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  • Open Access

Exploring the effect of antenatal depression treatment on children’s epigenetic profiles: findings from a pilot randomized controlled trial

  • 1, 2Email authorView ORCID ID profile,
  • 3, 4,
  • 5,
  • 1, 2,
  • 3,
  • 3,
  • 5,
  • 6 and
  • 2
Clinical Epigenetics201911:18

https://doi.org/10.1186/s13148-019-0616-2

  • Received: 28 June 2018
  • Accepted: 14 January 2019
  • Published:

Abstract

Background

Children prenatally exposed to maternal depression more often show behavioral and emotional problems compared to unexposed children, possibly through epigenetic alterations. Current evidence is largely based on animal and observational human studies. Therefore, evidence from experimental human studies is needed. In this follow-up of a small randomized controlled trial (RCT), DNA-methylation was compared between children of women who had received cognitive behavioral therapy (CBT) for antenatal depression and children of women who had received treatment as usual (TAU). Originally, 54 women were allocated to CBT or TAU. A beneficial treatment effect was found on women’s mood symptoms.

Findings

We describe DNA methylation findings in buccal swab DNA of the 3–7-year-old children (CBT(N) = 12, TAU(N) = 11), at a genome-wide level at 770,668 CpG sites and at 729 CpG sites spanning 16 a priori selected candidate genes, including the glucocorticoid receptor (NR3C1). We additionally explored associations with women’s baseline depression and anxiety symptoms and offspring DNA methylation, regardless of treatment. Children from the CBT group had overall lower DNA methylation compared to children from the TAU group (mean ∆β = − 0.028, 95% CI − 0.035 to − 0.022). Although 68% of the promoter-associated NR3C1 probes were less methylated in the CBT group, with cg26464411 as top most differentially methylated CpG site (p = 0.038), mean DNA methylation of all NR3C1 promoter-associated probes did not differ significantly between the CBT and TAU groups (mean ∆β = 0.002, 95%CI − 0.010 to 0.011). None of the effects survived correction for multiple testing. There were no differences in mean DNA methylation between the children born to women with more severe depression or anxiety compared to children born to women with mild symptoms of depression or anxiety at baseline (mean ∆β (depression) = 0.0008, 95% CI − 0.007 to 0.008; mean ∆β (anxiety) = 0.0002, 95% CI − 0.004 to 0.005).

Conclusion

We found preliminary evidence of a possible effect of CBT during pregnancy on widespread methylation in children’s genomes and a trend toward lower methylation of a CpG site previously shown by others to be linked to depression and child maltreatment. However, none of the effects survived correction for multiple testing and larger studies are warranted.

Trial registration

Trial registration of the original RCT: ACTRN12607000397415. Registered on 2 August 2007.

Keywords

  • DNA methylation
  • Epigenetics
  • Neurodevelopment
  • Antenatal depression
  • CBT
  • Programming

Background

Many pregnant women experience clinically significant depressive symptoms before delivery, with an estimated prevalence of 7.4 to 12.8% [1]. Mounting evidence demonstrates that children prenatally exposed to maternal depression more often have a difficult temperament [2], are more prone to develop internalizing and externalizing behavioral problems [37], show poorer performance on cognitive tasks [8, 9], and more often develop depression and anxiety symptoms themselves in (pre)adolescence [1012]. One mechanism by which antenatal depression might influence susceptibility for psychopathology is by epigenetic regulation of gene expression [13, 14]. Epigenetic mechanisms regulate the activity of DNA and include post-translational histone modification, micro-RNAs, and DNA methylation [15]. In contrast to the fixed genotype, the epigenome has shown to be highly variable early in development under the influence of environmental factors [16, 17].

Animal studies have provided evidence that antenatal stress alters methylation of offspring genes involved in neurodevelopment and is associated with behavioral changes. For example, exposure to chronic stress in early gestation in mice resulted in a stress-sensitive phenotype in male offspring, showing increased immobility in the tail suspension and forced swim test and heightened hypothalamic pituitary adrenal (HPA) axis responsivity, which was accompanied by increased DNA methylation and decreased gene expression of the glucocorticoid receptor in the hippocampus and amygdala [18]. Moreover, alterations in epigenetic profiles have been shown to remain stable across generations, passing on susceptibility for emotional and behavioral disorders from one generation to the next [19].

Since 2008, many human studies have investigated associations between prenatal stress exposure and offspring gene methylation, with a special focus on NR3C1, coding for the glucocorticoid receptor [20]. While the reported effect sizes are usually small, increased methylation status of NR3C1 has been linked to an increased HPA axis stress-response [21]. All studies to date are, however, observational and therefore susceptible to confounding by factors that are both associated with antenatal stress and with methylation patterns, such as maternal smoking during pregnancy [22]. Experimental designs including follow-up of children are currently scarce and urgently needed to establish causality between intrauterine exposures and later life outcomes [23].

The current study investigated effects of maternal depression treatment during pregnancy on DNA methylation profiles in the children. In the Beating the Blues before Birth (BBB) study, pregnant women with a confirmed Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) depressive disorder were randomized to either the intervention group, consisting of eight cognitive behavioral therapy (CBT) sessions, or to a control group, consisting of treatment as usual (TAU), which comprised case-managing by a midwife or referral to a general practitioner. Beneficial treatment effects favoring the intervention were found on maternal depression and anxiety. Anxiety symptoms significantly decreased, and depressive symptoms showed a decreasing trend nearly reaching significance, in the intervention versus the control group [24].

We hypothesized that compared to the control group, the intervention would be associated with a change in DNA methylation profiles of buccal swab DNA from the children, (1) at an epigenome-wide level, (2) at 16 a priori selected candidate genes, and (3) at promoter-associated glucocorticoid receptor (NR3C1) probes. We additionally explored whether severity of maternal symptoms of depression and anxiety at baseline would be associated with DNA methylation profiles in the children, regardless of treatment.

