Cited by 20Birth, Development, and HealthEpigenetics and DNA MethylationGestational Diabetes Research and Management

A meta-analysis of pre-pregnancy maternal body mass index and placental DNA methylation identifies 27 CpG sites with implications for mother-child health

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Fernandez-Jimenez, Nora, Fore, Ruby, Cilleros-Portet, Ariadna, Lepeule, Johanna, Perron, Patrice, Kvist, Tuomas, Tian, Fu-Ying, Lesseur, Corina +36 more·2022·Communications Biology

Abstract

Higher maternal pre-pregnancy body mass index (ppBMI) is associated with increased neonatal morbidity, as well as with pregnancy complications and metabolic outcomes in offspring later in life. The placenta is a key organ in fetal development and has been proposed to act as a mediator between the mother and different health outcomes in children. The overall aim of the present work is to investigate the association of ppBMI with epigenome-wide placental DNA methylation (DNAm) in 10 studies from the PACE consortium, amounting to 2631 mother-child pairs. We identify 27 CpG sites at which we observe placental DNAm variations of up to 2.0% per 10 ppBMI-unit. The CpGs that are differentially methylated in placenta do not overlap with CpGs identified in previous studies in cord blood DNAm related to ppBMI. Many of the identified CpGs are located in open sea regions, are often close to obesity-related genes such as GPX1 and LGR4 and altogether, are enriched in cancer and oxidative stress pathways. Our findings suggest that placental DNAm could be one of the mechanisms by which maternal obesity is associated with metabolic health outcomes in newborns and children, although further studies will be needed in order to corroborate these findings.

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  • A meta-analysis of 10 cohorts comprising 2631 mother-infant pairs identified 27 placental CpG sites associated with maternal prepregnancy BMI, with DNA methylation differences of up to 2.0% per 10-unit increase in prepregnancy BMI.

    “A meta-analysis of 10 cohorts comprising 2631 mother–infant pairs identified 27 placental CpG sites associated with maternal prepregnancy body mass index (BMI), with DNA methylation differences of up to 2.0% per 10-unit increase in prepregnancy BMI [30].”

