Appendices
Other packages’ registries and the operation inventory
Appendix A - Full pyaging clock registry (173 clocks)
Generated from pyaging clock_metadata.json. Columns: clock id, year, data type, species, tissue, predicts, training target, unit, model type, platform, n features, population, in-model preprocess, in-model postprocess, ships reference values.
| clock | yr | data | species | tissue | predicts | trained_on | unit | model | platform | n_feat | pop | pre | post | ref |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| abec | 2020 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | elastic net regression | Illumina EPIC | 1695 | adults | |||
| adbahadosingh | 2021 | DNA methylation | Homo sapiens | whole blood | late-onset Alzheimer’s disease | late-onset Alzheimer’s disease | probability | logistic regression | Illumina EPIC | 4 | older adults | sigmoid | ||
| altumage | 2022 | DNA methylation | Homo sapiens | multi-tissue | chronological age | chronological age | years | deep neural network | Illumina 27K; Illumina 450K | 20318 | all ages | scale | ref | |
| bitage | 2021 | transcriptomics | Caenorhabditis elegans | whole organism | biological age | biological age | hours | elastic net regression | RNA-seq | 576 | Caenorhabditis elegans | binarize | ||
| bocklandt | 2011 | DNA methylation | Homo sapiens | saliva | EDARADD methylation | chronological age | beta value | single-CpG score | Illumina 27K | 1 | adults | |||
| bohlin | 2016 | DNA methylation | Homo sapiens | cord blood | gestational age | gestational age | weeks | LASSO regression | Illumina 450K | 251 | newborns | days_to_weeks | ||
| cabec | 2020 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | elastic net regression | Illumina 450K; Illumina EPIC | 1892 | adults | |||
| camilloh3k27ac | 2025 | histone modification | Homo sapiens | multi-tissue | chronological age | chronological age | years | PCA + elastic net + ARD regression | ChIP-seq | 1275 | all ages | |||
| camilloh3k27me3 | 2025 | histone modification | Homo sapiens | multi-tissue | chronological age | chronological age | years | PCA + elastic net + ARD regression | ChIP-seq | 922 | all ages | |||
| camilloh3k36me3 | 2025 | histone modification | Homo sapiens | multi-tissue | chronological age | chronological age | years | PCA + elastic net + ARD regression | ChIP-seq | 870 | all ages | |||
| camilloh3k4me1 | 2025 | histone modification | Homo sapiens | multi-tissue | chronological age | chronological age | years | PCA + elastic net + ARD regression | ChIP-seq | 892 | all ages | |||
| camilloh3k4me3 | 2025 | histone modification | Homo sapiens | multi-tissue | chronological age | chronological age | years | PCA + elastic net + ARD regression | ChIP-seq | 1240 | all ages | |||
| camilloh3k9ac | 2025 | histone modification | Homo sapiens | multi-tissue | chronological age | chronological age | years | PCA + elastic net + ARD regression | ChIP-seq | 102 | all ages | |||
| camilloh3k9me3 | 2025 | histone modification | Homo sapiens | multi-tissue | chronological age | chronological age | years | PCA + elastic net + ARD regression | ChIP-seq | 341 | all ages | |||
| camillopanhistone | 2025 | histone modification | Homo sapiens | multi-tissue | chronological age | chronological age | years | PCA + elastic net + ARD regression | ChIP-seq | 3739 | all ages | |||
| cellpopage | 2024 | DNA methylation | Homo sapiens | cultured fibroblasts | cell-population passage age | cell passage number | passages | elastic net regression | Illumina EPIC | 42 | human cell cultures | |||
| compil6 | 2021 | DNA methylation | Homo sapiens | whole blood | interleukin-6 | interleukin-6 | unitless | elastic net regression | Illumina 450K | 35 | older adults | |||
| corticalclock | 2020 | DNA methylation | Homo sapiens | brain cortex | chronological age | chronological age | years | elastic net regression | Illumina 450K | 347 | human, age unspecified | anti_log_linear | ref | |
| cpgptgrimage3 | 2024 | DNA methylation | Homo sapiens | whole blood | biological age; mortality risk | mortality | years | Cox proportional hazards regression | Illumina 450K | 24 | adults | scale | cox_to_years | |
| cpgptpcgrimage3 | 2024 | DNA methylation | Homo sapiens | whole blood | biological age; mortality risk | mortality | years | PCA + Cox regression | Illumina 450K | 31 | adults | scale | cox_to_years | |
| ctsliver | 2024 | DNA methylation | Homo sapiens | liver | chronological age | chronological age | years | LASSO regression | Illumina EPIC | 90 | adults | |||
| cvdwesterman | 2020 | DNA methylation | Homo sapiens | whole blood | cardiovascular disease risk | cardiovascular disease | probability | elastic net Cox ensemble | Illumina 450K | 235 | adults | sigmoid | ||
| deconvolutebloodepicbcell | 2018 | DNA methylation | Homo sapiens | purified blood leukocytes | B cell proportion | cell-type-specific methylation contrast | proportion | reference-based constrained deconvolution | Illumina EPIC | 600 | adults | fill_with_reference_means | ref | |
| deconvolutebloodepiccd4tcell | 2018 | DNA methylation | Homo sapiens | purified blood leukocytes | CD4+ T cell proportion | cell-type-specific methylation contrast | proportion | reference-based constrained deconvolution | Illumina EPIC | 600 | adults | fill_with_reference_means | ref | |
| deconvolutebloodepiccd8tcell | 2018 | DNA methylation | Homo sapiens | purified blood leukocytes | CD8+ T cell proportion | cell-type-specific methylation contrast | proportion | reference-based constrained deconvolution | Illumina EPIC | 600 | adults | fill_with_reference_means | ref | |
| deconvolutebloodepicmonocyte | 2018 | DNA methylation | Homo sapiens | purified blood leukocytes | monocyte proportion | cell-type-specific methylation contrast | proportion | reference-based constrained deconvolution | Illumina EPIC | 600 | adults | fill_with_reference_means | ref | |
| deconvolutebloodepicneutrophil | 2018 | DNA methylation | Homo sapiens | purified blood leukocytes | neutrophil proportion | cell-type-specific methylation contrast | proportion | reference-based constrained deconvolution | Illumina EPIC | 600 | adults | fill_with_reference_means | ref | |
| deconvolutebloodepicnkcell | 2018 | DNA methylation | Homo sapiens | purified blood leukocytes | natural killer cell proportion | cell-type-specific methylation contrast | proportion | reference-based constrained deconvolution | Illumina EPIC | 600 | adults | fill_with_reference_means | ref | |
| depressionbarbu | 2021 | DNA methylation | Homo sapiens | whole blood | major depressive disorder | major depressive disorder | unitless | elastic net regression | Illumina EPIC | 196 | adults | |||
| dnamfili | 2022 | DNA methylation | Homo sapiens | whole blood | frailty risk | frailty | unitless | LASSO regression | Illumina EPIC; Illumina 450K | 20 | older adults | |||
| dnamfitage | 2023 | DNA methylation | Homo sapiens | whole blood | physical-fitness biological age | biological age | years | Klemera–Doubal composite | Illumina 450K | 630 | adults | ref | ||
| dnamfitagegaitf | 2023 | DNA methylation | Homo sapiens | whole blood | gait speed | gait speed | meters per second | LASSO regression | Illumina 450K | 53 | adult women | ref | ||