Results

Study sample characteristics

Of the original study group of 54 women, 2 women had moved overseas to unknown addresses, and 10 women could not be traced. This resulted in 42 women being invited to participate in the current study. In total, 19 women declined to participate. Reasons for declining were lack of time, a lack of interest in being involved, or not wanting their child’s DNA to be used for study purposes. This resulted in a study group of 23 women and their children who agreed to participate in the current study, 12 (42.9%) women from the intervention group and 11 (42.3%) women from the control group (flowchart; Fig. 1). Table 1 shows baseline characteristics of all women from the original study, women that did not participate, and women that did participate in the current follow-up. In the intervention and control group alike, women that responded to the current follow-up had lower Beck Depression Inventory (BDI-II) and lower Beck Anxiety Inventory (BAI) scores, less often reported using antidepressants, and were more highly educated with a higher annual income compared to non-responders at baseline. In the intervention group, participating women were more often born in Australia and married compared to women who did not participate, whereas in the control group, women were less often born in Australia and married compared to non-responders. Current demographics of the women and their children are shown in Table 2. Less women from the intervention group were currently using an antidepressant, their income was higher, and they more often drank one or more alcoholic unit per week, as compared to the control group.
Fig. 1
Fig. 1

Flow diagram of participant recruitment. *Comorbid axis I disorders, medical conditions at risk for interference with study participation, concurrent major psychiatric disorders for which the intervention was not designed (e.g., bipolar and psychotic disorder), risk requiring crisis management, current participation in other psychological programs, or significant difficulty with English. CBT cognitive behavioral therapy, TAU treatment as usual

Table 1

Baseline characteristics of all participants in a trial evaluating an antenatal cognitive behavioral therapy (CBT) versus treatment as usual (TAU), those that responded, and those that did not respond to the 5-year follow-up

 

All participants

Not participating in 5-year follow-up

Participating in 5-year follow-up

Baseline demographics

CBT (n = 28)

TAU (n = 26)

CBT (n = 16)

TAU (n = 15)

CBT (n = 12)

TAU (n = 11)

Mean (SD) BDI pre-treatment score

30.8 (9.5)

30.5 (8.9)

31.6 (9.7)

31.2 (7.8)

29.6 (9.5)

29.5 (10.4)

Mean (SD) BAI pre-treatment score

22.8 (10.0)

21.2 (10.2)

25.4 (10.1)

22.8 (12.2)

19.2 (9.0)

19.3 (7.1)

Mean (SD) BDI post-treatment score

13.0 (9.8)

17.4 (9.8)

12.9 (10.1)

17.3 (10.8)

13.0 (10.0)

17.6 (9.0)

Mean (SD) BAI post-treatment score

10.6 (7.6)

16.7 (11.8)

9.6 (5.4)

17.6 (14.3)

11.6 (9.9)

15.3 (7.1)

Mean (SD) ∆ BDI score (post-treatment − pre-treatment)

− 18.6 (10.0)

− 13.2 (12.8)

− 20.4 (12.0)

− 14.5 (10.4)

− 16.6 (7.3)

− 11.5 (16.1)

Mean (SD) ∆ BAI score (post-treatment − pre-treatment)

− 11.2 (9.4)

− 4.3 (8.3)

− 14.5 (10.1)

− 5.0 (9.8)

− 7.5 (7.2)

− 3.1 (6.0)

Mean (SD) maternal age in years

32.9 (5.9)

31.0 (5.8)

32.2 (6.5)

29.2 (5.6)

33.7 (5.7)

33.6 (5.2)

Mean (SD) gestational age in weeks

19.9 (7.7)

21.0 (6.0)

21.2 (8.0)

22.6 (6.1)

18.3 (7.2)

19.0 (5.5)

Antidepressant use (%)

7.1

22.7

14.3

26.7

11.1

Marital status (%)

 - Married

57.7

65.2

46.7

69.2

72.7

60.0

 - De Facto

34.6

21.7

46.7

15.4

18.2

30.0

 - Separated

8.7

7.7

10.0

 - Single

7.7

4.3

6.7

7.7

9.1

Birth location (%)

 - Australia

73.1

82.6

66.7

84.6

81.8

80.0

 - Other

26.9

17.4

33.3

15.4

18.2

20.0

Income (%)

 - Up to $ 20,000

4.5

10.0

 - $ 20,001–$ 40,000

8.0

22.7

7.1

25.0

9.1

20.0

 - $ 40,001–$ 60,000

20.0

13.6

28.6

16.7

9.1

10.0

 - $ 60,001–$ 80,000

28.0

27.3

21.4

33.3

36.4

20.0

 - > $ 80,001

32.0

31.8

28.6

25.0

36.4

40.0

 - Do not wish to divulge

12.0

-

14.3

-

9.1

Highest level of education (%)

 - Did not finish school

3.8

12.0

6.7

21.4

 - High School

7.7

24.0

13.3

21.4

27.3

 - Certificate Level/Apprenticeship

23.1

4.0

33.3

9.1

9.1

 - Advanced Diploma

19.2

4.0

6.7

7.1

36.4

 - Bachelor degree

11.5

24.0

20.0

28.6

18.2

 - Graduate diploma/certificate

19.2

16.0

6.7

7.1

36.4

27.3

 - Postgraduate Degree

15.4

16.0

13.3

14.3

18.2

18.2

Table 2

Current characteristics of women and their children participating in a DNA methylation study

Current demographics

CBT (n = 12)

TAU (n = 11)

Mean (SD) BDI score

16.1 (13.3)

14.9 (11.2)

Mean (SD) BAI score

11.3 (8.9)

10.9 (10.2)

Mean (SD) maternal age in years

40.0 (4.9)

40.6 (4.7)

Antidepressant use, n (%)

2 (16.7)

6 (54.4)

Mean (SD) child age in years

5.7 (1.2)

5.9 (1.0)

Mean (SD) child birth weight in grams

3547 (332)

3520 (590)

Gender (boys) (%)

58.3

63.6

Birth location (%)

 - Australia

81.8

80.0

 - Other

18.2

20.0

Marital status (%)

 - Married

66.7

54.4

 - De Facto

8.3

18.2

 - Separated

8.3

18.2

 - Single

16.7

9.1

Highest level of education (%)

