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 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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".