Risk-Benefit Balance Associated With Obstetric, Neonatal, and Child Outcomes After Metabolic and Bariatric Surgery

Claire Rives-Lange; Tigran Poghosyan; Aurelie Phan; Alexis Van Straaten; Yannick Girardeau; Jacky Nizard; Delphine Mitanchez; Cécile Ciangura; Muriel Coupaye; Claire Carette; Sébastien Czernichow; Anne-Sophie Jannot

doi:10.1001/jamasurg.2022.5450

. 2022 Nov 9;158(1):36–44. doi: 10.1001/jamasurg.2022.5450

Risk-Benefit Balance Associated With Obstetric, Neonatal, and Child Outcomes After Metabolic and Bariatric Surgery

Claire Rives-Lange ^1,^2,⁴, Tigran Poghosyan ^1,^2,³, Aurelie Phan ⁴, Alexis Van Straaten ⁵, Yannick Girardeau ⁵, Jacky Nizard ^6,⁷, Delphine Mitanchez ^8,⁹, Cécile Ciangura ¹⁰, Muriel Coupaye ^11,¹², Claire Carette ^1,^4,¹³, Sébastien Czernichow ^1,^4,^✉, Anne-Sophie Jannot ^1,^5,¹⁴

¹Université de Paris-Cité, F-75015, Paris, France

²Assistance Publique-Hôpitaux de Paris (AP-HP), Service de Chirurgie Digestive, Oeso-Gastrique et Bariatrique, Hôpital Bichat, Centre de Recherche Sur l'inflammation, Inserm UMR 1149, Paris, France

³Inserm UMRS 1149. Centre de Recherche des Cordeliers, Paris, France

⁴Assistance Publique-Hôpitaux de Paris (AP-HP), Service de Nutrition, Centre Spécialisé Obésité, Hôpital Européen Georges Pompidou, Paris, France

⁵Assistance Publique-Hôpitaux de Paris (AP-HP), Service d'informatique médicale, biostatistiques et santé publique, Hôpital Européen Georges Pompidou, Paris, France

⁶Assistance Publique-Hôpitaux de Paris (AP-HP), Service de gynécologie obstétrique, Hôpital Pitié-Salpêtrière, Paris, France

⁷Inserm U1150, CNRS UMR 7222, Sorbonne Université, Paris, France

⁸Service de néonatalogie, Hôpital Bretonneau, Université François Rabelais, F-37000 Tours, France

⁹INSERM UMR 938 Centre de Recherche Saint Antoine, F-75012 Paris, France

¹⁰Assistance Publique-Hôpitaux de Paris (AP-HP), Service de Nutrition, Hôpital Pitié-Salpêtrière, Paris, CSO Ile de France Centre, France

¹¹Assistance Publique-Hôpitaux de Paris (AP-HP), Service des Explorations Fonctionnelles, Centre Intégré Nord Francilien de l'Obésité (CINFO), Hôpital Louis Mourier, Colombes, France

¹²Centre de Recherche sur l'Inflammation, INSERM UMR 1149, Université de Paris, Paris, France

¹³Centre d’investigation clinique, Inserm 1418, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France

¹⁴HeKA INSERM, INRIA, Centre de Recherche des Cordeliers Paris, France

Accepted for Publication: July 23, 2022.

Published Online: November 9, 2022. doi:10.1001/jamasurg.2022.5450

Correction: This article was corrected on December 21, 2022, to fix typographical errors in the article.

^✉

Corresponding Author: Sebastien Czernichow, MD, PhD, Assistance Publique-Hôpitaux de Paris (AP-HP), Service de Nutrition (Centre Spécialisé Obésité), Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75015 Paris, France (sebastien.czernichow@aphp.fr).

Author Contributions: Drs Jannot and Van Straaten had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Rives-Lange, Van Straaten, Girardeau, Nizard, Czernichow, Jannot.

Acquisition, analysis, or interpretation of data: Poghosyan, Phan, Van Straaten, Girardeau, Nizard, Mitanchez, Ciangura, Coupaye, Carette, Czernichow, Jannot.

Drafting of the manuscript: Rives-Lange, Van Straaten, Jannot.

