Skip to main content

Population-based cohort study: proton pump inhibitor use during pregnancy in Sweden and the risk of maternal and neonatal adverse events

Abstract

Background

Approximately half of all women suffer from heartburn at some stage during pregnancy. The most effective treatment is proton pump inhibitors, but the safety of use during pregnancy cannot be guaranteed. This study aimed to elucidate the effect of proton pump inhibitors on the risk of pre-eclampsia, gestational diabetes mellitus, preterm birth, an Apgar score at 5 min below 7, and a child being small or large for its gestational age.

Methods

This Swedish population-based study included 1,089,514 live singleton deliveries between July 2006 and December 2016 in Sweden. Multiple logistic regression was used to model the outcomes as a function of the covariates. Results were presented as odds ratios with 95% confidence intervals.

Results

In 1.4% of all pregnancies, the mother used proton pump inhibitors in the period from 3 months before the last menstrual period up to delivery. The use of proton pump inhibitors was associated with higher odds of pre-eclampsia (odds ratio = 1.19, 1.10–1.29), gestational diabetes mellitus (odds ratio = 1.29, 1.16–1.43), preterm birth (odds ratio = 1.23, 1.14–1.32), and small for gestational age (odds ratio = 1.27, 1.16–1.40) and lower odds of large for gestational age (odds ratio = 0.84, 0.77–0.91). No significant association was found with a low Apgar score 5 min after birth.

Conclusions

Proton pump inhibitor use was associated with a higher risk of pre-eclampsia, gestational diabetes, preterm birth, and being born small for gestational age.

Peer Review reports

Background

Proton pump inhibitors (PPIs) are the most effective treatment of heartburn [1], but contra-indicated during pregnancy. Yet PPIs are still prescribed in approximately 1% of all pregnancies according to our recent meta-analysis [2], and also available over-the-counter in several countries including Sweden [3, 4]. Maternal PPI use might affect the child via different mechanisms. PPIs have been shown to cross the placenta [1] and prenatal exposure to PPIs is seemingly associated with an increased risk of developing childhood asthma [5]. In addition, the initial gut colonization is highly influenced by the maternal microbiome (vaginal and fecal) [6, 7], and the maternal microbiome seems to play an important role in the onset of pregnancy complications [8,9,10]. In turn, PPI use has been associated with important changes in the gut microbiome that appear to be more prominent than those related to antibiotic use [11], also in infants as shown in our small pilot study [12]. The Food and Drug Administration (FDA) used to classify (up till 2015) most PPIs as category B drugs (“No risk in animal studies”), except for omeprazole which was categorized as type C (“Risk cannot be ruled out”) [13, 14]. Current recommendations are that omeprazole is not recommended during breastfeeding and that it “should be used during pregnancy only if the benefit outweighs the risk to the fetus” [15]. There were no reports of teratogenicity, and PPI use was not associated with major adverse pregnancy outcomes or birth defects [16,17,18,19], but was related to a lower birth weight [16, 17] and an increased risk of pre-eclampsia [20]. However, our recent meta-analysis reported an increased risk of congenital malformations associated with PPI use during pregnancy [2]. Little research is done concerning the effect of PPIs on less severe health risks that might have long-term implications for the mother and her offspring including maternal complications (gestational diabetes, pre-eclampsia), preterm birth, and small or large for gestational age. In our previous meta-analysis, we did find a handful of studies addressing neonatal adverse events, yet none of the pooled analyses (beyond congenital malformations) reached statistical significance, which may be due to power issues and low prevalences of exposure to PPIs [2]. This highlights the need to determine the effect of PPIs on the pregnant woman and her developing child.

This large nationwide population-based Swedish cohort study aimed to investigate the relation between the use of PPIs shortly before and during pregnancy on the risk of maternal and neonatal health complications.

Methods

The study was performed using a Swedish cohort including all live singleton births delivered between July 2006 and December 2016, and the terminology “women” and “mother” were defined based on their biological sex and pregnancy status, not their gender identity. The cohort was created by linking information from four high-quality nationwide Swedish health data registries maintained by the National Board of Health and Welfare (Socialstyrelsen), as described earlier [21, 22]: the Medical Birth Registry [23,24,25] (established in 1973), the Prescribed Drug Registry [26] (established in July 2005), the Patient Registry (in- and outpatient care) [27, 28], and the Causes of Death Registry (since 1952). Information was linked through the unique Swedish personal identification number [29]. The study was approved by the Regional Ethics Committee of Stockholm (2017/2423–31), without the need for informed consent because of the registry-based nature of the data.

