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. 2019 Dec 30;14(12):e0227120. doi: 10.1371/journal.pone.0227120

The association between exposure to interferon-beta during pregnancy and birth measurements in offspring of women with multiple sclerosis

Sarah Burkill 1,2,*, Pia Vattulainen 3, Yvonne Geissbuehler 4, Meritxell Sabido Espin 5, Catrinel Popescu 6, Kiliana Suzart-Woischnik 7, Jan Hillert 8, Miia Artama 9, Auli Verkkoniemi-Ahola 10, Kjell-Morten Myhr 11, Sven Cnattingius 2, Pasi Korhonen 3, Scott Montgomery 2,12,13, Shahram Bahmanyar 1,2
Editor: Cheryl S Rosenfeld14
PMCID: PMC6936848  PMID: 31887199

Abstract

Background

Interferon-beta (IFN-beta) is a commonly used treatment for multiple sclerosis (MS). Current guidelines recommend cessation of treatment during pregnancy, however the results of past studies on the safety of prenatal exposure to IFN-beta have been conflicting. A large scale study of a population of MS women is therefore warranted.

Objectives

To assess whether, among those born to women with MS, infants prenatally exposed to IFN-beta show evidence of smaller size at birth relative to infants which were not prenatally exposed to any MS disease modifying drugs.

Methods

Swedish and Finnish register data was used. Births to women with MS in Sweden and Finland between 2005–2014 for which a birth measurement for weight, height, and head circumference was available were included. The exposure window was from 6 months prior to LMP to the end of pregnancy.

Results

In Sweden, 411 pregnancies were identified as exposed to IFN-beta during the exposure window, and 835 pregnancies were counted as unexposed to any MS DMD. The corresponding numbers for Finland were 232 and 331 respectively. Infants prenatally exposed to interferon-beta were on average 28 grams heavier (p = 0.17), 0.01 cm longer (p = 0.95), and had head circumferences 0.14 cm larger (p = 0.13) in Sweden. In Finland, infants were 50 grams lighter (p = 0.27), 0.02 cm shorter (p = 0.92) and had head circumferences 0.22 cm smaller (p = 0.15) relative to those unexposed.

Conclusions

This study provides evidence that exposure to IFN-beta during pregnancy does not influence birth weight, length, or head circumference.

Introduction

Multiple sclerosis (MS) is an immune mediated disease causing demyelination and axonal loss in the central nervous system (CNS) [1]. Women with MS often cease treatment during pregnancy, in part due to immunological adaptations during pregnancy including suppression of T-cell activity[2], which occur to allow foetal growth without the foetus being recognised as a foreign agent by the immune system, often reducing symptoms[3]. Interferon beta (IFN-beta) is a commonly used disease modifying treatment for MS, with current guidelines recommending cessation during pregnancy. Pregnant women are often excluded from clinical trials, meaning the effects of prenatal exposure are not known, resulting in a preference to cease treatment. If the pregnancy is planned, decisions need to be made as to whether treatment should be continued. However not all pregnancies are planned and some women will conceive whilst on treatment, making foetal exposure to IFN-beta inevitable in some instances. The possible effects of this are not known with certainty. Results from smaller scale studies generally show no adverse pregnancy outcomes for mothers exposed to IFN-beta during pregnanc[4–8]. However, some studies have concluded that exposure could potentially be detrimental, making the benefit–risk assessment for mother and child and therefore the decision to continue or discontinue treatment after conception difficult[9]. Given conflicting outcomes in past research[4–9], an investigation into potential adverse pregnancy outcomes related to foetal exposure to IFN-beta using larger populations of women with MS rather than smaller samples is therefore warranted.

This study utilized administrative data from Sweden and Finland to compare birth measurements (birth weight, height, and head circumference) of infants of women with MS exposed to IFN-beta, and infants of women with MS unexposed to MS disease modifying drugs (DMD).

Methods

Study population and data sources

Pregnancies resulting in a live birth to women with a diagnosis of MS were identified using the Medical Birth Registers between the years of 2005 and 2014 in Sweden and Finland. These dates were selected because the PDR in Sweden began in 2005, so it would not be possible to identify prescriptions of IFN-beta prior to this date. The corresponding date was chosen as the beginning of the study in Finland to ensure results were comparable. The end of the study was 31st December 2014, because at the time of the data application this was the most recently available data. In Sweden, the mother’s diagnosis of MS was identified using the National Patient Registers (NPR) (International Classification of Diseases tenth revision [ICD 10] code G35), and the National Multiple Sclerosis Register (MSR). Informed consent is required for a patient to be included in the MSR. Data for identification of exposure to IFN-beta and other disease modifying treatments was retrieved from the Prescribed Drugs Register (PDR), which records all collected prescriptions within Sweden and allows for identification of treatments through the use of anatomical therapeutic chemical classification (ATC) system codes. The MS Register was also used to identify when treatment had been initiated in hospital. In Finland, an MS diagnosis was identified using the National Reimbursement Register, and the Care Register for Health Care, and exposure to IFN-beta was retrieved from the Finnish PDR, using ATC codes.