| dnamfitagegaitm | 2023 | DNA methylation | Homo sapiens | whole blood | gait speed | gait speed | meters per second | LASSO regression | Illumina 450K | 59 | adult men | ref | ||
| dnamfitagegripf | 2023 | DNA methylation | Homo sapiens | whole blood | grip strength | grip strength | kilograms | LASSO regression | Illumina 450K | 91 | adult women | ref | ||
| dnamfitagegripm | 2023 | DNA methylation | Homo sapiens | whole blood | grip strength | grip strength | kilograms | LASSO regression | Illumina 450K | 93 | adult men | ref | ||
| dnamfitagevo2max | 2023 | DNA methylation | Homo sapiens | whole blood | VO2max | VO2max | milliliters per kilogram per minute | LASSO regression | Illumina 450K | 41 | adults | ref | ||
| dnamic | 2025 | DNA methylation | Homo sapiens | whole blood | intrinsic capacity | intrinsic capacity | unitless | elastic net regression | Illumina EPIC | 91 | older adults | |||
| dnamphenoage | 2018 | DNA methylation | Homo sapiens | whole blood | phenotypic age | phenotypic age | years | elastic net regression | Illumina 27K; Illumina 450K; Illumina EPIC | 513 | adults | |||
| dnamstress | 2023 | DNA methylation | Homo sapiens | whole blood | stress exposure | stress exposure | unitless | bootstrap-stabilized elastic net regression | Illumina EPIC | 211 | adults | |||
| dnamtl | 2019 | DNA methylation | Homo sapiens | whole blood | leukocyte telomere length | leukocyte telomere length | kilobases | elastic net regression | Illumina 450K; Illumina EPIC | 140 | adults | |||
| downsyndrome | 2021 | DNA methylation | Homo sapiens | neonatal blood spots | Down syndrome methylation score | not applicable | unitless | weighted linear score | Illumina EPIC | 652 | newborns | |||
| dunedinpace | 2022 | DNA methylation | Homo sapiens | whole blood | pace of aging | pace of aging | biological years per chronological year | elastic net regression | Illumina EPIC | 20000 | adults | quantile_normalization_with_gold_standard | ref | |
| dunedinpoam38 | 2020 | DNA methylation | Homo sapiens | whole blood | pace of aging | pace of aging | biological years per chronological year | elastic net regression | Illumina 450K | 46 | adults | |||
| eabec | 2020 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | elastic net regression | Illumina EPIC | 1791 | adults | |||
| encen100 | 2023 | DNA methylation | Homo sapiens | whole blood; saliva; buccal epithelium | chronological age | chronological age | years | elastic net regression | Illumina 450K; Illumina EPIC | 198 | centenarians | |||
| encen40 | 2023 | DNA methylation | Homo sapiens | whole blood; saliva; buccal epithelium | chronological age | chronological age | years | elastic net regression | Illumina 450K; Illumina EPIC | 559 | older adults | |||
| ensembleagehumanmouse | 2025 | DNA methylation | Homo sapiens and Mus musculus | multi-tissue; whole blood | relative age | relative age | relative age | elastic net regression | mammalian methylation array | 100 | humans and mice | |||
| ensembleagestatic | 2025 | DNA methylation | Mus musculus | multi-tissue | intervention-responsive epigenetic age | intervention-responsive epigenetic age | years | elastic net regression | Horvath MammalMethylChip40; Horvath MammalMethylChip320 | 288 | mice | |||
| ensembleagestatictop | 2025 | DNA methylation | Mus musculus | multi-tissue | intervention-responsive epigenetic age | intervention-responsive epigenetic age | years | elastic net regression | Horvath MammalMethylChip40; Horvath MammalMethylChip320 | 431 | mice | |||
| epicga | 2021 | DNA methylation | Homo sapiens | cord blood | gestational age | gestational age | days | LASSO regression | Illumina EPIC | 176 | newborns | days_to_weeks | ||
| epicmithyper | 2020 | DNA methylation | Homo sapiens | B cells | mitotic age | replicative history | proportion | mean methylation aggregation | Illumina 450K; Illumina EPIC | 184 | human, age unspecified | mean | ref | |
| epicmithypo | 2020 | DNA methylation | Homo sapiens | B cells | mitotic age | replicative history | proportion | complement of mean methylation | Illumina 450K; Illumina EPIC | 1164 | human, age unspecified | mean | ref | |
| epitoc1 | 2016 | DNA methylation | Homo sapiens | multi-tissue; whole blood | mitotic age | chronological age | beta value | mean methylation aggregation | Illumina 450K | 385 | all ages | mean | ref | |
| epitoc2 | 2020 | DNA methylation | Homo sapiens | whole blood | mitotic age | chronological age | cell divisions per stem cell | dynamic methylation transmission model | Illumina 450K | 163 | adults | nan_to_zero | ref | |
| epitoc3 | 2020 | DNA methylation | Homo sapiens | cultured primary human cells; whole blood; multi-tissue; cord blood | mitotic age | population doublings | cell divisions per stem cell | dynamic methylation transmission model | Illumina 450K; Illumina EPIC | 170 | all ages | nan_to_zero | ref | |
| garagnani | 2012 | DNA methylation | Homo sapiens | whole blood | ELOVL2 methylation | not applicable | beta value | single-CpG score | Illumina 450K | 1 | all ages | |||
| gliasin | 2024 | DNA methylation | Homo sapiens | brain cortex | chronological age | chronological age | years | elastic net regression | Illumina 450K | 220 | adults | |||
| grimage | 2019 | DNA methylation | Homo sapiens | whole blood | mortality risk | mortality | years | two-stage elastic net + Cox regression | Illumina 450K | 1032 | adults | cox_to_years | ref | |
| grimage2 | 2022 | DNA methylation | Homo sapiens | whole blood | mortality risk | mortality | years | elastic net Cox regression | Illumina 450K | 1032 | older adults | cox_to_years | ref | |
| grimage2adm | 2022 | DNA methylation | Homo sapiens | whole blood | adrenomedullin | adrenomedullin | picograms per milliliter | elastic net regression | Illumina 450K | 187 | older adults | cox_to_years | ref | |
| grimage2b2m | 2022 | DNA methylation | Homo sapiens | whole blood | beta-2-microglobulin | beta-2-microglobulin | picograms per milliliter | elastic net regression | Illumina 450K | 92 | older adults | cox_to_years | ref | |
| grimage2cystatinc | 2022 | DNA methylation | Homo sapiens | whole blood | cystatin C | cystatin C | picograms per milliliter | elastic net regression | Illumina 450K | 88 | older adults | cox_to_years | ref | |
| grimage2gdf15 | 2022 | DNA methylation | Homo sapiens | whole blood | GDF-15 | GDF-15 | picograms per milliliter | elastic net regression | Illumina 450K | 138 | older adults | cox_to_years | ref | |
| grimage2leptin | 2022 | DNA methylation | Homo sapiens | whole blood | leptin | leptin | picograms per milliliter | elastic net regression | Illumina 450K | 187 | older adults | cox_to_years | ref | |
| grimage2loga1c | 2022 | DNA methylation | Homo sapiens | whole blood | hemoglobin A1c | hemoglobin A1c | natural-log percent | elastic net regression | Illumina 450K | 87 | older adults | cox_to_years | ref | |
| grimage2logcrp | 2022 | DNA methylation | Homo sapiens | whole blood | C-reactive protein | C-reactive protein | natural-log milligrams per liter | elastic net regression | Illumina 450K | 132 | older adults | cox_to_years | ref | |
