Association of cord blood methylation with neonatal leptin: An epigenome wide association study

Autoři: Rachel Kadakia aff001;  Yinan Zheng aff002;  Zhou Zhang aff002;  Wei Zhang aff002;  Jami L. Josefson aff001;  Lifang Hou aff002
Působiště autorů: Division of Endocrinology, Ann and Robert H. Lurie Children’s Hospital of Chicago and Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America aff001;  Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226555



Neonatal adiposity is a risk factor for childhood obesity. Investigating contributors to neonatal adiposity is important for understanding early life obesity risk. Epigenetic changes of metabolic genes in cord blood may contribute to excessive neonatal adiposity and subsequent childhood obesity. This study aims to evaluate the association of cord blood DNA methylation patterns with anthropometric measures and cord blood leptin, a biomarker of neonatal adiposity.


A cross-sectional study was performed on a multiethnic cohort of 114 full term neonates born to mothers without gestational diabetes at a university hospital. Cord blood was assayed for leptin and for epigenome-wide DNA methylation profiles via the Illumina 450K platform. Neonatal body composition was measured by air displacement plethysmography. Multivariable linear regression was used to analyze associations between individual CpG sites as well as differentially methylated regions in cord blood DNA with measures of newborn adiposity including anthropometrics (birth weight, fat mass and percent body fat) and cord blood leptin. False discovery rate was estimated to account for multiple comparisons.


247 CpG sites as well as 18 differentially methylated gene regions were associated with cord blood leptin but no epigenetic changes were associated with birth weight, fat mass or percent body fat. Genes of interest identified in this study are DNAJA4, TFR2, SMAD3, PLAG1, FGF1, and HNF4A.


Epigenetic changes in cord blood DNA are associated with cord blood leptin levels, a measure of neonatal adiposity.

Klíčová slova:

Adipose tissue – Blood – DNA methylation – Epigenetics – Fats – Gene regulation – leptin – Neonates


1. Catalano PM, Farrell K, Thomas A, Huston-Presley L, Mencin P, de Mouzon SH, et al. Perinatal risk factors for childhood obesity and metabolic dysregulation. Am J Clin Nutr. 2009;90(5):1303–13. doi: 10.3945/ajcn.2008.27416 19759171

2. Ogden CL, Carroll MD, Lawman HG, Fryar CD, Kruszon-Moran D, Kit BK, et al. Trends in Obesity Prevalence Among Children and Adolescents in the United States, 1988–1994 Through 2013–2014. Jama. 2016;315(21):2292–9. doi: 10.1001/jama.2016.6361 27272581

3. Arslanian S. Type 2 diabetes in children: clinical aspects and risk factors. Horm Res. 2002;57 Suppl 1:19–28.

4. Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med. 2004;350(23):2362–74. doi: 10.1056/NEJMoa031049 15175438

5. Barker DJ. The fetal and infant origins of adult disease. BMJ. 1990;301(6761):1111. doi: 10.1136/bmj.301.6761.1111 2252919

6. Nicholas LM, Morrison JL, Rattanatray L, Zhang S, Ozanne SE, McMillen IC. The early origins of obesity and insulin resistance: timing, programming and mechanisms. Int J Obes (Lond). 2016;40(2):229–38.

7. Desai M, Jellyman JK, Ross MG. Epigenomics, gestational programming and risk of metabolic syndrome. Int J Obes (Lond). 2015;39(4):633–41.

8. El Hajj N, Schneider E, Lehnen H, Haaf T. Epigenetics and life-long consequences of an adverse nutritional and diabetic intrauterine environment. Reproduction. 2014;148(6):R111–20. doi: 10.1530/REP-14-0334 25187623

9. Sharp GC, Salas LA, Monnereau C, Allard C, Yousefi P, Everson TM, et al. Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium. Hum Mol Genet. 2017;26(20):4067–85. doi: 10.1093/hmg/ddx290 29016858

10. Kadakia R, Zheng Y, Zhang Z, Zhang W, Hou L, Josefson JL. Maternal pre-pregnancy BMI downregulates neonatal cord blood LEP methylation. Pediatr Obes. 2016.