Outcomes

The maternal outcomes were pre-eclampsia, characterized by hypertension (systolic blood pressure > 140 mmHg and/or diastolic blood pressure > 90 mmHg) combined with proteinuria (24-h urine protein level > 300 mg), and gestational diabetes mellitus (GDM), defined as any degree of glucose intolerance with the onset during pregnancy (Additional file 1: Table A1) [30]. The neonatal outcomes included preterm birth (birth < 37 weeks of gestation), an Apgar score 5 min after birth (AS5min) < 7, and small (SGA) and large for gestational age (LGA) based on birth weight in the 10th or 90th percentile based on gestational age, defined as birthweight below the 10th and above the 90th population percentile, respectively. [30, 31]

Study exposure

The drugs in the Prescribed Drug Registry are classified according to the Anatomical Therapeutic Chemical (ATC) Classification System and the duration of use is expressed in defined daily doses (DDDs) per package. The exposure was prescribed PPI (ATC-code: A02BC) in the period ranging from 3 months before the last menstrual period (LMP) up to the delivery date. Women filling at least two prescriptions during the study period were considered users since lower compliance is expected for those with only a single prescription. [32]

Covariates

Potential confounders included maternal characteristics (age at delivery, body mass index (BMI), tobacco consumption (smoking or moist snuff use), other prescribed drug use, comorbidities), pregnancy, and obstetric characteristics (Additional file 1: Table A1). Missingness in BMI was adjusted for by creating an additional dummy variable. The use of prescription drugs was split into histamine-2 receptor antagonist (H2RA) use and other drugs (NSAIDs, low-dose aspirin, and antibiotics). The use of H2RA was separated because it is prescribed for similar indications as PPIs. Maternal comorbidities were identified according to their ICD-10 codes or by the prescription of associated drugs (Additional file 1: Table A1). The comorbidities included hypertension, GDM, diabetes mellitus (type 1 and type 2), and hypo- and hyperthyroidism.

Pregnancy and obstetric variables, of which some are also outcomes, expected to be associated with at least one outcome were pre-eclampsia, mode of delivery (cesarean section or vaginal delivery), preterm birth, neonatal birthweight (SGA, average for gestational age (AGA) or LGA), parity, time in months since previous delivery, and whether the outcome was present in a previous pregnancy.

Statistical analysis

The effect of PPIs was assessed by comparing PPI users with non-users. Multiple logistic regression models were used to evaluate the association between the exposure and the odds that the outcome occurred, corrected for covariates, and presented as odds ratios (OR) with 95% confidence intervals (CI).

Models were built independently for each outcome, based on the purposeful selection method described by Hosmer et al. [33]. For more detail, see Additional file 2: Additional Methods. [33,34,35,36,37]

All final models included PPI use, irrespective of whether it was significant, because it was the exposure of interest. The model was concluded as the final model after assessing the adequacy and fit of the model. The cohort contained women with one or more pregnancies resulting in live birth. Generalized Estimating Equations (GEE) were used to take the correlation between siblings into account [38].

All analyses were performed in R version 3.6.1 [39,40,41,42]. Observations with missing information on one of the outcomes (n = 6105) were removed from all analyses. All covariates were categorical and, if necessary, included a separate category for missing values.

Sensitivity analysis (only firstborns)

Multiple linear logistic regression was performed for each outcome, including only the firstborn children. Covariates included all before-mentioned maternal characteristics. Pregnancy and obstetric characteristics included were pre-eclampsia, mode of delivery, preterm birth, and neonatal birthweight, excluding covariates considering multiple pregnancies.

Use of PPIs at different timepoints (trimesters)

To determine if the use of PPIs had a different effect depending on the timing of the prescription, the exposure was split into four time periods. The first period ranged from 3 months before up to the last menstrual period (LMP). The time between the LMP and the delivery was divided into trimesters. The first trimester lasted until 97 days after LMP, the second starting at 98 days until 202 days of gestation, and the third trimester ranged from 203 days of gestation to delivery.

Dose–response association

The dose–response association between PPI use and the outcomes was assessed in two models. First, PPI use was replaced by a categorical variable describing the number of prescriptions (≤ 1, 2, 3, ≥ 4). Second, PPI use was described by a categorical variable based on the quantiles of the DDD presented as the number of weeks.

Results

Descriptive characteristics

The study included 1,089,514 live singleton births delivered between July 2006 and December 2016 in Sweden. PPIs were used by 1.4% (n = 14,787) of the women 3 months before or during gestation (Table 1). Overall, PPI users were older and had a higher BMI at the start of pregnancy. Among PPI users, the reported obesity was almost double (21.6%) compared to PPI non-users (11.7%). Users had a higher frequency of comorbidities (11.9% of users and 5.8% of non-users) and/or exposure to other drugs than PPIs (53.6% and 31.8% respectively). The use of PPIs was more prevalent among the third or higher pregnancy of the mother.

Table 1 Distribution of maternal and obstetric characteristics among all singleton pregnancies resulting in livebirth in Sweden, by exposure to proton pump inhibitors (PPIs)

Among PPI users, 4.9% developed pre-eclampsia and 3.2% GDM, whereas these complications occurred in respectively 3.3% and 1.4% of pregnancies in non-users (Table 2). Children were born preterm in 6.5% and had a low AS5min in 1.6% of the deliveries among PPI users and respectively 4.6% and 1.2% in non-users. The birthweight of the child was SGA or LGA in respectively 3.2% and 3.9% among users and 2.3% and 3.5% among non-users.

