Allostatic load and comorbidities: A mitochondrial, epigenetic, and evolutionary perspective
Bibliographic record
Abstract
Stress-related pathophysiology drives comorbid trajectories that elude precise prediction. Allostatic load algorithms that quantify biological "wear and tear" represent a comprehensive approach to detect multisystemic disease processes of the mind and body. However, the multiple morbidities directly or indirectly related to stress physiology remain enigmatic. Our aim in this article is to propose that biological comorbidities represent discrete pathophysiological processes captured by measuring allostatic load. This has applications in research and clinical settings to predict physical and psychiatric comorbidities alike. The reader will be introduced to the concepts of allostasis, allostasic states, allostatic load, and allostatic overload as they relate to stress-related diseases and the proposed prediction of biological comorbidities that extend rather to understanding psychopathologies. In our transdisciplinary discussion, we will integrate perspectives related to (a) mitochondrial biology as a key player in the allostatic load time course toward diseases that "get under the skin and skull"; (b) epigenetics related to child maltreatment and biological embedding that shapes stress perception throughout lifespan development; and
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".