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Differential genome-wide DNA methylation patterns in childhood obesity



Exposure to stress during pregnancy may program susceptibility to the development of obesity in offspring. Our goal was to determine whether prenatal maternal stress (PNMS) due to a natural disaster was associated with child obesity, and to compare the DNA methylation profiles in obese versus non-obese children at age 13½ years. Women and their children were involved in the longitudinal natural disaster study—Project Ice Strom, which served as a human model to study PNMS. Blood was collected from 31 children (including five obese children). Infinium HumanMethylation450 BeadChip Array was performed for genome-wide DNA methylation analyses.


Results demonstrated a well-defined obesity-associated genome-wide DNA methylation pattern. There were 277 CpGs, corresponding to 143 genes, were differentially-methylated. IPA analyses revealed 51 canonical pathways, and enrichment of pathways was involved in immune function. Although no significant association was found between PNMS and child obesity, the preliminary data in the study revealed obesity-associated methylation patterns on a genome-wide level in children.


Obesity is a multifactorial condition caused by the complex interaction between genetic and environmental factors. As genetics alone cannot fully explain the rapid global increase in obesity, epigenetic mechanisms have been suggested as playing a role in its etiology [1, 2]. DNA methylation, the best understood epigenetic mechanism, has been suggested to be associated with the development of obesity.

As the intrauterine period is recognized as a critical developmental window, exposure to stress during this period may program susceptibility to the development of obesity. For instance, the Dutch Famine study showed that adverse in utero conditions greatly impacted later adiposity and metabolic risk status [3]. In another study, elevated body mass index (BMI) and percent body fat were observed in young adults whose mothers experienced stressful life events during pregnancy [4]. In addition, maternal bereavement during pregnancy predicted increased rates of offspring obesity [5]. The presumed mechanism may be that prenatal maternal stress (PNMS) induces excessive cortisol which can pass the placental barrier early and could program fetal development of metabolic functioning via the HPA axis [6].

Unlike in animal studies of PNMS, stress in human pregnancy is rarely randomly assigned, which can compromise causal conclusions. Project Ice Storm, a natural disaster study, serves as a quasi-randomized human model to study PNMS [7]. Project Ice Storm was conceived following one of Canada’s worst natural disasters in history: the 1998 Quebec ice storm. Project Ice Storm provides an opportunity to examine the effects of an independent stressor on a number of developmental outcomes prospectively. We have shown that greater objective severity of the women’s exposure to the ice storm was associated with greater BMI in their children at the age of 5½ years, and through the age of 15 [8]. Recently, using selected candidate genes (Type 1 and 2 diabetes-related genes) we reported that DNA methylation mediated the effect of objective severity of exposure to the ice storm on levels of BMI and central adiposity at age 13½ [9]. However, the relationship between PNMS and clinically-defined child obesity at age 13½, and the obesity-related genome-wide DNA methylation profile, has not been explored. The aim of the current study was to determine whether clinically-defined obesity in Project Ice Storm children was associated with PNMS, and to compare obese versus non-obese children at age 13½ years in terms of their DNA methylation on a genome-wide level.

Main text



For the assessment of children at age 13½ years in 2011, 36 children agreed to provide a blood sample. DNA samples from two children were not used for DNA methylation analyses due to very low T-cell DNA concentrations. We identified three women who developed gestational diabetes, therefore, their children were excluded in order to avoid confounding effects of maternal gestational diabetes on DNA methylation [10]. A total of 31 children including 5 obese (4 boys and 1 girl) and 26 non-obese (14 boys and 12 girls) were used for further analysis.


Objective hardship was assessed with the questionnaire, which has been previously published elsewhere [11], by measuring the severity of storm-related events experienced by the pregnant women with four categories of exposure: Threat, Loss, Scope, and Change. Each dimension was scored on a scale of 0–8, ranging from no exposure to high exposure. A total objective hardship score (Storm32) was calculated by summing scores from all four dimensions using McFarlane’s approach [12]. Subjective distress was assessed using a validated French version of the Impact of Event Scale-Revised (IES-R) which has been previously published elsewhere [13]. This 22-item scale, widely used for assessing distress following trauma exposure, describes symptoms from three categories relevant to post-traumatic stress disorder: Intrusive Thoughts, Hyperarousal, and Avoidance. Participants responded on a 5-point Likert scale, from “Not at all” to “Extremely”, the extent to which each behavior described how they felt over the preceding 7 days.