Table 2 Distribution of the outcomes among all singleton pregnancies resulting in livebirth in Sweden, by proton pump inhibitor (PPI) exposure

Other risk factors

Large increasing effects on at least one of the outcomes were shown for BMI, smoking, comorbidities, and mode of delivery (all time periods combined). Women with a higher BMI or comorbidities had higher odds of pre-eclampsia and gestational diabetes and giving birth to a LGA child (Additional file 3: Table A2). Smoking was associated with over twofold odds of the child being SGA (OR = 2.03, 95% CI 1.95–2.11) and a c-section affected the odds of a low AS5min (OR = 3.33, 95% CI 3.21–3.46). Among women diagnosed with pre-eclampsia during the pregnancy, the odds of preterm birth (OR = 4.74, 95% CI 4.60–4.89) and SGA (OR = 4.46, 95% CI 4.30–4.63) were increased. If the women had a history of pre-eclampsia or GDM, they had an OR of respectively 5.20 (4.76–5.68) and 8.98 (7.92–10.17) to have the outcome again. The odds of delivering preterm and a low AS5min were slightly higher if it occurred during a previous pregnancy of the women. The child had higher odds of being SGA or LGA if their sibling was SGA (OR = 8.16, 95% CI 7.55–8.81) or LGA (OR = 11.05, 95% CI 10.52–11.61), respectively.

Use of PPIs and the risk of maternal and neonatal health effects

The use of PPIs increased the odds of the mother developing pre-eclampsia and GDM respectively with 19 and 29% compared to non-users (OR = 1.19, 95% CI 1.10–1.29 and OR = 1.29, 95% CI 1.16–1.43) (Table 2). The neonate of a PPI user had an OR of 1.23 (95% CI 1.14–1.32) to be born preterm compared to non-users. The odds of a low AS5min was not significantly affected by PPI use of the mother. The use of PPIs increased the odds of a SGA child (OR = 1.27, 95% CI 1.16–1.40), whereas it was associated with a decrease in the odds for LGA (OR = 0.84, 95% CI 0.77–0.91).

Subset analysis (only firstborns)

Including only the firstborn children in the data resulted in similar associations as the analysis including all live births (Additional file 4: Table A3).

Use of PPIs at different timepoints (trimesters)

The odds of developing pre-eclampsia increased when PPIs were used during the second (OR = 1.38, 95% CI 1.16–1.65) or third (OR = 1.45, 95% CI 1.20–1.76) trimester of pregnancy (Table 3). If a woman had PPIs prescribed during the second and third trimester, her odds of pre-eclampsia was 1.29 (95% CI 1.12–1.50). The use of PPIs only in the 3 months before the LMP was significantly associated with and increased odds of giving birth to a child preterm (OR = 1.26, 95% CI 1.12–1.42) and of the child being SGA (OR = 1.42, 95% CI 1.11–1.82). PPI use only in the first trimester gave a slight increase in the odds of preterm birth (OR = 1.16, 95% CI 1.02–1.30) and SGA (OR = 1.25, 95% CI 1.00–1.57). The odds of preterm birth were mainly affected by the use of PPIs during the second trimester (OR = 1.58, 95% CI 1.40–1.78). In contrast, PPI use in the third trimester reduced the odds of preterm birth (OR = 0.54, 95% CI 0.47–0.61).

Table 3 Associations between PPI exposure at different timepoints during pregnancy and maternal and neonatal health outcomes expressed as odds ratios (OR) with 95% confidence interval (CI) obtained by multiple logistic regression including all live births

Dose–response association

Women with 2 (OR = 1.21, 95% CI 1.09–1.36) or 3 (OR = 1.30, 95% CI 1.11–1.52) prescriptions had higher odds of pre-eclampsia compared to non-users (Table 4). An increased odds of GDM was associated with any number of PPI prescriptions, yet without large differences in the odds of GDM between the different categories. All prescriptions increased the odds of preterm birth, with 3 prescriptions (OR = 1.56, 95% CI 1.37–1.78) having a larger effect compared to 2 (OR = 1.11, 95% CI 1.01–1.23) or at least 4 (OR = 1.19, 95% CI 1.03–1.36) prescriptions. The odds of SGA was higher in women with 2 (OR = 1.31, 95% CI 1.14–1.49) or at least 4 (OR = 1.28, 95% CI 1.05–1.56) prescriptions compared to non-users, and the effect was not higher in women with 4 or more prescriptions than in women with 2 prescriptions. Filling of 2 prescriptions was significantly associated with lower odds of LGA (OR = 0.81, 95% CI 0.71–0.92).

Table 4 Association between the use of PPI and maternal and neonatal outcomes presented as the odds ratio of having the outcome compared to non-users

A DDD up to and including 36 weeks increased the odds of developing pre-eclampsia compared to non-users. No significant difference was found between the different categories. DDDs up to 20 weeks and over 36 weeks were associated with higher odds of GDM, without differences across the categories. No association was found between a DDD of 20–36 weeks and the odds of GDM. An increased odds of preterm birth and SGA was associated with PPI use longer than 12 weeks, but no large differences between the DDD categories. A slight decrease in the odds of LGA was associated with a DDD of 0–12 (OR = 0.82, 95% CI 0.69–0.97), 20–36 (OR = 0.79, 95% CI 0.66–0.95), and more than 36 (OR = 0.83, 95% CI 0.70–0.98) weeks.