 - Did not finish school

 - High School

27.3

 - Certificate Level/Apprenticeship

8.3

9.1

 - Advanced Diploma

8.3

-

 - Bachelor degree

25.0

9.1

 - Graduate diploma/certificate

41.7

18.2

 - Postgraduate Degree

16.7

36.4

Income (%)

 - Up to $ 20,000

18.2

 - $ 20,001–$ 40,000

8.3

18.2

 - $ 40,001–$ 60,000

9.1

 - $ 60,001–$ 80,000

8.3

9.1

 - > $ 80,001

83.3

45.5

 - Do not wish to divulge

Smokinga (%)

8.3

9.1

Alcoholb (%)

58.3

27.3

CBT cognitive behavioral therapy, TAU treatment as usual

a,bDefined as “currently consuming one or more alcoholic units per week or smoking one or more cigarettes per week”

Association between genome-wide DNA methylation and allocation

Linear regression analysis was used to identify specific differentially methylated probes according to allocation. This took into account variation associated with the following covariates: birth weight, HM850 array chip position, sex and age, as identified by principal component analysis (PCA). Linear regression analysis revealed a total of 4780 differentially methylated probes at a nominal significance level (p < 0.01, uncorrected for multiple testing) between the intervention and the control group, showing higher DNA methylation in the control group (mean ∆β = − 0.028, 95% CI − 0.035 to − 0.022, p < 0.001). Adding current income as an additional covariate did not significantly alter the results (mean ∆β = − 0.026, 95% CI − 0.031 to − 0.021, p < 0.001). The top 100 differentially methylated probes are presented in Table 3 of the Appendix. Table 4 shows the ten most differentially methylated probes. Of the top five differentially methylated probes, three probes with annotated genes were probe cg15495292 on the AIG1 gene (uncorrected p = 4.01E-06, corrected p = 0.999), cg05155812 on the SUN1 gene (uncorrected p = 1.56E-05, corrected p = 0.999), and cg18818484 on the PTCHD2 gene (uncorrected p = 2.20E-05, corrected p = 0.999). After correcting for multiple testing (corrected p ≤ 0.01), no probes remained significantly associated with the intervention.
Table 3

Top 100 differentially methylated probes according to intervention

CpG

p

Adjusted pa

Gene

Gene region

∆β

cg19908420

3.40E-06

0.999997557

  

0.049137862

cg15495292

4.01E-06

0.999997557

AIG1

Body

0.079710136

cg05155812

1.56E-05

0.999997557

SUN1

TSS1500

− 0.280713404

cg18818484

2.20E-05

0.999997557

PTCHD2

 

0.022078691

cg17622532

2.21E-05

0.999997557

  

0.024836631

cg14034519

2.27E-05

0.999997557

SNX1

Body

0.053471841

cg26436424

3.24E-05

0.999997557

NGEF

Body

0.033261363

cg21494953

3.48E-05

0.999997557

C5orf23

TSS1500

0.036133838

cg19232929

3.58E-05

0.999997557

  

0.054387673

cg22342380

3.86E-05

0.999997557

  

0.03688025

cg13719771

5.98E-05

0.999997557

NDUFA9

Body

0.13765872

cg10356363

6.06E-05

0.999997557

CEBPB

TSS1500

0.026639222

cg05205351

6.20E-05

0.999997557

NOP56

Body

0.05930508

cg14231326

6.23E-05

0.999997557

  

0.031289864

cg14358699

7.14E-05

0.999997557

  

0.047991502

cg06961812

8.01E-05

0.999997557

PRODH2

Body

0.058582642

cg16007230

8.39E-05

0.999997557

ABCC3

ExonBnd

0.036161879

cg25968469

8.53E-05

0.999997557

ARHGAP22

Body

0.056699144

cg23619591

8.80E-05

0.999997557

C19orf81

Body

0.057592082

cg09240747

0.000101189

0.999997557

  

0.067301777

cg18077049

0.000101567

0.999997557

GLRA3

Body

0.116790545

cg24435401

0.000110721

0.999997557

NPAS4

TSS1500

0.021387283

cg23274420

0.000110944

0.999997557

  

0.068615769

cg09223928

0.000111509

0.999997557

  

0.030359585

cg18666104

0.000115314

0.999997557

CORO1C

Body

0.058415174

cg16273469

0.000115391

0.999997557

  

0.036049214

cg00541777

0.000120288

0.999997557

COLEC11

TSS1500

0.120518141

cg06646082

0.0001208

0.999997557

BTBD17

TSS1500

0.0430183

cg03711840

0.000127893

0.999997557

PLXNA1

Body

0.043191584

cg19465002

0.000130791

0.999997557

  

0.033852961

cg14687471

0.000134464

0.999997557

NBR2

Body

0.023128809

cg27243560

0.000134814

0.999997557

  

0.031689225

cg05510714

0.000135017

0.999997557

KYNU

Body

0.153887531

cg12987887

0.000136898

0.999997557

UPB1

ExonBnd

− 0.01972518

cg26836955

0.000138572

0.999997557

LONP1

Body

0.039104166

cg26330841

0.000138665

0.999997557

  

0.032962344

cg16720807

0.000142967

0.999997557

FAM176A

5′UTR

0.042119403

cg01440210

0.000143289

0.999997557

  

0.030341728

cg17068417

0.000144326

0.999997557

EEFSEC

Body

0.030665165

cg15313810

0.000144443

0.999997557

ST6GALNAC4

Body

0.029787439

cg07545731

0.000147518

0.999997557

COL22A1

Body

0.04468122

cg14684297

0.000150469

0.999997557

ARHGAP33

5′UTR

0.032019831

cg10727673

0.000154265

0.999997557

TMEM22

TSS1500

0.089444195

cg04798314

0.000155738

0.999997557

SMYD3

Body

0.323390033

cg11035122

0.000160944

0.999997557

MIR758

TSS1500

0.055539324

cg12360330

0.000168181

0.999997557

CENPJ

Body

0.032193572

cg07469546

0.000172234

0.999997557

  

