Implementation of the eleven pyrosequencing assays and the six blood-based age prediction models

Six blood-based age prediction models using pyrosequencing for DNA methylation analysis were selected for evaluation on blood samples from 100 French donors aged between 19 and 65 years. The selected models were those of Bekaert1, Park2, Thong3, Zbiec-Piekarska 15, Zbiec-Piekarska 26, and Weidner4, which use 4, 3, 3, 2, 5 and 3 CpG sites in 4 (ASPA, EDARADD, ELOLV2 and PDE4C), 3 (CCDC102B, ELOVL2 and ZNF423), 3 (ELOVL2, KLF14 and TRIM59), 1 (ELOVL2), 5 (C1orf132, ELOVL2, FHL2, KLF14 and TRIM59) and 3 (ASPA, ITGA2B and PDE4C) genes of interest respectively (Fig. 1). In total, 11 genes including 52 CpG sites were analyzed by pyrosequencing (Supplementary Tables 1 and 2). Contrary to the original studies, the DNA extractions were performed on buffy coats instead of whole blood. However this modification should not impact the DNA methylation analysis, as the buffy coat is the main source of DNA in whole blood.

Figure 1 Description of the CpGs included in the six blood-based age prediction models using DNA methylation analysis by pyrosequencing. Full size image

The 11 different, previously published, pyrosequencing assays were first evaluated against standards of known degrees of DNA methylation (0, 25, 50, 75 and 100%) in order to determine their efficiency and linearity, and to detect any possible PCR-induced biases (Supplementary Figs 2 and 3). All pyrosequencing assays presented an observed DNA methylation of the 0% and 100% DNA methylation standards close to the expected values, with a higher variability of the observed value of the 100% standard for some CpG sites (Supplementary Figs 2 and 3). The 25%, 50% and 75% DNA methylation standards presented a quantification close to or slightly lower than their expected value, with the notable exception of the PDEC assay for which all the observed values were close to 0 (Supplementary Figs 2 and 3). In most assays, these results indicated the presence of only a slight PCR bias in favor of the unmethylated allele, however this bias was very strong for the PDE4C gene. To also evaluate the possible amplification biases induced by the use of different PCR cycles, we performed replicate experiments for the eleven assays with the same bisulfite-treated commercial DNA sample using either 45 or 50 PCR cycles (Supplementary Fig. 3). The DNA methylation values obtained for all CpGs included in the age-prediction models were very similar for both experimental conditions and presented no statistically significant differences (Supplementary Fig. 3), indicating that the use of 45 or 50 cycles of PCR should not modify the quantification of DNA methylation or the prediction of age.

The correlation analysis of DNA methylation of all CpGs and the chronological age of all individuals revealed a strong correlation present overall (mean absolute r = 0.640), which was stronger for the CpGs included in the six age-prediction models (mean absolute r = 0.758), although these CpGs correlations were not systematically the strongest within a given region (Supplementary Fig. 5 and Table 1). It should also be noted that all the CpGs of ITGA2B presented a weak correlation (−0.464≥ r ≥−0.325 while all the CpGs of ELOVL2, 4 of which are included in 5 different age prediction models, presented a very strong correlation (0.742≥ r ≥0.862); this explains 55.1% to 74.3% of the age variance in our group of individuals (Supplementary Fig. 5 and Table 1). The Pearson correlation coefficient of the CpGs included in the six age prediction models was very similar between men and women, with the exception of ASPA and C1orf132 which presented a difference of 0.165 and 0.166 respectively in their r coefficients (Table 1).

Table 1 Correlation between chronological age and DNA methylation for all CpGs analyzed. Full size table

The formulas used to predict age in the six different age prediction models given in Table 2 were obtained from previous studies21,22,28 or personal communications by the authors of the models (Bekaert, Thong and Zbiec-Piekarska 1 age prediction models).

Table 2 Formulas of the different age prediction models used. Full size table

Evaluation and comparison of the six blood-based age prediction models

The predicted age obtained with the six age-prediction models was plotted against the chronological age (Fig. 2A). The first observation for all age prediction models was that there was no visible and statistically significant difference between men and women for their predicted age, indicating that the six models are not in fact influenced by gender (Fig. 2A, Supplementary Table 4), as had been assumed in the original studies where the models were developed. Correlation analysis indicated a strong correlation (0.783≤ r ≤0.883) between predicted and chronological age for the six models, which explained 61.3% to 77.8% of the age variation (Table 3). The Weidner model showed the lowest correlation in all individuals (r = 0.783) and in women (r = 0.755), and the second lowest correlation in men (r = 0.792). The Bekaert model, in contrast, presented the highest correlation in men (r = 0.883), in women (r = 0.888) and in all individuals (r = 0.883) (Table 3).