Discussion

In this large Swedish population-based study, PPI use shortly before and during pregnancy was associated with a higher probability of pre-eclampsia, GDM, preterm birth, and being born SGA. Analysis of only the firstborn child of a mother yielded similar results. Differences in the outcomes were seen by the different exposure periods based on all pregnancies. PPI use in the period ranging from three months before LMP until the end of the first trimester was associated with increased odds of preterm birth and SGA. Similarly, the odds of preterm birth were also higher when PPIs were used during the second trimester. PPI use in the second and third trimesters of pregnancy was associated with higher odds of pre-eclampsia. No evidence for a dose–response relation between PPI use and any of the outcomes was found.

Our results are consistent with previous studies relating PPI use to a higher (or not reduced) risk of pre-eclampsia [20, 43, 44] and low birth weight [16, 17]. Contradicting to our results, other observational studies have reported no significant relation between PPI use and low birthweight and/or preterm birth [17, 19]. However, compared to this large nationwide study, both studies had a much lower number of observations available and mainly focused on major anomalies. To our knowledge, there are no randomized clinical trials investigating the safety of maternal PPI use, regarding maternal and neonatal adverse events [2, 45, 46]. We also question if it is still ethically defendable to conduct these on PPI use during pregnancy with the accumulating safety concerns based on association studies, and our increasing understanding of drug interactions and the microbiome [2, 5, 12, 47, 48].

The maternal and neonatal adverse events investigated can affect short- and long-term health of both the mother and the child. Pre-eclampsia is a cause of worldwide maternal and perinatal morbidity and mortality [49]. GDM has previously been associated with an increased frequency of maternal hypertensive disorders and an increased risk of type 2 diabetes after pregnancy. GDM has been associated with the child having a higher odds of developing obesity, glucose intolerance, and diabetes in late adolescence and young adulthood [50]. Preterm birth is a major cause of neonatal and infant morbidity. Children that are too small or too light at birth, have a higher risk of hypertension, obesity, and diabetes mellitus type 2 later in life [51] and a lower quality of life when young adults [52]. We do acknowledge that causality cannot be established and that unknown confounders may still affect the results. Although we did adjust for BMI, we did see that obesity was more prevalent among PPI users (21.6% vs. 11.7%) and was associated with higher odds of pre-eclampsia and gestational diabetes, preterm birth and large for gestational age, as previously described in the literature. [53, 54]

Despite being contra-indicated, 1.4% of pregnant women in this cohort were PPI users (excluding over-the-counter use). Many studies reporting PPI utilization during pregnancy report a prevalence below 2% [2, 5, 55, 56] with a few studies reporting up to 6% [43, 57, 58]. Although this is lower than reported utilization in non-pregnant adults [59,60,61,62,63], over-the-counter use is usually not included [64]. With 4 million pregnancies born in the European Union in 2020 alone, a 1% PPI prevalence equals 40,000 pregnancies per year [65]. As our results do support previous safety concerns [16,17,18,19], more awareness to potential consequences of PPI use during pregnancy seems warranted. To note, PPI use has been considered inappropriate in up to 70% of (non-pregnant) long-term users [66, 67].

The underlying mechanisms on how PPIs affect our health need further exploration; as well as safer (non-pharmaceutical) alternatives for treating of gastro-intestinal symptoms and discomfort during pregnancy. PPIs may still have a place for restricted indications during pregnancy, yet widespread and unsupervised over-the-counter use should be discouraged.

This study has several strengths including the large registry-based nature of the data and its high completeness, resulting in a large nationwide and population-based study with highly valid data on outcomes, exposure, and covariates.

Despite the high completeness of the registries and adjustments for confounders, confounding by indication could not be entirely ruled out. Nausea, vomiting (hyperemesis gravidarum), gastro-esophageal reflux, and/or peptic ulcers may be more common and/or severe among PPI users than non-users. Although these indications may increase the risk for adverse events, it remains unclear if PPI use can reduce the risk [68]. Reverse causation could be affecting the associations, particularly for third-trimester exposure, for which the effect could also be underestimated since not all deliveries reach the end of the third trimester.

In addition, information on the outcomes and some covariates was incomplete (< 1% overall). Observations with any of the outcome variables missing (0.006%) were removed. Information on the exposure was limited by the availability of PPIs over the counter and lack of confirmation whether the women actually used the drugs, although it is expected that most women will use prescribed drugs only (after advice from their midwife/clinician). We included only women with at least two dispensed prescriptions, indicating that they were utilizing the drugs. This, however, could lead to misclassification of women filling a single prescription taking the drug, or women who only took PPI over the counter. Another potential concern is that a woman with previously diagnosed diabetes might have been missed and/or misclassified as having GDM. Due to a lack of power (only 0.2% of this cohort used H2RA), it was not possible to assess the effect of H2RA use on the odds of an outcome. H2RA is prescribed for similar indications as PPIs and it is recommended only to prescribe PPIs if antiacids and H2RA do not sufficiently relieve symptoms [1, 13]. The registries did not provide data on potential confounders such as whether the mothers took tocolytics to suppress preterm delivery, chronic hypertension, and hyperemesis. We also lacked information on ethnicity and socio-economic status. Nonetheless, our results were adjusted for important factors including diabetes mellitus, gestational hypertension, and hypo- and hyperthyroidism. The current analysis only included whether the outcome of interest was present in a previous pregnancy, but not if any of the other outcomes was. Others reported on a previous LGA child increasing the risk of GDM [69] and previous SGA the risk of preterm birth [70]. Furthermore, we would not expect socio-economic differences to have influenced our results significantly, because pregnancy-related health care in Sweden is highly standardized, equally accessible for the entire country, and free for the expecting mother. We chose to categorize exposure by trimester above time-varying exposures, since this is the most applicable to clinical antenatal practice (in particular since any antenatal PPI use is contra-indicated). We also do not have the exact period of exposure, since the duration of use is estimated based on the average use per package.