Only observations for which a birth height, birth weight, and birth head circumference measurement had been recorded were included. All three measurements needed to be present for the birth to be included in the study to ensure the same infants were being compared throughout the analyses. All pregnancies to women with MS within Sweden and Finland between 2005 and 2014 were included in this population based study.

Exposure and outcome

Exposure to IFN-beta was counted as having occurred if the individual collected a prescription of IFN-beta during the exposure window, here defined as up to 6 months prior to the last menstrual period (LMP) until the end of the pregnancy according to the PDR of Sweden or Finland, or if a date of initiation for IFN-beta was recorded in the MSR in Sweden. It was assumed that the woman would have been exposed to IFN-beta in the 3 months before LMP or later, because the prescriptions are intended to last 3 months. Exposure to any MS DMD was also identified using the same registers and exposure window (see S1 Table for the list of ATC codes) aside from cladribine and mitoxantrone, for which exposure 6 months prior (purchase 9 months prior) to LMP was considered as exposed.

Within the MSR, exposure definitions are reliant on dates of treatment initiation and cessation. The accuracy of using such dates to determine exposure may be considered questionable. Clinicians who only temporarily cease treatment due to the pregnancy would not necessarily consider this a cessation of treatment. In these instances, date of cessation indicating the treatment was stopped during pregnancy would go unrecorded. It is also possible that no cessation date was recorded because the woman continued treatment during the exposure window. To examine whether results changed when applying different exposure criteria from the MSR, a sensitivity analysis identified all pregnancies with recorded treatment before the 6 months prior to LMP exposure window but with no cessation date during the exposure window. This analysis provides numbers exposed and mean values for each outcome, rather than comprising the main definition due to the previously mentioned limitations. It was only possible to conduct this sensitivity analysis using Swedish data, since such information is not available in the Finnish registers.

Pregnancies exposed to IFN-beta only were compared to pregnancies unexposed to any MS DMD. Birth measurements for weight in grams, height in cm, and head circumference in cm were used in a continuous form, and information was taken directly from the Medical Birth Registers of Sweden and Finland.

Statistical analysis

Linear regression using generalized estimating equations (GEEs) was undertaken in which the mother’s ID was used as a cluster identifier which demarcates siblings. Maternal age at LMP, gestational age in weeks, maternal smoking status, and within Sweden highest maternal educational attainment (separated into compulsory school or less, upper secondary, or higher education) were considered to be possible confounders, and included in the adjusted models. Sex of the newborn was also included in the model due to its association with birth measurements. An analysis studying differently exposed siblings was also undertaken and included the same confounders as covariates. Adjusting for maternal age at LMP meant birth order was considered in this model.

A sensitivity analysis which compared women exposed to any MS DMD (including but not limited to IFN-beta) to women unexposed to any MS DMD was also undertaken to assess whether the outcomes differed to when IFN-beta exposure only was studied.

Continuous measures which compare mean measurements between groups are the primary outcomes used in this paper.

Ethics statement

In Finland, the study was given a positive opinion by the Helsinki University Hospital Ethics Committee (Finland; 159/13/03/00/2016). Data permit approvals were granted by the National Institute for Health and Welfare (Dnro THL/635/5.05.00/2016) and the Social Insurance Institution (Dnro 42/522/2016). In Sweden, the study was approved by the Regional Ethical Review Board in Stockholm (Sweden; 2016/874-31/2). Data permit approvals were granted by National Board of Health and Welfare (Dnr 23981/2016) and Swedish MS Registry (Dnr 53). All individuals within Sweden and Finland are automatically included in administrative records through entry into national registers at birth or immigration. Informed consent is not gained for this. These databases are primarily administrative in nature, and are not set up with research as the intended outcome, however they are commonly used for such purposes. The datasets are held at the institutions listed in the data access statement, and were fully anonymised by the holding institutions before delivery.

