Diagnostic Performance of Anthropometric Weight and Height Markers Associated With Insulin Resistance Diagnosis
Bibliographic record
Abstract
Background: The detection of insulin resistance (IR) is crucial to avoid long-term complications. Given that the classic methods for its measurement are challenging to implement, simpler methods are sought for its detection. The aim of the study is to determine the association and diagnostic performance of four anthropometric markers based on weight and height for IR in a sample of Peruvians. Methods: This study is a secondary analysis of the data. The variables were body mass index (BMI), the triponderal index (TPI), the new BMI (NBMI), and the University of Navarra Clinic-Body Fat Estimator index (CUN-BAE index). IR was measured using the homeostatic model assessment of insulin resistance (HOMA-IR). The association was evaluated using the odds ratio (OR), while for diagnostic performance, the receiver operating characteristic (ROC) curve and the corresponding area under it (AUC) were applied. Results: The prevalence of IR was 17.11%. The adjusted multivariate analysis found that the association with IR significantly increased with the increase of their levels, especially in the third tertile in BMI (adjusted odds ratio (aOR): 18.2; 95% confidence interval (CI): 8.73 - 44.6), TPI (aOR: 17.2; 95% CI: 8.34 - 40.6), NBMI (aOR: 16.5; 95% CI: 8.12 - 38.3) and CUN-BAE index (aOR: 20.8; 95% CI: 10.6 - 47.1). In addition, BMI had the highest AUC = 0.854 (0.824 - 0.884), cutoff = 27.44, sensitivity = 85.03 (78.70 - 90.07) and specificity = 73.42 (70.23 - 76.44). Conclusions: Based on the markers that only use weight and height, BMI showed the best association and diagnostic performance for detecting IR. It is advisable to conduct prospective studies to verify these findings. If such results are corroborated, BMI could become a valuable predictor for identifying IR in different populations. J Endocrinol Metab. 2024;14(3):149-157 doi: https://doi.org/10.14740/jem891
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".