Associations between insomnia and pregnancy and perinatal outcomes: Evidence from mendelian randomization and multivariable regression analyses.

BACKGROUND: Insomnia is common and associated with adverse pregnancy and perinatal outcomes in observational studies. However, those associations could be vulnerable to residual confounding or reverse causality. Our aim was to estimate the association of insomnia with stillbirth, miscarriage, gestational diabetes (GD), hypertensive disorders of pregnancy (HDP), perinatal depression, preterm birth (PTB), and low/high offspring birthweight (LBW/HBW). METHODS AND
FINDINGS: We used 2-sample mendelian randomization (MR) with 81 single-nucleotide polymorphisms (SNPs) instrumenting for a lifelong predisposition to insomnia. Our outcomes included ever experiencing stillbirth, ever experiencing miscarriage, GD, HDP, perinatal depression, PTB (gestational age <37 completed weeks), LBW (<2,500 grams), and HBW (>4,500 grams). We used data from women of European descent (N = 356,069, mean ages at delivery 25.5 to 30.0 years) from UK Biobank (UKB), FinnGen, Avon Longitudinal Study of Parents and Children (ALSPAC), Born in Bradford (BiB), and the Norwegian Mother, Father and Child Cohort (MoBa). Main MR analyses used inverse variance weighting (IVW), with weighted median and MR-Egger as sensitivity analyses. We compared MR estimates with multivariable regression of insomnia in pregnancy on outcomes in ALSPAC (N = 11,745). IVW showed evidence of an association of genetic susceptibility to insomnia with miscarriage (odds ratio (OR): 1.60, 95% confidence interval (CI): 1.18, 2.17, p = 0.002), perinatal depression (OR 3.56, 95% CI: 1.49, 8.54, p = 0.004), and LBW (OR 3.17, 95% CI: 1.69, 5.96, p < 0.001). IVW results did not support associations of insomnia with stillbirth, GD, HDP, PTB, and HBW, with wide CIs including the null. Associations of genetic susceptibility to insomnia with miscarriage, perinatal depression, and LBW were not observed in weighted median or MR-Egger analyses. Results from these sensitivity analyses were directionally consistent with IVW results for all outcomes, with the exception of GD, perinatal depression, and PTB in MR-Egger. Multivariable regression showed associations of insomnia at 18 weeks of gestation with perinatal depression (OR 2.96, 95% CI: 2.42, 3.63, p < 0.001), but not with LBW (OR 0.92, 95% CI: 0.69, 1.24, p = 0.60). Multivariable regression with miscarriage and stillbirth was not possible due to small numbers in index pregnancies. Key limitations are potential horizontal pleiotropy (particularly for perinatal depression) and low statistical power in MR, and residual confounding in multivariable regression.
CONCLUSIONS: In this study, we observed some evidence in support of a possible causal relationship between genetically predicted insomnia and miscarriage, perinatal depression, and LBW. Our study also found observational evidence in support of an association between insomnia in pregnancy and perinatal depression, with no clear multivariable evidence of an association with LBW. Our findings highlight the importance of healthy sleep in women of reproductive age, though replication in larger studies, including with genetic instruments specific to insomnia in pregnancy are important.

Introduction

Insomnia, which affects approximately 10% to 20% of the adult population, is usually defined as a difficulty in getting to sleep or remaining asleep, or having a nonrestorative sleep, and such sleep impairment can be associated with daytime sleepiness [1,2]. Physical and hormonal changes during pregnancy increase susceptibility to insomnia [3,4].

Most evidence on the relationship between insomnia during pregnancy and adverse pregnancy and perinatal outcomes has come from observational studies. The most recently updated systematic reviews of observational studies suggest that pregnancy-related insomnia and poor sleep quality are associated with higher risks of gestational diabetes (GD) [5,6], hypertensive disorders of pregnancy (HDP) [6], perinatal depression [7], and preterm birth (PTB) [6]. Other observational studies have shown that specific conditions that relate to insomnia are also associated with adverse pregnancy and perinatal outcomes. Sleep-disordered breathing, obstructive sleep apnoea, and restless legs syndrome have also been shown to associate with higher risks of GD, HDP, large-for-gestational age, and low offspring birthweight (LBW) [6]. However, it remains unclear whether insomnia causes adverse pregnancy outcomes or whether these associations are explained by confounding, e.g., due to socioeconomic status and lifestyle factors. It is also possible that some of these studies reflect reverse causation. For example, all 4 studies included in the systematic review for perinatal depression were cross-sectional [7], in which disturbed sleep could be either a symptom of or a risk factor for depression. Furthermore, most individual studies focus on just 1 or 2 outcomes. Examining potential effects on a range of adverse pregnancy and perinatal outcomes is important to understand the overall health impact of insomnia during pregnancy.

Three randomized control trials assessing the effects of interventions to prevent insomnia on adverse pregnancy and perinatal outcomes have been published [8-10]. All 3 of these used cognitive behavioural interventions targeted at reducing insomnia, with the primary outcome being Edinburgh Postnatal Depression Scale scores. The small number of randomized control trials, their small sample sizes, and directional inconsistency, but overlapping 95% confidence intervals (CIs), make it difficult to draw conclusions, and none of them explored other adverse pregnancy or perinatal outcomes.

Mendelian randomization (MR) provides an alternative way to assess the impact of insomnia on adverse pregnancy and perinatal outcomes by using genetic variants (mostly single-nucleotide polymorphisms [SNPs]) as instrumental variables (IVs) for insomnia [11,12]. MR is less prone to confounding than observational studies, as genetic variants are randomly allocated at meiosis and cannot be influenced by the wide range of sociodemographic or behavioural factors which conventionally confound observational studies nor can they be influenced by health status [11,12]. Under key assumptions (discussed in Methods), MR can be used to estimate a causal association from the SNPs-exposure and SNPs-outcome associations, if the underlying assumptions (in Discussion) are true. In 2-sample MR, the SNP-exposure and SNP-outcome associations are estimated using different (ideally independent) studies from the same underlying population [13]. This approach has previously been used to evaluate causal associations of insomnia with type 2 diabetes [14,15], hypertension [16], and cardiovascular disease [15,17,18] in non-pregnant populations, but to the best of our knowledge not pregnancy and perinatal outcomes.

The aims of this study are to (I) explore the causal associations of maternal genetic susceptibility to insomnia with stillbirth, miscarriage, GD, HDP, perinatal depression, PTB, LBW, and high offspring birthweight (HBW), using 2-sample MR; and (II) compare MR findings with conventional multivariable regression analyses of self-reported insomnia during pregnancy with these outcomes, where possible.

Methods

Study populations

This study was undertaken using data from the MR-PREG collaboration, which aims to explore causes and consequences of different pregnancy and perinatal outcomes [19]. We used individual-level data from UK Biobank (UKB) women (N = 208,140, recruited between 2006 to 2010) and mother-offspring pairs from Avon Longitudinal Study of Parents and Children (ALSPAC, N = 6,826, recruited between 1991 to 1992), Born in Bradford (BiB, N = 2,940, recruited between 2007 to 2010), and the Norwegian Mother, Father and Child Cohort (MoBa, N = 14,584, recruited between 1999 to 2009). To be comparable across all cohorts, only genetically unrelated women of European descent with qualified genotype data (and with singleton offspring in birth cohorts) were eligible for inclusion in our analyses (S1 Fig). We also used summary-level genetic association data from FinnGen—the national wide network of Finnish biobanks (N = up to 123,579 women) [20]. All studies had ethical approval from relevant national or local bodies and participants provided written informed consent. Details of the recruitment, information on genetic data, and measurements of baseline characteristics of each cohort are described in S1 Text. This study was initiated using UKB in January 2018, with extra exploration of insomnia IVs and MR sensitivity analyses completed in February 2020 [21]. We searched for additional cohorts till July 2021, and harmonization across the cohorts had to be made continuously. Therefore, we did not have a prespecified analysis plan.

Outcomes measures

We explored potential effects of insomnia on 8 binary outcomes: ever experiencing stillbirth, ever experiencing miscarriage, GD, HDP, perinatal depression, PTB (gestational age <37 completed weeks), LBW (<2,500 grams), and HBW (>4,500 grams). Full details about how these outcomes were measured and derived in each participating study and how we harmonised them across studies can be found in S1 Table. We were not able to measure pre-eclampsia and gestational hypertension separately, because of the small number of definite cases of pre-eclampsia, and because of differences between studies in data collection and definitions.

