Screening for Impaired Glucose Homeostasis: A Novel Metric of Glycemic Control
Bibliographic record
Abstract
ObjectiveTo investigate the use of a mathematical model of glucose homeostasis, fit to continuous glucose monitor data, as a metric of dysfunctional glycemic control.Patients and MethodsThree hundred eighty four participants recruited from 2 studies between October 2020 and June 2022 were equipped with a continuous glucose monitor, and interstitial glucose data were automatically collected for 2 weeks. The participants were assessed by a physician and diagnosed as being diabetic, prediabetic, or healthy according to the American Diabetes Association guidelines. A mathematical model of glucose homeostasis was fitted to the glucose data, and model parameter values were obtained. The participants were classified into the following 2 groups on the basis of their glucose homeostasis parameters: effective and impaired. Finally, glycemic variability metrics were compared with glucose homeostasis classification.ResultsThe homeostasis classification resulted in a specificity, sensitivity of individuals with prediabetes, and sensitivity of individuals with type 2 diabetes (T2D) of 0.78, 0.86, and 1.00, respectively, for women and 0.71, 0.86, and 1.00, respectively, for men. This sensitivity was similar to that of glycated hemoglobin A1c measurement (a sensitivity of 0.89 for women and 0.90 for men for prediabetes and a sensitivity of 1.00 for T2D) and superior to that of the oral glucose tolerance test (a sensitivity of 0.18 for women and 0.24 for men for prediabetes and a sensitivity of 0.75 for women and 0.86 for men for T2D). Overall, the individuals classified as impaired had increased glucose variability metrics than the individuals classified as effective (P<.05).ConclusionThe classification of glucose homeostasis on the basis of mathematical modeling of continuous measurements has promising applications as a new metric of dysfunctional glycemic control.Trial Registrationclinicaltrials.gov Identifier: NCT04529239; clinical trial registry identifier: CTRI/2021/08/035957
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".