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Associations between maternal risk factors of adverse pregnancy and birth outcomes and the offspring… Girchenko, Polina; Lahti, Jari; Czamara, Darina; Knight, Anna K; Jones, Meaghan J; Suarez, Anna; Hämäläinen, Esa; Kajantie, Eero; Laivuori, Hannele; Villa, Pia M; Reynolds, Rebecca M; Kobor, Michael S; Smith, Alicia K; Binder, Elisabeth B; Räikkönen, Katri May 8, 2017

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SHORT REPORT Open AccessAssociations between maternal risk factorsof adverse pregnancy and birth outcomesand the offspring epigenetic clock ofgestational age at birthPolina Girchenko1* , Jari Lahti1,2, Darina Czamara3, Anna K. Knight11, Meaghan J. Jones14, Anna Suarez1,Esa Hämäläinen5, Eero Kajantie6,7, Hannele Laivuori8,9, Pia M. Villa8, Rebecca M. Reynolds10, Michael S. Kobor14,Alicia K. Smith11,12,13, Elisabeth B. Binder3,4 and Katri Räikkönen1AbstractBackground: A recent study has shown that it is possible to accurately estimate gestational age (GA) at birth fromthe DNA methylation (DNAm) of fetal umbilical cord blood/newborn blood spots. This DNAm GA predictor mayprovide additional information relevant to developmental stage. In 814 mother-neonate pairs, we evaluated theassociations between DNAm GA and a number of maternal and offspring characteristics. These characteristicsreflect prenatal environmental adversity and are expected to influence newborn developmental stage.Results: DNAm GA acceleration (GAA; i.e., older DNAm GA than chronological GA) of the offspring at birth wasassociated with maternal age of over 40 years at delivery, pre-eclampsia and fetal demise in a previous pregnancy,maternal pre-eclampsia and treatment with antenatal betamethasone in the index pregnancy, lower neonatal birthsize, lower 1-min Apgar score, and female sex. DNAm GA deceleration (GAD; i.e., younger DNAm GA thanchronological GA) of the offspring at birth was associated with insulin-treated gestational diabetes mellitus (GDM)in a previous pregnancy and Sjögren’s syndrome. These findings were more accentuated when the DNAm GAcalculation was based on the raw difference between DNAm GA and GA than on the residual from the linearregression of DNAm GA on GA.Conclusions: Our findings show that variations in the DNAm GA of the offspring at birth are associated with anumber of maternal and offspring characteristics known to reflect exposure to prenatal environmental adversity.Future studies should be aimed at determining if this biological variation is predictive of developmental adversity.Keywords: Aging, Cord blood methylation, Epigenetic clock, Gestational age, Prenatal programmingBackgroundBiomarkers of cellular aging have attracted increasing at-tention over the past few years. Such biomarkers holdthe potential to identify individuals who are at risk ofaging-related diseases so that they may be offered timely,targeted preventive interventions, hopefully decades be-fore the onset of disease. DNA methylation (DNAm) isan epigenetic mechanism characterized by the additionof one methyl group primarily to cytosine-phosphate-guanine (CpG) sites on DNA. The epigenome is knownto undergo age-related changes [1–6], and specific meth-ylated CpG sites have been strongly correlated withchronological age in humans. Hannum et al. identified71 CpG sites in whole blood [7], and Horvath et al. iden-tified 353 CpG sites from multiple tissues that couldpredict chronological age with high accuracy (r > 0.91).The median absolute difference between these methyla-tion age biomarkers and chronological age has beenshown to vary from between 2.9 and 4.9 years [8]. Epi-genetic age acceleration (AA; higher epigenetic thanchronological age) has been associated with negative* Correspondence: polina.girchenko@helsinki.fi1Institute of Behavioral Sciences, University of Helsinki, Helsinki 00014, FinlandFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Girchenko et al. Clinical Epigenetics  (2017) 9:49 DOI 10.1186/s13148-017-0349-zhealth outcomes [9–11], and a recent meta-analysis ofover 13,000 individuals has confirmed that it can predictall-cause mortality [12].Epidemiological studies have shown that exposure toadverse environmental events in the prenatal period pre-dicts increased risk of aging-related diseases [13–18].These studies are consistent with the Developmental Or-igins of Health and Diseases (DOHaD) hypothesis [19],which proposes that prenatal exposures alter develop-mental trajectories [19]. However, it remains unclearwhether epigenetic AA could identify individuals at birthwho were exposed to environmental adversity in the pre-natal period. A recent study assessed epigenetic AA inperipheral blood from children and adolescents based onthe Hannum and the Horvath age predictors. In thisstudy, epigenetic AA, which was higher in boys, was alsoassociated with higher maternal body mass index (BMI)and lower maternal selenium and cholesterol levels dur-ing pregnancy, and higher birth weight of the offspring[20]. Furthermore, epigenetic AA at birth measured inDNA isolated from fetal cord blood was higher in theoffspring born to mothers who had smoked during preg-nancy and who were delivered by cesarean section [20].The Hannum and Horvath epigenetic age predictorsare not, however, suitable for epigenetic age estimationusing fetal cord blood. The Hannum age predictor wasbased on whole blood taken from 19–101-year-old indi-viduals [7] and the Horvath age predictor was based onmultiple tissues taken from 0–100-year-old individuals,which includes fetal cord blood [8]. However, it shouldbe noted that the correlation of both predictors withgestational age (GA) is nearly zero [20]. A recent studygenerated a novel DNAm GA predictor designed specif-ically for use on fetal umbilical cord blood or newbornblood spots [21]. This predictor identified methylation of148 CpG sites that showed a strong correlation (overallr = 0.91) with ultrasound-based GA [21]. The averageabsolute difference between the predicted DNA methyla-tion GA and GA was 1.49 weeks [21]. Using fetal umbil-ical cord blood samples, another recent study identified96 and 58 CpG sites, which however correlated lessstrongly with ultrasound-based (r = 0.81) and last men-strual period-based GA (r = 0.71) [22]; Only 23 of theultrasound- and last menstrual period-based GA pre-dictor CpG sites overlapped [22].In the present study, we studied the DNAm GA pre-dictor at birth based on fetal umbilical cord bloodmethylation data as developed by Knight et al. [21]. Wetested whether DNAm GA is associated with a numberof maternal and offspring characteristics