Conclusions

Our large study suggests an increase in the risk of pre-eclampsia, GDM, preterm birth, and SGA associated to maternal PPI use during pregnancy. Therefore, we believe PPIs should be prescribed more cautiously and only be used under clinical supervision during pregnancy.

Availability of data and materials

Upon reasonable request and after required approvals from the Ethics Committee and National Board of Health and Welfare are obtained. Because the data belong to the National Board of Health and Welfare (Socialstyrelsen) and the detailed level of clinical information, it is not allowed to share these data publicly.

Abbreviations

AGA:

Appropriate for gestational age

AS:

Apgar score

ATC:

Anatomical therapeutical classification

CI:

Confidence interval

DDD:

Defined daily dose

FDA:

Food and Drug Administration

GDM:

Gestational diabetes mellitus

GEE:

Generalized Estimating Equations

H2RA:

Histamine-2 receptor antagonist

OR:

Odds ratio

LGA:

Large for gestational age

PPI:

Proton pump inhibitor

SGA:

Small for gestational age

References

  1. Nikfar S, Abdollahi M, Moretti ME, Magee LA, Koren G. Use of proton pump inhibitors during pregnancy and rates of major malformations: a meta-analysis. Dig Dis Sci. 2002;47(7):1526–9.

    Article  CAS  Google Scholar 

  2. Li CM, Zhernakova A, Engstrand L, Wijmenga C, Brusselaers N. Systematic review with meta-analysis: the risks of proton pump inhibitors during pregnancy. Aliment Pharmacol Ther. 2020;51(4):410–20.

    Article  Google Scholar 

  3. Boardman HF, Heeley G. The role of the pharmacist in the selection and use of over-the-counter proton-pump inhibitors. Int J Clin Pharm. 2015;37(5):709–16.

    Article  Google Scholar 

  4. Cohen J. Switching omeprazole in Sweden and the United States. Am J Ther. 2003;10(5):370–6.

    Article  Google Scholar 

  5. Andersen AB, Erichsen R, Farkas DK, Mehnert F, Ehrenstein V, Sorensen HT. Prenatal exposure to acid-suppressive drugs and the risk of childhood asthma: a population-based Danish cohort study. Aliment Pharmacol Ther. 2012;35(10):1190–8.

    Article  CAS  Google Scholar 

  6. Goulet O. Potential role of the intestinal microbiota in programming health and disease. Nutr Rev. 2015;73(suppl_1):32–40.

    Article  Google Scholar 

  7. Payne MS, Bayatibojakhi S. Exploring preterm birth as a polymicrobial disease: an overview of the uterine microbiome. Front Immunol. 2014;5:595.

    Article  Google Scholar 

  8. Dunlop AL, Mulle JG, Ferranti EP, Edwards S, Dunn AB, Corwin EJ. Maternal microbiome and pregnancy outcomes that impact infant health: a review. Adv Neonatal Care. 2015;15(6):377–85.

    Article  Google Scholar 

  9. Bayar E, Bennett PR, Chan D, Sykes L, MacIntyre DA. The pregnancy microbiome and preterm birth. Semin Immunopathol. 2020;42(4):487–99.

    Article  Google Scholar 

  10. Fuhler GM. The immune system and microbiome in pregnancy. Best Pract Res Clin Gastroenterol. 2020;44–45:101671.

    Article  Google Scholar 

  11. Imhann F, Bonder MJ, Vila AV, Fu J, Mujagic Z, Vork L, et al. Proton pump inhibitors affect the gut microbiome. Gut. 2016;65(5):740–8.

    Article  CAS  Google Scholar 

  12. Brusselaers N, Pereira M, Alm J, Engstrand L, Lilja Engstrand H. Effect of proton pump inhibitors in infants with esophageal atresia on the gut microbiome: a pilot cohort Gut Pathogens. 2022:In press (acceptance pending)

  13. Richter J. The management of heartburn in pregnancy. Aliment Pharmacol Ther. 2005;22(9):749–57.

    Article  CAS  Google Scholar 

  14. Mahadevan U, Kane S. American Gastroenterological Association Institute Medical Position Statement on the use of gastrointestinal medications in pregnancy. Gastroenterology. 2006;131(1):278–82.

    Article  Google Scholar 

  15. Omeprazole Pregnancy and Breastfeeding Warnings 2022 [cited 2022 October 2022]. Available from: https://www.drugs.com/pregnancy/omeprazole.html

  16. Nielsen G, Sørensen H, Thulstrup A, Tage-Jensen U, Olesen C, Ekbom A. The safety of proton pump inhibitors in pregnancy. Aliment Pharmacol Ther. 1999;13(8):1085–9.

