Screening for Impaired Glucose Homeostasis: A Novel Metric of Glycemic Control
Bibliographic record
Abstract
Objective To 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 Methods Three 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. Results The 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). Conclusion The 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 Registration clinicaltrials.gov Identifier: NCT04529239; clinical trial registry identifier: CTRI/2021/08/035957
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".