Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Clinical Research

Neonatal adiposity is associated with microRNAs in adipocyte-derived extracellular vesicles in maternal and cord blood, a discovery analysis

International Journal of Obesity volume 48, pages 403–413 (2024)Cite this article

Subjects

Abstract

Background

Maternal body size, nutrition, and hyperglycemia contribute to neonatal body size and composition. There is little information on maternal-fetal transmission of messages which influence fetal growth. We analyzed adipocyte-derived small extracellular vesicular (ADsEV) microRNAs in maternal and cord blood to explore their adipogenic potential.

Methods

There were 279 mother-neonate pairs with all phenotypic data (normal glucose tolerant NGT = 148, gestational diabetes mellitus GDM = 131). Neonates with adiposity were those in the highest tertile (T3) of sex-specific sum of skinfolds and those without adiposity (lean) in the lowest tertile T1 of NGT pregnancies. We studied ADsEV miRNAs in 76 and 51 neonates with and without adiposity respectively and their mothers based on power calculations (68 NGT and 59 GDM pregnancies). ADsEV miRNAs from maternal and cord blood plasma samples were profiled on Agilent 8*60 K microarray. Differential expression (DE) of ADsEV miRNAs in adipose vs. lean groups was studied before and after adjustment for maternal GDM, adiposity, and vitamin B12-folate status.

Results

Multiple miRNAs were common in maternal and cord blood and positively correlated. We identified 24 maternal and 5 cord blood miRNAs differentially expressed (discovery p ≤ 0.1) in the adipose group in unadjusted, and 19 and 26, respectively, in the adjusted analyses. Even though DE miRNAs were different in maternal and cord blood, they targeted similar adipogenic pathways (e.g., the forkhead box O (FOXO) family of transcription factors, mitogen‑activated protein kinase (MAPK) pathway, transforming growth factor beta (TGF-β) pathway). Maternal GDM and adiposity were associated with many DE ADsEV miRNAs.

Conclusion

Our results suggest that the ADsEV miRNAs in mothers are potential regulators of fetal adiposity. The expression and functionality of miRNAs appear to be influenced by maternal adiposity, hyperglycemia, and micronutrient status during pregnancy.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Differentially expressed miRNAs between lean Vs adipose groups in maternal and cord blood samples.
Fig. 2: Differentially expressed ADsEV miRNAs highlighted in maternal A. unadjusted and B. adjusted analysis.
Fig. 3: Differentially expressed ADsEV miRNAs highlighted in cord blood A. unadjusted and B. adjusted analysis.
Fig. 4: Complex interplay of DE ADsEVs miRNAs from maternal and cord blood in adipogenesis related pathways.
Fig. 5: Comparison of differentially expressed miRNAs in the 3 analyses.

Similar content being viewed by others

Data availability

The raw microarray data is available at Gene Expression Omnibus (Accession ID: GSE217933). The demographic, and phenotypic data is available with the authors and can be made available for a reasonable request following institutional clearances.

Code availability

Code used for calculating differential expression of miRNAs can be accessed from GitHub on the following address: https://github.com/poojakunte24/IJO_ADsEV_manuscript/tree/f16066c106f8ed7f7725b8d4eba67cc14646b2b8.

References

  1. Williams RH, Larsen PR. Chapter 20. Endocrine Changes in Pregnancy. 10 ed. Williams textbook of endocrinology: Philadelphia, Pa. : Saunders; (2003).

  2. Hales CN, Barker DJ. Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. 1992. Int J Epidemiol. 2013;42:1215–22.

    Article  CAS  PubMed  Google Scholar 

  3. Yajnik CS, Deshmukh US. Maternal nutrition, intrauterine programming and consequential risks in the offspring. Rev Endocr Metab Disord. 2008;9:203–11.

    Article  CAS  PubMed  Google Scholar 

  4. Prachi Katre CSY. Pune Experience. Influence of early life environment on risk of non-nommunicable diseases (NCDs) in Indians. SIGHT AND LIFE. 2015;19.

  5. Yajnik CS, Fall CH, Coyaji KJ, Hirve SS, Rao S, Barker DJ, et al. Neonatal anthropometry: the thin-fat Indian baby. The Pune Maternal Nutrition Study. Int J Obes Relat Metab Disord. 2003;27:173–80.

    Article  CAS  PubMed  Google Scholar 

  6. Yajnik CS, Lubree HG, Rege SS, Naik SS, Deshpande JA, Deshpande SS, et al. Adiposity and hyperinsulinemia in Indians are present at birth. J Clin Endocrinol Metab. 2002;87:5575–80.