Figure 2 Comparison of the predicted ages obtained with the six age-prediction models. (A) Scatterplot of predicted age and chronological age obtained with the six age-prediction models. (B) Differences between chronological age and predicted age plotted against chronological age. Full size image

Table 3 Correlation between chronological age and predicted age obtained with the six age prediction models. Full size table

When the differences between chronological and predicted age were plotted for the six models, we observed that some models presented overestimations or underestimations of different magnitudes for predicted age compared to chronological age, and these over/under-estimations also seemed to be influenced by chronological age (Fig. 2B). Therefore we divided our cohort into three groups composed of young adults (Group I, aged 19–34 years, n = 34), middle-aged adults (Group II, aged 35–49 years, n = 33) and older adults (Group III, aged 50–65 years, n = 33), and we measured the mean and median differences between the predicted and the chronological age of the different groups (Supplementary Table 3). Contrary to gender, statistically significant differences were observed for all models between the three age groups indicating that the models have different capacity of age prediction depending on the age range of the samples (Supplementary Fig. 4). The models of Bekaert, Thong and Zbiec-Pierkarska 1 presented very slight over- and under-estimations of the predicted age compared to the chronological age with mean and median age differences of about 2.5 years or less when all individuals were considered (Fig. 2B, Supplementary Table 3). Moreover, these models all tended to slightly overestimate the age of younger individuals and to underestimate the age of older individuals (Fig. 2B, Supplementary Table 3). The Park and Weidner models presented overall overestimations of the predicted age (mean and median over-estimation of 4.74–7.41 years), which were stronger in younger individuals (mean and median over-estimation of 7.50–9.61 years, Fig. 2B, Supplementary Table 3). Finally, the Zbiec-Pierkarska 2 model tended to underestimate the predicted age (mean and median underestimation of 5.99 and 6.41 years respectively) more often in older individuals (mean and median under-estimation of around 10 years) than in younger individuals (mean and median underestimation of around 2 years, Fig. 2B, Supplementary Table 3).

The performance and accuracy of the six age prediction models were evaluated by calculating the mean absolute deviation (MAD), the standard error of estimate (SEE) and the percentage of correct predictions (PCP), considering a difference of 5, 7.5 and 10 years between the predicted and chronological ages for all individuals, as well as for men and for women, and for the three groups based on their chronological age (Table 4). When all individuals were considered, the models of Bekaert and Thong presented the best performance (MAD of 4.5 and 5.2 and SEE of 6.8 and 7.1), while the models of Zbiec-Piekarska 1 & 2 and Weidner presented a lower performance (MAD of 6.8–7.2 and SEE of 8.6–9.6) and the model of Park presented the lowest performance of all (MAD of 8.7 and SEE of 10.2); the same tendencies were observed when men and women were analyzed in two distinct groups (Table 4). Notably, the model of Bekaert, together with the models of Zbiec-Piekarska 2, Thong and Weidner showed the best performance for young adults (MAD of 4.2 and SEE of 5.8–6.3), middle-age adults (MAD of 4.5–4.7 and SEE of 6.8–7.6) and older adults (MAD of 4.7–4.9 and SEE of 6.8–7.7) respectively (Table 4). The poorest performance was observed in the groups of the young and middle-age adults with the Park and Weiner models (MAD of 8.9–9.9 and SEE of 10.3–11.8); while in older adults the poorest performance was observed with the Zbiec-Piekarska 2 model (MAD of 10.5 and SEE of 12.6, Table 4).

Table 4 Evaluation of the accuracy of the six age prediction models. Full size table

When a threshold of 5 years was chosen, and regardless of how the individuals were grouped, the age prediction accuracy was best in the Bekaert model (65–73% of correct predictions), while higher thresholds identified both the Bekaert and Thong models as giving the best age prediction accuracies (76–94% of correct predictions, followed by Zbiec-Piekarska 1 model (59−86% of correct predictions, Table 4). The age prediction accuracies of the Weidner and Zbiec-Piekarska 2 models were highest in the young (62–94% of correct predictions) and older (64–82% of correct predictions) adults regardless of the threshold applied, and were lowest in the older (21–53% of correct predictions) and young (15–48% of correct predictions) adults (Table 4). Finally, the Park model presented an overall low age prediction accuracy for all groups (24–36% of correct predictions with a threshold of 5 years), and this was less pronounced in the group of men and in the older individuals (Table 4).

In order to evaluate the impact of a second measure of DNA methylation on the age prediction performance, we performed a duplicate PCR and pyrosequencing experiment for ELOVL2 on all samples and compared the age predictions obtained with each replicate and with the mean of duplicates (Supplementary Fig. 6). While the age predictions calculated from each replicate dataset showed similar performances (MAD = 5.8–6.8 and SEE = 7.8–8.6), an improvement was observed when the predicted age was calculated with the mean of duplicates (MAD = 5.2 and SEE = 6.8) (Supplementary Fig. 6).