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. 2022 Mar 31;17(3):e0266203. doi: 10.1371/journal.pone.0266203

Pregnancy loss and risk of multiple sclerosis and autoimmune neurological disorder: A nationwide cohort study

Anders Pretzmann Mikkelsen 1,2,*, Pia Egerup 2,3,4, Astrid Marie Kolte 2,4, David Westergaard 4,5,6, Henriette Svarre Nielsen 2,3,4, Øjvind Lidegaard 1,2
Editor: Angela Lupattelli7
PMCID: PMC8970484  PMID: 35358256

Abstract

Background

The loss of one or more pregnancies before viability (i.e. pregnancy loss or miscarriage), has been linked to an increased risk of diseases later in life such as myocardial infarction and stroke. Recurrent pregnancy loss (i.e. three consecutive pregnancy losses) and multiple sclerosis have both been linked to immunological traits, which could predispose to both occurrences. The objective of the current study was to investigate if pregnancy loss is associated with later autoimmune neurological disease.

Methods

This register-based cohort study, included the Danish female population age 12 or older between 1977–2017. Women were grouped hierarchically: 0, 1, 2, ≥3 pregnancy losses, primary recurrent pregnancy loss (i.e. not preceded by a delivery), and secondary recurrent pregnancy loss (i.e. preceded by a delivery). The main outcome was multiple sclerosis and additional outcomes were amyotrophic lateral sclerosis, Guillain-Barré syndrome, and myasthenia gravis. Bayesian Poisson regression estimated incidence rate ratios [IRR] and 95% credible intervals [CI] adjusted for year, age, live births, family history of an outcome, and education.

Results

After 40,380,194 years of follow-up, multiple sclerosis was diagnosed among 7,667 out of 1,513,544 included women (0.5%), median age at diagnosis 34.2 years (IQR 27.4–41.4 years), and median age at symptom onset 31.2 years (IQR 24.8–38.2). The adjusted IRR of multiple sclerosis after 1 pregnancy loss was: 1.03 (95% CI 0.95–1.11), 2 losses: 1.02 (95% CI 0.86–1.20), ≥3 non-consecutive losses: 0.81 (95% CI 0.51–1.24), primary recurrent pregnancy loss: 1.18 (95% CI 0.84–1.60), secondary recurrent pregnancy loss: 1.16 (95% CI 0.81–1.63), as compared to women with no pregnancy losses. Seven sensitivity analyses and analyses for additional outcomes did not show significantly elevated adjusted risk estimates.

Conclusions

In this nationwide study, pregnancy loss was not significantly associated with autoimmune neurological disorder.

Introduction

Multiple sclerosis is the most prevalent demyelinating neurological disease and the incidence highest among women of reproductive age [1]. Although the etiology is unknown, the disease is presumed to be an autoimmune disorder with a hereditary element, were disease development is effected by unknown environmental interactions [2, 3]. Single nucleotide polymorphisms in genes essential for immune regulation; such as interleukin receptor genes, and genes in the human leukocyte antigen (HLA) locus, are associated with multiple sclerosis [4, 5].

During pregnancy, changes in maternal immune response occur in order not to reject the genetically dissimilar fetus [6]. Whether this mechanism could be responsible for the advantageous course of multiple sclerosis during pregnancy is unknown [7]. Pregnancy loss occurs in 10–15% of pregnancies seen in a hospital setting or confirmed by ultrasonography [8, 9]. Although most pregnancy losses are due to fetal genetic defects [10], some pregnancy losses may be due to maternal autoimmune disease and possibly defects in feto-maternal immune interactions [11, 12]. The fetal rate of aneuploidy decreases with increasing number of pregnancy losses [13]. Recurrent pregnancy loss, in Denmark defined as three consecutive pregnancy losses, (although some countries use a definition of two consecutive losses), occurs for 1–2% of women trying to conceive [14, 15]. Recurrent pregnancy loss can be divided into primary recurrent pregnancy loss which is not preceded by a delivery, and secondary recurrent pregnancy loss which is preceded by a delivery. Primary and secondary pregnancy loss are possibly linked to a different spectrum of diseases [16, 17], and have further been associated to specific maternal HLA alleles [18, 19].

The current study investigates the association between multiple and recurrent pregnancy loss and risk of developing autoimmune neurological disorders.

Methods

Study design

Register-based historical cohort study.

Participants

All women born between 1957–1997 and living in Denmark between 1977–2017 were eligible for inclusion in the study cohort and identified in the Danish Civil Registration System [20]. The unique personal identification number provided to each resident enabled linkage to other national registers. Immigrants were eligible for inclusion if immigration occurred before age 20. Persons with an outcome of interest before age 12 were excluded. Follow-up commenced at age 12, immigration, or start of follow-up (January 1, 1977), whichever came last. Censoring occurred due to emigration, death, end of follow-up (December 31, 2017), or an event of interest, whichever came first. The study was approved by the Danish Health Data Authority. No informed consent is need for register-based studies in Denmark. A complete list of definitions and registers used can be found in the S1 Table.