| grimage2packyrs | 2022 | DNA methylation | Homo sapiens | whole blood | smoking exposure | smoking exposure | pack-years | elastic net regression | Illumina 450K | 173 | older adults | cox_to_years | ref | |
| grimage2pai1 | 2022 | DNA methylation | Homo sapiens | whole blood | PAI-1 | PAI-1 | picograms per milliliter | elastic net regression | Illumina 450K | 211 | older adults | ref | ||
| grimage2timp1 | 2022 | DNA methylation | Homo sapiens | whole blood | TIMP-1 | TIMP-1 | picograms per milliliter | elastic net regression | Illumina 450K | 43 | older adults | cox_to_years | ref | |
| han | 2020 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | linear regression | Illumina 450K | 65 | all ages | anti_log_linear | ||
| hannum | 2013 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | elastic net regression | Illumina 450K | 71 | adults | |||
| hep | 2024 | DNA methylation | Homo sapiens | liver | chronological age | chronological age | years | LASSO regression | Illumina EPIC | 70 | adults | |||
| hepatoxu | 2017 | DNA methylation | Homo sapiens | plasma cell-free DNA | hepatocellular carcinoma | hepatocellular carcinoma | unitless | feature-selected logistic regression | targeted bisulfite sequencing | 10 | adults | |||
| horvath2013 | 2013 | DNA methylation | Homo sapiens | multi-tissue | chronological age | chronological age | years | elastic net regression | Illumina 27K; Illumina 450K | 353 | all ages | anti_log_linear | ref | |
| hrsinchphenoage | 2022 | DNA methylation | Homo sapiens | whole blood | phenotypic age | phenotypic age | years | weighted linear score | Illumina 450K; Illumina EPIC | 959 | adults | |||
| hypoclock | 2020 | DNA methylation | Homo sapiens | multi-tissue | mitotic age | not applicable | beta value | mean aggregation | Illumina 450K | 678 | human, age unspecified | mean | one_minus | ref |
| intrinclock | 2024 | DNA methylation | Homo sapiens | multi-tissue | chronological age | chronological age | years | two-stage elastic net regression | Illumina 450K; Illumina EPIC | 380 | all ages | anti_log_linear | ||
| knight | 2016 | DNA methylation | Homo sapiens | cord blood; neonatal blood spots | gestational age | gestational age | weeks | elastic net regression | Illumina 27K; Illumina 450K | 148 | newborns | ref | ||
| leecontrol | 2019 | DNA methylation | Homo sapiens | placenta | gestational age | gestational age | weeks | elastic net regression | Illumina 450K; Illumina EPIC | 546 | pregnancies | |||
| leerefinedrobust | 2019 | DNA methylation | Homo sapiens | placenta | gestational age | gestational age | weeks | elastic net regression | Illumina 450K; Illumina EPIC | 395 | pregnancies | |||
| leerobust | 2019 | DNA methylation | Homo sapiens | placenta | gestational age | gestational age | weeks | elastic net regression | Illumina 450K; Illumina EPIC | 558 | pregnancies | |||
| lin | 2016 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | linear regression | Illumina 27K; Illumina 450K | 99 | adults | |||
| mammalian1 | 2023 | DNA methylation | multiple species | multi-tissue | chronological age | chronological age | years | elastic net regression | Horvath MammalMethylChip40 | 335 | multiple mammalian species | anti_logp2 | ||
| mammalian2 | 2023 | DNA methylation | multiple species | multi-tissue | chronological age | relative age | years | elastic net regression | Horvath MammalMethylChip40 | 2572 | multiple mammalian species | mammalian2 | ref | |
| mammalian3 | 2023 | DNA methylation | multiple species | multi-tissue | chronological age | chronological age | years | elastic net regression | Horvath MammalMethylChip40 | 2467 | multiple mammalian species | mammalian3 | ref | |
| mammalianblood2 | 2023 | DNA methylation | multiple species | blood | chronological age | relative age | years | elastic net regression | Horvath MammalMethylChip40 | 2257 | multiple mammalian species | mammalian2 | ref | |
| mammalianblood3 | 2023 | DNA methylation | multiple species | blood | chronological age | chronological age | years | elastic net regression | Horvath MammalMethylChip40 | 2097 | multiple mammalian species | mammalian3 | ref | |
| mammalianfemale | 2023 | DNA methylation | multiple species | multi-tissue | sex | sex | probability | elastic net regression | mammalian methylation array | 101 | multiple mammalian species | sigmoid | ||
| mammalianlifespan | 2024 | DNA methylation | multiple species | multi-tissue | species maximum lifespan | species maximum lifespan | years | elastic net regression | Horvath MammalMethylChip40 | 152 | multiple mammalian species | anti_log | ref | |
| mammalianskin2 | 2023 | DNA methylation | multiple species | skin | chronological age | relative age | years | elastic net regression | Horvath MammalMethylChip40 | 2240 | multiple mammalian species | mammalian2 | ref | |
| mammalianskin3 | 2023 | DNA methylation | multiple species | skin | chronological age | chronological age | years | elastic net regression | Horvath MammalMethylChip40 | 2055 | multiple mammalian species | mammalian3 | ref | |
| mayne | 2017 | DNA methylation | Homo sapiens | placenta | gestational age | gestational age | weeks | elastic net regression | Illumina 27K; Illumina 450K | 62 | pregnancies | |||
| mccartneyalcohol | 2018 | DNA methylation | Homo sapiens | whole blood | alcohol consumption | alcohol consumption | units per week | LASSO regression | Illumina EPIC | 450 | adults | |||
| mccartneybmi | 2018 | DNA methylation | Homo sapiens | whole blood | body mass index | body mass index | unitless | LASSO regression | Illumina EPIC | 1109 | adults | sigmoid | ||
| mccartneybodyfat | 2018 | DNA methylation | Homo sapiens | whole blood | body fat percentage | body fat percentage | unitless | LASSO regression | Illumina EPIC | 968 | adults | sigmoid | ||
| mccartneyeducation | 2018 | DNA methylation | Homo sapiens | whole blood | educational attainment | educational attainment | unitless | LASSO regression | Illumina EPIC | 373 | adults | sigmoid | ||
| mccartneyhdlcholesterol | 2018 | DNA methylation | Homo sapiens | whole blood | HDL cholesterol | HDL cholesterol | unitless | LASSO regression | Illumina EPIC | 737 | adults | sigmoid | ||
| mccartneyldlcholesterol | 2018 | DNA methylation | Homo sapiens | whole blood | LDL cholesterol | LDL cholesterol | unitless | LASSO regression | Illumina EPIC | 233 | adults | sigmoid | ||
| mccartneysmoking | 2018 | DNA methylation | Homo sapiens | whole blood | smoking exposure | smoking exposure | pack-years | LASSO regression | Illumina EPIC | 233 | adults | |||
| mccartneytotalcholesterol | 2018 | DNA methylation | Homo sapiens | whole blood | total cholesterol | total cholesterol | unitless | LASSO regression | Illumina EPIC | 204 | adults | sigmoid | ||
| mccartneytotalhdlratio | 2018 | DNA methylation | Homo sapiens | whole blood | total-to-HDL cholesterol ratio | total-to-HDL cholesterol ratio | ratio | LASSO regression | Illumina EPIC | 412 | adults | |||
| mccartneywhr | 2018 | DNA methylation | Homo sapiens | whole blood | waist-to-hip ratio | waist-to-hip ratio | ratio | LASSO regression | Illumina EPIC | 226 | adults | |||
| meer | 2018 | DNA methylation | Mus musculus | multi-tissue | chronological age | chronological age | days | elastic net regression | RRBS | 435 | mice | |||
