Placental proteomic signatures of preterm birth, gestational age, and birthweight
Notice bibliographique
Résumé
BACKGROUND: Preterm birth and low birthweight are leading contributors to infant morbidity and mortality, yet underlying mechanisms remain poorly understood. Proteomics can provide insights into biological pathways that may be targets for prevention and reveal predictive markers of at-risk pregnancies. The placenta plays a critical role in parturition, yet few studies have investigated proteomic signatures in the placenta associated with birth outcomes. METHODS: Using untargeted, mass spectrometry-based label-free proteomics, 1,221 proteins were quantified in placental samples from 99 participants in the Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE) study. Associations of placental proteomics with binary spontaneous preterm birth, continuous gestational age at birth, and birthweight-for-gestational age z-scores were evaluated via differential abundance analysis, pathway enrichment, and principal component analysis (PCA) adjusting for numerous potential confounders. Sparse partial least squares discriminant analysis (sPLS-DA) was employed in a classification analysis to predict preterm versus term birth using the placental proteomics data. RESULTS: Preterm birth was associated with expression of 295 proteins and 15 molecular pathways, while gestational age was associated with expression of 367 proteins and 28 molecular pathways. Among the proteins significantly associated with either outcome, 264 (72%) overlapped. Proteins most strongly associated with both birth timing measures included Steryl-sulfatase (STS; preterm birth: LogFC = 2.08, FDR < 0.0001; gestational age at birth: LogFC= -0.57, FDR < 0.000001) and Collagen alpha-2(I) chain (COL1A2; preterm birth: LogFC= -2.11, FDR < 0.001; gestational age at birth: LogFC = 0.49, FDR < 0.0001). No associations were identified with birthweight z-scores. Proteins with the strongest links to preterm birth were major contributors to variance explained in PCA, and four of ten retained PCs were significantly associated with preterm birth. The top component in sPLS-DA classified preterm birth with 86.9% accuracy using 30 proteins, while the optimal sPLS-DA solution retaining three components classified preterm birth with 87.7% accuracy using 120 proteins. CONCLUSIONS: Many of the top proteins and molecular pathways associated with birth timing measures have been previously implicated in birth outcomes and pregnancy complications, and point to mechanisms including energy production, inflammation, and oxidative stress that may drive these risks. These proteins may serve as targets in future mechanistic and therapeutic research. Proteins identified through sPLS-DA demonstrated high accuracy in distinguishing preterm from term birth, pointing to potential targets for clinical screening tools, but necessitating validation in independent studies and more accessible biospecimens.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».