Why and How to Improve Nutritional Care for Pregnant Women after Bariatric Surgery: The NUMASURG Study Protocol.

Why and How to Improve Nutritional Care for Pregnant Women after Bariatric Surgery: The NUMASURG Study Protocol - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Obes Facts . 2025 Apr 6;18(4):404–414. doi: 10.1159/000545123 Why and How to Improve Nutritional Care for Pregnant Women after Bariatric Surgery: The NUMASURG Study Protocol Cécile Ciangura a, ✉ , Claire Rives-Lange b, c , Bénédicte Lelièvre d , Martine Laville e , Marie-Aline Charles f, g , Bérénice Ségrestin e, h , Barbara Heude f ; on behalf of the NUMASURG Study Group Author information Article notes Copyright and License information a Nutrition Department, Pitié-Salpêtrière Hospital, AP-HP, Sorbonne Université, Paris, France b Nutrition Department, Georges Pompidou European Hospital, AP-HP, Université Paris Cité, Paris, France c INSERM- INRIA-Université Paris Cité, UMR 1346, HeKA Team, Paris, France d Pharmacology Toxicology Laboratory, Angers University Hospital, Angers, France e F-CRIN - FORCE (French Obesity Research Centre of Excellence), CARMEN Lab INSERM U1060/University of Lyon/INRA U1235, Lyon1 University, Lyon, France f Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France g INED, INSERM EFS, Joint Unit ELFE, Paris, France h Endocrinology-Diabetes-Nutrition Department, Hospices Civils de Lyon, Lyon, France ✉ Correspondence to: Cécile Ciangura, cecile.ciangura@aphp.fr ✉ Corresponding author. Received 2024 Nov 15; Accepted 2025 Feb 11; Collection date 2025 Oct. © 2025 The Author(s). Published by S. Karger AG, Basel This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC) ( http://www.karger.com/Services/OpenAccessLicense ). Usage and distribution for commercial purposes requires written permission. PMC Copyright notice PMCID: PMC12162123  PMID: 40188828 Abstract Introduction Pregnancies after bariatric surgery (BS) are considered at risk because of increased rate of maternal micronutrient deficiencies, small for gestational age, and prematurity. Longer-term data on child health are scarce and conflicting. The objective of the NUMASURG project is to understand the consequences of micronutrient deficiencies during pregnancy after BS for the mother and the offspring and to better document the child’s health outcomes after maternal BS. Methods NUMASURG is a collaborative project bringing together clinicians, biologists, epidemiologists, clinical researchers, and the French Obesity Research Center of Excellence network. The project is organized into four specific tasks: (1) establish a cohort of 1,000 pregnant women with a history of BS; (2) establish reference values for nutritional biomarkers during pregnancy (vitamins A, B9, B12, 25[OH]-D, zinc, ferritin) from two birth cohorts of the French general population (EDEN and ELFE); (3) compare the nutritional status between pregnant women after BS and women from the general population and investigate the associations between nutritional biomarkers and small for gestational age and prematurity in both populations; and (4) describe the health of children born from mothers with a history of BS using the French national health data system. Started in September 2023, the project will run for 4 years. Conclusion The NUMASURG project will allow for implementing updated recommendations concerning the monitoring and nutritional supplementation of pregnant women with a history of BS. Ultimate objective are to improve the nutritional status of these women and consequently limit the main risks currently observed during pregnancy after BS. Structuring a clinical database will help standardize practices and enable future research projects in this area and will be a key first step in creating a prospective cohort of children born from mothers with a history of BS. Keywords: Pregnancy, Child, Bariatric surgery, Micronutrients, Biomarkers Introduction Bariatric surgery (BS) is the most successful therapeutic option for patients with severe obesity and has been increasingly performed worldwide [ 1 – 3 ]. This treatment is associated with sustained weight loss, amelioration and even remission of multiple comorbidities, improved quality of life, and long-term reduction of mortality [ 4 , 5 ]. About 80% of people who undergo BS are women, most of childbearing age [ 1 ]. Consequently, the number of pregnancies after BS is increasing, especially because weight loss improves spontaneous fertility [ 6 ]. BS also reduces adverse outcomes of pregnancy such as maternal hypertension, gestational diabetes, and macrosomia [ 7 , 8 ]. However, emerging evidence suggests that BS is associated with an estimated doubling of the risk of giving birth to a small-for-gestational-age (SGA) neonate (weight ≤10th percentile for gestational age) and a modest increase in premature births (birth term ≤37 gestational weeks [GWs]) [ 7 , 8 ]. Micronutrient deficiencies are among the most common and compelling problems in people undergoing BS [ 9 ]. However, micronutrients are essential for fetal development, and deficiencies in some of them (mainly iron, folates, vitamin B12, and zinc) have been associated with SGA and/or prematurity [ 10 – 15 ]. BS-induced changes in the digestive tract, such as intestinal shunt and reduced gastric acidity and volume, may limit the usual adaptive mechanisms developed during pregnancy to increase micronutrient transfer to the fetoplacental unit and meet the increased requirements during pregnancy [ 10 , 16 ]. Therefore, surgery-induced maternal nutritional deficiencies represent one of the main hypotheses for increased risk of SGA and prematurity after BS but remains to be proven. Specific guidelines emphasize the importance of monitoring maternal nutritional status and supplementation in this context [ 9 , 17 – 19 ]. However, disentangling whether a blood concentration of micronutrients corresponds to deficiencies requiring adjustment of supplementation or simply reflects the hemodilution physiologically induced by pregnancy may be difficult. Previous works have reported the absence of specific reference values during pregnancy for most of the micronutrients included in nutritional monitoring [ 17 , 20 ]. Of note, this shortcoming also constitutes a major pitfall in terms of the risk of micronutrient overload when supplementing at high doses [ 11 , 12 , 21 – 23 ]. Although pregnancy after BS has become increasingly common, little is known about whether and how maternal BS affects the