Association of monocyte CCR<sub>2</sub> expression with obesity and insulin resistance in postmenopausal women
Bibliographic record
Abstract
PURPOSE: Monocytes actively participate in inflammatory mechanisms that contribute to the development of adipose tissue dysfunction and atherogenesis. The aim of this study was to evaluate the association of monocyte CCR2 chemokine receptor expression and intracellular oxidative burst with the metabolic and inflammatory factors related to body weight. METHODS: The study was performed in 67 postmenopausal women with normal, overweight and obese body mass index. Monocyte CCR2 surface expression and intracellular oxidative burst activity (determined using 2', 7'-dichlorofluorescin diacetate) were analyzed by flow cytometry. Serum levels of HMW adoponectin, monocyte chemoattractant protein-1 (MCP-1), insulin, glucose, lipids and C-reactive protein were determined. RESULTS: Subjects with homeostasis model assessment-estimated insulin resistance (HOMA-IR) above the median had significantly higher proportion of CCR2+ monocytes and higher mean fluorescence intensity (MFI) of CCR2 and oxidative burst. The proportion of CCR2+ monocytes and CCR2 MFI were correlated with body weight, body mass index, fat mass, insulin and HOMA-IR. Oxidative burst also correlated with anthropometric measures, fat mass and expression of CCR2. No correlations were found between these markers of monocyte activation and HMW adiponectin or monocyte chemoattractant protein-1. The absolute number of monocytes was associated with insulin and this association remained significant after adjusting for C-reactive protein. In the multiple regression model the monocyte number was determined to be an independent predictor of insulin level. CONCLUSION: These results provide support for significant associations of monocyte number and markers involved in monocyte activation with obesity and insulin resistance.
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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".