0.014405304

cg17785398

0.000172977

0.999997557

KCNJ6

Body

0.022656857

cg18291664

0.000173083

0.999997557

PRKAR1B

Body

0.040654976

cg09319487

0.000181803

0.999997557

  

0.033053753

cg11510586

0.000186082

0.999997557

  

0.107251714

cg25441526

0.000188457

0.999997557

WDFY4

Body

0.025251026

cg19379103

0.000188787

0.999997557

SSBP3

Body

0.031870653

cg19769811

0.00019183

0.999997557

RASGRF2

TSS1500

0.046395706

cg26221509

0.000199233

0.999997557

SCUBE1

Body

0.039685931

cg14700416

0.000199451

0.999997557

SPOCK3

5′UTR

0.049430209

cg22746421

0.000200331

0.999997557

  

0.02669027

cg23553242

0.000200938

0.999997557

USP2

Body

0.043740484

cg06617093

0.000206244

0.999997557

  

0.032231234

cg08670534

0.000206305

0.999997557

COL2A1

Body

0.032117847

cg15791944

0.000212127

0.999997557

  

0.055152706

cg17562896

0.000216404

0.999997557

SV2C

Body

0.037479302

cg02018176

0.000217297

0.999997557

KIAA1530

Body

0.047057842

cg11576176

0.000220243

0.999997557

GSX2

1stExon

0.03556139

cg09480336

0.0002295

0.999997557

POLD1

Body

0.03212232

cg21592262

0.000233681

0.999997557

  

0.06371313

cg12472342

0.000234117

0.999997557

  

− 0.069235248

cg18361948

0.00023564

0.999997557

  

0.029932491

cg00945089

0.000236572

0.999997557

GFRA1

Body

0.033266209

cg07442357

0.000238546

0.999997557

  

0.01892614

cg09193498

0.000239232

0.999997557

SEZ6

Body

0.042776024

cg02438610

0.000240811

0.999997557

SUN1

TSS1500

− 0.013139753

cg15037661

0.00024103

0.999997557

NR1D2

TSS1500

0.00946764

cg26264656

0.000243011

0.999997557

SKI

Body

0.034797294

cg24367840

0.000243465

0.999997557

PSMD14

Body

0.057487682

cg05289897

0.000259274

0.999997557

  

0.012403078

cg16419764

0.000261486

0.999997557

CDYL

Body

0.026043028

cg00248302

0.000266776

0.999997557

FCRL5

Body

0.028022889

cg24900542

0.000269678

0.999997557

  

0.085875055

cg15078841

0.000272298

0.999997557

  

0.022837528

cg12541879

0.000282436

0.999997557

PTPRN2

Body

0.056208383

cg01976641

0.000283246

0.999997557

  

0.05497368

cg17121322

0.000286514

0.999997557

  

0.025193249

cg17547875

0.000288231

0.999997557

  

0.01236688

cg18169610

0.000296554

0.999997557

CD81

Body

0.038708

cg04801704

0.000304651

0.999997557

TLL2

Body

0.025096532

cg23425290

0.000307508

0.999997557

ABCC1

Body

0.023343856

cg22680931

0.00030882

0.999997557

TMEM167B

TSS1500

0.122894387

cg01723825

0.000310423

0.999997557

URI1

TSS200

0.039217642

cg16261251

0.000311941

0.999997557

  

0.06722457

cg01400541

0.000314878

0.999997557

C10orf128

Body

0.042719378

cg26796807

0.000318004

0.999997557

  

0.04717045

cg10038145

0.000319876

0.999997557

POR

Body

0.045738894

cg09078103

0.000320468

0.999997557

SNX9

Body

0.027261168

cg08880699

0.000322485

0.999997557

  

0.043133838

cg03116452

0.00032398

0.999997557

PLD3

5′UTR

0.034421382

cg03071994

0.000324145

0.999997557

NR4A1

Body

0.029626215

cg21485062

0.000324634

0.999997557

C7orf25

Body

0.024308813

cg11504793

0.000326763

0.999997557

NOL4L

Body

0.025196146

cg04837576

0.00032871

0.999997557

ADRBK2

Body

0.030823149

∆β = mean β (treatment as usual) − mean β (cognitive behavioral therapy)

TSS transcription start site, UTR untranslated region

aAdjusted for multiple testing [45]

Candidate gene-specific DNA methylation and allocation

In addition to an exploratory genome-wide analysis (above), we also tested for associations with a list of a priori chosen candidate genes. Table 5 shows the results of the unpaired Mann-Whitney-Wilcoxon tests, comparing mean DNA methylation of 16 candidate genes between the intervention and control group. No genes were significantly differentially methylated at a nominal significance level p < 0.01. Trends toward lower DNA methylation in the CBT group compared to the TAU group were seen in the OXTR, MEST, MEG3, H19, and CRHR2 genes. Table 6 of the Appendix shows the probes of the candidate genes that were differentially methylated at a nominal significance level p < 0.01.
Table 4

Top 10 differentially methylated genes according to allocation

CpG

p

Adjusted pa

Gene

Gene region

Δß

cg19908420

3.40E-06

0.999998

  

0.049137862

cg15495292

4.01E-06

0.999998

AIG1

Body

0.079710136

cg05155812

1.56E-05

0.999998

SUN1

TSS1500

-?0.280713404

cg18818484

2.20E-05

0.999998

PTCHD2

Body

0.022078691

cg17622532

2.21E-05

0.999998

  

0.024836631

cg14034519

2.27E-05

0.999998

SNX1

Body

0.053471841

cg26436424

3.24E-05

0.999998

NGEF

Body

0.033261363

cg21494953

3.48E-05

0.999998

C5orf23

TSS1500

0.036133838

cg19232929

3.58E-05

0.999998

  

0.054387673

cg22342380

3.86E-05

0.999998

  

0.03688025

?ß = mean ß(TAU) – mean ß(CBT)

CBT cognitive behavioral therapy, TAU treatment as usual, TSS transcription start site, UTR untranslated region

aCorrected for multiple testing [46]