development and health of the next generation, in the mid and long term. Current literature relying mainly on small sample size studies suggests that maternal BS may modify offspring weight status and body composition [ 24 – 26 ] as well as epigenetic, cardiometabolic, immune, inflammatory, and appetite regulation markers [ 27 – 29 ]. Whether these modifications translate to adverse clinical outcomes still needs to be deciphered and more globally raises the question of the benefit-risk balance of this treatment before pregnancy with respect to the offspring’s health and development. Therefore, there is a great need to better understand the links between maternal nutritional status during pregnancy, and fetal growth and offspring health and development after BS. In this context, we have developed the collaborative and multidisciplinary NUMASURG (NUtritional MAternal biomarkers during pregnancy and pregnancy and offspring outcomes: a study in the general population and after bariatric SURGery) project, with a main objective to better understand the consequences of maternal nutritional deficiencies during pregnancy and provide recommendations to improve nutritional care in pregnant women with a history of BS. Methods The NUMASURG project is based on an original collaboration between five academic and clinical teams covering expertise in epidemiology, statistical analysis of cohort and medical-administrative data, biology of micronutrients, clinical management of pregnant women who had BS, and clinical research. It started in September 2023 and will be running for 4 years. This research was funded by the MESSIDORE 2022 research project call, the main objective of which was to financially support multidisciplinary projects addressing clinical issues and exploiting health data or existing biobanks. NUMASURG is structured around the four following specific objectives: (1) establish a cohort of 1,000 pregnant women with a history of BS, gathering clinical and biological routine data (hereafter called the BS cohort); (2) establish reference values of selected micronutrients during pregnancy within the general population; (3) investigate the associations between nutritional biomarkers and birth outcomes in pregnant women from the BS cohort and the general population; and (4) study the postnatal consequences of BS for offspring in terms of hospitalization and recourse to care. The project is organized into four corresponding complementary work packages (WPs) as illustrated in Figure 1 and is described in more detail below. Fig. 1. Open in a new tab Conceptual framework of the NUMASURG project on improving nutritional care for pregnant women after BS. WP1: Establishing a Cohort of 1,000 Pregnant Women with a History of BS – The BS Cohort In France, a large proportion of pregnant women with a history of BS are followed in Centers Specialized for Obesity (CSOs) [ 30 ]. We will extract relevant data collected in clinical routine care from the medical files of these centers and set up a structured database. We previously conducted a survey of the 37 French CSOs to ensure data collection feasibility and discussed its results with our board consisting of scientists and healthcare providers in order to consolidate the most relevant data to be collected. The data are listed in Table 1 . We aim to prospectively include 1,000 singleton pregnant women over a 2-year period. If this number is not reached after 18 months, we will complete the cohort with retrospective data collection from women whose pregnancy occurred after 2019, the year that national guidelines for pregnancy management after BS were published in France [ 17 ]. We expect to enroll women who have had mainly sleeve or gastric bypass surgery, the two most common procedures in France, but all types of surgery could be included, including revisional surgeries (i.e., secondary bariatric interventions). Only data from routine care will be collected, and no specific intervention will be added. The detailed protocol and descriptive statistics of this BS cohort will be shared with the clinical and scientific community as a report or a scientific publication. Table 1. Data collected for the BS cohort, general population cohorts (EDEN and ELFE), and the French National Heath Data System (SNDS) cohort BS cohort EDEN ELFE SNDS Bariatric surgery ✓ Not considered Not considered ✓ Type of surgery, date of surgery, time between surgery and pregnancy, complications Medical history ✓ ✓ ✓ ✓ Pregestational diabetes, pregestational hypertension, chronic illness, treatment Sociodemographic factors ✓ ✓ ✓ ✓ Age, social insurance/social deprivation area, parity, height + country of origin and profession + country of origin, profession, and household income + country of origin, profession, and household income Lifestyle ✓ ✓ ✓ ∅ Smoking, alcohol or other toxic substance intake, dietary intake, micronutrient supplementation, medications + physical activity + physical activity Maternal ponderal history ✓ ✓ ✓ ✓ Prepregnancy weight, gestational weight gain Weight before BS, before and during pregnancy, maximal in the life, and nadir after BS (medical record) Reported by the mothers Reported by the mothers Class of obesity during pregnancy only Father characteristics ✓ ✓ ✓ ∅ Smoking, weight, height, age + dietary intake Nutritional status ✓ ✓ ✓ ∅ Each trimester from first appointment: hemoglobin, ferritin, vitamin B12, folates, calcium, vitamin D and parathyroid hormone, albumin and prealbumin (transthyretin), retinol and retinol binding protein, prothrombin as proxy for vitamin K, ionography, zinc, selenium 24–28 GWs 37–41 GWs Ferritin, vitamin B12, folates, vitamin D, retinol and retinol binding protein, zinc Ferritin, vitamin B12, folates, vitamin D, retinol and retinol binding protein, zinc Biomarker analysis methods ✓ ✓ ✓ ∅ Ultrasound fetal growth parameters ✓ ✓ ✓ ∅ 22, 32, 37 GWs 22, 32 GWs 22, 32 GWs Pregnancy-related maternal outcomes ✓ ✓ ✓ ✓ Reproductive history, gravid hypertension, preeclampsia, gestational diabetes mellitus Pregnancy-related obstetrical outcomes ✓ ✓ ✓ live births only ✓ Miscarriage, stillbirth, termination of pregnancy, elective abortion, ectopic pregnancy, hydatidiform mole, delivery, cesarean, post-partum hemorrhage Pregnancy-related neonatal outcomes ✓ ✓ ✓ ✓ Sex, birth weight, height, and head circumference, gestational age at birth, perinatal asphyxia, hypoglycemia, APGAR score, malformation, neonatal unit transfer, neonatal death Except APGAR score and head circumference