Results

In Sweden, 411 pregnancies were identified as exposed to IFN-beta during the exposure window for which all birth measurements were available, and 835 pregnancies were counted as unexposed to any MS DMD. The corresponding numbers for Finland were 232 and 331, respectively. Within Sweden, there were 1131, and within Finland 442 women with MS registered as having a pregnancy during the study time frame. In Sweden, there were 101 pregnancies comprised of 50 sibling sets identified as being differently exposed to IFN-beta. The corresponding numbers for Finland were 83 pregnancies comprised of 41 sibling sets. The study population characteristics showed most women in Sweden had received higher education (studying at university) as their highest educational attainment (data on education were unavailable in Finland), and had a parity of 2 overall. When considering all pregnancies, the unexposed cohort had on average a slightly older age at LMP relative to the exposed cohort in Sweden, and in Finland the ages were very similar at LMP (Table 1). Gestational age at birth was not significantly different among the exposed and unexposed cohorts, with mean gestational age of 39.7 weeks for the exposed and 39.5 weeks (p = 0.10) for the unexposed cohorts in Sweden, and 39.4 weeks for the exposed and 39.5 weeks (p = 0.67) for the unexposed cohorts in Finland. Births prior to 22 weeks are not recorded in the medical birth registers. Very early births under 32 weeks are rare. S1–S3 Figs show the distribution of birth measurements and allow for identification of outliers.

Table 1. Exposed and unexposed cohort characteristics.

  Sweden      
All Differently exposed siblings
  Exposed Unexposed Exposed Unexposed
Number of pregnancies 411 835 50 51
Mean (SE) Gestational age, weeks, 39.7 (0.1) 39.5 (0.1) 40.0 (0.2) 39.2 (0.3)
Mean (SE) Birth weight, grams 3465.9 (27.7) 3414.8 (19.4) 3475.5 (66.3) 3346.6 (81.7)
Mean (SE) Birth height, cm's 50.1 (0.1) 50.0 (0.1) 50.3 (0.4) 49.7 (0.4)
Mean (SE) Head circumference, cm 35.0 (0.1) 35.0 (0.1) 35.0 (0.2) 34.7 (0.3)
Infant Sex
Male (%) 207 (50.4) 441 (52.8) 24 (48.0) 26 (51.0)
Female (%) 204 (49.6) 394 (47.2) 25 (52.0) 25 (49.0)
Mean (SE) maternal age, years 31.3 (0.2) 32.3 (0.2) 31.0 (0.6) 30.9 (0.5)
Maternal education
Compulsory school or less (%) 20 (4.9) 56 (6.7) 2 (4.0) 2 (3.9)
Upper secondary (%) 113 (27.5) 242 (30.0) 15 (30.0) 17 (33.3)
Higher education (%) 277 (67.4) 534 (64.0) 33 (66.0) 32 (62.8)
Missing data (%) 1 (0.2) 3 (0.4) 0 (0) 0 (0)
Smoking status
Smoker (%) 18 (4.4) 54 (6.5) 45 (90.0) 47 (92.2)
Nonsmoker (%) 378 (92.0) 740 (88.6) 3 (6.0) 0 (0)
Not known (%) 15 (3.7) 41 (4.9) 2 (4.0) 4 (7.8)
  Finland      
All Differently exposed siblings
  Exposed Unexposed Exposed Unexposed
Number of pregnancies 232 331 41 42
Mean (SE) Gestational age, weeks, 39.4 (2.4) 39.5 (1.9) 39.4 (2.9) 40.0 (1.2)
Mean (SE) Birth weight, grams 3357.5 (628.3) 3410.4 (541.0) 3306.6 (649.2) 3508.4 (441.7)
Mean (SE) Birth height, cm's 49.5 (3.1) 49.6 (2.5) 49.2 (3.8) 49.9 (1.9)
Mean (SE) Head circumference, cm 34.5 (2.2) 34.8 (1.7) 34.4 (2.6) 35.0 (1.4)
Infant Sex
Male 117 (50.4) 171 (51.7) 18 (43.9) 18 (43.9)
Female 115 (49.6) 160 (48.3) 23 (56.1) 24 (57.1)
Mean (SE) maternal age, years 30.0 (4.2) 30.6 (4.5) 30.0 (4.2) 30.6 (4.5)
Maternal education Not available Not available Not available Not available
Smoking status
Smoker (%) 33 (14.2) 49 (14.8) 2 (4.9) 3 (7.1)
Nonsmoker (%) 195 (84.1) 277 (83.7) 39 (95.1) 39 (92.9)
Not known (%) 4 (1.7) 5 (1.5) 0 (0) 0 (0)
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In the adjusted analyses, infants prenatally exposed to IFN-beta were on average 28 grams heavier (p = 0.17) in Sweden, and 50 grams lighter (p = 0.26) in Finland than those unexposed (Table 2). For birth height, those exposed to IFN-beta were 0.01 cm longer (p = 0.95) in Sweden, and 0.02 cm shorter (p = 0.92) in Finland compared to those unexposed. For head circumference, those in Sweden had measurements 0.14 cm larger (p = 0.13) and in Finland 0.22 cm smaller (p = 0.15) relative to those unexposed (unadjusted analyses shown in Table 2, adjusted analyses shown in Table 3).

