Investigating the Stress-Disease Connection: Insights from Chronic Glucocorticoid Stimulation in Human Primary Fibroblasts
Bibliographic record
Abstract
While the stress response represents an example of allostasis that enables the organism to cope with environmental and psychosocial challenges, its chronic activation imposes an allostatic load that contributes to the cumulative wear and tear of the system and induces negative mental and physical health outcomes. Nonetheless, the underlying basis of the stress-disease connection is still poorly understood and represents a gap in the knowledge that requires further research. We investigated the effects of chronic glucocorticoid stimulation in three independent human primary fibroblast lines, as an in vitro model of chronic psychosocial stress. By deploying a longitudinal, high-frequency, repeated-measures strategy across their entire lifespan, we were able to determine that chronically stressed cells present a significant increase in their total energy expenditure and that this stress-induced hypermetabolism is linked to an acceleration of their biological aging. Expanding from our results and placing emphasis on the energetic costs associated with the activation of the stress response, we proposed the “Energetic Model of Allostatic Load”. This model proposes that chronic stress causes a redirection of the energetic resources towards allostatic responses and away from growth, maintenance, and repair processes, which in turn leads to the accumulation of damage that will further contribute to the development of disease and increased risk of mortality. Finally, we highlighted new avenues to quantify allostatic load and its link to health via the integration of systemic and cellular energy expenditure measurements together with classic biomarkers, that could contribute to further advances in the stress field.
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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 | 0.000 |
| 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) | 0.001 | 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".