Critical revision of the manuscript for important intellectual content: Rives-Lange, Poghosyan, Phan, Girardeau, Nizard, Mitanchez, Ciangura, Coupaye, Carette, Czernichow, Jannot.

Statistical analysis: Van Straaten, Girardeau, Jannot.

Administrative, technical, or material support: Jannot.

Supervision: Poghosyan, Girardeau, Carette, Czernichow, Jannot.

Conflict of Interest Disclosures: Dr Rives-Lange reported personal fees from Nestlé Health Science and congress fees from Fit for Me and Nestlé Home. Dr Poghosyan reported being a member of an advisory board for Bariatek and consultancy fees from Gore and Novonordisk. Dr Carette reported personal fees from Ipsen Pharma, Pfizer, AstraZeneca, Novo Nordisk, Lilly, Vitalaire, and Merck France outside the submitted work. Dr Czernichow reported participation fees from Mygoodlife, and personal fees from Novonordisk, Fresenius, Lilly, Janssen, Bristol Myers Squibb, and other fees from Jellynov outside the submitted work. No other disclosures were reported.

^✉

Corresponding author.

PMCID: PMC9647576 PMID: 36350637

This study assesses the risk-benefit balance associated with metabolic and bariatric surgery around obstetric, neonatal, and child outcomes.

Key Points

Question

What is the association of metabolic and bariatric surgery (MBS) with obstetric, neonatal, and child outcomes?

Findings

In this study, among 3686 women who had 1 pregnancy both before and after MBS, the study team found that after MBS there was a significant increase in the rate of births that were small for gestational age, but a significantly reduced rate of births that were large for gestational age. The study team also found fewer cases of gestational hypertension and diabetes in pregnant women, and in newborns found fewer rates of fetal skeletal injuries, febrile convulsions, viral intestinal infections, and carbohydrate metabolism disorders, but more respiratory failure rates associated with bronchiolitis in the first months of life.

Meaning:

The risk-benefit balance associated with MBS appears to be favorable for pregnancies but requires further analyses to assess benefit-risk in children.

Abstract

Importance

Metabolic and bariatric surgery (MBS) is the most efficient therapeutic option for severe obesity. Most patients who undergo MBS are women of childbearing age. Data in the scientific literature are generally of a low quality due to a lack of well-controlled prospective trials regarding obstetric, neonatal, and child outcomes.

Objective

To assess the risk-benefit balance associated with MBS around obstetric, neonatal, and child outcomes.

Design, Setting, and Participants

The study included 53 813 women on the French nationwide database who underwent an MBS procedure and delivered a child between January 2012 and December 2018. Each women was their own control by comparing pregnancies before and after MBS.

Exposures

The women included were exposed to either gastric bypass or sleeve gastrectomy.

Main Outcomes and Measures

The study team first compared prematurity and birth weights in neonates born before and after maternal MBS with each other. Then they compared the frequencies of all pregnancy and child diagnoses in the first 2 years of life before and after maternal MBS with each other.

Results

A total of 53 813 women (median [IQR] age at surgery, 30 [26-35] years) were included, among 3686 women who had 1 pregnancy both before and after MBS. The study team found a significant increase in the small-for-gestational-age neonate rate after MBS (+4.4%) and a significant decrease in the large-for-gestational-age neonate rate (−12.6%). The study team highlighted that compared with pre-MBS births, after MBS births had fewer occurrences of gestational hypertension (odds ratio [OR], 0.16; 95% CI, 0.10-0.23) and gestational diabetes for the mother (OR, 0.39; 95% CI, 0.34-0.45), as well as fewer birth injuries to the skeleton (OR, 0.27; 95% CI, 0.11-0.60), febrile convulsions (OR, 0.39; 95% CI, 0.21-0.67), viral intestinal infections (OR, 0.56; 95% CI, 0.43-0.71), or carbohydrate metabolism disorders in newborns (OR, 0.54; 95% CI 0.46-0.63), but an elevated respiratory failure rate (OR, 2.42; 95% CI, 1.76-3.36) associated with bronchiolitis.

Conclusions and Relevance

The risk-benefit balance associated with MBS is highly favorable for pregnancies and newborns but may cause an increased risk of respiratory failure associated with bronchiolitis. Further studies are needed to better assess the middle- and long-term benefits and risks associated with MBS.

