Epigenetic aging in children from a small‐scale farming society in The Congo Basin: Associations with child growth and family conflict
Bibliographic record
Abstract
Developmental environments influence individuals' long-term health trajectories, and there is increasing emphasis on understanding the biological pathways through which this occurs. Epigenetic aging evaluates DNA methylation at a suite of distinct CpG sites in the genome, and epigenetic age acceleration (EAA) is linked to heightened chronic morbidity and mortality risks in adults. Consequently, EAA provides insights on trajectories of biological aging, which early life experiences may help shape. However, few studies have measured correlates of children's epigenetic aging, especially outside of the U.S. and Europe. In particular, little is known about how children's growth and development relate to EAA in ecologies in which energetic and pathogenic stressors are commonplace. We studied EAA from dried blood spots among Bondongo children (n = 54) residing in a small-scale, fisher-farmer society in a remote region of the Republic of the Congo. Here, infectious disease burdens and their resultant energy demands are high. Children who were heavier for height or taller for age, respectively, exhibited greater EAA, including intrinsic EAA, which is considered to measure EAA internal to cells. Furthermore, we found that children in families with more conflict between parents had greater intrinsic EAA. These results suggest that in contexts in which limited energy must be allocated to competing demands, more investment in growth may coincide with greater EAA, which parallels findings in European children who do not face similar energetic constraints. Our findings also indicate that associations between adverse family environments and greater intrinsic EAA were nonetheless observable but only after adjustment for covariates relevant to the energetically and immunologically demanding nature of the local ecology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".