Association of Epigenetic Age and Outcome in Critically Ill Patients
Bibliographic record
Abstract
OBJECTIVES: DNA methylation can be used to determine an individual’s biological age, as opposed to chronological age, an indicator of underlying health status. This study aimed to assess epigenetic age in critically ill patients with and without sepsis to determine if higher epigenetic age is associated with admission diagnosis or mortality. DESIGN: Secondary analysis of whole blood DNA methylation data generated from a nested case–control study of critically ill septic and nonseptic patients. SETTING: Four tertiary care hospitals in Canada. INTERVENTIONS: None. PATIENTS: Critically ill patients with and without sepsis. MEASUREMENTS AND MAIN RESULTS: Epigenetic age was derived from DNA methylation data using the Hannum and PhenoAge algorithms and deviation from the patient’s chronological age in years was determined. Of the 66 patients with sepsis, 34 were male (51.5%), the mean age was 65.03 years and 25 patients (37.8%) died before discharge. Of the 68 nonseptic patients, 47 were male (69.1%), the mean age was 64.92 years and 25 (36.7%) died before discharge. Epigenetic age calculated using the PhenoAge algorithm showed a significant age acceleration of 4.97 years in septic patients ( p = 0.045), but no significant acceleration in nonseptic patients. Epigenetic age calculated using the Hannum algorithm showed no significant acceleration in the septic or nonseptic patients. Similarly, in the combined septic and nonseptic cohorts, nonsurvivors showed an epigenetic age acceleration of 7.62 years ( p = 0.004) using the PhenoAge algorithm while survivors showed no significant age acceleration. Survivor status was not associated with age acceleration using the Hannum algorithm. CONCLUSIONS: In critically ill patients, epigenetic age acceleration, as calculated by the PhenoAge algorithm, was associated with sepsis diagnosis and mortality.
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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 | 0.002 |
| 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".