In UKB, gestational age was only available for a small subset of women (N = 7,280) who delivered a child during or after 1989, the earliest date for which linked hospital labour and perinatal data are available [22]. As a result, numbers with data on PTB are smaller than for any other outcome, and we a priori decided to examine associations with LBW and HBW rather than small-for-gestational age and large-for-gestational age. For most outcomes in UKB, women reported their experience retrospectively in a questionnaire completed at recruitment when they were aged 40 to 60 years.

In the 3 birth cohorts, most outcomes were prospectively obtained (from self-report or clinical records) during an index pregnancy and the perinatal period. The 2 exceptions were history of stillbirth and miscarriage, which were retrospectively reported at the time of the index pregnancy when women were asked if they had ever experienced a (previous) stillbirth or miscarriage. We explored the possibility of examining associations with miscarriage and stillbirth in the index pregnancy. However, numbers were too small for reliable results, and for miscarriage, we were concerned about misclassification or selection bias due to women who had experienced a miscarriage prior to recruitment. Therefore, we used MR to explore the association of susceptibility to insomnia on a history of miscarriage and stillbirth and did not undertake any multivariable regression analyses for these 2 outcomes as suggested during peer review. There were a small proportion of women who contributed more than 1 pregnancy (<5% of total N for each outcome). Given that choosing the first pregnancy could introduce selection towards younger age, lower parity, and higher morbidity of HDP [23], we followed EGG consortium convention [24] to randomly select 1 pregnancy per woman [25].

Data from FinnGen were available for 4 of our outcomes: ever experiencing miscarriage, GD, HDP, and PTB, which were defined based on International Classification of Diseases codes.

Insomnia measures

Self-reported information on insomnia was obtained from 2 of the studies. In UKB, information on lifetime insomnia was used to generate SNP-insomnia associations in women for use in MR analyses in UKB and the birth cohorts. ALSPAC collected data on insomnia during pregnancy, and this was used for conventional confounder-adjusted multivariable regression.

In UKB, insomnia was self-reported at recruitment via the question “Do you have trouble falling asleep at night or do you wake up in the middle of the night?” with responses “never/rarely,” “sometimes,” “usually,” and “prefer not to answer.” For our analyses, we collapsed these categories to generate a binary variable of usually experiencing insomnia (i.e., “usually” [cases] versus “sometimes” + “never/rarely” [controls]) as this was how the responses were categorised in the published genome-wide association study (GWAS) that we have used to select genetic IVs [15].

In ALSPAC, insomnia in pregnancy was self-reported, at 18 and 32 weeks of gestation, using the question “Can you get off to sleep alright?” with options “Very often,” “Often,” “Not very often,” and “Never.” At each time point, we compared “Not very often” + “Never” [cases] versus “Very often” + “Often” [controls]. We acknowledge that the 2 studies are using different questions and that definitions of insomnia vary across published literature [2]. For ease of reading throughout the paper, we refer to results reflecting genetic susceptibility to insomnia (MR) and reporting insomnia in pregnancy (multivariable regression).

SNP selection and SNP-insomnia associations

To identify genetic IVs for insomnia, we searched the GWAS published between January 2017 and February 2021 on PubMed and Neale Lab website [26]. We found 7 insomnia GWAS reporting genome-wide significant SNPs (details in S2 Table). Of these, we selected SNPs from the largest GWAS (total N = 709,986 women, 29% from UKB, and 71% from 23andMe), which provided female-specific results [15]. This GWAS identified 83 loci containing 87 lead SNPs that were robustly associated with insomnia (P-value < 5 × 10−8) after pooling UKB and 23andMe women together. We removed 6 SNPs that were correlated to other SNPs (linkage disequilibrium) at an R2 threshold of 0.01 or higher, based on all European samples from the 1,000 genome project [27]. Associations (reported in log odds ratios [ORs]) of the remaining 81 lead SNPs from the women only GWAS were extracted and listed in S3 Table.

We followed the standard IV approach [28], first using linear regression with individual-level data from 208,140 UKB women to obtain SNP-insomnia association summary data for 2-sample MR analyses. This provides estimates on the risk difference scale, which is more interpretable and comparable to our multivariable regression results [29]. We adjusted the linear models for genotyping batch, top 40 principal components (PCs) and women’s age. During peer review, we were asked to regenerate SNP-insomnia associations using logistic regression to repeat MR analyses. Therefore, we reconducted: (I) split-sample analyses in UKB by generating SNP-insomnia and SNP-outcome associations in logistic regression; (II) 2-sample MR using SNP-insomnia associations generated in logistic regression by the GWAS where we selected our IVs [15], and the pooled SNP-outcome associations combining ALSPAC, BiB, MoBa, and FinnGen; and (III) a meta-analysis of MR estimates from (I) and (II) using fixed-effects (with inverse variance weights) for each insomnia-outcome pair. Consistent with a previous MR study of binary exposures [30], our MR estimates were reported as odds ratios per 1 unit higher log-odds of insomnia.

SNP-outcome associations

We estimated the associations between maternal SNPs and outcomes (log OR and standard errors) for each of the 81 insomnia-related SNPs. In UKB, we randomly separating women in half (giving 2 datasets, A and B) for our split cross-over 2-sample MR [31], given UKB was also included in the GWAS of insomnia. We then estimated SNP-outcome associations in each split sample using logistic regression, adjusting for genotyping batch, top 40 PCs, and women’s age. In the birth cohorts, we estimated the SNP-outcome associations using logistic regression, adjusting for (I) top 20 PCs and women’s age in ALSPAC; (II) top 10 PCs and women’s age in BiB; and (III) genotyping batch, top 10 PCs, and women’s age in MoBa. We extracted associations of the 81 SNPs with the following from FinnGen (words in brackets are the outcome labels from FinnGen): miscarriage (O15_ABORT_SPONTAN), GD (GEST_DIABETES), HDP (O15_GESTAT_HYPERT), and PTB (O15_PRETERM). These summary data were generated by FinnGen using the R-package called SAIGE that fits mixed-effects logistic regression [32], adjusting for genotyping batch, top 10 PCs, and women’s age [20]. Then, we meta-analysed associations from ALSPAC, BiB, MoBa, and FinnGen using fixed-effects with inverse variance weights. Two SNPs (i.e., rs10947428 and rs117037340) were excluded from BiB analyses due to their minor allele frequency lower than 1%.

Assessment of confounders in ALSPAC for multivariable regression

We considered maternal age at time of delivery, education, body mass index at 12 weeks of gestation, smoking status in pregnancy, alcohol intake in the first 3 months of pregnancy, and household occupational social class as potential confounders based on their known or plausible associations with maternal insomnia and pregnancy and perinatal outcomes. Details of confounders were based on maternal self-report and are fully described in S1 Text.

Statistical analyses

Two-sample MR

As shown in Fig 1, we conducted 2-sample MR analyses of maternal insomnia on pregnancy and perinatal outcomes. In UKB, we conducted a split cross-over 2-sample MR [31]. Specifically, we used SNP-insomnia associations from dataset A and SNP-outcomes associations from dataset B (A on B) and vice-versa (B on A), and then meta-analysed the MR estimates from the 2 together for each insomnia-outcome pair using fixed-effects (with inverse variance weights). For the 2-sample MR using the rest of the cohorts, we used SNP-insomnia associations from UKB women and the pooled SNP-outcome associations combining ALSPAC, BiB, MoBa, and FinnGen. For each outcome, we pooled MR estimates from all cohorts using fixed-effects (with inverse variance weights) and used leave-one (study)-out analysis (initially across all cohorts and then among non-UKB cohorts during peer review) to assess the degree of heterogeneity between cohorts.

Fig 1

Summary of methods and data contributing to this study.

(a) Two-sample MR methods include: IVW, MR-Egger, weighted median, and leave-one-out analysis. (b) Multivariable regression analysis adjusted for maternal age at time of delivery, social class, education, body mass index at 12 weeks of gestation, smoking status in pregnancy, and alcohol intake in the first 3 months of ALSPAC pregnancy. ALSPAC, Avon Longitudinal Study of Parents and Children; BiB, Born in Bradford; GWAS, genome-wide association study; IVW, inverse variance weighted; MoBa, Norwegian Mother, Father and Child Cohort Study; MR, mendelian randomization; SNP, single-nucleotide polymorphism; UKB, UK Biobank.