Table 2. GEE’s unadjusted- differences in mean weight, height, and head circumference between exposed and unexposed cohorts.

Unadjusted
  Weight P-value Height P-value Head circumference P-value
Sweden
Overall 51.1 (37.4) 0.17 0.12 (0.2) 0.48 0.20 (0.1) 0.08
Differently exposed siblings 128.8 (110.0) 0.24 0.53 (0.5) 0.27 0.31 (0.3) 0.34
Finland            
Overall -52.8 (59.6) 0.38 -0.10 (0.3) 0.74 -0.26 (0.2) 0.21
Differently exposed siblings -198.2 (126.7) 0.12 -0.71 (0.8) 0.36 -0.57 (0.5) 0.3
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Table 3. GEE’s adjusted- differences in mean weight, height, and head circumference between exposed and unexposed cohorts.

  Adjusted*          
  Weight P-value Height P-value Head circumference P-value
Sweden
Overall 27.8 (20.1) 0.34 0.01 (0.1) 0.95 0.14 (0.1) 0.13
Differently exposed siblings -21.6 (77.1) 0.78 -0.10 (0.4) 0.78 -0.05 (0.3) 0.85
Finland            
Overall -50.3 (45.1) 0.27 -0.02 (0.2) 0.92 -0.21 (0.2) 0.15
Differently exposed siblings -83.6 (79.8) 0.30 0.07 (0.4) 0.85 -0.008 (0.3) 0.98
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*Adjusted for gestational age, sex of the newborn, smoking status of the mother, and maternal age at LMP

Analysis of groups of siblings in which at least one sibling was exposed, and at least one sibling was unexposed was undertaken (see Tables 2 and 3). Overall, differences between siblings exposed and siblings unexposed to IFN-beta during pregnancy were minimal, and not statistically significant. The same was true when considering exposure to any MS DMD, relative to non-exposure to MS DMD (see S3–S5 Tables).

Discussion

Infants born to women with MS exposed to IFN-beta during pregnancy did not show evidence of decreased intrauterine growth relative to infants born to MS women unexposed to IFN-beta during pregnancy. There were also no differences in mean gestational age at birth. No differences were found when comparing infants prenatally exposed to any MS DMD, relative to infants not exposed to MS DMD. This confirms evidence from some past studies which report no adverse effects for infants prenatally exposed to IFN-beta[5, 7, 10], and refutes the findings of other studies which have found IFN-beta is associated with birth measurement[11].

Birth measurements which are substantially below average have been reported to be associated with a number of adverse health outcomes, including behavioural difficulties such as ADHD[12], and other health conditions including coronary heart disease and diabetes[13, 14], although the extent to which these effects are confounded by social or genetic factors is incompletely understood[15]. The increased risks of adverse outcomes later in life highlights the importance of studying such measures.

Gestational age is one of the most critical factors determining birth measurements[16], with expected foetal weight gain of 24–26 grams per day in the third trimester for low risk pregnancies[17]. Gestational ages at birth were comparable across exposure groups, and maternal ages at LMP, indicating exposure to IFN-beta does not result in earlier gestational age at birth, and will therefore not have an impact on birth measurements through this mechanism.

One explanation as to why IFN-beta may not be influencing birth measurements or gestational age could be due to its pharmacokinetic characteristics. The placental barrier is a semipermeable tissue which separates foetal and maternal blood, and is only permeable for substances with a low molecular weight (between 600 and 800 Dalton)[18]. IFN-beta is categorized as a polypeptide with a molecular weight of 22.kDa (kiloDalton) for IFN-beta 1a and 18.5kDa for IFN-beta 1b[19], which is too large to permeate the placental barrier. The likelihood of IFN-beta therefore being able to directly affect the development of the foetus through permeation of foetal blood is unlikely, and suggests the lack of an effect of IFN-beta on foetal growth measurements is biologically plausible.

The maternal identification numbers included in our data allowed for observation of specific sets of siblings. This was particularly useful, as it enabled us to consider the effect of exposure to IFN-beta on an individual, relative to their unexposed sibling or siblings. In contrast to regular population analysis, sibling analysis controls by design for unmeasured shared familial (genetic and environmental) factors. Differences between the sibling exposure groups for all included birth measurements were not statistically significant for either Sweden or Finland.