| neusin | 2024 | DNA methylation | Homo sapiens | brain cortex | chronological age | chronological age | years | elastic net regression | Illumina 450K | 672 | adults | |||
| ocampoatac1 | 2023 | chromatin accessibility | Homo sapiens | peripheral blood mononuclear cells | chronological age | chronological age | years | elastic net regression | ATAC-seq | 228 | adults | tpm_norm_log1p | ||
| ocampoatac2 | 2023 | chromatin accessibility | Homo sapiens | peripheral blood mononuclear cells | chronological age | chronological age | years | elastic net regression | ATAC-seq | 380 | adults | tpm_norm_log1p | ||
| pasta | 2025 | transcriptomics | Homo sapiens | multi-tissue | transcriptomic age | age ordering | years | ridge logistic regression | RNA-seq; gene expression microarray | 8113 | human, age unspecified | median_fill_and_rank_normalization | scale_and_shift | ref |
| pastamouse | 2025 | transcriptomics | Mus musculus | multi-tissue | transcriptomic age | age ordering | years | orthologue-transferred ridge logistic regression | RNA-seq; gene expression microarray | 1600 | human, age unspecified | median_fill_and_rank_normalization | scale_and_shift | ref |
| pcdnamtl | 2022 | DNA methylation | Homo sapiens | whole blood | leukocyte telomere length | DNAmTL output | base pairs | PCA + elastic net regression | Illumina 450K | 78464 | adults | ref | ||
| pcgrimage | 2022 | DNA methylation | Homo sapiens | whole blood | mortality risk | DNAm GrimAge output | years | PCA + elastic net regression | Illumina 450K | 78466 | adults | ref | ||
| pchannum | 2022 | DNA methylation | Homo sapiens | whole blood | chronological age | Hannum clock output | years | PCA + elastic net regression | Illumina 450K | 78464 | adults | ref | ||
| pchorvath2013 | 2022 | DNA methylation | Homo sapiens | multi-tissue | chronological age | Horvath clock output | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 78464 | all ages | anti_log_linear | ref | |
| pcphenoage | 2022 | DNA methylation | Homo sapiens | whole blood | phenotypic age | phenotypic age | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 78464 | adults | ref | ||
| pcskinandblood | 2022 | DNA methylation | Homo sapiens | skin; whole blood; cultured fibroblasts | chronological age | skin-and-blood clock output | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 78464 | all ages | anti_log_linear | ref | |
| pedbe | 2020 | DNA methylation | Homo sapiens | buccal epithelium | chronological age | chronological age | years | elastic net regression | Illumina 450K; Illumina EPIC | 94 | children and adolescents | anti_log_linear | ||
| petkovich | 2017 | DNA methylation | Mus musculus | whole blood | chronological age | chronological age | months | elastic net regression | bisulfite sequencing | 90 | mice | petkovich | ||
| phenoage | 2018 | clinical biomarkers | Homo sapiens | blood | phenotypic age | mortality | years | penalized hazards regression with Gompertz calibration | clinical laboratory assays | 10 | adults | mortality_to_phenoage | ||
| pipekelasticnet | 2022 | DNA methylation | Homo sapiens | multi-tissue | chronological age | chronological age | years | elastic net regression | Illumina 27K; Illumina 450K; Illumina EPIC | 239 | all ages | anti_log_linear | ||
| pipekfilteredh | 2022 | DNA methylation | Homo sapiens | multi-tissue | chronological age | chronological age | years | elastic net regression | Illumina 27K; Illumina 450K; Illumina EPIC | 272 | all ages | anti_log_linear | ||
| pipekretrainedh | 2022 | DNA methylation | Homo sapiens | multi-tissue | chronological age | chronological age | years | linear regression | Illumina 27K; Illumina 450K; Illumina EPIC | 308 | all ages | anti_log_linear | ||
| prostatecancerkirby | 2017 | DNA methylation | Homo sapiens | prostate | prostate cancer | prostate cancer | log odds | logistic regression | Illumina 450K | 3 | adult men | |||
| reedbmi | 2020 | DNA methylation | Homo sapiens | whole blood; cord blood | BMI methylation score | body mass index | unitless | weighted methylation aggregation | Illumina 450K | 135 | all ages | |||
| reg | 2025 | transcriptomics | Homo sapiens | multi-tissue | chronological age | chronological age | years | ridge regression | RNA-seq; gene expression microarray | 8113 | human, age unspecified | median_fill_and_rank_normalization | add_constant | ref |
| replitali | 2022 | DNA methylation | Homo sapiens | cultured primary human cells | replicative history | population doublings | population doublings | elastic net regression | Illumina EPIC | 87 | human cell cultures | |||
| replitalinorm | 2022 | DNA methylation | Homo sapiens | cultured fibroblasts | replicative history | population doublings | population doublings | elastic net regression | Illumina EPIC | 218 | human cell cultures | |||
| retroelementagev1 | 2024 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | elastic net regression | Illumina EPIC | 1317 | all ages | |||
| retroelementagev2 | 2024 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | elastic net regression | Illumina EPIC | 1378 | all ages | |||
| senchronoage | 2026 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | elastic net regression | Illumina 450K; Illumina EPIC | 187 | adults | |||
| sencultureage | 2026 | DNA methylation | Homo sapiens | cultured fibroblasts; cultured mesenchymal stromal cells | cellular senescence | cellular senescence | log odds | elastic net logistic regression | Illumina 450K; Illumina EPIC | 142 | human cell cultures | |||
| senmortalityage | 2026 | DNA methylation | Homo sapiens | whole blood | mortality risk | mortality | log hazard | elastic net Cox regression | Illumina 450K; Illumina EPIC | 91 | adults | |||
| skinandblood | 2018 | DNA methylation | Homo sapiens | buccal epithelium; whole blood; epithelium; cultured fibroblasts; skin; cord blood | chronological age | chronological age | years | elastic net regression | Illumina 450K; Illumina EPIC | 391 | all ages | anti_log_linear | ||
| stemtoc | 2024 | DNA methylation | Homo sapiens | multi-tissue; cultured human cells; whole blood | mitotic age | population doublings; chronological age | beta value | 95th-percentile methylation aggregation | Illumina 450K; Illumina EPIC | 371 | all ages | 0.95 quantile | ref | |
| stemtocvitro | 2024 | DNA methylation | Homo sapiens | multi-tissue; cultured human cells | mitotic age | population doublings | beta value | 95th-percentile methylation aggregation | Illumina 450K; Illumina EPIC | 629 | prenatal and newborn | 0.95 quantile | ref | |
| stoch | 2024 | DNA methylation | Homo sapiens | sorted monocytes | chronological age | chronological age | years | elastic net regression | Illumina 450K | 353 | adults | |||
| stocp | 2024 | DNA methylation | Homo sapiens | sorted monocytes | chronological age | chronological age | years | elastic net regression | Illumina 450K | 513 | adults | |||
| stocz | 2024 | DNA methylation | Homo sapiens | sorted monocytes | chronological age | chronological age | years | elastic net regression | Illumina 450K | 514 | adults | |||
