Associations of resistin with inflammatory and fibrinolytic markers, insulin resistance, and metabolic syndrome in middle-aged and older Chinese
Bibliographic record
Abstract
OBJECTIVE: Resistin increases insulin resistance (IR) in mice. However, the role of resistin in human disease remains controversial. We aimed to assess plasma resistin levels and their associations with inflammatory and fibrinolytic markers, IR and metabolic syndrome (MetS) among Chinese. DESIGN AND METHODS: Plasma resistin was measured in a population-based cross-sectional survey of 3193 Chinese aged from 50 to 70 years in Beijing and Shanghai. RESULTS: The median resistin concentration was 8.60 ng/ml (interquartile range, 5.78-14.00) among all participants, and it was higher in women than in men (P=0.008). Resistin was correlated weakly with body mass index, waist circumference, high-density lipoprotein (HDL) cholesterol (negatively), homeostatic model assessment of IR and tumor necrosis factor-alpha receptor 2 (TNFR2; r=0.04, 0.07, -0.09 and 0.06 respectively, all P<0.05), and more highly with C-reactive protein (CRP), interleukin (IL)6 and plasminogen activator inhibitor (PAI)1 (r=0.12, 0.12 and 0.21 respectively, all P<0.001), but only HDL cholesterol, CRP, IL6, TNFR2, and PAI1 remained significantly associated with resistin in multiple regression analysis (all P<0.05). Furthermore, elevated resistin levels were associated with the higher prevalence of IR and MetS. However, the significant relationships disappeared after adjustment for inflammatory and fibrinolytic markers especially PAI1. CONCLUSIONS: This study suggests that resistin is more strongly associated with inflammatory and fibrinolytic markers than with obesity or IR status. The associations of resistin with IR and MetS could largely be explained by inflammatory and fibrinolytic markers especially PAI1 levels.
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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 | 0.001 | 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) | 0.001 | 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".