The glucocorticoid receptor (NR3C1) gene and allocation

Mean DNA methylation of 34 promoter-associated NR3C1 probes (Table 7 in Appendix) did not differ significantly between the intervention and control group (mean ∆β = 0.002, 95% CI − 0.010 to 0.011). One probe, cg26464411, showed a trend toward lower methylation in the intervention group (Table 7 in Appendix, Fig. 2).
Fig. 2
Fig. 2

Box plot indicates methylation values (%) for children that were prenatally exposed to the intervention compared to control for the CpG site on NR3C1 that was mostly associated with treatment exposure: cg26464411. p (unadjusted for multiple testing) = 0.039. CBT cognitive behavioral therapy, TAU treatment as usual

Association between genome-wide DNA methylation and baseline depression/anxiety

Depression

Linear regression analysis (adjusted for birth weight, HM850 array chip position, sex, age, and allocation) revealed a total of 3065 differentially methylated probes at a nominal significance level (p < 0.01) between the groups of children from the antenatally severely depressed women versus the group of children from the antenatally mildly depressed women. Mean DNA methylation values were not significantly different between children born to the severely depressed and the mildly depressed women (mean ∆β = 0.0008 95% CI − 0.007 to 0.008, p = 0.95). The top 100 differentially methylated probes according to depression severity at baseline are presented in Table 8 (Appendix). After correcting for multiple testing (corrected p ≤ 0.01), no probes remained significantly associated with maternal depression severity in pregnancy, prior to treatment.

Anxiety

Linear regression analysis (adjusted for birth weight, HM850 array chip position, sex, age, and allocation) revealed a total of 3292 differentially methylated probes at a nominal significance level (p < 0.01) between the groups of children from the antenatally severely anxious women versus the group of children from the antenatally mildly anxious women. Mean DNA methylation values were not significantly different between the children born to severely anxious and the mildly anxious women (mean ∆β = 0.0002 95% CI − 0.004 to 0.005, p < 0.01). The top 100 differentially methylated probes according to anxiety severity at baseline are presented in Table 9 in Appendix. After correcting for multiple testing (corrected p ≤ 0.01), no probes remained significantly associated with maternal anxiety severity in pregnancy, prior to treatment.

Candidate gene-specific DNA methylation and baseline depression/anxiety

Depression

Table 10 (Appendix) shows the results of the unpaired Mann-Whitney-Wilcoxon tests, comparing mean DNA methylation of 16 candidate genes between the groups of children from the highly depressed and the mildly depressed women. No genes were significantly differentially methylated at a nominal significance level p < 0.01. Table 11 of the Appendix shows the probes of the candidate genes that were differentially methylated according to depression symptom severity at a nominal significance level p < 0.01.

Anxiety

Table 12 (Appendix) shows the results of the unpaired Mann-Whitney-Wilcoxon tests, comparing mean DNA methylation of 16 candidate genes between the groups of children from the highly anxious and the mildly anxious women. No genes were significantly differentially methylated at a nominal significance level p < 0.01. A trend toward higher DNA methylation was seen in the children from the highly anxious mothers compared to the children of mildly anxious mothers in the MEST gene. Table 11 of the Appendix shows the probes of the candidate genes that were differentially methylated according to anxiety symptom severity at a nominal significance level p < 0.01.

The glucocorticoid receptor (NR3C1) gene and baseline depression/anxiety

Depression

Mean DNA methylation of 34 promoter-associated NR3C1 probes (Table 13, Appendix) did not differ significantly between the groups of children from the highly depressed and the mildly depressed women (mean ∆β = 0.006, 95% CI − 0.005 to 0.020).

Anxiety

Mean DNA methylation of 34 promoter-associated NR3C1 probes did not differ significantly between the groups of children from the highly anxious and the mildly anxious women (mean ∆β = 0.006, 95% CI − 0.005 to 0.020). Two probes, cg07515400 and cg22402730, showed a trend toward higher DNA methylation in the children from severely anxious mothers (Table 13, Appendix).

Discussion

In this follow-up of one of the first randomized controlled trials on the effect of antenatal psychological depression treatment (CBT) on children’s DNA methylation patterns, we found no robust evidence of widespread methylation differences between children of women in the control or intervention group. However, at a pre-specified nominal significance level of p < 0.01, 4780 differentially methylated probes according to allocation pointed to an overall 2.7% lower DNA methylation level of probes in children from the intervention group. Applying a candidate approach, non-significant trends toward lower DNA methylation in the intervention group were seen in OXTR, MEST, MEG3, H19, and CRHR2. We did not find a significant difference in mean DNA methylation of 34 NR3C1 promoter-associated probes between the intervention and control groups. Nevertheless, the majority of probes (68%) showed lower DNA methylation in the intervention group compared to the control group, with cg26464411 as topmost differentially methylated probe, a CpG site that has been associated with depression in earlier studies [25, 26]. Whether these trends are persistent and clinically relevant remains to be determined in future studies with larger sample size and longer follow-up.

Of the top five probes that were most differentially methylated between the intervention and the control group, three corresponded to annotated genes: cg15495292 on the AIG1 gene, which is a gene involved in androgen regulation; cg18818484 on the PTCHD2 gene, which is involved in neuronal proliferation and differentiation; and cg05155812 on SUN1, a gene that potentially plays a role in neuronal migration and cerebellar development. These findings may be relevant as the desired effect of a prenatal intervention would be to target genes that mediate the associations of prenatal stress, depression or anxiety with adverse neurodevelopmental disorders in children [27, 28]. Our results are promising, but evidently replication in larger studies is necessary.

Additionally, we revealed trends toward lower DNA methylation in children from the intervention group compared to the control group in 5 out of 16 candidate genes that have previously been associated with prenatal exposure to maternal stress, depression, or anxiety. These trends were observed in OXTR, the gene coding for the Oxytocin receptor; the MEST gene, a gene involved in metabolism; MEG3, a long noncoding RNA; H19, an imprinted gene; and CRHR1, a gene for corticotrophin releasing hormone receptors. We did not find a significant difference in mean DNA methylation between the intervention and control group on the promoter region of the NR3C1 gene, coding for the glucocorticoid receptor. Nevertheless, cg26464411 showed a trend toward lower DNA methylation in the intervention group. This CpG site has been positively correlated with depressive symptoms or hypercortisolism in earlier studies [25, 26]. Although our results were not significant, the trends we have observed were in line with our expectations, based on earlier findings from observational studies showing increased methylation of NR3C1 in newborns and young children of antenatally stressed, depressed, or anxious women [20, 29], which was associated with increased stress responses [21, 30].

