Variation in C-reactive Protein Following Weight Loss in Obese Insulin Resistant Postmenopausal Women: is there an Independent Contribution of Lean Body Mass?
Bibliographic record
Abstract
BACKGROUND: We showed that obese insulin resistant postmenopausal women are characterized by higher lean body mass and elevated C-reactive protein. Although counterintuitive, we hypothesized that losses in muscle mass following caloric restriction and increase in muscle quality will be associated with improvements in glucose homeostasis through decreases in C-reactive protein. OBJECTIVES: To determine 1) if improvements in C-reactive protein concentrations occurs through losses in lean body mass; and 2) if decreases in C-reactive protein levels contribute to improvements in insulin sensitivity. METHODS: 50 postmenopausal women (body mass index>26 kg/m(²)) with impaired glucose disposal (<7.5 mg/kg/min) completed a 6-month caloric restriction program. Outcome measures were: Glucose disposal rate: M value (by hyperinsulinemic-euglycemic clamp), body composition (total, trunk, and appendicluar). LBM and FM by DXA), LBM index (LBM (kg)/height (m(2)), body fat distribution (VAT and SAT by CT scan) and plasma high-sensitive C-reactive protein (hsCRP) and interleukin-6 (Il-6). RESULTS: Significant correlations were observed between Δ hsCRP levels with Δ Il-6 (r=0.33, p≤0.05), Δ total LBM index (r=0.44, p≤0.01), Δ trunk LBM (r=0.38, p≤0.01) Δ SAT (r=0.35, p≤0.05) and ∆ glucose disposal rate (r=- 0.44, p≤0.01). After including all the correlated variables in Stepwise linear regression model, Δ LBM index was the only independent predictor of the reduction in hsCRP levels (R(2)=0.20, p≤0.01). CONCLUSION: Losses in total lean body mass are independently associated with improvements in inflammatory state (CRP levels) in obese postmenopausal women with impaired glucose disposal.
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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 | 0.000 |
| 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".