- chronological age in human adults using DNA methylation data from the Illumina MethylationEPIC array. - Thus, it remains unclear to what extent EPIC contributes to increased precision and accuracy in the prediction of chronological age.. - This high precision is unlikely due to the use of EPIC, but rather due to the large sample size of the training set.. - Conclusions: Our ABECs predicted adults ’ chronological age precisely in independent cohorts. - The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. - Full list of author information is available at the end of the article. - Because chronological age is an imperfect surrogate of aging [3–6], the concept of biological aging that can capture the different rate of func- tional deterioration across individuals has been suggested [1]. - Among these, DNAm-based estimators of chronological age (referred to as epigenetic clocks) have garnered the most interest due to their re- markable precision in estimating chronological age, age- related diseases, and all-cause mortality . - Most of the previously published epigenetic clocks (the Han- num Blood-based clock [19], Horvath Pan-tissue clock [20], Levine PhenoAge clock [16], and Horvath Skin &. - 450,000 CpGs), in terms of the number of probes (>. - Thus, it remains unclear to what extent EPIC contrib- utes to increased precision and accuracy in the predic- tion of chronological age.. - The purpose of cABEC was to determine whether the additional CpGs on EPIC improved predictions of chronological age. - The chronological age of these adults ranged from 19 to 59 years (19 to 46 years for women and 19 to 59 years for men). - We used elastic net regression [32] to select the most pre- dictive CpGs for chronological age. - where DNAm Age j is the epigenetic age of the j th individ- ual, and X cgi, j refers to the DNAm level of the j th individ- ual at the i th CpG site. - Figure 2 shows the performance of ABEC in the training set (n = 1592, Fig. - 2a) and the test set (n = 424, Fig. - The prediction precision was quantified using the Pearson correl- ation coefficient (r) between DNAm age and chronological age. - The prediction accuracy was quantified using the me- dian absolute deviation (MAD) between DNAm age and chronological age. - ABEC showed high precision and accur- acy in both of the training (r = 0.999, MAD = 0.14, Fig. - 2a and b represents a perfect correlation between chronological age and DNAm age, and the dotted line refers to the regression of the predicted DNAm age on chrono- logical age.. - Despite its overall high precision, ABEC slightly underestimated the age of the older individuals, particu- larly those above 45 years of age (Fig. - Lee et al. - males, which may introduce a sex-bias in the prediction of chronological age.. - To reduce the underestimation bias and improve the precision of ABEC among older individuals in the MoBa-START dataset, we developed an extended ABEC (eABEC) by adding a publicly available DNAm dataset, GSE116339 (n from the GEO data repository (https://www.ncbi.nlm.nih.gov/geo/) [33] to the original training set for ABEC (Fig. - This increased the total sample size of the new training set to 2227. - Elastic net regression was used in the same manner as for ABEC above, and for this training set, the number of selected CpGs was 1791.. - We validated eABEC in an extended test set consisting of the test set for ABEC and two independent cohorts (GSE111165 and GSE115278) from GEO. - The training set of the other epigenetic clocks was mostly based on 450 K, except for the Horvath Skin &. - Table 1 Description of the peripheral whole-blood-derived DNAm data on the EPIC platform Cohort Tissue type Platform GEO submitter N Normalization. - EPIC Curtis et al. - EPIC Shinozaki et al.. - EPIC Arpon et al. - EPIC Kilaru et al. - a Pre-processing method for quantifying DNAm levels in the range of 0 to 1 Noob Normal-exponential out-of-band [29]. - However, compared to eABEC, the accuracy of cABEC in the test set was slightly diminished (MAD . - Both types of epigenetic clocks showed a remarkable improvement in precision and accuracy as the sample size of the training set in- creased (Fig. - This indicates that the additional CpGs on EPIC do not enhance the accur- acy or precision of the epigenetic clocks when the train- ing set is reduced.. - Blood clock (r = 0.94), and the Hannum Blood-based epigenetic clock (r = 0.87). - The 95% confi- dence intervals of the r values can be found in Supple- mentary File 2 (S-Table 1). - 5 because the dots in the scatter plots could deviate systematically from the 45-degree line (so-called systematic offset) but still form a very tight prediction, e.g., panel (D) in Fig. - We vali- dated ABEC, eABEC, cABEC, and the other published epi- genetic clocks in the EPIC-derived blood-based DNAm data from EPIPREG (n = 470. - 6), a sub-study of the STORK Groruddalen Cohort [24]. - resid- uals from the regression of DNAm age on chronological age) was higher in South Asian women than in Norwegian women. - Given that ABEC, eABEC, and cABEC were trained on the ethnically homogeneous training set of Europeans, they may be sub-optimal for predicting chronological age in other ethnicities. - 2 Chronological age estimation by ABEC. - a Scatter plot of chronological age against DNAm age estimated by ABEC in the training set. - b Scatter plot of chronological age against DNAm age estimated by ABEC in the test set. - c Residual plot in the training set. - d Residual plot in the test set. - The red line in panels (a) and (b) represents a perfect correlation between chronological age and DNAm age, and the dotted line is the regression of DNAm age on chronological age. - confidence intervals of the r values can be found in Sup- plementary File 2 (S-Table 1). - epigenetic clocks showed a more precise chronological age prediction than existing