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.

Epidemiology and Population Health

Maternal infection and antibiotic use in pregnancy and the risk of childhood obesity in offspring: a birth cohort study



The reported association between maternal antibiotic use and childhood obesity, if true, could change obstetric practice. However, it is unclear whether the reported association was due to antibiotics, or underlying infection or both. To examine the independent contributions of maternal infection and antibiotic use separately, we conducted a birth cohort study among Kaiser Permanente Northern California (KPNC) members.


The study consisted of 145,393 mother-child dyads. The KPNC electronic medical records provided data on maternal infections, antibiotic use during pregnancy, and longitudinal anthropometric measurements throughout childhood. Obesity was defined by BMI using CDC criteria. Mixed effects logistic regression for repeated measurements was used to analyze multiple BMI measurements per child (five measurements per child on average).


After controlling for confounders using propensity score methodology, there was no increased risk associated with maternal antibiotic use during pregnancy once underlying infection was controlled for: OR = 0.97 (95% CI: 0.92–1.01). There was also no association with timing of use or use of broad-spectrum antibiotics, nor a dose-response relationship. In contrast, maternal untreated infection (without antibiotic use) during pregnancy was associated with a statistically significant risk of childhood obesity compared with mothers without infection: odds ratio (OR) = 1.09 (95% confidence interval (CI): 1.03–1.16). The association was stronger for GBS positive infection (OR = 1.16) than GBS negative infections (OR = 1.08). These results were further confirmed by a discordant sibling study. This discordant sibling study allowed additional control of unmeasured confounders including genetic, maternal intrauterine, and familiar factors. The consistent findings from this sibling study enhances the reproducibility of our findings.


It is maternal infection, NOT antibiotic use, during pregnancy that is associated with increased risk of childhood obesity. While use of antibiotics should always be judicious, in the context of preventing childhood obesity, the focus should be on reducing maternal infections during pregnancy.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Skinner AC, Perrin EM, Skelton JA. Prevalence of obesity and severe obesity in US children, 1999–2014. Obesity. 2016;24:1116–23.

    Article  Google Scholar 

  2. 2.

    Centers for Disease Control and Prevention. Childhood obesity facts. USA: Centers for Disease Control and Prevention; 2015.

  3. 3.

    World Health Organization. Population-based approaches to childhood obesity prevention. Geneva, Switzerland: World Health Organization; 2012.

  4. 4.

    Institute of Medicine (IOM). Accelerating progress in obesity prevention: solving the weight of the nation. Washington, DC: IOM; 2012.

  5. 5.

    Mathur R, Barlow GM. Obesity and the microbiome. Expert Rev Gastroenterol Hepatol. 2015;9:1087–99.

    CAS  Article  Google Scholar 

  6. 6.

    Komaroff AL. The microbiome and risk for obesity and diabetes. JAMA. 2017;317:355–6.

    Article  Google Scholar 

  7. 7.

    Tilg H, Adolph TE. Influence of the human intestinal microbiome on obesity and metabolic dysfunction. Curr Opin Pediatr. 2015;26:496–501.

    Article  Google Scholar 

  8. 8.

    Fukuda S, Ohno H. Gut microbiome and metabolic diseases. Semin Immunopathol. 2014;36:103–14.

    CAS  Article  Google Scholar 

  9. 9.

    Fanaro S, Chierici R, Guerrini P, Vigi V. Intestinal microflora in early infancy: composition and development. Acta Paediatr. 2003;91:48–55.

    CAS  Google Scholar 

  10. 10.

    Penders J, Thijs C, Vink C, Stelma FF, Snijders B, Kummeling I, et al. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics. 2006;118:511–21.

    Article  Google Scholar 

  11. 11.

    Munyaka PM, Khafipour E, Ghia JE. External influence of early childhood establishment of gut microbiota and subsequent health implications. Front Pediatr. 2014;2:109.

    Article  Google Scholar 

  12. 12.

    Kalliomaki M, Collado MC, Salminen S, Isolauri E. Early differences in fecal microbiota composition in children may predict overweight. Am J Clin Nutr. 2008;87:534–8.

    CAS  Article  Google Scholar 

  13. 13.

    Soderborg TK, Borengasser SJ, Barbour LA, Friedman JE. Microbial transmission from mothers with obesity or diabetes to infants: an innovative opportunity to interrupt a vicious cycle. Diabetologia. 2016;59:895–906.

    CAS  Article  Google Scholar 

  14. 14.

    Rautava S. Microbial composition of the initial colonization of newborns. Nestle Nutr Inst Workshop Series. 2017;88:11–21.

    Article  Google Scholar 

  15. 15.

    Dunn AB, Jordan S, Baker BJ, Carlson NS. The maternal infant microbiome: considerations for labor and birth. MCN Am J Matern Child Nurs. 2017;42:318–25.

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Mueller NT, Whyatt R, Hoepner L, Oberfield S, Dominguez-Bello MG, Widen EM, et al. Prenatal exposure to antibiotics, cesarean section and risk of childhood obesity. Int J Obes. 2015;39:665–70.

    CAS  Article  Google Scholar 

  17. 17.

