Epigenetic clocks for gestational age: statistical and study design considerations

Simpkin, Andrew; Suderman, Matthew; Howe, Laura D

doi:10.1186/s13148-017-0402-y

Cited by 24 publications

(35 citation statements)

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“…Some reduction, however, was expected because the Bohlin et al [ 10 ] model was trained and tested in distinct subsets of the same Norwegian cohort. We elected not to use the model of Knight et al [ 9 ] because we were less confident that it would produce meaningful GAA estimates given its low correlation with GA in ARIES ( r = 0.37) [ 21 ].…”

Section: Resultsmentioning

confidence: 99%

“…Maternal pre-pregnancy overweight and obese status was associated with higher GAA compared with maternal pre-pregnancy body mass index (BMI) of < 25 kg/m 2 (MD = 1.6 days, 95% CI 0.7, 2.6 for overweight; MD = 2.9 days, 95% CI 1.3, 4.4 for obese, p < .001, see Table 4 ) after adjusting for sex, cell type proportion and parental socioeconomic factors. As GAA calculated using Bohlin et al methods [ 10 ] is correlated with birthweight [ 21 ], we further adjusted these models including birthweight as a covariate to assess whether birthweight may be driving this effect. The results were not substantially different when additionally adjusting for birthweight (Additional file 1 : Table S3).…”

Section: Resultsmentioning

confidence: 99%

“…Another explanation for the discrepancies between our findings and the findings of Girchenko et al [ 23 ] is their use of the Knight et al [ 9 ] GA prediction model rather than the Bohlin et al [ 10 ] model applied in this study. We have previously noted several key methodological differences in the derivation of the Knight et al [ 9 ] and the Bohlin et al [ 10 ] models that influence the accuracy of the GA prediction in this cohort [ 21 ], such as the inclusion of preterm infants in the test set of the Knight et al model [ 9 ] which is inappropriate for a data set with few pre-term births (as in this study). Additionally, the number of CpGs (148) included in the Knight et al [ 9 ] training model was close to the sample size of the training set (207) and the model was then tested with a much larger sample size which may have resulted in an overfitting of the model.…”

Section: Discussionmentioning

confidence: 99%

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Epigenetic gestational age acceleration: a prospective cohort study investigating associations with familial, sociodemographic and birth characteristics

et al. 2018

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BackgroundGestational age at delivery is associated with health and social outcomes. Recently, cord blood DNA methylation data has been used to predict gestational age. The discrepancy between gestational age predicted from DNA methylation and determined by ultrasound or last menstrual period is known as gestational age acceleration. This study investigated associations of sex, socioeconomic status, parental behaviours and characteristics and birth outcomes with gestational age acceleration.ResultsUsing data from the Avon Longitudinal Study of Parents and Children (n = 863), we found that pre-pregnancy maternal overweight and obesity were associated with greater gestational age acceleration (mean difference = 1.6 days, 95% CI 0.7 to 2.6, and 2.9 days, 95% CI 1.3 to 4.4, respectively, compared with a body mass index < 25 kg/m2, p < .001). There was evidence of an association between male sex and greater gestational age acceleration. Greater gestational age acceleration was associated with higher birthweight, birth length and head circumference of the child (mean differences per week higher gestational age acceleration: birthweight 0.1 kg, 95% CI 0.1 to 0.2, p < .001; birth length 0.4 cm, 95% CI 0.2 to 0.7, p < .001; head circumference 0.2 cm, 95% CI 0.1 to − 0.4, p < .001). There was evidence of an association between gestational age acceleration and mode of delivery (assisted versus unassisted delivery, odds ratio = 0.9 per week higher gestational age acceleration, 95% CI 0.7, 1.3 (p = .05); caesarean section versus unassisted delivery, odds ratio = 0.6, 95% CI 0.4 to 0.9 per week higher gestational age acceleration (p = .05)). There was no evidence of association for other parental and perinatal characteristics.ConclusionsThe associations of higher maternal body mass index and larger birth size with greater gestational age acceleration may imply that maternal overweight and obesity is associated with more rapid development of the fetus in utero. The implications of gestational age acceleration for postnatal health warrant further investigation.Electronic supplementary materialThe online version of this article (10.1186/s13148-018-0520-1) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Epigenetic gestational age acceleration: a prospective cohort study investigating associations with familial, sociodemographic and birth characteristics

et al. 2018

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“…For each sample, the estimated level of DNA methylation at each CpG site was reported as a beta value (β), ranging from 0 (no cytosine methylation) to 1 (complete cytosine methylation) (18). model (11) due to its stronger correlation with GA in ARIES (R=0.65 compared to R=0.37) (12,20). GA of subjects was gathered in ALSPAC from clinical records and was measured from last menstrual period (LMP) for the majority of subjects, although this measure was updated for some individuals following a dating ultrasound.…”

