identical to the original cell. Have you ever noticed how your lymph nodes swell up when you are fighting an illness? That’s because the police officers are busy cloning themselves. In a matter of days, a single activated lymphocyte creates an army of 10,000 or even 100,000 copies. This part is critical: this army of police clones only leaves the precinct when the multiplying process is completely finished—marching out all together as a fully formed regiment. If they went out one by one, they would never be able to build up the necessary crowd density.

The thing is, the clones marching out of the station are immediately met by the regulators—those police officers who realized their mugshots were wrong but didn't shoot themselves. The ratio is roughly five to one: for every 50,000 clones, there are 10,000 regulators. The first clash happens right outside the lymph node, and the second one occurs right near the organ the clones are planning to assault. The regulators strip the activated police officers of their vital fuel—interleukin-2—and the immune process grinds to a halt.

How do the regulators know they are dealing with an autoimmune gang? They don't know anything—they are just as brainless as the regular lymphocyte-cops. They take away interleukin from absolutely everyone, meaning you need a massive, powerful impulse to get an immune process off the ground. It’s a trial by fire: if you want to fight a threat, you first have to prove you can break through the cordons of regulators. The ones who are truly determined will make it through, and if they don't, it means they didn't want it badly enough.

Because of this, the success of an immune response depends entirely on the overall level of inflammation. If the army of clones is small, the regulators will easily starve it of fuel. How does the body manage to fight off viruses with all these checks and balances in place? The same way it always does—through sheer numbers. For instance, when dendritic cells bring fragments of a common cold virus into a lymph node, dozens of different police clone lines might activate at the same time. In a few days, they will stamp out an army of a couple of million cells, completely overwhelming the regulators. If an autoimmune reaction tried to start in a time of peace, it would never get anywhere—the regulatory cells simply wouldn't allow it. Glitches in the immune system happen exactly when it is pushed to its limit. Every single time you get sick, you create conditions for an autoimmune reaction—the infection sets up the necessary background of inflammation.

What happens next? The autoimmune cells spill out into the bloodstream, float around the body, and look for whatever matches their receptor. Fortunately, they only have enough steam to last a few weeks before they burn out and stop being a problem. However, if they actually cross paths with their target antigen, things take a very ugly turn. If the receptor matches the proteins of the thyroid gland, Hashimoto's thyroiditis begins: the patient has to take L-thyroxine for the rest of their life. If the receptor is specific to the gut wall, it triggers Crohn's disease; if it targets the lining of the joints, it's rheumatoid arthritis; and if it goes after myelin, it's multiple sclerosis. All autoimmune diseases start the exact same way: a lymphocyte that should have been executed immediately after birth wakes up, breaks through every single safety cordon, and reaches the target tissue.

How does a single lymphocyte manage to cause so much damage? It has already stopped multiplying; it is completely alone in its mission to tear down the body's tissues. The answer is simple—after undergoing secondary activation, it calls for backup from other immune cells. Once it encounters the specific antigen matching its receptor, this police officer releases cytokines—signaling molecules that summon heavy-duty macrophages to the scene of the crime. Macrophages are even dumber than lymphocytes; they don't look at anything at all. They just start ruthlessly burning the entire territory to the ground, whether it’s the thyroid, the gut, the joints, or the brain. The debris of the destroyed tissue washes out into the bloodstream, where it gets picked up by dendritic cells, leading to a repeat presentation of antigens—a phenomenon known as epitope spreading. New scouts, new mugshots, and new police officers are dragged into the mess. Without outside intervention, an autoimmune reaction only ends in one scenario—when the target is completely obliterated.

The exact moment a police officer spots a criminal in the target tissue marks the real beginning of the autoimmune process. How do you halt an autoimmune process? How do you fix this systemic error? The most logical and clinically proven method is to completely reboot the immunity. You have to wipe out all lymphocytes—the bad ones, the good ones, the useful ones, and the useless ones—and then build brand new ones from scratch. You have to erase the immune memory, force the body to forget the mistake it made, suppress the immunity entirely, and give it a chance to rebuild itself with a clean slate. How is this accomplished? Hematologists and immunologists solved this puzzle about 35 years ago through a procedure called Autologous Hematopoietic Stem Cell Transplantation (AHSCT)—which you will read about in detail in Chapter Eight of this book.

Chapter 2. Diagnosing and Assessing MS Progression

«If you don’t know where you’re going, any path can take you there»

Lewis Carroll, Alice in Wonderland

How Many Patients Are There in the World?

It seems astonishing, but there is no official, unified statistic on the number of multiple sclerosis patients worldwide. Different registries and studies use vastly different criteria, tracking scopes, and methodologies, making final estimates diverge. The explanation is logical and simple: multiple sclerosis has no standalone diagnostic marker. Sclerosis isn’t caused by an identifiable virus, fungus, or bacteria, which makes routine blood tests practically useless. Tests that can explicitly confirm or definitively rule out multiple sclerosis simply do not exist.