Introduction

The prevalence of obesity has increased worldwide to pandemic proportions with more than 2 billion people who are currently obese with higher incidence in women compared with men (14.9% vs 10.8%).^1,2,3 Pregnant women who live with obesity are more likely to have early pregnancy loss and to have an increased risk of gestational diabetes, congenital fetal malformations, hypertensive disorders, delivery of large for gestational age (LGA) infants, cesarean deliveries, shoulder dystocia, spontaneous and medically indicated premature births, or stillbirths.^4,5,6 Currently, metabolic and bariatric surgery (MBS) is the most successful therapeutic option for severe obesity with long-term positive effects.⁷ The International Federation for the Surgery of Obesity and Metabolic Disorders survey reported a total of 604 223 yearly primary interventions worldwide in January 2021, mostly in women of childbearing age.⁸ The surrounding literature is growing but remains weak, especially regarding the long-term effect on the children born to women who underwent MBS.^{9,10,11,12,13} Consistent results show that MBS is associated with a reduced risk of gestational diabetes,^14,15 LGA neonates, hypertensive disorders,^16,17 and delivery outcomes;¹⁸ however, the benefit to the newborn remains debated considering the potential risk of preterm birth and small for gestational age (SGA) infants.^14,19

Whether MBS has other benefits or risks remains largely unknown. Indeed, MBS could have many effects that have not been studied yet, but could be highlighted through an analysis with no a priori hypothesis on the possible associated diagnoses. Thus, a study assessing differences in frequencies before and after MBS for all recorded diagnoses would allow for a global picture of the risk-benefit associated to MBS for women and newborns. Such a study could highlight both diagnoses already associated with MBS confirming their association but also highlight new associations bringing to light new potential benefits or adverse events for pregnancies and newborns. The aim of this study was to perform a diagnosis-wide association study comparing all diagnoses of both pregnant women and newborns to clarify the risk-benefit balance associated with MBS for pregnancy and childhood outcomes.

Methods

Data Source

This study is based on the French nationwide hospitalization database Programme de Médicalisation des Systèmes d’Information (PMSI) covering approximately 67 million individuals during a 10-year follow-up from January 2011 to December 2020 and including a unique anonymous identifier for each patient and another identifier linking each newborn to his or her mother since 2012. This enables all hospital stays of the same patient to be linked together, even if the stays were in different hospitals, enabling us to perform such a diagnosis-wide association study for both pregnancy and resulting newborn outcomes. This database contains administrative and demographic data, diagnoses codes using International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), and procedures performed in private or public hospitals in France.²⁰ Discharge reports are mandatory since 2008 and serve as the basis for hospital funding.

Institutional Review Board and Informed Consent

Institutional review board approval was not required because the study was based on the PMSI discharge database. Written informed consent is not required according to French regulation on research reusing the PMSI discharge database.

Population

We included all women who had both an MBS procedure, such as a gastric bypass (GBP) or sleeve gastrectomy (SG), from January 2012 to December 2018 and at least 1 child delivery from January 2012 to December 2018. Women who underwent 2 MBS procedures were excluded. MBS were identified using procedure codes (HFCC0030 for GBP and HFFC0180 for SG procedures from the French National Procedures Classification). Child delivery was identified with ICD-10 codes O80 to O84. The considered inclusion periods allowed at least 2 years of follow-up after delivery (until December 2020) to collect all diagnoses related to the first 2 years of the child’s postnatal period and 1 year of follow-up before delivery to be able to catch all diagnoses related to pregnancies. Among this population, we distinguished the subpopulation, including women who had at least 1 pregnancy before and 1 pregnancy after MBS and the subpopulation, including women who had at least 2 consecutive pregnancies before MBS.

Variables

The type, BMI class, date, and age of the women at the time of the MBS procedure were collected along with the date and age of the women at delivery. We extracted all diagnoses of the mother related to pregnancies (obstetrical and delivery outcomes, ie, ICD-10 codes with O or Z letter). We also extracted diagnoses of their children during the first 2 years of life (all ICD-10 codes) along with their birth weight and term.