Section: Dna Methylationmentioning

confidence: 99%

Epigenetic gestational age and trajectories of weight and height during childhood: a prospective cohort study

Bright

Howe

Khouja

et al. 2019

Preprint

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BackgroundDifferences between an individual's estimated epigenetic gestational age (EGA) and their actual gestational age (GA) are defined as gestational age acceleration (GAA). GAA is associated with increased birthweight and birth length. Whether these associations persist through childhood is yet to be investigated. MethodsWe examined the association between GAA and trajectories of height and weight from birth to 10 years (n=785) in a British birth cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). EGA of participants was estimated using DNA methylation data from cord blood using a recently-developed prediction model. GA of participants was gathered in ALSPAC from clinical records and was measured from last menstrual period (LMP) for most participants. GAA of participants, measured in weeks, was calculated as the residuals from a regression model of EGA on actual GA. Height and weight were obtained from several sources including birth records, research clinics, routine child health clinics, links to health visitor records and parent-reported measures from questionnaires. Analyses were performed using linear spline multilevel models and adjusted for maternal age, maternal pre-pregnancy BMI, maternal smoking during pregnancy and maternal education. ResultsIn adjusted analyses, offspring with a one-week greater GAA were born on average 0.14 kg heavier (95% Confidence Interval (CI) 0.09, 0.19) and 0.55 cm taller (95% CI 0.33, 0.78) at birth. These differences in weight persisted up to approximately age 9 months but thereafter began to attenuate and reduce in magnitude. From age 5 years onwards, the association between GAA and weight reversed such that GAA was associated with lower weight and this association strengthened with age (mean difference at age 10 years -0.60 kg (95% CI, -1.19, -0.01)). Differences in height persisted only up to age 9 months (mean difference at 9 months 0.15 cm, (95% CI -0.09, 0.39)). From age 9 months to age 10 years, offspring with a one-week greater GAA were of comparable height to those with no GAA (mean difference at age 10 years -0.07 cm, (95% CI -0.64, 0.50)). ConclusionsGestational age acceleration is associated with increased birth weight and length and these differences persist to age 9 months. From 5 years onwards, the association of GAA and weight reverses such that by age 10 years greater GAA is associated with lower childhood weight. Further work is required to examine whether the weight effects of GAA strengthen further through adolescence and into early adulthood.

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“…Epigenetic mechanisms act to regulate gene expression developmentally via chemical modifications to DNA and histone proteins (Bernstein et al, 2007), conferring cell-typespecific patterns of gene expression and differing markedly between tissues and celltypes (Mendizabal and Yi, 2016). There has been recent interest in the dynamic changes in epigenetic processes over the life course, and a number of 'epigenetic clocks' based on one specific epigenetic modification -DNA methylation (DNAm) -have been developed that appear to be highly predictive of chronological age (Campisi and Vijg, 2009;Horvath, 2013;Horvath et al, 2012Horvath et al, , 2018Knight et al, 2016;Oberdoerffer and Sinclair, 2007;Simpkin et al, 2017). The landmark DNAm clock was developed by Horvath (Horvath, 2013), who applied elastic net regression to Illumina DNAm array data from a large number of samples derived from a range of tissues (n = ~ 8,000…”

Section: Introductionmentioning

confidence: 99%

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

Shireby

Davies

Francis

et al. 2020

Preprint

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Human DNA-methylation data have been used to develop biomarkers of ageingreferred to 'epigenetic clocks' -that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into 'training' and 'testing' samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts. mortem 6 samples were split into a "training" dataset (used to determine the DNAm sites included in the model and their weighted coefficients) and a "testing" dataset (used to profile the performance of the proposed model). To reduce the effects of experimental batch in our model, we maximised the number of different datasets included in the training data by combining the ten cohorts and randomly assigning individuals within them to either the training or testing dataset in a 3:1 ratio ( Table 1). In total, our training dataset (age range = 1-108 years, median = 57 years; see Supplementary Fig. S1) comprised DNAm data from 1,047 cortex samples (derived from 832 donors) and our testing dataset (age range = 1-108 years, median = 56 years; see Supplementary Fig. S1) comprised DNAm data from 350 cortex samples (derived from 323 donors). Individuals with a diagnosis of Alzheimer's disease and other major neurological phenotypes were excluded from our analysis given the ...

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Epigenetic clocks for gestational age: statistical and study design considerations

Cited by 24 publications

References 6 publications

Epigenetic gestational age acceleration: a prospective cohort study investigating associations with familial, sociodemographic and birth characteristics

Epigenetic gestational age acceleration: a prospective cohort study investigating associations with familial, sociodemographic and birth characteristics

Epigenetic gestational age and trajectories of weight and height during childhood: a prospective cohort study

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

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