    Mor A, Antonsen S, Kahlert J, Holsteen V, Jorgensen S, Holm-Pedersen J, et al. Prenatal exposure to systemic antibacterials and overweight and obesity in Danish schoolchildren: a prevalence study. Int J Obes. 2015;39:1450–5.

    CAS  Article  Google Scholar 

  18. 18.

    Cassidy-Bushrow AE, Burmeister C, Havstad S, Levin AM, Lynch SV, Ownby DR, et al. Prenatal antimicrobial use and early-childhood body mass index. Int J Obes. 2018;42:1–7.

    CAS  Article  Google Scholar 

  19. 19.

    Poulsen MN, Pollak J, Bailey-Davis L, Hirsch AG, Glass TA, Schwartz BS. Associations of prenatal and childhood antibiotic use with child body mass index at age 3 years. Obesity. 2017;25:438–44.

    CAS  Article  Google Scholar 

  20. 20.

    Wang B, Liu J, Zhang Y, Yan C, Wang H, Jiang F. et al. Prenatal exposure to antibiotics and risk of childhood obesity in a multi-center cohort study. Am J Epidemiol. 2018;187:2159–2167.

    Article  Google Scholar 

  21. 21.

    Centers for Disease Control and Prevention. Prevention of perinatal group B streptococcal disease: revised guidelines from CDC, 2010. Atlanta, GA: Centers for Disease Control and Prevention; 2010.

  22. 22.

    American College of Obstetricians and Gynecologists Committee on Obstetric Practice. ACOG Committee Opinion No. 485: Prevention of early-onset group B streptococcal disease in newborns. Obstet Gynecol. 2011;117:1019–27.

  23. 23.

    Dhurandhar NV. A framework for identification of infections that contribute to human obesity. Lancet Infect Dis. 2011;11:963–9.

    Article  Google Scholar 

  24. 24.

    Deriu E, Boxx GM, He X, Pan C, Benavidez SD, Cen L, et al. Influenza virus affects intestinal microbiota and secondary salmonella infection in the gut through type I interferons. PLoS Pathog. 2016;12:e1005572.

    Article  Google Scholar 

  25. 25.

    Suprunenko T, Hofer MJ. The emerging role of interferon regulatory factor 9 in the antiviral host response and beyond. Cytokine Growth Factor Rev. 2016;29:35–43.

    CAS  Article  Google Scholar 

  26. 26.

    Li DK, Chen H, Ferber JR, Odouli R. Infection and antibiotic use in infancy and risk of childhood obesity: a longitudinal birth cohort study. Lancet Diab Endocrinol. 2016;5:18–25.

    Article  Google Scholar 

  27. 27.

    Cocoros NM, Lash TL, Norgaard M, Farkas DK, DeMaria A Jr., Sorensen HT. Hospitalized prenatal and childhood infections and obesity in Danish male conscripts. Annals Epidemiol. 2013;23:307–13.

    Article  Google Scholar 

  28. 28.

    Gordon NP. Similarity of the adult kaiser permanente membership in northern california to the insured and general population in northern california: statistics from the 2011–12 California Health Interview Survey. Oakland, CA: Kaiser Permanente Division of Research; 2015.

  29. 29.

    Gordon NP. A comparison of sociodemographic and health characteristics of the kaiser permanente northern california membership derived from two data sources: the 2008 Member Health Survey and the 2007 California Health Interview Survey. Oakland, CA: Kaiser Permanente Division of Research; 2012.

  30. 30.

    Centers for Disease Control and Prevention. Body mass index: considerations for practitioners. Atlanta, GA: Centers for Disease Control and Prevention; 2011.

  31. 31.

    Kasai C, Sugimoto K, Moritani I, Tanaka J, Oya Y, Inoue H, et al. Comparison of the gut microbiota composition between obese and non-obese individuals in a Japanese population, as analyzed by terminal restriction fragment length polymorphism and next-generation sequencing. BMC Gastroenterol. 2015;15:100–9.

  32. 32.

    Korpela K, Zijlmans MA, Kuitunen M, Kukkonen K, Savilahti E, Salonen A, et al. Childhood BMI in relation to microbiota in infancy and lifetime antibiotic use. Microbiome. 2017;5:26.

    CAS  Article  Google Scholar 

  33. 33.

    Angelakis E, Armougom F, Million M, Raoult D. The relationship between gut microbiota and weight gain in humans. Future Microbiol. 2012;7:91–109.

    Article  Google Scholar 

  34. 34.

    Bailey LC, Forrest CB, Zhang P, Richards TM, Livshits A, DeRusso PA. Association of antibiotics in infancy with early childhood obesity. JAMA Pediatr. 2014;168:1063–9.

    Article  Google Scholar 

  35. 35.

    Qin N, Zheng B, Yao J, Guo L, Zuo J, Wu L, et al. Influence of H7N9 virus infection and associated treatment on human gut microbiota. Sci Rep. 2015;5:14771.

    CAS  Article  Google Scholar 

  36. 36.

    De Vlaminck I, Khush KK, Strehl C, Kohli B, Luikart H, Neff NF, et al. Temporal response of the human virome to immunosuppression and antiviral therapy. Cell. 2013;155:1178–87.