As a result, tracking the global population of sclerotics over time has turned out to be an incredibly non-trivial task. Searching Yandex and Google yields practically zero concrete data prior to the year 2000. I used to think that cases only started being registered once doctors gained the ability to confirm diagnoses via MRI, but that turned out to be a myth.

I was convinced that over the 25 years since MRI was universally adopted for MS diagnosis, the patient count must have doubled or tripled, but there was no clean way to verify it. So, I messaged my editor-in-chief and asked for the publishing house's help. Maria turned out to be far more resourceful than me and instantly figured out how to solve the bottleneck: look to ChatGPT, the artificial intelligence. By the way, last year I read halfway through The Autobiography of a Neural Network, a book completely generated by ChatGPT-4. I can't say I loved the book, but I definitely walked away believing it was genuinely written by AI. The terrifying future shown to us in the later Terminator movies is inching closer.

Ultimately, in the absence of official stats, using ChatGPT felt like the only accessible shortcut to rapidly aggregate fragmented historical data. Crucial clarification: the neural network doesn't act as a "fortune teller"; it merely summarizes official, scattered statistical registries. Since ChatGPT speaks every human language fluently, I asked it to gather published data from various countries—health ministry reports, epidemiological reviews, national registries, and massive meta-analyses—and use these sources to estimate the global patient population back in 1980. For the record, the neural network flagged this as a highly problematic task, since a standardized tracking system for such diseases didn't exist back then. Naturally, I didn't stop at 1980.

Global MS Patient Population Estimates (Aggregated by AI):

1980: 0.8 to 1.0 million

1990: 1.1 to 1.3 million

2000: 1.5 to 2.0 million

2010: 2.1 to 2.3 million

2020: 2.8 million

2025: 3.2 to 3.5 million

I think the trajectory toward a rapid population explosion is obvious to anyone, even those far removed from mathematics. Armed with these statistics, I couldn't resist asking: "Based on historical incidence data, project the global number of multiple sclerosis patients in the year 2100." ChatGPT complimented me on the unconventional question and then ran the projections.

The baseline scenario (50–60% probability) assumed that multiple sclerosis treatment variables would remain stagnant, with only diagnostic tools improving. In this track, the patient count would scale to 13–14 million by 2100. The pessimistic track, which the AI hit with a 20–30% probability, projected a surge up to 30 million cases. There was also an optimistic scenario: the arrival of radical new medical technologies, including genetic engineering, alongside a massive leap in differential diagnostics. In that track, the planet would harbor 6.2 million MS patients in 2100, but the probability of this outcome sat at a measly 10–20%.

As someone whose favorite school subject was math, I am deeply convinced that numbers do not lie. On the contrary, using mathematically sound models usually yields the most pinpoint projections—provided, of course, that no error sneaked into the baseline data. Furthermore, if a mathematically sound model starts projecting absolute, unadulterated nonsense, the error is almost certainly baked right into the initial inputs.

Based on official numbers, ChatGPT’s baseline projection (50% probability) stated that by the year 2200, the planet would see about 100 million cases of MS, while the pessimistic track (20% probability) claimed the patient population would swell to 270 million. Yes, I also immediately factored in proportional global population growth. However, those same mathematical models stated that the planet's human population in 2200 would cap out between 8 and 10 billion people.

So, the artificial intelligence suggested that if current trends hold, out of 10 billion people living on earth in 2200, the baseline track would yield 100 million MS patients, and the pessimistic track would yield 270 million. The pessimistic model calculated that by 2200, one out of every thirty-seven people on earth would suffer from multiple sclerosis; by 2500, one out of every six; and by roughly the year 3400, sclerosis would be diagnosed in literally every single human being without exception.

When I followed up with, "Doesn't it bother you that your projection requires the relative proportion of MS patients to skyrocket 20- to 30-fold by 2200?" the neural network replied, "A very pertinent observation—and yes, that absolutely raises flags." Shortly after, it heavily adjusted its projections, and after a few more clarifications and roasts on my part, it essentially told me it was "done with this nonsense" and that the global MS population would never exceed 15 million, no matter what happens, period. It seems I managed to train the neural network.

Obviously, such extreme conclusions don't map reality; they merely expose the bankruptcy of the initial data. A model built on numbers stripped of common sense rarely yields a solid forecast, but mapping a real forecast wasn't my objective anyway. The point lies elsewhere—this exercise unevocably proves that the MS diagnostic framework is corrupted by systematic errors. My next question was: "Given your previous answers, what percentage of people diagnosed with G35 do you think received that label by mistake?" The neural network praised the angle and stated quite clearly that the data (backed by a dozen research papers it attached to its reply) indicates that in recent years, MS has been misdiagnosed in 25 to 30% of all cases.

