Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-23T05:54:44.289Z Has data issue: false hasContentIssue false
  • English
  • Français

Maternal cardiometabolic factors and genetic ancestry influence epigenetic aging of the placenta

Published online by Cambridge University Press:  17 January 2020

Tsegaselassie Workalemahu Open the ORCID record for Tsegaselassie Workalemahu [Opens in a new window] ,
Deepika Shrestha ,
Salman M. Tajuddin  and
Fasil Tekola-Ayele
Tsegaselassie Workalemahu
Affiliation:
Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
Deepika Shrestha
Affiliation:
Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
Salman M. Tajuddin
Affiliation:
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
Fasil Tekola-Ayele*
Affiliation:
Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
*
Address for correspondence: Fasil Tekola-Ayele, Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Disruption of physiological aging of the placenta can lead to pregnancy complications and increased risk for cardiometabolic diseases during childhood and adulthood. Maternal metabolic and genetic factors need to operate in concert with placental development for optimal pregnancy outcome. However, it is unknown whether maternal cardiometabolic status and genetic ancestry contribute to differences in placental epigenetic age acceleration (PAA). We investigated whether maternal prepregnancy obesity, gestational weight gain (GWG), blood pressure, and genetic ancestry influence PAA. Among 301 pregnant women from 4 race/ethnic groups who provided placenta samples at delivery as part of the National Institute of Child Health and Human Development Fetal Growth Studies, placental DNA methylation age was estimated using 62 CpGs known to predict placental aging. PAA was defined to be the difference between placental DNA methylation age and gestational age at birth. Percentage of genetic ancestries was estimated using genotype data. We found that a 1 kg/week increase in GWG was associated with up to 1.71 (95% CI: −3.11, −0.32) week lower PAA. Offspring Native American ancestry and African ancestry were associated, respectively, with higher and lower PAA among Hispanics, and maternal East Asian ancestry was associated with lower PAA among Asians (p < 0.05). Among mothers with a male offspring, blood pressure was associated with lower PAA across all three trimesters (p < 0.05), prepregnancy obesity compared to normal weight was associated with 1.24 (95% CI: −2.24, −0.25) week lower PAA. In summary, we observed that maternal cardiometabolic factors and genetic ancestry influence placental epigenetic aging and some of these influences may be male offspring-specific.

Keywords

Obesitygestational weight gainblood pressureancestryplacental agingepigenetic aging
Type
Original Article
Information
Journal of Developmental Origins of Health and Disease , Volume 12 , Issue 1 , February 2021 , pp. 34 - 41
Check for updates
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chen, K, Chen, L, Lee, Y. Exploring the relationship between preterm placental calcification and adverse maternal and fetal outcome. Ultrasound Obst Gyn. 2011; 37(3), 328–334.CrossRefGoogle ScholarPubMed
Chen, K-H, Chen, L-R, Lee, Y-H. The role of preterm placental calcification in high-risk pregnancy as a predictor of poor uteroplacental blood flow and adverse pregnancy outcome. Ultrasound Med Biol. 2012; 38(6), 1011–1018.CrossRefGoogle ScholarPubMed
Biron-Shental, T, Sukenik-Halevy, R, Sharon, Y, et al. Short telomeres may play a role in placental dysfunction in preeclampsia and intrauterine growth restriction. Am J Obstet Gynecol. 2010; 202(4), 381. e381–381. e387.CrossRefGoogle ScholarPubMed
Maiti, K, Sultana, Z, Aitken, RJ, et al. Evidence that fetal death is associated with placental aging. Am J Obstet Gynecol. 2017; 217(4), 441. e441–441. e414.CrossRefGoogle ScholarPubMed
Sultana, Z, Maiti, K, Aitken, J, Morris, J, Dedman, L, Smith, R. Oxidative stress, placental ageing‐related pathologies and adverse pregnancy outcomes. Am J Reprod Immunol. 2017; 77(5), e12653.CrossRefGoogle ScholarPubMed
Thornburg, K, O’tierney, P, Louey, S. The placenta is a programming agent for cardiovascular disease. Placenta. 2010; 31, S54–S59.CrossRefGoogle ScholarPubMed
Arabin, B, Baschat, AA. Pregnancy: an underutilized window of opportunity to improve long-term maternal and infant health—An appeal for continuous family care and interdisciplinary communication. Front Pediatr. 2017; 5, 69.CrossRefGoogle ScholarPubMed
Sultana, Z, Maiti, K, Dedman, L, Smith, R. Is there a role for placental senescence in the genesis of obstetric complications and fetal growth restriction? Am J Obstet Gynecol. 2018; 218(2), S762–S773.CrossRefGoogle Scholar
Polettini, J, Dutta, E, Behnia, F, Saade, G, Torloni, M, Menon, R. Aging of intrauterine tissues in spontaneous preterm birth and preterm premature rupture of the membranes: a systematic review of the literature. Placenta. 2015; 36(9), 969–973.CrossRefGoogle ScholarPubMed
Rodier, F, Campisi, J. Four faces of cellular senescence. J Cell Biol. 2011. jcb.201009094.CrossRefGoogle ScholarPubMed
Van Deursen, JM. The role of senescent cells in ageing. Nature. 2014; 509(7501), 439.CrossRefGoogle ScholarPubMed
Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 2013; 14(10), 3156.CrossRefGoogle ScholarPubMed
Horvath, S, Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018; 19(6), 371.CrossRefGoogle ScholarPubMed
Jylhävä, J, Pedersen, NL, Hägg, S. Biological age predictors. EBioMedicine. 2017; 21, 29–36.CrossRefGoogle ScholarPubMed
Bell, JT, Tsai, P-C, Yang, T-P, et al. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLos Genet. 2012; 8(4), e1002629.CrossRefGoogle Scholar
Christensen, BC, Houseman, EA, Marsit, CJ, et al. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLos Genet. 2009; 5(8), e1000602.CrossRefGoogle ScholarPubMed
Boyd-Kirkup, JD, Green, CD, Wu, G, Wang, D, Han, J-DJ. Epigenomics and the regulation of aging. Epigenomics. 2013; 5(2), 205–227.CrossRefGoogle ScholarPubMed
Perna, L, Zhang, Y, Mons, U, Holleczek, B, Saum, K-U, Brenner, H. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin Epigenetics. 2016; 8(1), 64.CrossRefGoogle Scholar
Zheng, SC, Widschwendter, M, Teschendorff, AE. Epigenetic drift, epigenetic clocks and cancer risk. Epigenomics. 2016; 8(5), 705–719.CrossRefGoogle ScholarPubMed
Girchenko, P, Lahti, J, Czamara, D, et al. Associations between maternal risk factors of adverse pregnancy and birth outcomes and the offspring epigenetic clock of gestational age at birth. Clin Epigenetics. 2017; 9(1), 49.CrossRefGoogle ScholarPubMed
Bohlin, J, Håberg, SE, Magnus, P, et al. Prediction of gestational age based on genome-wide differentially methylated regions. Genome Biol. 2016; 17(1), 207.CrossRefGoogle ScholarPubMed
Knight, AK, Craig, JM, Theda, C, et al. An epigenetic clock for gestational age at birth based on blood methylation data. Genome Biol. 2016; 17(1), 206.CrossRefGoogle ScholarPubMed
Mayne, BT, Leemaqz, SY, Smith, AK, Breen, J, Roberts, CT, Bianco-Miotto, T. Accelerated placental aging in early onset preeclampsia pregnancies identified by DNA methylation. Epigenomics. 2017; 9(3), 279–289.CrossRefGoogle ScholarPubMed
Knight, AK, Conneely, KN, Smith, AK. Gestational age predicted by DNA methylation: potential clinical and research utility, 2017. Future Medicine.CrossRefGoogle Scholar
Yang, L, Cartier, J, Drake, A, Reynolds, R. DNA methylation differs between lean and obese placenta and is influenced by maternal environment and fetal sex. In Society for Endocrinology BES 2017, 2017. BioScientifica.CrossRefGoogle Scholar
Horvath, S, Gurven, M, Levine, ME, et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 2016; 17(1), 171.CrossRefGoogle Scholar
Cappetta, M, Berdasco, M, Hochmann, J, et al. Effect of genetic ancestry on leukocyte global DNA methylation in cancer patients. BMC Cancer. 2015; 15(1), 434.CrossRefGoogle ScholarPubMed
Zhang, W, Dolan, ME. Ancestry-related differences in gene expression: findings may enhance understanding of health disparities between populations. Pharmacogenomics. 2008; 9(5), 489–492.CrossRefGoogle ScholarPubMed
Bryc, K, Durand, EY, Macpherson, JM, Reich, D, Mountain, JL. The genetic ancestry of African Americans, Latinos, and European Americans across the United States. Am J Hum Genet. 2015; 96(1), 37–53.CrossRefGoogle ScholarPubMed
Yeganegi, M, Watson, CS, Martins, A, et al. Effect of Lactobacillus rhamnosus GR-1 supernatant and fetal sex on lipopolysaccharide-induced cytokine and prostaglandin-regulating enzymes in human placental trophoblast cells: implications for treatment of bacterial vaginosis and prevention of preterm labor. Am J Obstet Gynecol. 2009; 200(5), 532. e531–532. e538.CrossRefGoogle ScholarPubMed
Simpkin, AJ, Hemani, G, Suderman, M, et al. Prenatal and early life influences on epigenetic age in children: a study of mother–offspring pairs from two cohort studies. Hum Mol Genet. 2015; 25(1), 191–201.CrossRefGoogle ScholarPubMed
Grewal, J, Grantz, KL, Zhang, C, et al. Cohort Profile: NICHD Fetal Growth Studies–Singletons and Twins. Int J Epidemiol. 2017; 47(1), 25–25l.CrossRefGoogle Scholar
Buck Louis, GM, Grewal, J, Albert, PS, et al. Racial/ethnic standards for fetal growth: the NICHD Fetal Growth Studies. Am J Obstet Gynecol. 2015; 213(4), 449. e441–449. e441.CrossRefGoogle ScholarPubMed
Delahaye, F, Do, C, Kong, Y, et al. Genetic variants influence on the placenta regulatory landscape. PLos Genet. 2018; 14(11), e1007785.CrossRefGoogle ScholarPubMed
Teschendorff, AE, Marabita, F, Lechner, M, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2012; 29(2), 189–196.CrossRefGoogle ScholarPubMed
Troyanskaya, O, Cantor, M, Sherlock, G, et al. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001; 17(6), 520–525.CrossRefGoogle ScholarPubMed
Friedman, J, Hastie, T, Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010; 33(1), 1.CrossRefGoogle ScholarPubMed
Consortium, GP. A global reference for human genetic variation. Nature. 2015; 526(7571), 68.CrossRefGoogle Scholar
Li, JZ, Absher, DM, Tang, H, et al. Worldwide human relationships inferred from genome-wide patterns of variation. Science. 2008; 319(5866), 1100–1104.CrossRefGoogle ScholarPubMed
Alexander, DH, Novembre, J, Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009; 19, 1655–1664.CrossRefGoogle ScholarPubMed
Hinkle, SN, Johns, AM, Albert, PS, Kim, S, Grantz, KL. Longitudinal changes in gestational weight gain and the association with intrauterine fetal growth. Eur J Obstet Gynecol Reprod Biol. 2015; 190, 41–47.CrossRefGoogle ScholarPubMed
Roberts, VH, Smith, J, McLea, SA, Heizer, AB, Richardson, JL, Myatt, L. Effect of increasing maternal body mass index on oxidative and nitrative stress in the human placenta. Placenta. 2009; 30(2), 169–175.CrossRefGoogle ScholarPubMed
Siveska, E, Jasovic, V. Fetal Growth and Body Proportion during Pre-Eclamptic Pregnancy. Obstet Gynecol Int J. 2015; 2(3), 00038.Google Scholar
Hermida, RC, Ayala, DE, Iglesias, M. Predictable blood pressure variability in healthy and complicated pregnancies. Hypertension. 2001; 38(3), 736–741.CrossRefGoogle ScholarPubMed
Norwood, KA. Maternal obesity alters fetal development due to impaired placental function and has lasting effects on adult offspring, Theses and Dissertations in Animal Science, 69, UNL Digital Commons, 2013. Accessed 03/12/2019 from https://digitalcommons.unl.edu/animalscidiss/69.Google Scholar
Huang, L, Liu, J, Feng, L, Chen, Y, Zhang, J, Wang, W. Maternal prepregnancy obesity is associated with higher risk of placental pathological lesions. Placenta. 2014; 35(8), 563–569.CrossRefGoogle ScholarPubMed
Belkacemi, L, Kjos, S, Nelson, D, Desai, M, Ross, M. Reduced apoptosis in term placentas from gestational diabetic pregnancies. J Dev Orig Hlth Dis. 2013; 4(3), 256–265.CrossRefGoogle ScholarPubMed
Myatt, L. Placental adaptive responses and fetal programming. J Physiol. 2006; 572(1), 25–30.CrossRefGoogle ScholarPubMed
Walker, MG, Hindmarsh, PC, Geary, M, Kingdom, JC. Sonographic maturation of the placenta at 30 to 34 weeks is not associated with second trimester markers of placental insufficiency in low-risk pregnancies. J Obstet Gynaecol Can. 2010; 32(12), 1134–1139.CrossRefGoogle Scholar
Sood, R, Zehnder, JL, Druzin, ML, Brown, PO. Gene expression patterns in human placenta. Proc Natl Acad Sci USA. 2006; 103(14), 5478–5483.CrossRefGoogle ScholarPubMed
Scott, NM, Hodyl, NA, Murphy, VE, et al. Placental cytokine expression covaries with maternal asthma severity and fetal sex. J Immunol. 2009; 182(3), 1411–1420.CrossRefGoogle ScholarPubMed
Clifton, V. Sex and the human placenta: mediating differential strategies of fetal growth and survival. Placenta. 2010; 31, S33–S39.CrossRefGoogle ScholarPubMed
Muralimanoharan, S, Maloyan, A, Myatt, L. Evidence of sexual dimorphism in the placental function with severe preeclampsia. Placenta. 2013; 34(12), 1183–1189.Google ScholarPubMed
Shrestha, D, Workalemahu, T, Tekola-Ayele, F. Maternal dyslipidemia during early pregnancy and epigenetic aging of the placenta. Epigenetics. 2019; 14(10), 1030–1039.CrossRefGoogle Scholar
Steer, PJ, Little, MP, Kold-Jensen, T, Chapple, J, Elliott, P. Maternal blood pressure in pregnancy, birth weight, and perinatal mortality in first births: prospective study. BMJ. 2004; 329(7478), 1312.CrossRefGoogle ScholarPubMed
Ng, PH, Walters, WA. The effects of chronic maternal hypotension during pregnancy. Aust NZ J Obstet Gynaecol. 1992; 32(1), 14–16.Google ScholarPubMed
Melo, NADB, Araujo Júnior, E, Helfer, TM, et al. Assessment of maternal Doppler parameters of ophthalmic artery in fetuses with growth restriction in the third trimester of pregnancy: a case–control study. J Obstet Gynaecol Res. 2015; 41(9), 1330–1336.CrossRefGoogle ScholarPubMed
Lean, SC, Heazell, AE, Dilworth, MR, Mills, TA, Jones, RL. Placental dysfunction underlies increased risk of fetal growth restriction and stillbirth in advanced maternal age women. Sci Rep. 2017; 7(1), 9677.CrossRefGoogle ScholarPubMed
Shriner, D, Tekola-Ayele, F, Adeyemo, A, Rotimi, CN. Genome-wide genotype and sequence-based reconstruction of the 140,000 year history of modern human ancestry. Sci Rep. 2014; 4, 6055.CrossRefGoogle ScholarPubMed
Galanter, JM, Gignoux, CR, Oh, SS, et al. Methylation analysis reveals fundamental differences between ethnicity and genetic ancestry. BioRxiv. 2016, 036822.Google Scholar
Tekola-Ayele, F, Workalemahu, T, Gorfu, G, et al. Sex differences in the associations of placental epigenetic aging with fetal growth. Aging (Albany NY). 2019; 11, 5412–5432.CrossRefGoogle ScholarPubMed
Supplementary material: Image

Workalemahu et al. supplementary material

Figure S1

Download Workalemahu et al. supplementary material(Image)
Image 55.1 KB
Supplementary material: File

Workalemahu et al. supplementary material

Table S1

Download Workalemahu et al. supplementary material(File)
File 19.1 KB
Supplementary material: File

Workalemahu et al. supplementary material

Table S3

Download Workalemahu et al. supplementary material(File)
File 17.9 KB
Supplementary material: File

Workalemahu et al. supplementary material

Table S2

Download Workalemahu et al. supplementary material(File)
File 17.3 KB