    Article  Google Scholar 

  37. 37.

    Oever JT, Netea MG. The bacteriome-mycobiome interaction and antifungal host defense. Eur J Immunol. 2014;44:3182–91.

    Article  Google Scholar 

  38. 38.

    Hayes KS, Bancroft AJ, Goldrick M, Portsmouth C, Roberts IS, Grencis RK. Exploitation of the intestinal microflora by the parasitic nematode Trichuris muris. Science. 2010;328:1391–4.

    CAS  Article  Google Scholar 

  39. 39.

    Thompson MG, Li DK, Shifflett P, Sokolow LZ, Ferber JR, Kurosky S, et al. Effectiveness of seasonal trivalent influenza vaccine for preventing influenza virus illness among pregnant women: a population-based case-control study during the 2010–2011 and 2011–2012 influenza seasons. Clin Infect Dis. 2014;58:449–57.

    Article  Google Scholar 

  40. 40.

    Sokolow LZ, Naleway AL, Li DK, Shifflett P, Reynolds S, Henninger ML, et al. Severity of influenza and noninfluenza acute respiratory illness among pregnant women, 2010-2012. Am J Obstet Gynecol. 2015;212:202 e1–11.

    Article  Google Scholar 

  41. 41.

    Zerbo O, Qian Y, Yoshida C, Grether JK, Van de Water J, Croen LA. Maternal infection during pregnancy and autism spectrum disorders. J Autism Dev Disord. 2015;45:4015–25.

    Article  Google Scholar 

  42. 42.

    Barlow SE. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007;120:S164–S92.

    Article  Google Scholar 

  43. 43.

    Kuczmarski RJ, Ogden CL, Guo SS, Grummer-Strawn LM, Flegal KM, Mei Z, et al. 2000 CDC growth charts for the United States: methods and development. Vital Health Stat. 2002;11:1–190.

  44. 44.

    Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22.

    Article  Google Scholar 

  45. 45.

    Hubbard AE, Ahern J, Fleischer NL, Van der Laan M, Lippman SA, Jewell N, et al. To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology. 2010;21:467–74.

    Article  Google Scholar 

  46. 46.

    Hosmer DW, Lemeshow S. Logistic regression, 2nd ed. New York: John Wiley and Sons, Inc.; 2000.

  47. 47.

    Schwartz BS, Pollak J, Bailey-Davis L, Hirsch AG, Cosgrove SE, Nau C, et al. Antibiotic use and childhood body mass index trajectory. Int J Obes. 2016;40:615–21.

  48. 48.

    Wen X, Kleinman K, Gillman MW, Rifas-Shiman SL, Taveras EM. Childhood body mass index trajectories: modeling, characterizing, pairwise correlations and socio-demographic predictors of trajectory characteristics. BMC Med Res Methodol. 2012;12:38

    Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Chivers P, Hands B, Parker H, Beilin L, Kendall G, Bulsara M. Longitudinal modelling of body mass index from birth to 14 years. Obes Facts. 2009;2:302–10.

    Article  Google Scholar 

  50. 50.

    Aker AM, Johns L, McElrath TF, Cantonwine DE, Mukherjee B, Meeker JD. Associations between maternal phenol and paraben urinary biomarkers and maternal hormones during pregnancy: a repeated measures study. Environ Int. 2018;113:341–9.

    CAS  Article  Google Scholar 

  51. 51.

    Glynn RJ, Schneeweiss S, Sturmer T. Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin Pharmacol Toxicol. 2006;98:253–9.

    CAS  Article  Google Scholar 

  52. 52.

    Sturmer T, Joshi M, Glynn RJ, Avorn J, Rothman KJ, Schneeweiss S. A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. J Clin Epidemiol. 2006;59:437–47.

    Article  Google Scholar 

  53. 53.

    Wang J, Li F, Wei H, Lian ZX, Sun R, Tian Z. Respiratory influenza virus infection induces intestinal immune injury via microbiota-mediated Th17 cell-dependent inflammation. J Exp Med. 2014;211:2397–410.

    CAS  Article  Google Scholar 

Download references


This study was funded by The Kaiser Permanente Center for Effectiveness & Safety Research. We also thank Andrew Hirst for his help in data analysis.

Author contributions

D-KL conceived the concept, designed the study, obtained funding, oversaw the data gathering and analyses, and is responsible for the interpretation of results, and drafting and finalizing the manuscript. HC and JF were responsible for data management and analysis, and interpretation of the data. RO was involved in the study management and preparation of the manuscript. D-KL is the guarantor of this paper who took full responsibility for the conduct of the study, had access to the data, and controlled the decision to publish.

Author information



Corresponding author

Correspondence to De-Kun Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

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

Tweet: It is maternal infection, not antibiotic use, during pregnancy that is associated with the risk of childhood obesity.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, DK., Chen, H., Ferber, J. et al. Maternal infection and antibiotic use in pregnancy and the risk of childhood obesity in offspring: a birth cohort study. Int J Obes 44, 771–780 (2020).

Download citation

Further reading


Quick links