Younger women are more likely to be of lower parity, and to have smaller mean birth measurements for their offspring[20]. Previous studies have indicated the effect of young maternal age on low birth height and weight measurements does not persist when behavioural, biological, and socioeconomic confounders have been controlled for[21, 22], indicating that maternal age may primarily be a confounder. Our study indicated the age at LMP for the pregnancies exposed to IFN-beta was on average lower than the age at LMP for the pregnancies unexposed to any MS DMD. This has the potential to influence birth measurements through potentially unmeasured confounders[23], even though maternal age at LMP itself has been included in the adjusted models.

A strength of this register study was our ability to include all women with MS who had a pregnancy that ended with a birth in both Sweden and Finland during the study period. Previous studies have relied on samples which can be self-selecting, and have reduced power due to smaller numbers enrolled than is possible using register data[24]. Inclusion of a population of pregnant women, with information available for all pregnancies to a particular patient group, removes the possibility of selection bias through for example over-recruitment of low- or high-risk pregnancies, improving the reliability of the results.

Potential limitations of the study should also be considered. It was not possible to know whether exposure identified using the Prescribed Drugs Register resulted in an exposed pregnancy, because we cannot be certain the treatment was taken by the mother. Only data which stated the drug had been dispensed was available for prescriptions, with the assumption then made that the treatment was taken as instructed. Previous studies into different treatments have indicated that pregnant women are less likely to adhere to treatment than other patient groups[25, 26], which increases the likelihood that dispensation does not necessarily equate to exposure. Only pregnancies for which information was recorded for all three measurements (weight, height, and head circumference at birth) were included in the study. If missingness is differential by exposure status, bias may be induced. We were limited in the variables we were able to adjust for, due to limited information being provided in registers. For example, potentially informative data on diet and physical activity are not available. A live birth had to have occurred for inclusion in the study population. The Medical Birth Registers of Sweden and Finland record births occurring at 22 weeks or later of pregnancy. Elective terminations are additionally not identifiable in the Swedish data. If the rates of elective termination and spontaneous abortion differ by exposure group, there is the potential for bias to be induced.

In summary, the evidence from this large population based study indicate no association between IFN-beta exposure and fetal growth or gestational age among infants of women with MS.

Supporting information

S1 Fig. Birth weight in grams by gestational age, according to interferon-beta exposure status.

(DOCX)

Click here for additional data file. (144KB, docx)
S2 Fig. Birth height in cms by gestational age, according to interferon-beta exposure status.

(DOCX)

Click here for additional data file. (104.6KB, docx)
S3 Fig. Head circumference by gestational age, according to interferon-beta exposure status.

(DOCX)

Click here for additional data file. (94KB, docx)
S1 Table. ATC codes and brand names used to identify interferon-beta exposure.

(DOCX)

Click here for additional data file. (12.3KB, docx)
S2 Table. Exposed to interferon-beta according to all possibly exposed sensitivity analysis (Sweden only).

(DOCX)

Click here for additional data file. (12.7KB, docx)
S3 Table. Exposure to any MSDMD’s sensitivity analysis.

(DOCX)

Click here for additional data file. (13.2KB, docx)
S4 Table. GEE OLS unadjusted models.

(DOCX)

Click here for additional data file. (12.9KB, docx)
S5 Table. GEE OLS adjusted models.

(DOCX)

Click here for additional data file. (12.9KB, docx)
S6 Table. Exposure to any MSDMD’s all possibly exposed sensitivity analysis (Sweden only).

(DOCX)

Click here for additional data file. (12.7KB, docx)

Acknowledgments

We would like to acknowledge the European Interferon Beta Pregnancy Study Group for their contribution to this manuscript

Data Availability

The data that support the findings of this study are available from the Swedish National Board of Health and Welfare (Prescribed Drug Register, National Patient Register, and Swedish Medical Birth Register), Karolinska Institute (MS register), and Statistics Sweden (Total Population Register). In Finland, the respective data are available from the National Institute of Welfare and Health (Care Register for Health Care, and Medical Birth Register, Register) and from the Social Insurance Institute (National Reimbursement Register and National Prescription Register). Restrictions apply to the availability of these data, which were used under license for this study. The data supporting these findings can be obtained by applying to the respective data holders in Sweden and Finland. For Sweden, data access queries can be sent to Registerservice@socialstyrelsen.se for the PDR, NPR, and MBR. For the MS register, data access queries can be sent to the register co-ordinator, anna.cunningham@ki.se. For the total population register, data access queries can be sent to scb@scb.se. For Finland, data access queries can be sent to info@tfl.fi for the MBR and CRHC. For the NRR and NPR, data access queries can be sent to tilastot@kela.fi.

