Skip to main content

Novel insights from our special issue on maternal factors during pregnancy that influence maternal, fetal and childhood outcomes

Peer Review reports

As guest editors of BMC Medicine, we issued a call in 2022 for papers that shed critical insights into factors during pregnancy that impact the health of the mother and her offspring. We are now closing submissions for this call and are delighted to announce our finalised collection: 65 new papers offering fresh insights into maternal factors during pregnancy influencing maternal, fetal and childhood outcomes.

Our opening editorial underscored the critical importance of this topic to human health. We pointed out that pregnancy is a time of remarkable physiological and psychological adaptations for both the mother and the developing fetus. Events unfolding during the months of pregnancy can reverberate for many decades on the future health of mother and child: may it be an underperforming placenta, ill-timed exposures to medications during vulnerable gestational windows or developmental tangents taken by the fetus because of variations in the genome or epigenome.

We are very pleased with the final collection, as we had hoped for a breadth of topics and varied research approaches—and we got it. Among the collection are large-scale epidemiological studies, meta-analyses, high-quality cohort studies, one randomised trial and contributions linking laboratory-generated findings (measured on biological samples)—all exploring a fascinating range of questions.

Part of the collection are insightful reviews, including an umbrella review examining risk factors for preterm birth (an adverse outcome that frequently echoes upon the lifelong health of the offspring) [1], a meta-analysis exploring potential links between breastfeeding and mental health conditions in both mother and child [2] and a fascinating narrative review on how in utero fetal programming of the metabolic syndrome may be facilitated through a failure of proper differentiation of fetal adipocytes. This incorrect differentiation limits adipocyte storage capacity, leading to lipids spill-over into the circulation and causing obesity-associated diseases [3].

There are epidemiological studies that used big data—millions of births—to generate new insights, mostly clinical. A nationwide register-based study from Sweden examined just under three million births to investigate links between maternal smoking and type 1 diabetes in the offspring [4]. Interestingly, a reduction in type 1 diabetes incidence was reported among those who smoked, though the authors do not recommend this as a public health intervention. Another population-based cohort study from Sweden reported potential harms associated with pregnant women taking proton pump inhibitors (a treatment for gastric reflux symptoms), including increased risks of preeclampsia, gestational diabetes and the birth of a small for gestational age infant [5]. Another large-scale study from Taiwan studied the potential risks for mothers of antenatal corticosteroid treatment to improve fetal health. The administration of corticosteroids was linked to an increased risk of maternal sepsis, gastrointestinal bleeding and even heart failure (though reassuringly, the absolute risk of the latter outcome remained tiny) [6].

We published one randomised trial that demonstrated the effectiveness of a mobile app, which pregnant women could use to make an appointment to consult remotely with health professionals, in lowering the risk of postpartum depressive symptoms by about one-third [7].

There are also reports describing novel applications of machine learning. Liao et al. applied artificial intelligence on readily obtainable clinical information to predict the treatments needed to achieve glycaemic control for mothers newly diagnosed with gestational diabetes [8]. Abraham and colleagues leveraged dense phenotyping on data captured on electronic health medical records to generate an algorithm to predict preterm birth risk with reasonably good accuracy, including the prediction of important subtypes of spontaneous and medically indicated preterm birth [9]. Interestingly, an artificial intelligence algorithm based on billing codes (administrative data capturing information such as specific diagnoses) outperformed algorithms based on traditional risk factors for preterm birth that clinicians currently rely upon daily.

The collection includes reports describing novel insights gleaned from the analysis of data from cohort (or case-cohort) studies collecting rich phenotypic data, ranging from physiological variables to laboratory measurements on biological samples. Among many examples, some indicated that prenatal social supports in low-risk pregnancies may somehow shape the placental epigenome (methylation differences in genes were observed that may be associated with fetal growth, energy metabolism and neurodevelopment) [10].

Another early report made links between particular bacterial species in the vaginal microbiota in pregnant women with preterm premature rupture of membranes (a high-risk condition associated with infection risk) [11] and the risk of early-onset sepsis of the neonatal soon after birth, and a large cohort study identified umbilical cord blood methylation signatures that predict rapid postnatal weight growth [12]. Interesting findings were also obtained from the Copenhagen Baby Heart Study, concluding that women with elevated body mass index birthed infants with smaller systolic and diastolic left ventricular diameters [13].

The snapshots above provide ample evidence that our collection contains a rich tapestry of diverse findings advancing this exciting area of research. We are proud to note our collection offers proof that authors from all corners of the globe are working for the same cause—to generate critical discoveries so we may better understand the maternal factors on the health and wellbeing of mother and baby and make a clinical impact.

While this collection is complete, BMC Medicine continues to invite submission of papers relevant to all aspects of reproductive health. As a general medical journal aiming to publish translational research with strong clinical impact, we also accept important contributions that have a strong laboratory component (including intensely mechanistic studies and in vivo animal work).

