Waist Body Mass Index Outperforms Other Anthropometric Indicators in Identifying Obesity Using Bioimpedance
Bibliographic record
Abstract
Background: While the body mass index (BMI) has been widely used to diagnose overweight and obesity, other anthropometric markers, such as waist circumference (WC), waist-to-height ratio (WHtR), among others, have been proposed as alternative diagnostic measures for obesity. The objective was to determine which anthropometric marker has the best diagnostic accuracy for obesity. Methods: This was a diagnostic test study with the primary analysis in workers of an occupational clinic located in Lima, Peru. The percentage of fat measured by bioimpedance was used as the reference test. The WC, BMI, WHtR, tri-ponderal mass index, new BMI, Clinica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), and waist BMI (wBMI) were evaluated. Receiver operating characteristic (ROC) curve analysis was used as a statistical and graphical method to assess predictive capacity, as well as the area under the curve (AUC) corresponding to each response variable. Sensitivity and specificity, with their 95% confidence intervals (95% CIs), were calculated. Results: In our study on obesity according to the percentage of fat, 780 participants were included. The overall prevalence of obesity was 19.74%. Regarding the diagnostic test analysis, the measure with the highest accuracy in women was wBMI: AUC = 0.783 (95% CI: 0.735 - 0.830), sensitivity = 71.59% (95% CI: 60.98 - 80.69), and specificity = 74.54% (95% CI: 69.45 - 79.18). For men, the measure with the highest accuracy was wBMI: AUC = 0.828 (95% CI: 0.779 - 0.878), sensitivity = 89.39% (95% CI: 79.36 - 95.62), and specificity = 58% (95% CI: 52.19 - 63.65). Conclusions: Our study concludes that wBMI proved to be a superior tool for diagnosing obesity compared to conventional measures such as BMI, WC, WHtR, and other evaluated anthropometric metrics. J Endocrinol Metab. 2024;14(1):13-20 doi: https://doi.org/10.14740/jem918
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".