Chronic cannabis use and circulating biomarkers of neural health, stress, and inflammation in physically active individuals
Bibliographic record
Abstract
Previous research has associated cannabis use with altered circulating neurotrophins and biomarkers of immune health, but these relationships have yet to be fully explored in physically active individuals. The specific aim of this study was to explore the relationships between biomarkers of neural health: nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF), immune health: interleukin 6 (IL-6), C-reactive protein (CRP), and cortisol, as well as the presence of depression, in physically active cannabis users (CU) and nonusers (NU). Male and female participants (N = 30; CU, n = 15, NU, n = 15) provided intravenous blood samples and underwent assessment of body composition, maximal oxygen consumption, and depression (Beck Depression Inventory-II (BDI-II)). Samples were analyzed for concentrations of NGF, BDNF, IL-6, CRP, and cortisol using ELISAs. CU and NU were compared using an unpaired t test. Pearson’s correlation and multiple linear regression were used to evaluate relationships among variables. There were no significant differences in body size or composition, maximal oxygen consumption, total BDI-II Score, concentrations of NGF, IL-6, CRP, or cortisol between groups. BDNF was significantly lower in CU compared with NU (p = 0.02), with a significant negative relationship between BDNF and CRP (p = 0.02). Mean concentrations of CRP placed CU at higher risk for cardiovascular disease compared with NU. Total BDI-II score negatively correlated with BDNF (p = 0.02) and positively correlated with CRP (p = 0.02). Novelty Plasma BDNF was significantly lower in physically active cannabis users compared with NU. CU were classified at moderate risk for cardiovascular disease based on average circulating CRP compared with low risk for NU.
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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 | 0.000 |
| 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".