Childhood sexual abuse history amplifies the link between disease burden and inflammation among older adults with HIV
Bibliographic record
Abstract
As they age, people living with HIV (PLWH) experience greater rates of inflammation-related health conditions compared to their HIV-negative peers. Because early life adversity can exaggerate proinflammatory effects of later physiological challenges, inflammation may be higher among PLWH with these combined risks, which could inform intervention approaches to mitigate multimorbidity. In this cross-sectional analysis, we investigated individual and combined effects of childhood sexual abuse (CSA) history and physiological burden (Veterans Aging Cohort Study Index scores) on serum cytokine and C-reactive protein (CRP) levels among PLWH. Participants (n = 131; age 54 and older) were patients at an outpatient HIV clinic who completed a psychosocial survey and biomedical research visit as part of a larger study. 93% were virally suppressed, and 40% reported experiencing sexual abuse in childhood. Composite cytokine levels (summarizing IL-6, TNF-α, IFN-γ), CRP, and disease burden did not differ significantly between those who had a history of CSA and those who did not. Participants with greater disease burden had higher composite cytokine levels (r = 0.29, p = 0.001). The disease burden by CSA interaction effect was a significant predictor of composite cytokine levels (but not CRP), and remained significant after controlling for age, sex, race, BMI, anti-inflammatory medication use, selective serotonin reuptake inhibitor use, depressive symptoms, and smoking status (F(1, 114) = 5.68, p = 0.02). In follow-up simple slopes analysis, greater disease burden was associated with higher cytokine levels among those with CSA history (b = 0.03, SE = 0.008, p<0.001), but not among those without CSA history. Further, in the context of greater disease burden, individuals with a CSA history tended to have higher cytokine levels than those without a CSA history (b = 0.38, SE = 0.21, p = 0.07). These data suggest that the physiological sequelae of childhood trauma may persist into older age among those with HIV. Specifically, links between physiological burden and inflammation were stronger among survivors of CSA in this study. The combined presence of CSA history and higher disease burden may signal a greater need for and potential benefit from interventions to reduce inflammation, an area for future work.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".