Maternal and neonatal post-partum outcomes ✓ ✓ ✓ ∅ At 2 months of life, weight and height of the neonate, breastfeeding, post-partum depressive symptoms, hospitalization Child outcomes ∅ ✓ ✓ ✓ Hospital diagnoses, mortality, hospitalizations, number/type of medical and paramedical consultations Until 11 years Until 11 years Until 10 years + medication consumption Open in a new tab GW, gestational week. WP2: Nutritional Biomarkers in Pregnant Women with and without a History of BS This WP will rely on data and samples from two French birth cohorts, EDEN (Mother-child cohort on the prenatal and early postnatal determinants of child health and development) and ELFE (the French national cohort of children), that included women during pregnancy between 2003 and 2006 for EDEN or at delivery in 2011 for ELFE [ 31 , 32 ]. Their study protocols have been described in detail elsewhere [ 31 , 32 ] and are briefly presented in Table 2 . These cohorts are important sources of biological collection and clinical data on mothers and offspring within the general population. Collected informations include lifestyle during pregnancy as well as pregnancy- and child-related outcomes and are listed in more detail in Table 1 . Blood serum and plasma were collected between 24 and 28 GWs in EDEN and at delivery (between 33 and 41 GWs) in ELFE and frozen at −80°C. Constraints related to the quantity of available biological material from the above-mentioned cohorts and the cost of biological analyses necessitated the selection of limited number of nutritional markers for this specific objective: we will prioritize micronutrients for which maternal deficits have been consistently associated with severe neonatal or maternal consequences in the literature (within the general population), such as iron estimated by ferritin; folates; vitamins A, B12, and D; and zinc [ 11 , 33 ]. Table 2. Summary of EDEN and ELFE birth cohorts EDEN (Heude et al. [ 32 ], 2016) ELFE (Charles et al. [ 31 ], 2020) Enrolled population 2,002 pregnant women from two regional university hospitals (Nancy, Poitiers) enrolled before 24 GWs 18,040 pregnant women from a representative sample of maternity units in mainland France enrolled at their child’s birth after 33 GWs Nutritional data Prepregnancy weight reported by the mothers, measured height at clinical exam during pregnancy, food frequency questionnaire on usual diet before and in the last 3 months of pregnancy, gestational weight gain from obstetrical file Prepregnancy weight and height reported by the mother, food frequency questionnaire on usual diet in the last 3 months of pregnancy, gestational weight gain reported by mother Pregnancy outcomes Recorded from obstetrical and infant medical files Recorded from obstetrical and infant medical files Socio-economic status and way of life Maternal questionnaire during pregnancy and after delivery Maternal questionnaire after delivery and at 2-month follow-up Available biological samples for the project 1,700 plasma samples collected between 24 and 28 GWs and stored for 17–20 years at −80°C 2,100 plasma samples collected at delivery (33–41 GWs) and stored for 11 years at −196°C More information https://eden.vjf.inserm.fr www.elfe-france.fr Open in a new tab GW, gestational week. As a preliminary step before conducting biological analyses, we will review the different analytic methods and select the most reliable and widely used techniques in routine care in laboratories. Reliability will be assessed by inter- and intra-variability of the analytic methods, internal quality controls, and the external quality programs. The samples from the EDEN and ELFE cohorts will be selected according to appropriate preanalytical criteria (e.g., time from collection to storage). About 1,500 serum and/or plasma samples in EDEN and about the same number in ELFE will be analyzed. Because of the large number of samples, they will be distributed among three hospital laboratories. To select the appropriate population of women from which reference values will be derived, we will exclude women with a history of BS, body mass index (BMI) <18 kg/m 2 or >25 kg/m 2 , low diet quality (low adherence to the French nutritional recommendations from the Programme National Nutrition Santé [PNNS]), and pregnancy complications [ 34 – 36 ]. We will derive reference values, expressed as means, standard deviations, and percentiles using the Lambda-Mu-Sigma method at each available GW [ 37 , 38 ]. This method is generally used to derive childhood growth references or standards [ 39 , 40 ]. According to these references, we will derive thresholds permitting to define abnormal values and especially assess prevalences of deficiencies related to all micronutrients of interest in our BS cohort. WP3: Association between Nutritional Biomarkers and Birth Outcomes in Pregnant Women after BS and Women from the General Population This WP will investigate the association between nutritional biomarker values during pregnancy and birth outcomes in both the BS and general population (all EDEN and ELFE women) cohorts. An essential primary step will be to better characterize the factors associated with variation in micronutrient concentrations in these two populations. Using multiple linear regression models, we will investigate potential determinants of variations among sociodemographic, lifestyle (including diet) or pregnancy-related factors, weight status, micronutrient supplementation, biological dosage methods used in routine care and factors specific to BS including type of surgery and time between surgery and pregnancy ( Table 1 ). Correlations between concentrations of the different micronutrients will be described and the determinants of their covariation explored. Second, outcomes of interest will be gestational age at birth and birth size for gestational age, considered both as continuous and binary variables (premature birth [before 37 GWs] and SGA [weight ≤10th percentile for gestational age]). The shape of the association between each micronutrient value and outcomes will be described by cubic splines to detect potential threshold or ceiling effects in different groups depending on the dietary supplementation history. Other potential determinants of the outcomes will be identified in the literature and taken into account by multivariable analyses after careful selection by directed acyclic graphs [ 41 ]. Interactions between micronutrient values and prepregnancy BMI, type of surgery, and other potentially relevant