known to re-flect a suboptimal prenatal developmental milieu of theoffspring. These characteristics include maternal pre-pregnancy risk factors of pre-eclampsia and intrauterinegrowth restriction, maternal pregnancy disorders,maternal treatment with antenatal corticosteroids, parityand mode of delivery, as well as newborn body size atbirth, cord blood pH, and Apgar score. For comparison,we also present these associations with the Horvath epi-genetic age, which shares only six overlapping CpG siteswith the DNAm GA predictor of Knight et al. [21].ResultsThe associations of maternal and neonatal characteris-tics with the raw DNAm GA difference (arithmetic dif-ference between DNAm GA and GA) and with theDNAm GA residual (the residual from a linear regres-sion of DNAm GA on GA) were tested in 814 womenand their singleton neonates participating in thePrediction and Prevention of Pre-eclampsia andIntrauterine Growth Restriction (PREDO) study(Additional file 1: Figure S1). The mean GA at birth ofthis cohort was 39.76 (standard deviation (SD) 1.60; me-dian 39.86; range 31.0–42.71) weeks, and the meanDNAm GA at birth was 38.45 (SD 2.05; median 38.60;range 28.50–47.13) weeks. There was a positivecorrelation between the DNAm GA and GA (r = 0.51;p < 0.0001; Fig. 1). The average absolute difference(arithmetic difference between DNAm GA absolutevalues and GA) between DNAm GA and GA was 1.78(SD 1.41; median 1.51) weeks, and the raw meandifference was −1.32 (SD 1.85; median −1.36; range−10.64–6.97) weeks. There was a weak negative correl-ation between the raw DNAm GA difference and GAFig. 1 Association between offspring DNAm gestational age (GA) atbirth based on cord blood methylation data and offspring chronologicalultrasound-based GA at birth. Scatterplot shows regression line and 95%confidence intervals. p value refers to the significance level ofthe associationGirchenko et al. Clinical Epigenetics  (2017) 9:49 Page 2 of 14(r = −0.30; p < 0.0001). DNAm GA residual did not cor-relate with GA (r = 0, p = 1.0).Pearson correlations between the Horvath DNAm ageand our values were as follows: GA 0.03 (p = 0.32),DNAm GA 0.11 (p = 0.002), raw DNAm GA difference0.09 (p = 0.01), and DNAm GA residual 0.11 (p = 0.01).Characteristics of the study population are presentedin Table 1.Maternal characteristics during pregnancy and offspringDNAm GA at birthFigures 2, 3, and 4 show the associations between ma-ternal characteristics during pregnancy with the rawDNAm GA difference and the DNAm GA residual ofthe offspring. The regression models are adjusted forcellular heterogeneity and population stratification.When based on the raw DNAm GA difference, GAAwas associated with a maternal age of above 40 years atdelivery, pre-eclampsia in a previous pregnancy, fetaldemise in a previous pregnancy, and having three ormore of the pre-pregnancy risk factors for pre-eclampsia and intrauterine growth restriction (Fig. 2).GAA was also associated with the presence, onset, andseverity of maternal pre-eclampsia in the index preg-nancy and maternal treatment with betamethasone inthe index pregnancy, particularly if the treatment wasstarted a maximum of 30 days before delivery (Fig. 3).GAD was associated with insulin-treated GDM in aprevious pregnancy (Fig. 2).When based on the DNAm GA residual, GAA was as-sociated with a maternal age of above 40 years at deliv-ery, and GAD with insulin-treated GDM in a previouspregnancy and maternal Sjögren’s syndrome (Fig. 4).Additional file 2: Table S1 shows the unstandardizedregression coefficients and 95% confidence intervals forthe associations depicted in Figs. 2, 3, and 4 and for theassociations between the other tested maternal charac-teristics during pregnancy and offspring DNAm GA atbirth. Additional file 2: Table S2 shows that all of the sig-nificant associations remained significant when addition-ally adjusted for the birth weight SD score based onFinnish national growth references [23].Additional file 2: Table S3 shows the associations be-tween maternal characteristics and the offspring’sHorvath epigenetic age at birth.Offspring characteristics and DNAm GA at birthGAA, based on the raw DNAm GA difference, was asso-ciated with lower birth weight, birth length, ponderalindex at birth, birth head circumference, placentalweight (Fig. 5), being a lower birth weight for GA (con-tinuous and being small-for-gestational-age, <−2 SD), alower 1-min Apgar score, and female sex (Fig. 6). Allmodels were adjusted for cellular heterogeneity,population stratification, and additionally for sex in theanalyses of the offspring birth anthropometry.When based on the DNAm GA residual, GAA was as-sociated with a lower 1-min Apgar score and female sex(Fig. 7). Additional file 2: Table S4 shows the unstan-dardized regression coefficients and 95% Confidence In-tervals for the associations depicted in Figs. 5, 6, and 7and for the associations between the other tested off-spring characteristics and offspring DNAm GA at birth.Additional file 2: Table S5 shows the associations be-tween offspring characteristics and the offspring Horvathepigenetic age at birth.DiscussionWe show that a number of maternal and offspring char-acteristics known to reflect the offspring’s exposure toenvironmental adversity during the prenatal period wereassociated with variations in the offspring’s DNAm GAat birth. These characteristics are expected to influencethe newborn developmental stage and fetal organ andtissue maturation. Characteristics associated withDNAm GAA included a number of pre-eclampsia andintrauterine growth restriction pre-pregnancy risk fac-tors: maternal age of over 40 years, pre-eclampsia andfetal demise in a previous pregnancy, and having ahigher number of pre-pregnancy risk factors of pre-eclampsia and intrauterine growth restriction that weremeasured in this study. DNAm GAA was also associatedwith maternal pre-eclampsia in the index pregnancy andtreatment with antenatal betamethasone, which hastensfetal lung maturation and maturation of some other tis-sues [24]. It was also associated with a smaller body sizeat birth and being born small-for-gestational age, lower1-min Apgar score, and female sex. Furthermore, ourfindings show that DNAm GAD was associated withinsulin-treated GDM in a previous pregnancy andSjögren’s syndrome. These findings were more accentu-ated when the DNAm GA calculation was based on theraw difference between DNAm GA and GA (whichshared 9% of variance with GA) than on the residualfrom linear regression of DNAm GA on GA (which re-moved the effect of GA entirely from DNAm GA, andhence, was uncorrelated with GA). Our findingsemphasize that