The role of an addictive tendency towards food and patterns of body fat distribution in obesity and metabolic health
Bibliographic record
Abstract
Food addiction (FA) is a contributing factor to obesity. Individuals with similar total body fat (BF) %, exhibit a large amount of heterogeneity in how BF is distributed. Certain BF distribution (BFD) patterns produce different outcomes regarding metabolic health. Little is known about how FA influences BFD and metabolic profiles. The study was designed to evaluate the correlation between FA symptom counts and metabolic characteristics, the correlation between FA symptoms and BFD patterns with emphasis on central obesity and Visceral fat (VF), and the role of android fat (AF) in women’s metabolic health. Data from the CODING study was used for analysis. FA symptoms are correlated with HOMA-β, triglycerides (TG), inversely correlated with high-density lipoprotein (HDL) in men and are correlated with TG in post-menopausal women. FA symptom counts were also associated with central obesity markers in men and women, including trunk fat (TF) and VF. Women exhibited slightly stronger correlations for all BFD measures except for VF and AF than in men. AF to GF ratio (AGR) affected metabolic characteristics and metabolic syndrome (MetS) risk in women. When separated into AGR tertiles, women in each tertile differed significantly in levels of insulin, glucose, TG, HDL, low-density lipoprotein (LDL), total cholesterol (TC), blood pressure (BP), and waist circumference (WC). Women in the top tertile exhibited higher levels of HOMA-IR and HOMA-β. When women in the top AGR quartile, matched by age and body mass index (BMI) with a control group while controlling for VF, were 2.4x more likely to have MetS. In conclusion, FA symptoms exhibit correlations with markers of metabolic disturbance in men and to a smaller degree in women. FA symptoms are also correlated with central obesity in men and women. Women with high levels of AF are at increased risk of developing MetS when compared to women of similar age and BMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".