Late-pregnancy immune and kynurenine signatures predict postpartum depression and brain morphology
Harrington, Y. A.; Shin, H. K.; Milano, C.; Mauri, M. C.; Marano, A.; Maggioni, E.; Squarcina, L.; Tassi, E.; Enrico, P.; Persico, N.; Conte, G.; Melloni, E. M. T.; Agnoletto, E.; Spadini, S.; Lorenzi, C.; Nasini, S.; DallAcqua, S.; Comai, S.; Poletti, S.; Origoni, M.; Brambilla, P.; Benedetti, F.; Milano PPDINFLAME Study Group,
Show abstract
Background: Pregnancy involves profound immune adaptations that may contribute to the development of postpartum depression (PPD); however, the relevant biomarkers and critical time window remain unclear. Therefore, we investigated whether third-trimester and postpartum cytokines and kynurenine pathway (KP) metabolites predict PPD and whether these biological signatures are related to postpartum brain morphology. Methods: Circulating cytokines and KP metabolites were assessed in 140 pregnant women during the third trimester and postpartum. Partial least squares (PLS) regression models were used to identify multivariate biological profiles predictive of PPD, followed by classification and regression tree (CART) analyses to evaluate predictive performance. In a neuroimaging subsample (n=61), voxel-based morphometry examined associations among PPD status, significant PLS-derived components, selected individual biomarkers, and postpartum grey matter volume. Results: A third-trimester cytokine signature including 21 cytokines significantly predicted PPD with high specificity. A third-trimester KP profile, driven primarily by the kynurenic acid/quinolinic acid ratio, also predicted PPD, and the combination of cytokine and KP components improved overall predictive performance to 93.3%. Postpartum biomarkers showed substantially weaker associations with PPD. The cytokine component was differentially associated with postpartum grey matter volume according to PPD status, with opposite associations observed in occipital regions between women who did and did not develop PPD. KP measures also showed distinct associations within sensorimotor regions based on PPD status Conclusions: Late pregnancy immune-inflammatory alterations are associated with subsequent PPD and postpartum brain morphology, supporting the potential utility of pregnancy-derived biological signatures for identifying women at increased risk for PPD.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The disappointment centre of the brain gets exciting: A systematic review of habenula dysfunction in depression 94%
- Association between DNA Methylation Levels in Brain Tissue and Late-Life Depression in Community-Based Participants 94%
- Applying Machine-Learning and Deep-Learning to Predict Depression from Brain MRI and Identify Depression-Related Brain Biology 93%
Similar papers in this journal
- Peripheral inflammation is associated with micro-structural and functional connectivity changes in depression-related brain networks 93%
- Immunometabolic Blood Biomarkers of Developmental Trajectories of Depressive Symptoms: Findings From the ALSPAC Birth Cohort 93%
- Potential depression and antidepressant-response biomarkers in human lymphoblast cell lines from treatment-responsive and treatment-resistant subjects: roles of SSRIs and omega-3 polyunsaturated fatty acids 92%
Similar papers in this journal
- Pro-cognitive effects of 5-HT4 receptor agonism in individuals with remitted depression 93%
- Linking brain structure to stress reactivity: Cingulate surface area predicts acute cortisol responses 93%
- Increased Insular Functional Connectivity During Repetitive Negative Thinking in Major Depression and Healthy Volunteers 93%
Similar papers in this journal
- Metabolomic and Inflammatory Signatures of Symptom Dimensions in Major Depression 94%
- Maternal prenatal immune activation associated with brain tissue microstructure and metabolite concentrations in newborn infants 93%
- Reverse cholesterol transport and lipid peroxidation biomarkers in major depression and bipolar disorder: a systematic review and meta-analysis 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.