The Association Between an Addictive Tendency Toward Food and Metabolic Characteristics in the General Newfoundland Population
Bibliographic record
Abstract
Background: Our previous study of 29 obese food addiction (FA) patients found that FA is associated with lipid profiles and hormones which may be a factor in cardiovascular disease (CVD) and insulin resistance (IR). However, there is currently no data available regarding the relationship between FA symptoms and metabolic characteristics of CVD and IR in the general population. We designed this study to investigate the correlation between FA symptoms with lipid profiles and IR in men and women of the general Newfoundland population. Methods: 739 individuals (450 women & 289 men) recruited from the general Newfoundland population were used in analysis. FA symptoms were evaluated using the Yale Food Addiction Scale (YFAS). Glucose, insulin, HDL, LDL, total cholesterol and triglycerides levels were measured. IR was evaluated using the homeostatic model of assessment (HOMA). Participants were grouped by sex and menopausal status. Age, physical activity, calories and total % body fat were controlled. Results: Partial correlation analysis revealed that in men, YFAS symptom counts were significantly correlated with insulin (r=0.149 p=0.047), HOMA-IR (r=0.172 p<0.038), HOMA-β (r=0.173 p=0.036), triglycerides (r=0.204 p=.001) and inversely correlated with HDL (r= -0.147 p=0.015). In women there was a small correlation between YFAS symptoms and triglycerides (r=0.096 p=0.045). After separating by menopausal status, pre-menopausal women exhibited no correlation and post-menopausal women had a much higher correlation with triglycerides (r=0.249 p=0.002). Conclusion: FA is significantly correlated with several markers of metabolic disturbance in men and to a lesser extent, post-menopausal women, in the general population. Further research is required to explain sex specific associations and elucidate any potentially causal mechanisms behind this correlation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".