config.json

{
 "approved_by_author": "\u231b",
 "citation": "Higgins-Chen, Albert T., et al. \"A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking.\" Nature Aging 2 (2022): 644\u2013661.",
 "citations": 546,
 "citations_date": "2026-10-02",
 "clock_name": "pcgrimagepai1",
 "data_type": "DNA methylation",
 "doi": "https://doi.org/10.1038/s43587-022-00248-2",
 "journal": "Nature Aging",
 "last_author": "Morgan E. Levine",
 "model_type": "PCA + elastic net regression",
 "n_features": 78465,
 "notes": "Standalone principal-component proxy trained to reproduce the original DNAm PAI1 score. Coefficients are composed exactly from the author PCA rotation and proxy regression. CpGs use the author GSE40279 reference. Sex (female=1, male=0) and chronological age must be supplied when selected by the original component regression; missing demographic inputs are rejected. Selected demographic inputs: female.",
 "platform": [
  "Illumina 450K"
 ],
 "population": "adults",
 "predicts": [
  "PAI-1"
 ],
 "reference_values": true,
 "research_only": null,
 "species": "Homo sapiens",
 "tissue": [
  "whole blood"
 ],
 "training_target": [
  "DNAm PAI1 output"
 ],
 "unit": [
  "picograms per milliliter"
 ],
 "version": "0.5.4",
 "year": 2022
}