Inflammatory Cytokine Profiles in Visceral and Subcutaneous Adipose Tissues of Obese Patients Undergoing Bariatric Surgery Reveal Lack of Correlation With Obesity or Diabetes
Bibliographic record
Abstract
Population studies have linked insulin resistance to systemic low-grade chronic inflammation and have reported elevated levels of inflammatory cytokines such as TNFα, IL-1β and IL-6, individually or in certain combinations, in adipose tissues or in the serum. We undertook this comprehensive study to simultaneously evaluate the expression of several pro-inflammatory and anti-inflammatory cytokines in serum and in the visceral and subcutaneous adipose tissues from obese patients undergoing bariatric surgery. We observed that several inflammatory cytokines implicated in obesity-associated inflammation showed no significant difference in protein or gene expression between obese patients with or without diabetes and control groups. IL1B gene expression was significantly elevated in the visceral adipose tissues of obese patients, but did not correlate with their diabetes status. Despite the significant increase in IL1B expression in the obese group, a significant proportion of obese patients did not express TNFA, IL1B or IL6 in visceral adipose tissues. Certain inflammatory cytokines showed correlation with the chemokine CCL2 and VEGF-A in visceral adipose tissues. Our findings suggest that the inflammatory cytokine profile in metabolic syndrome is more complex than what is currently perceived and that chronic inflammation in obese patients likely results from incremental contribution from different cytokines and possibly other inflammatory mediators from within and outside the adipose tissues. It is possible that this obesity associated chronic inflammation is not predicted by a single mediator, but rather includes a large spectrum of possible profiles.
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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 | 0.001 |
| 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) | 0.001 | 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".