Diagnostic Accuracy of Anthropometric Markers of Obesity for Prediabetes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Prediabetes is a significant public health concern due to its high risk of progressing to diabetes. Anthropometric measures of obesity, including body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) have been demonstrated as key risk factors in the development of prediabetes. However, there is a lack of clarity on the diagnostic accuracy and cut-off points of these measures. Objective: To determine the diagnostic accuracy of these anthropometric measures for their most effective use in identifying prediabetes. Methodology: A systematic review (SR) with metanalysis of observational studies was carried out. The search was conducted in four databases: Pubmed/Medline, SCOPUS, Web of Science, and EMBASE. For the meta-analysis, sensitivity and specificity, together with their 95% confidence intervals (CI 95%) were calculated. Results: Among all the manuscripts chosen for review, we had four cross-sectional studies, and three were classified as cohort studies. The forest plots showed the combined sensitivity and specificity for both cross-sectional and cohort studies. For cross-sectional studies, the values were as follows: BMI had a sensitivity of 0.63 and specificity of 0.56, WC had a sensitivity of 0.59 and specificity of 0.58, and WHtR had a sensitivity of 0.63 and specificity of 0.73. In the cohort studies, the combined sensitivity and specificity were: BMI at 0.70 and 0.45, WC at 0.68 and 0.56, and WHtR at 0.68 and 0.56, respectively. All values are provided with 95% confidence intervals. Conclusions: This systematic review and meta-analysis evaluated the diagnostic accuracy of BMI, WC, and WHtR in identifying prediabetes. The results showed variations in sensitivity and specificity, with WHtR having the highest specificity in cross-sectional studies and BMI having improved sensitivity in cohort studies.
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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 | 0.034 | 0.511 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".