The Visceral Adiposity Index: Relationship with cardiometabolic risk factors in obese and overweight postmenopausal women – A MONET group study
Bibliographic record
Abstract
A recent study suggested visceral adipose index (VAI) as an indicator of adipose tissue distribution and function associated with cardiometabolic risk. We aim to examine the association between VAI and visceral adipose tissue (VAT), insulin sensitivity, and a large panel of associated cardiometabolic risk factors, and to determine if changes in VAI after weight loss intervention will reflect changes in VAT. We performed a secondary analysis using the data of 99 overweight and postmenopausal women that completed a 6-month weight loss program (Montreal Ottawa New Emerging Team Study). VAI was calculated according to the equation by Amato et al. (2010; Diabetes Care, 33(4):920-922). At baseline, VAI was associated with VAT (r = 0.284, p < 0.01) but not with subcutaneous adipose tissue (SAT) while body mass index (BMI) and waist circumference (WC) were significantly related to both. BMI and WC demonstrated significantly stronger predictive value of VAT accumulation (area under the curve = 0.84 and 0.86, respectively) than VAI (area under the curve = 0.61; p < 0.01). However, VAT, BMI, WC, and VAI were similarly related to fasting insulin and glucose disposal rates. After a 6-month weight loss program, VAI decreased significantly and similarly in both intervention groups (p < 0.01). In addition, the percentage of change in VAI showed the significantly weakest correlation (r = 0.25) with the percentage of change in VAT than BMI (r = 0.56; p < 0.01 for r comparisons) and was not a significant predictor of interindividual percentage of change in VAT while BMI accounted for 33.7%. VAI is a weak indicator of VAT function and did not predict changes in VAT after weight loss. Furthermore, this index was not superior to BMI or WC. However, VAI is a good indicator of metabolic syndrome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".