The women in the current study were treated at a mean of 18.6 weeks gestational age, and it may be possible that the effect of treatment on offspring DNA methylation would have been stronger if the women had been treated earlier in their pregnancies. Increased attention is currently focused on the period of early pregnancy, and even the preconception period, as an important time window for adverse environmental factors inducing prenatal programming, which has been shown in animal studies [18]. Further evidence in humans is derived from studies examining prenatal famine, in which the largest effect on offspring methylation was found after prenatal exposure to undernutrition in early pregnancy [31]. We did not test for an interaction between allocation status and gestational age on mean methylation in candidate genes because of the lack of significance in the initial analyses, but in larger future studies, exploring moderation through gestational age would be highly informative to identify treatment effects on DNA methylation during specific stages of pregnancy.

A limitation of the study was a lack of statistical power, as we were only able to include approximately half (23/54 = 43%) of the original sample in this follow-up. Nevertheless, associations between prenatal stress and methylation status of NR3C1 have been reported in studies with a similar sample size [30, 32]. It was of interest that women who participated in the current follow-up study had lower levels of depression and anxiety at baseline compared to the participants that were lost to follow-up (Table 1). Also, they were observed to have higher incomes and were more highly educated at baseline. However, attrition bias is not likely to have occurred as this was the case in both groups [33]. Despite no formal statistical tests being conducted [34], it was evident that the difference in anxiety (BAI) scores before and after treatment between the intervention and control group was twice as high in the non-responders compared to the responders (14.5 versus 7.5), indicating that women with greater response to treatment were relatively underrepresented in the current sample. Additionally, some women in the control group also reported accessing psychological or medical treatment outside the trial [24]. This, and the lower participation of those who responded better to treatment, might have led to an underestimation of the effect of therapy on methylation profiles in the children in the current study.

Although both groups were reasonably balanced in terms of psychological and sociodemographic factors at the time of follow-up, it is still possible that other, unmeasured factors are (partly) responsible for the trends observed in the children’s epigenetic profiles according to allocation status. Because of the small sample size of our study, we chose to include only those variables that were likely to attribute mostly to the variation in DNA methylation, such as child gender, age, birth weight, and income. We did not include educational attainment, although this also appeared to be somewhat higher in the intervention group (although not statistically significant, results not shown). In addition, maternal body composition in pregnancy, pregnancy complications, and mode of delivery were not recorded in the original study files, and hence, not included in the current study. As these factors may act as mediators in the causal path from improved mood in pregnancy to better child outcomes, in future studies these variables should be included as well. Nevertheless, we did have access to the children’s birth weight, an important marker for general health of the baby, which showed to be similar between both groups. Also, we were unable to control for PC5 in the analyses, as none of the variables included in the model was associated with PC5. Nevertheless, the contribution fraction of PC5 to the variation in DNA methylation was very marginal compared to the contribution fraction of PC1, PC2, PC3, and PC4, which were associated with known variables and therefore were controlled for in our analyses. Finally, we did not adjust for cellular heterogeneity in our study. The most widely applied method is the reference-based deconvolution method originally described by Houseman et al., which permits the estimation of the proportion of various cell types within a sample based on existing reference data sets [35]. For blood, several studies have analyzed the methylation profile of the specific cell- types present in whole blood, which can serve as reference data. However, for saliva, this has not been performed systematically, but studies that have applied the Houseman deconvolution method on salivary genome wide DNA methylation data (combining reference methylomes from leucocyte subtypes and buccal epithelial cells references methylomes) have shown that saliva is less heterogenic compared to blood [36].

The impact of the postnatal environment on methylation profiles in children also cannot be ignored. Exposure to stressful life events from birth to adolescence has been associated with higher NR3C1 methylation [37]. Although in both intervention and control group, more women were currently using antidepressant medication compared to when they were pregnant at enrollment of the original study, this was much more pronounced in the control group (relative increase of 43.3%) compared to the intervention group (relative increase of 16.7%). These observations may be consistent with a potential longer-term beneficial effect of treatment in the women, which in turn, might have positively affected child outcomes. Women from the intervention group also reported higher incomes compared to baseline, which was not the case in the control group, although including income as additional covariate did not significantly alter the results. To be able to isolate the effect of antenatal CBT on offspring DNA methylation in utero, prior to any postnatal confounding, evidence from trials that include cord blood and/or placenta samples for DNA methylation (and gene expression) are needed.

Finally, it has not yet been fully elucidated how maternal depression affects child adversity. Nevertheless, epigenetic modification of fetal genes in response to increased cortisol exposure, either directly or via a decrease in placental inactivation, has been widely accepted as a potential underlying mechanism. Although our study findings could not robustly support this hypothesis, the trends observed are in line with earlier evidence. The existing evidence is nearly exclusively based on findings from experiments in animals and observational human studies. The fact that the exploratory findings from this novel experimental study in humans are in line with the available evidence is therefore promising. It must be noted that we mostly looked at statistically significant results at an uncorrected p-value level. The results of our study should therefore be interpreted with caution. Although the observed effect sizes were small, with mean differences of 1–5% in methylation status, they are in line with earlier evidence [20]. Because of the lack of studies with a comparable study design, it is not yet possible to replicate our findings in a similar trial; however, plans for a larger trial are currently in progress.

Conclusion

We found preliminary evidence of a possible effect of cognitive behavioral therapy during pregnancy on widespread methylation and a non-significant trend towards lower methylation of a specific CpG site previously linked to depressive symptoms and child maltreatment in the intervention group. However, none of the effects survived correction for multiple testing. Larger studies are now warranted.