11. 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(5):1528–34. doi: 10.2337/db10-0979 21471513

12. Sharp GC, Lawlor DA, Richmond RC, Fraser A, Simpkin A, Suderman M, et al. Maternal pre-pregnancy BMI and gestational weight gain, offspring DNA methylation and later offspring adiposity: findings from the Avon Longitudinal Study of Parents and Children. Int J Epidemiol. 2015;44(4):1288–304. doi: 10.1093/ije/dyv042 25855720

13. 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(6):937–50. doi: 10.2217/epi.15.45 26011824

14. 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(10):913–21. doi: 10.1080/15592294.2015.1078963 26252179

15. Agha G, Hajj H, Rifas-Shiman SL, Just AC, Hivert MF, Burris HH, et al. Birth weight-for-gestational age is associated with DNA methylation at birth and in childhood. Clin Epigenetics. 2016;8:118. doi: 10.1186/s13148-016-0285-3 27891191

16. Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study: associations with neonatal anthropometrics. Diabetes. 2009;58(2):453–9. doi: 10.2337/db08-1112 19011170

17. Josefson JL, Simons H, Zeiss DM, Metzger BE. Excessive gestational weight gain in the first trimester among women with normal glucose tolerance and resulting neonatal adiposity. J Perinatol. 2016;36(12):1034–8. doi: 10.1038/jp.2016.145 27583397

18. Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, Damm P, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33(3):676–82. doi: 10.2337/dc09-1848 20190296

19. Walsh CA, Mahony RT, Foley ME, Daly L, O'Herlihy C. Recurrence of fetal macrosomia in non-diabetic pregnancies. J Obstet Gynaecol. 2007;27(4):374–8. doi: 10.1080/01443610701327545 17654189

20. Ellis KJ, Yao M, Shypailo RJ, Urlando A, Wong WW, Heird WC. Body-composition assessment in infancy: air-displacement plethysmography compared with a reference 4-compartment model. Am J Clin Nutr. 2007;85(1):90–5. doi: 10.1093/ajcn/85.1.90 17209182

21. Ma G, Yao M, Liu Y, Lin A, Zou H, Urlando A, et al. Validation of a new pediatric air-displacement plethysmograph for assessing body composition in infants. Am J Clin Nutr. 2004;79(4):653–60. doi: 10.1093/ajcn/79.4.653 15051611

22. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3. doi: 10.1093/bioinformatics/bts034 22257669

23. Michels KB, Binder AM, Dedeurwaerder S, Epstein CB, Greally JM, Gut I, et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat Methods. 2013;10(10):949–55. doi: 10.1038/nmeth.2632 24076989

24. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. 1995;57(1):289–300.

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

26. Stouffer SA, Suchman EA, DeVinney LC, Star SA, W RM Jr. The American Soldier, Vol. 1: Adjustment During Army Life. Princeton: Princeton University Press; 1949.

27. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57–74. doi: 10.1038/nature11247 22955616

28. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. doi: 10.1038/nprot.2008.211 19131956

29. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1–13. doi: 10.1093/nar/gkn923 19033363

30. Choy L, Derynck R. Transforming growth factor-beta inhibits adipocyte differentiation by Smad3 interacting with CCAAT/enhancer-binding protein (C/EBP) and repressing C/EBP transactivation function. J Biol Chem. 2003;278(11):9609–19. doi: 10.1074/jbc.M212259200 12524424

31. Hibbard MK, Kozakewich HP, Dal Cin P, Sciot R, Tan X, Xiao S, et al. PLAG1 fusion oncogenes in lipoblastoma. Cancer Res. 2000;60(17):4869–72. 10987300

32. Hutley L, Shurety W, Newell F, McGeary R, Pelton N, Grant J, et al. Fibroblast growth factor 1: a key regulator of human adipogenesis. Diabetes. 2004;53(12):3097–106. doi: 10.2337/diabetes.53.12.3097 15561939

33. Choi Y, Jang S, Choi MS, Ryoo ZY, Park T. Increased expression of FGF1-mediated signaling molecules in adipose tissue of obese mice. J Physiol Biochem. 2016;72(2):157–67. doi: 10.1007/s13105-016-0468-6 26847131

34. Roifman M, Choufani S, Turinsky AL, Drewlo S, Keating S, Brudno M, et al. Genome-wide placental DNA methylation analysis of severely growth-discordant monochorionic twins reveals novel epigenetic targets for intrauterine growth restriction. Clin Epigenetics. 2016;8:70. doi: 10.1186/s13148-016-0238-x 27330572

35. Gotardo EM, dos Santos AN, Miyashiro RA, Gambero S, Rocha T, Ribeiro ML, et al. Mice that are fed a high-fat diet display increased hepcidin expression in adipose tissue. J Nutr Sci Vitaminol (Tokyo). 2013;59(5):454–61.

