Early Life Adversity and Inflammation in African Americans and Whites in the Midlife in the United States Survey
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".