In the main analyses, we used the MR inverse variance weighting (IVW) method, which is a regression of the estimates for SNP-outcomes associations on SNP-insomnia associations weighted by the inverse of the SNP-outcome associations variances, with the intercept of the regression line forced through zero [33]. The IVW estimates should provide an unbiased estimate of a causal effect in the absence of unbalanced horizontal pleiotropy [33]. To explore potential unbalanced horizontal pleiotropy, our sensitivity analyses included (I) estimating between-SNP heterogeneity (which if present may be due to one or more SNPs having horizontal pleiotropic effects on the outcome) using Cochran’s Q-statistic and leave-one (SNP)-out analysis; and (II) undertaking analyses with weighted median [34] and MR-Egger [35], which are more likely to be robust in the presence of invalid IVs. The weighted median method is unbiased so long as less than 50% of the weight is from invalid instruments (i.e., if 1 SNP contributing more than 50% of the weight across the SNP-insomnia associations or several SNPs that contribute more than 50% introduce horizontal pleiotropy the effect estimate is likely to be biased) [34]. MR-Egger is similar to IVW except it does not constrain the regression line to go through zero; if the MR-Egger intercept is not null, it suggests the presence of unbalanced horizontal pleiotropy, and the MR-Egger slope provides an effect estimate corrected for unbalanced horizontal pleiotropy [35]. However, MR-Egger has considerably less statistical power than IVW. Further details of these MR methods are provided in our previous study [21]. When using MR to assess the effect of maternal exposures in pregnancy on offspring outcomes, results might be biased via a path from maternal genotypes to maternal/offspring outcomes due to fetal genotype [36]. To explore this, we compared SNP-outcome associations with versus without adjustments for fetal genotypes in the pooled birth cohort analyses.

We evaluated the strength of IVs using both proportion of variances of maternal insomnia explained by the 81 SNPs (R2) and F-statistic [37]. We selected SNPs robustly related to insomnia in the general female population rather than in pregnant women. Therefore, we explored associations of the 81 SNPs with woman’s insomnia measured at 18 and 32 weeks of gestation in ALSPAC using logistic regressions to determine whether those SNPs related similarly to insomnia in pregnancy. We adjusted for the top 20 PCs and women’s age. As suggested during peer review, we used Steiger filtering to identify SNPs explaining substantially more of the variance in an outcome than in insomnia (i.e., P-value < 0.05) [38] and reconducted MR IVW after removing those SNPs (listed in S3 Table).

Multivariable regression in ALSPAC

In ALSPAC, we explored the observational associations of insomnia at 18 weeks of gestation with binary outcomes using logistic regression, with adjustment for measured confounders. During peer review, insomnia at 32 weeks of gestation was not considered in the analysis due to potential reverse causality for some outcomes.

All analyses were performed using R 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria). Two-sample MR analyses were conducted using the “TwoSampleMR” R package [27]. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline, specific for MR (S1 STROBE Checklist) [39].

Results

Table 1 summarizes the characteristics of included women from UKB, ALSPAC, BiB, MoBa, and FinnGen. The SNP-insomnia associations in UKB and ALSPAC are listed in S4 Table. The 81 SNPs explained approximately 0.42% of the variance of insomnia among the 208,140 UKB women included in this study (S4 Table), and the mean F-statistic of the 81 SNPs was 11. The pooled 81 SNP-insomnia associations at 18 (OR 1.02 per effect allele, 95% CI: 1.01, 1.03, p = 0.004) and 32 (OR 1.02 per effect allele, 95% CI: 1.01, 1.03, p < 0.001) weeks of gestation in ALSPAC were in the same direction as (but weaker than) the pooled association in the original GWAS of UKB plus 23andMe women (OR 1.05 per effect allele, 95% CI: 1.05, 1.06, p < 0.001). The SNP-outcome associations in UKB, ALSPAC, BiB, and MoBa are listed in S5 Table.

Table 1

Characteristics of the women in UKB, ALSPAC, BiB, MoBa, and FinnGen.

Variablea	UKB (N = 208,140)	ALSPAC (N = 6,826)	BiB (N = 2,940)	MoBa (N = 14,584)	FinnGen (N = ~123,579)
	Mean (standard deviation)
Maternal age at delivery (years)	25.5 (4.6)b	28.7(4.7)	26.8 (6.0)	30.0 (4.4)	Not available
Maternal height (cm)	162.7 (6.2)	164.3 (6.7)	164.4 (6.1)	168.3 (5.5)	Not available
Maternal body mass index (kg/m2)	27.0 (5.1)	22.9 (3.7)	26.7 (6.0)	24.0 (4.2)	Not available
Gestational age (weeks)	38.9 (3.8)c	39.6 (1.7)	39.7 (1.9)	39.6 (1.7)	Not available
Offspring birthweight (grams)	3,186.7 (547.6)	3,441.5 (523.0)	3,357.9 (571.2)	3,640.8 (513.4)	Not available
	N (%)
Maternal educationd
O levels/GCSEs or equivalent and below	91,093 (44.2)	4,043 (59.5)	1,400 (47.6)	260 (1.9)	Not available
A levels/AS levels or equivalent	48,059 (23.3)	1,719 (25.3)	485 (16.5)	4,356 (31.8)	Not available
College or university degree	66,873 (32.5)	1,035 (15.2)	551 (18.7)	9,072 (66.3)	Not available
Maternal ever smoking	85,501 (41.3)	1,450 (21.6)e	911 (31.0)e	1,106 (8.8)e	Not available
Maternal ever drinking	191,010 (91.2)	4,580 (70.2)e	1,793 (61.0)e	3,644 (29.7)e	Not available
Offspring sex, male	Not available	3,430 (50.2)	1,504 (51.2)	7,412 (50.9)	Not available
Number with fetal genotype data	0	4,625 (67.8)	1,855 (63.1)	12,183 (83.5)	Not available
	N cases/N controls (Prevalence, %)
History of stillbirth	4,907/139,034 (3.4)	48/4,546 (1.0)	31/2,588 (1.2)	51/9,998 (0.5)	Not available
History of miscarriage	42,717/139,034 (23.5)	1,378/4,546 (23.3)	14/2,588 (0.5)	2,677/9,998 (21.1)	9,113/89,340 (9.3)
GD	726/200,536 (0.4)	34/6,283 (0.5)	136/2,657 (4.9)	113/14,375 (0.8)	5,687/117,892 (4.6)
HDP	2,138/206,002 (1.0)	1,099/5,698 (16.2)	347/2,159 (13.8)	1,892/12,652 (13.0)	4,255/114,735 (3.6)
Perinatal depression	5,178/25,130 (17.1)	423/5,896 (6.2)	312/2,245 (12.2)	579/13,865 (4.0)	Not available
PTB	556/4,862 (10.3) c	285/4,931 (5.5)	172/2,706 (6.0)	495/12,846 (3.7)	5,480/98,626 (5.3)
LBW	13,429/149,084 (8.3)	337/6,376 (5.0)	167/2,725 (5.8)	245/13,690 (1.8)	Not available
HBW	2,716/149,084 (1.8)	113/6,376 (1.7)	42/2,725 (1.5)	621/13,690 (4.3)	Not available

aIn UKB, these variables were measured at the recruitment that is typically 31.1 years after pregnancy.

bWe report maternal ages at giving their first live birth. UKB women were recruited with an average age of 56.5 (standard deviation 7.9) years.

cGestational age was available only in a small subset of UKB women (N = 7,280).

dO level, General Certificate Education (GCE) Ordinary Level; GCSE, General Certificate of Secondary Education; A level, GCE Advanced level; AS level, GCE Advanced Subsidiary level.

e These were maternal ever smoking/drinking in pregnancy.

ALSPAC, Avon Longitudinal Study of Parents and Children; BiB, Born in Bradford; GD, gestational diabetes; HBW, high offspring birthweight; HDP, hypertensive disorders of pregnancy; LBW, low offspring birthweight; MoBa, the Norwegian Mother, Father and Child Cohort; PTB, preterm birth; UKB, UK Biobank.

