Human aging reflects increases in entropy across organ networks
Bibliographic record
Abstract
Summary Aging involves diminished homeostatic control and changes in individual biomarker levels/states. However, it is unknown whether these alterations reflect a rise in entropy during the aging process, and whether entropy disrupts broad systemic interrelationships 1–4 . The entropy of human aging has not been well characterized, but measures of systemic entropy could reveal aging dynamics that may not be apparent even by integration of state-based aging metrics 5–7 . Here, we leverage the Distance of Covariance (DISCO), which quantifies entropy in large ensembles of biological information, to demonstrate that organs and systems exhibit interconnected increased entropy with age. We validate DISCO on multiple data substrates (clinical biomarkers, proteomics, metabolomics, and microbiomes) in five cohort datasets: UK BioBank, National Health and Nutrition Examination Survey, and three Chinese cohorts of older adults. DISCO consistently outperforms mortality prediction of existing metrics of dysregulation and is comparable to the best-in-class epigenetic clocks. It also strongly predicts frailty and incidence of age-related chronic conditions. Crucially, organ- and system-specific DISCO scores derived from circulating proteomics demonstrate broad predictive power with little to no specificity of a given organ predicting its own diseases and mortality. Network analysis of organ- and system-specific DISCO shows that more central, connected organ DISCOs predict health outcomes more strongly. For example, for each mortality cause, brain entropy is one of the strongest predictors. These findings challenge current notions of independent organ-specific aging signatures 8–10 , suggesting instead that while pathology may be organ-specific, entropy spills readily across systems, and thus conversely that health during aging requires integrated homeostatic coordination across multiple systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".