An integrated molecular landscape implicates the regulation of dendritic spine formation through insulin-related signalling in obsessive–compulsive disorder
Bibliographic record
Abstract
BACKGROUND: Obsessive-compulsive disorder (OCD) is a neuropsychiatric disorder with onset in childhood and is characterized by obsessions (recurrent, intrusive, persistent thoughts, impulses and/or ideas that often cause anxiety or distress) and compulsions (ritualized and stereotypic behaviours or mental acts that are often performed to relieve anxiety or distress associated with obsessions). Although OCD is a heritable disorder, its complex molecular etiology is poorly understood. METHODS: We combined enrichment analyses and an elaborate literature review of the top-ranked genes emerging from the 2 published genome-wide association studies of OCD and candidate genes implicated through other evidence in order to identify biological processes that, when dysregulated, increase the risk for OCD. RESULTS: The resulting molecular protein landscape was enriched for proteins involved in regulating postsynaptic dendritic spine formation - and hence synaptic plasticity - through insulin-dependent molecular signalling cascades. LIMITATIONS: This study is a first attempt to integrate molecuar information from different sources in order to identify biological mechanisms underlying OCD etiology. Our findings are constrained by the limited information from hypothesis-free studies and the incompleteness and existing limitations of the OCD literature and the gene function annotations of gene enrichment tools. As this study was solely based on in silico analyses, experimental validation of the provided hypotheses is warranted. CONCLUSION: Our work suggests a key role for insulin and insulin-related signalling in OCD etiology and - if confirmed by independent studies - could eventually pave the way for the development of novel OCD treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".