Reproductive history

In the Danish Medical Birth Register [21], the number of live births and stillbirths were identified. The lower gestational threshold for stillbirths in this register was 28 gestational weeks before 2004 and 22 gestational weeks after. Therefore, pregnancies ending at earlier gestational ages were identified in the Danish National Patient Register [22]. A pregnancy loss was defined as the registered loss of a pregnancy before viability and included miscarriage, blighted ovum (ie. a pregnancy with a visible gestational sac, but no visible embryo), and missed abortion (ie. a nonviable pregnancy remaining in the uterus). Recurrent pregnancy loss was defined as three consecutive pregnancy losses without a delivery or induced abortion in between or a specific diagnosis of recurrent pregnancy loss. Recurrent pregnancy loss was subdivided by whether the sequence was preceded by a delivery (secondary) or not (primary). As complications of early pregnancy could lead to multiple hospital contacts during clinical care, an algorithm using restriction periods was used (S1 Appendix) to ascertain each pregnancy was only counted once. Therefore, additional pregnancy outcomes were also identified: ectopic pregnancy, molar pregnancy, and induced abortion, to separate individual pregnancies. The latter were identified in the Danish Register of Legally Induced Abortions [23].

Exposure

Pregnancy loss was the exposure of interest and defined hierarchically with no pregnancy losses at the lowest level and recurrent pregnancy loss at the highest level, as these women were a priori hypothesized to have a higher probability of underlying immune disorder. Women could increase exposure level during follow-up but not revert, and could only contribute risk time in one category at a given time during follow-up (categories: no pregnancy losses, 1 pregnancy loss, 2 pregnancy losses, ≥3 non-consecutive pregnancy losses [not fulfilling criteria for recurrent pregnancy loss], primary recurrent pregnancy loss, and secondary recurrent pregnancy loss).

Outcome

The primary outcome of interest was an incident diagnosis of multiple sclerosis in the Danish Multiple Sclerosis Register [24]. In case the date of diagnosis or date of symptom onset was given as the calendar year, June 1 was chosen as the date of diagnosis or symptom onset. Kinship information was available since 1968 in the Civil Registration System. As the Danish Multiple Sclerosis Register was only available for women, multiple sclerosis among first degree relatives (mother, listed father, or full sibling) was identifiable since 1977 in the Danish National Patient Register using the International Classification of Diseases and Related Health Problems, 8th revision (ICD-8), and 10th revision (ICD-10) codes for multiple sclerosis: ICD-8: 340, ICD-10: G35. Additional outcomes examined were amyotrophic lateral sclerosis, Guillain-Barré syndrome, and myasthenia gravis, all identified in the Danish National Patient Register.

Covariates

The study included the following potential confounders in the main analysis: (i) the number of live births (categories: 0, 1, 2, and ≥3), (ii) obtainment of a bachelor’s degree or higher education (categories: yes, no, or unknown), (iii) family history of multiple sclerosis, defined as mother, listed father, or full-sibling diagnosed with multiple sclerosis (categories: yes, no, or unknown), (iv) calendar period (categories: 1977–1989, 1990–1999, 2000–2009, and 2010–2017), and (v) maternal age (categories: 12–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, and 50–61 years). Persons with missing data were grouped in a separate ‘unknown’ category in analyses.

Statistical analyses

Follow-up was conducted in a time-dependent manner and risk time in years with three decimal places, was split at changes in age, other covariates, or exposure status. The number of events and person-years was summarized, and crude incidence rates per 10,000 person-years were calculated. The primary model estimated crude and adjusted incidence rate ratios (IRR) using Bayesian Poisson regression. A sensitivity analysis used a negative binomial model to better account for potential overdispersion. The Bayesian framework was chosen as it provided an intuitive interpretation of uncertainty in estimates and modeled the complete posterior distribution based on the statistical model, data, and prior beliefs. The chosen priors in the main analysis were modestly informative. A sensitivity analysis used minimally informative uniform priors, approximating estimates which would be obtained by a corresponding frequentist model. The IRR and 95% credible interval (CI) were extracted from the posterior distribution. Convergence of chains was confirmed for all analyses [25]. For detailed specifications of the statistical analyses including chosen priors, see the S2 Appendix.

Sensitivity analyses

To assess the robustness of the findings, we performed a series of sensitivity analyses. Due to the risk of type I error due to multiple comparisons, these should be interpreted as exploratory. First, as patients diagnosed with multiple sclerosis may have had a prolonged period of prodromal symptoms, a secondary analysis used the date of symptom onset as the event date. The date of symptom of onset was assessed and registered by the clinician upon diagnosing multiple sclerosis. Second, as women who never achieve pregnancy may include a higher proportion of women with underlying medical conditions, these women were excluded. Third, as smoking status (categories: yes/no) is associated with multiple sclerosis, we conducted an analysis including this covariate [26]. Smoking status has in Denmark routinely been assessed for live births and stillbirths since 1997. This analysis only included women with non-missing data in these variables (i.e., complete case analysis) and women were included at the delivery where their smoking status was first registered. The last known values for these variables were used until a newer was available. Fourth, as treatment of pregnancy loss was mainly provided in a hospital setting in Denmark before the year 2000, we conducted an analysis concluding the study this year to minimize exposure misclassification. Fifth, exposure to stillbirth (categories: 0, ≥1) was examined, as stillbirth has been associated with other diseases such as cardiovascular disease [27]. Sixth, an analysis used minimal-informative priors. Finally, we conducted an analysis using a negative binomial model where the covariate calendar year was modeled to have a linear effect, and the covariate age was modeled using a cubic spline with five knots. The knots were chosen as the quartiles of the variable in the full dataset. This analysis improved modelling in case of overdispersion and non-linear effects of age on the outcome.

