| --- |
| 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 |
| |
