https://www.sciencerepository.org/hpa-axis-functioning-and-food-addiction-among-individuals-suffering-from-severe-obesity-and-awaiting-bariatric-surgery_PDR-2019-3-103
Bibliographic record
Abstract
Similarities have been observed between substance dependence and overconsumption of food, leading to the development of the food addiction (FA) concept. While psychological markers of FA have often been documented, data on physiological markers remains scarce. This study aimed to investigate HPA-axis functioning through cortisol awakening response (CAR) in relation to FA among bariatric candidates. We hypothesized that participants presenting high FA symptomatology would present a blunted CAR when compared to participants presenting low FA symptomatology and that significant associations between CAR and eating behaviors would be observed within both groups. The final sample comprised 40 participants, who were invited to complete questionnaires and provide saliva samples upon awakening (T0, T15, and T30). Results from the two-way ANOVA with repeated measures showed a non-significant “time x group” interaction, indicating that CAR did not differ between groups. Moreover, results from correlational analyses showed different patterns of associations between CAR and eating behaviors within each group; further analyses showed that the relationship between CAR and food cravings triggered by cues in the environment was significantly moderated by FA symptomatology, as higher CAR was related to lower cue-triggered food cravings only in individuals presenting low FA symptomatology. While these findings do not support the presence of HPA-axis functioning differences in relation to FA, they suggest that the association of CAR with eating behaviors may depend on whether or not addictive tendencies are present. Further investigation of the association between CAR and eating behaviors in the context of FA will thus be essential.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.627 | 0.428 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".