Two-sample MR

In MR IVW combining all cohorts, point estimates for associations between lifetime susceptibility to insomnia (versus no insomnia) and outcomes ranging from ORs of 1.20 (95% CI: 0.52, 2.77, p = 0.67) for GD, to 3.56 (95% CI: 1.49, 8.54, p = 0.004) for perinatal depression (Fig 2). Despite combining data from the largest genetic studies available estimates were imprecise, with 95% CIs for all but 3 outcomes including the null. The 3 that did not include the null were miscarriage, perinatal depression, and LBW (Fig 2). S2 Fig shows IVW results for leave-one (study)-out analysis. Results were broadly consistent but dominated by large cohorts (e.g., UKB and FinnGen), with the point estimates inflated and very wide CIs in small birth cohorts. We further removed 26, 1, and 7 SNPs from analyses for stillbirth, perinatal depression, and LBW, respectively (S3 Table), because Steiger filtering suggested these SNPs potentially more associated with the respective outcome than with susceptibility to insomnia (see Methods). MR IVW estimates after Steiger filtering were consistent for perinatal depression, slightly attenuated for LBW, and in the opposite direction for stillbirth with overlapped CIs both including the null (Fig 2).

Fig 2

Two-sample MR estimates for causal effects of insomnia on adverse pregnancy and perinatal outcomes, meta-analysing UKB, the birth cohorts, and FinnGen.

(a) For those outcomes that FinnGen did not contribute to. (b) Steiger filtering did not suggest a removal of any SNPs from MR analyses (details shown in S3 Table). (c) p for Cochran’s Q-statistic <0.05 suggests between-study heterogeneity in the meta-analysis. CI, confidence interval; IVW, inverse variance weighted; MR, mendelian randomization; OR, odds ratio; SNP, single-nucleotide polymorphism; UKB, UK Biobank.

Sensitivity analyses using weighted median and MR-Egger for all outcomes were directionally consistent with IVW but attenuated to the null for all outcomes (except stillbirth), and MR-Egger results for GD, perinatal depression and PTB were attenuated to the null (Fig 2). Between-SNP heterogeneity for MR analyses was observed with LBW and HDP (S6A Table), but leave-one (SNP)-out analyses were consistent with the main IVW estimates including all SNPs for all outcomes (S3–S5 Figs). The MR-Egger intercept p-value indicated unbalanced horizontal pleiotropy only for perinatal depression in UKB (S6A Table). Adjusting for fetal genotype (only possible in the birth cohorts) did not alter the SNP-outcome associations with stillbirth, miscarriage, LBW, or HBW; SNP-outcome associations with GD, HDP, and perinatal depression were slightly attenuated; SNP-PTB associations moved slightly away from the null (S6 Fig).

After combining all cohorts, most MR estimates based on SNP-insomnia associations from linear (Fig 2) versus logistic (S6B Table) regression were in the same directions (S7 Table). An association of lifetime susceptibility to insomnia with HBW was observed using IVW (S6B Table), which previously had a wide 95% CI including the null (Fig 2).

Multivariable regression in ALSPAC

Tables 2 and S7 summarize the characteristics of women from ALSPAC. After adjusting for maternal age, education, BMI, smoking, alcohol intake, and household occupational social class, there was an association of insomnia (versus no insomnia) at 18 weeks of gestation with perinatal depression (OR 2.96, 95% CI: 2.42, 3.63, p < 0.001, Fig 3). Associations with other outcomes had imprecise 95% CIs including the null, although their point estimates were in similar magnitudes to those seen in MR (Fig 3).

Table 2

Characteristics of women in ALSPAC in confounder-adjusted multivariable regression.

Variable		Insomnia at 18 weeks of gestation (N = 10,540)
		Yes	No
		Mean (standard deviation)
Maternal age at delivery (years)		27.1 (5.0)	28.6 (4.7)
Maternal height (cm)		163.1 (6.8)	164.3 (6.7)
Maternal body mass index (kg/m2)		23.4 (4.3)	22.9 (3.7)
Gestational age (weeks)		39.5 (1.8)	39.6 (1.7)
Offspring birthweight (grams)		3,409.0 (546.6)	3,446.6 (522.4)
		N (%)
Insomnia at 32 weeks of gestation	Yes	1,073 (10.2)	1,984 (18.8)
	No	599 (5.7)	6,781 (64.3)
Maternal educationa
O levels/GCSEs or equivalent and below		1,308 12.4	5,433 (51.5)
A levels/AS levels or equivalent		280 (2.7)	2,126 (20.2)
College or university degree		95 (0.9)	1,244 (11.8)
Household occupational social class
I Professional occupations		29 (0.3)	288 (2.7)
II Managerial and technical occupations		240 (2.3)	2,041 (19.4)
III Skilled non-manual occupations		353 (3.3)	2,185 (20.7)
III Skilled manual occupations		515 (4.9)	2,444 (23.2)
IV Partly skilled occupations		321 (3.0)	1,221 (11.6)
V Unskilled occupations		106 (1.0)	334 (3.2)
Maternal smoking status in pregnancy	Ever	590 (5.6)	1,971 (18.7)
	Never	1,106 (10.5)	6,873 (65.2)
Maternal drinking status in pregnancy	Ever	1,032 (9.8)	5,968 (56.6)
	Never	539 (5.1)	2,556 (24.3)
Offspring sex	Male	886 (8.4)	4,539 (43.1)
	Female	810 (7.7)	4,304 (40.8)
GD	Case	8 (0.1)	36 (0.3)
	Control	1,538 (14.6)	8,210 (77.9)
HDP	Case	295 (2.8)	1,403 (13.3)
	Control	1,393 (13.2)	7,389 (70.1)
Perinatal depression	Case	242 (2.3)	460 (4.4)
	Control	1,207 (11.5)	7,759 (73.6)
PTB	Case	79 (0.7)	369 (3.5)
	Control	1,170 (11.1)	6,324 (60.0)
LBW	Case	90 (0.9)	421 (4.0)
	Control	1,578 (15.0)	8,263 (78.4)
HBW	Case	28 (0.3)	160 (1.5)
	Control	1,578 (15.0)	8,263 (78.4)

aO level, General Certificate Education (GCE) Ordinary Level; GCSE, General Certificate of Secondary Education; A level, GCE Advanced level; AS level, GCE Advanced Subsidiary level.

ALSPAC, Avon Longitudinal Study of Parents and Children; GD, gestational diabetes; HBW, high offspring birthweight; HDP, hypertensive disorders of pregnancy; LBW, low offspring birthweight; PTB, preterm birth.

Fig 3

Multivariable regression associations of insomnia at 18 weeks of gestation with adverse pregnancy and perinatal outcomes in ALSPAC.

(a) We adjusted for maternal age at time of delivery, education, body mass index at 12 weeks of gestation, smoking status in pregnancy and alcohol intake in the first 3 month of ALSPAC pregnancy, and household occupational social class. (b) The numbers of women in adjusted models are slightly smaller than those in crude models due to missingness (<8%) in these covariates. ALSPAC, Avon Longitudinal Study of Parents and Children; CI, confidence interval; OR, odds ratio.

Discussion

To the best of our knowledge, this is the first MR study to explore the relationship of insomnia with pregnancy and perinatal outcomes. We interpreted the MR results as reflecting a lifetime susceptibility to insomnia on the basis that SNPs are determined at conception, and evidence suggested that with similar analyses of other exposures (e.g., blood pressure and C-reactive protein) this is the case [40,41]. We interpreted the multivariable regression results as reflecting associations of insomnia during pregnancy, though we could not distinguish this from preexisting insomnia as we did not have information on sleep traits before conception. The associations of the insomnia genetic IVs with reported insomnia during pregnancy in ALSPAC provided some support that the exposures in our MR and multivariable regression analyses had some consistency with each other. Overall, our MR results provide some evidence that a lifetime susceptibility to insomnia might be associated with higher risks of miscarriage, perinatal depression, and LBW. We did not observe evidence to support associations between genetically predicted insomnia and stillbirth, GD, HDP, PTB, and HBW. In multivariable regression, we were unable to assess associations with miscarriage in the index pregnancy. Result for perinatal depression were consistent with the MR results, but this was not the case for LBW, for which no significant association with insomnia reported at 18 weeks gestation was observed.