neonates exposed to prenatal environ-mental adversity show differences at birth in theirDNAm GA and GA, confirmed by early pregnancyultrasound.This conclusion is in agreement with a recent study,which first generated the used biomarker for the epigen-etic clock for GA at birth and tested associations be-tween maternal socioeconomic status and newborn birthweight with DNAm GA [21]. However, in contrast toour findings, the previous study showed that DNAmGAD was associated with mother’s socioeconomicGirchenko et al. Clinical Epigenetics  (2017) 9:49 Page 3 of 14Table 1 Characteristics of the study population (N = 814)Maternal characteristics Mean (SD) or N (%)Pre-pregnancy risk factorsaMaternal age at delivery, years 33.3 (5.8)Below 20 years, yes 22 (3.0%)Above 40 years, yes 115 (14.1%)Pre-eclampsia in previous pregnancy, yes 178 (21.9%)Intrauterine growth estriction in previous pregnancy, yes 85 (10.4%)Gestational diabetes in previous pregnancy, yes 84 (10.3%)Diet treated 75 (9.2%)Insulin treated 9 (1.1%)Pre-pregnancy body mass index kg/m2 27.4 (6.4)≥30 kg/m2 287 (35.3%)Pre-pregnancy chronic hypertension, yes 109 (13.4%)Pre-pregnancy type 1 diabetes, yes 12 (1.5%)Pre-pregnancy systemic lupus erythematosus, yes 2 (0.3%)Sjögren’s syndrome, yes 11 (1.4%)Previous pregnancy with fetal demise (>22 gestational weeks or over 500 g), yes 28 (3.4%)Number of known pre-pregnancy risk factorsNo known pre-pregnancy risk factors 79 (9.7%)1 or 2 pre-pregnancy risk factors 696 (85.5%)3 or more pre-pregnancy risk factors 31 (3.8%)Data not available 8 (1.0%)Pregnancy disordersGestational diabetes, yes 183 (22.5%)Diet treated 147 (18.1%)Insulin treated 36 (4.4%)Data not available on gestational diabetes treatment 2 (0.2%)Data not available on gestational diabetes diagnosis 1 (0.1%)Hypertension spectrum pregnancy disorders, yesGestational hypertension 80 (9.8%)Pre-eclampsia 61 (7.5%)Early pre-eclampsia (diagnosis <34 weeks of gestation) 53 (6.5%)Late pre-eclampsia (diagnosis ≥34 weeks of gestation) 8 (1.0%)Non-severe pre-eclampsia 42 (5.2%)Severe pre-eclampsia 19 (2.3%)Chronic hypertension 134 (16.5%)Data not available 1 (0.1%)Other characteristicsEducation levelLower secondary or less 359 (44.1%)Upper secondary 184 (22.6%)Tertiary 248 (30.5%)Data not available 23 (2.8%)ParityPrimiparous 247 (30.3%)Girchenko et al. Clinical Epigenetics  (2017) 9:49 Page 4 of 14Table 1 Characteristics of the study population (N = 814) (Continued)Multiparous 566 (69.5%)Data not available 1 (0.1%)Smoking during pregnancyNon-smoker 780 (95.8%)Quit during first trimester 26 (3.2%)Smoked throughout the pregnancy 8 (1.0%)Data not available 0Alcohol use during pregnancyNo 588 (72.2%)Yes 117 (14.4%)Data not available 109 (13.4%)Mode of deliveryVaginal 640 (78.6%)Cesarean 173 (21.3%)Data not available 1 (0.1%)Antenatal betamethasone treatmentNo 750 (92.1%)Yes (n = 1 for 12 mg/24 h, n = 22 for 24 mg/24 h, n = 1 for 48 mg/24 h,n = 11 for no information on dose)35 (4.3%)Timing of antenatal betamethasone treatment, number of days before birth 33.4 (27.1)30 days or less before delivery 14 (1.7%)30 days or more before delivery 13 (1.6%)Data not available 29 (3.6%)Neonatal characteristicsSexGirls 384 (47.2%)Boys 430 (52.8%)Data not available 0Gestational age at birth, weeks 39.76 (1.60)Data not available 0DNA methylation gestational age, weeks 38.45 (2.05)Data not available 0Raw epigenetic gestational age difference, DNA methylation gestationalage-gestational age−1.32 (1.85)Data not available 0Absolute epigenetic gestational age difference, DNA methylationgestational age-gestational age in absolute values1.78 (1.41)Data not available 0Horvath DNA methylation age at birth, weeks 9.77 (19.51)Data not available 0Birth weight, g 3549 (546)Small for gestational age, yesb 23 (2.8%)Data not available 1 (0.1%)Birth length, cm 50 (2)Small for gestational age, yesb 21 (2.6%)Data not available 1 (0.1%)Girchenko et al. Clinical Epigenetics  (2017) 9:49 Page 5 of 14Table 1 Characteristics of the study population (N = 814) (Continued)Head circumference, cm 35 (2)Small for gestational age, yesb 14 (1.7%)Data not available 2 (0.3%)Ponderal index, kg/m3 27.8 (2.7)Data not available 1 (0.1%)Placenta weight, g 615 (134)Cord blood pHArterial 7.26 (0.09)Venous 7.31 (0.08)Apgar score9–10 611 (75.1%)7–8 145 (17.8%)≤6 47 (5.8%)Data not available 11 (1.4%)aPre-pregnancy risk factors served as inclusion criteria for the study as described [39]bSmall for gestational age indicates birth size for sex and gestational age SD ≤ −2 according to Finnish growth references [23]Fig. 2 Associations between maternal pre-pregnancy risk factors of pre-eclampsia and intrauterine growth restriction (panels a–e) and raw epigeneticgestational age (GA) difference (DNAm GA-GA) of the offspring at birth based on fetal cord blood methylation data. Associations are adjusted for cell-type composition and population stratification estimated with two multi-dimensional scaling components based on genome-wide data. Data shownare median, interquartiles, and range. p values refer to group differences. Ref referent groupGirchenko et al. Clinical Epigenetics  (2017) 9:49 Page 6 of 14disadvantage during pregnancy and the offspring’s lowerbirth weight [21]. Also in contrast to our report, they re-port no sex differences in the median errors betweenDNAm GA and GA, i.e., boys and girls did not differ intheir DNAm GA at birth [21]. Another recent study,which used the DNAm age predictors of Horvath andHannum on cord blood methylation data, also con-cluded that prenatal adversity is associated with epigen-etic age at birth [20]. Yet, of the 20 different maternaland neonatal characteristics tested in that study, includ-ing maternal age, education, alcohol use during preg-nancy, body mass index (BMI), parity, birth weight, andsex, only maternal smoking during pregnancy andcesarean section were associated with epigenetic AA, in-dependent of GA at birth. However, the Horvath andHannum epigenetic age did not correlate with the new-born chronological GA [20, 21]. This supports our study,as we found that the Horvath epigenetic age at birthbased on cord blood methylation data was uncorrelatedwith the newborn GA. So, while the overall conclusionfrom these prior studies is similar to ours, discrepancyin the direction of associations indicates that futurestudies are still needed. Yet, in effect size, the priorfindings and those of ours do not greatly differ. For in-stance, in the Knight et al. study [21], offspring birthweight accounted for 2% of the variance of the DNAmGA, when accounting for GA and the other covariates;in our study, birth weight was unrelated with DNAmGA adjusted for GA, cellular heterogeneity, and popu-lation stratification, but child’s sex accounted for 1%and Apgar score accounted for 1% of the variance ofthe DNAm GA. In the Simpkin et al. study [20], mater-nal smoking during pregnancy and cesarean sectiondelivery explained less than 1% of the variance of off-spring’s Horvath age at birth. In our study, these char-acteristics and the offspring’s Horvath age at birthwere generally unrelated. Thus, future studies willneed to determine to what extent the different associa-tions and their direction reflect differences betweenthe studies due to tissue type (cord blood plus new-born blood spots vs cord blood only), cellular compos-ition of samples, fetal cord blood contamination withFig. 