Cannabinoids in Late Life Parkinson’s Disease and Dementia: Biological Pathways and Clinical Challenges
Bibliographic record
Abstract
The use of cannabinoids as therapeutic drugs has increased among aging populations recently. Age-related changes in the endogenous cannabinoid system could influence the effects of therapies that target the cannabinoid system. At the preclinical level, cannabidiol (CBD) induces anti-amyloidogenic, antioxidative, anti-apoptotic, anti-inflammatory, and neuroprotective effects. These findings suggest a potential therapeutic role of cannabinoids to neurodegenerative disorders such as Parkinson's disease (PD) and Alzheimer. Emerging evidence suggests that CBD and tetrahydrocannabinol have neuroprotective therapeutic-like effects on dementias. In clinical practice, cannabinoids are being used off-label to relieve symptoms of PD and AD. In fact, patients are using cannabis compounds for the treatment of tremor, non-motor symptoms, anxiety, and sleep assistance in PD, and managing responsive behaviors of dementia such as agitation. However, strong evidence from clinical trials is scarce for most indications. Some clinicians consider cannabinoids an alternative for older adults bearing Parkinson's disease and Alzheimer's dementia with a poor response to first-line treatments. In our concept and experience, cannabinoids should never be considered a first-line treatment but could be regarded as an adjuvant therapy in specific situations commonly seen in clinical practice. To mitigate the risk of adverse events, the traditional dogma of geriatric medicine, starting with a low dose and proceeding with a slow titration regime, should also be employed with cannabinoids. In this review, we aimed to address preclinical evidence of cannabinoids in neurodegenerative disorders such as PD and AD and discuss potential off-label use of cannabinoids in clinical practice of these disorders.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".