On "Active Demyelination Lesions" on MRI Results

Today, it’s hard to believe, but 40 years ago, clinical practice possessed zero technology capable of visualizing what was happening inside a living brain. A person experiencing strange neurological symptoms would visit a doctor, undergo a long, tedious physical exam, and head home completely stripped of a definitive diagnosis. There were no McDonald criteria, no contrast enhancement, no "demyelination lesions"—or rather, they existed, but you could only find them during an autopsy. Before the arrival of MRI, a patient could feel systematically unwell for years without any way to map the structural warfare unfolding inside their skull.

One of the first to map strange neurological symptoms to physical changes in the brain was Jean-Martin Charcot. In 1868, he observed a patient presenting with tremors, nystagmus, and slurred speech. After her death, he conducted an autopsy and discovered characteristic "plaques" scattered through the brain's white matter. It was Charcot who coined the term scleróse en plaques—multiple sclerosis—and hypothesized that a hidden brain pathology was pulling the strings behind the outward symptoms. This launched a century-long era where MS was diagnosed entirely without MRIs, without spinal fluid analysis, and without any instrumental confirmation. A diagnosis was assembled purely from symptoms, their timeline, and the physician's clinical intuition. For decades, multiple sclerosis remained a "diagnosis of exclusion"—and the echoes of this strategy still form the bedrock of international diagnostic protocols today.

The first successful attempt to systemize this guesswork came in 1965 with the introduction of the "Schumacher criteria." According to these rules, a diagnosis could only be made if a patient experienced at least two relapses, each lasting more than 24 hours and separated by an interval of at least one month. Doctors also had to find objective neurological signs of damage in at least two separate areas of the central nervous system. The patient’s age had to sit strictly between 10 and 50, and any alternative explanations for the symptoms had to be completely ruled out. These criteria heavily prioritized the value of a physical exam and clinical observation over laboratory data, demanding a massive level of qualification, experience, and a willingness to monitor a patient over long stretches of time.

Without an MRI, a diagnosis was assembled literally "by eye." Everything hinged on the doctor’s ability to track and interpret subtle clinical cues: nystagmus, reflex asymmetries, pathological babinski signs, coordination, gait, and sensory maps. Any single detail could turn out to be the master key, but only when framed by the total clinical picture and how it shifted over time. Physicians had to know their patients practically intimately, not just as charts—otherwise, tracking the slippery dynamics of the disease was impossible.

An audit rarely wrapped up in a single visit; observation stretched across months or even years. A doctor had to recall and contrast today’s symptoms against how the patient presented six months or a year ago, ideally factoring in variables like weather, fatigue, stress, and other ambient triggers. Finding multiple sclerosis in a perfectly healthy person was incredibly easy; missing the disease entirely was just as easy; and objectively proving or disproving either was flat-out impossible. Naturally, a system wrapped in so much subjectivity and individual bias had zero chance of long-term stability: it simply left too much room for human error.

It wasn't until the late 1960s that the first genuine biomarker emerged: "oligoclonal bands" in the cerebrospinal fluid. Spotting OCBs offered the first concrete proof of a specific, localized inflammation within the CNS linked to multiple sclerosis. Before this breakthrough, the diagnosis was entirely "clinical"—meaning it was born strictly out of the doctor's office. Once science gained the ability to isolate intrathecal (intra-brain) antibody synthesis from the body's general immune response, MS diagnostics took a massive leap forward—oligoclonal IgG bands were detected in 95% of patients with genuine MS.

Yet, this first attempt to lock down the system ran into two distinct brick walls. First of all, harvesting cerebrospinal fluid via a lumbar puncture is an incredibly unpleasant procedure; it isn't 100% safe and frequently brings prolonged side effects. The second problem multiplies the first: roughly 10% of spinal fluid analyses yield a false negative.

A 10% margin of error is a massive vulnerability when dealing with a life-altering diagnosis. It threw physicians right back into a corner where clinical intuition and gut feeling carried more weight than laboratory data. What do you do if you are a young doctor facing a patient whose symptoms resemble MS but don't quite fit the textbook? What if the manual says one thing, but the latest revision of clinical guidelines says another? What if the spinal tap comes back completely clean, but the clinical mapping screams sclerosis? What do you do if you are only 70% certain, but the patient demands a definitive label and refuses to leave without one? Or conversely, what if the patient views MS as a death sentence, and you can't give them a straight "yes" or "no"?

The arrival of MRI didn’t just trigger a technological revolution—it fundamentally inverted the nature of diagnostics. Suddenly, "demyelination lesions" and "multiple sclerosis" could be visualized on a screen, which drastically simplified a doctor's workflow. The journey from the first experimental brain scan in 1981 to a fully standardized framework took exactly twenty years: in 2001, the McDonald criteria were adopted, legally equating the appearance of white spots on a tomograph to actual, physical clinical attacks.