| stubbs | 2017 | DNA methylation | Mus musculus | multi-tissue; liver; lung; heart; brain cortex; skeletal muscle; cerebellum; spleen | chronological age | chronological age | months | quadratically calibrated elastic net regression | RRBS | 17992 | mice | quantile_normalization_and_scale_with_gold_standard | stubbs | ref |
| systemsage | 2025 | DNA methylation | Homo sapiens | whole blood | multisystem biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsageblood | 2025 | DNA methylation | Homo sapiens | whole blood | blood-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsagebrain | 2025 | DNA methylation | Homo sapiens | whole blood | brain-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsageheart | 2025 | DNA methylation | Homo sapiens | whole blood | cardiovascular-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsagehormone | 2025 | DNA methylation | Homo sapiens | whole blood | endocrine-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsageimmune | 2025 | DNA methylation | Homo sapiens | whole blood | immune-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsageinflammation | 2025 | DNA methylation | Homo sapiens | whole blood | inflammatory-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsagekidney | 2025 | DNA methylation | Homo sapiens | whole blood | renal-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsageliver | 2025 | DNA methylation | Homo sapiens | whole blood | hepatic-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsagelung | 2025 | DNA methylation | Homo sapiens | whole blood | pulmonary-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsagemetabolic | 2025 | DNA methylation | Homo sapiens | whole blood | metabolic-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| systemsagemusculoskeletal | 2025 | DNA methylation | Homo sapiens | whole blood | musculoskeletal-system biological age | mortality | years | PCA + elastic net regression | Illumina 450K; Illumina EPIC | 125175 | older adults | ref | ||
| thompson | 2018 | DNA methylation | Mus musculus | adipose tissue; blood; cerebellum; brain cortex; heart; kidney; liver; lung; skeletal muscle; spleen | chronological age | chronological age | months | elastic net regression | RRBS | 582 | mice | |||
| twelvecelldeconvolutebloodepicbas | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | basophil proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepicbmem | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | memory B cell proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepicbnv | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | naive B cell proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepiccd4mem | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | memory CD4+ T cell proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepiccd4nv | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | naive CD4+ T cell proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepiccd8mem | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | memory CD8+ T cell proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepiccd8nv | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | naive CD8+ T cell proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepiceos | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | eosinophil proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepicmono | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | monocyte proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepicneu | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | neutrophil proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepicnk | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | natural killer cell proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| twelvecelldeconvolutebloodepictreg | 2022 | DNA methylation | Homo sapiens | purified blood leukocytes | regulatory T-cell proportion | cell-type proportions | proportion | reference-based constrained deconvolution | Illumina EPIC | 240 | adults | fill_with_reference_means | ref | |
| vidalbralo | 2016 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | linear regression | Illumina 27K | 8 | adults | |||
| weidner | 2014 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | linear regression | Illumina 27K; bisulfite sequencing | 3 | adults | |||
| wu | 2019 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | screened elastic net regression | Illumina 27K; Illumina 450K | 111 | children | anti_log_linear | ||
| xchrom | 2021 | DNA methylation | Homo sapiens | whole blood | X-chromosome dosage | X-chromosome dosage | unitless | principal component analysis | Illumina 450K; Illumina EPIC | 4047 | adults | sex_estimation_autosomal_zscore | ref | |
| ychrom | 2021 | DNA methylation | Homo sapiens | whole blood | Y-chromosome presence | Y-chromosome presence | unitless | principal component analysis | Illumina 450K; Illumina EPIC | 284 | adults | sex_estimation_autosomal_zscore | ref | |
| yingadaptage | 2024 | DNA methylation | Homo sapiens | whole blood | adaptive epigenetic age | chronological age | years | causality-weighted elastic net regression | Illumina 450K | 999 | adults | |||
| yingcausage | 2024 | DNA methylation | Homo sapiens | whole blood | chronological age | chronological age | years | causality-weighted elastic net regression | Illumina 450K | 585 | adults | |||
| yingdamage | 2024 | DNA methylation | Homo sapiens | whole blood | damaging epigenetic age | chronological age | years | causality-weighted elastic net regression | Illumina 450K | 1089 | adults | |||
| zhangblup | 2019 | DNA methylation | Homo sapiens | whole blood; saliva | chronological age | chronological age | years | best linear unbiased prediction | Illumina 450K; Illumina EPIC | 319607 | all ages | scale_row | ref | |
| zhangen | 2019 | DNA methylation | Homo sapiens | whole blood; saliva | chronological age | chronological age | years | elastic net regression | Illumina 450K; Illumina EPIC | 514 | all ages | scale_row | ref | |
| zhangmortality | 2017 | DNA methylation | Homo sapiens | whole blood | mortality risk | mortality | unitless | weighted linear score | Illumina 450K | 10 | older adults |
Appendix B - tAge transcriptomic clock registry (60 models)
From tAge clocks_metadata.csv. Weights on Zenodo record 18763485. lifespan_scaled = TRUE means the raw prediction is multiplied by the species maximum lifespan (human 122.5, mouse 48, rat 50.4, monkey 39) to reach age units; FALSE means the output is already on its native scale, log10(hazard ratio) for mortality clocks, or a fraction of maximum lifespan for normalized-age clocks. BR is Bayesian Ridge, EN is Elastic Net. Scaled clocks take per-sample z-scored log10 RLE expression; YuGene clocks take the YuGene uniformised version. All are relative models and require reference centring.
| filename | type | outcome | species | tissue | scaling | lifespan_scaled |
|---|---|---|---|---|---|---|
| BR_Chronoage_Mouse_Multitissue_scaleddiff.pkl | BR | Chronological | Mouse | Multi-Tissue | Scaled | TRUE |
| BR_Chronoage_Mouse_Multitissue_yugenediff.pkl | BR | Chronological | Mouse | Multi-Tissue | YuGene | TRUE |
| BR_Chronoage_Multispecies_Multitissue_scaleddiff.pkl | BR | Chronological | Multispecies | Multi-Tissue | Scaled | TRUE |
