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To utilize modern tools to assess depressive and anxiety symptoms, wellbeing and life conditions in pregnant women during the first two waves of the COVID-19 pandemic in Sweden. Pregnant women (n = 1577) were recruited through the mobile application Mom2B. Symptoms of depression, anxiety and wellbeing were assessed during January 2020–February 2021. Movement data was collected using the phone’s sensor. Data on Google search volumes for “Corona” and Covid-related deaths were obtained. Qualitative analysis of free text responses regarding maternity care was performed. Two peaks were seen for depressive symptoms, corresponding to the two waves. Higher prevalence of anxiety was only noted during the first wave. A moderating effect of the two waves in the association of depression, anxiety, and well-being with Covid deaths was noted; positive associations during the first wave and attenuated or became negative during the second wave. Throughout, women reported on cancelled healthcare appointments and worry about partners not being allowed in hospital. The association of mental health outcomes with relevant covariates may vary during the different phases in a pandemic, possibly due to adaptation strategies on a personal and societal/healthcare level. Digital phenotyping can help healthcare providers and governmental bodies to in real time monitor high-risk groups during crises, and to adjust the support offered.

Objective

Mechanisms driving temporal fluctuations of inflammatory markers during pregnancy, and how these might differ between distinct perinatal depressive trajectories, are not well understood. The aim of this study was to investigate cytokines levels over the course of pregnancy in women with different trajectories of depressive symptoms peripartum, and relate the levels to levels of non-pregnant controls.

Methods

Based on the Edinburgh Postnatal Depression Scale and/or selective serotonin reuptake inhibitors use, 131 women were categorized into: no (n = 65); antepartum (APD, n = 19), postpartum (PPD, n = 17) and persistent (n = 30) depressive symptoms. Plasma samples (n = 386) were analyzed for levels of interleukin (IL)-8, IL-18, Tumor necrosis factor-α, macrophage colony-stimulating factor (M-CSF), vascular endothelial growth factor A (VEGF-A) and fractalkine, at four different time-points (twice during pregnancy, during childbirth, and postpartum) using Bio-Plex Pro Human Cytokine Assays. Generalized linear mixed models were applied to analyze the associations between cytokine levels, time-point, perinatal depressive symptom trajectory group and their interaction.

Results

For all markers but VEGF-A, pregnancy was associated with higher cytokine levels compared to the non-pregnant controls, with delivery being the most prominent time-point. For M-CSF, IL-18 and VEGF-A, levels were back to the non-pregnant status at postpartum week 8. An effect of perinatal depressive symptom trajectory groups on cytokine levels was found for VEGF-A. Women with PPD and women with APD had lower levels of VEGF-A throughout the study period compared to women with persistent depression, and women with PPD had lower levels compared to non-depressed women.

Conclusions

Lower levels of VEGF-A were noted among women in some trajectories of depressive symptoms peripartum. The peripartum period is a time of tremendous immune system adaptations. Standardization of time-points for cytokine measurements in studies of perinatal depression are important in order to draw valid conclusions on the role of the immune system in perinatal depression.

Children of mothers with prenatal depressive symptoms (PND) have a higher risk of behavioral problems; fetal programming through DNA methylation is a possible underlying mechanism. This study investigated DNA methylation in cord blood to identify possible "at birth" signatures that may indicate susceptibility to behavioral problems at 18 months of age. Cord blood was collected from 256 children of mothers who had self-reported on symptoms of depression during pregnancy and the behavior of their child at 18 months of age. Whole genome DNA methylation was assessed using Illumina MethylationEPIC assay. The mother and child pairs were categorized into four groups, based on both self-reported depressive symptoms, PND or Healthy control (HC), and scores from the Child Behavior checklist (high or low for internalizing, externalizing, and total scores). Adjustments were made for batch effects, cell-type, and clinical covariates. Differentially methylated sites were identified using Kruskal-Wallis test, and Benjamini-Hochberg adjusted p values < 0.05 were considered significant. The analysis was also stratified by sex of the child. Among boys, we observed higher and correlated DNA methylation of one CpG-site in the promoter region of TPP1 in the HC group, with high externalizing scores compared to HC with low externalizing scores. Boys in the PND group showed lower DNA methylation in NUDT15 among those with high, compared to low, internalizing scores; the DNA methylation levels of CpGs in this gene were positively correlated with the CBCL scores. Hence, the differentially methylated CpG sites could be of interest for resilience, regardless of maternal mental health during pregnancy. The findings are in a relatively healthy study cohort, thus limiting the possibility of detecting strong effects associated with behavioral difficulties. This is the first investigation of cord blood DNA methylation signs of fetal programming of PND on child behavior at 18 months of age and thus calls for independent replications.