3 Associations between maternal pregnancy disorders in the index pregnancy and other maternal characteristics (panels a–e) and rawepigenetic gestational age (GA) difference (DNAm GA-GA) of the offspring at birth based on fetal cord blood methylation data. Associations havebeen adjusted for cell-type composition and population stratification estimated with two multi-dimensional scaling components based ongenome-wide data. Data shown are median, interquartiles, and range. p values refer to group differences. Ref referent groupGirchenko et al. Clinical Epigenetics  (2017) 9:49 Page 7 of 14maternal blood, and population genetic structure.These factors were only taken into account in ourstudy. Differences may also relate to sample character-istics: 89.3% of women in our sample had at least 1 ofthe 10 pre-pregnancy risk factors of pre-eclampsia andintrauterine growth restriction. This resulted ingreater variation in many maternal and offspring char-acteristics, including GA and DNAm GA, which areslightly different in this study from the study con-ducted by Knight et al. [21].Both DNAm GAA and GAD could be conceived asindicators of risk. The increased risk of future adverseoutcomes associated with DNAm GAA is congruentwith findings in children and adults showing that epi-genetic age acceleration is associated with a number ofadverse characteristics including higher BMI [25], lowerphysical and cognitive fitness [26], and increased mor-tality [9, 10, 12]. Lower developmental maturity, as in-dicated by DNAm GAD, is consistent with the DOHaDhypothesis and findings from previous studies showingan increased risk of aging-related diseases in individualsexposed to prenatal environmental adversity associatedwith pre-term birth [27–31]. Hence, the associationswith DNAm GAA or GAD may serve as summary indi-cators of epigenetic programming and indicate in-creased risk for adverse outcomes later in life. Assuggested by Knight et al. [21], an alternative explan-ation for the difference between DNAm GA and GAmay be the variable nature of the clinical GA estima-tion. Yet, in our sample, this explanation may be lesslikely as GA estimation in all women was based onultrasound performed between 12 + 0 and 13 + 6gestational weeks + days.However, it is important to note that when DNAmGA was based on the residual, which removed the ef-fect of GA on DNAm GA entirely, many of the mater-nal and neonatal characteristics were no longerassociated with DNAm GA. Only a maternal age ofabove 40 years, lower 1-min Apgar score, and femalesex were associated with residual GAA and insulin-treated GDM in a previous pregnancy and maternalSjögren’s syndrome were associated with residualGAD. If DNAm GA reflects developmental maturity,we cannot rule out it being independent of variousenvironmental factors, which may alter the matur-ational process. Associations may become moreevident later in childhood as the variation in methyla-tion increases [3, 7, 32–34].In our study, we used two measurements: raw DNAmGA and residual DNAm GA. Residual DNAm GA wascorrected for any confounding effect of GA on DNAmGA and hence did not correlate with GA. However, ifwe had only focused on this variable, any finding whichwas associated with both GA and DNAm GA would beomitted, and hence, our analysis might have been too re-strictive. Therefore, we decided to also present findingsfrom the raw DNAm GA in our study. These two mea-sures of DNAm GAA and GAD might serve differentapplications. The DNAm GA residual might be a moreappropriate measure for testing hypotheses on a popula-tion level. As it is dependent on population characteris-tics, it may not have clinical, individual level utilityFig. 4 Associations between maternal pre-pregnancy risk factors of pre-eclampsia and intrauterine growth restriction (Panels a–c) and epigeneticgestational (GA) residual (the residual from a linear regression of DNAm GA on GA) of the offspring at birth based on fetal cord blood methylationdata. Associations are adjusted for cell-type composition and population stratification estimated with two multi-dimensional scaling componentsbased on genome-wide data. Data shown are median, interquartiles, and range. p values refer to group differencesGirchenko et al. Clinical Epigenetics  (2017) 9:49 Page 8 of 14unless population level “DNAm GA standards” becomeavailable, analogous to national references for fetalgrowth [23]. The raw DNAm GA difference may be amore useful and clinically relevant measure for individ-ual level assessments.StrengthsThe main strength of our study is the use of a well-characterized prospective, ethnically homogenous cohortwith data on pre-pregnancy risk factors of pre-eclampsiaand intrauterine growth restriction, pregnancy disordersFig. 5 Associations between offspring anthropometry (panels a–d) and placental weight at birth (panel e) and raw epigenetic gestational (GA)difference (DNAm GA-GA) of the offspring at birth based on fetal cord blood methylation data. Associations have been adjusted for cell-typecomposition, population stratification estimated with two multi-dimensional scaling components based on genome-wide data, and neonatal sex.Scatterplots show regression lines and 95% confidence intervals. p values refer to significance levels of the associationsGirchenko et al. Clinical Epigenetics  (2017) 9:49 Page 9 of 14validated by a clinical jury consisting of two qualifiedphysicians and a nurse, and data on other maternaland neonatal characteristics extracted from both med-ical records and the Finnish Medical Birth Register(MBR) [35]. Our sample was enriched for women withknown risk factors for pre-eclampsia and intrauterinegrowth restriction. This resulted in greater variation ofthe pre- and neonatal characteristics, thus increasingthe possibility of being able to detect their effects onthe DNAm GA predictor. Furthermore, clinical GA es-timation was based on an ultrasound scan conductedin all women between 12 + 0 and 13 + 6 weeks + daysof gestation. DNAm GA was estimated from fetal um-bilical cord blood, and we applied novel methods toaccount for any contamination of the samples by ma-ternal blood. A number of studies have shown that cel-lular heterogeneity [36], and genetic variation in thepopulation structure [37, 38], can influence epigeneticprofiles. Therefore, we removed any potential effects ofcell type heterogeneity using bioinformatics methodsand corrected for population structure using principalcomponents derived from genome-wide genotypes.LimitationsSeveral strengths of this study are also limitations. Theethnic homogeneity of our sample may preclude gener-alizations for other ethnic groups. Our inclusion criteria,which resulted in enrichment of pregnancy disorders inFig. 