Medical Cannabis Use and Inflammatory Cytokines and Chemokines Among Adult Chronic Pain Patients
Bibliographic record
Abstract
Background: Utilizing cannabis as a therapeutic option for chronic pain (CP) has increased significantly. However, data regarding the potential immunomodulatory effects of cannabis in CP patients remain scarce. We aimed at exploring the relationship between cannabis use and inflammatory cytokines and chemokines among a cohort of CP patients. Methods: Adult patients with a CP diagnosis and medical authorization of cannabis were enrolled. Patients completed validated clinical questionnaires and self-reported the effectiveness of cannabis for symptom management. Patients' blood and cannabis samples were analyzed for the presence of four major cannabinoids, two major cannabinoid metabolites, 29 different cytokines/chemokines, and cortisol. The multivariable linear regression model was used to identify cannabis and patient factors associated with immune markers. Results: Fifty-six patients (48±15 years; 64% females) were included, with dried cannabis (53%) being the most common type of cannabis consumed. Seventy percent of products were considered delta-9-tetrahydrocannabinol (Δ 9 -THC)-dominant. The majority of patients (96%) self-reported effective pain management, and 76% reported a significant decrease in analgesic medication usage ( p ≤0.001). Compared with males, female patients had higher plasma levels of cannabidiol (CBD), cannabidiolic acid, Δ 9 -THC, and 11-hydroxy-Δ 9 -tetrahydrocannabinol but lower concentrations of delta-9-tetrahydrocannabinolic acid and 11-nor-9-carboxy-Δ 9 -tetrahydrocannabinol (THC-COOH). Females had significantly lower eotaxin levels ( p =0.04) in comparison to male patients. The regression analysis indicated that high cannabis doses were related to increased levels of interleukin (IL)-12p40 ( p =0.02) and IL-6 ( p =0.01), whereas female sex was associated with decreased eotaxin ( p ≤0.01) concentrations. Blood CBD levels were associated with lower vascular endothelial growth factor ( p =0.04) concentrations, and THC-COOH was a factor related to decreased tumor necrosis factor alpha ( p =0.02) and IL-12p70 ( p =0.03). Conclusion: This study provides further support for the patient-perceived effectiveness of cannabis in managing CP symptoms and reducing analgesic medication consumption. The results suggest a potential sex difference in metabolizing cannabinoids, and the varying immune marker concentrations may support a possible immunomodulatory effect associated with patient sex and cannabis product type. These preliminary findings provide grounds for further validation using larger, well-designed studies with longer follow-up periods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".