Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

DNA methylation in blood from neonatal screening cards and the association with BMI and insulin sensitivity in early childhood



There is increasing evidence that metabolic diseases originate in early life, and epigenetic changes have been implicated as key drivers of this early life programming. This led to the hypothesis that epigenetic marks present at birth may predict an individual’s future risk of obesity and type 2 diabetes. In this study, we assessed whether epigenetic marks in blood of newborn children were associated with body mass index (BMI) and insulin sensitivity later in childhood.


DNA methylation was measured in neonatal blood spot samples of 438 children using the Illumina Infinium 450 k BeadChip. Associations were assessed between DNA methylation at birth and BMI z-scores, body fat mass, fasting plasma glucose, insulin and homeostatic model assessment of insulin resistance (HOMA-IR) at age 5 years, as well as birth weight, maternal BMI and smoking status.


No individual methylation sites at birth were associated with obesity or insulin sensitivity measures at 5 years. DNA methylation in 69 genomic regions at birth was associated with BMI z-scores at age 5 years, and in 63 regions with HOMA-IR. The methylation changes were generally small (<5%), except for a region near the non-coding RNA nc886 (VTRNA2-1) where a clear link between methylation status at birth and BMI in childhood was observed (P=0.001). Associations were also found between DNA methylation, maternal smoking and birth weight.


We identified a number of DNA methylation regions at birth that were associated with obesity or insulin sensitivity measurements in childhood. These findings support the mounting evidence on the role of epigenetics in programming of metabolic health. Whether many of these small changes in DNA methylation are causally related to the health outcomes, and of clinical relevance, remains to be determined, but the nc886 region represents a promising obesity risk marker that warrants further investigation.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1


  1. 1

    Biro FM, Wien M . Childhood obesity and adult morbidities. Am J Clin Nutr 2010; 91: 1499S–1505S.

    Article  Google Scholar 

  2. 2

    McMillen IC, Rattanatray L, Duffield JA, Morrison JL, MacLaughlin SM, Gentili S et al. The early origins of later obesity: pathways and mechanisms. Adv Exp Med Biol 2009; 646: 71–81.

    Article  Google Scholar 

  3. 3

    Barker DJ, Godfrey K, Gluckman P, Harding J, Owens J, Robinson J . Fetal nutrition and cardiovascular disease in adult life. Lancet 1993; 341: 938–941.

    CAS  Article  Google Scholar 

  4. 4

    Ravelli A, van der Meulen J, Michels R, Osmond C, Barker D, Hales C et al. Glucose tolerance in adults after prenatal exposure to famine. Lancet 1998; 351: 173–177.

    CAS  Article  Google Scholar 

  5. 5

    Richmond RC, Timpson NJ . Sorensen TIa Exploring possible epigenetic mediation of early-life environmental exposures on adiposity and obesity development. Int J Epidemiol 2015; 44: 1191–1198.

    Article  Google Scholar 

  6. 6

    Waterland RA, Michels KB . Epigenetic epidemiology of the developmental origins hypothesis. Annu Rev Nutr 2007; 27: 363–388.

    CAS  Article  Google Scholar 

  7. 7

    Tobi EW, Goeman JJ, Monajemi R, Gu H, Putter H, Zhang Y et al. DNA methylation signatures link prenatal famine exposure to growth and metabolism. Nat Commun 2014; 5: 5592.

    CAS  Article  Google Scholar 

  8. 8

    Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aïssi D, Wahl S et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet 2014; 6736: 1–9.

    Google Scholar 

  9. 9

    Aslibekyan S, Demerath EW, Mendelson M, Zhi D, Guan W, Liang L et al. Epigenome-wide study identifies novel methylation loci associated with body mass index and waist circumference. Obesity 2015; 23: 1493–1501.

    CAS  Article  Google Scholar 

  10. 10

    Demerath EW, Guan W, Grove ML, Aslibekyan S, Mendelson M, Zhou Y-H et al. Epigenome-wide Association Study (EWAS) of BMI, BMI change, and waist circumference in African American adults identifies multiple replicated loci. Hum Mol Genet 2015; 24: 4464–4479.

    CAS  Article  Google Scholar 

  11. 11

    Soriano-Tárraga C, Jiménez-Conde J, Giralt-Steinhauer E, Mola-Caminal M, Vivanco-Hidalgo RM, Ois A et al. Epigenome-wide association study identifies TXNIP gene associated with type 2 diabetes mellitus and sustained hyperglycemia. Hum Mol Genet 2015; 25: 1–11.