moderators will be tested. The association between the combined status of all micronutrients studied and outcomes will also be explored using appropriate statistical techniques to take into account their potential collinearity. Missing data will be considered and imputation techniques applied when appropriate. The number of participants in the general and post-BS populations provides the necessary power (80%) to identify a significant association (alpha risk of 5%), for example, between micronutrient deficiency and risk of SGA, if this association exists and the odds ratio is at least 1.7. WP4: Consequences of a History of BS before Pregnancy for Offspring The objective of this WP is to compare pregnancy-related complications and offspring outcomes for mothers with or without a history of BS. We will use comprehensive data from the French national health data system (Système National des Données de Sante [SNDS]), which covers 99% of people residing in France (>66 million inhabitants). This database provides a unique anonymous identifier for each patient and since 2012 another identifier linking each newborn to their mother. It contains administrative and demographic data, diagnoses coded using the International Classification of Diseases, 10th Revision [ 42 ], procedures performed in private or public hospitals in France, and healthcare consumption outside hospitals covered by the national health insurance. Outcomes for pregnancies with delivery beyond 22 GWs will include gestational diabetes, gestational hypertension, preeclampsia or eclampsia, SGA, large for gestational age (weight ≥90th percentile for gestational age), termination of pregnancy, prematurity, stillbirth, and perinatal mortality (<7 days of life). Outcomes for pregnancies with delivery before 22 GWs will include elective abortion, termination of pregnancy, ectopic pregnancy, miscarriage, and hydatidiform mole. Outcomes for long-term health of offspring (up to 10 years of life) will include mortality, number and causes of hospitalizations, number/type of medical and paramedical consultations, and medication consumption. Pregnancy- and birth-related outcomes of mothers with or without a history of BS will be compared according to two distinct and complementary study designs, as described below. For the first analysis, data for all women with post-BS pregnancies occurring from January 2013 to December 2022, and their children, will be compared with data from control pregnancies (and children) matched on end-of-pregnancy date, age, BMI at pregnancy, pre-pregnancy diabetes (type 1 and type 2), hypertension, socio-economic status, and previous parity. The outcomes listed above will be compared using generalized estimating equations models. For the second analysis, all women who had a pregnancy both before and after BS from January 2013 to December 2022, and their children, will be included. Each woman will then serve as her own control, and post-BS pregnancies (and related children) will be compared to pre-BS pregnancies (and related children) by using conditional logistic regression models. A counterfactual analysis using two consecutive pre-BS pregnancies will be used to identify results that could be confounded by age and parity changes from pre- to post-BS pregnancies. Outcomes for children’s long-term health will be studied by comparing the frequencies of all child diagnoses during hospitalizations, mortality, and healthcare consumption by an outcome-wide association approach in the two previous designs [ 43 ]. Multiple comparison adjustments will involve using the false discovery rate method. Mediation analysis will be used for child-related outcomes to identify those potentially mediated by SGA or prematurity. Subgroup analyses will involve the type of BS, time from BS to pregnancy, and malnutrition from BS to pregnancy. Discussion Expected Results This ambitious collaborative project will provide important insights into the nutritional status of pregnant women with a history of BS, the usual values of micronutrients during pregnancy for comparison, and the associations between BS, nutrition, and pregnancy and child outcomes. It will allow for updating evidence-based clinical practice recommendations for optimized nutritional care of pregnant women after BS and hopefully limit the risk of SGA, prematurity, and adverse long-term outcomes. Finally, it will provide for the establishment of a long-term routine clinical database that will serve as a tool to optimize nutritional care for pregnant women after BS. The multidisciplinary partners (epidemiology, research, clinical, biology) have already worked and collaborated in the field of BS and pregnancy or BS and micronutrient deficiencies [ 8 , 17 , 22 , 44 – 46 ]. They are strongly involved in key research networks and scientific societies that will provide fertile ground for the wide dissemination and valorization of results at the national and international levels. Strengths and What This Project Will Add Current evidence is scarce regarding the role of BS-induced malnutrition or micronutrient deficiencies in the increased risk of SGA and prematurity observed in women with a history of BS. Recent reviews on maternal nutrition during pregnancy after BS did not investigate or identify associations between micronutrient deficiencies and fetal growth [ 10 , 16 ]. Some cohorts showed a negative association with risk of SGA when mothers received nutritional advice but only few associations with nutritional biomarkers, the main hypothesis being lack of power [ 22 , 47 , 48 ]. The organization of clinical care for patients with obesity around a CSO and the national coordination of the clinical research by the French Obesity Research Centre of Excellence (FORCE) are two strengths for the constitution of the BS cohort [ 30 ]. By combining the EDEN and ELFE general-population cohorts and the BS cohort, we expect data from approximately 4,000 women, including the dosages of at least ferritin; vitamins A, B9, B12, and D; and zinc, the value of which will cover two different periods of pregnancy, second and third trimesters. This unique worldwide dataset will provide substantial statistical power to thoroughly investigate the associations between nutritional biomarkers and the risk of SGA or prematurity, within the specific context of BS. Although physiological modifications mostly induced by hormonal variations in pregnancy are well established and associated with variations in biological values, most if not all