Analyses were conducted using R version 4.0.3 [28], and Bayesian models were fit using Stan version 2.21.2 [29] using the R interface package rstanarm. A two-sided 95% CI that did not overlap 1.00 was considered statistically significant. The study was reported using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [30].

Results

Of the 1,513,560 women eligible for inclusion, 16 were excluded due to a diagnosis of multiple sclerosis before inclusion, resulting in a cohort of 1,513,544 women. The median age at inclusion was 12.0 years (interquartile range [IQR] 12.0–12.2 years), median follow-up time was 28.2 years (IQR 16.0–38.8 years), and the median age at the end of follow-up was 40.4 years (IQR 28.3–50.9 years), with a total of 40,380,194 follow-up years. The cohort development by exposure group and age can be seen in Fig 1. At the end of follow-up, 7,667 women (0.5%) had been diagnosed with multiple sclerosis, the median age at diagnosis 34.2 years (IQR 27.4–41.4 years). At end of follow up, the distribution of persons by pregnancy loss group was: no pregnancy losses: 1,293,791 (85.5%), one loss: 172,150 (11.4%), two losses: 31,757 (2.1%), three or more non-consecutive losses: 4,248 (0.3%), primary recurrent pregnancy loss: 5,635 (0.4%), and secondary recurrent pregnancy loss: 5,963 (0.4%). The number of women diagnosed with multiple sclerosis in each group was: no losses: 6,654 (0.5%), one loss: 807 (0.5%), two losses: 137 (0.4%), three or more non-consecutive losses 12 (0.3%), primary recurrent pregnancy loss: 30 (0.5%), and secondary recurrent pregnancy loss: 27 (0.5%). As seen in Table 1, the adjusted IRR of multiple sclerosis in each group was: one loss: 1.03 (95% CI 0.95–1.11), two losses: 1.02 (95% CI 0.86–1.20), three or more non-consecutive losses: 0.81 (95% CI 0.51–1.24), primary recurrent pregnancy loss: 1.18 (95% CI 0.84–1.60), and secondary recurrent pregnancy loss: 1.16 (95% CI 0.81–1.63), as compared to women with no pregnancy losses. The prior and posterior density distributions of the adjusted IRR can be seen in S1 Fig. One or more live births and obtainment of a bachelor’s degree were negatively associated with multiple sclerosis, while calendar year after 1990, age above 20, and family history of multiple sclerosis were positively associated with the multiple sclerosis.

Fig 1. Cohort development during follow-up.

Fig 1

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In a study of the association of pregnancy loss with autoimmune neurological disorder, the entire Danish female population was followed-up in a time-dependent manner from age 12. Women could change exposure status during follow-up in a hierarchal manner from 0 to 1, 2, ≥3 non-consecutive pregnancy losses, primary recurrent pregnancy loss (Primary RPL, i.e. 3 consecutive losses not preceded by a delivery), and secondary recurrent pregnancy loss (Secondary RPL, i.e. 3 consecutive losses preceded by a delivery). The left stacked bar chart (a) shows the number at risk by age and exposure group in the cohort. The right chart (b) shows the proportion at risk by age and exposure group. The data used to create the figure can be found in the S2 Table.

Table 1. Association of pregnancy loss with multiple sclerosis, main analysis.

