Opioid Use Disorder in Three Samples of the Lebanese Population: Correlation with Clinical and Genetic Factors
Bibliographic record
Abstract
Introduction Opioid Use Disorder (OUD) is a severe and recurrent condition that contributes to a global prevalence of disabilities. Accumulating evidence suggests a potential convergence of clinical and genetic factors underlying OUD. Objectives This study explores the clinical and genetic factors associated with OUD in the Lebanese population. Methods A cross-sectional study in the Lebanese population included three different groups of participants stratified according to the cut-off of the revised Opioid Risk Tool (ORT-OUD): (1) Low-risk group for OUD (n=513; general population; ORT-OUD score <2.5); (2) High-risk group for OUD (n=87; general population; ORT-OUD score ≥3); (3) a third group consisting of patients clinically diagnosed with OUD according to the DSM-5 (n=46). The survey included sociodemographic information and used validated scales to assess other substance use disorders, sleep disturbances, depression, and anxiety. Genotyping for the COMT, MTHFR, and CRY2 genes was conducted for 91 patients using a real-time PCR (Roche®). Bivariate and multivariate analyses were conducted to identify the associations between OUD risk and sociodemographic, clinical, and genetic factors. Results This study enrolled 646 participants. Multivariate analysis showed significant associations between risk of developing an OUD and cigarette smoking (B=0.583), worse insomnia scores (B=0.074) and Alcohol, Smoking and Substance Involvement Screening Test-alcohol (B=0.053) scores, male gender (B=13.351), lack of education (B=4.159), unemployment (B=7.235), low income (B=11.285), lack of healthcare coverage (B=4.190), neuropsychiatric disorders (B=7.966). Conversely, OUD risk was negatively correlated with the morning chronotype (B=-0.372). Bivariate analysis showed that the CRY2 AA genotype was significantly associated with a higher risk of OUD; nevertheless, none of the genetic factors remained significant in the multivariable model. Conclusions This study identified several sociodemographic, clinical, and genetic factors that could potentially increase the risk of developing OUD in the Lebanese population. Further research is needed to clarify risk factors and underlying mechanisms, enabling the development of more effective prevention strategies. Disclosure of Interest None Declared
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".