Bidirectional Relationship between the Body Mass Index and Substance Use in Young Men
Bibliographic record
Abstract
BACKGROUND: Obesity and substance use are major concern in young people. This study explored the bidirectional longitudinal relationships between the body mass index (BMI) of young men and their use of (1) 4 classes of nonmedical prescription drugs; (2) alcohol; (3) tobacco; and (4) cannabis. METHODS: Baseline and follow-up data from the Cohort Study on Substance Use Risk Factors were used (N = 5007). A cross-lagged panel model, complemented by probit models as sensitivity analysis, was run to determine the bidirectional relationships between BMI and substance use. Alcohol was assessed using risky single-occasion drinking (RSOD); tobacco, using daily smoking; and cannabis, using hazardous cannabis use (defined as twice-weekly or more cannabis use). Nonmedical prescription drugs use (NMPDU) included opioid analgesics, sedatives/sleeping pills, anxiolytics, and stimulants. RESULTS: Different associations were found between BMI and substance use. Only RSOD (β = -.053, P = .005) and NMPDU of anxiolytics (β = .040, P = .020) at baseline significantly predicted BMI at follow-up. Baseline RSOD predicted a lower BMI at follow-up, whereas baseline NMPDU of anxiolytics predicted higher BMI at follow-up. Furthermore, BMI at baseline significantly predicted daily smoking (β = .050, P = .007) and hazardous cannabis use (β = .058, P = .030). CONCLUSIONS: These results suggest different associations between BMI and the use of various substances by young men. However, only RSOD and NMPDU of anxiolytics predicted BMI, whereas BMI predicted daily smoking and hazardous cannabis use.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".