| BR_Chronoage_Multispecies_Multitissue_yugenediff.pkl | BR | Chronological | Multispecies | Multi-Tissue | YuGene | TRUE |
| BR_Chronoage_Rodents_Brain_WT_scaleddiff.pkl | BR | Chronological | Rodents | Brain | Scaled | TRUE |
| BR_Chronoage_Rodents_Brain_WT_yugenediff.pkl | BR | Chronological | Rodents | Brain | YuGene | TRUE |
| BR_Chronoage_Rodents_Kidney_WT_scaleddiff.pkl | BR | Chronological | Rodents | Kidney | Scaled | TRUE |
| BR_Chronoage_Rodents_Kidney_WT_yugenediff.pkl | BR | Chronological | Rodents | Kidney | YuGene | TRUE |
| BR_Chronoage_Rodents_Liver_scaleddiff.pkl | BR | Chronological | Rodents | Liver | Scaled | TRUE |
| BR_Chronoage_Rodents_Liver_yugenediff.pkl | BR | Chronological | Rodents | Liver | YuGene | TRUE |
| BR_Chronoage_Rodents_Multitissue_scaleddiff.pkl | BR | Chronological | Rodents | Multi-Tissue | Scaled | TRUE |
| BR_Chronoage_Rodents_Multitissue_yugenediff.pkl | BR | Chronological | Rodents | Multi-Tissue | YuGene | TRUE |
| BR_Chronoage_Rodents_Skeletal muscle_WT_yugenediff.pkl | BR | Chronological | Rodents | Skeletal Muscle | YuGene | TRUE |
| BR_Chronoage_Rodents_Skeletal_muscle_WT_scaleddiff.pkl | BR | Chronological | Rodents | Skeletal Muscle | Scaled | TRUE |
| BR_Mortality_Mouse_Multitissue_scaleddiff.pkl | BR | Mortality | Mouse | Multi-Tissue | Scaled | FALSE |
| BR_Mortality_Mouse_Multitissue_yugenediff.pkl | BR | Mortality | Mouse | Multi-Tissue | YuGene | FALSE |
| BR_Mortality_Multispecies_Multitissue_scaleddiff.pkl | BR | Mortality | Multispecies | Multi-Tissue | Scaled | FALSE |
| BR_Mortality_Multispecies_Multitissue_yugenediff.pkl | BR | Mortality | Multispecies | Multi-Tissue | YuGene | FALSE |
| BR_Mortality_Rodents_Liver_scaleddiff.pkl | BR | Mortality | Rodents | Liver | Scaled | FALSE |
| BR_Mortality_Rodents_Liver_yugenediff.pkl | BR | Mortality | Rodents | Liver | YuGene | FALSE |
| BR_Mortality_Rodents_Multitissue_scaleddiff.pkl | BR | Mortality | Rodents | Multi-Tissue | Scaled | FALSE |
| BR_Mortality_Rodents_Multitissue_yugenediff.pkl | BR | Mortality | Rodents | Multi-Tissue | YuGene | FALSE |
| BR_NormalizedAge_Mouse_Multitissue_scaleddiff.pkl | BR | Normalized age | Mouse | Multi-Tissue | Scaled | FALSE |
| BR_NormalizedAge_Mouse_Multitissue_yugenediff.pkl | BR | Normalized age | Mouse | Multi-Tissue | YuGene | FALSE |
| BR_NormalizedAge_Multispecies_Multitissue_scaleddiff.pkl | BR | Normalized age | Multispecies | Multi-Tissue | Scaled | FALSE |
| BR_NormalizedAge_Multispecies_Multitissue_yugenediff.pkl | BR | Normalized age | Multispecies | Multi-Tissue | YuGene | FALSE |
| BR_NormalizedAge_Rodents_Liver_scaleddiff.pkl | BR | Normalized age | Rodents | Liver | Scaled | FALSE |
| BR_NormalizedAge_Rodents_Liver_yugenediff.pkl | BR | Normalized age | Rodents | Liver | YuGene | FALSE |
| BR_NormalizedAge_Rodents_Multitissue_scaleddiff.pkl | BR | Normalized age | Rodents | Multi-Tissue | Scaled | FALSE |
| BR_NormalizedAge_Rodents_Multitissue_yugenediff.pkl | BR | Normalized age | Rodents | Multi-Tissue | YuGene | FALSE |
| EN_Chronoage_Mouse_Multitissue_scaleddiff.pkl | EN | Chronological | Mouse | Multi-Tissue | Scaled | TRUE |
| EN_Chronoage_Mouse_Multitissue_yugenediff.pkl | EN | Chronological | Mouse | Multi-Tissue | YuGene | TRUE |
| EN_Chronoage_Multispecies_Multitissue_scaleddiff.pkl | EN | Chronological | Multispecies | Multi-Tissue | Scaled | TRUE |
| EN_Chronoage_Multispecies_Multitissue_yugenediff.pkl | EN | Chronological | Multispecies | Multi-Tissue | YuGene | TRUE |
| EN_Chronoage_Rodents_Brain_scaleddiff.pkl | EN | Chronological | Rodents | Brain | Scaled | TRUE |
| EN_Chronoage_Rodents_Brain_yugenediff.pkl | EN | Chronological | Rodents | Brain | YuGene | TRUE |
| EN_Chronoage_Rodents_Kidney_scaleddiff.pkl | EN | Chronological | Rodents | Kidney | Scaled | TRUE |
| EN_Chronoage_Rodents_Kidney_yugenediff.pkl | EN | Chronological | Rodents | Kidney | YuGene | TRUE |
| EN_Chronoage_Rodents_Liver_scaleddiff.pkl | EN | Chronological | Rodents | Liver | Scaled | TRUE |
| EN_Chronoage_Rodents_Liver_yugenediff.pkl | EN | Chronological | Rodents | Liver | YuGene | TRUE |
| EN_Chronoage_Rodents_Multitissue_scaleddiff.pkl | EN | Chronological | Rodents | Multi-Tissue | Scaled | TRUE |
| EN_Chronoage_Rodents_Multitissue_yugenediff.pkl | EN | Chronological | Rodents | Multi-Tissue | YuGene | TRUE |
| EN_Chronoage_Rodents_Skeletal_muscle_scaleddiff.pkl | EN | Chronological | Rodents | Skeletal Muscle | Scaled | TRUE |
| EN_Chronoage_Rodents_Skeletal_muscle_yugenediff.pkl | EN | Chronological | Rodents | Skeletal Muscle | YuGene | TRUE |
| EN_Mortality_Mouse_Multitissue_scaleddiff.pkl | EN | Mortality | Mouse | Multi-Tissue | Scaled | FALSE |
| EN_Mortality_Mouse_Multitissue_yugenediff.pkl | EN | Mortality | Mouse | Multi-Tissue | YuGene | FALSE |
| EN_Mortality_Multispecies_Multitissue_scaleddiff.pkl | EN | Mortality | Multispecies | Multi-Tissue | Scaled | FALSE |
| EN_Mortality_Multispecies_Multitissue_yugenediff.pkl | EN | Mortality | Multispecies | Multi-Tissue | YuGene | FALSE |
| EN_Mortality_Rodents_Liver_scaleddiff.pkl | EN | Mortality | Rodents | Liver | Scaled | FALSE |
| EN_Mortality_Rodents_Liver_yugenediff.pkl | EN | Mortality | Rodents | Liver | YuGene | FALSE |
| EN_Mortality_Rodents_Multitissue_scaleddiff.pkl | EN | Mortality | Rodents | Multi-Tissue | Scaled | FALSE |
| EN_Mortality_Rodents_Multitissue_yugenediff.pkl | EN | Mortality | Rodents | Multi-Tissue | YuGene | FALSE |
| EN_NormalizedAge_Mouse_Multitissue_scaleddiff.pkl | EN | Normalized age | Mouse | Multi-Tissue | Scaled | FALSE |
| EN_NormalizedAge_Mouse_Multitissue_yugenediff.pkl | EN | Normalized age | Mouse | Multi-Tissue | YuGene | FALSE |
| EN_NormalizedAge_Multispecies_Multitissue_scaleddiff.pkl | EN | Normalized age | Multispecies | Multi-Tissue | Scaled | FALSE |
| EN_NormalizedAge_Multispecies_Multitissue_yugenediff.pkl | EN | Normalized age | Multispecies | Multi-Tissue | YuGene | FALSE |
| EN_NormalizedAge_Rodents_Liver_scaleddiff.pkl | EN | Normalized age | Rodents | Liver | Scaled | FALSE |
| EN_NormalizedAge_Rodents_Liver_yugenediff.pkl | EN | Normalized age | Rodents | Liver | YuGene | FALSE |
| EN_NormalizedAge_Rodents_Multitissue_scaleddiff.pkl | EN | Normalized age | Rodents | Multi-Tissue | Scaled | FALSE |
| EN_NormalizedAge_Rodents_Multitissue_yugenediff.pkl | EN | Normalized age | Rodents | Multi-Tissue | YuGene | FALSE |
Appendix C - ComputAgeBench benchmark studies (65 datasets, blood/saliva/buccal)
From ComputAge BenchBSB_table.csv. HC content = HC means the study contains its own healthy controls and can run the AA2 test; No HC means only the AA1 test is possible. Condition abbreviations: AS atherosclerosis, IHD ischaemic heart disease, CVA cerebrovascular accident, IBD inflammatory bowel disease, CD Crohn’s, UC ulcerative colitis, HIV, TB tuberculosis, XOB extreme obesity, OBS obesity, T1D/T2D diabetes, ASD, RA rheumatoid arthritis, PA psoriatic arthritis, UA undifferentiated arthritis, OA osteoarthritis, OP osteoporosis, MS multiple sclerosis, AD Alzheimer’s, PD Parkinson’s, CJD Creutzfeldt–Jakob, COPD, WS Werner, aWS atypical Werner, MDPS mandibular dysplasia, HGPS Hutchinson–Gilford progeria, ncLMNA non-classical laminopathy, CGL congenital generalised lipodystrophy.