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  1. [1]Maffeis C Morandi A Effect of Maternal Obesity on Foetal Growth and Metabolic Health of the Offspring Obes. Facts. 2017 10 112 117 10.1159/000456668 28384625 PMC5644955 · DOI
  2. [2]Marchi J Risks associated with obesity in pregnancy, for the mother and baby: a systematic review of reviews Obes. Rev. 2015 16 621 638 10.1111/obr.12288 26016557 · DOI · Paper page
  3. [3]Howell KR Powell TL Effects of maternal obesity on placental function and fetal development Reproduction 2017 153 R97 R108 10.1530/REP-16-0495 27864335 PMC5432127 · DOI
  4. [4]Mamun AA Mannan M Doi SA Gestational weight gain in relation to offspring obesity over the life course: a systematic review and bias-adjusted meta-analysis Obes. Rev. 2014 15 338 347 10.1111/obr.12132 24321007 · DOI
  5. [5]Godfrey KM Influence of maternal obesity on the long-term health of offspring Lancet Diabetes Endocrinol. 2017 5 53 64 10.1016/S2213-8587(16)30107-3 27743978 PMC5245733 · DOI · Paper page
  6. [7]Desai M Jellyman JK Ross MG Epigenomics, gestational programming and risk of metabolic syndrome Int. J. Obes. (Lond.) 2015 39 633 641 10.1038/ijo.2015.13 25640766 · DOI
  7. [8]Felix JF Cohort profile: Pregnancy and childhood epigenetics (PACE) consortium Int. J. Epidemiol. 2018 47 22 23u 10.1093/ije/dyx190 29025028 PMC5837319 · DOI
  8. [9]Sharp GC 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 4067 4085 10.1093/hmg/ddx290 29016858 PMC5656174 · DOI
  9. [10]Sharp GC 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 1288 1304 10.1093/ije/dyv042 25855720 PMC4588865 · DOI
  10. [11]Shrestha D Placental DNA methylation changes associated with maternal prepregnancy BMI and gestational weight gain Int. J. Obes. (Lond.). 2020 44 1406 1416 10.1038/s41366-020-0546-2 32071425 PMC7261634 · DOI
  11. [12]Thakali KM Zhong Y Cleves M Andres A Shankar K Associations between maternal body mass index and diet composition with placental DNA methylation at term Placenta 2020 93 74 82 10.1016/j.placenta.2020.02.018 32250742 · DOI
  12. [13]Everson TM Placental DNA methylation signatures of maternal smoking during pregnancy and potential impacts on fetal growth Nat. Commun. 2021 12 5095 10.1038/s41467-021-24558-y 34429407 PMC8384884 · DOI
  13. [14]Joubert BR DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis Am. J. Hum. Genet. 2016 98 680 696 10.1016/j.ajhg.2016.02.019 27040690 PMC4833289 · DOI · Paper page
  14. [15]Muggli E Study protocol: Asking QUestions about Alcohol in pregnancy (AQUA): A longitudinal cohort study of fetal effects of low to moderate alcohol exposure BMC Pregnancy Childbirth 2014 14 302 10.1186/1471-2393-14-302 25187010 PMC4168250 · DOI
  15. [16]Newschaffer CJ Infant siblings and the investigation of autism risk factors J. Neurodev. Disord. 2012 4 7 10.1186/1866-1955-4-7 22958474 PMC3436647 · DOI
  16. [17]Heude B Cohort Profile: The EDEN mother-child cohort on the prenatal and early postnatal determinants of child health and development Int. J. Epidemiol. 2016 45 353 363 10.1093/ije/dyv151 26283636 · DOI
  17. [18]Guillemette L Genetics of Glucose regulation in Gestation and Growth (Gen3G): a prospective prebirth cohort of mother–child pairs in Sherbrooke, Canada BMJ Open 2016 6 e010031 10.1136/bmjopen-2015-010031 26842272 PMC4746442 · DOI
  18. [19]Aguilar-Lacasaña S Methylenetetrahydrofolate Reductase (MTHFR) Gene Polymorphism and Infant’s Anthropometry at Birth Nutrients 2021 13 831 10.3390/nu13030831 33802362 PMC7998581 · DOI
  19. [20]Michels KB Harris HR Barault L Birthweight, maternal weight trajectories and global DNA methylation of LINE-1 repetitive elements PLoS One 2011 6 e25254 10.1371/journal.pone.0025254 21980406 PMC3182185 · DOI
  20. [21]Guxens M Cohort Profile: The INMA—INfancia y Medio Ambiente—(Environment and Childhood) Proj. Int. J. Epidemiol. 2012 41 930 940 10.1093/ije/dyr054 21471022 · DOI
