| 1. |
- Axfors, Cathrine, et al.
(författare)
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Cohort profile : the Biology, Affect, Stress, Imaging and Cognition (BASIC) study on perinatal depression in a population-based Swedish cohort
- 2019
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Ingår i: BMJ Open. - : BMJ. - 2044-6055. ; 9:10
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: With the population-based, prospective Biology, Affect, Stress, Imaging and Cognition (BASIC) cohort, we aim to investigate the biopsychosocial aetiological processes involved in perinatal depression (PND) and to pinpoint its predictors in order to improve early detection.PARTICIPANTS: From September 2009 to November 2018, the BASIC study at Uppsala University Hospital, Sweden, has enrolled 5492 women, in 6478 pregnancies, of which 46.3% first-time pregnancies and with an average age of 31.5 years. After inclusion around gestational week 16-18, participants are followed-up with data collection points around gestational week 32, at childbirth, as well as three times postpartum: after 6 weeks, 6 months and 1 year. At the last follow-up, 70.8% still remain in the cohort.FINDINGS TO DATE: In addition to internet-based surveys with self-report instruments, participants contribute with biological samples, for example, blood samples (maternal and from umbilical cord), biopsies (umbilical cord and placenta) and microbiota samples. A nested case-control subsample also takes part in cognitive and emotional tests, heart rate variability tests and bioimpedance tests. Subprojects have identified various correlates of PND of psychological and obstetric origin in addition to factors of the hypothalamic-pituitary-adrenal axis and immune system.FUTURE PLANS: In parallel with the completion of data collection (final follow-up November 2019), BASIC study data are currently analysed in multiple subprojects. Since 2012, we are conducting an ongoing follow-up study on the participants and their children up to 6 years of age (U-BIRTH). Researchers interested in collaboration may contact Professor Alkistis Skalkidou (corresponding author) with their request to be considered by the BASIC study steering committee.
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| 2. |
- Bilal, Ayesha, et al.
(författare)
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Mom2B: a study of perinatal health via smartphone application and machine learning methods
- 2022
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Ingår i: European Psychiatry. - : Royal College of Psychiatrists. - 0924-9338 .- 1778-3585. ; 65:S1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Bilal, Ayesha, et al.
(författare)
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Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B) : study protocol
- 2022
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Ingår i: BMJ Open. - : BMJ Publishing Group Ltd. - 2044-6055. ; 12:4
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Bränn, Emma, 1988-
(författare)
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Biomarkers for Peripartum Depression : Focusing on aspects of the immune system and the metabolome
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 5. |
- Bränn, Emma, et al.
(författare)
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Inflammatory markers in women with postpartum depressive symptoms
- 2020
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Ingår i: Journal of Neuroscience Research. - : Wiley. - 0360-4012 .- 1097-4547. ; 98:7, s. 1309-1321
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Tidskriftsartikel (refereegranskat)abstract
- Postpartum depression (PPD) is a devastating disorder affecting not only more than 10% of all women giving birth, but also the baby, the family, and the society. Compiling evidence suggests the involvement of the immune system in the pathophysiology of major depression; yet, the immune response in perinatal depression is not as well studied. The aim of this study was to investigate the alterations in peripheral levels of inflammatory biomarkers in 169 Swedish women with and without depressive symptoms according to the Edinburgh postnatal depression scale or the M.I.N.I neuropsychiatric interview at eight weeks postpartum. Among the 70 markers analyzed with multiplex proximity extension assay, five were significantly elevated in women with postpartum depressive symptoms in the adjusted LASSO logistic regression analysis: Tumor necrosis factor ligand superfamily member (TRANCE) (OR-per 1 SD increase = 1.20), Hepatocyte growth factor (HGF) (OR = 1.17) Interleukin (IL)-18 (OR = 1.06), Fibroblast growth factor 23 (FGF-23) (OR = 1.25), and C-X-C motif chemokine 1 (CXCL1) (OR 1.11). These results indicate that women with PPD have elevated levels of some inflammatory biomarkers. It is, therefore, plausible that PPD is associated with a compromised adaptability of the immune system.
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| 6. |
- Bränn, Emma, 1988-, et al.
(författare)
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Metabolic Profiling Indicates Diversity in the Metabolic Physiologies Associated With Maternal Postpartum Depressive Symptoms
- 2021
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Ingår i: Frontiers in Psychiatry. - : Frontiers Media S.A.. - 1664-0640. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Liakea, Iliana, et al.
(författare)
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Working Memory During Late Pregnancy : Associations With Antepartum and Postpartum Depression Symptoms
- 2022
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Ingår i: Frontiers in Global Women's Health. - : Frontiers Media S.A.. - 2673-5059. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundFew 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.MethodWe 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.ResultsAPD 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).ConclusionThere 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.
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