Our findings in both MR and multivariable regression of an association of insomnia with perinatal depression are consistent with the systematic review and meta-analysis of observational studies [7], and with randomized control trials suggesting that pregnancy intervention with cognitive behavioural therapy to reduce insomnia decreases perinatal depression [8,9]. Recent systematic reviews have only identified 1 cross-sectional study of the association of insomnia with stillbirth [6,42]. This cross-sectional study compared outcomes between 190 women reporting poor sleep quality and 30 women who did not and found no association with stillbirth, although this was not the main focus of the paper [43]. We did not identify any previous studies of insomnia associations with miscarriage. Thus, our novel finding of a possible association of insomnia with miscarriage in MR warrants replication, and larger studies that support analyses with both miscarriage and stillbirth would be valuable. Previous systematic reviews of observational associations of insomnia with GD (OR 1.37, 95% CI: 1.12, 1.69), HDP (OR 1.72, 95% CI: 1.16, 2.56), and PTB (OR 1.49, 95% CI: 1.17, 1.90) are directionally consistent but with stronger ORs than our main MR results [6]. These stronger associations could be due to insufficient adjustment of potential confounders and reverse causality, as many cross-sectional studies and unadjusted associations were included in the meta-analyses.

Several mechanisms have been suggested for why insomnia might influence pregnancy and perinatal outcomes, including insomnia resulting in increased risks of adiposity and insulin resistance that could then influence related pregnancy outcomes (GD, HDP, and HBW). Insomnia has also been suggested to influence maternal blood pressure and placental function which in turn would increase risks of HDP, miscarriage, stillbirth, and PTB. MR analyses support causal associations of insomnia with coronary heart disease, higher glycated haemoglobin, and higher glycoprotein acetyls (an inflammatory marker) in general populations of women and men [17,44,45]. Thus, an increase in cardio-metabolic risk and inflammation may mediate effects of insomnia on miscarriage and LBW, and outcomes for which our MR analyses are currently imprecise. Similarly, MR analyses have found a causal association of insomnia with depressive symptoms [17], which is coherent with our findings in relation to perinatal depression.

Key strengths of our study are that (I) to the best of our knowledge, it is the first study to use MR to explore associations of insomnia with pregnancy and perinatal outcomes; (II) we conducted confounder-adjusted multivariable regression of insomnia in pregnancy in ALSPAC—a larger sample than most previous studies; and (III) we explored a range of pregnancy and perinatal outcomes in 1 paper.

Our MR analyses may be biased by horizontal pleiotropy, particularly given our previous research showing that SNPs for insomnia are also associated with several factors that could influence pregnancy and perinatal outcomes, including education, age at first live birth, and smoking [21]. We explored this potential with a range of sensitivity analyses, including exploring between-SNP heterogeneity and using weighted median and MR-Egger methods that are more robust to such bias than IVW [33]. Results from these sensitivity analyses were broadly consistent with point estimates from IVW; however, the associations between insomnia and miscarriage, perinatal depression, and LBW no longer reached statistical significance. The wider 95% CIs observed could be attribute to the fact that those sensitivity analyses are known to have less statistical power [46]. Those attenuations towards the null could be due to weak IVs, and MR-Egger point estimates are known to be attenuated more severely than weighted median ones [13,46]. Further MR studies in larger samples with more cases would be needed for all outcomes. Specially, our results for perinatal depression require further validation using multivariable MR to account for unbalanced horizontal pleiotropy. Adjusting for fetal genotype did not alter results suggesting that bias due to fetal genotypic effects is unlikely. We did not further adjust for paternal genotype because of limited data with paternal, maternal, and offspring genotype. Furthermore, the most plausible mechanism for paternal genotype to affect pregnancy outcomes is via fetal genotype, which we have adjusted for. Interpretation of our MR estimates requires a further assumption of monotonicity in the SNP-insomnia associations. This requires that all of the women with genetic IVs related to higher susceptibility to insomnia symptoms should report more symptoms (compared to those with fewer alleles related to insomnia)—i.e., that they are “compliers” [47]. The monotonicity assumption cannot be tested. A previous study indicated potential bias when the standard IV approach (see [28]) was used for a nonlinear model [48]. In our study, using linear versus logistic regression to obtain SNP-insomnia associations showed consistent directions between MR estimates. However, magnitudes of MR estimates cannot be compared directly due to their different scales. Further MR studies of binary exposures could apply both approaches to explore an association.

Both our MR and multivariable regression estimates could be vulnerable to selection bias, which has been extensively discussed in previous papers [25,49,50]. UKB is a selective sample (5.5% response to invitation) of adults who are healthier and better educated than the general UK adult population of the same age [51]. Information on perinatal depression and PTB was only available in a subsample of UKB women and such missingness might not be at random [52,53]. By definition our study only includes women who have experienced at least 1 pregnancy, and if insomnia influences fertility then our results might be biased [54]. However, we are not aware of robust evidence of insomnia (or SNPs related to insomnia) influencing infertility or number of children [55,56], suggesting any selection bias through only including pregnant women is unlikely to have a meaningful impact on our MR estimates [54,57].

Insomnia was measured via one self-administrated question in both UKB and ALSPAC, which could mean the binary exposure is misclassified. Non-differential misclassification of insomnia would be expected to bias MR results away from the null (given the attenuated genetic IVs-insomnia associations is the denominator), but multivariable regression results towards the null [58,59]. Similarly, there may be misclassification in some of our outcomes because of the absence of universal testing (e.g., GD in ALSPAC [60]), assessment via self-report questionnaires (e.g., birthweight in UKB), or differences between studies in definitions (e.g., in older women in UKB the gestational age thresholds for defining stillbirth and miscarriage would have differed from those used in the more contemporary birth cohorts). Non-differential misclassification of our binary outcomes would be expected to bias both MR and multivariable regression results towards the null [58,59]. Moreover, the first live-born babies of UKB women are known to be lighter than babies with various birth orders from the more contemporary birth cohorts [61,62].

Although we examined the possibility of reverse causality for individual SNPs using Steiger filtering in MR, this test could be influenced by measurement errors in insomnia and our outcomes and by confounding with opposite directions for insomnia and the outcomes [38]. Multivariable regression results for maternal outcomes could also be vulnerable to reverse causality, as tendency towards the outcomes might have influenced insomnia reported at 18 weeks of gestation. As the sources of bias in our 2 methods (MR and multivariable regression) differ, consistent results between them could strength confidence in the findings even considering different timings of an exposure [63-67]. Our previous study discussed how timings affected the interpretation of MR estimates for insomnia [21]. The similarity of the multivariable regression and MR results for GD, HDP, and perinatal depression suggests it is unlikely that residual confounding has biased regression results, horizontal pleiotropy has substantially affect MR results, or different sources of selection bias in the 2 have a strong impact, for these outcomes. The associations for PTB, LBW, and HBW were attenuated to the null compared to MR results. These suggest possible masking confounding, other biases specific to the multivariable regression, or that the MR is estimating an accumulative effect of insomnia across the life course [68], whereas the observational analyses reflect exposure only from 18 weeks of gestation to occurrence of outcome. MR analyses are statistically inefficient and despite combining relevant studies in order to increase sample size, several of our MR and multivariable regression estimates are imprecise due to small numbers of cases. Our study is limited to women of European ancestry, and we cannot assume that our results generalize to other populations.

Our findings provide some evidence for associations between insomnia and adverse pregnancy outcomes, raising the possibility that interventions to improve healthy sleep (e.g., cognitive behavioural therapy) in women of reproductive age might be beneficial to a healthy pregnancy. However, we acknowledge the need for further MR studies based on larger GWAS of pregnancy and perinatal outcomes, larger observational studies, and studies in women from ethnic backgrounds other than white European. Further studies on the association of insomnia with recurrent miscarriage would help policy makers decide whether to allocate sleep interventions to women with a history of miscarriage when they prepare to be pregnant again.

In conclusion, our study raises the possibility of associations between insomnia and miscarriage, perinatal depression, and LBW.

STROBE-MR checklist of recommended items to address in reports of mendelian randomization studies.

(DOCX)

Click here for additional data file.

Descriptions of each cohort.

(DOCX)

Click here for additional data file.

Flow chart of each cohort.

(DOCX)

Click here for additional data file.

Leave-one (study)-out analyses of MR IVW estimates.

(DOCX)

Click here for additional data file.

Leave-one-out sensitivity analysis in UK Biobank (dataset A on dataset B).

(DOCX)

Click here for additional data file.

Leave-one-out sensitivity analysis in UK Biobank (dataset B on dataset A).

