Adverse childhood experiences and cognitive function in adulthood: examining the roles of depressive symptoms and inflammation in a prospective cohort study
Bibliographic record
Abstract
PURPOSE: Adverse childhood experiences (ACEs) have been associated with cognitive decline in adulthood. However, the underlying mechanisms implicated remain unclear. This study investigated depressive symptoms and systemic inflammation as potential mediators of the association between ACEs and later cognitive function. METHODS: Participants were adults aged 50 + from the English Longitudinal Study of Ageing (N = 3029; 54.8% female). Measures included self-reported ACEs at wave 3 (2006-2007), C-reactive protein (CRP) and depressive symptoms at wave 4 (2008-2009), and cognitive function at waves 3 and 7 (2014-2015). Mediation analyses examined the direct associations between ACEs and cognitive function at wave 7 and the indirect associations via depressive symptoms and CRP at wave 4. In a first set of analyses, models were adjusted for sociodemographic factors and baseline cognitive function. In a second set of analyses, models were additionally adjusted for BMI and health behaviours (n = 1915). RESULTS: Cumulative ACEs exposure positively predicted depressive symptoms (b = 0.184, s.e. = 0.034, p < .001), which in turn predicted poorer cognitive function at wave 7 (b = - 0.035, s.e. = 0.008, p < .001). ACEs also positively predicted systemic inflammation as measured by CRP (b = 0.031, s.e. = 0.01, p = 0.0016). However, CRP did not mediate the association between ACEs and later cognitive function (b = - 0.0002, 95% CI: - 0.002, 0.002). CONCLUSION: These findings suggest that ACEs may be related to cognitive decline partly via depressive symptoms and corroborate prior research linking ACEs with systemic inflammation in adulthood.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".