Covariate Events / non-events Person-years Incidence rate a Crude IRR b (95% CI) Adjusted IRR b,c (95% CI)
n = 1,513,544
Pregnancy loss exposure d
    0 6,654 / 1,287,137 36,656,369 1.82 1 1
    1 807 / 171,343 2,986,620 2.70 1.49 (1.38–1.60) 1.03 (0.95–1.11)
    2 137 / 31,620 505,490 2.71 1.48 (1.25–1.75) 1.02 (0.86–1.20)
    ≥3 non-consecutive e 12 / 4,236 61,249 1.96 1.06 (0.60–1.73) 0.81 (0.51–1.24)
    Primary RPL f 30 / 5,605 87,626 3.42 1.80 (1.24–2.52) 1.18 (0.84–1.60)
    Secondary RPL f 27 / 5,936 82,839 3.26 1.71 (1.15–2.44) 1.16 (0.81–1.63)
Number of live births
    0 3,250 / 565,711 23,148,201 1.40 1 1
    1 1,278 / 242,573 5,597,152 2.28 1.62 (1.52–1.73) 0.80 (0.75–0.86)
    2 2,326 / 474,371 8,222,068 2.83 2.01 (1.91–2.12) 0.86 (0.81–0.92)
    ≥3 813 / 223,222 3,412,771 2.38 1.69 (1.57–1.82) 0.71 (0.65–0.77)
Obtained bachelor’s degree
    No 5,794 / 945,565 32,975,926 1.76 1 1
    Yes 1,840 / 504,421 6,909,812 2.66 1.51 (1.44–1.59) 0.88 (0.83–0.92)
    Unknown 33 / 55,891 494,455 0.67 0.40 (0.29–0.54) 0.90 (0.66–1.20)
Family history of MS g
    No 6,906 / 1,341,542 37,171,033 1.86 1 1
    Yes 387 / 17,844 311,766 12.41 6.62 (5.97–7.32) 5.17 (4.65–5.72)
    Unknown 374 / 146,491 2,897,394 1.29 0.70 (0.63–0.77) 0.75 (0.68–0.83)
Calendar period
    1977–1989 450 / 33,156 7,985,832 0.56 1 1
    1990–1999 1,499 / 29,862 9,513,828 1.58 2.74 (2.47–3.05) 1.56 (1.40–1.74)
    2000–2009 2,937 / 35,480 12,483,749 2.35 4.10 (3.72–4.52) 2.08 (1.87–2.31)
    2010–2017 2,781 / 1,407,379 10,396,784 2.67 4.66 (4.22–5.14) 2.31 (2.07–2.57)
Age group
    12–19 334 / 34,492 10,406,128 0.32 1 1
    20–24 969 / 217,839 6,830,089 1.42 4.07 (3.62–4.59) 4.14 (3.66–4.68)
    25–29 1,350 / 185,044 5,819,698 2.32 6.65 (5.95–7.48) 6.80 (6.04–7.71)
    30–34 1,397 / 151,132 4,977,287 2.81 8.05 (7.20–9.04) 8.08 (7.13–9.19)
    35–39 1,384 / 151,985 4,232,111 3.27 9.38 (8.38–10.54) 9.04 (7.95–10.33)
    40–44 1,096 / 172,693 3,415,026 3.21 9.20 (8.20–10.37) 8.41 (7.35–9.63)
    45–49 690 / 180,011 2,515,667 2.74 7.84 (6.93–8.90) 6.96 (6.04–8.04)
    50–61 447 / 412,681 2,184,188 2.05 5.84 (5.09–6.71) 5.10 (4.35–5.94)
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Abbreviations: CI, credible interval; IRR, incidence rate ratio; MS, multiple sclerosis

RPL: Recurrent pregnancy loss

a Incidence rate per 10,000 person-years.

b Estimated using a Poisson model.

c Estimates adjusted for the number of live births, obtained bachelor’s degree, family history of multiple sclerosis, calendar period, and age group.

d Pregnancy loss (i.e. miscarriage) was the exposure of interest defined hierarchically with no pregnancy losses at the lowest level and recurrent pregnancy loss at the highest level.

e ≥3 non-consecutive pregnancy losses (not fulfilling criteria for recurrent pregnancy loss).

f Recurrent pregnancy loss (RPL) defined as three consecutive pregnancy losses, either preceded by a delivery (secondary) or not (primary).

g First degree relative included mother, listed father, or full sibling.

Secondary analyses examined the effect of redefining the outcome date as the date of onset of symptoms (median age at onset 31.2 years, IQR 24.8–38.2), excluding women who never achieved pregnancy, (n = 1,029,795; 68.0% of the cohort), or adjusting for smoking status (n = 793,482; smoking data available for 52.4% of the cohort [among these women 20.1% were registered as ever tobacco smokers]), ending follow-up in the year 2000 (n = 1,107,618; 73.2% of the cohort), using minimally informative priors, or using a negative binomial model with a cubic spline on the covariate age. These secondary analyses did not change the statistical significance of the results, as seen in Table 2.

Table 2. Association of pregnancy loss with multiple sclerosis, sensitivity analyses.

Analysis Events Person-years Incidence rate b Crude IRRc (95% CI) Adjusted IRR c, d (95% CI)
Pregnancy loss exposure a
Symptom onset date as event date n = 1,513,500 (>99.9%)
     0 6,998 36,637,219 1.91 1 1
     1 743 2,984,173 2.49 1.30 (1.21–1.40) 1.05 (0.97–1.13)
    2 114 505,044 2.26 1.18 (0.98–1.40) 0.97 (0.81–1.15)
    ≥3 non-consecutive e 10 61,228 1.63 0.87 (0.48–1.46) 0.82 (0.50–1.28)
    Primary RPL f 28 87,550 3.20 1.61 (1.10–2.28) 1.21 (0.85–1.68)
    Secondary RPL f 21 82,781 2.54 1.29 (0.83–1.90) 1.08 (0.72–1.54)
Excluding never pregnant women n = 1,029,795 (68.0%)
    0 5,128 28,608,434 1.79 1 1
    1 807 2,986,620 2.70 1.50 (1.39–1.62) 1.04 (0.97–1.12)
    2 137 505,490 2.71 1.50 (1.26–1.77) 1.02 (0.86–1.21)
    ≥3 non-consecutive e 12 61,249 1.96 1.07 (0.61–1.74) 0.82 (0.52–1.26)
    Primary RPL f 30 86,642 3.46 1.84 (1.27–2.58) 1.22 (0.86–1.67)
    Secondary RPLf 27 82,839 3.26 1.73 (1.16–2.46) 1.17 (0.82–1.63)
Further adjusted for smoking status n = 793,482 (52.4%)
    0 2,555 9,320,898 2.74 1 1
    1 564 1,953,548 2.89 1.05 (0.96–1.15) 1.05 (0.95–1.14)
    2 99 359,427 2.75 1.00 (0.82–1.22) 1.00 (0.82–1.22)
    ≥3 non-consecutive e 10 48,229 2.07 0.78 (0.44–1.32) 0.77 (0.40–1.33)
    Primary RPL f 16 49,606 3.23 1.15 (0.69–1.79) 1.13 (0.68–1.75)
    Secondary RPL f 20 60,095 3.33 1.19 (0.76–1.76) 1.18 (0.74–1.77)
Ending study in year 2000 n = 1,107,618 (73.2%)
    0 1,667 15,679,891 1.06 1 1
    1 129 651,305 1.98 1.84 (1.53–2.18) 1.03 (0.86–1.23)
    ≥2 non-consecutive e 21 93,823 2.24 1.96 (1.25–2.91) 1.07 (0.72–1.55)
    Primary RPL f 5 17,997 2.78 1.93 (0.80–4.02) 1.29 (0.68–2.30)
    Secondary RPL f 5 12,052 4.15 2.45 (0.98–5.36) 1.46 (0.72–2.76)
Exposure to stillbirth n = 1,513,544 (100%)
    0 7,638 40,254,066 1.90 1 1
    ≥1 29 126,128 2.30 1.21 (0.83–1.68) 0.88 (0.64–1.18)
Minimally informative priors g n = 1,513,544 (100%)
    0 6,654 36,656,369 1.82 1 1
    1 807 2,986,620 2.70 1.49 (1.38–1.60) 1.03 (0.95–1.11)
    2 137 505,490 2.71 1.49 (1.25–1.77) 1.02 (0.86–1.21)
    ≥3 non-consecutive e 12 61,249 1.96 1.06 (0.57–1.79) 0.73 (0.39–1.24)
    Primary RPL f 30 87,626 3.42 1.87 (1.29–2.64) 1.22 (0.84–1.71)
    Secondary RPL f 27 82,839 3.26 1.78 (1.19–2.55) 1.22 (0.82–1.75)
Negative binomial model h n = 1,513,544 (100%)
    0 6,654 36,656,369 1.82 1 1
    1 807 2,986,620 2.70 1.49 (1.38–1.60) 1.09 (0.98–1.21)
    2 137 505,490 2.71 1.48 (1.25–1.75) 1.05 (0.86–1.26)
    ≥3 non-consecutive e 12 61,249 1.96 1.06 (0.60–1.73) 0.75 (0.40–1.27)
    Primary RPL f 30 87,626 3.42 1.80 (1.24–2.52) 1.26 (0.86–1.80)
    Secondary RPL f 27 82,839 3.26 1.71 (1.15–2.44) 1.24 (0.82–1.80)
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Abbreviations: CI, credible interval; IRR, incidence rate ratio

RPL: Recurrent pregnancy loss

a Pregnancy loss (i.e. miscarriage) was defined hierarchically with no pregnancy losses at the

lowest level and recurrent pregnancy loss at the highest level.

b Incidence rate per 10,000 person-years.

c Estimated using a Poisson model unless stated otherwise.

d Estimates adjusted for the number of live births, obtained bachelor’s degree, family history of multiple sclerosis, calendar period, and age group unless otherwise stated.

e ≥3 non-consecutive pregnancy losses (not fulfilling criteria for recurrent pregnancy loss).

f Recurrent pregnancy loss (RPL) defined as three consecutive pregnancy losses, either preceded by a delivery (secondary) or not (primary).

g Uniform priors for parameters and intercept. (continued)

h Using a negative binomial model and fitting the covariate calendar year as a linear predictor and the covariate age using a cubic spline with five knots.

In the analyses of other autoimmune neurological diseases seen in Table 3, some exposure groups were aggregated due to few events. Adjusted analyses found pregnancy loss or recurrent pregnancy loss was not significantly associated with developing amyotrophic lateral sclerosis, Guillain-Barré syndrome, or myasthenia gravis. During follow-up, seven women with recurrent pregnancy loss developed Guillain Barré syndrome, corresponding to an adjusted IRR of 1.40 (95% CI 0.79–2.37), as compared with women with no pregnancy losses.

Table 3. Association of pregnancy loss with other autoimmune neurological disorders.

Analysis Events / non-events Person-years Incidence rate b Crude IRR c (95% CI) Adjusted IRR c, d (95% CI)
Pregnancy loss exposure a
Amyotrophic lateral sclerosis n = 1,513,557
    0 164 / 1,293,328 36,731,181 0.04 1 1
    1 27 / 172,367 2,997,495 0.09 1.92 (1.26–2.79) 1.08 (0.75–1.51)
    ≥2 or RPLe,f 5 / 47,666 739,973 0.07 1.40 (0.62–2.78) 0.95 (0.57–1.54)
Guillain-Barré syndrome n = 1,513,492
    0 630 / 1,292,860 36,721,402 0.17 1 1
    1 62 / 172,286 2,996,260 0.21 1.19 (0.92–1.54) 1.12 (0.86–1.43)
    ≥2 (not including RPL) e 13 / 36,031 568,465 0.23 1.28 (0.73–2.07) 1.18 (0.75–1.79)
     RPL e,f 7 / 11,603 171,149 0.41 1.95 (0.92–3.70) 1.40 (0.79–2.37)
Myasthenia gravis n = 1,513,528
    0 404 / 1,293,099 36,726,232 0.11 1 1
    1 53 / 172,314 2,996,773 0.18 1.58 (1.18–2.08) 1.20 (0.92–1.57)
    ≥2 or RPL e,f 6 / 47,652 739,746 0.08 0.83 (0.40–1.52) 0.85 (0.54–1.31)
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Abbreviations: CI, credible interval; IRR, incidence rate ratio

