Psilocybin in late-life mental health: Addressing depression, loneliness, and existential anxiety
Bibliographic record
Abstract
The global demographic shift toward aging populations has intensified the need for innovative therapeutic interventions targeting late-life mental health conditions, notably depression, loneliness, and existential distress. Traditional pharmacological treatments often exhibit limited efficacy and poor tolerability in older patients, primarily due to age-related physiological changes and the challenges associated with polypharmacy. Recently, psychedelic-assisted therapy, particularly psilocybin, has gained attention for its potential antidepressant and psychological benefits. This comprehensive review critically evaluates the current evidence supporting psilocybin's effectiveness in older populations and elucidates its neurobiological mechanisms, including serotonergic modulation, enhanced neuroplasticity, and the disruption of maladaptive default mode network activity. Clinical trials in general adult samples demonstrate sustained improvements in depressive symptoms, existential anxiety, and social connectedness following psilocybin administration, suggesting its distinct therapeutic potential beyond conventional treatments. However, geriatric populations are underrepresented in psychedelic research, creating significant knowledge gaps regarding dosing, safety profiles, and long-term outcomes. Pharmacokinetic complexities, cardiovascular risks, and drug interactions necessitate age-specific therapeutic protocols. Ethical considerations, including the complexities of informed consent in cases of cognitive impairment, further underscore the importance of tailored approaches. Future directions must prioritize dedicated geriatric studies that incorporate rigorous safety assessments and integrate findings into existing geriatric care frameworks to fully assess the potential of psilocybin for enhancing late-life mental health and quality of life.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".