    Google Scholar 

  12. 12

    Ronn T, Volkov P, Gillberg L, Kokosar M, Perfilyev A, Jacobsen AL et al. Impact of age, BMI and HbA1c levels on the genome-wide DNA methylation and mRNA expression patterns in human adipose tissue and identification of epigenetic biomarkers in blood. Hum Mol Genet 2015; 1–22.

  13. 13

    Huang R-C . Genome wide methylation analysis identifies differentially methylated CpG loci associated with severe obesity in childhood. Clin Epigenetics 2015; 10: 995–1005.

    CAS  Article  Google Scholar 

  14. 14

    Wang S, Song J, Yang Y, Zhang Y, Wang H, Ma J et al. Methylation is associated with childhood obesity and ALT. PLoS ONE 2015; 10: e0145944.

    Article  Google Scholar 

  15. 15

    Clarke-Harris R, Wilkin TJ, Hosking J, Pinkney J, Jeffery AN, Metcalf BS et al. PGC1α promoter methylation in blood at 5-7 years predicts adiposity from 9 to 14 years (EarlyBird 50). Diabetes 2014; 63: 2528–2537.

    Article  Google Scholar 

  16. 16

    Dayeh T, Tuomi T, Almgren P, Perfilyev A, Jansson PA, de Mello VD et al. DNA methylation of loci within ABCG1 and PHOSPHO1 in blood DNA is associated with future type 2 diabetes risk. Epigenetics 2016; 11: 482–488.

    Article  Google Scholar 

  17. 17

    Chambers JC, Loh M, Lehne B, Drong A, Kriebel J, Motta V et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol 2015; 3: 526–534.

    CAS  Article  Google Scholar 

  18. 18

    Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 2016; 541: 81–86.

    Article  Google Scholar 

  19. 19

    Godfrey KM, Sheppard A, Gluckman PD, Lillycrop Ka, Burdge GC, McLean C et al. Epigenetic gene promoter methylation at birth is associated with child’s later adiposity. Diabetes 2011; 60: 1528–1534.

    CAS  Article  Google Scholar 

  20. 20

    Relton CL, Groom A St, Pourcain B, Sayers AE, Swan DC, Embleton ND et al. DNA methylation patterns in cord blood DNA and body size in childhood. PLoS One 2012; 7: e31821.

    CAS  Article  Google Scholar 

  21. 21

    Groom A, Potter C, Swan DC, Fatemifar G, Evans DM, Ring SM et al. Postnatal growth and DNA methylation are associated with differential gene expression of the TACSTD2 gene and childhood fat mass. Diabetes 2012; 61: 391–400.

    CAS  Article  Google Scholar 

  22. 22

    Makrides M, Gibson RA, McPhee AJ, Yelland L, Quinlivan J, Ryan P . Effect of DHA supplementation during pregnancy on maternal depression and neurodevelopment of young children: a randomized controlled trial. JAMA 2010; 304: 1675–1683.

    CAS  Article  Google Scholar 

  23. 23

    Muhlhausler BS, Yelland LN, McDermott R, Tapsell L, McPhee AJ, Gibson RA et al. DHA supplementation during pregnancy does not reduce BMI or body fat mass in children: follow-up of the DOMInO randomized controlled trial. Am J Clin Nutr 2016; 103: 1489–1496.

    CAS  Article  Google Scholar 

  24. 24

    WHO Multicentre Growth Reference Study GroupWHO child growth standards: methods and development. (2006). Available at

  25. 25

    Ellis KJ, Shypailo RJ, Wong WW . Measurement of body water by multifrequency bioelectrical impedance spectroscopy in a multiethnic pediatric population. Am J Clin Nutr 1999; 70: 847–853.

    CAS  Article  Google Scholar 

  26. 26

    Van Dijk SJ, Zhou J, Peters TJ, Buckley M, Sutcliffe B, Oytam Y et al. Effect of prenatal DHA supplementation on the infant epigenome: results from a randomized controlled trial. Clin Epigenetics 2016; 8: 114.

    Article  Google Scholar 

  27. 27

    Pidsley R, Y Wong CC, Volta M, Lunnon K, Mill J, Schalkwyk LC . A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 2013; 14: 293.

    CAS  Article  Google Scholar 

  28. 28

    Chen Y, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 2013; 8: 203–209.