RPL: Recurrent pregnancy loss

a Pregnancy loss (i.e. miscarriage) was the exposure of interest defined hierarchically with no pregnancy losses

at the lowest level and recurrent pregnancy loss at the highest level.

b Incidence rate per 10,000 person-years

c Estimated using a Poisson model

d Estimates adjusted for the number of live births, obtained bachelor’s degree, family history of the outcome of interest, calendar period, and age group.

e Groups aggregated due to few events.

f Recurrent pregnancy loss (RPL) defined as three consecutive pregnancy losses, either preceded by a delivery (secondary) or not (primary).

Discussion

This nationwide cohort study, including over 1.5 million women with over 40 million years of follow-up, found no statistically significant association between multiple or recurrent pregnancy loss and later multiple sclerosis, amyotrophic lateral sclerosis, Guillain-Barré syndrome, or myasthenia gravis. The incidence of multiple sclerosis and Guillain-Barré syndrome was estimated to be increased after primary or secondary pregnancy loss as compared to no pregnancy losses, however, the findings were not statistically significant. Considering the rarity of the outcomes in question, an increase in relative risk corresponds to a very low increase in absolute risk.

To our knowledge, no prior studies have examined the association between recurrent pregnancy loss and developing autoimmune neurological disease. A prior register-based cohort study found no association between pregnancy loss and later multiple sclerosis, however exposure to multiple or recurrent pregnancy loss was not investigated [31]. Studies examining the effect of one or more live births on development of multiple sclerosis have shown either no significant effect [32], a decreased risk [31, 33], or a delayed onset of disease [34]. However, the fact that the effect diminishes five years after delivery, and the decrease in risk is also detectable for partners have raised questions of reverse causality. This could for example be caused by the choice to postpone a planned pregnancy during the prodromal stage of multiple sclerosis, consequently an analysis will estimate that nulliparity or low parity to be risk factors for multiple sclerosis [31, 35]. The current study contributes significantly to the current scientific body of evidence examining pregnancy-related factors for autoimmune neurological disorder due to size, length of follow-up, and physician-assigned diagnoses of the primary and additional outcomes.

Limitations

This study has several limitations. First, as multiple sclerosis is often not diagnosed until years after initial symptoms, the study could have been susceptible to reverse causality. To further explore this, a sensitivity analysis was conducted using the date of symptom onset as the event date. This did not change the findings materially.

Second, women who never achieve pregnancy may comprise a heterogenous group not necessarily comparable with other women. To increase the homogeneity of the comparator group, women who were never registered pregnant were excluded in a sensitivity analysis. Again, this did not change the significance of the results. Third, although the primary analysis adjusted for many important confounders, residual confounding could not be ruled out. Therefore, an analysis further adjusted for smoking status as a proxy for unmeasured lifestyle factors, and the resulting estimates were materially unchanged. Smoking status was only known from year 1997 an onwards for women with a delivery (ie. live- or stillbirth), and consequently cannot necessarily be extrapolated to women with no prior deliveries. Therefore, a post-hoc sensitivity analysis (S3 Table), explored the effect of identifying smokers by hospital diagnosis code (ICD-10: F17) or fulfilling a prescription for a smoking cessation drug (Anatomical Therapeutic Chemical Classification code: N07BA), this did not change the significance of the results. Fourth, as the exposure only included clinical pregnancy losses, that is, those treated in a hospital setting and not including those only treated by private practitioners, misclassification of the exposure may have biased results. Assuming non-differential misclassification, this would bias our results towards the null. Until the year 2000, pregnancy losses in Denmark were routinely evaluated in a hospital setting [9]; therefore, a secondary analysis evaluated the effect of ending the study on December 31, 1999. Although the statistical power was reduced, this did not change the significance of the results. Further, a study found that a diagnosis of miscarriage in the National Patient Register had a high validity as the diagnosis was confirmed in 114 out of 117 hospital records [36].

Fifth, conducting Bayesian models facilitates and necessitates incorporating prior evidence and knowledge into the probability of an outcome. However, in the current study, only sparse evidence existed to guide the model, and the prior was used to guide posterior estimates into a plausible range. To further investigate the effect of this, a secondary model assessed the effect of minimally informative uniform priors on estimates. This analysis only changed the results minimally due to the abundant data guiding the model. Fifth, a sensitivity analysis was designed to better handle potential overdispersion and non-linear effects of the covariate age. This analysis did not change results substantially. Misclassification of the outcome was assumed to be minimal due to the high completeness (91%) and validity (94%) of the Danish Multiple Sclerosis Registry. Additional outcomes were based on diagnosis codes in the Danish Patient Register which have shown positive predictive values ranging from 83.8 to 92.5% [24, 37–39]. Family linkage was based on the Civil Registration System and has to our knowledge not been validated. Correct assignment of the mothers is likely very accurate as is occurs right after birth. Family history of multiple sclerosis was based on diagnosis codes in the Danish National Patient Register which have been validated against the Danish Multiple Sclerosis Registry and shown a completeness of 92.8% and validity of 95.1% [40]. In the cohort missing values were rare: 9.7% had missing information on one or both parents leading to unknown family history of an outcome, and 3.7% had missing information on educational status. We acknowledge this could be a potential source of bias.

