Association between infectious exposures in infancy and epigenetic age acceleration in young adulthood in metropolitan Cebu, Philippines
Bibliographic record
Abstract
OBJECTIVES: The drivers of human life expectancy gains over the past 200 years are not well-established, with a potential role for historical reductions in infectious disease. We investigate whether infectious exposures in infancy predict biological aging using DNA methylation-based markers that forecast patterns of morbidity and mortality later in life. METHODS: N = 1450 participants from the Cebu Longitudinal Health and Nutrition Survey-a prospective birth cohort initiated in 1983-provided complete data for the analyses. Mean chronological age was 20.9 years when venous whole blood samples were drawn for DNA extraction and methylation analysis, with subsequent calculation of three epigenetic age markers: Horvath, GrimAge, and DunedinPACE. Unadjusted and adjusted least squares regression models were evaluated to test the hypothesis that infectious exposures in infancy are associated with epigenetic age. RESULTS: Birth in the dry season, a proxy measure for increased infectious exposure in the first year of life, as well as the number of symptomatic infections in the first year of infancy, predicted lower epigenetic age. Infectious exposures were associated with the distribution of white blood cells in adulthood, which were also associated with measures of epigenetic age. CONCLUSIONS: We document negative associations between measures of infectious exposure in infancy and DNA methylation-based measures of aging. Additional research, across a wider range of epidemiological settings, is needed to clarify the role of infectious disease in shaping immunophenotypes and trajectories of biological aging and human life expectancy.
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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".