    CAS  Article  Google Scholar 

  29. 29

    Oytam Y, Sobhanmanesh F, Duesing K, Bowden JC, Osmond-McLeod M, Ross J . Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets. BMC Bioinformatics 2016; 17: 332.

    Article  Google Scholar 

  30. 30

    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43: e47.

    Article  Google Scholar 

  31. 31

    Benjamini Y, Hochberg Y . Controlling the false discovery rate: a practical and powerful approach to multiple testing on JSTOR. J R Stat Soc 1995; 57: 289–300.

    Google Scholar 

  32. 32

    Bakulski KM, Feinberg JI, Andrews SV, Yang J, Brown S, McKenney S et al. DNA methylation of cord blood cell types: Applications for mixed cell birth studies. Epigenetics 2016; 2294: 354–362.

    Article  Google Scholar 

  33. 33

    Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 2012; 13: 86.

    Article  Google Scholar 

  34. 34

    Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, V Lord R et al. De novo identification of differentially methylated regions in the human genome. Epigenetics Chromatin 2015; 8: 6.

    Article  Google Scholar 

  35. 35

    Romanelli V, Nakabayashi K, Vizoso M, Moran S, Iglesias-Platas I, Sugahara N et al. Variable maternal methylation overlapping the nc886/vtRNA2-1 locus is locked between hypermethylated repeats and is frequently altered in cancer. Epigenetics 2014; 9: 783–790.

    CAS  Article  Google Scholar 

  36. 36

    Treppendahl MB, Qiu X, Søgaard A, Yang X, Nandrup-Bus C, Hother C et al. Allelic methylation levels of the noncoding VTRNA2-1 located on chromosome 5q31.1 predict outcome in AML. Blood 2012; 119: 206–216.

    CAS  Article  Google Scholar 

  37. 37

    Voisin S, Almén MS, Zheleznyakova GY, Lundberg L, Zarei S, Castillo S et al. Many obesity-associated SNPs strongly associate with DNA methylation changes at proximal promoters and enhancers. Genome Med 2015; 7: 103.

    Article  Google Scholar 

  38. 38

    Acevedo N, Reinius LE, Vitezic M, Fortino V, Söderhäll C, Honkanen H et al. Age-associated DNA methylation changes in immune genes, histone modifiers and chromatin remodeling factors within 5 years after birth in human blood leukocytes. Clin Epigenetics 2015; 7: 34.

    Article  Google Scholar 

  39. 39

    Horvath S . DNA methylation age of human tissues and cell types. Genome Biol 2013; 14: R115.

    Article  Google Scholar 

  40. 40

    Michaud J, Simpson KM, Escher R, Buchet-Poyau K, Beissbarth T, Carmichael C et al. Integrative analysis of RUNX1 downstream pathways and target genes. BMC Genomics 2008; 9: 363.

    Article  Google Scholar 

  41. 41

    Shah S, Bonder MJ, Marioni RE, Zhu Z, McRae AF, Zhernakova A et al. Improving phenotypic prediction by combining genetic and epigenetic associations. Am J Hum Genet 2015; 97: 75–85.

    CAS  Article  Google Scholar 

  42. 42

    Gaulton KJ, Ferreira T, Lee Y, Raimondo A, Mägi R, Reschen ME et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat Genet 2015; 47: 1415–1425.

    CAS  Article  Google Scholar 

  43. 43

    Coughlin GM, Kurrasch DM . Protocadherins and hypothalamic development: do they play an unappreciated role? J Neuroendocrinol 2015; 544–555.

    CAS  Article  Google Scholar 

  44. 44

    Silver MJ, Kessler NJ, Hennig BJ, Dominguez-salas P, Laritsky E, Baker MS et al. Independent genomewide screens identify the tumor suppressor VTRNA2-1 as a human epiallele responsive to periconceptional environment. Genome Biol 2015; 1–14.

  45. 45

    Nilsson E, Jansson PA, Perfilyev A, Volkov P, Pedersen M, Svensson MK et al. Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes 2014; 63: 2962–2976.

    Article  Google Scholar 

  46. 46

    Koppes E, Himes KP, Chaillet JR . Partial loss of genomic imprinting reveals important roles for Kcnq1 and Peg10 imprinted domains in placental development. PLoS One 2015; 10: e0135202.