laboratories provide no reference ranges specific to pregnancy. An exhaustive review of studies published between 1975 and 2008 provided reference values during pregnancy for a number of biomarkers [ 20 ]. The included studies were conducted in different geographical areas and ethnic groups, relied on discrepant pre-analytical conditions including various timing of blood samples and assay methods. Such heterogeneity may explain why the published usual values are too wide and not fully suitable to define micronutrient deficiency in clinical routine care. We believe that the large number of biological samples available from the EDEN and ELFE cohorts for two different periods of pregnancy, the screening of very healthy women, the attention to preanalytical conditions, and the restricted choice of reliable biological techniques will allow for determining robust reference values during the second and third trimesters of pregnancy. These reference values are essential for the appropriate treatment of women exposed to deficiencies in the post-BS context while avoiding overdosing. Previous studies of the health and development of children born to mothers with a history of BS mainly focused on weight and metabolic outcomes [ 24 – 26 ] and did not allow for demonstrating a protective effect of BS on childhood overweight and obesity risks. The recent Belgium EFFECTOR cohort of 36 children born after maternal BS enlarged the evaluated outcomes to eating habits [ 49 ], psychomotor and behavior outcomes [ 28 ], and endothelial function [ 50 ]. In NUMASURG, we will conduct analyses at a national scale, in real life, on a very large nonselected comprehensive population of more than 50,000 children born after BS, for the largest cohort from a medical-administrative database in this field to date. The absence of an a priori hypothesis will contribute to confirm associations already suggested in the literature and highlight new ones. Limitations The first limitation is that selection biases may occur in both the EDEN and ELFE cohorts and the BS cohort. Indeed, participants from higher socio-economic levels are over-represented in population-based cohorts such as EDEN and ELFE [ 31 , 32 ]. Regarding the BS cohort, exclusive recruitment from the CSOs may also select women particularly concerned by nutritional follow-up or conversely women who are referred because of a complex medical situation. Therefore, we will need to interpret our results with caution before the generalization of our recommendations. Finally, some limitations will be inherent to the nature of the SNDS database. We will not have exhaustive data on clinical aspects or the indication for reimbursed treatments, which will limit the search for certain diagnoses. Conclusion and Perspectives The NUMASURG project will allow for determining the reference values and micronutrient targets during pregnancy and better understanding the consequences of maternal nutritional deficiency during pregnancy after BS. These new insights will allow for updating guidelines regarding the optimal set of maternal, obstetric, and pediatric clinical and biological factors to be monitored in routine care. These guidelines will detail how to assess the maternal nutritional status, the biological targets for micronutrients, and the supplementation protocols for preventing and correcting micronutrient deficiencies. We hope that these recommendations will help limit the risks of SGA and prematurity observed in pregnancy after BS and improve long-term health outcomes. Structuring a standardized clinical database within each CSO will enable future research projects in this field and is a first step in the constitution of a prospective cohort of children born after BS. Acknowledgments The authors thank Mrs Nesrine Zaabar, Project Manager of FORCE Network, all clinicians in the CSOs, biological laboratories and obstetrics departments involved in the data collection of BS cohort. The authors thank the scientific coordinators (B. Geay, H. Léridon, C. Bois, J.L. Lanoé, X. Thierry, and C. Zaros), IT employees and data managers, statisticians (M. Cheminat, C. Ricourt, A. Candea, and S. de Visme), administrative and family communication staff, and study technicians (C. Guevel, M. Zoubiri, L.G.L. Gravier, I. Milan, and R. Popa) from the ELFE coordination team. The authors are also grateful to the families who gave their consent to participate in the EDEN and ELFE cohorts. Statement of Ethics The database of pregnancies after BS established in the CSOs will be hosted with all the security and accessibility requirements for personal data hosting in compliance with General Data Protection Regulation, after submission to the Scientific and Ethics Committee, along with a declaration of conformity to the required ethics regulation (MR004) to the French data protection authority (CNIL). Regarding the EDEN cohort study, written informed consent was obtained twice from parents: once at enrollment for their own participation and once after delivery for the participation of their child. The study received approval from the Ethics Committee (CCPPRB, No. 02-70) of Kremlin Bicêtre on 12 December 2002 and from CNIL (Commission Nationale Informatique et Liberté), the French data privacy institution. Implication of the EDEN cohort data and biological samples in the NUMASURG project has been approved by the EDEN steering committee on 12 October 2022. Regarding the ELFE cohort study, written informed consent was obtain from parents for their own and their child’s participation after delivery. The ELFE study received approvals from the Advisory Committee for the Processing of Information for Health Research (Comité Consultatif sur le Traitement des Informations pour la Recherche en Santé: CCTIRS; authorization 10.623), the committee for protection of persons engaged in research (CPP: Comité de Protection des Personnes; authorization CPP-IDF IX-11-024), the National Data Protection Authority (Commission National Informatique et Libertés: CNIL, authorization 910504), and the National Council for Statistical Information (CNIS). Implication of the ELFE cohort data and biological samples in the NUMASURG project has been approved by the Elfe biological subcommittee of the scientific council on 14 September 2023. Regarding the SNDS, the project is registered with the SNDS access rights authority and the Health Data Hub (No. 16515049). The Health Data Hub and the National Health Insurance Fund ensure subject information and rights