Methods

Study population

For the BBB study, women aged 18 years or over, and less than 30 weeks pregnant were recruited through screening programs at the Northern Hospital and Mercy Hospital for Women, Melbourne, Australia, and via other health professionals and services in the public (e.g., obstetricians, GPs, and PaNDA; a Perinatal Anxiety and Depression helpline) and private sector (e.g., Northpark Private Hospital). The participating institutions were reached through advertisement and encouraged to refer women with suspected clinical depression. Women scoring 13 points or higher on the Edinburgh Postnatal Depression Scale (EPDS), the optimal score for detecting depression during pregnancy [38], were referred to the study for assessment by a psychologist if they consented. They were included in the study if they met DSM-IV criteria for a minor or major depressive disorder or an adjustment disorder with mixed depression and anxiety [39]. Severity of depression and anxiety symptoms was measured with the Beck Depression and Anxiety Inventories [40, 41]. Women with comorbid axis I disorders or medical conditions that were likely to interfere with study participation, risk requiring crisis management, participation in other psychological programs, or significant difficulty with English were excluded [24]. Women included in the study (N = 54) were randomized to receive pregnancy-specific CBT (N = 28) or TAU (N = 26). The CBT program consisted of seven individual sessions and one partner-session. TAU consisted of case-management by a midwife or a general practitioner and referral to other services of agencies as necessary. For ease of interpretation, in the results sections of this paper, the group of children of mothers from the CBT group will be referred to as the “intervention” group, and the group of children of mothers from the TAU group will be referred to as the “control” group. For participation in the current study, starting approximately 5 years after the BBB program had ended, all participants were invited through a letter. If they agreed to participate, an appointment at the Melbourne Brain Institute was planned, and informed consent was signed prior to or on the day of their visit to the clinic. If women were not able to attend the clinic, they were invited to send a buccal sample through the mail. The study was approved by the Human Research Ethics Committees of Austin Health, Melbourne, Australia.

Data collection

A questionnaire on current sociodemographic data and current symptoms of depression and anxiety was sent to each woman’s home address. Baseline demographics, including symptoms of depression and anxiety as well as the child’s birth weight, were taken from the BBB study files. At the Melbourne Brain Centre, a cognitive assessment by means of the Wechsler Preschool and Primary Intelligence Scale (WWPSI-III) [42] was performed on the child, an MRI scan of the child’s brain was conducted, of which results are described elsewhere, and a buccal cell sample from the child was obtained by a researcher who was blinded to the allocation status of the women.

Buccal cell samples

Buccal cells were collected using a dedicated swab (OraCollect 100, DNA Genotek Inc., Ontario, Canada). Children were instructed not to eat or drink 30 min prior to taking the swab. Women who were not able to visit the Melbourne Brain Centre were instructed how to apply the swab on their child, and asked to send the sample via mail. The swabs were stored at room temperature at the Parent-Infant Research Institute and transported to the Murdoch Children’s Research Institute (Melbourne, Australia) for DNA extraction within 2 weeks after collection.

DNA extraction and genome-wide methylation detection

DNA extraction of all samples was performed using the NucleoBond CB20 DNA extraction kit. Purification of DNA was assessed using Nanodrop Spectrophotometry. Bisulfite conversion was performed using the EZ-96 DNA methylation kit (ZYMO Research Corporation) according to the manufacturer’s instructions. DNA methylation profiling was performed at the Australian Genome Research Facility, on bisulfite converted DNA using the Illumina Infinium Methylation EPIC BeadChip Array (HM850) (Illumina), which measures CpG methylation at > 850,000 genomic sites.

Candidate gene approach

We extracted 729 probes spanning 16 a priori selected genes for linear regression analysis. Candidate genes were those that had previously been assessed in relation to prenatal exposure to maternal stress, depression, and/or anxiety in earlier studies [20]. Genes of interest were genes encoding brain-derived neurotrophic factor (BDNF; 91 probes), corticotrophin releasing hormone (CRH; 21 probes), corticotrophin-releasing factor-binding protein (CRHBP; 25 probes), corticotrophin-releasing hormone receptors 1 and 2 (CRHR1; 41 probes, CRHR2; 40 probes), FK506 binding protein (FKBP5; 49 probes), a long noncoding RNA (H19; 57 probes), hydroxysteroid 11-beta dehydrogenase 1 and 2 (HSD11B1; 25 probes, HSD11B2; 23 probes), insulin-like growth factor (IGF2; 15 probes), maternally expressed 3 (MEG3; 87 probes), mesoderm-specific transcript homolog protein (MEST; 63 probes), the glucocorticoid receptor (NR3C1; 89 probes), the mineralocorticoid receptor (NR3C2; 50 probes), the oxytocin receptor (OXTR; 22 probes), and the serotonin transporter (SLC6A4; 31 probes) [20]. Additionally, considering the especially strong evidence for this gene, we separately analyzed the probes of the promoter region of the glucocorticoid receptor gene (NR3C1 promoter-associated probes; 34 probes) for differential methylation.

Statistical analysis

DNA methylation was defined as a continuous variable varying from 0 (completely unmethylated) to 1 (completely methylated). Methylation data were processed in R using the minfi package. Normalization of the data was performed using the SWAN method [43]. Probes on X and Y chromosomes, probes that were associated with SNPs with a minor allele frequency > 1%, and cross-reactive probes [44] were removed from the dataset. This resulted in data for 770,668 probes available for subsequent analysis.