IntroductionPeripartum depression (PPD) impacts around 12% of women globally and is a leading cause of maternal mortality. However, there are currently no accurate methods in use to identify women at high risk for depressive symptoms on an individual level. An initial study was done to assess the value of deep learning models to predict perinatal depression from women at six weeks postpartum. Clinical, demographic, and psychometric questionnaire data was obtained from the “Biology, Affect, Stress, Imaging and Cognition during Pregnancy and the Puerperium” (BASIC) cohort, collected from 2009-2018 in Uppsala, Sweden. An ensemble of artificial neural networks and decision trees-based classifiers with majority voting gave the best and balanced results, with nearly 75% accuracy. Predictive variables identified in this study were used to inform the development of the ongoing Swedish Mom2B study.ObjectivesThe aim of the Mom2be study is to use digital phenotyping data collected via the Mom2B mobile app to evaluate predictive models of the risk of perinatal depression.MethodsIn the Mom2B app, clinical, sociodemographic and psychometric information is collected through questionnaires, including the Edinburgh Postnatal Depression Scale (EPDS). Audio recordings are recurrently obtained upon prompts, and passive data from smartphone sensors and activity logs, reflecting social-media activity and mobility patterns. Subsequently, we will implement and evaluate advanced machine learning and deep learning models to predict the risk of PPD in the third pregnancy trimester, as well as during the early and late postpartum period, and identify variables with the strongest predictive value.ResultsAnalyses are ongoing.ConclusionsPending results.DisclosureNo significant relationships.

Introduction: Perinatal complications, such as perinatal depression and preterm birth, are major causes of morbidity and mortality for the mother and the child. Prediction of high risk can allow for early delivery of existing interventions for prevention. This ongoing study aims to use digital phenotyping data from the Mom2B smartphone application to develop models to predict women at high risk for mental and somatic complications.

Methods and analysis: All Swedish-speaking women over 18 years, who are either pregnant or within 3 months postpartum are eligible to participate by downloading the Mom2B smartphone app. We aim to recruit at least 5000 participants with completed outcome measures. Throughout the pregnancy and within the first year postpartum, both active and passive data are collected via the app in an effort to establish a participant's digital phenotype. Active data collection consists of surveys related to participant background information, mental and physical health, lifestyle, and social circumstances, as well as voice recordings. Participants' general smartphone activity, geographical movement patterns, social media activity and cognitive patterns can be estimated through passive data collection from smartphone sensors and activity logs. The outcomes will be measured using surveys, such as the Edinburgh Postnatal Depression Scale, and through linkage to national registers, from where information on registered clinical diagnoses and received care, including prescribed medication, can be obtained. Advanced machine learning and deep learning techniques will be applied to these multimodal data in order to develop accurate algorithms for the prediction of perinatal depression and preterm birth. In this way, earlier intervention may be possible.

Ethics and dissemination: Ethical approval has been obtained from the Swedish Ethical Review Authority (dnr: 2019/01170, with amendments), and the project fully fulfils the General Data Protection Regulation (GDPR) requirements. All participants provide consent to participate and can withdraw their participation at any time. Results from this project will be disseminated in international peer-reviewed journals and presented in relevant conferences.

Background

Few studies, with conflicting results, report on the association between memory performance and depressive symptoms during the perinatal period. In this study, we aimed to evaluate whether memory performance during late pregnancy is associated with antepartum (APD) and postpartum depression (PPD) symptoms.

