Migraine as a risk factor for mixed symptoms of peripartum depression and anxiety in late pregnancy: A prospective cohort study
Abstract
BACKGROUND
Migraine has been identified as a risk factor for peripartum depression. However, little is known about the contribution of anxiety to this association or potential changes throughout the peripartum period.
METHODS
In a sample of 4,831 women from the Biology, Affect, Stress, Imaging and Cognition cohort in Sweden, participants were asked about history of migraine prior to pregnancy. The participants completed the Edinburgh Postnatal Depression Scale (EPDS) at gestational weeks 17 and 32 and postpartum week 6. Multinomial logistic regression analyses were used to assess associations between migraine and symptoms of depression, anxiety or mixed depression and anxiety, while adjusting for potential confounders.
RESULTS
In crude estimates, migraine was associated with separate and mixed symptoms of depression and anxiety at most time points. After adjustments, migraine was associated with anxiety at week 17 (adjusted odds ratio: 1.69; 95% confidence interval: 1.11-2.54) and with mixed depression and anxiety at week 32 (adjusted odds ratio: 1.45; 95% confidence interval: 1.06-1.99). None of the other associations remained statistically significant after adjustments.
LIMITATIONS
Migraine history was self-reported. Symptoms of depression and anxiety were based on the screening tool EPDS and not on clinical diagnoses.
CONCLUSIONS
The results demonstrate that migraine may be a risk factor for anxiety in mid- pregnancy and mixed symptoms of peripartum depression and anxiety…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 4,831 participants or records.
Read the paper
Related studies
- Calcium Ions in the Physiology and Pathology of the Central Nervous System
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene
- In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy
- Targeting serotonin receptors with phytochemicals – an in-silico study
- In silico exploration of anti-prostate cancer compounds from differential expressed genes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.