Activation of the Peripheral Endocannabinoid System in Human Obesity
Bibliographic record
Abstract
Obesity is the main risk factor for the development of type 2 diabetes. Activation of the central endocannabinoid system increases food intake and promotes weight gain. Blockade of the cannabinoid type 1 (CB-1) receptor reduces body weight in animals by central and peripheral actions; the role of the peripheral endocannabinoid system in human obesity is now being extensively investigated. We measured circulating endocannabinoid concentrations and studied the expression of CB-1 and the main degrading enzyme, fatty acid amide hydrolase (FAAH), in adipose tissue of lean (n = 20) and obese (n = 20) women and after a 5% weight loss in a second group of women (n = 17). Circulating levels of anandamide and 1/2-arachidonoylglycerol were increased by 35 and 52% in obese compared with lean women (P < 0.05). Adipose tissue mRNA levels were reduced by -34% for CB-1 and -59% for FAAH in obese subjects (P < 0.05). A strong negative correlation was found between FAAH expression in adipose tissue and circulating endocannabinoids. Circulating endocannabinoids and CB-1 or FAAH expression were not affected by 5% weight loss. The expression of CB-1 and FAAH was increased in mature human adipocytes compared with in preadipocytes and was found in several human tissues. Our findings support the presence of a peripheral endocannabinoid system that is upregulated in human obesity.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".