Food Addiction and Binge Eating Disorder in Relation to Dietary Patterns and Anthropometric Measurements: A Descriptive-Analytic Cross-Sectional Study in Iranian Adults with Obesity
Bibliographic record
Abstract
Obesity is associated with maladaptive eating behaviors, including food addiction (FA) and binge eating disorder (BED). However, the key factors influencing the development of maladaptive eating behaviors remain unknown. Adherence to specified dietary patterns has been suspected of making indirect impacts. This study investigates the association of FA and BED with dietary patterns and anthropometric measurements among 400 Iranian adults (aged 18–60; 66.25% women) living with obesity (body mass index [BMI] ≥ 30 kg/m2). The Binge Eating Scale and Yale Food Addiction Scale were used to measure BED and FA. A validated 147-item semi-quantitative food frequency questionnaire underwent principal component analysis and identified three major dietary patterns: mixed, unhealthy, and healthy dietary pattern. After adjusting for confounders, higher adherence to unhealthy dietary patterns was associated with an increased risk of FA, while higher adherence to healthy dietary patterns was associated with a lower risk of FA. Also, those within obesity class III had a significantly higher risk of FA and BED than those in obesity class I. This study suggests that adherence to an unhealthy dietary pattern may be associated with a higher risk of FA. It also highlights the link between higher BMI and maladaptive eating behaviors.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".