Age prediction of children and adolescents aged 6-17 years: an epigenome-wide analysis of DNA methylation

Li, Chunxiao; Gao, Wenjing; Gao, Ying; Chen, Yu; Lv, Jun; Ruo-Ran, LV; Duan, Jiali; Sun, Yingxian; Xiang-hui, Guo; Cao, Weihua; Li, Liming

doi:10.18632/aging.101445

Cited by 21 publications

(12 citation statements)

References 45 publications

(53 reference statements)

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“…We did not see any increased enrichment of age-related CpGs identified in previous childhood and adolescent studies with advancing age in our models (all P values > 0. 19), making it unlikely that our results represent a strong effect of age [69,70].…”

Section: Interpretation Of Main Findingsmentioning

confidence: 92%

DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies

et al. 2020

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Background DNA methylation has been shown to be associated with adiposity in adulthood. However, whether similar DNA methylation patterns are associated with childhood and adolescent body mass index (BMI) is largely unknown. More insight into this relationship at younger ages may have implications for future prevention of obesity and its related traits. Methods We examined whether DNA methylation in cord blood and whole blood in childhood and adolescence was associated with BMI in the age range from 2 to 18 years using both cross-sectional and longitudinal models. We performed meta-analyses of epigenome-wide association studies including up to 4133 children from 23 studies. We examined the overlap of findings reported in previous studies in children and adults with those in our analyses and calculated enrichment. Results DNA methylation at three CpGs (cg05937453, cg25212453, and cg10040131), each in a different age range, was associated with BMI at Bonferroni significance, P < 1.06 × 10−7, with a 0.96 standard deviation score (SDS) (standard error (SE) 0.17), 0.32 SDS (SE 0.06), and 0.32 BMI SDS (SE 0.06) higher BMI per 10% increase in methylation, respectively. DNA methylation at nine additional CpGs in the cross-sectional childhood model was associated with BMI at false discovery rate significance. The strength of the associations of DNA methylation at the 187 CpGs previously identified to be associated with adult BMI, increased with advancing age across childhood and adolescence in our analyses. In addition, correlation coefficients between effect estimates for those CpGs in adults and in children and adolescents also increased. Among the top findings for each age range, we observed increasing enrichment for the CpGs that were previously identified in adults (birth Penrichment = 1; childhood Penrichment = 2.00 × 10−4; adolescence Penrichment = 2.10 × 10−7). Conclusions There were only minimal associations of DNA methylation with childhood and adolescent BMI. With the advancing age of the participants across childhood and adolescence, we observed increasing overlap with altered DNA methylation loci reported in association with adult BMI. These findings may be compatible with the hypothesis that DNA methylation differences are mostly a consequence rather than a cause of obesity.

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Section: Interpretation Of Main Findingsmentioning

confidence: 92%

DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies

et al. 2020

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“…Based on information in the section above, developing a proxy measure of biological aging for humans still requires work but is a very dynamic and promising area of investigation with strong potential for translation. Some of the measures described-namely mitochondrial function, DNA methylation, and, to a lesser extent, cellular senescence and autophagy-are ready to be implemented based on several epidemiological studies, although refinements are always possible Choi et al, 2016;Cohen, Morissette-Thomas, Ferrucci, & Fried, 2016;Jylhävä, Pedersen, & Hägg, 2017;Jylhävä et al, 2014;Kananen et al, 2016;Kent & Fitzgerald, 2016;Kim & Jazwinski, 2015;Levine et al, 2018;Li et al, 2018;Marioni et al, 2019;Marttila et al, 2015;Putin et al, 2017;Sillanpää et al, 2018). Measures of telomere length are hampered by noise and wide longitudinal variations that cannot be explained by health events and at this stage are not useful for measuring biological age (Arai et al, 2015;Jodczyk, Fergusson, Horwood, Pearson, & Kennedy, 2014;Tomaska & Nosek, 2009).…”

