Abstract
Several blood-based age prediction models have been developed using less than a dozen to more than a hundred DNA methylation biomarkers. Only one model (Z-P1) based on pyrosequencing has been developed using DNA methylation of a single locus located in the ELOVL2 promoter, which is considered as one of the best age-prediction biomarker. Although multi-locus models generally present better performances compared to the single-locus model, they require more DNA and present more inter-laboratory variations impacting the predictions. Here we developed 17,018 single-locus age prediction models based on DNA methylation of the ELOVL2 promoter from pooled data of four different studies (training set of 1,028 individuals aged from 0 and 91 years) using six different statistical approaches and testing every combination of the 7 CpGs, aiming to improve the prediction performances and reduce the effects of inter-laboratory variations. Compared to Z-P1 model, three statistical models with the optimal combinations of CpGs presented improved performances (MAD of 4.41–4.77 in the testing set of 385 individuals) and no age-dependent bias. In an independent testing set of 100 individuals (19–65 years), we showed that the prediction accuracy could be further improved by using different CpG combinations and increasing the number of technical replicates (MAD of 4.17).
Highlights
Aging is a complex biological process influenced by both genetic and environmental factors and characterized by the progressive decline of several physiological, cellular and molecular functions[1,2]
We evaluated multiple quadratic regression (MQR), as some CpGs from ELOVL2 were shown to present a better correlation with the chronological age using a quadratic rather than a linear regression model[27], support-vector machines with radial (SVMr), linear (SVMl) and polynomial (SVMp) functions, the latter function presenting the best age prediction accuracy in a study using DNA methylation of 12 multi-locus CpG sites obtained by NGS that evaluated 17 statistical m odels[38], gradient boosting regressor (GBR) that presented the best age prediction accuracy in a 6 loci age-prediction model using epigenotyping microarray DNA methylation data[39] and missMDA40,41, which has never been used to date in an age prediction model
A previous study showed that age-related DNA methylation changes were logarithmic[42], we did not include this function in our regression models, as the relationship between chronological age and DNA methylation of ELOVL2 was better fitted in our data by a linear or quadratic regression for most CpGs (Supplementary Table 3)
Summary
Aging is a complex biological process influenced by both genetic and environmental factors and characterized by the progressive decline of several physiological, cellular and molecular functions[1,2]. The models presenting the best age prediction accuracy were the multi-locus models of Bekaert and Thong (MAD of 4.5–5.2 years and SEE of 6.8–7.2 years) followed by the single-locus model of Zbiec-Piekarska 1 (MAD of 6.8 year and SEE of 8.6 years) while the models presenting the worst age prediction performances (MAD of 7.2–8.7 years and SEE of 9.2–10.3 years) were the three other multi-locus models of Weidner, Park and Zbiec-Piekarska 226 The latter MAD were much higher than the ones described in their original studies, and we suggested that these differences could be principally attributed to inter-laboratory variations during the implementation of the different pyrosequencing assays[26]. The use of several loci and pyrosequencing assays might increase the variability in the predicted age estimates of the models when run in different laboratories
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