Sources of variation

Main contributors to the variation in the methylation data were identified by principal component analysis (PCA). We included the following variables in the analysis to assess associations with PC’s: participant ID, chip ID, HM850 array chip position, allocation, sex, child age, birth weight, maternal age, gestational age, current income, baseline depression symptoms, baseline anxiety symptoms, current depression symptoms, and current anxiety symptoms. Results of the PCA showed that the first five principal components contributed most to the variation in the methylation data, and all variables associated with any of these PC’s were added as covariate in all analyses (Fig. 3a). The heatmap demonstrated that allocation was associated with the third principal component. Birth weight, child age, sex, and HM850 array chip position were associated with the first four principal components and they were included in the analyses as covariates. None of the variables included in our model was significantly associated with the fifth principal component, and this PC was therefore not included in our model as covariate (Fig. 3b). Unsupervised analysis by multidimensional scaling was conducted in order to examine sources of variation within the dataset. Beta values (methylation level) at all HM850 probes for all samples were used to produce multidimensional scaling (MDS) plots, with samples colored according to intervention (turquoise)/control (orange) status, showing the relatedness of samples over the first two principal components of variation (Fig. 4a). Coloring by intervention/control revealed no distinct separation by allocation. Additional MDS plots of samples over other principal components also failed to show a distinct separation between the two groups (Figs. 4b c).
Fig. 3
Fig. 3

Principal component analysis results of the variation in the HM850 methylation data. Principal component analysis revealed birth weight as the major contributor to variation in the dataset with intervention status as the fifth largest contributor to variation in buccal cell DNA methylation profiles. a Scree plot generated with M values for 770,668 probes on the HM850 array. Variance is shown on the y-axis, principal components are shown on the x-axis. b Heatmap showing correlation coefficients, direction of correlations, and p values (bracketed) between principal components and various clinical parameters. Shaded boxes indicate correlations between principal components and clinical parameters (set at p ≤ 0.1)

Fig. 4
Fig. 4

MDS plots, with samples colored according to CBT (turquoise)/TAU (orange) status, showing the relatedness of samples over the first four principal components of variation. CBT cognitive behavioral therapy, TAU treatment as usual

Differential methylation according to allocation

Linear regression analysis was used to identify associations between the intervention status and epigenome-wide DNA methylation. We took into account variation associated with the covariates birth weight, HM850 array chip position, child sex and age, to account for PC1, PC2, PC3, and PC4, as identified by PCA. The Benjamini-Hochberg False-Discovery-Rate method [45] was used to correct for multiple testing. However, none of the analyses yielded significant differentially methylated probes between the intervention and control group after correcting for multiple testing. In an explorative analysis, we extracted differentially methylated probes between the intervention and control group at a nominal significance level set at p < 0.01, prior to correcting for multiple testing. We assessed differences in mean DNA methylation of all significant probes between the intervention and control group using an unpaired Mann-Whitney-Wilcoxon test. We additionally compared mean beta differences of 16 candidate genes, and the promoter region of the NR3C1 gene between the intervention and control group using an unpaired Mann-Whitney-Wilcoxon test.

Differential methylation according to baseline depression or anxiety symptom score

As additional explorative analyses, two separate linear regression models were also used to investigate associations between baseline depression (BDI–II score) and baseline anxiety (BAI- score) with methylation profiles in the children. For ease of interpretation, the sample was divided into two groups in both analyses. The rationale behind this approach was to explore widespread methylation variation between women with severe symptoms compared to those with mild symptoms using clinically relevant cut-offs, rather than investigating the direction of correlations between increasing depression and anxiety scores on all probes separately. Baseline depression was converted to a dichotomous variable using clinically relevant Beck questionnaire cut-offs. Women with BDI-II ≥ 29 were classified as “highly depressed” (n = 13), whereas those with a score below 29 were classified as “mildly depressed” (n = 9) [46]. This procedure was repeated for baseline anxiety (BAI-score). The cut-off for clinically relevant anxiety is set at 16, and therefore we classified women with BAI ≥ 16 as “highly anxious” (n = 8), and women with BAI below 16 as “mildly anxious” (n = 14) [47]. One woman had missing data on baseline depression and anxiety and was excluded from the analysis. We took into account allocation status, birth weight, HM850 array chip position, child sex, and age as covariates, as identified by PCA. Differentially methylated probes at a nominal significance level set at p < 0.01, prior to correction for multiple testing, were extracted. We compared differences in mean DNA methylation in groups of children of women with high baseline symptoms and low baseline symptoms using an unpaired Mann-Whitney-Wilcoxon test, both for depression and anxiety. We additionally compared mean beta differences of 16 candidate genes, and the promoter region of the NR3C1 gene between groups of children of women with high baseline symptoms and low baseline symptoms using an unpaired Mann-Whitney-Wilcoxon test, both for depression and anxiety.

Abbreviations

BAI: 

Beck Anxiety Inventory

BBB: 

Beating the Blues before Birth

BDI-II: 

Beck Depression Inventory-II

CBT: 

Cognitive behavioral therapy

DSM-IV: 

Diagnostic and Statistical Manual of Mental Disorders, 4th Edition

MDS: 

Multidimensional scaling

PCA: 

Principal component analysis

RCT: 

Randomized controlled trial

TAU: 

Treatment as usual

Declarations

Acknowledgements

We are particularly grateful to all the families who took part in this study.

Funding

This work was supported by the Brain and Behavior research foundation, under the NARSAD Young Investigator Grant, project 22975, and DynaHealth, under Grant Agreement no 633595, Horizon2020. The funding organizations had no role in the design and conduct of the study; collection, management, and analysis of the data; or preparation, review, and approval of the manuscript.

Availability of data and materials

The data sets used and/or analyzed during the current study are available from the corresponding author, on reasonable request.

Authors’ contributions

JM, AG, and CH contributed to the design and implementation of the original randomized controlled trial. RS and AS assisted in the analysis of DNA methylation data. SdR and LB contributed to the collection of the 5-year follow data including the statistical analysis and preparing of the manuscript. TR and HB aided in interpreting the results and writing of the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The original RCT and the current follow-up study were both approved by the Human Research Ethics Committees of Austin Health, Melbourne, Australia. Trial Registration of the original RCT: ACTRN12607000397415. Registered on 2 August 2007, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=82169. Informed consent was given by one of the children’s parents at the outset of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the 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.

Authors’ Affiliations

(1)
Department of Obstetrics and Gynecology, Amsterdam UMC, location AMC, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
(2)
Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam UMC, location AMC, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
(3)
Austin Health, Parent-Infant Research Institute, 300 Waterdale Road, Heidelberg West, VIC, 3081, Australia
(4)
Melbourne School of Psychological Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
(5)
Murdoch Children’s Research Institute—Cancer and Disease Epigenetics, Royal Children’s Hospital, Flemington Road, Parkville, Melbourne, VIC, 3052, Australia
(6)
Department of General Practice, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands

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