Aging of blood can be tracked by DNA methylation changes at just three CpG sites

Abstract

Background: Human aging is associated with DNA methylation changes at specific sites in the genome. These epigenetic modifications may be used to track donor age for forensic analysis or to estimate biological age.

Results: We perform a comprehensive analysis of methylation profiles to narrow down 102 age-related CpG sites in blood. We demonstrate that most of these age-associated methylation changes are reversed in induced pluripotent stem cells (iPSCs). Methylation levels at three age-related CpGs--located in the genes ITGA2B, ASPA and PDE4C--were subsequently analyzed by bisulfite pyrosequencing of 151 blood samples. This epigenetic aging signature facilitates age predictions with a mean absolute deviation from chronological age of less than 5 years. This precision is higher than age predictions based on telomere length. Variation of age predictions correlates moderately with clinical and lifestyle parameters supporting the notion that age-associated methylation changes are associated more with biological age than with chronological age. Furthermore, patients with acquired aplastic anemia or dyskeratosis congenita--two diseases associated with progressive bone marrow failure and severe telomere attrition--are predicted to be prematurely aged.

Conclusions: Our epigenetic aging signature provides a simple biomarker to estimate the state of aging in blood. Age-associated DNA methylation changes are counteracted in iPSCs. On the other hand, over-estimation of chronological age in bone marrow failure syndromes is indicative for exhaustion of the hematopoietic cell pool. Thus, epigenetic changes upon aging seem to reflect biological aging of blood.

Figure 1

Age-associated DNAm is reversed by reprogramming into induced pluripotent stem cells. (a) A heatmap of 102 AR-CpG sites from 575 DNAm profiles derived from blood cells from donors of different ages (HumanMethylation27 BeadChip platform). All of these loci revealed relatively linear DNAm changes during aging (r < −0.85 or r > 0.85). (b) Based on these AR-CpGs, we generated a multivariate model to predict donor age and these predictions were compared to the corresponding chronological age. A combination of all 102 AR-CpGs facilitated reliable age predictions with a mean absolute deviation (MAD) of about 3.34 years. (c) Age-related-CpGs were subsequently analyzed in mesenchymal stromal cells (MSCs), induced pluripotent stem cells (iPSCs), and embryonic stem cells (ESCs) (heatmap clustered by Euclidean distance). Overall, AR-CpGs that are hypomethylated during aging are highly methylated in pluripotent stem cells and vice versa. (d) Subsequently, we used a multivariate model to predict donor age in these cells (early passage, P2 or P3; late passage, P7 to P16). Notably, iPSCs generated from these MSCs as well as ESCs were predicted to be of negative age, indicating that AR-DNAm changes are, overall, reversed by reprogramming into pluripotent cells.

Figure 2

Development of the epigenetic aging signature. (a) The most relevant AR-CpGs were selected by iterative division of 575 DNAm profiles into training and testing sets (different split ratios). Age predictions were made for training sets using either 51 AR-CpGs or subsets of 5 CpGs. The results indicated that subsets with five CpGs (selected by recursive feature elimination) can enable age predictions with a mean absolute deviation (MAD) from chronological age of less than 6 years. (b) The frequency of occurrence of individual AR-CpGs in the best performing subsets of five CpGs. Five specific CpGs occurred in more than 50% of these filtered subsets and hence seemed to provide the best complement for age predictions. (c) DNAm at relevant AR-CpG sites was subsequently analyzed by pyrosequencing after bisulfite conversion. The sequences surrounding three of the five AR-CpGs were particularly suitable for this approach (CpG sites represented on the HumanMethylation27 BeadChip platform are indicated). (d) DNAm levels at these AR-CpGs were analyzed in a training set from 82 blood samples. The results were in line with the microarray data and revealed a clear age-associated correlation for each of the three CpGs. For cg17861230 (PDE4C) this correlation was even better at a neighboring CpG locus, which was therefore preferred for further analysis. (e) Based on the results with these three AR-CpGs, we generated a multivariate model that enabled relatively precise age predictions (MAD of 5.4 years). (f) Notably, the precision was even slightly higher when we validated this method in an independent set of 69 samples (MAD of 4.5 years).

Figure 3

Parameters with age-independent impacts on AR-CpGs. Age predictions with our epigenetic aging signature were associated with various clinical and lifestyle parameters (105 samples from the HNR study). Deviations from chronological age revealed a moderate association with (a) gender (P = 0.28), (b) body mass index (BMI; P = 0.67), (c) alcohol consumption (P = 0.049), and (d) number of children (P = 0.0043 for females).

Figure 4

Age-related DNAm correlates with telomere length. (a) Telomere length of granulocytes was analyzed by flow-FISH in samples from 104 healthy donors (grey), patients with aplastic anemia (AA; red) and dyskeratosis congenita (DKC; blue). (b, c) Age predictions with our epigenetic aging signature demonstrated that several patients with AA or DKC - particularly those with telomere attrition - were predicted to be significantly older than their chronological age (in comparison to age predictions for the validation set in Figure 2f).