Method

We conducted a prospective follow-up of 283 pregnant women, nested within a large cohort of women enrolled in the BASIC study in Uppsala University hospital between 2009 and 2019. The Wechsler Digit Span Task (forward-DSF, backward-DSB and total score-DST) was performed to evaluate short-term memory/attention (DSF) and working memory (DSB) around the 38th gestational week; the Edinburgh Postnatal Depression Scale (EPDS), evaluating depressive symptoms, was filled out at 17, 32, 38 gestational weeks, as well as at 6 weeks postpartum. Unadjusted and multivariate logistic regression was used to assess the association between performance on the Digit Span Task and outcome, namely depressive symptoms (using a cut-off of 12 points on the EPDS) at 38 gestational weeks, as well as at 6 weeks postpartum.

Results

APD symptoms were not significantly associated with DSF (p = 0.769) or DSB (p = 0.360). APD symptoms were significantly associated with PPD symptoms (p < 0.001). Unadjusted regression modeling showed that DSF in pregnancy was a significant predictor of PPD symptoms (OR 1.15; 95% CI, 1.00, 1.33, p = 0.049), and remained a significant predictor when adjusted for confounders (education and feeling rested at assessment; OR 1.21, 95% CI 1.03, 1.42, p = 0.022). DSF was a predictor of PPD symptoms only for women without a pre-pregnancy history of depression (OR 1.32; 95% CI 1.04, 1.67, p = 0.024) and also those without APD (OR 1.20, 95% CI 1.01, 1.43, p = 0.040).

Conclusion

There was no significant association between working and short-term memory performance and APD symptoms. Among all women, but especially non-depressed earlier in life and/or at antepartum, those scoring high on the forward memory test, i.e., short-term memory, had a higher risk for PPD. Future studies are required to further explore the pathophysiology behind and the predictive value of these associations.

Peripartum depression is a common, multifactorial, and potentially devastating disease among new mothers. A biological marker for peripartum depression would facilitate early detection, better understanding of the pathophysiology, and identification of targets for treatment. Evidence is growing for a potential role of the immune system in depression outside the peripartum period. Major adaptations of the immune system occur during pregnancy, justifying the search for immunological markers for peripartum depression. The immune system is very complex and dynamic during pregnancy, complicating the study of associations with depression. The metabolome is also affected by pregnancy and is linked to the immune system via, e.g., the microbiota. Hence, metabolomic profiling could increase the understanding of peripartum depression. 

This thesis aimed to explore inflammatory markers and metabolic profiles in the peripartum period, in order to discover possible biomarkers, and to increase the understanding of the pathophysiology of peripartum depression.

All studies were conducted within the Biology, Affect, Stress, Imaging, and Cognition (BASIC) study. The Edinburgh Postnatal Depression Scale and the Mini International Neuropsychiatric Interview were used to assess depressive symptoms. Multiplex Proximity Extension assays were used to analyze inflammatory markers in pregnancy and postpartum. Luminex Bio-Plex Pro Human Cytokine Assays were used to analyze cytokine levels across the peripartum period, and gas chromatography-mass spectrometry metabolomics were used for metabolic profiling. 

No marker was discriminative enough to be used on its own as a biomarker for peripartum depression. However, several inflammatory markers (such as STAM-BP, TRANCE, HGF, IL-18, FGF-23, and CXCL1) were identified as possible candidates for more advanced diagnostic algorithms. The results further pointed towards the importance of adaptation of the immune system during pregnancy and postpartum, where levels of cytokines such as VEGF-A might have an important role in antenatal and postpartum depression. The results even highlight the importance of examination timing. Lastly, the metabolic profiling suggested different subgroups of women with postpartum depressive symptoms, supporting theories of peripartum depression being a heterogeneous disease in need of subgroup definition. 

