Early Life Adversity and Inflammation in African Americans and Whites in the Midlife in the United States Survey
Why this work is in the frame
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Bibliographic record
Abstract
Objectives: To determine whether early life adversity (ELA) was predictive of inflammatory markers and to determine the consistency of these associations across racial groups. Methods: We analyzed data from 177 African Americans and 822 whites aged 35 to 86 years from two preliminary subsamples of the Midlife in the United States biomarker study. ELA was measured via retrospective self-report. We used multivariate linear regression models to examine the associations between ELA and C-reactive protein, interleukin-6, fibrinogen, endothelial leukocyte adhesion molecule-1, and soluble intercellular adhesion molecule-1, independent of age, gender, and medications. We extended race-stratified models to test three potential mechanisms for the observed associations. Results: Significant interactions between ELA and race were observed for all five biomarkers. Models stratified by race revealed that ELA predicted higher levels of log interleukin-6, fibrinogen, endothelial leukocyte adhesion molecule-1, and soluble intercellular adhesion molecule-1 among African Americans (p < .05), but not among whites. Some, but not all, of these associations were attenuated after adjustment for health behaviors and body mass index, adult stressors, and depressive symptoms. Conclusions: ELA was predictive of high concentrations of inflammatory markers at midlife for African Americans, but not whites. This pattern may be explained by an accelerated course of age-related disease development for African Americans. BMI = body mass index; CRP = C-reactive protein; CVD = cardiovascular diseases; E-selectin = endothelial leukocyte adhesion molecule-1; ELA = early life adversity; GCRC = general clinical research center; IL = interleukin; MIDUS = Midlife in the U.S. survey; SEP = socioeconomic position; sICAM-1 = soluble intercellular adhesion molecule-1.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it