Child outcome measures at age 13½ years

During a face-to-face assessment, height, weight and waist measurements were collected following standard guidelines [14], repeating each measure twice and taking the means. Sex- and age-specific BMI (kg/m2) percentiles were computed based on World Health Organization growth references adapted for Canadians ( The children were classified as obese if they exceeded the 97th percentile of BMI.

Infinium HumanMethylation450 BeadChip Array

DNA extraction from T-cells was performed using Wizard-Genomic-DNA-Purification-kit (Promega) according to the manufacturer’s instructions. Infinium HumanMethylation450 BeadChip Array and data analysis have been described previously [15].

Ingenuity pathway analysis (IPA)

Differentially methylated genes were classified by IPA software. A right-tailed Fisher’s exact test was used to calculate the Gene enrichment. Biological functions with a cut-off p-value < 0.05 were considered statistically significant.

Statistical analysis

Infinium HumanMethylation450 BeadChip Array analyses were performed using R packages. All other analyses were performed using SPSS (Version 20, SPSS Inc., Chicago IL, USA). T-test was used to compare the differences between two groups. All p-values reported are two-sided.


Participants’ characteristics

At the time of assessment, these 31 children (18 boys and 13 girls) were on average 13.6 years of age (SD = 0.1), 5 children (16.1%) were classified as obese (4 boys and 1 girl). Their mothers were in their first (n = 9), second (n = 9), or third (n = 6) trimester of pregnancy on January 9, 1998, or conceived within 3 months of the storm (n = 7). The BMI values in the non-obese group ranged from 15.96 to 24.76, while those in the obese group ranged from 26.76 to 36.38. Waist-to-height ratio, skinfolds, BMI and birth weight were significantly greater in children who were classified as obese than in the non-obese group (Table 1).

Table 1 Summary of descriptive statistics of study participants characteristics of non-obese and obese samples

Relationship between PNMS and child obesity

Although the mothers of the obese children tended to have higher objective PNMS than those of non-obese children, these differences were not great enough to be considered statistically significant. Similarly, there were no significant differences in subjective PNMS between the mothers of the obese children and these of the non-obese children (Table 1).

Genome-wide DNA methylation pattern in obese group compared to non-obese group

In total, significant group differences were observed in 277 CpG sites (p < 0.005, false discovery rate (FDR) < 0.2) (Additional file 1: Table S1) including 10 CpGs with FDR < 0.01 (Table 2) and 4 CpGs with FDR < 0.005. These 277 CpGs were identified to correspond to 143 genes. 122 CpGs were hypo-methylated and 155 CpGs were hyper-methylated in the obese group compared to the non-obese group. Absolute differences (obese group versus non-obese) in DNA methylation beta values for those CpG sites ranged from 0.04 to 0.57. The mean of Cohen’s d effect sizes is 1.028, with minimum and maximum effect sizes of 0.690 and 3.096 respectively. These differentially methylated loci are represented in a Heatmap (Fig. 1). Hierarchical clustering analysis of individual methylation patterns was performed; the results are represented in the dendrogram as shown on the left of the Heatmap. The methylation profiles of the obese and non-obese groups are well distinguished.

Table 2 10 CpG sites (FDR < 0.01) whose DNA methylation levels were significantly different between obese and non-obese group
Fig. 1
figure 1

CpG sites which are differentially methylated between obese and non-obese groups. Methylation of the 277 differentially methylated CpGs (p < 0.05, FDR < 0.2) across all 31 individuals are shown in the Heatmap. Each column represents a child and each row a single CpG site. Methylation changes are expressed via a color gradient intensity scale at the lower right-hand corner of the Heatmap: the darkest red indicates the highest DNA methylation level and the darkest green indicates the lowest DNA methylation level. A color gradient intensity scale at the higher right-hand corner expresses children’s BMI level: the darkest red indicates the highest BMI level and the darkest blue indicates the lowest BMI level. The color bar above the Heatmap indicates children categorized by their BMIs: blue indicates a non-obese individual, red indicates an obese individual