    Article  CAS  PubMed  Google Scholar 

  7. Yajnik CS, Yajnik PC. Fetal adiposity epidemic in the modern world: a thrifty phenotype aggravated by maternal obesity and diabetes. Am J Clin Nutr. 2020;112:8–10.

    Article  PubMed  Google Scholar 

  8. D’Angelo S, Yajnik CS, Kumaran K, Joglekar C, Lubree H, Crozier SR, et al. Body size and body composition: a comparison of children in India and the UK through infancy and early childhood. J Epidemiol Community Health. 2015;69:1147–53.

    Article  PubMed  Google Scholar 

  9. Bavdekar A, Yajnik CS, Fall CH, Bapat S, Pandit AN, Deshpande V, et al. Insulin resistance syndrome in 8-year-old Indian children: small at birth, big at 8 years, or both? Diabetes. 1999;48:2422–9.

    Article  CAS  PubMed  Google Scholar 

  10. Yajnik CS, Deshpande SS, Jackson AA, Refsum H, Rao S, Fisher DJ, et al. Vitamin B12 and folate concentrations during pregnancy and insulin resistance in the offspring: the Pune Maternal Nutrition Study. Diabetologia. 2008;51:29–38.

    Article  CAS  PubMed  Google Scholar 

  11. Kaspar D, Hastreiter S, Irmler M, Hrabe de Angelis M, Beckers J. Nutrition and its role in epigenetic inheritance of obesity and diabetes across generations. Mamm Genome. 2020;31:119–33.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Reichetzeder C. Overweight and obesity in pregnancy: their impact on epigenetics. Eur J Clin Nutr. 2021;75:1710–22.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wang K, Li WD, Zhang CK, Wang Z, Glessner JT, Grant SF, et al. A genome-wide association study on obesity and obesity-related traits. PLoS One. 2011;6:e18939.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Czernek L, Duchler M. Exosomes as Messengers Between Mother and Fetus in Pregnancy. Int J Mol Sci. 2020;21.

  15. Sarker S, Scholz-Romero K, Perez A, Illanes SE, Mitchell MD, Rice GE, et al. Placenta-derived exosomes continuously increase in maternal circulation over the first trimester of pregnancy. J Transl Med. 2014;12:204.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Thery C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles. 2018;7:1535750.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Thomou T, Mori MA, Dreyfuss JM, Konishi M, Sakaguchi M, Wolfrum C, et al. Adipose-derived circulating miRNAs regulate gene expression in other tissues. Nature. 2017;542:450–5.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Boon RA, Vickers KC. Intercellular transport of microRNAs. Arterioscler Thromb Vasc Biol. 2013;33:186–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ferrante SC, Nadler EP, Pillai DK, Hubal MJ, Wang Z, Wang JM, et al. Adipocyte-derived exosomal miRNAs: a novel mechanism for obesity-related disease. Pediatr Res. 2015;77:447–54.

    Article  CAS  PubMed  Google Scholar 

  20. Hubal MJ, Nadler EP, Ferrante SC, Barberio MD, Suh JH, Wang J, et al. Circulating adipocyte-derived exosomal MicroRNAs associated with decreased insulin resistance after gastric bypass. Obesity (Silver Spring). 2017;25:102–10.

    Article  CAS  PubMed  Google Scholar 

  21. Hromadnikova I, Kotlabova K, Dvorakova L, Krofta L, Sirc J. Substantially Altered Expression Profile of Diabetes/Cardiovascular/Cerebrovascular Disease Associated microRNAs in Children Descending from Pregnancy Complicated by Gestational Diabetes Mellitus-One of Several Possible Reasons for an Increased Cardiovascular Risk. Cells. 2020;9.

  22. Xie Z, Wang X, Liu X, Du H, Sun C, Shao X, et al. Adipose-Derived Exosomes Exert Proatherogenic Effects by Regulating Macrophage Foam Cell Formation and Polarization. J Am Heart Assoc. 2018;7.

  23. Weisberg SP, McCann D, Desai M, Rosenbaum M, Leibel RL, Ferrante AW Jr. Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest. 2003;112:1796–808.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Xu H, Barnes GT, Yang Q, Tan G, Yang D, Chou CJ, et al. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. J Clin Invest. 2003;112:1821–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sonali S Wagle SP, Bhat D, Kalyanaraman K, Rajashree K, Sayali W, et al. Adiposity and Glucose Intolerance in Offspring of Diabetic Mothers in India. medRxiv. 2021.

  26. International Association of D, Pregnancy Study Groups Consensus P, Metzger BE, Gabbe SG, Persson B, Buchanan TA, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33:676–82.