For each child, we estimated the growth percentile at birth using Audipog reference curves giving growth percentiles according to the gestational age based on 204 316 children born in France between 1999 and 2005 depending on age, sex, and weight but not on race and ethnicity. SGA was defined as having a growth percentile below 10% and LGA was defined as having a growth percentile above 90%.

Statistical Methods

The characteristics of women and their children were summarized according to bariatric procedures (GBP and SG) using median and interquartile range for continuous variables and number (percentage) for qualitative variables, for the whole population, for the subpopulation including women who had at least 1 pregnancy before and 1 pregnancy after MBS, and for the subpopulation including women who had at least 2 consecutive pregnancies before MBS. We compared the distribution of birth weight and the gestational ages and the proportions of SGA and LGA, according to the delay before and after MBS.

We then focused on the population having had a child before and after MBS and performed a self-controlled association test (χ² test of exact Fisher test when necessary) for each diagnosis (diagnosis-wide association study) by estimating odds ratios (ORs) and their associated 95% CIs, comparing diagnosis frequencies during the first pregnancy after MBS and the last pregnancy before MBS. Each woman who had an MBS acts as their own control by comparing pregnancies before and after MBS. We then performed the same analysis for the diagnoses related to the children delivered during these pregnancies, ie, comparing diagnoses of the children delivered before and after MBS. We then performed a sensitivity analysis with a center effect as a random effect (center of MBS).

In such analyses, highlighted associations could be due either to MBS or to confounders, such as age or parity because of the chosen design, as pregnancies after MBS are of greater parity and from older women than pregnancies before MBS. Therefore, to take into account the fact that pregnancies after bariatric surgery are of greater parity and from older women than pregnancies before MBS, we used a counterfactual approach that we called a falsification study comparing pregnancies and newborns in 2 consecutive deliveries before MBS. Significantly associated diagnoses in this falsification study will be due to association with either age and/or parity and will not be considered as associated with MBS if also found to be significantly associated in the main analysis. This falsification study thus allows to indirectly take into account age and parity confounding factors. We performed a diagnosis-wide association study, including women who had at least 2 deliveries before MBS and compared diagnoses between the delivery before MBS and the penultimate delivery before MBS. Because of the multiple tests performed, we corrected the significance threshold by assuming that 50 independent tests were performed; therefore, we considered that the level of significance was α = .001. All analyses were performed using R software version 4.1 (R Project).

Results

A total of 53 813 women were included. There were 3686 women who had at least 1 pregnancy before and after MBS and 7383 women who had at least 2 pregnancies before MBS. The Table describes the characteristics of these different populations, according to the type of MBS procedure.

Table. Characteristics of the Different Population Studied, According to the type of MBS Procedures.

Characteristic	No. (%)^a			P value^b
Characteristic	Whole sample	GBP	SG	P value^b
Women included
No. of patients	53 813 (100.0)^c	15 172 (28.2)^c	38 641 (71.8)^c	NA
Age at surgery, (IQR), y	30 (26-35)	31 (27-35)	30 (26-35)	<.001
BMI at surgery
30-40	17 624 (32.75)	4144 (27.31)	13 480 (34.89)	<.001
40-50	31 865 (59.21)	9554 (62.97)	22 311 (57.74)
≥50	4126 (7.67)	1420 (9.36)	2706 (7)
Unspecified	198 (0.37)	54 (0.36)	144 (0.37)
Follow-up times after surgery, median, y	3.75	4.25	3.59	NA
Before MBS
No. of pregnancies	45 063 (100)	11 546 (25.6)	33 517 (74.4)	NA
Age at pregnancy, y	29 (26-33)	30 (26-34)	29 (25-33)	<.001
After MBS
No. of pregnancies	24 101 (100)	7791 (32.3)	16 310 (67.7)	NA
Age at pregnancy, (IQR), y	31 (28-35)	31 (28-35)	31 (27-34)	<.001
Women who have had 2 pregnancies before MBS
No. of patients	7383	1847	5536	NA
Age at surgery, (IQR), y	31 (28-35)	32 (28-35)	31 (28-35)	<.001
BMI at surgery				<.001
30-40	2705 (36.64)	592 (32.05)	2113 (38.17)	NA
40-50	4186 (56.7)	1107 (59.94)	3079 (55.62)
≥50	464 (6.28)	139 (7.53)	325 (5.87)
Unspecified	28 (0.38)	9 (0.49)	19 (0.34)
Age at pregnancy, y
1 Before MBS	27 (24-31)	28 (25-31)	27 (24-31)	<.001
2 Before MBS	30 (26-33)	30 (27-34)	929 (26-33)	<.001
Women who have had 1 pregnancy before and after MBS
No. of patients	3 686	934	2 752	NA
Age at surgery, (IQR), y	28 (25-31)	28 (25-32)	28 (25-31)	<.001
BMI at surgery
30-40	1149 (31.17)	246 (26.34)	903 (32.81)	<.001
40-50	2242 (60.82)	587 (62.85)	1655 (60.14)
≥50	278 (7.54)	99 (10.6)	179 (6.5)
Unspecified	17 (0.46)	2 (0.21)	15 (0.55)
Follow-up times after surgery, median, y	4.59	4.76	4.50	NA
Age at pregnancy, (IQR), y
Before MBS	27 (24-30)	27 (24-30)	26 (24-30)	<.001
After MBS	31 (28-34)	31 (28-34)	30 (27-34)	<.001