Differentially methylated pathways related to obesity

Based on the 143 genes differentially methylated between obese and non-obese groups, IPA classified and revealed 52 canonical pathways (Additional file 2: Table S2). The biological functions of these pathways were predominantly involved in immune system. Antigen Presentation Pathway, the primary pathway, included 5 genes: NLRC5 (NLR Family CARD Domain Containing 5), HLA-DRB1 (Major Histocompatibility Complex, Class II, DR Beta 1), HLA-B (Major Histocompatibility Complex, Class I, B), TAP1 (Transporter 1, ATP Binding Cassette Subfamily B Member) and HLA-DRB5 (Major Histocompatibility Complex, Class II, DR Beta 5) which were found to be associated with obesity in the current study.


DNA methylation, the best understood epigenetic mechanism, has been suggested to be associated with the development of obesity. As reviewed by Demetriou et al. [16], studies on obesity in childhood or adolescence demonstrate significant associations between childhood or adolescent BMI/obesity and DNA methylation in peripheral blood.

Our results demonstrated a well-defined obesity-associated genome-wide DNA methylation pattern. There were 277 CpGs, corresponding to 143 genes that were differentially-methylated. IPA analyses revealed 51 canonical pathways and enrichment of pathways was involved in immune function.

The top CpG cg16507569 with FDR < 0.001 is located on the collagen type XI alpha 2 (COL11A2) gene which encodes one of the two alpha chains of type XI collagen. This gene has been reported to be involved in metabolic syndrome. For instance, in a study investigating the contribution of genetic and epigenetic factors in the development of the metabolic syndrome, 2 methylation quantitative trait loci (meQTL) disrupting CpG sites located within the COL11A2 gene were revealed for associations with the metabolic syndrome and its components in obese individuals, and interestingly, CpG cg16507569 showed a reduction in methylation levels in the presence of a minor allele of rs114894582 [17]. The CpG cg25013753 is located on Rho GTPase Activating Protein 22 (ARHGAP22) gene which has been identified as one of the T2D risk loci [18, 19] and also implicated in a novel insulin regulated pathway [20, 21]. The CpG cg14007688 is located on dopamine beta-hydroxylase (DBH) gene. Mice with inactivation of the dopamine beta-hydroxylase gene (Dbh-null mice) gain weight on a high fat diet comparing to the control mice [22]. Similarly, the mouse model overexpressing NPY driven by DBH gene promoter (OE-NPYDBH) displays obesity and impaired glucose metabolism [23,24,25].

Among the 143 differentially methylated genes in this study, the HLA set is of particular interest. Evidence is growing that the HLA set is associated with obesity. For example, the HLA-DQ genotype was reported to increase risk for obesity among 2–4 year-old children with genetic risk for type-1 diabetes [26]. Similarly, the HLA-DQ genotype was found to be associated with increased BMI in type-1 diabetes children [27]. Furthermore, the HLA genotype was observed to interact with BMI status in relation to the risk of developing multiple sclerosis [28].

Moreover, we identified 52 canonical obesity-associated pathways from the 143 genes that were differentially methylated between the obese and non-obese groups. The top pathway was the Antigen Presentation Pathway. It is well known that the major histocompatibility (MHC) class I Antigen Presentation Pathway plays an important role in altering the immune system in response to virally infected cells. Furthermore, this pathway has been reported to play an essential role in obesity-induced adipose inflammation [29]. Similarly, this pathway was observed in visceral adipose tissue in obese men discordant for metabolic disturbances [30]. Interestingly, by comparing our finding with the latter study, we observed 7 common pathways including Antigen Presentation Pathway, Graft-versus-Host Disease Signaling, Autoimmune Thyroid Disease Signaling, Cdc42 Signaling, Dendritic Cell Maturation, Calcium-induced T Lymphocyte Apoptosis, and Signaling by Rho Family GTPases. The considerable overlap at the pathway level between our finding from T-cells and that from adipose tissue suggests that peripheral blood tissue holds great promise for the further application of DNA methylation signatures of psychosocial exposures for determining biomarkers of methylation status.

