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SHR Neuro Cancer Cardio Lipid Metab Microb

Riedl, D; Kirchhoff, C; Egle, UT; Nolte, T; Tschuggnall, M; Rumpold, G; Kantner-Rumplmair, W; Grote, V; Fischer, MJ; Lampe, A.
Adverse Childhood Experiences (ACEs) in Specific Vulnerable Developmental Periods Can Increase the Likelihood of Chronic Pain in Adulthood-Results from a Cross-Sectional Study.
Diagnostics (Basel). 2025; 15(7): Doi: 10.3390/diagnostics15070839 [OPEN ACCESS]
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Co-authors Med Uni Graz: Grote Vincent Thomas
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Abstract:: Background: Adverse childhood experiences (ACEs) have been identified as a major risk factor for physical and mental ill health in adulthood. So far, no studies have investigated whether ACEs during sensitive periods in infant development are associated with a higher likelihood of chronic pain (CP) in adulthood. Methods: Patients of the University Hospital of Innsbruck (Austria) completed questionnaires retrospectively assessing their ACEs as well as their current levels of CP, psychological distress, and trauma-related symptoms in this cross-sectional study. Dose-dependent associations of ACEs with CP were investigated through binary logistic regressions. To detect vulnerable developmental periods, a machine learning grid search was applied. Results: A total of n = 2577 patients were included in the analyses, with 31.5% reporting CP. Polytraumatization (i.e., four or more types of ACEs) was associated with a threefold increase for CP (OR: 3.01; 95% CI: 2.16-4.19; p < 0.001). The machine learning approach revealed maltreatment between 10 and 12 years to be the most predictive of CP (Ada Boost Classifier). Discussion: CP is a highly prevalent symptom among hospital patients and is clearly associated with ACEs. This is the first study to present evidence for a timing-dependent association of ACEs with CP. Early identification and appropriate psychosocial support for patients with ACEs is a crucial task for healthcare professionals.
Find related publications in this database (Keywords): adverse childhood experiences; ACEs; chronic pain; childhood trauma; traumatization; machine learning