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Abbreviations: BMI (calculated as weight in kilograms divided by height in meters squared), body mass index; GBP, gastric bypass; IQR, interquartile range; MBS, metabolic and bariatric surgery; NA, not applicable; SG, sleeve gastrectomy.

^{^a}

Results are expressed by median (IQR) for continuous data and No. (%) for categorical data. P values shown result from t test for continuous data.

^{^b}

t Test.

^{^c}

Median (IQR); n (%).

Birth weight significantly decreased after MBS, with a mean difference of 320.3 g (3421.6 g [SD 561.7; 95%CI, 3416.4-3426.8] before MBS and 3101.3 g [SD 536.9; 95% CI, 3094.5-3108.1] after MBS) (P<.01). After MBS, birth weight increased with time from MBS without reaching the weight before MBS during the study time. Birth weight was significantly lower for the GBP group born after MBS (GBP mean of 3074.8 g [SD, 535; 95% CI, 3062.9-3086.7] and SG mean of 3114 g [SD, 537.4; 95% CI, 3105.7-3122.2]) (P < .001), although it did not differ before maternal SG and GBP. SGA rates were relatively stable with time before MBS but increased after MBS. SGA tended to decrease with time from MBS (Figure 1) but remained higher than before MBS. eTable 1 and eFigure 1 in the Supplement showed a significant increase in SGA rates from 5.76% (95% CI, 5.54-5.98) to 15.19% (95% CI, 14.74-15.65) in the whole population (the increase remained significant in the subpopulation, including only women who had a child delivery before and after MBS). In contrast, LGA neonate rates sharply decreased after MBS from 19.14% (95% CI, 18.78-19.51) to 4.98% (95% CI, 4.71-5.26) in the whole population (the decrease remained significant in the subpopulation, including women having both 1 pregnancy before and after MBS). There was no difference in SGA rates after MBS between the 2 MBS procedures, ie, GBP and SG (eTable 2 in the Supplement).

Prematurity was significantly higher after MBS than before in the whole population (7.6% vs 6.3%), but this was not the case for the subpopulation including only women having both 1 pregnancy before and after MBS (6.5% vs 5.9%) (eTable 1 in the Supplement). We first performed the falsification study to highlight diagnoses associated with age and parity. Sixty-four diagnoses truncated to 1 letter and 2 digits were considered for this study for pregnancies and 16 were significant at the 0.1% level (Figure 2). For the resulting children, 558 diagnoses were truncated to 1 letter, 2 digits were considered, and 13 were significant at the 0.1% level (Figure 3).

Figure 2. — *ICD-10* indicates *International Statistical Classification of Diseases and Related Health Problems, Tenth Revision*; OR, odds ratio.

^aSignificant diagnoses in the falsification study.

^bReverse association in the falsification study.

