SAT-122 The Role of Targeted Plasma Proteomics for Identifying Inflammatory Signatures Associated with Risk for Perinatal Depression
Notice bibliographique
Résumé
Abstract Disclosure: E. Braybrook: None. K. Natasha: None. D. Grammatopoulos: None. Depressive symptoms experienced either during pregnancy or postpartum, collectively termed perinatal depression (PND) affects around 17% of women globally, with significant impact to both the mother and child’s health. The underpinning mechanisms are not yet fully understood, however dysregulation of the HPA axis is believed to be central, with impairment of neurotransmitter function also linked to inflammation. Additionally, the occurrence of depression antenatally is shown to be a significant risk factor in the development of postpartum depression. Clinicians currently use questionnaires as the primary tool to identify risk of depression, however their performance is inadequate and only one fifth of women who experience PND actively seek help. Biomarker-based screening strategies might offer an additional tool for earlier identification, stratification and development of targeted therapies. Plasma proteomics is emerging as a powerful tool, enabling both improved understanding of key biological processes through the generation of molecular profiles, and the identification of novel biomarkers for disease prediction. This study analysed serum samples from 260 women between 24-28 weeks gestation, with risk of depression assessed through the Edinburgh Postnatal Depression Questionnaire. To capture depressive symptoms either during pregnancy or postpartum, scores were obtained between 24-29 weeks gestation and again 6-10 weeks postpartum, with a cut-off score of 10 used to indicate increased risk. 92 inflammatory markers were analysed in the serum samples using Olink Proseek Multiplex Inflammation I panel, utilising a proximity extension assay. Differential expression analysis revealed distinct profiles between the antenatal and postnatal depression groups. Machine learning models (e.g. Random Forrest, Classification and Regression Tree and Pearsons Chi-square Statistic) were applied to the data using SPSS Modeler, with similarities across the outputs in key proteins identified (STAMPB, SIRT2, AXIN1, LAP TGF-beta-1, IL-10, MMP-10 and IL17C). In addition, differing psychosocial variables were highlighted as contributing factors across the two groups (history of anxiety or depression in antenatal and family history of PND in postnatal). Functional enrichment analyses further explored the biological functions of key proteins. This work highlights the value of targeted proteomics approaches coupled with machine learning in uncovering biomarker signatures that add to our understanding of the underlying biological mechanisms of PND. Alterations in inflammatory protein networks suggest distinct mechanisms between antenatal and postnatal depression. Application of biomarker tools, incorporating key proteins alongside patient history, could pave the way for personalised PND diagnosis and development of novel therapies. Presentation: Saturday, July 12, 2025
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».