| Class | Condition | Conditions | Dataset ID | PMID | Cited | First author, year | Tissue | N samples | HC content | Comment |
|---|---|---|---|---|---|---|---|---|---|---|
| CVD | AS | AS | GSE56046 | 25404168 | y | Reynolds, 2014 | Blood | 1202 | HC | |
| CVD | AS | AS | GSE56581 | 25404168 | y | Reynolds, 2014 | Blood | 214 | HC | |
| CVD | IHD | IHD | GSE62867 | 25856389 | y | Nazarenko, 2014 | Blood, vessels | 24 | No HC | |
| CVD | CVA | CVA | GSE69138 | 26643952 | y | Soriano-Tárraga, 2016 | Blood | 185 w/ages | No HC | |
| CVD | AS | AS | GSE107143 | 28698603 | y | Istas, 2017 | Blood | 16 (8 HC) | HC | |
| CVD | CVA | CVA | GSE203399 | 36180927 | y | Cullell, 2022 | Blood | 121 | No HC | |
| ISD | IBD | IBD | GSE32148 | 22467598 | y | Harris, 2012 | Blood | 48 (20 HC) | HC | |
| ISD | HIV | HIV | GSE53840 | 25969563 | y | Horvath, 2015a | Blood | 120 | No HC | |
| ISD | HIV | HIV | GSE53841 | 25969563 | y | Horvath, 2015a | Blood | 24 | No HC | |
| ISD | HIV | HIV | GSE67705 | 27105112 | y | Gross, 2016 | Blood | 284 | HC | |
| ISD | HIV | HIV | GSE67751 | 25969563 | y | Horvath, 2015a | Blood | 92 (69 HC) | HC | |
| ISD | HIV | HIV | GSE77696 | 27672717 | y | Zhang, 2016 | Blood | 378 (117 HC) | HC | |
| ISD | IBD | IBD (CD) | GSE81961 | 27279921 | y | Li Yim, 2016 | Blood | 40 (25 HC) | HC | |
| ISD | IBD | IBD (CD, UC) | GSE87640 | 27886173 | y | Ventham, 2016 | Blood | 240 (84 HC) | HC | |
| ISD | IBD | IBD (CD, UC) | GSE87648 | 27886173 | y | Ventham, 2016 | Blood | 382 (190 HC) | HC | |
| ISD | HIV | HIV | GSE100264 | 29269866 | v | Zhang, 2017 | Blood | 386 | No HC | |
| ISD | HIV | HIV | GSE107080 | 29269866 | v | Zhang, 2017 | Blood | 405 | No HC | |
| ISD | HIV | HIV | GSE117859 | 30545403 | v | Zhang, 2018 | Blood | 608 | No HC | |
| ISD | HIV | HIV | GSE117860 | 30545403 | v | Zhang, 2018 | Blood | 529 | No HC | |
| ISD | HIV | HIV | GSE140800 | 32760119 | v | Oriol-Tordera, 2020 | Blood | 70 | No HC | |
| ISD | HIV | HIV | GSE143942 | Unpublished | Unpublished | Blood | 85 | HC | ||
| ISD | HIV_TB | HIV, TB | GSE145714 | 32125282 | v | DiNardo, 2020 | Blood | 32 | HC | |
| ISD | HIV | HIV | GSE185389 | 35325780 | v | Oriol-Tordera, 2022 | Blood | 56 | No HC | |
| ISD | HIV | HIV | GSE185390 | 35325780 | v | Oriol-Tordera, 2022 | Blood | 30 | No HC | |
| ISD | HIV | HIV | GSE217633 | 36640455 | v | Esteban-Cantos, 2023 | Blood | 413 (44 HC) | HC | |
| MBD | XOB | XOB, OBS, ASD | GSE49909 | 24034465 | v | Day, 2013 | Blood, brain | 283 | HC | |
| MBD | T1D | T1D | GSE56606 | 21980303 | v | Rakyan, 2011 | Blood | 100 (68 HC) | HC | MZ twins |
| MBD | T2D | T2D | GSE62003 | 25585531 | v | Lunnon, 2015 | Blood | 70 | No HC | |
| MBD | XOB | XOB | GSE131461 | 31333715 | v | Ramos-Molina, 2019 | Blood, adipose | 40 | No HC | |
| MBD | XOB | XOB, OBS | GSE166611 | Unpublished | Unpublished | Blood | 32 (17 HC) | HC | ||
| MBD | XOB | XOB | GSE193836 | 35369101 | v | Noronha, 2022 | Blood | 21 (11 HC) | HC | |
| MSD | RA | RA | GSE42861 | 23334450 | v | Liu, 2013 | Blood | 689 | HC | |
| MSD | RA | RA | GSE71841 | Unpublished | Unpublished | Blood | 24 (12 HC) | HC | Unpublished | |
| MSD | OP | OP | GSE99624 | 28926142 | v | Fernandez-Rebollo, 2018 | Blood | 48 | HC | |
| MSD | RA | RA | GSE131989 | 27723282 | v | Rhead, 2017 | Blood | 371 | HC | |
| MSD | RA | RA | GSE134429 | Unpublished | Unpublished | Blood | 64 (17 HC) | HC | Unpublished | |
| MSD | RA | RA, PA, UA, OA | GSE137593 | 31945409 | v | Clark, 2020 | Blood | 103 | No HC | Early arthritis |
| MSD | RA | RA, PA, UA, OA | GSE137594 | 31945409 | v | Clark, 2020 | Blood | 119 | No HC | Early arthritis |
| MSD | RA | RA | GSE138653 | 32909363 | v | Tao, 2021 | Blood | 80 | No HC | Before medication |
| MSD | RA | RA, UA | GSE175364 | 34105306 | v | de la Calle-Fabregat, 2021 | Blood | 101 | HC | UA -> RA/OA |
| MSD | RA | RA | GSE176168 | 35576644 | v | Julià, 2022 | Blood | 113 | No HC | Longitudinal (0,12 w), anti-TNF |
| MSD | RA | RA | GSE228104 | 37744384 | v | Chen, 2023 | Blood | 40 | No HC | Leflunomide |
| NDD | MS | MS | GSE43976 | 23422812 | v | Marabita, 2013 | Blood | 95 (20 HC) | No HC | |
| NDD | AD | AD | GSE59685 | 25129077 | v | Lunnon, 2014 | Brain, blood | 531 | HC | |
| NDD | PD | PD | GSE72774 | 26655927 | v | Horvath, 2015b | Blood | 508 (219 HC) | HC | |
| NDD | PD | PD | GSE72776 | 26655927 | v | Horvath, 2015b | Blood | 84 (38 HC) | HC | |
| NDD | MS | MS, OBS | GSE103929 | 30981648 | v | Castro, 2019 | Blood | 49 | No HC | BMI |
| NDD | MS | MS | GSE106648 | 29921915 | v | Kular, 2019 | Blood | 279 (139 HC) | HC | |
| NDD | PD | PD | GSE111223 | 28851441 | v | Chuang, 2017 | Saliva | 259 (131 HC) | HC | |
| NDD | PD | PD | GSE111629 | 30958317 | v | Chuang, 2017 | Blood | 572 (237 HC) | HC | |
| NDD | MS | MS | GSE112596 | 30698680 | v | Ntranos, 2019 | Blood | 112 | No HC | |
| NDD | PD | PD | GSE122244 | Unpublished | Unpublished | Blood | 70 | HC | Non-Neurological controls | |
| NDD | MS | MS | GSE130029 | 31053557 | v | Ewing, 2019 | Blood | 31 (11 HC) | HC | |
| NDD | MS | MS | GSE130030 | 31053557 | v | Ewing, 2019 | Blood | 28 (14 HC) | HC | |
| NDD | MS | MS | GSE130491 | 31300673 | v | Carlström, 2019 | Blood | 82 (19x3x2) | No HC | |
| NDD | AD | AD | GSE144858 | 32745807 | v | Roubroeks, 2020 | Blood | 300 | HC | |
| NDD | PD | PD | GSE151355 | 32650713 | v | Go, 2020 | Blood, brain | 40 | No HC | |
| NDD | CJD | CJD | GSE156994 | 32918118 | v | Dabin, 2020 | Blood | 219 (106 HC) | HC | |
| NDD | MS | MS | GSE219293 | 36685876 | v | Bingen, 2023 | Blood | 47 (18 HC) | HC | |
| RSD | TB | TB | GSE72338 | 26374119 | v | Esterhuyse, 2015 | Blood | 38 | HC | HC = LTBI household concats |
| RSD | COPD | COPD | GSE118468 | 33658578 | v | Chen, 2021 | Blood | 21 (6 HC) | HC | |
| RSD | TB | TB | GSE118469 | 32365959 | v | Chen, 2020 | Blood | 21 (6 HC) | HC | |
| RSD | TB | HIV, TB | GSE145714 | 32125282 | v | DiNardo, 2020 | Blood | 32 | HC | |
| PGS | WS | WS, aWS, MDPS | GSE131752 | 31259468 | v | Maierhofer, 2019 | Blood | 48 (24 HC) | HC | |
| PGS | HGPS | HGPS, ncLMNA | GSE182991 | 35045206 | v | Bejaoui, 2022 | Blood | 27 (12 HC) | HC | |
| PGS | CGL | CGL | GSE214297 | 36715159 | v | Qannan, 2022 | Blood | 16 (9 HC) | HC |
Appendix D - organAging organ protein panels
Organ assignment rule: a plasma protein belongs to an organ if its GTEx expression in that tissue is at least 4× its mean expression across all other tissues. Counts from organAging GTEx_4x_FC_genes.json. Three model families ship per organ: full (Olink Explore 3072), feature-reduced (Olink Explore 1536), and CSF. Each family has first-generation (chronological age, with intercept) and mortality (Cox elastic net, relative log hazard, no intercept) variants, each fitted over five folds, use fold 1 unless working in UK Biobank itself.
