Diagnostic Strategies to Detect Glucose Intolerance in a Multiethnic Population
Bibliographic record
Abstract
OBJECTIVE: Identifying individuals who have elevated glucose concentrations is important for clinicians so that preventive strategies can be invoked, and it is useful for researchers who study associations between elevated glucose and adverse health outcomes. These methods should be applicable worldwide across different ethnic groups. Therefore, the objective of our analysis was to determine whether using the fasting glucose and HbA(1c) together could improve the classification of individuals with impaired glucose tolerance and diabetes in a multiethnic cohort randomly assembled in Canada. RESEARCH DESIGN AND METHODS: We determined the optimum diagnostic criteria to identify people with abnormal glucose tolerance using fasting plasma glucose, 2-h post-glucose load plasma glucose, and HbA(1c) in 936 Canadians of South Asian, Chinese, and European descent. RESULTS: The sensitivity of the American Diabetes Association (ADA) criteria to diagnose diabetes compared with the World Health Organization definitions was poor at 48.3% (95% confidence interval [CI] 35.7-61.0). Using a receiver operator characteristic curve, the optimum combined cut-point using fasting glucose and HbA(1c) to diagnose diabetes was a fasting glucose > or =5.7 mmol/l and an HbA(1c) > or =5.9%. These cut-points were associated with a sensitivity and specificity of 71.7% (60.3-83.1) and 95.0% (93.5-96.4), respectively, a positive likelihood ratio (LR) of 14.3 (9.6-19.0), and a negative LR of 0.3 (0.2-0.4). Significant ethnic variation in the sensitivity and specificity of this approach was observed: 47.4% (24.9-69.8) and 97.6% (95.9-99.4) among Europeans, 78.6% (57.1-100) and 95.9% (93.6-98.2) among Chinese, and 85.2% (71.8-98.6) and 91.3% (88.1-94.6) among South Asians, respectively. Participants with impaired glucose tolerance could not be identified reliably using the fasting glucose or HbA(1c) alone or in combination. CONCLUSIONS: The sensitivity of the ADA criteria to diagnose diabetes is low, and there is substantial variation between ethnic groups. Fasting glucose and HbA(1c) may be used together to improve the identification of individuals who have diabetes, allowing clinicians to streamline the use of the oral glucose tolerance test.
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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 | 0.001 |
| 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".