compliance with the Public Health Code (Article R1461-9). Ethical approval and consent were not required as this study was based on publicly available data, according to French regulation on research reusing the SNDS database. Conflict of Interest Statement The authors have no conflicts of interest to declare. Funding Sources This study was supported by a grant from INSERM and the French Ministry of Health (Messidore 2022, No. 97 – innovative methodologies, devices, tools, and research in clinical trials using health data and biobanks). The EDEN study was supported by the Foundation for Medical Research (FRM), National Agency for Research (ANR), National Institute for Research in Public health (IRESP: TGIR cohorte santé 2008 program), French Ministry of Health (DGS), French Ministry of Research, INSERM Bone and Joint Diseases National Research (PRO-A), and Human Nutrition National Research Programs, Paris-Sud University, Nestlé, French National Institute for Population Health Surveillance (InVS), French National Institute for Health Education (INPES), the European Union FP7 programmes (FP7/2007–2013, HELIX, ESCAPE, ENRIECO, Medall projects), Diabetes National Research Program (through a collaboration with the French Association of Diabetic Patients), French Agency for Environmental Health Safety (now ANSES), Mutuelle Générale de l’Education Nationale – a complementary health insurance (MGEN), French national agency for food security, and French-speaking association for the study of diabetes and metabolism (ALFEDIAM). The ELFE survey is a joint project between the French Institute for Demographic Studies (INED) and the National Institute of Health and Medical Research (INSERM), in partnership with the French blood transfusion service (Etablissement français du sang, EFS), Santé publique France, National Institute for Statistics and Economic Studies (INSEE), Direction générale de la santé (DGS, part of the Ministry of Health and Social Affairs), Direction générale de la prévention des risques (DGPR, Ministry for the Environment), Direction de la recherche, des études, de l’évaluation et des statistiques (DREES, Ministry of Health and Social Affairs), Département des études, de la prospective et des statistiques (DEPS, Ministry of Culture), and Caisse nationale des allocations familiales (CNAF), with the support of the Ministry of Higher Education and Research and the Institut national de la jeunesse et de l’éducation populaire (INJEP). Via the RECONAI platform, it receives a government grant managed by the National Research Agency under the “Investissements d'avenir” program (ANR-11-EQPX-0038). The funders had no role in the design, data collection, data analysis, and reporting of this study. Author Contributions C.C., C.R.-L., B.L., M.L., M.-A.C., B.S., and B.H. designed the research and contributed to fundraising. Members of the NUMASURG study group participated in the conception of the project. C.C. and B.H. wrote the first draft of manuscript. All authors reviewed the draft, provided critical feedback, and approved the final version of the manuscript. The NUMASURG study group members are cited in the online supplementary material (for all online suppl. material, see https://doi.org/10.1159/000545123 ). Funding Statement This study was supported by a grant from INSERM and the French Ministry of Health (Messidore 2022, No. 97 – innovative methodologies, devices, tools, and research in clinical trials using health data and biobanks). The EDEN study was supported by the Foundation for Medical Research (FRM), National Agency for Research (ANR), National Institute for Research in Public health (IRESP: TGIR cohorte santé 2008 program), French Ministry of Health (DGS), French Ministry of Research, INSERM Bone and Joint Diseases National Research (PRO-A), and Human Nutrition National Research Programs, Paris-Sud University, Nestlé, French National Institute for Population Health Surveillance (InVS), French National Institute for Health Education (INPES), the European Union FP7 programmes (FP7/2007–2013, HELIX, ESCAPE, ENRIECO, Medall projects), Diabetes National Research Program (through a collaboration with the French Association of Diabetic Patients), French Agency for Environmental Health Safety (now ANSES), Mutuelle Générale de l’Education Nationale – a complementary health insurance (MGEN), French national agency for food security, and French-speaking association for the study of diabetes and metabolism (ALFEDIAM). The ELFE survey is a joint project between the French Institute for Demographic Studies (INED) and the National Institute of Health and Medical Research (INSERM), in partnership with the French blood transfusion service (Etablissement français du sang, EFS), Santé publique France, National Institute for Statistics and Economic Studies (INSEE), Direction générale de la santé (DGS, part of the Ministry of Health and Social Affairs), Direction générale de la prévention des risques (DGPR, Ministry for the Environment), Direction de la recherche, des études, de l’évaluation et des statistiques (DREES, Ministry of Health and Social Affairs), Département des études, de la prospective et des statistiques (DEPS, Ministry of Culture), and Caisse nationale des allocations familiales (CNAF), with the support of the Ministry of Higher Education and Research and the Institut national de la jeunesse et de l’éducation populaire (INJEP). Via the RECONAI platform, it receives a government grant managed by the National Research Agency under the “Investissements d'avenir” program (ANR-11-EQPX-0038). The funders had no role in the design, data collection, data analysis, and reporting of this study. Data Availability Statement The data used in this project will not be made publicly available as they are personal data containing sensitive information. However, data access may be granted upon reasonable request by contacting the corresponding author. Each request will be reviewed by a cohort-specific steering committee, and data-sharing agreements ensuring compliance with GDPR regulations will be signed between the parties. Supplementary Material. Supplementary_material-Suppl.1-s1.xlsx (18.1KB, xlsx) References 1. Angrisani L, Santonicola A, Iovino P, Ramos A, Shikora S, Kow L. Bariatric surgery survey 2018: similarities and disparities among the 5 IFSO chapters. Obes Surg. 2021;31(5):1937–48. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Lazzati A. Epidemiology of the surgical management of obesity. J Visc Surg. 2023;160(2S):S3–6. [ DOI ] [ PubMed ] [ Google Scholar ] 3. Philippe OBERLIN et Christine de PERETTI . Chirurgie de l’obésité: 20 fois plus d’interventions depuis. 1997. 2018 Feb [cited 2024 May 27]. Available from: https://drees.solidarites-sante.gouv.fr/sites/default/files/2020-10/er1051.pdf [ Google Scholar ] 4. Sjöström L, Lindroos A-K, Peltonen M, Torgerson J, Bouchard C, Carlsson B, et al. Lifestyle, diabetes, and cardiovascular risk factors 10 years after bariatric surgery. N Engl J Med. 2004;351(26):2683–93. [ DOI ] [ PubMed ] [ Google Scholar ] 5. Sjöström L, Narbro K, Sjöström CD, Karason K, Larsson B, Wedel H, et al. Effects of bariatric surgery on mortality in Swedish obese subjects. N Engl J Med. 2007;357(8):741–52. [ DOI ] [ PubMed ] [ Google Scholar ] 6. Samarasinghe SNS, Leca B, Alabdulkader S, Dimitriadis GK, Davasgaium A, Thadani P, et al. Bariatric surgery for spontaneous ovulation in women living with polycystic ovary syndrome: the BAMBINI multicentre, open-label, randomised controlled trial. Lancet. 2024;403(10443):2489–503. [ DOI ] [ PubMed ] [ Google Scholar ] 7. Kwong W, Tomlinson G, Feig DS. Maternal and neonatal outcomes after bariatric surgery; a systematic review and meta-analysis: do the benefits outweigh the risks? Am J Obstet Gynecol. 2018;218(6):573–80. [ DOI ] [ PubMed ] [ Google Scholar ] 8. Rives-Lange C, Poghosyan T, Phan A, Van Straaten A, Girardeau Y, Nizard J, et al. Risk-benefit balance associated with obstetric, neonatal, and child outcomes after metabolic and bariatric surgery. JAMA Surg. 2023;158(1):36–44. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Busetto L, Dicker D, Azran C, Batterham RL, Farpour-Lambert N, Fried M, et al. Practical recommendations of the obesity management task force of the European association for the study of obesity for the post-bariatric surgery medical management. Obes Facts. 2017;10(6):597–632. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Bretón I, Ballesteros-Pomar MD, Calle-Pascual A, Alvarez-Sala LA, Rubio-Herrera MA. Micronutrients in pregnancy after bariatric surgery: a narrative review. J Clin Med. 2023;12(16):5429. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Berti C, Biesalski HK, Gärtner R, Lapillonne A, Pietrzik K, Poston L, et al. Micronutrients in pregnancy: current knowledge and unresolved questions. Clin Nutr. 2011;30(6):689–701. [ DOI ] [ PubMed ] [ Google Scholar ] 12. Lyon P, Strippoli V, Fang B, Cimmino L. B vitamins and one-carbon metabolism: implications in human health and disease. Nutrients. 2020;12(9):2867. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Alwan NA, Cade JE, McArdle HJ, Greenwood DC, Hayes HE, Simpson NAB. Maternal iron status in early pregnancy and birth outcomes: insights from the Baby’s Vascular health and Iron in Pregnancy study. Br J Nutr. 2015;113(12):1985–92. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Theodoratou E, Tzoulaki I, Zgaga L, Ioannidis JPA. Vitamin D and multiple health outcomes: umbrella review of systematic reviews and meta-analyses of observational studies and randomised trials. BMJ. 2014;348:g2035. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Atazadegan MA, Heidari-Beni M, Riahi R, Kelishadi R. Association of selenium, zinc and copper concentrations during pregnancy with birth weight: a systematic review and meta-analysis. J Trace Elem Med Biol. 2022;69:126903. [ DOI ] [ PubMed ] [ Google Scholar ] 16. Alamri SH, Abdeen GN. Maternal nutritional status and pregnancy outcomes post-bariatric surgery. Obes Surg. 2022;32(4):1325–40. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Ciangura C, Coupaye M, Deruelle P, Gascoin G, Calabrese D, Cosson E, et al. Clinical practice guidelines for childbearing female candidates for bariatric surgery, pregnancy, and post-partum management after bariatric surgery. Obes Surg. 2019;29(11):3722–34. [ DOI ] [ PubMed ] [ Google Scholar ] 18. Mechanick JI, Youdim A, Jones DB, Garvey WT, Hurley DL, McMahon MM, et al. Clinical practice guidelines for the perioperative nutritional, metabolic, and nonsurgical support of the bariatric surgery patient: 2013 update: cosponsored by American Association of Clinical Endocrinologists, the Obesity Society, and American Society for Metabolic & Bariatric Surgery. Obes Silver Spring Md. 2013;21 Suppl 1(0 1):S1–27. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Shawe J, Ceulemans D, Akhter Z, Neff K, Hart K, Heslehurst N, et al. Pregnancy after bariatric surgery: consensus recommendations for periconception, antenatal and postnatal care. Obes Rev. 2019;20(11):1507–22. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Abbassi-Ghanavati M, Greer LG, Cunningham FG. Pregnancy and laboratory studies: a reference table for clinicians. Obstet Gynecol. 2009;114(6):1326–31. [ DOI ] [ PubMed ] [ Google Scholar ] 21. Bastos Maia S, Rolland Souza AS, Costa Caminha MF, Lins da Silva S, Callou Cruz RSBL, Carvalho Dos Santos C, et al. Vitamin A and pregnancy: a narrative review. Nutrients. 2019;11(3):681. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Coupaye M, Legardeur H, Sami O, Calabrese D, Mandelbrot L, Ledoux S. Impact of Roux-en-Y gastric bypass and sleeve gastrectomy on fetal growth and relationship with maternal nutritional status. Surg Obes Relat Dis. 2018;14(10):1488–94. [ DOI ] [ PubMed ] [ Google Scholar ] 23. Hwang J-Y, Lee J-Y, Kim K-N, Kim H, Ha E-H, Park H, et al. Maternal iron intake at mid-pregnancy is associated with reduced fetal growth: results from Mothers and Children’s Environmental Health (MOCEH) study. Nutr J. 2013;12:38. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Kral JG, Biron S, Simard S, Hould FS, Lebel S, Marceau S, et al. Large maternal weight loss from obesity surgery prevents transmission of obesity to children who were followed for 2 to 18 years. Pediatrics. 2006;118(6):e1644–9. [ DOI ] [ PubMed ] [ Google Scholar ] 25. Willmer M, Berglind D, Sørensen TIA, Näslund E, Tynelius P, Rasmussen F. Surgically induced interpregnancy weight loss and prevalence of overweight and obesity in offspring. PLoS One. 2013;8(12):e82247. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Smith J, Cianflone K, Biron S, Hould FS, Lebel S, Marceau S, et al. Effects of maternal surgical weight loss in mothers on intergenerational transmission of obesity. J Clin Endocrinol Metab. 2009;94(11):4275–83. [ DOI ] [ PubMed ] [ Google Scholar ] 27. Damti P, Friger M, Landau D, Sergienko R, Sheiner E. Offspring of women following bariatric surgery and those of patients with obesity are at an increased risk for long-term pediatric endocrine morbidity. Arch Gynecol Obstet. 