It is important to note pathways involved in immune system in the current study are prominent. Impaired immune functions have been described in both humans and genetically obese rodents. Obesity has been associated with inflammation, and adipokines modulate immune function [31]. Therefore, our finding that immune functions were involved in obesity-associated pathways is in accordance with that reported in our recent immunity exploration in which we have shown that objective PNMS significantly predicted reductions of CD4+ lymphocyte and increases in TNF-α, IL-1β, and IL-6 levels, and an enhancement of the Th2 cytokines IL-4 and IL-13 in the Project Ice Storm cohort [32] using the same blood draws as for the epigenetic study. Therefore, our finding presents evidence to support the crucial and widely role of immune system on obesity.


(1) The small sample size of only 5 obese children is major issue that limits the significance of the findings; furthermore, the sex specificity of the DNA methylation cannot be tested based on such cohort; (2) We have no access to detect gene expression of relevant genes due to the lack of RNA samples; (3) Although the current study revealed obesity-associated methylation differences on a genome-wide level, we cannot draw any conclusions about whether the methylation differences are the cause, or the consequence, of obesity; (4) Because the DNA analyzed was obtained from the children at age 13½, we cannot rule out the possibility that confounders, such as prenatal maternal anxiety, which differed significantly between the groups, could be responsible for our results on both obesity patterns and DNA methylation levels. In summary, future studies with larger sample sizes are warranted to address these issues, and further investigation is required to better test the causality between DNA methylation and obesity.



Prenatal maternal stress


Ingenuity pathway analysis


Body mass index


False discovery rate


  1. Stoger R. Epigenetics and obesity. Pharmacogenomics. 2008;9(12):1851–60.

    Article  CAS  Google Scholar 

  2. Herrera BM, Keildson S, Lindgren CM. Genetics and epigenetics of obesity. Maturitas. 2011;69(1):41–9.

    Article  CAS  Google Scholar 

  3. Lumey LH, Stein AD, Kahn HS, Romijn JA. Lipid profiles in middle-aged men and women after famine exposure during gestation: the Dutch Hunger Winter Families Study. Am J Clin Nutr. 2009;89(6):1737–43.

    Article  CAS  Google Scholar 

  4. Entringer S, Buss C, Wadhwa PD. Prenatal stress and developmental programming of human health and disease risk: concepts and integration of empirical findings. Curr Opin Endocrinol Diabetes Obes. 2010;17(6):507–16.

    Article  Google Scholar 

  5. Hohwu L, Li J, Olsen J, Sorensen TI, Obel C. Severe maternal stress exposure due to bereavement before, during and after pregnancy and risk of overweight and obesity in young adult men: a Danish National Cohort Study. PLoS ONE. 2014;9(5):e97490.

    Article  Google Scholar 

  6. Reynolds RM. Glucocorticoid excess and the developmental origins of disease: two decades of testing the hypothesis—2012 Curt Richter Award Winner. Psychoneuroendocrinology. 2013;38(1):1–11.

    Article  CAS  Google Scholar 

  7. King S, Laplante DP. Using natural disasters to study prenatal maternal stress in humans. In: Antonelli MC, editor. Perinatal programming of neurodevelopment. Berlin: Springer; 2015.

    Google Scholar 

  8. Liu GT, Dancause K, Elgbeili G, Laplante DP, King S. Disaster-related prenatal maternal stress explains increasing amounts of variance in body composition through childhood and adolescence: Project Ice Storm. Environ Res. 2016;150:1–7.

    Article  CAS  Google Scholar 

  9. Cao-Lei L, Dancause KN, Elgbeili G, Massart R, Szyf M, Liu A, Laplante DP, King S. DNA methylation mediates the impact of exposure to prenatal maternal stress on BMI and central adiposity in children at age 13(1/2) years: Project Ice Storm. Epigenetics. 2015;10(8):749–61.

    Article  Google Scholar 

  10. Finer S, Mathews C, Lowe R, Smart M, Hillman S, Foo L, Sinha A, Williams D, Rakyan VK, Hitman GA. Maternal gestational diabetes is associated with genome-wide DNA methylation variation in placenta and cord blood of exposed offspring. Hum Mol Genet. 2015;24:3021–9.

    Article  CAS  Google Scholar 

  11. Bromet E, Dew MA. Review of psychiatric epidemiologic research on disasters. Epidemiol Rev. 1995;17(1):113–9.

    Article  CAS  Google Scholar 

  12. McFarlane AC. Relationship between psychiatric impairment and a natural disaster: the role of distress. Psychol Med. 1988;18(1):129–39.

    Article  CAS  Google Scholar 

  13. Brunet A, St-Hilaire A, Jehel L, King S. Validation of a French version of the impact of event scale—revised. Can J Psychiatry. 2003;48:55–60.

    Article  Google Scholar 

  14. Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Champaign: Human Kinetics Books; 1988.

    Google Scholar 

  15. Cao-Lei L, Massart R, Suderman M, Machnes Z, Laplante D, Szyf M, King S. DNA methylation signatures of prenatal maternal objective stress exposure to a natural disaster: Project Ice Storm. PLoS ONE. 2014;9(9):e107653.

    Article  Google Scholar 

  16. Demetriou CA, van Veldhoven K, Relton C, Stringhini S, Kyriacou K, Vineis P. Biological embedding of early-life exposures and disease risk in humans: a role for DNA methylation. Eur J Clin Invest. 2015;45(3):303–32.

    Article  CAS  Google Scholar 

  17. Guenard F, Tchernof A, Deshaies Y, Biron S, Lescelleur O, Biertho L, Marceau S, Perusse L, Vohl MC. Genetic regulation of differentially methylated genes in visceral adipose tissue of severely obese men discordant for the metabolic syndrome. Transl Res. 2017;184(1–11):e12.

    Google Scholar 

  18. Huang YC, Lin JM, Lin HJ, Chen CC, Chen SY, Tsai CH, Tsai FJ. Genome-wide association study of diabetic retinopathy in a Taiwanese population. Ophthalmology. 2011;118(4):642–8.

    Article  Google Scholar 

  19. Li J, Wei J, Xu P, Yan M, Li J, Chen Z, Jin T. Impact of diabetes-related gene polymorphisms on the clinical characteristics of type 2 diabetes Chinese Han population. Oncotarget. 2016;7(51):85464–71.

    PubMed  PubMed Central  Google Scholar 

  20. Larance M, Rowland AF, Hoehn KL, Humphreys DT, Preiss T, Guilhaus M, James DE. Global phosphoproteomics identifies a major role for AKT and 14-3-3 in regulating EDC3. Mol Cell Proteomics. 2010;9(4):682–94.

    Article  CAS  Google Scholar 

  21. Rowland AF, Larance M, Hughes WE, James DE. Identification of RhoGAP22 as an Akt-dependent regulator of cell motility in response to insulin. Mol Cell Biol. 2011;31(23):4789–800.

    Article  CAS  Google Scholar 

  22. Ste Marie L, Luquet S, Curtis W, Palmiter RD. Norepinephrine- and epinephrine-deficient mice gain weight normally on a high-fat diet. Obes Res. 2005;13(9):1518–22.

    Article  CAS  Google Scholar 

  23. Ruohonen ST, Pesonen U, Moritz N, Kaipio K, Roytta M, Koulu M, Savontaus E. Transgenic mice overexpressing neuropeptide Y in noradrenergic neurons: a novel model of increased adiposity and impaired glucose tolerance. Diabetes. 2008;57(6):1517–25.

    Article  CAS  Google Scholar 

  24. Ruohonen ST, Vahatalo LH, Savontaus E. Diet-induced obesity in mice overexpressing neuropeptide Y in noradrenergic neurons. Int J Pept. 2012;2012:452524.

    Article  Google Scholar 

  25. Vahatalo LH, Ruohonen ST, Makela S, Kovalainen M, Huotari A, Makela KA, Maatta JA, Miinalainen I, Gilsbach R, Hein L, et al. Neuropeptide Y in the noradrenergic neurones induces obesity and inhibits sympathetic tone in mice. Acta Physiol. 2015;213(4):902–19.