Funding Statement

This study was funded by Bayer AG, Biogen Netherlands B.V., Merck KGaA, and Novartis Europharm Limited. YG is employed by Novartis Pharma AG, MS is employed by Merck group, CP is employed by Biogen and KSW is employed by Bayer AG. Novartis Pharma AG, Merck group, Biogen and Bayer AG provided support in the form of salaries for authors YG, MS, CP, and KSW. The funders were not involved in data collection or analysis, decision to publish, or preparation of the manuscript beyond the contributions of these authors, however they were able to comment on study design in the initial stages of the project. The specific roles of the authors employed by the funders are articulated in the ‘author contributions’ section. PV and PK are employed by EPID research. EPID research provided support in the form of salaries for authors PV and PK, and were involved in data collection and decision to publish, but were not involved in data analysis or manuscript preparation beyond the contributions of these authors. The specific roles of the authors are articulated in the ‘author contributions’ section.

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Decision Letter 0

Cheryl S Rosenfeld

30 Sep 2019

PONE-D-19-21273

The association between exposure to interferon-beta during pregnancy and birth measurements in offspring of women with multiple sclerosis

PLOS ONE

Dear Miss Burkill,

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Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

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Reviewer #1: This is a registry study, with limitations inherent to that methodology, of women with MS in Sweden and, separately in Finland, who were exposed to BIFN and to other DMDS compared to MS women who delivered live births who were not.

1. Reviewer would like to see in Methods or elsewhere comments by the authors on the powering of the study. Although no significant difference in bodily measures of newborns were uncovered in either Sweden or Finland, there were trends in each country’s cohort. Can a statement be made regarding what degree of difference would these sized groups be empowered to detect?

2. This study examined the size of newborns. To be counted, a live birth must have occurred. One limitation not mentioned is termination of pregnancies (miscarriages or abortions) which may have occurred in these women. This should be mentioned. Is there any way to uncover the rate of selective or non-selective terminations and how that may have affected the study results?

3. The investigators identified sibling pairs of mothers with MS, in which one was exposed and the sibling was not exposed to BIFN, and found no differences in body measures of infant siblings based on exposures. It would also be of interest to know what the power for detecting a difference would be. For example, would it be powered sufficiently to detect a 10% difference in one of the measures? And, could the authors mention what degree of difference in head circumference or other measures is considered significant?

4. The study results are averages. Is there a way to identify the numbers of outliers in each group? Numbers/rates of premature infants?

5. The authors speculate that, due to size, beta-interferons will not cross the placental barrier due to its low permeability for high molecular weight substances. Are there not data (even from animal studies) to cite which can provide more definitive information on whether beta-interferons can cross the placenta, or not?

Reviewer #2: The following points are addressed to the authors: A)Why was the period 2005-2014 chosen? B) Please note the number of sibs studied in the man text. C) On page 7, reference 11 (Betaseron Pregnancy registry) did NOT report birth measurement abnormalities with IFN beta exposure. D)Tables 2 and 3 are totally confusing and not truly discussed in the text. Why are sibs in there? Where is Sweden?

**********

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Attachment

Submitted filename: Birth registry review.docx

Click here for additional data file. (13.3KB, docx)
PLoS One. 2019 Dec 30;14(12):e0227120. doi: 10.1371/journal.pone.0227120.r002

Author response to Decision Letter 0

30 Oct 2019

We would like to thank the reviewers for their constructive comments, which have been invaluable for the improvement of the manuscript. Our responses to specific comments can be seen below.

Reviewer #1:

This is a registry study, with limitations inherent to that methodology, of women with MS in Sweden and, separately in Finland, who were exposed to BIFN and to other DMDS compared to MS women who delivered live births who were not.

Comment 1: Reviewer would like to see in Methods or elsewhere comments by the authors on the powering of the study. Although no significant difference in bodily measures of newborns were uncovered in either Sweden or Finland, there were trends in each country’s cohort. Can a statement be made regarding what degree of difference would these sized groups be empowered to detect?

Response 1: Power calculations are an attempt to assess how precisely estimates reflect the ‘true’ value of the population based on sample values and variance. However, the study population was not sampled. Instead we included the entire population of women with MS with a live birth. In the method section, we stress that the entire population of available individuals was included in the study (page 4 paragraph 2).

“All pregnancies to women with MS within Sweden and Finland between 2005 and 2014 were included in this population based study”.

Comment 2: This study examined the size of newborns. To be counted, a live birth must have occurred. One limitation not mentioned is termination of pregnancies (miscarriages or abortions) which may have occurred in these women. This should be mentioned. Is there any way to uncover the rate of selective or non-selective terminations and how that may have affected the study results?

Response 2: This has now been added to the limitation section on page 10 paragraph 1. It is not possible to identify elective terminations for the study dates within Sweden, because they are not recorded in the birth register or any other patient registers. The birth register only includes births over 22 weeks, making miscarriage difficult to identify. This has now been discussed in the limitations section.