(DOCX)

Click here for additional data file.

Leave-one-out sensitivity analysis in ALSPAC, BiB, MoBa, and FinnGen.

(DOCX)

Click here for additional data file.

Maternal SNP-outcome associations comparing unadjusted to adjusted for fetal genotypes.

(DOCX)

Click here for additional data file.

Definitions of pregnancy and perinatal outcomes.

(XLSX)

Click here for additional data file.

Key characteristics of recently conducted genome-wide association studies of insomnia.

(XLSX)

Click here for additional data file.

Characteristics of genome-wide significant genetic variants for insomnia in women.

(XLSX)

Click here for additional data file.

Associations of the 81 SNPs with insomnia in UKB and ALSPAC.

(XLSX)

Click here for additional data file.

Associations of the 81 SNPs with outcomes in UKB, ALSPAC, BiB, and MoBa.

(XLSX)

Click here for additional data file.

Two-sample MR estimates in UKB and in the birth cohorts and FinnGen.

(XLSX)

Click here for additional data file.

Characteristics of women in ALSPAC by insomnia at 32 weeks of gestation.

(XLSX)

Click here for additional data file.

9 Oct 2021

Dear Dr Yang,

Thank you for submitting your manuscript entitled "Associations of insomnia with pregnancy and perinatal outcomes: Findings from Mendelian randomization and conventional observational studies in up to 356,069 women" for consideration by PLOS Medicine.

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2 Mar 2022

Dear Dr. Yang,

Thank you very much for submitting your manuscript "Associations of insomnia with pregnancy and perinatal outcomes: Findings from Mendelian randomization and conventional observational studies in up to 356,069 women" (PMEDICINE-D-21-04244R1) for consideration at PLOS Medicine.

Your paper was evaluated by three independent reviewers, and discussed among all the editors here and with an academic editor with relevant expertise. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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Comments from the Academic Editor:

In the introduction there is some conflation between insomnia and supine sleep position which I do not think is helpful. It seems likely that associations between supine sleep position and perinatal outcomes are mediated by mechanisms distinct from those described as insomnia.

In the ALPSAC logistic regression the numbers of miscarriages and stillbirths seem very high. I may have misunderstood the methodology but I think they have looked at causal effects of reported insomnia at 18 weeks and any previously reported pregnancy loss. I do not think this is correct- and would recommend that they look at the effect of insomnia reported at 18 weeks on any subsequent pregnancy complication in the same pregnancy.

In particular, I think it's necessary for the authors to fully address the concerns raised by Reviewer 2 regarding possible reverse causation, pleiotropy and shared aetiology. Some of the causal claims should also be toned down a bit.

Requests from the editors:

General comments:

Please include line numbers in your revised manuscript, ideally not starting from 1 with each new page.

Your study is observational and therefore causality cannot be inferred. Please avoid use of causal language such as ‘effect’, referring to ‘associations’ instead. Please replace ‘causal effects’ with ‘causal associations’ throughout your manuscript.

Throughout the paper, please adapt reference call-outs to the following style: "... risks of gestational diabetes (GD) [5,6]..." (noting the absence of spaces within the square brackets).

Title: Please revise your title according to PLOS Medicine's style. Please place the study design in the subtitle (ie, after a colon). We suggest “Association between insomnia and pregnancy and perinatal outcomes: A Mendelian randomization analysis” or similar

Abstract Background:

Please avoid use of causal language. Please revise to “Our aim was to estimate the association between insomnia and stillbirth, miscarriage, …”

Abstract Methods and Findings:

Please revise to “IVW showed evidence of an association between…”

Please revise to “For other outcomes IVW indicated potentially clinically important associations…”

Please provide brief demographic details of the study population (e.g. age, ethnicity, etc)

Abstract Conclusions:

Please begin your Abstract Conclusions with "In this study, we observed ..." or similar, to summarize the main findings from your study, without overstating your conclusions. Please emphasize what is new and address the implications of your study more specifically, being careful to avoid assertions of primacy.

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Author Summary:

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Introduction:

Please temper assertions of primacy by adding ‘to the best of our knowledge’ or similar to the penultimate paragraph.

Please replace ‘causal effects’ with ‘causal associations’

Methods:

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section. If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript. If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and if/when reported analyses differed from those that were planned. Changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale. If a reported analysis was performed based on an interesting but unanticipated pattern in the data, please be clear that the analysis was data-driven.

Please ensure that the study is reported according to the STROBE-MR guideline, and include the completed STROBE-MR checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline, specific for mendelian randomization (S1 Checklist)."

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Please remove all subheadings within your Discussion e.g. Study strengths and limitations

Please avoid causal language such as “effect” and “increased risk”. For example, please revise to “the strongest association using MR evidence was observed between insomnia and the risk of miscarriage, …” or similar.

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Comments from the reviewers:

Reviewer #1: This study examined the effects of insomnia on pregnancy and perinatal outcomes using two-sample Mendelian randomization design. It is a well-designed and carefully conducted study. Here are my specific comments on this study.

1. Please provide line number for easier reference.

2. There is competing interest that needs to be carefully reviewed

3. The data are not fully available to the public.

4. page 5, Introduction: "three RCT assessing the effects… or perinatal outcomes". I feel that this part can be briefer. No need to show the numbers here. Or you can discuss the results in more details in the discussion section.

5. Page 8, outcome measures: "if multiple pregnancies were enrolled in the birth cohorts, we randomly selected one pregnancy per woman". First, how many multiple pregnancies are there in the dataset? Second, is this the best way to deal with multiple pregnancies? Can you adjust for the multiple pregnancies using techniques such as random effect model? Also, if you have to choose one of the pregnancies, is that better to choose the first pregnancy rather than randomly select one pregnancy. I assume most the single pregnancies are the first pregnancy?

6. Page 9, insomnia measures: The definitions of insomnia in ALSPAC and in other studies are different. I would say lifelong exposure to insomnia and insomnia during pregnancy are drastically different. Is that possible to adjust for the two measurements to make them more consistent with each other? For example, using studies collecting data for both definitions to examine the relationship between them and adjust the data of two definitions accordingly. Given the difference between the two definitions, I would say that comparison between the MR and conventional approach become less useful.

7. Page 9, SNP selection and SNP-insomnia associations: fitting linear regression on a binary outcome (i.e., insomnia) is inappropriate. Can you fit logistic or probit model and predict the log-odds of insomnia instead? I don't see why you have to worry about non-collapsibility and interpretation here. This are intermediate estimates anyway.

8. Page 13: "if the MR-Egger intercept is not null it suggests the presence of horizontal pleiotropy, and the MR-Egger slope provides an effect estimate corrected for unbalanced horizontal pleiotropy" Then, why not use the MR-Egger directly to avoid making assumption on horizontal pleiotropy? Given the sample size you have, do you really have to worry about statistical power here?

Reviewer #2: The paper applies Mendelian randomization and multivariable linear regression to study the effects of lifetime tendency for insomnia on pregnancy related outcomes. Overall, I think this is a well written and presented study, and an interesting use of Mendelian randomization methods. There is some evidence of an effect of insomnia on some pregnancy outcomes, although many of the methods used are reasonably low powered and sensitivity analyses do not always support the primary analyses. Nonetheless, the authors place the results in the context of existing literature and their results provide the basis for future study of the identified associations.

I have the following comments.

- Although it is noted in the Discussion, I feel that potential reverse causation could be particularly relevant here, especially given that in many cases it is unknown if the exposure occurred before or after the outcome. A bidirectional analysis and/or the MR Steiger test of directionality could be used to analyse potential reverse causation.

- The Discussion suggests there are known risk factors which may sit on pleiotropic pathways from the genetic instruments to the outcomes, and hence be causing bias in the MR results. Do the genetic instruments associate with any of these possibly pleiotropic risk factors? The conclusion regarding the effect of insomnia on perinatal depression seems somewhat weak: there is evidence of unbalanced pleiotropy from the MR-Egger intercept test, and the direction of the MR-Egger point estimate is inconsistent with the MR-IVW result. Could these results be explained by horizontal pleiotropy, and if so, could this be explored in a multivariable MR analysis?