blood-based epigenetic clocks (e.g., the Hannum Blood-based clock and Horvath Skin &. - 3 Chronological age estimation by eABEC. - a Scatter plot of chronological age against DNAm age estimated by eABEC in the extensive training set. - b Scatter plot of chronological age against DNAm age estimated by eABEC in the test set. - Other clocks (the Hor- vath Pan-tissue clock and Levine PhenoAge clock) may not be directly comparable to eABEC for chronological age prediction. - These individuals had been exposed to the endocrine-disrupting chemical polybrominated bi- phenyl when an agricultural accident introduced it into the food supply in the 1970s. - The distribution of the total PBB exposure was highly right-skewed.. - The high precision of eABEC cannot be attributed solely to the use of the EPIC platform as the additional 413,743 CpGs on EPIC did not improve age prediction. - Yet, Pidsley et al. - As we regressed chronological age on DNAm levels (chronological age = DNAm levels + error), a scatter plot that displays chronological age on the x-axis and DNAm age on the y-axis may lead to the misconception that DNAm age is overestimated in the oldest age group and underestimated in the youngest age group (Supplementary File, S-Figure 4). - The strength of the current scatter plots lies in the visualization of EAA (the residuals of the regression of. - a Scatter plot of the Pearson correlation (r) in the test set against the sample size of the training set. - b Scatter plot of MAD in the test set against the sample size of the training set. - In panel (a), we fit the smoothing splines of the Fisher ’ s Z-transformed r values on the sample size, derived the confidence intervals, and inverse-transformed them. - DNAm age on chronological age. - 5 Chronological age estimation by ABEC, eABEC, and the other published epigenetic age estimators. - The red line in the panels represents a perfect correlation between chronological age and DNAm age, and the dotted line is the regression of DNAm age on chronological age. - to be dependent on chronological age. - Therefore, for other researchers who are interested in the associ- ation between EAA and a given phenotype, we rec- ommend redefining EAA (e.g., regressing DNAm age on chronological age using a piecewise cubic regres- sion or a smoothing spline rather than an ordinary linear regression) so that EAA is independent of chronological age.. - 6 Application of ABEC, eABEC, and other epigenetic clocks to DNAm data in the EPIPREG sub-study of the STORK Groruddalen cohort. - EUR indicates the r between chronological age and DNAm age in 305 women of European ancestry, whereas SAS refers to the r between chronological age and DNAm age in 165 women of South Asian ancestry. - [38] and McCartney et al. - Three blood-based epigenetic clocks were developed to esti- mate adults’ chronological age using EPIC-derived DNAm data. - The precise chronological age in days at blood draw was calculated for the fathers and mothers. - The age distributions of all the individuals included in the training and test sets can be found in Supplementary File 2 (S-Figure 5 and 6).. - For MoBa-START, 500 nanograms of DNA stored in the MoBa Biobank (see Paltiel et al. - Further details of the DNA extraction and quality control process of EPIPREG can be found in Supplementary File 3.. - Chronological age in days was regressed on 770,586 autosomal CpGs that remained after quality control. - The mixing parameter (alpha) was set to 0.5 and the shrinkage parameter (lambda) leading to the minimum mean square error was selected after 10- fold cross-validation in the training set. - For each of the reduced sample sizes (n and 1784;. - We made the sequence of the reduced sample sizes denser around 100 and sparser around 2227 because epigenetic clocks gradually improved their preci- sion and accuracy when the training set was larger than 1145. - Next, we vali- dated these clocks in the test set of eABEC (n = 485) and calculated r and MAD accordingly. - 4a, we fit the smoothing splines of the Fisher’s Z-transformed r values (F ð r Þ ¼ 0:5 logð 1 1þr − r Þ) on the sam- ple size, derived the confidence intervals and inverse- transformed them.. - This file includes CpG sites for ABEC, eABEC, and cABEC, their corresponding coefficients, overlap with the other published clocks, genomic locations, neighboring genes, presence in the Illumina HumanMethylation450K and 27 K array, and the SNP annotations generated by Zhou et al. - and McCartney et al. - 4, 5 and 6) figures displaying the age prediction of the ABECs and the other published clocks in EPIP REG and GSE132203, 4) a figure illustrating the regression-to-the-mean ef- fect and 5) histograms displaying the age distribution of individuals in each cohort.. - determination of the reduced sample sizes for Fig. - GHM performed the quality control and normalization of the EPIPREG samples. - SL-Ø validated all the epigenetic clocks in the DNAm data from EPIPREG. - LS participated in data acquisition in the STORK Groruddalen study. - The funding body played no role in the design of the study, analysis or interpretation of data, nor in writing the manuscript.. - The participation in the STORK Groruddalen study was based on informed written consent, and the study and its sub-study, EPIPREG, were approved by the REK South-East . - DNA methylation age of blood predicts future onset of lung cancer in the women's health initiative. - Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. - The accessible chromatin landscape of the human genome. - Systematic underestimation of the epigenetic clock and age acceleration in older subjects. - The biobank of the Norwegian mother and child cohort Study: a resource for the next 100 years. - The biobank of the Norwegian Mother and Child Cohort Study – present status
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