“A live birth had to have occurred for inclusion in the study population. The Medical Birth Registers of Sweden and Finland record births occurring at 22 weeks or later of pregnancy. Elective terminations are additionally not identifiable in the Swedish data. If the rates of elective termination and spontaneous abortion differ by exposure group, there is the potential for bias to be induced.”

Comment 3: The investigators identified sibling pairs of mothers with MS, in which one was exposed and the sibling was not exposed to BIFN, and found no differences in body measures of infant siblings based on exposures. It would also be of interest to know what the power for detecting a difference would be. For example, would it be powered sufficiently to detect a 10% difference in one of the measures? And, could the authors mention what degree of difference in head circumference or other measures is considered significant?

Response 3: It is not possible to state exactly what difference would be counted as statistically significant, and what non-significant, because this also depends in large part on variance and the extent to which results are heterogenous. It is not possible with real data showing non-significance to state that a difference of X would result in an outcome being statistically significant. It may simply be that there are no differences between the cohorts. A lack of statistical power is one possible reason for non-significance, but we cannot prove either way whether it is the case.

Comment 4: The study results are averages. Is there a way to identify the numbers of outliers in each group? Numbers/rates of premature infants?

Response 4: Gestational ages were comparable across the two cohorts, and preterm delivery was not statistically significantly more common in either group relative to the other. This was included the results section on page 7 paragraph 1. There were very few outliers in which births were very early. Differences between cohorts would therefore be difficult to detect. In order to show distributions of birth measurements more clearly, scatterplots which showed birth measurement by gestational age have been included in the supplementary materials.

“Gestational age at birth was not statistically significantly different between the exposed and unexposed cohorts, with mean gestational age of 39.7 weeks for the exposed and 39.5 weeks (p=0.10) for the unexposed cohorts in Sweden, and 39.4 weeks for the exposed and 39.5 weeks (p=0.67) for the unexposed cohorts in Finland. Births prior to 22 weeks are not recorded in the Medical Birth Registers. Very early births under 32 weeks are rare Figures 1-3 in the supplementary materials show the distribution of birth measurements and allow for identification of outliers.”.

Comment 5: The authors speculate that, due to size, beta-interferons will not cross the placental barrier due to its low permeability for high molecular weight substances. Are there not data (even from animal studies) to cite which can provide more definitive information on whether beta-interferons can cross the placenta, or not?

Response 5: The literature, as far as we are aware, is based on observations (even in animal studies) for exposed vs unexposed subjects. These references are included currently in the manuscript (references 18 and 19). We have not been able to identify studies which take e.g. specific placental measurements of IFN-beta levels. If the reviewer can provide any we are happy to add them.

Reviewer #2:

The following points are addressed to the authors:

Comment 1: Why was the period 2005-2014 chosen?

Response 1: The Prescribed Drugs Register started in 2005 in Sweden. Therefore, it would not be possible to identify instances of IFN-beta prescription dispensation prior to this date. To ensure comparability of results, we decided to use the same study period in both Sweden and Finland. The date 2014 was chosen as study end, because at the point of data order, this was the most recent available data. This has been clarified in the methods section in page 4 paragraph 1.

“These dates were selected because the PDR in Sweden began in 2005, so it would not be possible to identify prescriptions of IFN-beta prior to this date. The corresponding date was chosen as the beginning of the study in Finland to ensure results were comparable. The end of the study was 31st December 2014, because at the time of the data application this was the most recently available data.”

Comment 2: Please note the number of sibs studied in the man text.

Response 2: For the differently exposed sibling analysis, the number of sibling groups has now been included in the results section on page 7 paragraph 1. Only pregnancies to women with MS were included in this study. This means that any children born before the woman was diagnosed, are not included in the study population, so sibling sets will only refer to births after MS diagnosis. It therefore seemed to make more sense to simply report the number of women included in the study. This has now also been added into the results (page 7 paragraph 1).

“Within Sweden, there were 1131, and within Finland 422 women with MS registered as having a pregnancy during the study time frame. In Sweden, there were 101 pregnancies comprised of 50 sibling sets identified as being differently exposed to IFN-beta. The corresponding numbers for Finland were 83 pregnancies comprising 41 sibling sets. ”

Comment 3: On page 7, reference 11 (Betaseron Pregnancy registry) did NOT report birth measurement abnormalities with IFN beta exposure.

Response 3: This reference has been moved and added into the list of references demonstrating no measurement abnormalities.

Comment 4: Tables 2 and 3 are totally confusing and not truly discussed in the text. Why are sibs in there? Where is Sweden?