The study team then performed the main study analysis, which compared deliveries before and after MBS. Sixty-one diagnoses were considered associated with pregnancies. Among them, 21 were significant at the 0.1% level (Figure 2). The highest ORs, after exclusion of diagnoses highlighted in the falsification analysis, were observed for multiple delivery (OR, 2.48; 95% CI, 1.47-4.34) and full-term uncomplicated delivery (OR, 1.47; 95% CI, 1.34-1.61). The lowest ORs, after exclusion of diagnoses highlighted in the falsification analysis, were observed for gestational hypertension (OR, 0.16; 95% CI, 0.10-0.23), preeclampsia (OR, 0.19; 95% CI, 0.13-0.28), maternal hypertension (OR, 0.22; 95% CI, 0.10-0.42), and other puerperal infections (OR, 0.35; 95% CI, 0.22-0.53). Among highlighted diagnoses, gestational diabetes had among the lowest ORs (OR, 0.39; 95% CI, 0.34-0.45) with risk reversal compared with the falsification analysis (Figure 2). Another highlighted diagnosis was cesarean delivery without indication (OR, 0.7; 95% CI, 0.63-0.78). The decrease in the prolonged labor was also founded in the falsification analysis and therefore this association was probably related to parity or maternal age. ORs and 95% CIs did not vary when taking into account the center effect of MBS. The trends were similar for the GBP and SG subgroups and for the BMI subgroups (eFigures 2 and 3 in the Supplement). eFigure 4 in the Supplement reports the association for the sublevels of these diagnoses. Noteworthy, there was risk reversal for preexisting diabetes and hypertension with OR, 0.47; 95% CI, 0.34-0.45 and OR, 0.7; 95% CI, 0.44-0.96 vs OR, 1.7; 95% CI, 1.55-1.82 and OR, 1.5 95%; CI, 1.14-1.91, respectively, in the falsification study.

A total of 394 diagnoses were considered concerning for children’s outcomes. Among them, 17 were significant at the 0.1% level (Figure 3). The highest OR was observed for respiratory failure (OR, 2.42; 95% CI, 1.76-3.36) and for newborn disorders related to slow fetal growth and fetal malnutrition (OR, 1.53; 95% CI, 1.26-1.86). The sublevel leading this association was newborn SGA (OR, 1.77; 95% CI, 1.5-2.2) (eFigure 7 in the Supplement) with risk reversal in comparison with the falsification analysis (Figure 3). The lowest ORs were observed for birth injury to the skeleton (OR, 0.27; 95% CI, 0.11-0.60) and for convulsions (OR, 0.43; 95% CI, 0.26-0.68). The sublevel leading this association was febrile convulsions (eFigure 5 in the Supplement) for viral and other intestinal infections (OR, 0.56; 95% CI, 0.43-0.71). The sublevel leading this association for newborns affected by other complications of labor and delivery was cesarean delivery (OR, 0.81; 95% CI, 0.73-0.90) (eFigure 4 in the Supplement), and for disorders of newborns related to long gestation (OR, 0.39; 95% CI, 0.32-0.48) and for disorders of carbohydrate metabolism specific to newborns (OR, 0.54; 95% CI, 0.46-0.63) with risk reversal in comparison with the falsification analysis (Figure 3). The trends were similar for the GBP and SG subgroups and for the BMI subgroups (eFigure 6 and 7 in the Supplement). Respiratory failure after the neonatal period achieved one of the highest ORs and was associated with acute bronchiolitis in most cases (96%). A multivariate analysis with prematurity (less than 37 weeks of amenorrhea) and diagnosis of SGA added as explaining factor demonstrate and did not explain this association. This diagnosis was also significant in the falsification analysis (OR, 1.37; 95% CI, 1.11-1.69) but with a much lower effect size.

Discussion

Our study showed that the highest benefits for pregnancy outcomes after MBS were obtained for hypertension and gestational diabetes. Indeed, the risk of hypertension experienced a 6.7-fold reduction after MBS. Notably, prematurity was not increased by MBS, while SGA increased but to a lesser degree and LGA decreased. Convulsions, injury to the skeleton, and neonatal infections sharply decreased, while respiratory failure due to bronchiolitis was the only diagnosis shown to be increased after MBS. Our innovative methodological approach allowed us to describe for the first time an increase in the occurrence of hospitalization for respiratory failure in children after MBS.

