Evaluation of a DNA methylation-based measure of chronic inflammation in two generations of adults in metropolitan Cebu, Philippines
Bibliographic record
Abstract
ABSTRACT Objectives Proxy measures of chronic inflammation derived from DNA methylation (DNAm) data have emerged as promising predictors of cardiometabolic disease risk in high income countries. This study investigates the performance of a recently validated DNAm-based measure of C-reactive protein (DNAm-CRP) in two generations of adults in the Philippines to evaluate its utility in lower and middle income settings experiencing high levels of endemic infections as well as rising rates of chronic degenerative diseases. Methods DNAm-CRP was calculated from 1,468 CpG sites on the Infinium MethylationEPIC v1.0 array applied to genomic DNA from leukocytes in young adults (N=1,665; 20-22 years) and older women (N=1,070; 35-68 years). C-reactive protein was determined in plasma using a high sensitivity immunoturbidimetric assay. Pearson correlation and least squares regression were implemented to evaluate the strength of association between DNAm-CRP and plasma CRP, and to investigate patterns of association between DNAm-CRP and established predictors of chronic inflammation. Results For younger adults, the correlation between DNAm-CRP and log-transformed CRP was 0.41, and DNAm-CRP explained 17.2% of the variance in CRP. For older women, the correlation was 0.47, with 22.7% explained variance in CRP. For both cohorts larger waist circumference was associated with higher DNAm-CRP. The presence of infectious symptoms at the time of blood collection and leukocyte composition were both significant predictors of DNAm-CRP. Conclusions In two generations of adults in the Philippines, we document strong correlations between DNAm-CRP and plasma CRP. DNAm-CRP may be a useful tool for research on chronic inflammation across a range of epidemiological and ecological settings globally, but future applications should consider how recent infections and the distribution of leukocyte subsets may confound or mediate associations of interest.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".