2019;300(5):1253–9. [ DOI ] [ PubMed ] [ Google Scholar ] 28. Van De Maele K, Bogaerts A, De Schepper J, Provyn S, Ceulemans D, Guelinckx I, et al. Adiposity, psychomotor and behaviour outcomes of children born after maternal bariatric surgery. Pediatr Obes. 2021;16(5):e12749. [ DOI ] [ PubMed ] [ Google Scholar ] 29. Yu Y, Lyo V, Groth SW. The impact of maternal bariatric surgery on long-term health of offspring: a scoping review. Pediatr Res. 2023;94(5):1619–30. [ DOI ] [ PubMed ] [ Google Scholar ] 30. Les centres spécialisés obésité (CSO). [cited 2024 May 28]. Available from: https://sante.gouv.fr/soins-et-maladies/prises-en-charge-specialisees/obesite/article/les-centres-specialises-obesite-cso [ Google Scholar ] 31. Charles MA, Thierry X, Lanoe J-L, Bois C, Dufourg M-N, Popa R, et al. Cohort Profile: the French national cohort of children (ELFE): birth to 5 years. Int J Epidemiol. 2020;49(2):368–9j. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Heude B, Forhan A, Slama R, Douhaud L, Bedel S, Saurel-Cubizolles M-J, et al. Cohort Profile: the EDEN mother-child cohort on the prenatal and early postnatal determinants of child health and development. Int J Epidemiol. 2016;45(2):353–63. [ DOI ] [ PubMed ] [ Google Scholar ] 33. Ladipo OA. Nutrition in pregnancy: mineral and vitamin supplements. Am J Clin Nutr. 2000;72(1 Suppl l):280S–90S. [ DOI ] [ PubMed ] [ Google Scholar ] 34. Chaltiel D, Adjibade M, Deschamps V, Touvier M, Hercberg S, Julia C, et al. Programme National Nutrition Santé: guidelines score 2 (PNNS-GS2): development and validation of a diet quality score reflecting the 2017 French dietary guidelines. Br J Nutr. 2021;125(1):118–20. [ DOI ] [ PubMed ] [ Google Scholar ] 35. Hercberg S, Chat-Yung S, Chaulia M. The French national nutrition and health program: 2001-2006-2010. Int J Public Health. 2008;53(2):68–77. [ DOI ] [ PubMed ] [ Google Scholar ] 36. Kadawathagedara M, Ahluwalia N, Dufourg M, Forhan A, Charles MA, Lioret S, et al. Diet during pregnancy: influence of social characteristics and migration in the ELFE cohort. Matern Child Nutr. 2021;17(3):e13140. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Rigby RA, Stasinopoulos DM. Smooth centile curves for skew and kurtotic data modelled using the Box-Cox power exponential distribution. Stat Med. 2004;23(19):3053–76. [ DOI ] [ PubMed ] [ Google Scholar ] 38. Cole TJ. The LMS method for constructing normalized growth standards. Eur J Clin Nutr. 1990;44(1):45–60. [ PubMed ] [ Google Scholar ] 39. Heude B, Scherdel P, Werner A, Le Guern M, Gelbert N, Walther D, et al. A big-data approach to producing descriptive anthropometric references: a feasibility and validation study of paediatric growth charts. Lancet Digit Health. 2019;1(8):e413–23. [ DOI ] [ PubMed ] [ Google Scholar ] 40. WHO Multicentre Growth Reference Study Group . WHO Child Growth Standards based on length/height, weight and age. Acta Paediatr Suppl. 2006;450:76–85. [ DOI ] [ PubMed ] [ Google Scholar ] 41. Ferguson KD, McCann M, Katikireddi SV, Thomson H, Green MJ, Smith DJ, et al. Evidence Synthesis for Constructing Directed Acyclic Graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs. Int J Epidemiol. 2020;49(1):322–9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Hedegaard H, Johnson RL. An updated international classification of Diseases, 10th revision, clinical modification (ICD-10-CM) surveillance case definition for injury hospitalizations. Natl Health Stat Rep. 2019;125:1–8. [ PubMed ] [ Google Scholar ] 43. VanderWeele TJ. Outcome-wide epidemiology. Epidemiol Camb Mass. 2017;28(3):399–402. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Lecorguillé M, Lioret S, de Lauzon-Guillain B, de Gavelle E, Forhan A, Mariotti F, et al. Association between dietary intake of one-carbon metabolism nutrients in the year before pregnancy and birth anthropometry. Nutrients. 2020;12(3):838. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Lecorguillé M, Charles M-A, Lepeule J, Lioret S, de Lauzon-Guillain B, Forhan A, et al. Association between dietary patterns reflecting one-carbon metabolism nutrients intake before pregnancy and placental DNA methylation. Epigenetics. 2022;17(7):715–30. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Rives-Lange C, Girardeau Y, Poghosyan T, Ciangura C, Coupaye M, Nizard J, et al. Change in birth rate before and after bariatric surgery in France. Obes Surg. 2021;31(10):4657–61. [ DOI ] [ PubMed ] [ Google Scholar ] 47. Akhter Z, Heslehurst N, Ceulemans D, Rankin J, Ackroyd R, Devlieger R. Pregnancy after bariatric surgery: a nested case-control study of risk factors for small for gestational age babies in AURORA. Nutrients. 2021;13(5):1699. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. Lecot-Connan T, Jeannin A-C, Baptiste A, Dechartres A, Genser L, Oppert J-M, et al. Pregnancy outcomes after bariatric surgery: importance of maternal ferritin on birth weight. Obes Surg. 2024;34(7):2305–14. [ DOI ] [ PubMed ] [ Google Scholar ] 49. Van De Maele K, De Geyter C, Vandenplas Y, Gies I, Devlieger R. Eating habits of children born after maternal bariatric surgery. Nutrients. 2020;12(9):2577. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 50. Van De Maele K, Devlieger R, De Schepper J, Gies I. Endothelial function and its determinants in children born after maternal bariatric surgery. Pediatr Res. 2022;91(3):699–704. [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary_material-Suppl.1-s1.xlsx (18.1KB, xlsx) Data Availability Statement The data used in this project will not be made publicly available as they are personal data containing sensitive information. However, data access may be granted upon reasonable request by contacting the corresponding author. Each request will be reviewed by a cohort-specific steering committee, and data-sharing agreements ensuring compliance with GDPR regulations will be signed between the parties. Articles from Obesity Facts are provided here courtesy of Karger Publishers ACTIONS View on publisher site PDF (417.4 KB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Bariatric Surgery and Consequences for Mother and Baby in Pregnancy#1072097
• Predictors of Bariatric Surgery Weight Loss#325238
• Metabolic Responses to Bariatric Surgery#859233
• Genetic Susceptibility to Predict Weight Loss After Bariatric Surgery#349882
• Glycemic Variability During Pregnancy After Bariatric Surgery#844323