Regarding the decrease of hypertension, few studies in the literature reported such a large effect size, even though it was comparable with results published by Bennet et al¹⁶ but more important than those of other authors.^16,17,18 Gestational diabetes risk was reduced 2.5-fold after MBS, reinforcing previous data from the scientific literature.^16,21,22,23 A significant increase in SGA rates after MBS was found in previous studies.^14,24 This study adds to the literature information around the fact that SGA increases were lower than LGA decreases and that LGA decreases have very beneficial consequences for the newborn, including a decrease of skeletal injuries, carbohydrate metabolism disorders, and of fetal consequences of cesarean delivery while SGA increases have no consequences for the newborn with no highlighted diagnoses related to SGA. Moreover, we did not find increased medical termination of pregnancy or fetal malformation, as was expected from the literature.¹⁴ Meanwhile, an increase in occurrences of respiratory failure in children after MBS was observed, constituting a new signal highlighted thanks to an approach considering all diagnoses without any a priori hypothesis. Indeed, one of the main strengths of our work lies in this data mining strategy. Data-mining strategies have become common since the first genome-wide association studies²⁵ and have been adapted to many kinds of data, such as phenotypes²⁶ and medications.²⁷ This type of strategy has been made possible for this study because of the availability of medical administrative databases in which all diagnoses are recorded. Thus, our study both confirmed diagnoses already associated with MBS and highlighted new associations, such as the respiratory failure.

Of note, we have found a significant increase in multiple deliveries but with a frequency comparable with that of the general population, as reported in the 2021 National Perinatal Survey.²³ Weight loss due to MBS allows access to assisted reproductive technology²⁸and also spontaneously improved the fertility of these women by through the resolution of polycystic ovary syndrome.

We also showed a decrease in viral intestinal infections and the risk of febrile convulsions after MBS. These last elements could be explained by a potential role of an improvement of the intestinal microbiota generated by the MBS^29,30,31,32 having a positive impact on the immunity of the child. No differences were found between GBP and SG.

Limitations

One of the limitations of our study was that despite the realization of the falsification analysis, it includes the possibility of confusing factors for premature labor, SGA, and LGA, which could differ from one pregnancy to another. Another limitation is the use of a medical administrative database, which may include coding biases because the latter are recorded for reimbursement purposes and not for research, and in which some relevant data for research are not collected, such as the weight of women at the time of MBS, at the beginning of pregnancy and at delivery. However, these limitations are counterbalanced by the exhaustiveness of such a database over a population of approximately 67 million patients and the long available follow-up. Other strengths of our study include the use of a self-control design, the use of a falsification study to take into account parity and the age of women as confounding factors, and the lack of an a priori hypothesis on potentially associated diagnoses allowing us to highlight new signals, such as respiratory failure, that requires further investigation.

Conclusions

To our knowledge, this study is the first nationwide study using a lack of an a priori hypothesis on potentially associated diagnoses, considering a very large set of diagnoses, giving a global picture of the risk-benefit associated to MBS for pregnant women and newborns. On the basis of these associations, the risk-benefit balance associated to MBS seems to be favorable for pregnancy and newborns. Our approach without an a priori hypothesis on potentially associated diagnoses allows us to discover that infants had more frequent respiratory failures associated with bronchiolitis after MBS, which constitutes a novel association that should be investigated and that demonstrates the need to monitor pregnancies after MBS both by specialized obstetrician and pediatrician teams.

Supplement.

eTable 1: Comparison of birth weight, gestational age, LGA and SGA according to gestational age, before and after MBS

eFigure 1: SGA and LGA by Gestational Age, before and after MBS, in the whole population

eTable 2: Comparison of birth weight, gestational age, LGA and LGA according to gestational age, before and after MBS, according to GBP and SG

eFigure 2: Comparison of diagnostic occurrence in pregnancies before and after MBS, according to GBP and SG group

eFigure 3: Comparison of diagnostic occurrence in pregnancies before and after MBS, according to the BMI subgroup

eFigure 4: Comparison of diagnostic with 3 numbers occurrence in pregnancies before and after MBS

eFigure 5: Comparison of diagnostic with 3 numbers, occurrence in children before and after MBS

eFigure 6: Comparison of diagnostic occurrence in children before and after MBS, according to GBP and SG group

eFigure 7: Comparison of diagnostic occurrence in children before and after MBS, according to the BMI subgroup

Click here for additional data file.^{(1.2MB, pdf)}

References

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