6 Associations between offspring small for gestational age (GA) weight at birth (panel a), sex (panel b), and Apgar score (panel c), and rawDNAm GA difference (DNAm GA-GA) of the offspring at birth based on fetal cord blood methylation data. Associations are adjusted for cell-typecomposition and population stratification estimated with two multi-dimensional scaling components based on genome-wide data. Data shownare median, interquartiles, and range. p values refer to group differences. Ref referent groupFig. 7 Associations between offspring sex (panel a) and Apgar score (panel b) and epigenetic gestational age (GA) residual (the residual from alinear regression of DNAm GA on GA) of the offspring at birth based on fetal cord blood methylation data. Associations are adjusted for cell-typecomposition and population stratification estimated with two multi-dimensional scaling components based on genome-wide data. Data shownare median, interquartiles, and range. p values refer to group differences. Ref referent groupGirchenko et al. Clinical Epigenetics  (2017) 9:49 Page 10 of 14the study population and increased statistical power todetect their effects, also limit generalizability of thesefindings to women without such risk factors. Finally, ourfindings are limited to one tissue type; therefore, wecould not test cross-tissue correlations. It is also import-ant to note that for some prenatal characteristics, e.g.,for insulin-treated diabetes in a previous pregnancy, ma-ternal Sjögren’s syndrome, less than ten pairs of womenand neonates were present in the risk group. Therefore,although we observed significant associations, theyshould be interpreted with caution and need to be repli-cated. Finally, while maternal pre-eclampsia in the indexpregnancy, maternal treatment with betamethasone,lower birth weight and length, and lower placentalweight remained associated with GAA after Bonferronicorrection for multiple testing, this correction may betoo conservative as the tested associations were notindependent.ConclusionsOur findings show that a number of maternal and off-spring characteristics known to reflect the offspring’sprenatal environment are associated with variations inthe offspring’s DNAm GA at birth based on data fromcord blood DNA methylation. Whether this variation ispredictive of developmental outcomes in later life is thesubject of ongoing studies.MethodsStudy populationData were taken from the PREDO study, which is a longi-tudinal multicenter pregnancy cohort study of Finnishwomen and their singleton children born alive between2006 and 2010 [39]. We recruited 1079 pregnant women,of whom 969 had 1 or more, and 110 had none of theknown risk factors for pre-eclampsia and intrauterinegrowth restriction (Table 1). The recruitment took placein arrival order when these women attended the firstultrasound screening at 12 + 0–13 + 6 weeks + days of ges-tation in 1 of the 10 hospital maternity clinics participat-ing in the study. The cohort profile [39] contains details ofthe study design, inclusion criteria, and all the data thatare available. Additional file 1: Figure S1 shows a flowchartof the 814 mother-offspring pairs with data available forthe current study.Offspring DNA methylation, GA, and DNAm GA at birthFetal cord blood samples were collected according tostandard procedures. DNA was extracted at theNational Institute for Health and Welfare, Helsinki,Finland, and the Department of Medical and ClinicalGenetics, University of Helsinki, Finland. Methyla-tion analyses were performed at the Max PlanckInstitute of Psychiatry in Munich, Germany. DNAwas bisulphite-converted using the EZ-96 DNAMethylation kit (Zymo Research, Irvine, CA).Genome-wide methylation status of over 485,000CpG sites was measured using the Infinium HumanMethylation 450 BeadChip (Illumina Inc., San Diego,USA) according to the manufacturer’s protocol. Thearrays were scanned using the iScan System (Illu-mina Inc., San Diego, USA). The quality control(QC) pipeline was set up using the R-package minfi(http://bioconductor.org/packages/release/bioc/html/minfi.html). The samples were excluded if they wereduplicates, outliers in the median intensities, and be-cause of sex discrepancy. Furthermore, any probeson chromosome X or Y, cross-hybridizing probes aswell as probes containing SNPs, and CpGs with adetection P value > 0.01 in at least 50% of the sam-ples, or maternal blood contamination were ex-cluded. Maternal blood contamination was testedusing DNAm data at 10 CpGs independently identi-fied as differentially methylated between cord andadult blood and indicative of maternal blood con-tamination (paper under review). The samples withDNAm values above the previously identified thresh-olds at five or more of these CpGs were consideredcontaminated and removed from all future analyses.The final dataset contained 428,619 CpGs. Additionalfile 1: Figure S1 shows that of the 876 samples avail-able for these analyses, 51 were excluded. Methylationbeta-values were normalized using the funnorm func-tion and incorporating the first ten principal compo-nents from the internal control probes. To check forbatch effects, principal components were computed onthese beta values. Two batches, i.e., slide and well,were significantly associated to the main principalcomponents and were removed iteratively using thecombat package.DNAm GA was calculated using the method pub-lished by Knight et al. [21] and was based on themethylation profile of 148 selected CpGs.We calculated a raw DNAm GA difference by sub-tracting the chronological GA assessed at the firstultrasound screening conducted at 12 + 0–13 + 6 weeks+ days of gestation from the predicted DNAm GA.DNAm GA residual was extracted from a linear regres-sion of predicted DNAm GA on ultrasound-based GA.Offspring cord blood cell counts at birthSeven cell types (nucleated red blood cells, granulo-cytes, monocytes, natural killer cells, B cells, CD4(+)Tcells, and CD8(+)T cells) were estimated from cordblood methylation using the method of Bakulski et al.[40] which was also incorporated in the R-packageminfi.Girchenko et al. Clinical Epigenetics  (2017) 9:49 Page 11 of 14Offspring genome-wide genotyping and multi-dimensionalscaling analysisGenotyping was performed on Illumina Human OmniExpress Exome Arrays containing 964,193 SNPs. Onlymarkers with a call rate of at least 98%, a minor allelefrequency of 1%, and a P value for deviation fromHardy-Weinberg-Equilibrium (P > 1.0 e-06) were kept inthe analysis. After QC, 587,290 SNPs were available.Any sample pair with IBD estimates >0.125 was checkedfor relatedness. For most pairs, high IBD estimates couldbe explained due to partly African origin. As we cor-rected for admixture in our analyses, these samples werekept except for one pair. For this pair, the high IBD esti-mate could not be resolved and the other one of thispair was excluded from further analysis. The samplesshowing discrepancies between phenotypic and geno-typic sex were removed. We also checked for heterozy-gosity outliers, but found none. In total, we genotyped996 samples of which 13 were excluded. Of the sampleswith DNA methylation data, eight were excluded basedon the above criteria (Additional file 1: Figure S1).We performed multi-dimensional scaling (MDS) ana-lysis on the IBS matrix of quality-controlled genotypes.Apart of those samples with African admixture, no out-liers were detected. The first two components depict thisadmixture and were included as covariates in the regres-sion analysis.Maternal characteristics during pregnancyPre-pregnancy risk factors of pre-eclampsia and intra-uterine growth restriction were derived from medical re-cords screened for by trained study nurses or clinicpersonnel at maternity clinics of study hospitals at enrol-ment into the study. These pre-pregnancy risk factorsare listed in Table 1.Pregnancy disorders, derived from hospital recordsand further verified by a clinical jury [39, 41], in-cluded gestational diabetes, which was defined asfasting, 1- or 2-h plasma glucose during a 75-g oralglucose tolerance test ≥5.1, 10.0 or 8.5 mmol/L, re-spectively, that emerged or was first identified duringpregnancy, and further categorized according totreatment as diet or insulin treated; gestationalhypertension, which was defined as systolic bloodpressure ≥140 mmHg and/or diastolic blood pressure≥90 mmHg on ≥2 occasions at least 4 h apart in awoman who was normotensive before 20 weeks ofgestation; pre-eclampsia, which was defined as sys-tolic blood pressure ≥140 mmHg and/or diastolicblood pressure ≥90 mmHg on ≥2 occasions at least4 h apart with proteinuria ≥300 mg/24 h. Pre-eclampsia diagnosis was further divided into early(diagnosis before 34 weeks of gestation) and latepre-eclampsia (diagnosis 34 weeks of gestation orlater), and also into severe (blood pressure≥160 mmHg systolic and/or ≥110 mmHg diastolicand/or proteinuria ≥5 g/24 h) and non-severe pre-eclampsia (blood pressure 140–159.9 mmHg systolicand/or 90–109.9 mmHg diastolic and/or proteinuria0.3–4.9 g/24 h); chronic hypertension was defined assystolic/diastolic blood pressure ≥140/90 mmHg orantihypertensive medication before 20 weeks of ges-tation (in 24 out of 135 women chronic hypertensionwas diagnosed during pregnancy). In addition to pre-pregnancy obesity, data on maternal pre-pregnancyBMI calculated from measured weight and height atthe first antenatal clinic visit at 8 + 4 (SD 1 + 3)weeks + days were derived from the Finnish MedicalBirth Register (MBR) [35] and data on weight changeduring pregnancy from medical records.Information on the antenatal corticosteroid (beta-methasone) treatment was derived from hospitalrecords (one woman received half a standard dose, i.e.,12 mg/24 h, 22 women received one standard dose of24 mg/24 h, and one woman received two standarddoses totalling 48 mg/24 h), and timing of exposurewas further defined as the number of days before birth(over 30 vs 30 days or less before birth).Maternal smoking during pregnancy (non-smoker, quitduring first trimester, smoked throughout), parity (prim-iparous vs multiparous) and mode of delivery (vaginaldelivery vs caesarian section) were derived from theMBR, and alcohol use (yes vs no) and education level(lower secondary or less, upper secondary, tertiary) werereported at 12 + 0–13 + 6 gestational weeks + days.Offspring characteristics at birthWeight (kg), length (cm), head circumference (cm), fetalcord blood venous and arterial pH, and 1-min Apgarscore were measured at birth, and the birth ponderalindex (kg/m3) was calculated. We further divided birthweight and length into normal and small (≤ − 2SD) forGA using Finnish national growth references [23].Statistical analysisWe tested the associations between maternal and off-spring characteristics with the raw DNAm GA differ-ence, DNAm GA residual, and Horvath epigenetic agewith linear regressions. All models were adjusted forcell-type composition and population stratification esti-mated with two multi-dimensional scaling componentsbased on genome-wide data. Maternal characteristic datawere further adjusted for birth weight SD score and neo-natal anthropometric data for child’s sex. Unstandard-ized regression coefficients and 95% confidence intervals(CI) represent effect sizes in weeks (raw DNAm GA dif-ference and Horvath epigenetic age) and SD units with amean of 0 and SD 1 (DNAm GA residual). Nominal 2-Girchenko et al. Clinical Epigenetics  (2017) 9:49 Page 12 of 14tailed p values are given in the tables and Bonferroni-corrected p value threshold reaching a level of p < 0.05in footnotes. All statistical analyses were performedusing SAS 9.4 (SAS Institute, Inc., Cary, NC, USA).Additional fileAdditional file 1: Figure S1. Flowchart of the study participants andsample attrition. (PPTX 38 kb)Additional file 2: Table S1. Associations between maternalcharacteristics during pregnancy and offspring DNAm GA at birth basedon cord blood methylation data. Table S2. Associations betweenmaternal characteristics during pregnancy and offspring DNAm GA atbirth based on cord blood methylation data when additionally adjustingfor offspring birth weight SD score at birth*. Table S3. Associationsbetween maternal characteristics and the offspring Horvath epigeneticage at birth based on cord blood methylation data*. Table S4.Associations between offspring characteristics and DNAm GA at birthbased on cord blood methylation