    Article  Google Scholar 

  47. 47

    Richmond RC, Sharp GC, Ward ME, Fraser A, Mcardle WL, Ring SM et al. DNA methylation and body mass index: investigating identified methylation sites at HIF3A in a causal framework. Diabetes 2016; 65: 1231–1244.

    CAS  Article  Google Scholar 

  48. 48

    Pan H, Lin X, Wu Y, Chen L, Teh AL, Soh SE et al. HIF3A association with adiposity: the story begins before birth. Epigenomics 2015; 7: 937–950.

    CAS  Article  Google Scholar 

  49. 49

    Joubert BR, Felix JF, Yousefi P, Bakulski KM, Just AC, Breton C et al. DNA methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis. Am J Hum Genet 2016; 98: 680–696.

    CAS  Article  Google Scholar 

  50. 50

    Joubert BR, Håberg SE, Nilsen RM, Wang X, Vollset SE, Murphy SK et al. 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environ Health Perspect 2012; 120: 1425–1431.

    CAS  Article  Google Scholar 

  51. 51

    Richmond RC, Simpkin AJ, Woodward G, Gaunt TR, Lyttleton O, McArdle WL et al. Prenatal exposure to maternal smoking and offspring DNA methylation across the lifecourse: Findings from the Avon Longitudinal Study of Parents and Children (ALSPAC). Hum Mol Genet 2015; 24: 2201–2217.

    CAS  Article  Google Scholar 

  52. 52

    Burris HH, Baccarelli AA, Byun HM, Cantoral A, Just AC, Pantic I et al. Offspring DNA methylation of the aryl-hydrocarbon receptor repressor gene is associated with maternal BMI, gestational age, and birth weight. Epigenetics 2015; 10: 913–921.

    Article  Google Scholar 

  53. 53

    Singmann P, Shem-Tov D, Wahl S, Grallert H, Fiorito G, Shin S-Y et al. Characterization of whole-genome autosomal differences of DNA methylation between men and women. Epigenetics Chromatin 2015; 8: 43.

    Article  Google Scholar 

  54. 54

    Van Dongen J, Nivard MG, Willemsen G, Hottenga J-J, Helmer Q, Dolan CV et al. Genetic and environmental influences interact with age and sex in shaping the human methylome. Nat Commun 2016; 7: 11115.

    CAS  Article  Google Scholar 

  55. 55

    Shah S, Mcrae AF, Marioni RE, Harris SE, Gibson J, Henders AK et al. Genetic and environmental exposures constrain epigenetic drift over the human life course. Genome Res 2014; 24: 1725–1733.

    CAS  Article  Google Scholar 

  56. 56

    Rolland-Cachera MF, Deheeger M, Bellisle F, Sempé M, Guilloud-Bataille M, Patois E . Adiposity rebound in children: a simple indicator for predicting obesity. Am J Clin Nutr 1984; 39: 129–135.

    CAS  Article  Google Scholar 

  57. 57

    Simmonds M, Llewellyn A, Owen CG, Woolacott N . Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev 2016; 17: 95–107.

    CAS  Article  Google Scholar 

  58. 58

    Relton CL, Davey Smith G . Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol 2012; 41: 161–176.

    Article  Google Scholar 

Download references


This work was supported by the Science and Industry Endowment Fund (RP03-064), Diabetes Australia, and the National Health and Medical Research Council of Australia (NHMRC) (349301, 570109, APP1004211 to BSM, APP1046207 to RAG, APP1061074 to MM) and National Institutes of Health grant R35 CA 209859 to PJ.

Author information



Corresponding author

Correspondence to P L Molloy.

Ethics declarations

Competing interests

MM serves on scientific advisory boards for Fonterra and RAG serves on scientific advisory boards for Fonterra and Ferrero. BSM serves on the scientific advisory board for Nestle and has given lectures on maternal nutrition for Aspen Nutrition and Danone Nutricia. Associated honoraria for MM, RAG and BSM are paid their institutions to support conference travel and continuing education for postgraduate students and early career researchers. PJ is a paid consultant for Zymo, Inc. The remaining authors declare no conflict of interest.

Additional information

Supplementary Information accompanies this paper on International Journal of Obesity website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

van Dijk, S., Peters, T., Buckley, M. et al. DNA methylation in blood from neonatal screening cards and the association with BMI and insulin sensitivity in early childhood. Int J Obes 42, 28–35 (2018).

Download citation

Further reading


Quick links