Whole blood DNA methylation aging markers predict colorectal cancer survival: a prospective cohort study

Clinical Epigenetics

Table 1 Overview of DNA methylation aging algorithms

DNAm aging algorithm	Original study	Tissue	n_CpGs	Surrogate measure of biological age*
AgeAccelHorvath	Horvath et al. [5]	Multiple tissues^#	353	Calibrated chronological age
AgeAccelHannum	Hannum et al. [6]	Whole blood	71	Chronological age
DNAmMRscore	Zhang et al. [7]	Whole blood	10(8)^§	All-cause mortality
AgeAccelPheno	Levine et al. [8]	Whole blood	513	9 markers^†, chronological age
AgeAccelGrim	Lu et al. [9]	Whole blood	1030	7 Plasma proteins^‡, smoking pack-years

AgeAccel, age acceleration; DNAm, DNA methylation; MRscore, mortality risk score
^*DNAm aging algorithms are usually constructed by regressing mortality and/or a surrogate measure of biological age on a set of CpG sites
^#Horvath’s epigenetic clock was originally developed based on CpG sites from DNA of 51 different tissues and cell types. In our study, AgeAccelHorvath was calculated based on CpG sites from DNA of whole blood samples
^§DNAmMRscore was initially developed based on ten CpG sites, of which two CpG sites are not included in Illumina EPIC microarray data. An adapted formula based on the remaining eight CpG sites has been developed using the data from an external cohort, the German ESTHER cohort
^†9 markers include albumin, creatinine, serum glucose, C-reactive protein, lymphocyte percent, mean cell volume, red cell distribution width, alkaline phosphatase and white blood cell count
^‡7 plasma proteins include adrenomedullin, beta-2-microglobulim, cystatin C, growth/differentiation factor 15, leptin (Leptin), plasminogen activator inhibitor-1 and tissue inhibitor metalloproteinases 1

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