  21. [22]Dieckmann L Characteristics of epigenetic aging across gestational and perinatal tissues Clin. Epigenetics 2021 13 97 10.1186/s13148-021-01080-y 33926514 PMC8082803 · DOI
  22. [23]Hertz-Picciotto I A Prospective Study of Environmental Exposures and Early Biomarkers in Autism Spectrum Disorder: Design, Protocols, and Preliminary Data from the MARBLES Study Environ. Health Perspect. 2018 126 117004 10.1289/EHP535 30465702 PMC6371714 · DOI
  23. [24]Gilbert-diamond D Emond JA Baker ER Korrick SA Karagas MR Relation between in Utero Arsenic Exposure and Birth Outcomes in a Cohort of Mothers and Their Newborns from New Hampshire Environ. Health Perspect. 2016 124 1299 1307 10.1289/ehp.1510065 26955061 PMC4977046 · DOI
  24. [25]Appleton AA Prenatal Programming of Infant Neurobehaviour in a Healthy Population Paediatr. Perinat. Epidemiol. 2016 30 367 375 10.1111/ppe.12294 27004434 PMC5054721 · DOI
  25. [26]Houseman EA Reference-free deconvolution of DNA methylation data and mediation by cell composition effects BMC Bioinforma. 2016 17 259 10.1186/s12859-016-1140-4 PMC4928286 27358049 · DOI
  26. [27]Magi R Morris AP GWAMA: software for genome-wide association meta-analysis BMC Bioinforma. 2010 11 288 10.1186/1471-2105-11-288 PMC2893603 20509871 · DOI
  27. [29]Hamada H Allele-Specific Methylome and Transcriptome Analysis Reveals Widespread Imprinting in the Human Placenta Am. J. Hum. Genet. 2016 99 1045 1058 10.1016/j.ajhg.2016.08.021 27843122 PMC5097938 · DOI
  28. [30]Ernst J Kellis M ChromHMM: automating chromatin-state discovery and characterization Nat. Methods 2012 9 215 216 10.1038/nmeth.1906 22373907 PMC3577932 · DOI
  29. [31]Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes Nature 2015 518 317 329 10.1038/nature14248 25693563 PMC4530010 · DOI · Paper page
  30. [32]Schroeder DI The human placenta methylome Proc. Natl Acad. Sci. 2013 110 6037 6042 10.1073/pnas.1215145110 23530188 PMC3625261 · DOI
  31. [33]Horikoshi M Genome-wide associations for birth weight and correlations with adult disease Nature 2016 538 248 252 10.1038/nature19806 27680694 PMC5164934 · DOI
  32. [34]Beaumont RN Genome-wide association study of offspring birth weight in 86-577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics Hum. Mol. Genet. 2018 27 742 775 10.1093/hmg/ddx429 29309628 PMC5886200 · DOI
  33. [35]van der Valk RJP A novel common variant in DCST2 is associated with length in early life and height in adulthood Hum. Mol. Genet. 2015 24 1155 1168 10.1093/hmg/ddu510 25281659 PMC4447786 · DOI
  34. [36]Taal H Common variants at 12q15 and 12q24 are associated with infant head circumference Nat. Genet. 2012 44 532 538 10.1038/ng.2238 22504419 PMC3773913 · DOI
  35. [37]Zhang G Genetic Associations with Gestational Duration and Spontaneous Preterm Birth N. Engl. J. Med. 2017 377 1156 1167 10.1056/NEJMoa1612665 28877031 PMC5561422 · DOI
  36. [38]Warrington NM Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors Nat. Genet. 2019 51 804 814 10.1038/s41588-019-0403-1 31043758 PMC6522365 · DOI
  37. [39]Decker M EH3 (ABHD9): the first member of a new epoxide hydrolase family with high activity for fatty acid epoxides J. Lipid Res. 2012 53 2038 2045 10.1194/jlr.M024448 22798687 PMC3435537 · DOI
  38. [40]De Taeye BM Expression and regulation of soluble epoxide hydrolase in adipose tissue Obesity 2010 18 489 498 10.1038/oby.2009.227 19644452 PMC2864128 · DOI
  39. [41]Sun L CRISPR/Cas9 mediated establishment of a human CSRP3 compound heterozygous knockout hESC line to model cardiomyopathy and heart failure Stem Cell. Res. 2020 49 102077 10.1016/j.scr.2020.102077 33176267 · DOI
  40. [42]Hall E The effects of high glucose exposure on global gene expression and DNA methylation in human pancreatic islets Mol. Cell. Endocrinol. 2018 472 57 67 10.1016/j.mce.2017.11.019 29183809 · DOI
