The association between hematological markers of inflammation and chronic cannabis use: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Introduction: Cannabinoids, both natural and synthetic, are a subject of scientific interest. Cannabis is widely used, and its impact on health and the immune system is being studied. The endocannabinoid system influences inflammation, including the Neutrophil-to-Lymphocyte Ratio (NLR), a potential diagnostic tool. Our study investigates the connection between cannabis use and NLR. Methods: Our systematic review was registered in Prospero (#CRD42023463539). We searched six databases (PubMed, Scopus, Embase, PsycINFO, Web of Science, and CINAHL Complete) for records in English from inception to May 2024. We included observational studies that measured the Neutrophil-to-Lymphocyte Ratio (NLR) in cannabis users and control participants. We used the Newcastle-Ottawa Quality Assessment Scale to assess the quality of the included studies. We selected a random-effects model, and the statistical analysis was performed using Stata software version 17. Results: Out of a total of 4,054 records, only five articles were selected for inclusion in the meta-analysis. All of these chosen studies utilized a retrospective design. Furthermore, it's worth noting that all of the studies included were of high quality. In five studies involving 3,359 cannabis users and 10,437 non-users, no significant difference in NLR was found (WMD: 0.12 [-0.16, 0.41], I2: 39.89%). Subgroup analysis on healthy and schizophrenia participants didn't show significant NLR differences (p=0.76). Secondary analysis revealed cannabis users had higher Platelet-to-Lymphocyte Ratio (PLR) (67.80 [44.54, 91.06]), neutrophil count (0.68 [0.25, 1.12]), white blood cell count (0.92 [0.43, 1.41]), monocyte count (0.11 [0.05, 0.16]), and Systemic Immune Inflammation Index (SII) (83.48 [5.92, 157.04]) compared to non-users. Conclusion: Our systematic review and meta-analysis reveal that cannabis use may affect NLR and hematologic parameters, suggesting a potential immune impact. Complex associations exist, requiring further research. Schizophrenia and pro-inflammatory factors are discussed, highlighting the need for ongoing investigation into cannabis-related immune changes and mental health. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023463539.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".