Dataset Information

DNA methylation clocks for estimating biological age in Chinese cohorts.

ABSTRACT: Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation (DNAm) at specific CpG sites. However, a systematic comparison between DNA methylation data and other omics datasets has not yet been performed. Moreover, available DNAm age predictors are based on datasets with limited ethnic representation. To address these knowledge gaps, we generated and analyzed DNA methylation datasets from two independent Chinese cohorts, revealing age-related DNAm changes. Additionally, a DNA methylation aging clock (iCAS-DNAmAge) and a group of DNAm-based multi-modal clocks for Chinese individuals were developed, with most of them demonstrating strong predictive capabilities for chronological age. The clocks were further employed to predict factors influencing aging rates. The DNAm aging clock, derived from multi-modal aging features (compositeAge-DNAmAge), exhibited a close association with multi-omics changes, lifestyles, and disease status, underscoring its robust potential for precise biological age assessment. Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace, providing the basis for evaluating aging intervention strategies.

SUBMITTER: Zheng Z

PROVIDER: S-EPMC11259550 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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DNA methylation clocks for estimating biological age in Chinese cohorts.

Zheng Zikai Z Li Jiaming J Liu Tianzi T Fan Yanling Y Zhai Qiao-Cheng QC Xiong Muzhao M Wang Qiao-Ran QR Sun Xiaoyan X Zheng Qi-Wen QW Che Shanshan S Jiang Beier B Zheng Quan Q Wang Cui C Liu Lixiao L Ping Jiale J Wang Si S Gao Dan-Dan DD Ye Jinlin J Yang Kuan K Zuo Yuesheng Y Ma Shuai S Yang Yun-Gui YG Qu Jing J Zhang Feng F Jia Peilin P Liu Guang-Hui GH Zhang Weiqi W

Protein & cell 20240701 8

Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation (DNAm) at specific CpG sites. However, a systematic comparison between DNA methylation data and other omics datasets has not yet been performed. Moreover, available DNAm age predictors are based on datasets with limited ethnic representation. To address these knowledge gaps, we generated and analyzed DNA methylation datasets from two independent Chinese cohorts, revealing age-related DNA ...[more]

PMID: 38482631

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Project description:Study questionAre reproductive characteristics associated with genome-wide DNA methylation and epigenetic age?Summary answerOur data suggest that increasing parity is associated with differences in blood DNA methylation and small increases in epigenetic age.What is known alreadyA study of 397 young Filipino women (ages 20-22) observed increasing epigenetic age with an increasing number of pregnancies.Study design, size, durationWe used data from 2356 non-Hispanic white women (ages 35-74) enrolled in the Sister Study cohort.Participants/materials, setting, methodsData on reproductive history were ascertained via questionnaire. Of the 2356 women, 1897 (81%) reported at least one live birth. Among parous women, 487 (26%) women reported ever experiencing a pregnancy complication. Three epigenetic clocks (i.e. Hannum, Horvath and Levine) and genome-wide methylation were measured in DNA from whole blood using Illumina's HumanMethylation450 BeadChip. We estimated association β-values and 95% CIs using linear regression.Main results and the role of chanceAll three epigenetic clocks showed weak associations between number of births and epigenetic age (per live birth; Hannum: β = 0.16, 95% CI = 0.02, 0.29, P = 0.03; Horvath: β = 0.12, 95% CI = -0.04, 0.27, P = 0.14; Levine: β = 0.27, 95% CI = 0.08, 0.45, P = 0.01); however, additional adjustment for current BMI attenuated the associations. Among parous women, a history of abnormal glucose tolerance during pregnancy was associated with increased epigenetic age by the Hannum clock (β = 0.96; 95% CI = 0.10, 1.81; P = 0.03) and Levine clocks (β = 1.69; 95% CI = 0.54, 2.84; P < 0.01). In epigenome-wide analysis, increasing parity was associated with methylation differences at 17 CpG sites (Bonferroni corrected P≤ 1.0 × 10-7).Limitations, reasons for cautionWe relied on retrospective recall to ascertain reproductive history and pregnancy complications.Wider implications of the findingsOur findings suggest that parity is associated with small increases in epigenetic age and with DNA methylation at multiple sites in the genome.Study funding/competing interest(s)This research was supported by the Intramural Research program of the NIH, National Institute of Environmental Health Sciences (Z01-ES049033, Z01-ES049032 and Z01-ES044055). None of the authors have a conflict of interest.Trial registration numberNot applicable.

Project description:BackgroundDNA methylation (DNAm) age has been widely accepted as an epigenetic biomarker for biological aging. Emerging evidence suggests that DNAm age can be tissue-specific and female breast tissue ages faster than other parts of the body. The Horvath clock, which estimates DNAm age across multiple tissues, has been shown to be poorly calibrated in breast issue. We aim to develop a model to estimate breast tissue-specific DNAm age.MethodsGenome-wide DNA methylation sequencing data were generated for 459 normal, 107 tumor, and 45 paired adjacent-normal breast tissue samples. We determined a novel set of 286 breast tissue-specific clock CpGs using penalized linear regression and developed a model to estimate breast tissue-specific DNAm age. The model was applied to estimate breast tissue-specific DNAm age in different breast tissue types and in tumors with distinct clinical characteristics to investigate cancer-related aging effects.ResultsOur estimated breast tissue-specific DNAm age was highly correlated with chronological age (r = 0.88; p = 2.9 × 10-31) in normal breast tissue. Breast tumor tissue samples exhibited a positive epigenetic age acceleration, where DNAm age was on average 7 years older than respective chronological age (p = 1.8 × 10-8). In age-matched analyses, tumor breast tissue appeared 12 and 13 years older in DNAm age than adjacent-normal and normal breast tissue (p = 4.0 × 10-6 and 1.0 × 10-6, respectively). Both HER2+ and hormone-receptor positive subtypes demonstrated significant acceleration in DNAm ages (p = 0.04 and 3.8 × 10-6, respectively), while no apparent DNAm age acceleration was observed for triple-negative breast tumors. We observed a non-linear pattern of epigenetic age acceleration with breast tumor grade. In addition, early-staged tumors showed a positive epigenetic age acceleration (p = 0.003) while late-staged tumors exhibited a non-significant negative epigenetic age acceleration (p = 0.10).ConclusionsThe intended applications for this model are wide-spread and have been shown to provide biologically meaningful results for cancer-related aging effects in breast tumor tissue. Future studies are warranted to explore whether breast tissue-specific epigenetic age acceleration is predictive of breast cancer development, treatment response, and survival as well as the clinical utility of whether this model can be extended to blood samples.

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DNA methylation clocks for estimating biological age in Chinese cohorts.

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DNA methylation clocks for estimating biological age in Chinese cohorts.

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