Obesity has limited behavioural overlap with addiction and psychiatric phenotypes
Bibliographic record
Abstract
Obesity is a widespread health condition1, likely driven by increased availability of inexpensive high-calorie food2. People vary in their behavioural response to food plenty. Such variation is likely driven by behavioural styles3,4, as behaviour accounts for entire food intake5. A prominent hypothesis is that people with obesity respond to rewards similarly to people with addictions such as alcohol abuse or smoking6,7. For instance, perceived overeating or “Uncontrolled Eating” (UE) is the most common obesity-associated personality trait8 and resembles the perceived loss of control seen in drug addiction. Likewise, both obesity and addictive behaviours have similar correlations with broad personality domains3. Here, we seek to empirically test whether obesity and UE overlap behaviourally with addiction and psychiatric disorders, collectively referred to as phenotypes. We test for behavioural similarity by linking the personality profiles of each phenotype. NEO Personality Inventory (NEO PI-R/3) profiles of 28 phenotypes were extracted from 22 studies, encompassing summary statistics from 18,611 unique participants. Obesity had moderate and UE high behavioural similarity with addictions. UE also overlapped behaviourally with most psychiatric phenotypes, whereas obesity was behaviourally similar with mood disorders and certain personality disorders. Facet-based phenotype profiles provided more information than domain-based profiles.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".