    Article  Google Scholar 

  27. Warnes GR LP, Li F. ssize: Estimate Microarray Sample Size. 2023.

  28. Wickham H. ggplot2: Elegant Graphics for Data Analysis 2016 [Available from: https://ggplot2.tidyverse.org.

  29. Licursi V, Conte F, Fiscon G, Paci P. MIENTURNET: an interactive web tool for microRNA-target enrichment and network-based analysis. BMC Bioinformatics. 2019;20:545.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Moritz Marbach FB, Heike H, Pedro J, Ming-Yu L. ggnet2 - Plot a network with ggplot2.

  31. Statnet Development Team. Krivitsky PN, Handcock MS, Hunter DR, Butts CT, Klumb C, Goodreau SM, Morris M (2003-2023). statnet: Software tools for the Statistical Modeling of Network Data. http://statnet.org.

  32. Bogart S. SankeyMATIC: A Sankey diagram builder for everyone [Available from: https://sankeymatic.com/.

  33. Bardou P, Mariette J, Escudie F, Djemiel C, Klopp C. jvenn: an interactive Venn diagram viewer. BMC Bioinformatics. 2014;15:293.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Ferland-McCollough D, Fernandez-Twinn DS, Cannell IG, David H, Warner M, Vaag AA, et al. Programming of adipose tissue miR-483-3p and GDF-3 expression by maternal diet in type 2 diabetes. Cell Death Differ. 2012;19:1003–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Nardelli C, Iaffaldano L, Pilone V, Labruna G, Ferrigno M, Carlomagno N, et al. Changes in the MicroRNA Profile Observed in the Subcutaneous Adipose Tissue of Obese Patients after Laparoscopic Adjustable Gastric Banding. J Obes. 2017;2017:6754734.

  36. Joglekar MV, Kunte PS, Wong WKM, Bhat DS, Satoor SN, Patil RR, et al. Circulating microRNAs from early childhood and adolescence are associated with pre-diabetes at 18 years of age in women from the PMNS cohort. J Dev Orig Health Dis. 2022;13:806–11.

    Article  CAS  PubMed  Google Scholar 

  37. Wang L, Xu L, Xu M, Liu G, Xing J, Sun C, et al. Obesity-Associated MiR-342-3p Promotes Adipogenesis of Mesenchymal Stem Cells by Suppressing CtBP2 and Releasing C/EBPalpha from CtBP2 Binding. Cell Physiol Biochem. 2015;35:2285–98.

    Article  CAS  PubMed  Google Scholar 

  38. He H, Chen K, Wang F, Zhao L, Wan X, Wang L, et al. miR-204-5p promotes the adipogenic differentiation of human adipose-derived mesenchymal stem cells by modulating DVL3 expression and suppressing Wnt/beta-catenin signaling. Int J Mol Med. 2015;35:1587–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Li G, Fu S, Chen Y, Jin W, Zhai B, Li Y, et al. MicroRNA-15a Regulates the Differentiation of Intramuscular Preadipocytes by Targeting ACAA1, ACOX1 and SCP2 in Chickens. Int J Mol Sci. 20 (2019).

  40. Suchacki KJ, Stimson RH. Nutritional Regulation of Human Brown Adipose Tissue. Nutrients. 13 (2021).

  41. Bakker LE, Boon MR, van der Linden RA, Arias-Bouda LP, van Klinken JB, Smit F, et al. Brown adipose tissue volume in healthy lean south Asian adults compared with white Caucasians: a prospective, case-controlled observational study. Lancet Diabetes Endocrinol. 2014;2:210–7.

    Article  PubMed  Google Scholar 

  42. Chang YJ, Li YS, Wu CC, Wang KC, Huang TC, Chen Z, et al. Extracellular MicroRNA-92a Mediates Endothelial Cell-Macrophage Communication. Arterioscler Thromb Vasc Biol. 2019;39:2492–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Machado SA, Pasquarelli-do-Nascimento G, da Silva DS, Farias GR, de Oliveira Santos I, Baptista LB, et al. Browning of the white adipose tissue regulation: new insights into nutritional and metabolic relevance in health and diseases. Nutr Metab (Lond). 2022;19:61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhu Z, Cao F, Li X. Epigenetic Programming and Fetal Metabolic Programming. Front Endocrinol (Lausanne). 2019;10:764.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Sebo ZL, Jeffery E, Holtrup B, Rodeheffer MS A mesodermal fate map for adipose tissue. Development. 145 (2018).

  46. Ambele MA, Dhanraj P, Giles R, Pepper MS Adipogenesis: A Complex Interplay of Multiple Molecular Determinants and Pathways. Int J Mol Sci. 21 (2020).