    Article  CAS  Google Scholar 

  17. Diav-Citrin O, Arnon J, Shechtman S, Schaefer C, Van Tonningen M, Clementi M, et al. The safety of proton pump inhibitors in pregnancy: a multicentre prospective controlled study. Aliment Pharmacol Ther. 2005;21(3):269–75.

    Article  CAS  Google Scholar 

  18. Pasternak B, Hviid A. Use of proton-pump inhibitors in early pregnancy and the risk of birth defects. N Engl J Med. 2010;363(22):2114–23.

    Article  CAS  Google Scholar 

  19. Matok I, Levy A, Wiznitzer A, Uziel E, Koren G, Gorodischer R. The safety of fetal exposure to proton-pump inhibitors during pregnancy. Dig Dis Sci. 2012;57(3):699–705.

    Article  CAS  Google Scholar 

  20. Hastie R, Bergman L, Cluver CA, Wikman A, Hannan NJ, Walker SP, et al. Proton pump inhibitors and preeclampsia risk among 157 720 women: a Swedish population register–based cohort study. Hypertension. 2019;73(5):1097–103.

    Article  CAS  Google Scholar 

  21. Fornes R, Simin J, M.N. N, Cruz G, Crisosto N, Van Der Schaaf M et al. Pregnancy, perinatal and childhood outcomes in women with and without Polycystic Ovary Syndrome and metformin during pregnancy: a nationwide population-based study. Reprod Biol Endocrinol. 2022;In press

  22. Nguyen H, Fornes R, Kamau N, Danielsson H, Callens S, Fransson E et al. Antibiotic use during pregnancy and the risk of preterm birth: a population-based Swedish cohort study. J Antimicrob Chemother (JAC). 2022;In press

  23. Källén BaK, Karin. The Swedish Medical Birth Register - a summary of content and quality. The Centre of Epdemiology - The National Board of Health and Welfare; 2003

  24. The Swedish Medical Birth Registry - Statistics Sweden 2021 [Available from: https://www.socialstyrelsen.se/en/statistics-and-data/registers/register-information/the-swedish-medical-birth-register/.

  25. Axelsson O. The Swedish medical birth register. Acta Obstet Gynecol Scand. 2003;82(6):491–2.

    Article  Google Scholar 

  26. Wettermark B, Hammar N, Fored CM, Leimanis A, Otterblad Olausson P, Bergman U, et al. The new Swedish Prescribed Drug Register–opportunities for pharmacoepidemiological research and experience from the first six months. Pharmacoepidemiol Drug Saf. 2007;16(7):726–35.

    Article  Google Scholar 

  27. Ludvigsson JF, Andersson E, Ekbom A, Feychting M, Kim JL, Reuterwall C, et al. External review and validation of the Swedish national inpatient register. BMC Public Health. 2011;11:450.

    Article  Google Scholar 

  28. Socialstyrelsen. Kodningskvalitet i patientregistret - Ett nytt verktyg för att mäta kvalitet. 2013

  29. Ludvigsson JF, Otterblad-Olausson P, Pettersson BU, Ekbom A. The Swedish personal identity number: possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol. 2009;24(11):659–67.

    Article  Google Scholar 

  30. Fornes R, Simin J, Nguyen MH, Cruz G, Crisosto N, van der Schaaf M, et al. Pregnancy, perinatal and childhood outcomes in women with and without polycystic ovary syndrome and metformin during pregnancy: a nationwide population-based study. Reprod Biol Endocrinol: RB&E. 2022;20(1):30.

    Article  CAS  Google Scholar 

  31. Marsal K, Persson PH, Larsen T, Lilja H, Selbing A, Sultan B. Intrauterine growth curves based on ultrasonically estimated foetal weights. Acta Paediatr. 1996;85(7):843–8.

    Article  CAS  Google Scholar 

  32. Lam WY, Fresco P. Medication adherence measures: an overview. Biomed Res Int. 2015;2015:217047.

    Article  Google Scholar 

  33. Hosmer DW, Lemeshow S, Sturdivant RX, Hosmer DW, Jr. Applied logistic regression. New York, UNITED STATES: Wiley, Incorporated; 2013

  34. Agresti A. Categorical data analysis. Third;3rd; ed. Hoboken, N.J: Wiley-Interscience; 2013

  35. Statistics Sweden. [January 2017]. Available from: http://www.socialstyrelsen.se/barnochfamilj/graviditet

  36. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W. Applied linear statistical models. 4th ed. Chicago, Ill: Irwin; 1996.

    Google Scholar 

  37. Midi H, Sarkar SK, Rana S. Collinearity diagnostics of binary logistic regression model. J Interdisc Math. 2010;13(3):253–67.

    Article  Google Scholar 

  38. Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73(1):13–22.

    Article  Google Scholar 

  39. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019

  40. Conway J, Eddelbuettel D, Nishiyama T, Prayaga SK, Tiffin N. RPostgreSQL: R Interface to the ‘PostgreSQL’ Database System. R Found Stat Comput. 2017. https://cran.r-project.org/web/packages/RPostgreSQL/RPostgreSQL.pdf.