Background: Prenatal symptoms of depression (PND) and anxiety affect up to every third pregnancy. Children of mothers with mental health problems are at higher risk of developmental problems, possibly through epigenetic mechanisms together with other factors such as genetic and environmental. We investigated DNA methylation in cord blood in relation to PND, taking into consideration a history of depression, co-morbidity with anxiety and selective serotonin reuptake inhibitors (SSRI) use, and stratified by sex of the child. Mothers (N = 373) prospectively filled out web-based questionnaires regarding mood symptoms and SSRI use throughout pregnancy. Cord blood was collected at birth and DNA methylation was measured using Illumina MethylationEPIC array at 850 000 CpG sites throughout the genome. Differentially methylated regions were identified using Kruskal-Wallis test, and Benjamini-Hochberg adjusted p-values < 0.05 were considered significant.

Results: No differential DNA methylation was associated with PND alone; however, differential DNA methylation was observed in children exposed to comorbid PND with anxiety symptoms compared with healthy controls in ABCF1 (log twofold change - 0.2), but not after stratification by sex of the child. DNA methylation in children exposed to PND without SSRI treatment and healthy controls both differed in comparison with SSRI exposed children at several sites and regions, among which hypomethylation was observed in CpGs in the promoter region of CRBN (log2 fold change - 0.57), involved in brain development, and hypermethylation in MDFIC (log2 fold change 0.45), associated with the glucocorticoid stress response.

Conclusion: Although it is not possible to assess if these methylation differences are due to SSRI treatment itself or to more severe depression, our findings add on to existing knowledge that there might be different biological consequences for the child depending on whether maternal PND was treated with SSRIs or not.

Exploration of photoplethysmography (PPG), a technique that can be translated to the clinic, has the potential to assess the autonomic nervous system (ANS) through heart rate variable (HRV) in pregnant individuals. This novel study explores the complexity of mental health of individuals in a clinical sample responding to a task in late pregnancy; finding those with several types of past or current anxiety disorders, greater trait anxiety, or greater exposure to childhood traumatic events had significantly different HRV findings from the others in the cohort. Lower high frequency (HF), a measure of parasympathetic activity, was found for women who met the criteria for the history of obsessive-compulsive disorder (OCD) (p = 0.004) compared with women who did not meet the criteria for OCD, and for women exposed to greater than five childhood traumatic events (p = 0.006) compared with those exposed to four or less childhood traumatic events. Conversely higher low frequency (LF), a measure thought to be impacted by sympathetic system effects, and the LF/HF ratio was found for those meeting criteria for a panic disorder (p = 0.006), meeting criteria for social phobia (p = 0.002), had elevated trait anxiety (p = 0.006), or exposure to greater than five childhood traumatic events (p = 0.004). This study indicates further research is needed to understand the role of PPG and in assessing ANS functioning in late pregnancy. Study of the impact of lower parasympathetic functioning and higher sympathetic functioning separately and in conjunction at baseline and in relation to tasks during late pregnancy has the potential to identify individuals that require more support and direct intervention.

Background: Postpartum depression (PPD) is a devastating disease requiring improvements in diagnosis and prevention. Blood metabolomics identifies biological markers discriminatory between women with and those without antenatal depressive symptoms. Whether this cutting-edge method can be applied to postpartum depressive symptoms merits further investigation. Methods: As a substudy within the Biology, Affect, Stress, Imagine and Cognition Study, 24 women with PPD symptom (PPDS) assessment at 6 weeks postpartum were included. Controls were selected as having a score of ≤ 6 and PPDS cases as ≥12 on the Edinburgh Postnatal Depression Scale. Blood plasma was collected at 10 weeks postpartum and analyzed with gas chromatography-mass spectrometry metabolomics. Results: Variations of metabolomic profiles within the PPDS samples were identified. One cluster showed altered kidney function, whereas the other, a metabolic syndrome profile, both previously associated with depression. Five metabolites (glycerol, threonine, 2-hydroxybutanoic acid, erythritol, and phenylalanine) showed higher abundance among women with PPDSs, indicating perturbations in the serine/threonine and glycerol lipid metabolism, suggesting oxidative stress conditions. Conclusions: Alterations in certain metabolites were associated with depressive pathophysiology postpartum, whereas diversity in PPDS physiologies was revealed. Hence, plasma metabolic profiling could be considered in diagnosis and pathophysiological investigation of PPD toward providing clues for treatment. Future studies require standardization of various subgroups with respect to symptom onset, lifestyle, and comorbidities.