Similar Associations of Total Adiponectin and High Molecular Weight Adiponectin with Cardio-metabolic Risk Factors in a Population of Overweight and Obese Postmenopausal Women: A MONET Study
Bibliographic record
Abstract
The aim of the study was to examine the association between total adiponectin and high molecular weight (HMW) adiponectin levels with cardio-metabolic risk factors in a population of sedentary, overweight, and obese postmenopausal women. Cross-sectional study was carried out on 55 nondiabetic sedentary overweight and obese postmenopausal women aged between 50 and 70 years. Insulin sensitivity was assessed by euglycemic-hyperinsulinemic clamp technique. Body composition and visceral fat were measured using dual X-ray absorptiometry and computed tomography, respectively. Other cardio-metabolic risk factors included: plasma lipids, hsC-reactive protein, energy expenditure (doubly labeled water), peak oxygen consumption, muscle strength (using weight training equipment) as well as total and HMW adiponectin. Correlations of total and HMW adiponectin with various cardio-metabolic risk factors were comparable. In addition, regression analysis results showed similar independent predictors of total and HMW adiponectin. Finally, the receiver operator characteristic (ROC) curves for total and HMW adiponectin to predict insulin sensitivity showed no difference between the areas under curve (AUC) (AUC total adiponectin=0.80 [95% CI: 0.66-0.95] versus AUC HMW adiponectin=0.76 [95% CI: 0.60-0.91], p=0.36). The present study indicates that HMW adiponectin does not seem to provide additional information than total adiponectin in relation to cardio-metabolic risk factors in overweight/obese postmenopausal women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".