We thank all the authors of papers in this collection for publishing their research with us and BMC Medicine for inviting us to be guest editors. Finally, as continuing members of the editorial board, we hope those in our field will keep the manuscripts rolling in. This call for papers is evidence of the journal’s commitment to supporting pregnancy research. As the flagship journal of the BMC series, we think BMC Medicine is a prestigious place to publish future influential research in this area.

Availability of data and materials

Not applicable.


  1. Mitrogiannis I, Evangelou E, Efthymiou A, Kanavos T, Birbas E, Makrydimas G, Papatheodorou S. Risk factors for preterm birth: an umbrella review of meta-analyses of observational studies. BMC Med. 2023;21(1):494.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Bugaeva P, Arkusha I, Bikaev R, Kamenskiy I, Pokrovskaya A, El-Taravi Y, Caso V, Avedisova A, Chu DK, Genuneit J, et al. Association of breastfeeding with mental disorders in mother and child: a systematic review and meta-analysis. BMC Med. 2023;21(1):393.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Scheidl TB, Brightwell AL, Easson SH, Thompson JA. Maternal obesity and programming of metabolic syndrome in the offspring: searching for mechanisms in the adipocyte progenitor pool. BMC Med. 2023;21(1):50.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wei Y, Andersson T, Edstorp J, Lofvenborg JE, Talback M, Feychting M, Carlsson S. Maternal smoking during pregnancy and type 1 diabetes in the offspring: a nationwide register-based study with family-based designs. BMC Med. 2022;20(1):240.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Breddels EM, Simin J, Fornes R, Lilja Engstrand H, Engstrand L, Bruyndonckx R, Brusselaers N. Population-based cohort study: proton pump inhibitor use during pregnancy in Sweden and the risk of maternal and neonatal adverse events. BMC Med. 2022;20(1):492.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tsai HJ, Wallace BI, Waljee AK, Hong X, Chang SM, Tsai YF, Cheong ML, Wu AC, Yao TC. Association between antenatal corticosteroid treatment and severe adverse events in pregnant women. BMC Med. 2023;21(1):413.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Arakawa Y, Haseda M, Inoue K, Nishioka D, Kino S, Nishi D, Hashimoto H, Kondo N. Effectiveness of mHealth consultation services for preventing postpartum depressive symptoms: a randomized clinical trial. BMC Med. 2023;21(1):221.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Liao LD, Ferrara A, Greenberg MB, Ngo AL, Feng J, Zhang Z, Bradshaw PT, Hubbard AE, Zhu Y. Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study. BMC Med. 2022;20(1):307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Abraham A, Le B, Kosti I, Straub P, Velez-Edwards DR, Davis LK, Newton JM, Muglia LJ, Rokas A, Bejan CA, et al. Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth. BMC Med. 2022;20(1):333.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tesfaye M, Wu J, Biedrzycki RJ, Grantz KL, Joseph P, Tekola-Ayele F. Prenatal social support in low-risk pregnancy shapes placental epigenome. BMC Med. 2023;21(1):12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Dos Anjos Borges LG, Pastuschek J, Heimann Y, Dawczynski K. group Ps, Schleussner E, Pieper DH, Zollkau J: Vaginal and neonatal microbiota in pregnant women with preterm premature rupture of membranes and consecutive early onset neonatal sepsis. BMC Med. 2023;21(1):92.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Alfano R, Zugna D, Barros H, Bustamante M, Chatzi L, Ghantous A, Herceg Z, Keski-Rahkonen P, de Kok TM, Nawrot TS, et al. Cord blood epigenome-wide meta-analysis in six European-based child cohorts identifies signatures linked to rapid weight growth. BMC Med. 2023;21(1):17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Norregaard MMO, Basit S, Sillesen AS, Raja AA, Jorgensen FS, Iversen KK, Bundgaard H, Boyd HA, Vogg ROB. Impact of maternal age and body mass index on the structure and function of the heart in newborns: a Copenhagen Baby Heart Study. BMC Med. 2023;21(1):499.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


We thank Dr. Susanne Kröncke for her outstanding editorial support and her convivial engagement during our guest editorship of this collection.


None of the authors received funding for this editorial.

Author information

Authors and Affiliations



ST wrote the first draft. KB, LM and SO provided critical input. All authors read and approved the final manuscript.

Authors’ Twitter/X handles

@ProfStephenTong, @LouisMuglia. @ozannelab

Corresponding author

Correspondence to Stephen Tong.

Ethics declarations

Ethics approval and consent to participate

Not applicable; individual studies described in this editorial have ethical approvals.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tong, S., Benhalima, K., Muglia, L. et al. Novel insights from our special issue on maternal factors during pregnancy that influence maternal, fetal and childhood outcomes. BMC Med 22, 79 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: