The Influence of <scp>GBA</scp> and <scp>LRRK2</scp> on Mood Disorders in Parkinson's Disease
Bibliographic record
Abstract
ABSTRACT Background Mood disorders have emerged as major non‐motor comorbidities in Parkinson's disease (PD) even at the prodromal stage of the disease. Mutations in the LRRK2 and GBA genes are common among Ashkenazi Jews, with more severe phenotype reported for GBA ‐PD. Objective To explore the association between genetic status and mood related disorders before and after diagnosis of PD and the association between mood‐related medications, phenotype, and genetic status. Methods Participants were genotyped for mutations in the LRRK2 and GBA genes. State of depression, anxiety and non‐motor features were evaluated using validated questionnaires. History of mood disorders prior to diagnosis of PD and use of mood‐related medications were assessed. Results The study included 105 idiopathic PD (iPD), 55 LRRK2 ‐PD and 94 GBA ‐PD. Scores on mood related questionnaires and frequency of depression and anxiety before diagnosis were similar between the groups ( p >0.05). However, more GBA ‐PD patients used mood related medications before PD diagnosis than LRRK2 ‐PD and iPD (16.5% vs 7.1% and 8.2%, p =0.044). LRRK2 ‐PD and GBA ‐PD receiving mood‐related medications at time of assessment had worse motor and non‐motor phenotype compared to those that did not ( p <0.05). LRRK2 ‐PD receiving mood related‐medications at time of assessment, scored higher on mood‐related questionnaires compared to LRRK2 ‐PD not receiving such medications ( p <0.04). Conclusions Prodromal GBA ‐PD are more frequently treated with mood related‐medications despite equal rates of reported mood‐related disorders, while LRRK2 ‐PD with mood‐related disorders experience high rates of anxiety and depression despite treatment, attesting to the need of more precise assessment and treatment of these genetic subgroups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".