Birth weight and postnatal microbial exposures predict the distribution of peripheral blood leukocyte subsets in young adults in the Philippines
Bibliographic record
Abstract
The immune system not only provides protection against infectious disease but also contributes to the etiology of neoplastic, atopic, and cardiovascular and metabolic diseases. Prenatal and postnatal nutritional and microbial environments have lasting effects on multiple aspects of immunity, indicating that immune processes may play important roles in the developmental origins of disease. The objective of this study is to evaluate the association between birth weight and the distribution of leukocyte (white blood cell) subsets in peripheral blood in young adulthood. Postnatal microbial exposures were also considered as predictors of leukocyte distribution. Participants (n=486; mean age=20.9 years) were drawn from a prospective birth cohort study in the Philippines, and analyses focused on the following cell types: CD4 T lymphocytes, CD8 T lymphocytes, B lymphocytes, natural killer cells, monocytes, granulocytes. Higher birth weight was a strong predictor of higher proportion of CD4 T lymphocytes (B=0.12, s.e.=0.041, P=0.003), lower proportion of CD8 T lymphocytes (B=-0.874, s.e.=0.364, P=0.016), higher CD4:CD8 ratio (B=1.964, s.e.=0.658, P=0.003), and higher B lymphocytes (B=0.062, s.e.=0.031, P=0.047). Measures of microbial exposure in infancy were negatively associated with proportions of B lymphocytes and granulocytes, and lower CD4:CD8 ratio. Leukocytes are the key regulators and effectors of innate and specific immunity, but the origins of variation in the distribution of cell type across individuals are not known. Our findings point toward nutritional and microbial exposures in infancy as potentially important determinants of immune-phenotypes in adulthood, and they suggest that leukocyte distribution is a plausible mechanism through which developmental environments have lasting effects on disease risk in adulthood.
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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 | 0.000 | 0.000 |
| 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".