Impact of Vitamin D Status and Supplementation on Brain-Derived Neurotrophic Factor and Mood–Cognitive Outcomes: A Structured Narrative Review
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Vitamin D deficiency is prevalent in higher-latitude regions and among older adults, and has been linked to depressive symptoms and cognitive decline, although the neurobiological link remains unclear. Brain-derived neurotrophic factor (BDNF) may be a key modulator and mediator of vitamin D-related neuroprotection. METHODS: Selected databases (2009-2025) were searched for specific studies reporting vitamin D exposure, BDNF, and mood or cognitive outcomes. Risk of bias was appraised with RoB 2, Newcastle-Ottawa Scale or SYRCLE. RESULTS: Thirteen studies were included. High-dose vitamin D improves mood primarily when levels are low. Supplementation of at least 2000 IU/day for 12 weeks reduced BDI scores by 1.7-7.6 points and increased BDNF levels by ~7%. Each 1 ng/mL increase in 25(OH)D levels decreased the likelihood of depressive symptoms, especially when BDNF levels were high. In animal studies vitamin D increases hippocampal BDNF and reverses stress-induced depressive behavioral deficits. Adequate vitamin D intake is associated with improved cognitive performance and a dose-dependent increase in BDNF. Each 10 ng/mL increase in 25(OH)D was associated with a 0.6-point increase in MMSE scores and a 15% increase in serum BDNF. Low vitamin D status in children may predict cognitive decline. Animal studies have shown that supplementation with 500-10,000 IU/kg for at least 3 weeks increased hippocampal BDNF and improved biochemical markers of aging. CONCLUSIONS: Vitamin D supplementation may support mood and cognition via BDNF modulation, especially in people with insufficient vitamin D levels (<30 ng/mL), but long-term, adequately powered studies with objective tools are required.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".