Burn Injuries Accelerate Biological Aging and Increase the Epigenetically Inferred Risk of Mortality and Frailty
Bibliographic record
Abstract
Biological aging is closely associated with heightened disease risk, frailty, and mortality. Interestingly, physical traumas, such as burn injuries, exhibit physiological effects that resemble those of aging. However, the impact of burn injuries on biological aging remains underexplored, creating a gap in the literature that could inform better prognosis and outcomes. We conducted a prospective cohort study to investigate the effects of burn injuries on various epigenetic clocks, including HorvathAge, GrimAge, PhenoAge, and DunedinPoAm, using whole blood. The study included 59 burn patients and 25 healthy controls and was validated using a murine model of thermal injury. Our study demonstrates that burn injuries accelerate biological aging and the rate of aging, with these effects persisting for up to 28 days post-injury. The extent of biological aging was positively correlated with burn size, with severe burns resulting in an acceleration of 13-14 years in biological age as measured by GrimAge and PhenoAge-double the acceleration observed with chronic long-term smoking. This acceleration occurred irrespective of age or sex, though older patients were the most vulnerable to the aging effects of burn injuries. The role of burns as an accelerator of aging was further confirmed in mice, which exhibited the equivalent of 3-6 human years of accelerated aging (8 mouse months, or 7 human days) after the injury, reinforcing the chronic nature of the effect. Additionally, burn injuries increased epigenetically inferred risks of frailty and mortality in humans, highlighting their long-term and enduring consequences. Collectively, our findings identify burn injuries as the most significant and chronic accelerant of biological aging reported to date. To our knowledge, this study is one of the first to link burn injuries-or any form of physical trauma-to accelerated cellular and biological aging.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".