Opioids and Immunosuppression: Clinical Evidence, Mechanisms of Action, and Potential Therapies
Bibliographic record
Abstract
Background: In addition to the more well-known adverse effects of opioids, such as constipation, mounting evidence supports underlying immunosuppressive effects as well. Methods: In this study, we provide a narrative review of preclinical and clinical evidence of opioid suppression of the immune system as well as possible considerations for therapies. Results: and animal studies have shown clear effects of opioids on inflammatory cytokine expression, immune cell activity, and pathogen susceptibility. Observational data in humans have so far supported preclinical findings, with multiple reports of increased rates of infections in various settings of opioid use. However, the extent to which this risk is due to the impact of opioids on the immune system compared with other risk factors associated with opioid use remains uncertain. Considering the data showing immunosuppression and increased risk of infection with opioid use, measures are needed to mitigate this risk in patients who require ongoing treatment with opioids. In preclinical studies, administration of opioid receptor antagonists blocked the immunomodulatory effects of opioids. Conclusions: As selective antagonists of peripheral opioid receptors, peripherally acting mu-opioid receptor (MOR) antagonists may be able to protect against immune impairment while still allowing for opioid analgesia. Future research is warranted to further investigate the relationship between opioids and infection risk as well as the potential application of peripherally acting MOR antagonists to counteract these risks.
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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 | 0.001 |
| 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 | 0.001 |
| 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".