data. Table S5. Associations betweenoffspring characteristics and the offspring Horvath epigenetic age at birthbased on cord blood methylation data*. (DOCX 65 kb)AbbreviationsDNAm GA: DNA methylation gestational age; DOHaD: Developmental Originsof Health and Disease; GA: Gestational age; GAA: Gestational age acceletaion;GAD: Gestational age deceleration; GDM: Gestational diabetes mellitus;PREDO: Prediction and Prevention of Pre-eclampsia and Intrauterine GrowthRestriction Study; SD: Standard deviation; SGA: Small for gestational ageAcknowledgementsThe PREDO study would not have been possible without the dedicatedcontribution of the PREDO study group members: Anu-Katriina Pesonen, AAitokallio-Tallberg, A-M Henry, VK Hiilesmaa, T Karipohja, R Meri, S Sainio, TSaisto, S Suomalainen-Konig, V-M Ulander, T Vaitilo (Department of Obstetricsand Gynaecology, University of Helsinki and Helsinki University Central Hospital,Helsinki, Finland), L Keski-Nisula, Maija-Riitta Orden (Kuopio University Hospital,Kuopio Finland), E Koistinen, T Walle, R Solja (Northern Karelia Central Hospital,Joensuu, Finland), M Kurkinen (Päijät-Häme Central Hospital, Lahti, Finland),P.Taipale. P Staven (Iisalmi Hospital, Iisalmi, Finland), J Uotila (Tampere UniversityHospital, Tampere, Finland). We also thank the PREDO cohort participants fortheir enthusiastic participation. We thank the scientific writer, Jessica Keverne,for editing the final version of our manuscript.FundingThis work was supported by the Academy of Finland, EVO (a special statesubsidy for health science research), and University of Helsinki Research Funds.Availability of data and materialsAny interested researchers can obtain a de-identified dataset after havingobtained an approval from the PREDO Study Board. Data requests may besubject to further review by the National Register Authority and EthicalCommittees. Any requests for data use should be addressed to the PREDOStudy Board (predo.study@helsinki.fi) or individual researchers.Authors’ contributionsPG contributed to the statistical analyses, writing/editing the manuscript, andprepared the tables and figures. JL contributed to acquisition of the data,analysis of the data, and writing/editing the manuscript. DC and EBperformed the methylation analyses and contributed to writing/editing themanuscript. EH, EK, HL, RR, and PV contributed to the acquisition of data andediting the manuscript. AK, MJ, MK, and AS contributed to interpretation ofthe results and editing the manuscript. AF contributed to writing/editing themanuscript. KR contributed to the conception and design of the work,acquisition of data, analysis of the data, and writing/editing the manuscript.All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.Consent for publicationNot applicable.Ethics approval and consent to participateThe study protocol was approved by the Ethical Committees of the Helsinkiand Uusimaa Hospital District and by all participating hospitals. A writteninformed consent was obtained from all mothers involved in the study.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Institute of Behavioral Sciences, University of Helsinki, Helsinki 00014,Finland. 2Helsinki Collegium of Advanced Studies, University of Helsinki,Helsinki 00014, Finland. 3Department of Translational Research in Psychiatry,Department of Psychiatry and Behavioral Sciences, Max-Planck Institute ofPsychiatry, Munich 80804, Germany. 4Department of Psychiatry andBehavioral Sciences, School of Medicine, Emory University, Atlanta 30322, GA,USA. 5HUSLAB and Department of Clinical Chemistry, Helsinki UniversityHospital, Helsinki 00029, Finland. 6National Institute for Health and Welfare,Helsinki and Oulu, Helsinki 00271, Finland. 7Children’s Hospital, HelsinkiUniversity Central Hospital and University of Helsinki, Helsinki 00029, Finland.8Obstetrics and Gynaecology, University of Helsinki and Helsinki UniversityHospital Helsinki, Helsinki 00029, Finland. 9Medical and Clinical Genetics andInstitute for Molecular Medicine Finland, University of Helsinki and HelsinkiUniversity Hospital, Helsinki 00014, Finland. 10BHF Centre for CardiovascularScience, Queen’s Medical Research Institute, University of Edinburgh,Edinburgh EH16 4TJ, UK. 11Genetics and Molecular Biology Program, EmoryUniversity, Atlanta 30322, GA, USA. 12Department of Gynecology andObstetrics, School of Medicine, Emory University, Atlanta 30322, GA, USA.13Department of Psychiatry and Behavioral Sciences, School of Medicine,Emory University, Atlanta, GA, USA. 14Centre for Molecular Medicine andTherapeutics, BC Children’s Hospital and University of British Columbia,Vancouver V6T 1Z4, Canada.Received: 3 April 2017 Accepted: 28 April 2017References1. Johnson AA, Akman K, Calimport SR, Wuttke D, Stolzing A, de Magalhaes JP.The role of DNA methylation in aging, rejuvenation, and age-relateddisease. Rejuvenation Res. 2012;15(5):483–94.2. Garagnani P, Bacalini MG, Pirazzini C, Gori D, Giuliani C, Mari D, Di Blasio AM,Gentilini D, Vitale G, Collino S, et al. Methylation of ELOVL2 gene as a newepigenetic marker of age. Aging Cell. 2012;11(6):1132–4.3. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP, van Eijk K, van denBerg LH, Ophoff RA. Aging effects on DNA methylation modules in humanbrain and blood tissue. Genome Biol. 2012;13(10):R97.4. Rakyan VK, Down TA, Maslau S, Andrew T, Yang TP, Beyan H, Whittaker P,McCann OT, Finer S, Valdes AM, et al. Human aging-associated DNAhypermethylation occurs preferentially at bivalent chromatin domains.Genome Res. 2010;20(4):434–9.5. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ,Shen H, Campan M, Noushmehr H, Bell CG, Maxwell AP, et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is ahallmark of cancer. Genome Res. 2010;20(4):440–6.6. Bell JT, Tsai PC, Yang TP, Pidsley R, Nisbet J, Glass D, Mangino M, Zhai G,Zhang F, Valdes A, et al. Epigenome-wide scans identify differentiallymethylated regions for age and age-related phenotypes in a healthy ageingpopulation. PLoS Genet. 2012;8(4):e1002629.7. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B,Bibikova M, Fan JB, Gao Y, et al. Genome-wide methylation profiles revealquantitative views of human aging rates. Mol Cell. 2013;49(2):359–67.8. Horvath S. DNA methylation age of human tissues and cell types. GenomeBiol. 2013;14(10):R115.9. Perna L, Zhang Y, Mons U, Holleczek B, Saum KU, Brenner H. Epigenetic ageacceleration predicts cancer, cardiovascular, and all-cause mortality in aGerman case cohort. Clin Epigenetics. 