- Three SNPs were excluded from the BiB dataset due to having a minor allele frequency < 0.01. Does this indicate that the genetic distribution of individuals in BiB is not similar to the other cohorts? For example, rs10947428 has a minor allele frequency of approx 0.2 in the other datasets, which seems to be a big difference to BiB. Is it therefore valid to combine BiB with the other datasets? Also, rs9943753 is given in the text as a SNP which is excluded from BiB, but doesn't seem to be in the list of instruments in the supplementary tables.

- For the leave-one-out analysis of cohorts shown in S2 Fig, it might be relevant to also do this analysis without UKB. UKB dominates the others (due to its size), so I don't think the analysis is able to show whether there is heterogeneity between the birth cohorts. This may, for example, help to show whether BiB is from a similar population group to the other cohorts or not.

- Page 13: "if the MR-Egger intercept is not null it suggests the presence of horizontal pleiotropy" - should "horizontal" be "unbalanced" in this sentence?

Reviewer #3: Thank you for the opportunity to review this manuscript. The paper addresses an important question, whether sleep disturbances during pregnancy is causally linked to adverse pregnancy outcomes. Insomnia is becoming increasingly common, and is recognized to be associated with a variety of adverse health outcomes. As highlighted by the authors, several epidemiological studies have highlighted potential links between sleep disorders including insomnia and pregnancy outcome, though whether insomnia is causally linked to these outcomes are not clear.

The authors sought to address this series of research question using several datasets, including UK Biobank, 3 birth cohorts (ALSPAC, BIB, MoBa) and FinnGen. Results obtained from the different datasets were then meta-analyzed, as appropriate. They also performed multivariable regression on association between insomnia, as reported at 18 weeks gestation and 32 weeks gestation, with different pregnancy outcome, with adjustment of the known confounders. The main findings were significant, and clinically relevant association between insomnia and miscarriage, perinatal depression, and low birthweight. Some of the sensitivity analyses gave additional support, though some of the other analyses had CI that overlapped with null.

The study is overall well-performed, and utilized several of the largest datasets available to address the research question. The outcome definitions have been harmonized. The MR methodology and sensitivity analyses are clearly described, as would be expected given the extensive expertise on MR from this group of investigators from Bristol.

Results from the main analysis (using IVW), and the sensitivity analyses using weighted median and MR-Egger, were mostly consistent in direction and size of effects. Perhaps rather surprisingly, the confidence interval for the insomnia-adverse pregnancy outcome and perinatal outcome analysis was quite wide for the majority of outcomes, despite the large sample size. The between-study heterogeneity appeared limited for most outcomes, suggesting the source of the variation is prob not due to study differences.

Some limitations include the timing of the assessment of insomnia, which was at 18 and 32weeks gestation in ALSPAC, but with history of stillbirth or history of miscarriage used as the outcome. Another major limitation is that insomnia was defined according to self-reported measures.

Major comments

1. The confidence intervals for the estimates were rather large for most outcomes examined despite the large sample size. Can the authors discuss and speculate more on why this would be the case?

2. In general, genetically determined exposures (e.g. insomnia in this case) would show larger effects by the instrumental variable (reflecting lifelong predisposition to the exposure) compared to effect size observed in the multivariable regression analysis. This was however not the case for the outcomes examined in this analysis. Is this related to the impact of the exposure being limited to that during pregnancy. Perhaps some brief discussion of this would be useful.

Any attachments provided with reviews can be seen via the following link:

[LINK]

19 Apr 2022

Submitted filename: response to reviewers comments_KT_DAL_submit.docx

Click here for additional data file.

26 May 2022

Dear Dr. Yang,

Thank you very much for re-submitting your manuscript "Associations between insomnia and pregnancy and perinatal outcomes:  Mendelian randomization and conventional observational analyses" (PMEDICINE-D-21-04244R2) for consideration at PLOS Medicine.

Your revised paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to two of the original reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of the remaining comments from Reviewer 1, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

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Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Caitlin Moyer, PhD

Associate Editor

PLOS Medicine

on behalf of,

Louise Gaynor-Brook, MBBS PhD

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Points for a second major:

1. Response to reviewers: Please completely address the remaining points of reviewer 1.

2. Title: We suggest: “Associations between insomnia and pregnancy and perinatal outcomes: Evidence from Mendelian randomization and observational analyses” or similar.

3. Abstract: Background: If feasible, it might be helpful to add a few words or a sentences to explain the gap in knowledge being addressed by your study, particularly the MR aspect (given that you point out previous work has already revealed observational evidence for associations between insomnia and adverse pregnancy/perinatal outcomes).

4. Abstract: Methods and Findings: Please explicitly mention the main outcome measures.

5. Abstract: Methods and Findings: Please quantify the main results (with 95% CIs and p values).

6. Abstract: Line 33: Please define LBW at first use.

7. Abstract: Methods and Findings: Line 33-34: In light of the results, please revise the wording to: “For other outcomes IVW results did not support associations with insomnia, yielding observations that were directionally similar (OR range 1.20 to 2.43), but with 95% CIs that were wide and included the null.” or similar. It may also help to mention which specific outcomes are being described here, and please report OR, 95% CI, and p values for each.

8. Abstract: Line 35: We suggest revising to: “Associations between genetically predicted insomnia and miscarriage, perinatal depression, and LBW were not observed in weighted median and MR Egger analyses. Results from these sensitivity analyses were directionally consistent with IVW analyses for all outcomes, with the exception of gestational diabetes, perinatal depression, and preterm birth.

9. Abstract: Methods and Findings: Would it be considered a limitation also that genetic instruments for insomnia were not specific to women in pregnancy (could there be different mechanisms related to pregnancy insomnia)?

10. Abstract: Conclusions: Line 42: Please revise the wording to “...we observed some evidence supporting a possible causal relationship between genetically-predicted insomnia and miscarriage, perinatal depression, and LBW.” We suggest also mentioning that you also observed evidence supporting a relationship between insomnia at 18 weeks gestation and perinatal depression, but no observational evidence to support other outcomes.

11. Abstract: Conclusions: Here and throughout (also Line 67-68), please rephrase, and avoid wording that implies “insomnia causes miscarriage” or similar.

12. Author summary: Line 63: Please remove the word “consistent” given the findings from the various sensitivity analyses.

13. Author summary: Line 64-65: We suggest re-wording to: “...and MR analyses also suggested evidence for an association between genetically-predicted insomnia and risk of miscarriage and low offspring birthweight.”

14. Author summary: Line 67: We suggest: “These findings raise the possibility that interventions to improve healthy sleep may be beneficial for a healthy pregnancy.”

15. Introduction: Line 89-90: Please clarify this sentence: “As disturbed sleep is a symptom of depression it is unclear whether these studies reflect a causal effect of insomnia, or it is part of the diagnostic criteria.”

16. Methods: Line 130-133: Thank you for mentioning the lack of a pre-specified analysis plan. Please also make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

17. Results: Throughout this section, please present the OR, with 95% CIs and p values, for all main outcomes described.

18. Results: Line 309: Please mention here that results of IVW analyses were attenuated for all associations identified in weighted median and MR-Egger analyses.

19. Results: Lines 319-325: Please present the OR, 95% CIs, p values for associations with the primary outcomes in the text for the multivariable regression analyses.

20. Results: Lines 314-317: Please describe in the text, or in the S6 Fig legend, how it was established whether adjustment for fetal genotype did or did not significantly alter SNP-outcome associations.

21. Discussion: Line 340-348: Given the potential for this to be misinterpreted, we suggest rewording to clarify: “Overall, our MR results provide evidence that a lifetime susceptibility to insomnia might associate with higher risks of miscarriage, perintatal depression, and LBW. We did not observe evidence to support associations between genetically-predicted insomnia and stillbirth, GD, HDP, PTB, and HBW. In multivariable regression, we were unable to assess associations with miscarriage in the index pregnancy. Results for perinatal depression were consistent with the MR results, but this was not the case for LBW, for which no significant association with insomnia reported at 18 weeks gestation was observed.”

22. Discussion: Line 352-353: “Whilst previous studies have shown that pregnancy supine sleeping position (which is associated with insomnia) to be associated with stillbirth [6,39]” We suggest removing this, as per the discussion in the rebuttal letter.

23. Discussion: Line 387-388: “Results from these sensitivity analyses were broadly consistent with our main

388 analyses but were less precise (i.e. had wider CIs).” Please provide a sentence or so of discussion on the implications of the fact that the results from the IVW analysis were not found to be robust in sensitivity analyses, in terms of how that affects your interpretations.

