Steroids‐Dopamine Interactions in the Pathophysiology and Treatment of CNS Disorders
Bibliographic record
Abstract
INTRODUCTION: Dopamine cell loss is well documented in Parkinson's disease and dopamine hypofunction is proposed in certain depressive states. At the opposite, dopamine hyperactivity is an enduring theory in schizophrenia with extensive supporting evidence. AIMS: This article reviews the sex differences in these diseases that are the object of many studies and meta-analyses and could be explained by genetic differences but also an effect of steroids in the brain. This article then focuses on the extensive literature reporting on the effect of estrogens in these diseases and effects of the other ovarian hormone progesterone as well as androgens that are less documented. Moreover, dehydroepiandrosterone, the precursor of estrogens and androgens, shows effects on brain dopamine neurotransmission that are reviewed. To investigate the mechanisms implicated in the human findings, animal studies are reviewed showing effects of estrogens, progesterone, and androgens on various markers of dopamine neurotransmission under intact as well as lesioned conditions. DISCUSSION: For possible future avenues for hormonal treatments in these central nervous system diseases, we discuss the effects of selective estrogen receptor modulators (SERMs), the various estrogen receptors and their specific drugs as well as progesterone drugs. CONCLUSION: Clinical and experimental evidence supports a role of steroid-dopamine interactions in the pathophysiology of schizophrenia, depression and Parkinson's disease. Specific steroidal receptor agonists and SERMs are available for endocrine and cancer treatments and could find other applications as adjunct treatments in central nervous system diseases.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".