    Article  CAS  Google Scholar 

  26. Yang J, Lernmark A, Uusitalo UM, Lynch KF, Veijola R, Winkler C, Larsson HE, Rewers M, She JX, Ziegler AG, et al. Prevalence of obesity was related to HLA-DQ in 2–4-year-old children at genetic risk for type 1 diabetes. Int J Obes. 2014;38(12):1491–6.

    Article  CAS  Google Scholar 

  27. Carlsson A, Kockum I, Lindblad B, Engleson L, Nilsson A, Forsander G, Karlsson AK, Kernell A, Ludvigsson J, Marcus C, et al. Low risk HLA-DQ and increased body mass index in newly diagnosed type 1 diabetes children in the Better Diabetes Diagnosis study in Sweden. Int J Obes. 2012;36(5):718–24.

    Article  CAS  Google Scholar 

  28. Hedstrom AK, Lima Bomfim I, Barcellos L, Gianfrancesco M, Schaefer C, Kockum I, Olsson T, Alfredsson L. Interaction between adolescent obesity and HLA risk genes in the etiology of multiple sclerosis. Neurology. 2014;82(10):865–72.

    Article  Google Scholar 

  29. Deng T, Lyon CJ, Minze LJ, Lin J, Zou J, Liu JZ, Ren Y, Yin Z, Hamilton DJ, Reardon PR, et al. Class II major histocompatibility complex plays an essential role in obesity-induced adipose inflammation. Cell Metab. 2013;17(3):411–22.

    Article  CAS  Google Scholar 

  30. Guenard F, Tchernof A, Deshaies Y, Perusse L, Biron S, Lescelleur O, Biertho L, Marceau S, Vohl MC. Differential methylation in visceral adipose tissue of obese men discordant for metabolic disturbances. Physiol Genomics. 2014;46(6):216–22.

    Article  Google Scholar 

  31. de Heredia FP, Gomez-Martinez S, Marcos A. Obesity, inflammation and the immune system. Proc Nutr Soc. 2012;71(2):332–8.

    Article  Google Scholar 

  32. Veru F, Laplante DP, Luheshi G, King S. Prenatal maternal stress exposure and immune function in the offspring. Stress. 2014;17(2):133–48.

    Article  CAS  Google Scholar 

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Authors’ contributions

SK and DPL oversaw all data collection for Project Ice Storm. SK and MSzyf conceived and directed the present project. LCL designed epigenetic experiments, performed experiments and interpreted data. GE interpreted the Infinium methylation assay data. LCL, MSzyf, DPL and SK wrote the manuscript. Each author listed on this manuscript has seen and approved this submission and takes full responsibility for the manuscript. All authors read and approved the final manuscript.


We are grateful to families for their continued participation in Project Ice Storm.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Consent for publication

Not applicable.

Ethics approval and consent to participate

All phases of this study were approved by the Research Ethics Board of the McGill University-affiliated Douglas Hospital Research Center in Montreal, Canada. We obtained written informed consent from parents and written assent from children.


This research was supported by grants from the Canadian Institute of Health Research (CIHR) (Grant Number MOP-1150067) to Suzanne King, principal investigator, and colleagues. The funding body has no role in the design of the study, and collection, analysis, and interpretation of data, and in writing the manuscript.

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Correspondence to Suzanne King.

Additional files

Additional file 1: Table S1.

277 CpG sites (FDR < 0.2) whose DNA methylation levels were significantly different between obese and non-obese group. Significant group differences were observed in 277 CpG sites (p < 0.005, FDR < 0.2) including 10 CpGs with FDR < 0.01 and 4 CpGs with FDR < 0.005. These 277 CpGs were identified to correspond to 143 genes.

Additional file 2: Table S2.

52 canonical pathways classified with IPA. IPA classified and revealed 52 canonical pathways based on the 143 genes differentially methylated between obese and non-obese groups. The biological functions of these pathways were predominantly involved in immune system.

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Cao-Lei, L., Elgbeili, G., Szyf, M. et al. Differential genome-wide DNA methylation patterns in childhood obesity. BMC Res Notes 12, 174 (2019).

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