Mediating effect of adiponectin between free fatty acid and tumor necrosis factor-α in patients with diabetes
Bibliographic record
Abstract
Abstract Background/Objectives Increased free fatty acid (FFA) promotes adiponectin secretion in healthy subjects and induces inflammation in diabetes. Given the potential pro-inflammatory role of adiponectin in “adiponectin paradox”, we performed this study in patients with type 2 diabetes mellitus (T2DM) to assess the association of FFA with adiponectin and to investigate whether adiponectin mediates FFA-related inflammation. Methods This cross-sectional study consisted of adult patients with T2DM. FFA, adiponectin, and tumor necrosis factor-α (TNF-α) were assayed from fasting venous blood after overnight fasting for at least 8 h. Multivariable linear regression analysis and restricted cubic splines (RCS) analysis were performed to identify the association between FFA and adiponectin. Mediation analysis was performed to determine the mediating effect of adiponectin on the association between FFA and TNF-α. Results This study included 495 participants, with 332 males (67.1%) and a mean age of 47.0 ± 11.2 years. FFA was positively associated with adiponectin (b = 0.126, 95%CI: 0.036–0.215, P = 0.006) and was the main contributor to the increase of adiponectin (standardized b = 0.141). The RCS analysis demonstrated that adiponectin increased with FFA when FFA was less than 0.7 mmol/L but did not further increase thereafter (Poverall < 0.001 and Pnon-linear < 0.001). In addition, adiponectin mediated the association between FFA and TNF-α. The mediating effect was 0.08 (95%CI: 0.03–0.13, P = 0.003) and the mediating effect percentage was 26.8% (95%CI: 4.5–49.2, P = 0.02). Conclusions In patients with T2DM, FFA was positively associated with adiponectin when FFA was less than 0.7 mmol/L. Elevated adiponectin mediated FFA-related inflammation. This study may provide insights into the pro-inflammatory effect of adiponectin in T2DM.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".