  41. Yee TW. Vector generalized linear and additive models: with an implementation in R. New York, USA: Springer; 2015.

    Book  Google Scholar 

  42. Halekoh U, Højsgaard S, Yan J. The R package geepack for generalized estimating equations. J Stat Softw. 2006;15(2):1–11.

    Article  Google Scholar 

  43. Bello NA, Huang Y, Syeda SK, Wright JD, D’Alton ME, Friedman AM. Receipt of proton-pump inhibitors during pregnancy and risk for preeclampsia. Am J Perinatol. 2021;38(14):1519–25.

    Article  Google Scholar 

  44. Choi A, Noh Y, Park SH, Choe SA, Shin JY. Exploration of proton pump inhibitors use during pregnancy and preeclampsia. JAMA Netw Open. 2021;4(9):e2124339.

    Article  Google Scholar 

  45. Phupong V, Hanprasertpong T. Interventions for heartburn in pregnancy. Cochrane Database Syst Rev. 2015;2015(9):Cd011379.

    Google Scholar 

  46. Christopher L. The role of proton pump inhibitors in the treatment of heartburn during pregnancy. J Am Acad Nurse Pract. 2005;17(1):4–8.

    Article  Google Scholar 

  47. Salvo EM, Ferko NC, Cash SB, Gonzalez A, Kahrilas PJ. Umbrella review of 42 systematic reviews with meta-analyses: the safety of proton pump inhibitors. Aliment Pharmacol Ther. 2021;54(2):129–43.

    Article  CAS  Google Scholar 

  48. Imhann F, Vich Vila A, Bonder MJ, Lopez Manosalva AG, Koonen DPY, Fu J, et al. The influence of proton pump inhibitors and other commonly used medication on the gut microbiota. Gut Microbes. 2017;8(4):351–8.

    Article  CAS  Google Scholar 

  49. Sibai B, Dekker G, Kupferminc M. Pre-eclampsia. The Lancet. 2005;365(9461):785–99.

    Article  Google Scholar 

  50. American Diabetes Association. Gestational diabetes mellitus. Diabetes Care. 2004;27:S88.

    Article  Google Scholar 

  51. Chatelain P. Children born with intrauterine growth retardation (IUGR) or small for gestational age (SGA): long term growth and metabolic consequences. Endocr Regul. 2000;34(1):33–6.

    CAS  Google Scholar 

  52. Lund LK, Vik T, Lydersen S, Løhaugen GC, Skranes J, Brubakk A-M, et al. Mental health, quality of life and social relations in young adults born with low birth weight. Health Qual Life Outcomes. 2012;10(1):1–10.

    Article  Google Scholar 

  53. Stubert J, Reister F, Hartmann S, Janni W. The risks associated with obesity in pregnancy. Deutsches Arzteblatt Int. 2018;115(16):276–83.

    Google Scholar 

  54. Catalano PM, Shankar K. Obesity and pregnancy: mechanisms of short term and long term adverse consequences for mother and child. BMJ. 2017;356:j1.

    Article  Google Scholar 

  55. Chen YH, Lin HC, Lou HY. Increased risk of low birthweight, infants small for gestational age, and preterm delivery for women with peptic ulcer. Am J Obstet Gynecol. 2010;202(2):164.e1-8.

    Article  Google Scholar 

  56. Wilton LV, Pearce GL, Martin RM, Mackay FJ, Mann RD. The outcomes of pregnancy in women exposed to newly marketed drugs in general practice in England. Br J Obstet Gynaecol. 1998;105(8):882–9.

    Article  CAS  Google Scholar 

  57. Hastie R, Bergman L, Cluver CA, Wikman A, Hannan NJ, Walker SP, et al. Proton pump inhibitors and preeclampsia risk among 157 720 women. Hypertension. 2019;73(5):1097–103.

    Article  CAS  Google Scholar 

  58. Ruigomez A, Garcia Rodriguez LA, Cattaruzzi C, Troncon MG, Agostinis L, Wallander MA, et al. Use of cimetidine, omeprazole, and ranitidine in pregnant women and pregnancy outcomes. Am J Epidemiol. 1999;150(5):476–81.

    Article  CAS  Google Scholar 

  59. Halfdanarson OO, Pottegard A, Bjornsson ES, Lund SH, Ogmundsdottir MH, Steingrimsson E, et al. Proton-pump inhibitors among adults: a nationwide drug-utilization study. Therap Adv Gastroenterol. 2018;11:1756284818777943.

    Article  Google Scholar 

  60. Liu Y, Zhu X, Li R, Zhang J, Zhang F. Proton pump inhibitor utilisation and potentially inappropriate prescribing analysis: insights from a single-centred retrospective study. BMJ Open. 2020;10(11):e040473.

    Article  Google Scholar 

  61. Pottegard A, Broe A, Hallas J, de Muckadell OB, Lassen AT, Lodrup AB. Use of proton-pump inhibitors among adults: a Danish nationwide drug utilization study. Ther Adv Gastroenterol. 2016;9(5):671–8.

