Developmental stage of childhood trauma exposure and markers of inflammation at age 24
Notice bibliographique
Résumé
BACKGROUND: It is largely unknown whether the specific developmental stage at which childhood trauma occurs is related to inflammatory dysregulation in adulthood. We aimed to explore if trauma exposure at distinct developmental stages in childhood is differentially associated with the novel marker of chronic inflammation - soluble urokinase plasminogen activator receptor (suPAR), as well as with C-Reactive Protein (CRP) and Interleukin-6 (IL-6) levels in early adulthood. METHODS: Participants were drawn from the Avon Longitudinal Study of Parents and Children (n = 3272). The trauma variables represent any trauma exposure within early (0-4.9 years), middle (5-10.9 years), or late (11-17 years) childhood, and were derived from the responses to 121 questions collected via standardised questionnaires regarding traumatic experiences including physical abuse, sexual abuse, emotional abuse, emotional neglect, domestic violence, and bullying. Plasma suPAR, CRP and IL-6 samples were collected at age 24. Linear regression models assessed the relationship between trauma exposure at different developmental stages and the inflammatory markers, adjusting for sex, socio-economic status (SES) and child ethnicity. Latent profile analysis (LPA) identified age 24 inflammatory profiles and multinomial logistic regressions identified associations between childhood trauma and these latent groups. RESULTS: After adjustment for confounders, late childhood trauma was significantly associated with age 24 suPAR (β = 0.06, 95 % CI [.03, 0.1], p = 0.001), CRP (β = 0.09, 95 % CI [.01, 0.17], p = 0.04) and IL-6 (β = 0.1, 95 % CI [.02, 0.19], p = 0.02). The relationship between late trauma and suPAR survived additional adjustment for prior trauma (β = 0.06, 95 % CI [.01, 0.11], p = 0.03). Middle childhood trauma was significantly associated with IL-6 (β = 0.1, 95 % CI [.02, 0.18], p = 0.02). This attenuated after additionally adjusting for prior trauma (β = 0.11, 95 % CI [-0.09, 0.3], p = 0.29). There was little evidence of an association between early trauma and any inflammatory marker. Exposure to any trauma from 0-17 years was associated with elevated suPAR (β = 0.04, 95 % CI [.005, 0.07], p = 0.025) and IL-6 (β = 0.1, 95 % CI [.02, 0.18], p = 0.02) after adjustment for confounders. Additionally, LPA identified three distinct inflammatory profiles: 1. no inflammatory dysregulation; 2. elevated CRP and IL-6 levels; and 3. a high inflammatory group characterised by elevated levels of suPAR, CRP and IL-6. After adjustment for confounders, individuals with trauma either in early (RR = 2.31, 95 % CI [1.16, 4.6], p = 0.017), middle (RR = 2.72, 95 % CI [1.4, 5.29], p = 0.003) or late (RR = 3.37, 95 % CI [1.7, 6.64], p < 0.001) childhood had an increased risk of being in the high inflammatory group. The association between late childhood trauma and this high inflammatory group survived adjustment for prior trauma (RR = 3.69, 95 % CI [1.44, 9.47], p = 0.007). DISCUSSION: When the inflammatory markers were analysed independently, late childhood trauma showed a strong association with age 24 suPAR levels after adjusting for confounders and prior trauma. When the inflammatory markers were analysed in combination, those with late childhood trauma also were likely to have an elevated suPAR, CRP and IL-6 inflammatory profile. Collectively, the findings highlight the propensity of late childhood trauma (rather than early or mid-childhood trauma) for the dysregulation of suPAR in early adulthood and support the measurement of suPAR in combination with other markers to better characterise the effects of childhood trauma on adult inflammation. Future studies should use suPAR in combination with CRP and IL-6 to further explore the inflammatory contribution in the relationship between trauma and adverse health outcomes in adulthood.
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 | 0,000 |
| 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 ».