24. Discussion: Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion. Specifically, please include a paragraph discussing the implications, next steps for research, clinical practice, and/or public policy between the discussion of study limitations and the overall conclusion paragraph.

25. Discussion: Line 438: We suggest revising to: “In conclusion, our study raises the possibility of associations between insomnia and miscarriage, perinatal depression and LBW.”

26. Line 443: Data availability statement: Please remove this section from the main text of the manuscript, and make sure all information is completely and accurately entered into the data availability section of the manuscript submission system.

27. Line 460: Funding: Please remove this section from the main text of the manuscript, and make sure all information is completely and accurately entered into the funding section of the manuscript submission system.

28. Line 486: Competing interests: Please remove this section from the main text of the manuscript, and make sure all information is completely and accurately entered into the competing interests section of the manuscript submission system.

29. References: Please use the "Vancouver" style for reference formatting, and see our website for other reference guidelines https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

For reference 23, please provide complete information for the reference.

For reference 36, the journal abbreviation should be BMJ.

For reference 37, the journal abbreviation should be JAMA.

For reference 57, the journal abbreviation should be BJOG.

For reference 64, the journal abbreviation should be Nat Rev Methods Primers.

30. Table 1: It might be helpful to clarify in the legend that “miscarriage/stillbirth” represent any history of those (if this is accurate).

31. Figure 2: Please present the results taking Steiger filtering into account for all of the outcomes, not just those that were significant. Please present this in the figure rather than mentioning the results in the legend. Please present p values in addition to 95% CIs for each association.

32. Figure 3: Please correct the title to: “...insomnia at 18 weeks of gestation…”Please present p values in addition to 95% CIs for each association.

33. S3 Table: Please clarify where the footnotes indicating the Steiger filtering of individual SNPs are indicated (e.g. footnotes d, e, f were not found in the table).

34. S7 Table: Please move this table to the main text (at least the 18 weeks gestation dataset). Please clarify what is meant by “Insomnia at the other time point” in the variables list.

35. S6 Fig. Please display all plots on the same axis scale. Please describe interpretations in the figure legend (e.g. attenuation of relationships).

Comments from the reviewers:

Reviewer #1: Thank you for the thoughtful responses from the authors. I think the authors have addressed most concerns from me and other reviewers. However, there are still a few remaining concerns I have about the current manuscript.

1. Response to my point # 5:

The explanations given by the authors are great. I think it would be great to include these detailed explanations in the manuscript (possibly in the discussion section). The current revisions made by the authors are insufficient to me.

2. Response to my point # 6:

(page 27, line 474) We added: "As the sources of bias in our two methods (MR and multivariable regression) differ, where we have consistency between them this increases our confidence in those consistent results being the causal association [17-21]."

I would still be cautious about making any causal inference/claim here based on the consistency in the results.

3. Response to my point # 7:

Using conventional linear IV method in nonlinear models can lead to substantial bias (https://pubmed.ncbi.nlm.nih.gov/18546544/). Also, as I mentioned, since estimates of the association between SNP and insomnia are just intermediate results for the IV approach, why do we care so much about the interpretation of the intermediate results? Should we prioritize having unbiased estimates of association between insomnia and perinatal outcomes as these associations are more important?

Reviewer #2: I thank the authors for their response to my comments, which have all been addressed satisfactorily.

Any attachments provided with reviews can be seen via the following link:

[LINK]

7 Jul 2022

Submitted filename: response letter.docx

Click here for additional data file.

4 Aug 2022

Dear Dr. Yang,

Thank you very much for re-submitting your manuscript "Associations between insomnia and pregnancy and perinatal outcomes:  Evidence from Mendelian randomization and multivariable regression analyses" (PMEDICINE-D-21-04244R3) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by one of the reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Aug 11 2022 11:59PM.

Sincerely,

Caitlin Moyer, PhD

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

1. Abstract: Methods and Findings: Please explicitly state the outcomes of interest early in this section when describing the study methods: ever experiencing stillbirth, ever experiencing miscarriage, GD, HDP, perinatal depression, PTB (gestational age <37 completed weeks), LBW (<2,500 grams) and HBW (>4,500 grams).

2. Abstract: Methods and Findings: Line 34, Line 41: Here, and throughout, please report p values as p<0.001 where applicable, unless there is a statistical reason to present exact p values smaller than this.

3. Abstract: Line 35: Please define “HDP” at first use in the text.

4. Abstract: Conclusions: Line 46-47: Please revise to: “In this study, we observed evidence s

5. Author summary: Line 59-60: Please revise to: “To the best of our knowledge, Mendelian randomization (MR) has not been used to explore whether there is evidence to support a causal association between insomnia and adverse pregnancy and perinatal outcomes.”

6. Author summary: We suggest starting off this point to focus on the interpretation of the study findings: “These findings raise the possibility that insomnia may be related to pregnancy outcomes…”.

7. Results: Line 294 and 296: Here, and throughout the Results section, please use p<0.001 where appropriate.

8. Results: Line 299-301: Please revise to: “In MR IVW combining all cohorts, point estimates for associations between lifetime susceptibility to insomnia (vs no insomnia) and outcomes ranged from ORs of 1.20 (95% CI: 0.52, 2.77, p value=0.67) for GD, to 3.56 (95% CI: 1.49, 8.54, p-value=0.004) for perinatal depression (Fig 2).”

9. Results: Line 309: Please revise to: “MR IVW estimates after Steiger filtering were consistent for…”

10. Discussion: Line 362: Please revise to “...provide some evidence that a lifetime susceptibility to insomnia might be associated with….”

11. Discussion: Line 405-406: Please revise to: “Results from these sensitivity analyses were broadly consistent with point estimates from IVW, however, the associations between insomnia and miscarriage, perinatal depression, and low offspring birth weight no longer reached statistical significance. The wider 95% CIs observed could be attributable to the fact that these sensitivity analyses are known to be statistically less efficient [45].” It might be helpful to provide a sentence of explanation of what is meant by statistically less efficient, and alternative interpretations.

12. Discussion: Line 464: We suggest revising the first sentence to more closely address the implications of the study without overreaching what can be concluded (i.e. the study does not exactly provide evidence that suggests a sleep intervention could be beneficial in healthy pregnancy). We suggest: “Our findings provide some evidence for associations between insomnia and pregnancy outcomes, raising the possibility that interventions to improve healthy sleep (e.g. cognitive behavioural therapy) in women of reproductive age might be beneficial strategy to promote healthy pregnancy.” or similar.

13. References: When providing a DOI please include both the label and full DOI included at the end of the reference (e.g. doi: 10.1016/j.molimm.2014.11.005). Please do not provide a shortened DOI or the URL.

14. Figure 3: Please use p<0.001 for p values when appropriate (for perinatal depression).

15. S6 Table: Please provide p values for associations reported.

16. Table 1, Table 2, and S7 Table: Please define the abbreviations used for maternal education (GCSE) in the legend.

17. S2 Text: We suggest moving this to the main text of the Methods section at line 206.

Comments from Reviewers:

Reviewer #1: Thank you for the response. I think the authors have adequately addressed my comments from last round. I would recommend acceptance of the paper. Congratulations!

Any attachments provided with reviews can be seen via the following link:

[LINK]

11 Aug 2022

Submitted filename: response letter.docx

Click here for additional data file.

15 Aug 2022

Dear Dr Yang,

On behalf of my colleagues and the Academic Editor, Sarah Stock, I am pleased to inform you that we have agreed to publish your manuscript "Associations between insomnia and pregnancy and perinatal outcomes:  Evidence from Mendelian randomization and multivariable regression analyses" (PMEDICINE-D-21-04244R4) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

Please address the following editorial points:

-Abstract: Line 48-49: Apologies for the confusion with the previous comment on this sentence. We suggest revising to: “In this study, we observed some evidence in support of a possible causal relationship between genetically-predicted insomnia and miscarriage, perinatal depression, and LBW. Our study also found observational evidence in support of an association between insomnia in pregnancy and perinatal depression, with no clear multivariable evidence of an association with LBW.”

-Abstract: Line 52: Please remove the word “improving” from the sentence.

-Author summary: Line 59: Please change to “not clear” in this sentence.

-Supporting information files: Please remove the unpublished manuscript from the supporting information files.

PRESS

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine