Sense of Control and Epigenetic Aging: The Mediating Role of Lifestyle Behaviors
Bibliographic record
Abstract
Abstract Psychosocial factors such as purpose in life have been found to contribute to epigenetic aging and lifestyle behaviors may mediate these associations. Sense of control is another promising protective psychosocial factor that has been associated with greater engagement in protective lifestyle behaviors and better health. The present study tested whether sense of control was related to epigenetic age and whether lifestyle behaviors (diet, exercise, sleep, alcohol consumption, and smoking history) were a mediator. We predicted that individuals with a higher sense of control would engage in more protective lifestyle behaviors which would be related to a lower epigenetic age. Data were collected from two sub-study samples, the second (M2; n = 1,255) and refresher (R1; n = 863) waves of the Midlife in the United States Study (MIDUS). We tested our hypothesis with a mediation model using the PROCESS Macro in SPSS. Results showed that the relationships between sense of control and three second-generation epigenetic clocks (DunedinPACE, GrimAge, and PhenoAge) were fully mediated by lifestyle behaviors, adjusting for chronological age, sex, education, race, and study sample. In this model, sense of control was not significantly related to two first-generation epigenetic clocks (Hannum or Horvath clocks), possibly due to their higher correlations with chronological age. Taken together, these findings suggest that sense of control is a promising modifiable psychosocial variable that could be used to promote engagement in protective lifestyle behaviors potentially slowing age-related declines.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".