Measures of General Intelligence and Risk for Alcohol Use Disorder
Bibliographic record
Abstract
Importance: Associations among general intelligence (IQ), educational attainment (EA), and alcohol use disorder (AUD) are not well understood. Objective: To examine the relationship between IQ, EA, and AUD risk. Design, Setting, and Participants: The association between IQ and AUD risk was examined in a Swedish national conscription cohort. Potential causality was explored using mendelian randomization (MR) analyses, and the association of polygenic scores (PGS) for cognitive performance with AUD diagnosis was assessed. Participant data were obtained from cross-linked Swedish national registers, genome-wide association study (GWAS) summary statistics, and the US Yale-Penn cohort. Exposures: IQ and genetic variants associated with cognitive performance. Main Outcomes and Measures: Hazard ratios (HRs; time-to-event analyses) or odds ratios (ORs) for AUD. Results: Included in this study was a national cohort of 645 488 males, born between 1950 and 1962, from the Swedish Military Conscription Register, of whom 573 855 individuals were included in this analysis. All individuals were aged 18 years at IQ assessment with no substance use disorder diagnosis at conscription, and mean (SD) follow-up time (SD) was 60.5 (7.9) years. Summary statistics from GWAS of cognitive performance (n = 257 481) and AUD (total = 753 248; cases = 113 325) in individuals of European-like genetic ancestry (EUR), with FinnGen AUD GWAS as a replication sample (total = 500 348; cases = 20 597), were used for MR analyses. PGS analyses were conducted using the data of EUR individuals from the Yale-Penn cohort (n = 5424). IQ at age 18 years was inversely associated with AUD risk in Swedish males (adjusted HR, 1.43; 95% CI, 1.40-1.47; P < .001), adjusting for parental substance use disorder, probands' psychiatric disorders, socioeconomic factors, and birth year strata. MR analyses suggested a causal relationship between lower cognitive performance and AUD risk (β [SE], 0.11 [0.02]; P = 2.6 × 10-12). The mediating role of EA differed between national contexts. Higher cognitive performance PGS were associated with reduced odds of AUD in Yale-Penn participants (OR, 0.83; 95% CI, 0.78-0.89). Conclusions and Relevance: IQ and cognitive performance have a significant but context-dependent association with AUD risk, highlighting the need for a better understanding of the interplay among genetic factors, cognitive traits, and sociocultural influences on AUD susceptibility.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".