| Organ panel | Proteins |
|---|---|
| Adipose | 5 |
| Adrenal | 4 |
| Artery | 14 |
| Bladder | 0 (excluded from training) |
| Brain | 118 |
| Esophagus | 5 |
| Female (reproductive) | 6 |
| Heart | 7 |
| Immune | 124 |
| Intestine | 31 |
| Kidney | 9 |
| Liver | 79 |
| Lung | 11 |
| Male (reproductive) | 55 |
| Muscle | 20 |
| Pancreas | 29 |
| Pituitary | 12 |
| Salivary | 10 |
| Skin | 17 |
| Stomach | 12 |
| Thyroid | 2 |
| Organismal (any organ-enriched protein) | 2,347 |
| Multi-organ (enriched in >1 organ) | 569 |
| Conventional (all measured proteins) | 2,916 |
Applying them: multiply Olink NPX values by the coefficients and sum; add the intercept for first-generation models only. For non-Olink platforms, log-transform to symmetry, z-score each protein, then multiply by the published Olink NPX standard deviations (Table S3 of the paper) before scoring. The authors ignored missing values rather than imputing, and warn that performance degrades with the number of missing high-coefficient proteins.
Appendix E - Preprocess and postprocess operation inventory
Every distinct operation a clock can request, with the clocks that use it and the shape of the computation. This is the complete op set a new engine must implement to cover the pyaging registry.
Preprocess (runs on the clock’s feature slice, after feature selection)
| Op | Signature | Dependencies stored with model | Clocks |
|---|---|---|---|
identity |
x → x |
- | most |
fill_nan_with_reference |
where(isnan(x), ref, x) |
reference_values (len = n_features) |
~90 (LinearReferenceClock and subclasses) |
scale |
(x − median) / std |
median vector, std vector | AltumAge, CpGPTGrimAge3 |
scale_mean |
(x − mean) / std |
mean vector, std vector | CpGPTPCGrimAge3 |
scale_row |
(x − rowmean) / rowstd |
- | ZhangEN, ZhangBLUP |
quantile_normalize_with_gold_standard |
rank map onto sorted gold means, then subset | 20,000-length gold mean vector + scoring index list | DunedinPACE |
quantile_normalize_and_scale |
as above, then (x − gold_mean)/gold_std, then subset |
gold means, gold stds, index list | Stubbs |
tpm_norm_log1p |
log1p(1e6 · (1000·x/len) / rowsum), then subset |
region lengths, index list | OcampoATAC1/2 |
binarize |
per row, mask zeros, threshold at non-zero median | - | BiTAge |
median_fill_and_rank |
fill NaN with global median, then per-row average ranks | - | Pasta, PastaMouse, REG |
mean_over_present |
mean of entries ≠ −1 per row | - | epiTOC1, HypoClock, epiCMITHyper, epiCMITHypo |
quantile95_over_present |
0.95 quantile of entries ≠ −1 per row | - | stemTOC, stemTOCvitro |
nan_to_zero |
nan_to_num(x, 0) |
- | epiTOC2, epiTOC3 |
autosomal_zscore |
z-score all probes against the autosomal mean/SD of that sample | autosome index list | XChrom, YChrom |
expand_with_reference |
insert reference values for out-of-species features, then run the shared preprocess | full feature list, full reference vector, species index list | PastaMouse |
Postprocess (runs on the scalar model output)
Listed in the transform table in §4.20. Fifteen distinct forms, of which identity, anti_log_linear and cox_to_years cover the large majority.
What FALCONAge implements of this
Seven per-clock preprocess ops, one dataset-level op and 21 postprocess transforms, in models/ops.py; 35 of the 69 bundled clocks declare at least one. Several are more general than their row above: scale takes centre and spread vectors stored with the model and so covers scale and scale_mean; quantile_normalize takes the gold vector and the scoring index, covering the DunedinPACE and Stubbs rows. scale_row needs statistics from the sample itself, over every probe the array carries, so it runs on the dataset before alignment.
| Shipped | Covers |
|---|---|
beta_to_m, m_to_beta |
the β ↔︎ M logit pair, which no row above names because pyaging folds it into each clock |
scale |
scale, scale_mean |
standardise_within_sample (dataset-level) |
scale_row, over the whole array as the Zhang 2019 pred.R does |
rank_normalize |
median_fill_and_rank |
quantile_normalize |
quantile_normalize_with_gold_standard, quantile_normalize_and_scale |
binarize |
binarize |
simplex_projection |
the deconvolution row of the special passes |
add, add_constant, multiply, divide_by, days_to_months, days_to_weeks, clip, exp, expit, sigmoid, one_minus, log_linear, anti_log, anti_log_linear, anti_log_log, anti_logp2, scale_and_shift, cox_to_years, mortality_to_phenoage, petkovich_blood, stubbs_multitissue |
the postprocess set |
Reference filling is not an op here. fill_nan_with_reference is the largest row in the table and FALCONAge does it in align() instead, before any op runs, because the imputation choice is a property of the dataset rather than of the clock and has to be recorded in the manifest either way. Zero is never a fill value: in β space zero means completely unmethylated, which is a real and extreme measurement, and a clock with a large positive coefficient on a probe the array does not carry will read “totally unmethylated” and shift the prediction by whole years.
Not ops, because the statistic is the whole model: mean_over_present and quantile95_over_present are AggregationClock, and the epiTOC2 / epiTOC3 row of the special passes below is DivisionClock, which averages over the sites present, as the published implementation does, where the table has nan_to_zero. The deconvolution row is DeconvolutionClock, by the authors’ non-negative least squares rather than the pseudo-inverse and simplex projection the row describes. Unbuilt: tpm_norm_log1p, autosomal_zscore, expand_with_reference, and the other eight special forward passes. Each belongs to a clock family whose coefficients are licensed, not yet traced to a primary source, or absent from the registry, so building the op first would produce code with no real answer to test against, see the tier table in Architecture.
Special forward passes (not expressible as preprocess → linear → postprocess)
| Clock family | Structure |
|---|---|
| GrimAge / GrimAge2 | 8 or 10 sub-linear-models on disjoint feature index sets → concat with Age and Female in a fixed order → Cox linear layer → cox_to_years |
| PCGrimAge | centre + rotate 78,464 → 1,933 PCs → concat Female, Age → 8 PC sub-models → Cox layer |
| CpGPTPCGrimAge3 | scale → split age from proxies → rotate 30 → 29 PCs → concat → rescale PCs → linear → cox_to_years |
| DNAmFitAge | split by sex → 5 sub-models (GaitF/M, GripF/M, VO2Max) → affine standardisation with sex-specific constants → sex-specific KDM layer → recombine |
| Mammalian 2/3 and blood/skin variants | split CpG block from species one-hot (1,756 or 1,707 wide) → linear on CpGs → species-dependent inverse age transform using AnAge gestation / maturity / max lifespan |
| SystemsAge | centre + rotate 125,175 CpGs → project to system components → 11 system linear modules + a PC→age model with quadratic correction → optional composite PCA layer → affine calibration → /12 |
| Deconvolution | pseudo-inverse of the signature matrix → simplex projection → select one cell-type column |
| epiTOC2 / epiTOC3 | 2/k · Σ (β − β₀)/(δ(1−β₀)) with per-CpG δ and β₀ vectors |
| XChrom / YChrom | autosomal z-score → select sex-chromosome probes → Σ (z − mean) · coef |
| PastaMouse | expand mouse features into the 8,113-gene human space with reference values → shared Pasta forward |