Caffeine in Aging Brains: Cognitive Enhancement, Neurodegeneration, and Emerging Concerns About Addiction
Bibliographic record
Abstract
This narrative review examines the effects of caffeine on brain health in older adults, with particular attention to its potential for dependence-an often-overlooked issue in geriatric care. Caffeine acts on central adenosine, dopamine, and glutamate systems, producing both stimulating and rewarding effects that can foster tolerance and habitual use. Age-related pharmacokinetic and pharmacodynamic changes prolong caffeine's half-life and increase physiological sensitivity in the elderly. While moderate consumption may enhance alertness, attention, and possibly offer neuroprotective effects-especially in Parkinson's disease and Lewy body dementia-excessive or prolonged use may lead to anxiety, sleep disturbances, and cognitive or motor impairment. Chronic exposure induces neuroadaptive changes, such as adenosine receptor down-regulation, resulting in tolerance and withdrawal symptoms, including headache, irritability, and fatigue. These symptoms, often mistaken for typical aging complaints, may reflect a substance use disorder yet remain under-recognized due to caffeine's cultural acceptance. The review explores caffeine's mixed role in neurological disorders, being beneficial in some and potentially harmful in others, such as restless legs syndrome and frontotemporal dementia. Given the variability in individual responses and the underestimated risk of dependence, personalized caffeine intake guidelines are warranted. Future research should focus on the long-term cognitive effects and the clinical significance of caffeine use disorder in older populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".