Higher Inflammation Is Associated with Cardiometabolic Phenotype and Biochemical Health in Women with Obesity
Bibliographic record
Abstract
INTRODUCTION: Metabolic or inflammatory markers may predict adverse outcomes in women with obesity. We sought to describe metabolic-obesity phenotypes of women using novel staging tools and investigate relationships with inflammation. METHODS: In a cross-sectional study, we collected fasting blood samples from sixty-four females with body mass index (BMI) ≥28 kg/m2. Participants were classified as metabolically healthy or metabolically unhealthy obesity (MUO) using the cardiometabolic disease staging system (CMDS) and Edmonton obesity staging system (EOSS). Data were analyzed using independent sample t tests, Pearson's correlations, and multiple logistic regression. RESULTS: Mean (SD) age was 40.2 (9.3) years with median (IQR) BMI 31.8 (30.3-35.7) kg/m2. The prevalence of MUO was 46.9% and 81.3% using CMDS and EOSS criteria, respectively. Women with raised CMDS scores had higher C3 (1.34 [0.20] vs. 1.18 [0.15], p = 0.001) and C-reactive protein (CRP) (2.89 [1.31-7.61] vs. 1.39 [0.74-3.60], p = 0.034). C3 correlated with insulin (r = 0.52), hemoglobin A1c (r = 0.37), and C-peptide (r = 0.58), all p < 0.05. C3 above the median (>1.23 g/L) increased odds of raised CMDS score, when controlled for age, BMI, ethnicity, and smoking (OR = 6.56, 95% CI: 1.63, 26.47, p = 0.008). CONCLUSION: The prevalence of MUO was lower using CMDS than EOSS. C3 and CRP may be useful clinical biomarkers of risk or treatment targets in women with obesity.
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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) | 0.002 | 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".