  41. [43]Usha Kalyani R Fer1L5, a Dysferlin Homologue Present in Vesicles and Involved in C2C12 Myoblast Fusion and Membrane Repair Biology 2020 9 386 10.3390/biology9110386 33182221 PMC7695329 · DOI
  42. [44]Knöfler M Human placenta and trophoblast development: key molecular mechanisms and model systems Cell. Mol. Life Sci. 2019 76 3479 3496 10.1007/s00018-019-03104-6 31049600 PMC6697717 · DOI
  43. [45]Almomani SN Identification and validation of DNA methylation changes in pre-eclampsia Placenta 2021 110 16 23 10.1016/j.placenta.2021.05.005 34098319 · DOI
  44. [46]Diceglie C Placental Antioxidant Defenses and Autophagy-Related Genes in Maternal Obesity and Gestational Diabetes Mellitus Nutrients 2021 13 1303 10.3390/nu13041303 33920886 PMC8071310 · DOI
  45. [47]Zou Y Association of a gain-of-function variant in LGR4 with central obesity Obes. (Silver Spring) 2017 25 252 260 10.1002/oby.21704 27925416 · DOI
  46. [48]Coorens THH Inherent mosaicism and extensive mutation of human placentas Nature 2021 592 80 85 10.1038/s41586-021-03345-1 33692543 PMC7611644 · DOI
  47. [49]Costanzo V Bardelli A Siena S Abrignani S Exploring the links between cancer and placenta development Open Biol. 2018 8 180081 10.1098/rsob.180081 29950452 PMC6030113 · DOI
  48. [50]Calabuig-Navarro V Effect of Maternal Obesity on Placental Lipid Metabolism Endocrinology 2017 158 2543 2555 10.1210/en.2017-00152 28541534 PMC5551552 · DOI
  49. [51]Alcala M Antioxidants and Oxidative Stress: Focus in Obese Pregnancies Front. Physiol. 2018 9 1569 10.3389/fphys.2018.01569 30459642 PMC6232303 · DOI
  50. [52]Malti N Oxidative stress and maternal obesity: feto-placental unit interaction Placenta 2014 35 411 416 10.1016/j.placenta.2014.03.010 24698544 · DOI
  51. [53]Yuen RK Chen B Blair JD Robinson WP Nelson DM Hypoxia alters the epigenetic profile in cultured human placental trophoblasts Epigenetics 2013 8 192 202 10.4161/epi.23400 23314690 PMC3592905 · DOI
  52. [54]Fernandez-Twinn DS Exercise rescues obese mothers’ insulin sensitivity, placental hypoxia and male offspring insulin sensitivity Sci. Rep. 2017 7 44650 10.1038/srep44650 28291256 PMC5349590 · DOI · Paper page
  53. [55]Fernandez-Jimenez N Comparison of Illumina 450 K and EPIC arrays in placental DNA methylation Epigenetics 2019 14 1177 1182 10.1080/15592294.2019.1634975 31250700 PMC6791701 · DOI
  54. [56]Casas M Maternal pre-pregnancy overweight and obesity, and child neuropsychological development: two Southern European birth cohort studies Int. J. Epidemiol. 2013 42 506 517 10.1093/ije/dyt002 23569191 · DOI
  55. [57]Chen J Fast and robust adjustment of cell mixtures in epigenome-wide association studies with SmartSVA BMC Genomics 2017 18 413 10.1186/s12864-017-3808-1 28549425 PMC5446715 · DOI
  56. [59]Fortin J-P Functional normalization of 450k methylation array data improves replication in large cancer studies Genome Biol. 2014 15 503 10.1186/s13059-014-0503-2 25599564 PMC4283580 · DOI
  57. [60]Teschendorff AE A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data Bioinformatics 2013 29 189 196 10.1093/bioinformatics/bts680 23175756 PMC3546795 · DOI
  58. [61]Chen J CpGFilter: model-based CpG probe filtering with replicates for epigenome-wide association studies Bioinformatics 2016 32 469 471 10.1093/bioinformatics/btv577 26449931 PMC4757944 · DOI
  59. [62]Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002).
  60. [63]Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics14, (2013). PMC3637064 23586463
  61. [64]Lawrence M Software for Computing and Annotating Genomic Ranges PLOS Computational Biol. 2013 9 e1003118 10.1371/journal.pcbi.1003118 PMC3738458 23950696 · DOI
  62. [65]Fernandez-Jimenez, N. et al. A meta-analysis of pre-pregnancy maternal body mass index and placental DNA methylation identifies 27 CpG sites with implications for mother-child health. [Data set]. Zenodo. 10.5281/zenodo.7314534 (2022). PMC9709064 36446949 · DOI

