A DNA methylation based measure outperforms circulating CRP as a marker of chronic inflammation and partly reflects the monocytic response to long‐term inflammatory exposure: A Canadian Longitudinal Study on Aging analysis
Bibliographic record
Abstract
A key hallmark in the age-related dysfunction of physiological systems is disruption related to the regulation of inflammation, often resulting in a chronic, low-grade inflammatory state (i.e., inflammaging). In order to understand the causes of overall system decline, methods to quantify the life-long exposure or damage related to chronic inflammation are critical. Here, we characterize a comprehensive epigenetic inflammation score (EIS) based on DNA methylation loci (CpGs) that are associated with circulating levels of C-reactive protein (CRP). In a cohort of 1446 older adults, we show that associations to age and health-related traits such as smoking history, chronic conditions, and established measures of accelerated aging were stronger for EIS than CRP, while the risk of longitudinal outcomes such as outpatient or inpatient visits and increased frailty were relatively similar. To determine whether variation in EIS actually reflects the cellular response to chronic inflammation we exposed THP1 myelo-monocytic cells to low levels of inflammatory mediators for 14 days, finding that EIS increased in response to both CRP (p = 0.011) and TNF (p = 0.068). Interestingly, a refined version of EIS based only on those CpGs that changed in vitro was more strongly associated with many of the aforementioned traits as compared to EIS. In conclusion, our study demonstrates that EIS outperforms circulating CRP with regard to its association to health-traits that are synonymous with chronic inflammation and accelerated aging, and substantiates its potential role as a clinically relevant tool for stratifying patient risk of adverse outcomes prior to treatment or following illness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".