Associations Between Cannabis Use, Abdominal Fat Phenotypes and Insulin Traits
Bibliographic record
Abstract
BACKGROUND: General obesity has been linked to dysregulation of the endocannabinoid system in humans. However, there is a lack of studies on the relationship between cannabis use and specific abdominal fat phenotypes. The aim was to investigate the associations between cannabis use and magnetic resonance imaging-derived fat phenotypes, as well as indices of insulin sensitivity and insulin secretion. METHODS: In this cross-sectional study, magnetic resonance imaging was used to quantify subcutaneous fat volume (SFV), visceral fat volume (VFV), intra-hepatic fat deposition (IHFD), intra-pancreatic fat deposition (IPFD) and skeletal muscle fat deposition (SMFD) by two independent observers. Insulin sensitivity was determined based on HOMA-IS, Raynaud index and Matsuda index, whereas insulin secretion was determined based on HOMA-β, insulinogenic index 30' and insulinogenic index 60'. A validated questionnaire was used to ascertain participants' cannabis use. Linear regression models were constructed, adjusting for demographics, glycated hemoglobin, physical activity, tobacco smoking and alcohol consumption. RESULTS: A total of 120 individuals were included. Cannabis use explained 9.2% of variance in IHFD, 4.4% in SMFD, 3.4% in VFV, 0.4% in SFV and 0.2% in IPFD. Regular cannabis users had significantly greater IHFD compared with never users, in both the unadjusted (P = 0.002) and all adjusted (P = 0.002; P = 0.008) analyses. The other fat phenotypes did not differ significantly between either regular or non-regular users compared with never users. Regular cannabis users had significantly greater insulin secretion (as defined by the insulinogenic index 60') compared with never users, in both the unadjusted (P = 0.049) and all adjusted (P = 0.003; P = 0.004) analyses. Cannabis use explained 20.3% of variance in the insulinogenic index 60', but was not significantly associated with the other indices of insulin secretion. There were no significant differences in indices of insulin sensitivity in either regular or non-regular cannabis users compared with never users. CONCLUSION: Regular cannabis use may be a risk factor for non-alcoholic fatty liver disease (but not IPFD) and may alter the neuromodulation of insulin secretion. Further investigations are now warranted to elucidate the mechanisms underlying these associations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".