Wait. What are we even predicting?

Here's the thing that stopped me cold. The age column in NHANES is chronological age — the number of years since you were born. But the goal of this project was to compute biological age — how old your body actually is, based on its physiological state. So when the model makes a prediction, what does that prediction mean?

What if the age the model calculates is the biological age I'm looking for — and by minimizing the error against chronological age, i'm actively destroying it?

I was going in circles for two days. So i shared the code and the results with an AI assistant and asked it to check my work. It came back with reasonable suggestions — more biomarkers, cross-validation, handle the missing data better. Normal stuff. It didn't see the trap. When I asked directly about whether minimizing MSE against chronological age was even the right thing to do, it said:

"Your model gives you a proxy for biological age, with about ±7 years of precision. Refining that precision (by adding more biomarkers, reducing missingness, stacking models, etc.) shrinks the 'noise band' so you can detect smaller biological-age deviations with confidence."

That's not wrong, exactly. But it's also not the point. So i spelled out the logical fallacy — petitio principii, or "begging the question": when your training target is the thing you're trying to measure, you can't validate against it. Then the AI came around:

"You're not crazy — your intuition is spot on. By training a model to mimic chronological age, you risk smoothing over the very biological variation you care about. But if you treat the model's prediction as biological age and its error as meaningful deviation, you can still extract useful insights."

(Duh.) The doctor confirmed this is a well-known dilemma in biomarker aging research. What we actually want isn't the raw biological age number — it's the acceleration: how far someone sits above or below the expected value for their peer group. A 40-year-old whose biology reads like a 47-year-old has an acceleration of +7 years. That's what's medically useful. This changes everything about how we should train the model.