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Background Ageing displays clear sexual dimorphism, evident in both morbidity and mortality. Ageing is also associated with changes in DNA methylation, but very little focus has been on the sex chromosomes, potential biological contributors to the observed sexual dimorphism. Here, we sought to identify DNA methylation changes associated with ageing in the Y and X chromosomes, by utilizing datasets available in data repositories, comprising in total of 1240 males and 1191 females, aged 14–92 years. Results In total, we identified 46 age-associated CpG sites in the male Y, 1327 age-associated CpG sites in the male X, and 325 age-associated CpG sites in the female X. The X chromosomal age-associated CpGs showed significant overlap between females and males, with 122 CpGs identified as age-associated in both sexes. Age-associated X chromosomal CpGs in both sexes were enriched in CpG islands and depleted from gene bodies and showed no strong trend towards hypermethylation nor hypomethylation. In contrast, the Y chromosomal age-associated CpGs were enriched in gene bodies, and showed a clear trend towards hypermethylation with age. Conclusions Significant overlap in X chromosomal age-associated CpGs identified in males and females and their shared features suggest that despite the uneven chromosomal dosage, differences in ageing-associated DNA methylation changes in the X chromosome are unlikely to be a major contributor of sex dimorphism in ageing. While age-associated CpGs showed good replication across datasets in the present study, only a limited set of previously reported age-associated CpGs were replicated. One contributor to the limited overlap are differences in the age range of individuals included in each data set. Further study is needed to identify biologically significant age-associated CpGs in the sex chromosomes. Supplementary Information The online version contains supplementary material available at 10.1186/s13072-021-00407-6.

Additional file 1. Supplementary tables and figures.

Additional file 1. Supplementary Figures S1-6, Supplementary Tables S1-3 and S11-13 and Supplementary materials and methods.

Additional file 5: Figures and Supplementary analysis.

The research focuses on the co-morbidity study of atherosclerosis and osteoporosis.

Smoking as a major risk factor for morbidity affects numerous regulatory systems of the human body including DNA methylation. Most of the previous studies with genome-wide methylation data are based on conventional association analysis and earliest threshold-based gene set analysis that lacks sensitivity to be able to reveal all the relevant effects of smoking. The aim of the present study was to investigate the impact of active smoking on DNA methylation at three biological levels: 5'-C-phosphate-G-3' (CpG) sites, genes and functionally related genes (gene sets). Gene set analysis was done with mGSZ, a modern threshold-free method previously developed by us that utilizes all the genes in the experiment and their differential methylation scores. Application of such method in DNA methylation study is novel. Epigenome-wide methylation levels were profiled from Young Finns Study (YFS) participants' whole blood from 2011 follow-up using Illumina Infinium Hu-manMethylation450 BeadChips. We identified three novel smoking related CpG sites and replicated 57 of the previously identified ones. We found that smoking is associated with hypomethylation in shore (genomic regions 0-2 kilobases from CpG island). We identified smoking related methylation changes in 13 gene sets with false discovery rate (FDR)

The linear regression model explaining the variation in ΔAGE of middle-aged adults. We used BMI, gender, smoking, and alcohol consumption as predictors. Of these, the BMI was the best independent predictor of ΔAGE (p=0.002). The regression method was stepwise, criteria=PIN(0.05) POUT(0.10). (DOCX 14 kb)

The quality control of the DNA methylation of old DNA samples. YFS1986 samples (young adults) were collected 30 years ago, and the methylation profiling was performed in different facility than others. To ensure that these samples did not include technical biases, we compared the mean methylation of X chromosome in YFS1986 (X axis) and YFS2011 (Y axis) follow-up samples. These two cohorts cannot be distinguished from the plot, and only gender stands out, as expected in the case of DNA methylation. (DOCX 59 kb)

Gender-specific correlations of ΔAGE and BMI in middle-aged individuals. In panel A, scatterplot of ΔAGE and BMI in middle-aged female individuals (r=−0265, p=0.05). In panel B, scatterplot of ΔAGE and BMI in middle-aged male individuals (r=−0.270, p=0.022). (DOCX 51 kb)

Changes in the DNA methylation (DNAm) landscape have been implicated in aging and cellular senescence. To unravel the role of specific DNAm patterns in late-life survival, we performed genome-wide methylation profiling in nonagenarians (n=111) and determined the performance of the methylomic predictors and conventional risk markers in a longitudinal setting. The survival model containing only the methylomic markers was superior in terms of predictive accuracy compared with the model containing only the conventional predictors or the model containing conventional predictors combined with the methylomic markers. At the 2.55-year follow-up, we identified 19 mortality-associated (false-discovery rate