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BackgroundObservational studies have reported maternal short/long sleep duration to be associated with adverse pregnancy and perinatal outcomes. However, it remains unclear whether there are nonlinear causal effects. Our aim was to use multivariable regression (MVreg) and Mendelian randomization (MR) to examine nonlinear effects of sleep duration on stillbirth, miscarriage, gestational diabetes, hypertensive disorders of pregnancy, perinatal depression, preterm birth, low/high offspring birthweight (LBW/HBW).MethodsWe used data from European women in UK Biobank (UKB, N=208,140), FinnGen (N=∼123,579), Avon Longitudinal Study of Parents and Children (N=6826), Born in Bradford (N=2940) and Norwegian Mother, Father and Child Cohort Study (MoBa, N=14,584). We used 78 previously identified genetic variants as instruments for sleep duration, and investigated its effectsusing two-sample MR and one-sample nonlinear MR (in UKB only). We compared MR findings with MVreg in MoBa (N=76,669), where maternal sleep duration was measured at 30 weeks.ResultsIn UKB, MR provided evidence of nonlinear effects of sleep duration on stillbirth, perinatal depression and LBW, but not for other outcomes. Shorter and longer duration increased stillbirth and LBW; shorter duration increased perinatal depression. For example, there was a lower risk of LBW with increasing duration (odds ratio 0.79 per one-hour/day (95% confidence interval (CI): 0.68, 0.93) in the shortest duration group and a higher risk (odds ratio 1.38 (95% CI: 1.06, 1.81) in the longest duration group, suggesting shorter and longer duration increased the risk. These were supported by the lack of evidence of a linear effect of sleep duration with any outcome using two-sample MR. In MVreg, risks of all outcomes (except for stillbirth showing opposite directions) were higher in the women reporting −52), except for stillbirth and gestational diabetes.ConclusionsOur results supported possible nonlinear sleep duration effects on perinatal depression and LBW. Statistical support for nonlinear models across outcomes suggests potential nonlinear effects on other outcomes that larger studies could detect. |