  47. Ghaben AL, Scherer PE. Adipogenesis and metabolic health. Nat Rev Mol Cell Biol. 2019;20:242–58.

    Article  CAS  PubMed  Google Scholar 

  48. Samblas M, Milagro FI, Martinez A. DNA methylation markers in obesity, metabolic syndrome, and weight loss. Epigenetics. 2019;14:421–44.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Crocker KC, Domingo-Relloso A, Haack K, Fretts AM, Tang WY, Herreros M, et al. DNA methylation and adiposity phenotypes: an epigenome-wide association study among adults in the Strong Heart Study. Int J Obes (Lond). 2020;44:2313–22.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Rosen ED, MacDougald OA. Adipocyte differentiation from the inside out. Nat Rev Mol Cell Biol. 2006;7:885–96.

    Article  CAS  PubMed  Google Scholar 

  51. Yajnik CS. Transmission of obesity-adiposity and related disorders from the mother to the baby. Ann Nutr Metab. 2014;64:8–17. Suppl 1

    Article  CAS  PubMed  Google Scholar 

  52. Fowden AL, Hill DJ. Intra-uterine programming of the endocrine pancreas. Br Med Bull. 2001;60:123–42.

    Article  CAS  PubMed  Google Scholar 

  53. Pedersen J. Diabetes and pregnancy; blood sugar of newborn infants during fasting and glucose administration. Ugeskr Laeger. 1952;114:685.

    CAS  PubMed  Google Scholar 

  54. Freinkel N. Banting Lecture 1980. Of pregnancy and progeny. Diabetes. 1980;29:1023–35.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank Deepa Raut, Neelam Memane, Sayali Wadke and Rajashree Kamat for their help in processing cord blood samples and laboratory assays. Madhura Deshmukh for help in study coordination. Mrs. Pallavi Yajnik and Rasika Ladkat for administrative help. Dr. Leelavati Narlikar for her guidance in data analysis. Dr. Satyajit Rath for his advice in writing the discussion. We also thank the co-investigators of the InDiaGDM study Dr. Giriraj Chandak, Kalyanaraman Kumaran, Dr. Gundu Rao for their valuable contribution in conducting the InDiaGDM study.

Funding

The work was supported by InDiaGDM grant of the Department of Biotechnology, New Delhi, India (BT/IN/Denmark/02/CSY/2014) and National Institute of Health (NIH), the U.S government (R21HD094127-01).

Author information

Author notes

  1. These authors contributed equally: Pooja Kunte, Matthew Barberio.

Authors and Affiliations

  1. Diabetes Unit, KEM Hospital Research Centre, Pune, India

    Pooja Kunte, Pradeep Tiwari, Krishna Sukla, Dattatray Bhat & Chittaranjan Yajnik

  2. Diabetes and Islet Biology Group, School of Medicine, Western Sydney University, Sydney, NSW, Australia

    Pooja Kunte

  3. Department of Exercise and Nutrition Sciences, The Milken Institute School of Public Health, George Washington University, Washington, D.C, USA

    Matthew Barberio

  4. Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA

    Pradeep Tiwari

  5. Tata Consultancy Services, Life Sciences Research, Tata Research Development and Design Centre, Pune, India

    Krishna Sukla

  6. Center for Genetic Medicine Research, Children’s National Hospital, Washington, D.C., USA

    Brennan Harmon, Samuel Epstein, Kayla Authelet, Madeleine Goldberg & Robert J. Freishtat

  7. Genotypic Technology Pvt. Ltd., Bangalore, India

    Sudha Rao

  8. S K Navle hospital, Pune, India

    Hemant Damle

  9. Uncommon cures, 5550 Friendship Blvd., Suite 580, Chevy Chase, MD, 2081, USA

    Robert J. Freishtat

Authors
  1. Pooja Kunte
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew Barberio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pradeep Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krishna Sukla
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Brennan Harmon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Samuel Epstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dattatray Bhat
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kayla Authelet
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Madeleine Goldberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sudha Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hemant Damle
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Robert J. Freishtat
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Chittaranjan Yajnik
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CSY designed the InDiaGDM study. CSY and HD contributed to managing the clinical cohort. DB, RKK, BH, ES, MB contributed to exosome extraction and protocol standardization. SR contributed to microRNA data generation. PSK, PT, MB, RKK, KA, MG contributed to the data analysis. PSK, MB, CSY, and RF contributed to the manuscript writing. CSY and RF contributed to the discussion and interpretation of the results. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Robert J. Freishtat or Chittaranjan Yajnik.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kunte, P., Barberio, M., Tiwari, P. et al. Neonatal adiposity is associated with microRNAs in adipocyte-derived extracellular vesicles in maternal and cord blood, a discovery analysis. Int J Obes 48, 403–413 (2024). https://doi.org/10.1038/s41366-023-01432-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-023-01432-z

Search

Advanced search

Quick links