Socioeconomic Inequalities in Inflammation in Childhood and Adolescence: An International Study across Five Cohorts
Bibliographic record
Abstract
Chronic inflammation may be a key mechanism driving social to biological transitions across the lifecourse. We investigated the effect of early-life socioeconomic position (SEP) on inflammatory biomarkers in childhood and adolescence and assessed mediation by body mass index (BMI). We conducted cohort-specific analysis in five birth cohorts: the Barwon Infant Study (BIS, Australia, n = 708, 4-5 years); Born in Bradford (BiB, United Kingdom, n = 4576, 7-11 years); the Longitudinal Study of Australian Children's Child Health CheckPoint (LSAC-CP, Australia, n = 1874, 11-12 years); the Northern Finland Birth Cohort 1986 (NFBC1986, Finland, n = 9467, 15-16 years); and the Avon Longitudinal Study of Parents and Children (ALSPAC, United Kingdom, n = 4875, 17-18 years). Exposures were neighbourhood disadvantage, household economic conditions, and maternal education, in pregnancy or infancy. Outcomes were log-transformed high sensitivity C-reactive protein (hsCRP) and glycoprotein acetyls (GlycA) levels. We conducted confounder-adjusted linear regression to estimate the effect of exposures on outcomes, and mediation analysis using interventional effects to assess mediation by BMI. Lower early-life SEP, particularly neighbourhood disadvantage and lower maternal education, was associated with higher inflammation from the age of 7-11 years. Effects were most apparent for GlycA (e.g., mean GlycA difference between BiB children in most and least disadvantaged neighbourhoods: 0.1 SD [95% CI 0.03-0.2]; between NFBC1986 adolescents with most and least educated mothers: 0.2 SD [0.02, 0.3]). BMI partially mediated many of these effects (20-100%). Neighbourhood disadvantage and lower maternal education may increase inflammation in children and adolescents, in part through higher adiposity. This underscores the importance of addressing socioeconomic conditions in early life and of preventing excess BMI.
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How this classification was reachedexpand
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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.009 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".