Association of Immune and Metabolic Receptors<i>C5aR</i>and<i>C5L2</i>with Adiposity in Women
Bibliographic record
Abstract
Adipose tissue receptors C5aR and C5L2 and their heterodimerization/functionality and interaction with ligands C5a and acylation stimulating protein (ASP) have been evaluated in cell and rodent studies. Their contribution to obesity factors in humans remains unclear. We hypothesized that C5a receptors, classically required for host defense, are also associated with adiposity. Anthropometry and fasting blood parameters were measured in 136 women divided by body mass index (BMI): normal/overweight (≤30 kg/m(2); n = 34), obese I (≤45 kg/m(2); n = 33), obese II (≤51 kg/m(2); n = 33), and obese III (≤80 kg/m(2); n = 36). Subcutaneous and omental adipose tissue C5aR and C5L2 expression were analysed. C5L2 expression was comparable between subcutaneous and omental across all BMI groups. Plasma ASP and ASP/omental C5L2 expression increased with BMI (P < 0.001 and P < 0.01, resp.). While plasma C5a was unchanged, C5aR expression decreased with increasing BMI in subcutaneous and omental tissues (P < 0.01 and P < 0.05, resp.), with subcutaneous omental depots. Omental C5L2/C5aR ratio increased with BMI (P < 0.01) with correlations between C5L2/C5aR and waist circumference, HDL-C, and adiponectin. Tissue and BMI differences in receptors and ligands, particularly in omental, suggest relationship to metabolic disturbances and highlight adipose-immune interactions.
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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 | 0.000 | 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".