The Effects of Opioids and Opioid Analogs on Animal and Human Endocrine Systems
Bibliographic record
Abstract
Opioid abuse has increased in the last decade, primarily as a result of increased access to prescription opioids. Physicians are also increasingly administering opioid analgesics for noncancer chronic pain. Thus, knowledge of the long-term consequences of opioid use/abuse has important implications for fully evaluating the clinical usefulness of opioid medications. Many studies have examined the effect of opioids on the endocrine system; however, a systematic review of the endocrine actions of opioids in both humans and animals has, to our knowledge, not been published since 1984. Thus, we reviewed the literature on the effect of opioids on the endocrine system. We included both acute and chronic effects of opioids, with the majority of the studies done on the acute effects although chronic effects are more physiologically relevant. In humans and laboratory animals, opioids generally increase GH and prolactin and decrease LH, testosterone, estradiol, and oxytocin. In humans, opioids increase TSH, whereas in rodents, TSH is decreased. In both rodents and humans, the reports of effects of opioids on arginine vasopressin and ACTH are conflicting. Opioids act preferentially at different receptor sites leading to stimulatory or inhibitory effects on hormone release. Increasing opioid abuse primarily leads to hypogonadism but may also affect the secretion of other pituitary hormones. The potential consequences of hypogonadism include decreased libido and erectile dysfunction in men, oligomenorrhea or amenorrhea in women, and bone loss or infertility in both sexes. Opioids may increase or decrease food intake, depending on the type of opioid and the duration of action. Additionally, opioids may act through the sympathetic nervous system to cause hyperglycemia and impaired insulin secretion. In this review, recent information regarding endocrine disorders among opioid abusers is presented.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".