36. Mahoney SE, Yao Z, Keyes CC, Tapscott SJ, Diede SJ. Genome-wide DNA methylation studies suggest distinct DNA methylation patterns in pediatric embryonal and alveolar rhabdomyosarcomas. Epigenetics. 2012;7(4):400–8. doi: 10.4161/epi.19463 22419069

37. Alholle A, Brini AT, Gharanei S, Vaiyapuri S, Arrigoni E, Dallol A, et al. Functional epigenetic approach identifies frequently methylated genes in Ewing sarcoma. Epigenetics. 2013;8(11):1198–204. doi: 10.4161/epi.26266 24005033

38. Becker C, Orozco M, Solomons NW, Schumann K. Iron metabolism in obesity: how interaction between homoeostatic mechanisms can interfere with their original purpose. Part I: underlying homoeostatic mechanisms of energy storage and iron metabolisms and their interaction. J Trace Elem Med Biol. 2015;30:195–201. doi: 10.1016/j.jtemb.2014.10.011 25467855

39. Gonzalez-Nahm S, Mendez MA, Benjamin-Neelon SE, Murphy SK, Hogan VK, Rowley DL, et al. DNA methylation of imprinted genes at birth is associated with child weight status at birth, 1 year, and 3 years. Clin Epigenetics. 2018;10:90. doi: 10.1186/s13148-018-0521-0 29988473

40. van Dijk SJ, Peters TJ, Buckley M, Zhou J, Jones PA, Gibson RA, et al. DNA methylation in blood from neonatal screening cards and the association with BMI and insulin sensitivity in early childhood. Int J Obes (Lond). 2018;42(1):28–35.

41. Sainz N, Barrenetxe J, Moreno-Aliaga MJ, Martinez JA. Leptin resistance and diet-induced obesity: central and peripheral actions of leptin. Metabolism. 2015;64(1):35–46. doi: 10.1016/j.metabol.2014.10.015 25497342

42. Dyck DJ, Heigenhauser GJ, Bruce CR. The role of adipokines as regulators of skeletal muscle fatty acid metabolism and insulin sensitivity. Acta Physiol (Oxf). 2006;186(1):5–16.

43. Sandoval J, Heyn H, Moran S, Serra-Musach J, Pujana MA, Bibikova M, et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics. 2011;6(6):692–702. doi: 10.4161/epi.6.6.16196 21593595

44. Bock C. Analysing and interpreting DNA methylation data. Nat Rev Genet. 2012;13(10):705–19. doi: 10.1038/nrg3273 22986265

45. Chu SY, Callaghan WM, Bish CL, D'Angelo D. Gestational weight gain by body mass index among US women delivering live births, 2004–2005: fueling future obesity. American journal of obstetrics and gynecology. 2009;200(3):271 e1–7.

46. Kadakia R, Zheng Y, Zhang Z, Zhang W, Hou L, Josefson JL. Maternal pre-pregnancy BMI downregulates neonatal cord blood LEP methylation. Pediatr Obes. 2017;12 Suppl 1:57–64.

47. Geary M, Pringle PJ, Persaud M, Wilshin J, Hindmarsh PC, Rodeck CH, et al. Leptin concentrations in maternal serum and cord blood: relationship to maternal anthropometry and fetal growth. Br J Obstet Gynaecol. 1999;106(10):1054–60. doi: 10.1111/j.1471-0528.1999.tb08113.x 10519431

48. Considine RV, Sinha MK, Heiman ML, Kriauciunas A, Stephens TW, Nyce MR, et al. Serum immunoreactive-leptin concentrations in normal-weight and obese humans. N Engl J Med. 1996;334(5):292–5. doi: 10.1056/NEJM199602013340503 8532024

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