    Article  Google Scholar 

  62. Rotman SR, Bishop TF. Proton pump inhibitor use in the U.S. ambulatory setting, 2002-2009. PloS one. 2013;8(2):e56060.

    Article  CAS  Google Scholar 

  63. Tosetti C, Nanni I. Use of proton pump inhibitors in general practice. World J Gastrointest Pharmacol Ther. 2017;8(3):180–5.

    Article  Google Scholar 

  64. Cohen J. Switching omeprazole in Sweden and the United States. Am J Ther. 2003;10(5):370–6.

    Article  Google Scholar 

  65. Fertility Statistics - Eurostat statistics explained [cited 2022. Available from: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Fertility_statistics#The_birth_rate_in_the_EU_decreased_at_a_slower_pace_between_2000_and_2020_than_previously

  66. Boghossian TA, Rashid FJ, Thompson W, Welch V, Moayyedi P, Rojas-Fernandez C, et al. Deprescribing versus continuation of chronic proton pump inhibitor use in adults. Cochrane Database Syst Rev. 2017;3:CD011969.

    Google Scholar 

  67. Kim J, Blackett JW, Jodorkovsky D. Strategies for effective discontinuation of proton pump inhibitors. Curr Gastroenterol Rep. 2018;20(6):27.

    Article  Google Scholar 

  68. Cluver CA, Hannan NJ, van Papendorp E, Hiscock R, Beard S, Mol BW, et al. Esomeprazole to treat women with preterm preeclampsia: a randomized placebo controlled trial. Am J Obstet Gynecol. 2018;219(4):388 e1-e17.

    Article  Google Scholar 

  69. Fadl HE, Simmons D. Trends in diabetes in pregnancy in Sweden 1998–2012. BMJ Open Diabetes Res Care. 2016;4(1):e000221.

    Article  Google Scholar 

  70. Berkowitz GS, Blackmore-Prince C, Lapinski RH, Savitz DA. Risk factors for preterm birth subtypes. Epidemiology. 1998:9(3):279–85.

Download references

Acknowledgements

None

Funding

Open access funding provided by Karolinska Institute. NB received funding from the Swedish Research Council (Vetenskapsrådet) (2020–01058). RF received funding from the “National Commission for Scientific and Technological Research.” CONICYT, scholarship program “Becas Chile, Postdoctorado en el extranjero.” This funding organization did not have any role in the design of the study, the analyses, or the decision to publish.

Author information

Authors and Affiliations

Authors

Contributions

NB is the guarantor of the study. All authors (EB, JS, RF, HEL, LE, RB, NB) were involved in the conceptualization and design of the study. NB and RF were responsible for the data acquisition. Data cleaning was done by RF and JS. Analysis and interpretation were done by EB under the supervision and with support of NB, RB, and JS. Drafting of the article was done by EB, NB, and JS. All authors (EB, JS, RF, HEL, LE, RB, NB) revised and contributed to the intellectual content of the article. All authors (EB, JS, RF, HEL, LE, RB, NB) approved the final version of the article, including the authorship list.

Corresponding author

Correspondence to Nele Brusselaers.

Ethics declarations

Ethics approval and consent to participate

Approved by the Regional Ethics Committee of Stockholm (2017/2423–31).

Consent for publication

Waived because of registry-based nature.

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.

Esmeralda (Esmee) M. Breddels and Johanna Simin should be considered joined first authors.

Robin Bruyndonckx and Nele Brusselaers should be considered joined senior authors.

Supplementary information

Additional file 1: Table A1.

Description of the dependent and independent variables used and if appropriate the ICD-10 and ATC-codes used to identify the presence of the variable.

Additional file 2.

Additional methods.

Additional file 3: Table A2.

Associations between PPI exposure and maternal and neonatal health outcomes including all live births. Results were obtained by multiple logistic regression and expressed as odds ratios (OR) with 95% confidence interval (CI). Empty cells indicated the variable was not included in the final model for the outcome. Abbreviations: AGA, average for gestational age; AS5min, Apgar score 5 min after birth; BMI, body mass index; GDM, gestational diabetes; LGA, large for gestational age; NA, not available; PPI, proton pump inhibitors; SGA, small for gestational age.

Additional file 4: Table A3.

Associations between PPI exposure and maternal and neonatal health outcomes including only firstborns. Results were obtained by multiple logistic regression and expressed as odds ratios (OR) with 95% confidence interval (CI). Empty cells indicated the variable was not included in the final model for the outcome. Abbreviations: AGA, average for gestational age; AS5min, Apgar score 5 min after birth; BMI, body mass index; GDM, gestational diabetes; LGA, large for gestational age; NA, not available; PPI, proton pump inhibitors; SGA, small for gestational age.

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 http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) 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

Verify currency and authenticity via CrossMark

Cite this article

Breddels, E.M., Simin, J., Fornes, R. et al. Population-based cohort study: proton pump inhibitor use during pregnancy in Sweden and the risk of maternal and neonatal adverse events. BMC Med 20, 492 (2022). https://doi.org/10.1186/s12916-022-02673-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12916-022-02673-x

Keywords

  • Proton pump inhibitors
  • Pregnancy
  • Maternal and neonatal health
  • Logistic regression