Although the population was large, some exposure groups and outcomes were rare. Therefore, the possibility of insufficient power to accurately detect a small increase in risk cannot be excluded, and future studies may well aim to reproduce estimates for the outcomes of multiple sclerosis and Guillain-Barré syndrome after exposure to recurrent pregnancy loss.

Conclusion

This nationwide study found no significant association between pregnancy loss and multiple sclerosis, amyotrophic lateral sclerosis, Guillain-Barré syndrome, or myasthenia gravis. This evidence should be reassuring to women already burdened by the loss of one or more pregnancies.

Supporting information

S1 Fig. Prior and posterior probability density.

(DOCX)

Click here for additional data file. (221.4KB, docx)
S1 Table. Registers and definitions used.

(DOCX)

Click here for additional data file. (16KB, docx)
S2 Table. Data used to create Fig 1.

(DOCX)

Click here for additional data file. (20.9KB, docx)
S3 Table. Association of pregnancy loss with multiple sclerosis, further adjusted for smoking status defined by hospital admission for smoking (ICD-10: F17) or fulfilling prescription for a smoking cessation drug (ATC: N07AB).

(DOCX)

Click here for additional data file. (15.8KB, docx)
S1 Appendix. Restriction periods between pregnancies.

(DOCX)

Click here for additional data file. (14.1KB, docx)
S2 Appendix. Specification of the Bayesian model.

(DOCX)

Click here for additional data file. (13KB, docx)

Data Availability

The underlying data used in this study is not publicly available and cannot be made publicly available by the authors due to Danish legal restrictions. Researchers can apply for access to Danish healthcare data at the Danish Health Data Authority (https://sundhedsdatastyrelsen.dk/da/english). Aggregated versions of the original data can be made available upon reasonable request to the corresponding author. We confirm the data located in the paper and its Supporting Information files constitutes the minimal data set.

Funding Statement

All funding for the study were provided by grants from The Research Fund of Rigshospitalet, Copenhagen University Hospital [grant number E-22515-01] awarded to APM and ØL. URL: https://www.forskningspuljer-rh.dk/ and Ole Kirks Foundation [no grant number] awarded to HSN. URL: https://www.olekirksfond.dk/). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

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11 Feb 2022

PONE-D-21-39062Pregnancy loss and risk of multiple sclerosis and autoimmune neurological disorder:  A nationwide cohort studyPLOS ONE

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Reviewer #1: Well done strudy with rigorous analysis. Excellent that researchers looked at symptom onset rather than date of diagnosis for MS onset. In general therwe was little pregnancy loss. Surprislingly with 52.4% of the cohort smokers, smoking had litle impact on pregnancy loss or risl for MS. The authors explained this well. The appendix contsained a detailed review ofthe Bayesian Model- appreciated.

Although these questions will not hold publication, I wondered what was the definition of a "sensitgivity analysis", when was the study completed? and definitions for the nongynecologist would be helpful (blighteed ovum? and missed abortion?)

Well done- this may go to press

Reviewer #2: This is well written paper from Denmark utilizing the Danish nationwide register data. Authors studied whether pregnancy loss (miscarriage) is associated with autoimmune neurological disorders including multiple sclerosis (MS), amyotrophic lateral sclerosis, Guillain-Barré syndrome and myasthenia gravis. Study material consisted of female population aged 12 and older living in Denmark between 1977 and 2017. Findings from the main analysis and bunch of secondary and sensitivity analyses were reassuring: pregnancy loss was not significantly associated with autoimmune neurological disorder. Previous live births and higher education were negatively associated with MS while calendar year, age and family history increased the risk of MS. Conclusions are supported by the data.

Study exposure i.e. pregnancy loss was identified from the Danish Patient Register using diagnostic codes that had been previously validated. Also, an algorithm to identify same and different pregnancy episode are clinically justified. Authors write that they may not have captured early spontaneous pregnancy losses that does not require treatment at the hospitals. There is no reason to anticipate misclassification according to disease status. Also, sensitivity analysis was conducted restricting the study end into year 2000 as before that miscarriages were mainly treated at hospitals.

Study outcome was incident diagnosis recorded at the Danish Multiple Sclerosis Register with high completeness and validity. Secondary outcomes were identified from the Danish Patient Register.

Authors have conducted Bayesian Poisson regression to estimate adjusted incidence rate ratios and 95% credible intervals. Bayesian approach is justified based on rare event. However, Bayesian approach necessitates prior information on the probability of the outcome, but only sparse data existed to guide the model. A sensitivity analysis using a negative binomial model was done which did not change results substantially.

The study presents the results of the original research and have not been published elsewhere. Study material and analyses are described in sufficient detail and study reports scientifically sound results following the STROBE guidelines. Data cannot be made available due to Danish law for secondary use of register data. I have mainly minor issues where text could be improved and/or clarification added.

