README.md

---
license: other
license_name: original-author-license
license_link: https://huggingface.co/pyaging/organagemortalitythyroid/blob/main/organagemortalitythyroid.LICENSE.txt
library_name: pyaging
tags:
- pyaging
- aging-clock
- biology
- proteomics
---

# organagemortalitythyroid

Thyroid OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs. Values are assay-normalized Olink NPX on the log2 relative-abundance scale, not raw counts, concentrations, linear abundances or z-scores. No additional centering or cohort scaling is applied. Absent proteins contribute zero; NaNs in supplied proteins propagate, matching the original Example_Script.R scoring expressions. For other assays, the authors recommend a symmetric abundance distribution, protein-wise mean subtraction and SD division, then multiplication by the full-model UKB protein SDs in Table S3. This cohort-dependent cross-platform preparation is not performed by pyaging. The optional Example_Script.R SD-matching step is also external; NPX labeling alone does not establish comparability across Olink panels, batches, plasma and serum. Output is relative natural-log mortality hazard, not age in years. The 2026 Insilico trial used conventional OrganAge models on Olink Explore 3072 serum; organ-specific variants are not the six-clock comparison endpoints. pyaging returns the original raw score and does not apply the trial wrapper's cohort harmonization or mortality-to-years conversion. Models labelled Female and Male contain protein features; neither requires a sex covariate. Academic/non-commercial research use only; commercial use requires a separate license from the authors.

Model weights retain the original authors' terms; the pyaging software license does not relicense them. See the [original author license](organagemortalitythyroid.LICENSE.txt). These weights are restricted to research use under the authors' terms.

| | |
|---|---|
| **Predicts** | mortality risk |
| **Species** | Homo sapiens |
| **Tissue** | plasma |
| **Data type** | proteomics |
| **Model type** | elastic net Cox regression |
| **Year** | 2025 |

## Use with pyaging

```python
import pyaging as pya

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

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

## Citation

Goeminne, L. J. E., et al. "Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems." Cell Metabolism 37, 205–222.e6 (2025).

https://doi.org/10.1016/j.cmet.2024.10.005