Beyond BMI: Personality traits’ associations with adiposity and metabolic rate
Bibliographic record
Abstract
Various personality traits are known to correlate with body mass index (BMI). However, this index of adiposity conflates fat mass with lean body mass and may therefore lead to biased estimates of correlations. Yet, rarely have studies looked beyond BMI to understand how adiposity and other physiological characteristics relate to these psychological traits. Using previously validated formulas, we calculated an improved measure of adiposity (relative fat mass, RFM), as well as basal metabolic rate (BMR); explored their associations with various personality traits; and assessed how personality traits' associations with RFM differ from their associations with BMI. In a subsample of the Estonian Biobank (N = 3535), we compared how the five domains and 30 facets of NEO Personality Inventory-3 correlated with RFM, BMI, and BMR. Various traits, notably Openness to Experience and its facets, were associated with RFM above and beyond BMI; these traits may relate to lower adiposity through eating habits. Assertiveness, a facet of Extraversion, correlated more strongly with BMI than with RFM and also correlated with BMR. These correlations mirror associations of metabolic rate with conceptually similar traits in non-human animals and are consistent with Assertiveness being based on biological processes. Finally, BMI-personality trait correlations appeared to conflate personality traits' associations with fat mass and lean mass; the use of BMI as an indicator of adiposity can lead to both attenuated and inflated estimates of personality trait-adiposity associations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".