On p. 6 authors describe smoking as a proxy for unmeasured lifestyle and environmental factors possibly associated with outcome. Only women with non-missing data were included in the sensitivity analysis and sample size was limited almost to a half. Also, smoking status was only known for women after a delivery and cannot be extrapolated to women without delivery history. How this could affect the results? Would there be a possibility to identify smokers from the patient registry data e.g. ICD-10 F17? Or drug refills of varenicline ATC code N07A03? The missing information on smoking in pregnancies ending before viability seems a limitation that has not been fully addressed. Possibly associated with the outcome is an understatement as in the literature smoking has been associated both with increased risk of disease and with MS disease activity (see eg. https://pubmed.ncbi.nlm.nih.gov/32259516/).

Missing information is always a challenge in register-based studies. Complete-case analysis would restrict the sample size substantially. Modelling missing data as a separate category is an alternative, however that may result in study findings that are hard to interpret. Have you considered multiple imputation? In Table 1, unknown information on education was significantly associated with MS. Any ideas what could explain the finding? It is also uncertain where does unknown family history derive from? First degree relatives not found from the Patient Registry? Look-back period can have an impact on how reliable information can be obtained for the parents. Please explain and add into p.5 line 108 what years were available from the Danish Patient registry to identify MS in first degree relatives.

-abstract says age 11 while on page 4 age 12

-p3. lines 59-64 extremely long and fluctuating sentence. Please split. Also, the concept of secondary and primary pregnancy loss needs to be explained here when mentioned for the first time in the main text.

-p4 line 90 other pregnancies were identified but not directly used in analyses. What do you mean? Was used or wasn´t used?

-p5 line 101 3 or more pregnancy losses (not fulfilling criteria for recurrent pregnancy loss) is a bit unclear. It should be possible to understand the design without S1 Table. It is crucial to understand the difference between category 3+ and recurrent primary and secondary as these are repeated in result tables and text with often slightly different working in the brackets. Couldn´t you just simply name the category ≥ 3 non-consecutive pregnancy losses?

-p5 line 105 explain the difference between date of diagnosis and onset of disease? Which one was used as date of incidence in the analysis? In p. 6 sensitivity analysis date of symptom onset was used as an event date. Is onset of disease the same as date of symptom onset? Check the wording and be consistent.

p.6 line 140 delete of

p.7 line 151. Rationale for studying exposure to stillbirth is lacking. Also, the definition changed in the Danish Medical Birth Register over time from 28 weeks before 2004 to 22 weeks, how that was taken into account in the analysis?

p.14 lines 261-263 data quality for secondary outcomes in Danish Patient Register is provided. What about the validity of MS diagnosis i.e. capture of familial disease which was adjusted in the models?

S1 Figure. Should the red distribution be 0.25-4.00 in the figure? Mean being IRR=1 upper credible limit cannot be 0.40, right?

**********

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Reviewer #1: Yes: Heidi Maloni PhD

Reviewer #2: Yes: Maarit Leinonen

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Decision Letter 1

Angela Lupattelli

16 Mar 2022

Pregnancy loss and risk of multiple sclerosis and autoimmune neurological disorder:  A nationwide cohort study

PONE-D-21-39062R1

Dear Dr. Mikkelsen,

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

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Kind regards,

Angela Lupattelli, PhD

Academic Editor

PLOS ONE

Acceptance letter

Angela Lupattelli

22 Mar 2022

PONE-D-21-39062R1

Pregnancy loss and risk of multiple sclerosis and autoimmune neurological disorder:  A nationwide cohort study

Dear Dr. Mikkelsen:

I'm 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 let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, 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.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Angela Lupattelli

Academic Editor

PLOS ONE

Associated Data

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

    Supplementary Materials

    S1 Fig. Prior and posterior probability density.

    (DOCX)

    Click here for additional data file. (221.4KB, docx)
    S1 Table. Registers and definitions used.

    (DOCX)

    Click here for additional data file. (16KB, docx)
    S2 Table. Data used to create Fig 1.

    (DOCX)

    Click here for additional data file. (20.9KB, docx)
    S3 Table. Association of pregnancy loss with multiple sclerosis, further adjusted for smoking status defined by hospital admission for smoking (ICD-10: F17) or fulfilling prescription for a smoking cessation drug (ATC: N07AB).

    (DOCX)

    Click here for additional data file. (15.8KB, docx)
    S1 Appendix. Restriction periods between pregnancies.

    (DOCX)

    Click here for additional data file. (14.1KB, docx)
    S2 Appendix. Specification of the Bayesian model.

    (DOCX)

    Click here for additional data file. (13KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (42KB, docx)

    Data Availability Statement

    The underlying data used in this study is not publicly available and cannot be made publicly available by the authors due to Danish legal restrictions. Researchers can apply for access to Danish healthcare data at the Danish Health Data Authority (https://sundhedsdatastyrelsen.dk/da/english). Aggregated versions of the original data can be made available upon reasonable request to the corresponding author. We confirm the data located in the paper and its Supporting Information files constitutes the minimal data set.

    Articles from PLoS ONE are provided here courtesy of PLOS

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