Authors
Fernandez-Jimenez, N., Fore, R., Cilleros-Portet, A., Lepeule, J., Perron, P., Kvist, T., Tian, F.-Y., Lesseur, C., Binder, A. M., Lozano, M., Martorell-Marugán, J., Loke, Y. J., Bakulski, K. M., Zhu, Y., Forhan, A., Sammallahti, S., Everson, T. M., Chen, J., Michels, K. B., Belmonte, T., Carmona-Sáez, P., Halliday, J., Daniele Fallin, M., LaSalle, J. M., Tost, J., Czamara, D., Fernández, M. F., Gómez-Martín, A., Craig, J. M., Gonzalez-Alzaga, B., Schmidt, R. J., Dou, J. F., Muggli, E., Lacasaña, M., Vrijheid, M., Marsit, C. J., Karagas, M. R., Räikkönen, K., Bouchard, L., Heude, B., Santa-Marina, L., Bustamante, M., Hivert, M.-F., & Bilbao, J. R.
Affiliations
  • Harvard University (US)
  • Centre National de la Recherche Scientifique (FR)
  • Centre de recherche du CHUS (CA)
  • University of Helsinki (FI)
  • Emory University (US)
  • Icahn School of Medicine at Mount Sinai (US)
  • University of California, Los Angeles (US)
  • Universitat Jaume I (ES)
  • The University of Melbourne (AU)
  • University of Michigan (US)
  • University of California, Davis (US)
  • Inserm (FR)
  • Erasmus MC (NL)
  • University of Freiburg (DE)
  • Max Planck Institute of Psychiatry (DE)
  • Universidad de Granada (ES)
  • Deakin University (AU)
  • Instituto de Investigación Biosanitaria de Granada (ES)
  • Universitat Pompeu Fabra (ES)
  • Université de Sherbrooke (CA)
Venue
Communications Biology
Published
2022-11-30
Volume
5
Issue
1
Pages
1313-1313
ISSN
2399-3642 (ISSN-L)
OpenAlex
W4310368196
Cited by
20
References
66
Citations by year
2026: 4 · 2025: 5 · 2024: 8 · 2023: 2 · 2022: 1
FWCI
1.03
Type
journal article
Language
en
Open access
Locations
  • Communications Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, cc-by)
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  • Repository of Digital Objects for Teaching Research and Culture (University of Valencia) (submittedVersion, cc-by-nc)
  • RISalud Institutional Health Repository of Andalucía (submittedVersion, cc-by)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, cc-by)
  • Communities in ADDI (University of the Basque Country) (submittedVersion, cc-by)
  • Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by)
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  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
Funders
  • Ministry of Economy and Competitiveness | Instituto de Salud Carlos III (PI18/01142)
  • Osasun Saila, Eusko Jaurlaritzako (GVSAN2018111086)
Topics
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Birth, Development, and Health
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Health Sciences › Medicine › Obstetrics and Gynecology › Gestational Diabetes Research and Management
Keywords
pre-pregnancy body mass index, maternal obesity, placental DNA methylation, epigenome-wide association study, CpG sites, maternal and child health, neonatal morbidity, pregnancy complications, offspring metabolic outcomes, glutathione peroxidase 1, LGR4, oxidative stress, cancer pathways
MeSH terms
Child Health, Child, Female, Humans, Infant, Newborn, Mothers, Placenta, Pregnancy, Body Mass Index, DNA Methylation

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