Testosterone suppression in opioid users: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Whether used for pain management or recreation, opioids have a number of adverse effects including hormonal imbalances. These imbalances have been reported to primarily involve testosterone and affect both males and females to the point of interfering with successful treatment and recovery. We conducted a systematic review and meta-analysis to determine the extent that opioids affect testosterone levels in both men and women, which may be relevant to improved treatment outcomes for opioid dependence and for pain management. METHODS: We searched PubMed, EMBASE, PsycINFO, and CINAHL for relevant articles and included studies that examined testosterone levels in men and women while on opioids. Data collection was completed in duplicate. RESULTS: Seventeen studies with 2769 participants (800 opioid users and 1969 controls) fulfilled the review inclusion criteria; 10 studies were cross-sectional and seven were cohort studies. Results showed a significant difference in mean testosterone level in men with opioid use compared to controls (MD=-164.78; 95% CI: -245.47, -84.08; p<0.0001). Methadone did not affect testosterone differently than other opioids. Testosterone levels in women were not affected by opioids. Generalizability of results was limited due to high heterogeneity among studies and overall low quality of evidence. CONCLUSIONS: Our findings demonstrated that testosterone level is suppressed in men with regular opioid use regardless of opioid type. We found that opioids affect testosterone levels differently in men than women. This suggests that opioids, including methadone, may have different endocrine disruption mechanisms in men and women, which should be considered when treating opioid dependence.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".