You’re basically asking:
“If −15 years is ‘X’ rare on epigenetic clocks, how rare is the same −15 on proteomic / metabolomic / transcriptomic clocks?”
Good question, mildly cursed.
I’ll treat the “age gap” (bio age − chronological) as roughly normal for each clock type, use plausible SDs from the literature, and then compare the tail at −15 years.
1. Anchor: epigenetic clocks (DNAm)
State-of-the-art blood DNAm clocks (GrimAge, PhenoAge, etc.) usually have residual spreads ~4–5 years for age acceleration in adult cohorts.
Let’s use SD ≈ 4.5 years as a reasonable ballpark for epigenetic age acceleration.
- −15 years → Z ≈ 15 / 4.5 ≈ 3.3 SD younger
- One-sided Gaussian tail ~0.0004→ around 1 in 2,000–3,000 people
So:
Epigenetic −15 yrs ≈ 1 / 2–3k
Rare, but you’d actually see a few of these in a big biobank.
2. Proteomic clocks
Large plasma proteomic clocks (PAC, Tanaka’s 76-protein signature, etc.) fit chronological age pretty tightly and show smaller residual variation than DNAm in mid/late-life cohorts.
- Many proteomic age-acceleration scores are either:
- Explicitly z-scored (SD = 1 by definition), or
- Reported in years but clearly tighter than DNAm, roughly SD ≈ 3 years is a sane scale for “age gap.”
Using SD ≈ 3 years for the proteomic age gap:
- −15 years → Z = 15 / 3 = 5 SD younger
- One-sided Gaussian tail ≈ 3 × 10⁻⁷→ about 1 in 3–4 million
So if −15 epi is 1 in ~2,300, then:
Proteomic −15 yrs is ~1,000–2,000× rarer (if taken literally, not as an artifact).
And realistically:
- Most proteomic age papers show extremes more like ±6–9 years linked to disease vs athletes, not ±15.
- A “clean” −15 on a good proteomic clock that replicates would be ridiculous outlier territory.
3. Metabolomic clocks
Metabolomic clocks (e.g. MileAge, robust plasma metabolomic age models, CSF clocks) tend to be a bit noisier than proteomic clocks, similar or slightly worse than DNAm in error metrics:
- R² around 0.8–0.85, MAE ≈ 5–7 years for metabolomic age prediction in big cohorts.
If we translate this to a residual SD, SD ≈ 5.5 years for metabolomic age acceleration is a reasonable ballpark.
Using SD ≈ 5.5 years :
- −15 years → Z ≈ 15 / 5.5 ≈ 2.7 SD younger
- Tail probability ≈ 0.003→ about 1 in 300–350 people
So relative to epigenetic:
- Epigenetic −15: ~1 / 2,300
- Metabolomic −15: ~1 / 300
Meaning:
Metabolomic −15 years is ~7× more common than epigenetic −15, given current clock noise levels.
Interpretation: metabolomic clocks are sloppier, so extreme age gaps are more frequent, and a −15 there is less “special” and more likely to contain noise / transient physiology.
4. Transcriptomic clocks
Transcriptomic clocks are the drama queens of this family:
- Early human transcriptomic clocks and newer ones (BiT age style, BayesAge 2.0, mixture-of-experts, etc.) explicitly admit lower absolute accuracy than DNAm clocks and often have larger prediction errors.
- MAE in humans can easily be ~7–10 years depending on tissue and cohort.
So a reasonable ballpark for whole-blood transcriptomic age acceleration:
- SD ≈ 7 years (honestly could be higher in messy real-world data)
Using SD ≈ 7 years :
- −15 years → Z ≈ 15 / 7 ≈ 2.1 SD younger
- Tail ≈ 0.016→ roughly 1 in 60 people
Relative to epigenetic:
- Epigenetic −15: ~1 / 2,300
- Transcriptomic −15: ~1 / 60
So:
Transcriptomic −15 is ~30–40× more common than epigenetic −15,
and much more likely to reflect acute state / cell-type shifts / model overfitting than some deep, stable youthfulness.
Transcriptomic clocks are hypersensitive to:
- cell-type composition
- inflammation, infections, stress, circadian state, etc.
so extreme deltas are easier to get and harder to trust.
5. Putting it all together
Using epigenetic −15 years as the baseline (~1 in 2–3k):
Clock type Approx SD of age gap −15 yrs Z-score Rough frequency Relative to epi −15 Epigenetic (DNAm) ~4.5 yrs ~3.3 SD ~1 / 2,000–3,000 1× (baseline) Proteomic ~3 yrs 5 SD ~1 / 3–4 million ~1,000–2,000× rarer Metabolomic ~5.5 yrs ~2.7 SD ~1 / 300–350 ~7× more common Transcriptomic ~7 yrs ~2.1 SD ~1 / 60 ~30–40× more common Big caveats:
- These SDs are ballpark, based on typical MAEs / spreads in the literature, not a single canonical number.
- Real distributions may be skewed / heavy-tailed.
- Commercial tests sometimes rescale / standardize the output in cute, nontransparent ways.
But directionally:
- Proteomic −15: insanely rare if real.
- Epigenetic −15: rare but believable outlier.
- Metabolomic −15: somewhat rare, but partly a side effect of higher noise.
- Transcriptomic −15: not that rare; often “your RNA right now is in an unusually low-inflammation / low-stress state” rather than “congratulations, you’re biologically 15 years younger in some deep, structural sense.”
So if some dashboard tells you you’re −15 on all four at once, don’t conclude immortality. Conclude “this pipeline needs a QA engineer.”
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