Response 4: Table 2 is the unadjusted, and table 3 the adjusted results of the generalised estimating equations. The first row in tables 2 and 3 should be labelled to show those results pertain to Sweden. We thank the referee for pointing this out. This has now been rectified. It is not all siblings, but differently exposed siblings which are included there (where at least one sibling is prenatally exposed and at least one sibling is prenatally unexposed to IFN beta) because differently exposed siblings were compared as part of the analysis. The results of table 3 (the adjusted analysis) are covered in the results section on page 7 paragraph 2.

“In the adjusted analyses (table 3), infants prenatally exposed to IFN-beta were on average 28 grams heavier (p=0.17) in Sweden, and 50 grams lighter (p=0.26) in Finland than those unexposed. For birth height, those exposed to IFN-beta were 0.01 cm longer (p=0.95) in Sweden, and 0.02 cm shorter (p=0.92) in Finland compared to those unexposed. For head circumference, those in Sweden had measurements 0.14 cm larger (p=0.13) and in Finland 0.22 cm smaller (p=0.15) relative to those unexposed (unadjusted analysis shown in table 2, adjusted analysis shown in table 3).

Attachment

Submitted filename: Response to reviewers comments.docx

Click here for additional data file. (22.2KB, docx)

Decision Letter 1

Cheryl S Rosenfeld

13 Dec 2019

The association between exposure to interferon-beta during pregnancy and birth measurements in offspring of women with multiple sclerosis

PONE-D-19-21273R1

Dear Dr. Burkill,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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Cheryl S. Rosenfeld, DVM, PhD

Section Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The authors have answered all points in a satisfactory manner. I might just add in the Abstract/Results: No significant differences were noted between interferon beta exposed vs. unexposed infants.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: Yes: Patricia K. Coyle, MD

Acceptance letter

Cheryl S Rosenfeld

19 Dec 2019

PONE-D-19-21273R1

The association between exposure to interferon-beta during pregnancy and birth measurements in offspring of women with multiple sclerosis

Dear Dr. Burkill:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Birth weight in grams by gestational age, according to interferon-beta exposure status.

    (DOCX)

    Click here for additional data file. (144KB, docx)
    S2 Fig. Birth height in cms by gestational age, according to interferon-beta exposure status.

    (DOCX)

    Click here for additional data file. (104.6KB, docx)
    S3 Fig. Head circumference by gestational age, according to interferon-beta exposure status.

    (DOCX)

    Click here for additional data file. (94KB, docx)
    S1 Table. ATC codes and brand names used to identify interferon-beta exposure.

    (DOCX)

    Click here for additional data file. (12.3KB, docx)
    S2 Table. Exposed to interferon-beta according to all possibly exposed sensitivity analysis (Sweden only).

    (DOCX)

    Click here for additional data file. (12.7KB, docx)
    S3 Table. Exposure to any MSDMD’s sensitivity analysis.

    (DOCX)

    Click here for additional data file. (13.2KB, docx)
    S4 Table. GEE OLS unadjusted models.

    (DOCX)

    Click here for additional data file. (12.9KB, docx)
    S5 Table. GEE OLS adjusted models.

    (DOCX)

    Click here for additional data file. (12.9KB, docx)
    S6 Table. Exposure to any MSDMD’s all possibly exposed sensitivity analysis (Sweden only).

    (DOCX)

    Click here for additional data file. (12.7KB, docx)
    Attachment

    Submitted filename: Birth registry review.docx

    Click here for additional data file. (13.3KB, docx)
    Attachment

    Submitted filename: Response to reviewers comments.docx

    Click here for additional data file. (22.2KB, docx)

    Data Availability Statement

    The data that support the findings of this study are available from the Swedish National Board of Health and Welfare (Prescribed Drug Register, National Patient Register, and Swedish Medical Birth Register), Karolinska Institute (MS register), and Statistics Sweden (Total Population Register). In Finland, the respective data are available from the National Institute of Welfare and Health (Care Register for Health Care, and Medical Birth Register, Register) and from the Social Insurance Institute (National Reimbursement Register and National Prescription Register). Restrictions apply to the availability of these data, which were used under license for this study. The data supporting these findings can be obtained by applying to the respective data holders in Sweden and Finland. For Sweden, data access queries can be sent to Registerservice@socialstyrelsen.se for the PDR, NPR, and MBR. For the MS register, data access queries can be sent to the register co-ordinator, anna.cunningham@ki.se. For the total population register, data access queries can be sent to scb@scb.se. For Finland, data access queries can be sent to info@tfl.fi for the MBR and CRHC. For the NRR and NPR, data access queries can be sent to tilastot@kela.fi.

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