2016;8:64.10. Marioni RE, Harris SE, Shah S, McRae AF, von Zglinicki T, Martin-Ruiz C, WrayNR, Visscher PM, Deary IJ. The epigenetic clock and telomere length areGirchenko et al. Clinical Epigenetics  (2017) 9:49 Page 13 of 14independently associated with chronological age and mortality. Int JEpidemiol. 2016;45(2):424-32.11. Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H, Ritz BR,Chen B, Lu AT, Rickabaugh TM, et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 2016;17(1):171.12. Chen BH, Marioni RE, Colicino E, Peters MJ, Ward-Caviness CK, Tsai PC,Roetker NS, Just AC, Demerath EW, Guan W, et al. DNA methylation-basedmeasures of biological age: meta-analysis predicting time to death. Aging.2016;8(9):1844–65.13. Langie SA, Lara J, Mathers JC. Early determinants of the ageing trajectory.Best Pract Res Clin Endocrinol Metab. 2012;26(5):613–26.14. Sayer AA, Syddall HE, Gilbody HJ, Dennison EM, Cooper C. Does sarcopeniaoriginate in early life? Findings from the hertfordshire cohort study. JGerontol A Biol Sci Med Sci. 2004;59(9):M930–934.15. Cameron N, Demerath EW. Critical periods in human growth and theirrelationship to diseases of aging. Am J Phys Anthropol. 2002;Suppl 35:159–84.16. Osmond C, Barker DJ. Fetal, infant, and childhood growth are predictors ofcoronary heart disease, diabetes, and hypertension in adult men andwomen. Environ Health Perspect. 2000;108 Suppl 3:545–53.17. D'Onofrio BM, Class QA, Rickert ME, Larsson H, Langstrom N, Lichtenstein P.Preterm birth and mortality and morbidity: a population-based quasi-experimental study. JAMA Psychiat. 2013;70(11):1231–40.18. Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in utero and early-life conditions on adult health and disease. N Engl J Med. 2008;359(1):61–73.19. Barker DJ. The fetal and infant origins of adult disease. BMJ Clin Res. 1990;301(6761):1111.20. Simpkin AJ, Hemani G, Suderman M, Gaunt TR, Lyttleton O, McArdle WL,Ring SM, Sharp GC, Tilling K, Horvath S, et al. Prenatal and early lifeinfluences on epigenetic age in children: a study of mother-offspring pairsfrom two cohort studies. Hum Mol Genet. 2016;25(1):191–201.21. Knight AK, Craig JM, Theda C, Baekvad-Hansen M, Bybjerg-Grauholm J,Hansen CS, Hollegaard MV, Hougaard DM, Mortensen PB, Weinsheimer SM,et al. An epigenetic clock for gestational age at birth based on bloodmethylation data. Genome Biol. 2016;17(1):206.22. Bohlin J, Haberg SE, Magnus P, Reese SE, Gjessing HK, Magnus MC, Parr CL,Page CM, London SJ, Nystad W. Prediction of gestational age based ongenome-wide differentially methylated regions. Genome Biol. 2016;17(1):207.23. Pihkala J, Hakala T, Voutilainen P, Raivio K. Characteristic of recent fetalgrowth curves in Finland. Duodecim. 1989;105(18):1540–6.24. Liggins GC, Howie RN. A controlled trial of antepartum glucocorticoidtreatment for prevention of the respiratory distress syndrome in prematureinfants. Pediatrics. 1972;50(4):515–25.25. Horvath S, Erhart W, Brosch M, Ammerpohl O, von Schonfels W, Ahrens M,Heits N, Bell JT, Tsai PC, Spector TD, et al. Obesity accelerates epigeneticaging of human liver. Proc Natl Acad Sci U S A. 2014;111(43):15538–43.26. Marioni RE, Shah S, McRae AF, Ritchie SJ, Muniz-Terrera G, Harris SE, GibsonJ, Redmond P, Cox SR, Pattie A, et al. The epigenetic clock is correlated withphysical and cognitive fitness in the lothian birth cohort 1936. Int JEpidemiol. 2015;44(4):1388–96.27. Osler M, Lund R, Kriegbaum M, Andersen AM. The influence of birth weightand body mass in early adulthood on early coronary heart disease riskamong Danish men born in 1953. Eur J Epidemiol. 2009;24(1):57–61.28. Frankel S, Elwood P, Sweetnam P, Yarnell J, Smith GD. Birthweight, body-mass index in middle age, and incident coronary heart disease. Lancet.1996;348(9040):1478–80.29. Barker DJ, Godfrey KM, Fall C, Osmond C, Winter PD, Shaheen SO. Relation ofbirth weight and childhood respiratory infection to adult lung function anddeath from chronic obstructive airways disease. BMJ. 1991;303(6804):671–5.30. Rich-Edwards JW, Stampfer MJ, Manson JE, Rosner B, Hankinson SE, Colditz GA,Willett WC, Hennekens CH. Birth weight and risk of cardiovascular disease in acohort of women followed up since 1976. BMJ. 1997;315(7105):396–400.31. Visentin S, Grumolato F, Nardelli GB, Di Camillo B, Grisan E, Cosmi E. Earlyorigins of adult disease: low birth weight and vascular remodeling.Atherosclerosis. 2014;237(2):391–9.32. Hernandez DG, Nalls MA, Gibbs JR, Arepalli S, van der Brug M, Chong S,Moore M, Longo DL, Cookson MR, Traynor BJ, et al. Distinct DNAmethylation changes highly correlated with chronological age in thehuman brain. Hum Mol Genet. 2011;20(6):1164–72.33. Massart R, Nemoda Z, Suderman MJ, Sutti S, Ruggiero AM, Dettmer AM,Suomi SJ, Szyf M. Early life adversity alters normal sex-dependentdevelopmental dynamics of DNA methylation. Dev Psychopathol. 2016;28(4pt2):1259–72.34. Steegenga WT, Boekschoten MV, Lute C, Hooiveld GJ, de Groot PJ, MorrisTJ, Teschendorff AE, Butcher LM, Beck S, Muller M. Genome-wide age-related changes in DNA methylation and gene expression in human PBMCs.Age (Dordr). 2014;36(3):9648.35. Gissler M. Finnish health and social welfare registers in epidemiologicalresearch. Norsk Epidemiol. 2004;14(1):113–20.36. Lam LL, Emberly E, Fraser HB, Neumann SM, Chen E, Miller GE, Kobor MS.Factors underlying variable DNA methylation in a human communitycohort. Proc Natl Acad Sci U S A. 2012;109 Suppl 2:17253–60.37. Barfield RT, Almli LM, Kilaru V, Smith AK, Mercer KB, Duncan R, Klengel T, MehtaD, Binder EB, Epstein MP, et al. Accounting for population stratification in DNAmethylation studies. Genet Epidemiol. 2014;38(3):231–41.38. Smith AK, Kilaru V, Kocak M, Almli LM, Mercer KB, Ressler KJ, Tylavsky FA,Conneely KN. Methylation quantitative trait loci (meQTLs) are consistentlydetected across ancestry, developmental stage, and tissue type. BMCGenomics. 2014;15:145.39. Girchenko P, Hamalainen E, Kajantie E, Pesonen AK, Villa P, Laivuori H,Raikkonen K. Prediction and Prevention of Preeclampsia and IntrauterineGrowth Restriction (PREDO) study. Int J Epidemiol. 2016;1-9.40. Bakulski KM, Feinberg JI, Andrews SV, Yang J, Brown S, LM S, Witter F, WalstonJ, Feinberg AP, Fallin MD. DNA methylation of cord blood cell types:applications for mixed cell birth studies. Epigenetics. 2016;11(5):354–62.41. Villa PM, Kajantie E, Raikkonen K, Pesonen AK, Hamalainen E, Vainio M,Taipale P, Laivuori H. Aspirin in the prevention of pre-eclampsia in high-riskwomen: a randomised placebo-controlled PREDO Trial and a meta-analysisof randomised trials. BJOG. 2013;120(1):64–74.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Girchenko et al. Clinical Epigenetics  (2017) 9:49 Page 14 of 14


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