Adverse childhood experiences influence markers of neurodegeneration risk in older adults
Bibliographic record
Abstract
INTRODUCTION: Adverse childhood experiences (ACEs) disrupt brain development and increase vulnerability to Alzheimer's disease and related dementias (ADRD). We explored how ACEs impact neuroimaging, plasma biomarkers, and cognition in older adults. METHODS: Data from 214 participants aged ≥ 55 years were analyzed using linear and logistic regression, adjusting for demographic covariates. RESULTS: Financial need associated negatively with Montreal Cognitive Assessment scores (β = -0.10, p = 0.011). Lower mean diffusivity across white matter tracts associated with parental violence (β = -0.01, p = 0.03). Lower glial fibrillary acidic protein associated with parental intimidation (β = -0.07, p = 0.01) and parental violence (β = -0.18, p = 0.006). Family problems and separation (β = -0.16, p = 0.003), financial need (β = -0.1, p = 0.04), and parental intimidation (β = -0.05, p = 0.01) inversely associated with neurofilament light chain. DISCUSSION: Findings challenge the notion that ACEs uniformly accelerate neurodegeneration. Longitudinal studies are needed to determine whether these results reflect resilience, survivorship, or cohort-specific factors influencing ADRD risk. HIGHLIGHTS: Adverse childhood experiences (ACEs) may elicit compensatory neural responses in aging. Financial need was associated with lower global cognition (Montreal Cognitive Assessment scores). Some ACEs (e.g., financial need, parental intimidation) linked to lower plasma neurofilament light chain. Parental violence linked to lower glial fibrillary acidic protein and mean diffusivity values, implying intact white mattery integrity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".