Section: Connec Ting the B I Ology Of Ag Ing With Ag E-a Sso Ciatedmentioning

confidence: 99%

Measuring biological aging in humans: A quest

et al. 2019

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The global population of individuals over the age of 65 is growing at an unprecedented rate and is expected to reach 1.6 billion by 2050. Most older individuals are affected by multiple chronic diseases, leading to complex drug treatments and increased risk of physical and cognitive disability. Improving or preserving the health and quality of life of these individuals is challenging due to a lack of well‐established clinical guidelines. Physicians are often forced to engage in cycles of “trial and error” that are centered on palliative treatment of symptoms rather than the root cause, often resulting in dubious outcomes. Recently, geroscience challenged this view, proposing that the underlying biological mechanisms of aging are central to the global increase in susceptibility to disease and disability that occurs with aging. In fact, strong correlations have recently been revealed between health dimensions and phenotypes that are typical of aging, especially with autophagy, mitochondrial function, cellular senescence, and DNA methylation. Current research focuses on measuring the pace of aging to identify individuals who are “aging faster” to test and develop interventions that could prevent or delay the progression of multimorbidity and disability with aging. Understanding how the underlying biological mechanisms of aging connect to and impact longitudinal changes in health trajectories offers a unique opportunity to identify resilience mechanisms, their dynamic changes, and their impact on stress responses. Harnessing how to evoke and control resilience mechanisms in individuals with successful aging could lead to writing a new chapter in human medicine.

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“…Our models perform better in young subjects but poorly for elders over 60 years old. We suppose that the deviation might attribute to the fact that adults and elders suffer from more confounding factors in the aspect of medication, smoking, or alcohol, which are not easily accessible for children and adolescents, as previously reported [14]. Another possible reason might be the smaller sample size included in our study especially elder samples, which leads to bias.…”

Section: Discussionmentioning

confidence: 83%

“…Hence, several sites of gene ELOVL2 is believed to be the most hopeful locus for age prediction [13]. An age prediction model of 83 newly CpG sites identified through Epigenome-wide association analysis with a correlation of 0.99 and the error of 0.23 years indicated that the chronological age can be accurately predicted among children and adolescents using DNA methylation biomarker [14]. Although a DNA methylation-based age prediction method can be developed in a way relatively high accuracy for individual age estimation.…”

Section: Introductionmentioning

confidence: 99%

Circular RNA as a potential biomarker for forensic age prediction using multiple machine learning models: A preliminary study

Wang

et al. 2020

Preprint

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In forensic science, accurate estimation of the age of a victim or suspect can facilitate the investigators to narrow a search and aid in solving a crime. Aging is a complex process associated with various molecular regulation on DNA or RNA levels. Recent studies have shown that circular RNAs (circRNAs) upregulate globally during aging in multiple organisms such as mice and elegans because of their ability to resist degradation by exoribonucleases. In the current study, we attempted to investigate circRNAs’ potential capability of age prediction. Here, we identified more than 40,000 circRNAs in the blood of thirteen Chinese unrelated healthy individuals with ages of 20-62 years according to their circRNA-seq profiles. Three methods were applied to select age-related circRNAs candidates including false discovery rate, lasso regression, and support vector machine. The analysis uncovered a strong bias for circRNA upregulation during aging in human blood. A total of 28 circRNAs were chosen for further validation in 50 healthy unrelated subjects aged between 19 and 72 years by RT-qPCR and finally, 7 age-related circRNAs were chosen for final age prediction models. Several different algorithms including multivariate linear regression (MLR), regression tree, bagging regression, random forest regression (RFR), and support vector regression (SVR) were compared based on root mean square error (RMSE) and mean average error (MAE) values. Among five modeling methods, random forest regression (RFR) performed better than the others with an RMSE value of 5.072 years and an MAE value of 4.065 years (R2 = 0.902). In this preliminary study, we firstly used circRNAs as additional novel age-related biomarkers for developing forensic age estimation models. We propose that the use of circRNAs to obtain additional clues for forensic investigations and serve as aging indicators for age prediction would become a promising field of interest.Author summaryIn forensic investigations, estimation of the age of biological evidence recovered from crime scenes can provide additional information such as chronological age or the appearance of a culprit, which could give valuable investigative leads especially when there is no eyewitness available. Hence, generating an accurate model for age prediction using body fluids such as blood commonly seen at a crime scene can be of vital importance. Various molecular changes on DNA or RNA levels were discovered that they upregulated or downregulated during a person’s lifetime. Although some biomarkers have been proved to be associated with aging and used to predict age, several disadvantages such as low sensitivity, prediction accuracy, instability and susceptibility of diseases or immune states, thus limiting their applicability in the field of age estimation. Here, we utilized a novel biomarker namely circular RNA (circRNA) to generate highly accurate age prediction models. We propose that circRNA is more suitable for forensic degradation samples because of its unique molecular structure. This preliminary research offers a new thought for exploring potential biomarker for age prediction.

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Age prediction of children and adolescents aged 6-17 years: an epigenome-wide analysis of DNA methylation

Cited by 21 publications

References 45 publications

DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies

DNA methylation and body mass index from birth to adolescence: meta-analyses of epigenome-wide association studies

Measuring biological aging in humans: A quest

Circular RNA as a potential biomarker for forensic age prediction using multiple machine learning models: A preliminary study

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