README.md

---
library_name: pyaging
tags:
- pyaging
- aging-clock
- biology
- proteomics
---

# pac

Mortality-equivalent proteomic age from the original Kuo et al. R release, using all 128 Olink Explore 3072 plasma protein NPX values plus chronological age in years. Supply original lowercase identifiers, including fut3_fut5 and ntprobnp; there is no sex covariate. NPX is assay-normalized log2 relative abundance, not raw counts, concentrations, linear abundances or z-scores. NPX labeling alone does not establish comparability across panels, batches, plasma and serum. No cohort scaling, normalization, or imputation is applied during scoring. Every predictor column is required; supplied NaNs propagate. The paper used k-nearest-neighbor imputation (k=10) before model fitting; any upstream imputation is a separate caller decision. The full-precision original Gompertz constants are retained, without the later Insilico wrapper's rounding or optional distribution matching. Output is mortality-equivalent age in years, not a clinical diagnosis or an age-acceleration residual. Development cohort: UK Biobank adults aged 39-70. The original repository specifies no code license; the publication is CC BY 4.0. No author approval of this conversion is claimed.

Model weights retain the original authors' terms; the pyaging software license does not relicense them. These weights are restricted to research use under the authors' terms.

| | |
|---|---|
| **Predicts** | biological age |
| **Species** | Homo sapiens |
| **Tissue** | plasma |
| **Data type** | proteomics |
| **Model type** | Gompertz proportional hazards model |
| **Year** | 2024 |

## Use with pyaging

```python
import pyaging as pya

pya.pred.predict_age(adata, ["pac"])
```

Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io).

## Citation

Kuo, C.-L., et al. "Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults." Aging Cell 23, e14195 (2024).

https://doi.org/10.1111/acel.14195