More Data Isn’t Better, Just Louder
credits DALL-e

More Data Isn’t Better, Just Louder

This morning I was standing in front of my bathroom mirror, feeling fine. I had finally slept well, had energy, and was getting ready for my day. Then, for reasons I can’t quite explain, I decided to glance into the magnifying mirror (my mom had build into her bathroom) —the kind that makes every pore, every wrinkle, every imperfection appear larger than life.

In seconds my glow disappeared, I was horrified. I wasn't aware of all those tiny blemishes before, how could i leave the house with all those imperfections? The pangs of anxiety filled my head again. This is exactly what happens when we start measuring things in our blood that we have no business measuring.

The human body is not a spreadsheet. It does not operate like a car engine, where adding a little more oil here and a little more coolant there gets everything running optimally. It is messy, adaptable, constantly in flux—adjusting, balancing, compensating for changes in diet, stress, hydration, and a thousand other variables we don’t even notice. It is dynamic, nonlinear, complex. Mechanistic binary theories are mere oversimplifications that do not do it any justice.

And yet, an entire industry has convinced people that if they just measure enough things, they can outsmart disease before it happens. The problem isn’t just that this isn’t true. The problem is that it actively misleads people into thinking they are broken when they are perfectly fine.

The Bloodwork That Tells You Nothing (But Sells You Everything)

Enter Whole Blood Mineral Analysis, the crown jewel of unnecessary lab tests. It promises to go beyond boring old serum bloodwork—which mainstream doctors use—to provide a deeper, more advanced picture of what’s happening inside your cells.

But here’s the first problem: serum bloodwork is boring for a reason. It has been tested, validated, and shown to correlate with actual disease risk. Whole Blood Analysis, on the other hand, is the equivalent of staring at a magnifying mirror and deciding you need plastic surgery.

Let’s take three minerals that are commonly tested: magnesium, zinc, and lithium.

Magnesium in Whole Blood (Low, Hematocrit-Corrected: -13%) Magnesium is essential for muscle function, nerve conduction, and energy production. 60% of it is stored in bones, 20% in muscle, and 19% in soft tissue. Less than 1% of total body magnesium is in blood.

Now, let’s ask a simple question: If 99% of your magnesium is in muscle and bone, why would testing it in red blood cells, white blood cells, and platelets be a good idea?

Hematocrit Correction: The Fake Fix to a Fake Problem Whole blood includes both plasma and the cellular components of blood (red, white, and platelets). If you have a slightly high hematocrit (more red blood cells than usual), your plasma magnesium might look artificially lower. So, they “correct” the number—by an arbitrary percentage.

The problem? Hematocrit does not determine how much magnesium you have in your bones, muscles, or nerves. This would be like assuming you know a person’s net worth based on how much cash they carry in their pocket.

Zinc in Whole Blood (Low, Hematocrit-Corrected: -17%) 60% of zinc is in muscle, 30% in bone. Less than 0.1% of body zinc is in blood. Red and white blood cells do not store meaningful amounts of zinc, making whole blood zinc a terrible proxy for true deficiency.

So why do people get tested? Because low zinc sounds like a problem—a problem that can be fixed with zinc supplements sold by the same people who ran the test. Presenting a correction as a negative percentage for an essential element for life is mathematical nonsense at its finest. A negative percentage of magnesium? Does that mean the body owes the universe magnesium? Are we in mineral debt? Did someone take out a loan against our intracellular reserves?

Lithium in Whole Blood (Untraceable, Despite Not Being on Lithium Therapy) Lithium is not an essential mineral. It is a psychiatric medication. You do not need lithium unless you have bipolar disorder.

And yet, some labs test lithium in healthy people and come back with an alarming "undetectable lithium" result. This is like going to a doctor and being diagnosed with an alarming lack of heroin in your bloodstream.

The Common Theme - These tests suggest problems where none exist. They use complex calculations to make meaningless results look precise. They create a market for supplements and treatments that patients don’t actually need.

A Conversation Between Blood Cells That Will Never Happen

At heart my big unrealized passion has always been filmmaking, so indulge me in reading this tiny scenario: Imagine a red blood cell, a white blood cell, and a platelet sitting around, staring at one another in a mild state of alarm.

?? Red Blood Cell: “Guys, we have a problem. The magnesium levels in here are lower than usual.”

?? White Blood Cell: “Wait, does that mean the whole body is magnesium deficient?”

?? Platelet: “I don’t know, but I heard that if we correct for hematocrit, it’ll look even worse.”

?? Muscle Tissue (Overhearing): “Uh… none of you actually store magnesium. I do. Maybe ask me instead?”

?? Red Blood Cell: “No thanks, the lab already decided you don’t matter.”

The sheer absurdity of treating whole blood mineral levels as more meaningful than tissue stores cannot be overstated. And yet, people are given meaningless numbers and convinced to take corrective action on problems that were never real to begin with.

The Fine Line Between Health Awareness and Health Anxiety

None of this means that seeking better health is bad. Curiosity about the body is a good thing. Taking charge of one’s well-being is a good thing. But there is a fine line between proactive health and obsessive medicalization.

True prevention is based on tests that actually predict disease and interventions that actually improve outcomes. Measuring blood pressure prevents strokes because lowering it reduces risk. Checking for high LDL cholesterol prevents heart attacks because lowering it with lifestyle interventions prevents cardiovascular disease. Removing pre-cancerous polyps in a colonoscopy prevents colon cancer.

Misleading health tests create anxiety and lead to unnecessary interventions. No one has ever prevented disease by correcting a -13% hematocrit-adjusted magnesium level. No one has lived longer by microdosing lithium when they don’t need it. And yet, because these tests exist, they create a market for their own solutions.

Final Thoughts:

Science is a tool for understanding health, not a weapon for making people feel perpetually unwell. The human body is complex, adaptable, and self-regulating. It does not require constant micromanagement via endless, unvalidated tests. It requires balance, real knowledge, and the ability to let go of things that do not serve us. Not everything that can be measured is meaningful. Not everything that is abnormal needs to be fixed, have you considered that the healthiest choice is to stop looking for problems that aren’t there?

Matthew Kavalek, MD

Board-Certified Family Medicine Physician Raising the standard of primary care.

1 周

Cholesterol levels, colonsocopies, and vaccines are boring to people. Basic diet, exercise, and sleep advice are boring to people. There are a lot of fascinating things to focus on in health but for some reason zinc and the latest supplement excite people. Can I judge? Probably not. Who says I wouldn't be the same way if I wasnt a doc. ??♂? Just our job to nudge our patients back onto the correct path.

stefan anastasescu

Urology Nurse Practitioner at Kingston Hospital NHS Foundation Trust

1 周

Beautifully presented, Arina. I think even AI will have great difficulty in understanding and recommending steps in addressing a negative magnesium balance…

Bernard Baah

CEO at Filly Coder || AI Innovator || Software Solutions Provider || Web & App Development || AR/VR & Fintech Solutions || Digital Transformation || Empowering Businesses with Innovation

1 周

With advancements in AI, do you think we are getting closer to discovering cures for diseases that would have remained undiscovered without recent technological progress? It seems that machine learning models generally perform better with larger datasets. I’d love to hear your thoughts on this!

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