(PM-19) INSIGHTS INTO THE PATHOPHYSIOLOGY OF HYPERSEXUALITY: A COMPREHENSIVE LITERATURE EXPLORATION
Bibliographic record
Abstract
Abstract Introduction Hypersexuality or Compulsive Sexual Behavior Disorder (CSBD) is characterized by a spectrum of sexual behaviors and fantasies marked by compulsion, obsession, and impulsivity. The pathophysiology of CSBD remains poorly understood despite the arousing of articles exploring the potential biomarkers associated with hypersexuality. Objective We aimed to review the literature regarding the investigation on CSBD pathophysiology from a neuroimaging, neuroendocrine and molecular perspective. Methods We raised papers related on neuroimaging, neuroendocrine and molecular biomarkers investigated in individuals that reached diagnosis of CSBD or similar, written in English language in the last 10 years, through PubMed, Scopus, and Web of Science with biomarkers and compulsive sexual behavior/hypersexuality key terms. Results Neuroimaging studies revealed the activation of the anterior cingulate cortex, ventral striatum, and amygdala, in individuals with CSBD. Neuroendocrine markers revealed abnormalities in the hypothalamic-pituitary-adrenal (HPA) axis, including elevated adrenocorticotropic hormone (ACTH) levels and non-suppression of dexamethasone. Findings concerning the hypothalamic-pituitary-gonadal (HPG) axis, responsible for sexual function regulation, varied among CSBD individuals, including testosterone and luteinizing hormone (LH) levels. Oxytocin levels have been noted to rise in CSBD individuals and subsequently decrease following psychotherapy. Genetic and epigenetic markers associated with CSBD suggest involvement of addiction-related genes and epigenetic modifications near the corticotropin-releasing hormone (CRH) gene. Conclusion Most of the findings point to HPA axis alterations. Molecular analysis is in the beginning, as well as more research on the HPG axis is needed. The exploration of CSBD biomarkers potentially will provide insights into its pathophysiology, diagnosis, and treatment. Financing FAPESP.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".