A Novel, Non-Invasive 13C-Glucose Breath Test to Estimate Insulin Resistance in Obese Prepubertal Children
Bibliographic record
Abstract
Insulin resistance (IR) is an important risk factor for the development of type 2 diabetes mellitus in obese boys and girls. Because needle-associated fear and anxiety are common in children, non-invasive methods to determine IR are desirable. Our objective in this cross-sectional study of obese prepubertal children (n = 39) was to compare estimates of IR using a novel, non-invasive technique (13C-glucose breath test) with common indices of IR derived from an oral glucose tolerance test (OGTT). For the 13C-glucose breath test, samples were collected before and 90 minutes after ingestion of 25 mg 13C-labelled glucose. For the OGTT, glucose and insulin samples were collected at 0, 15, 30, 45, 60, 90 and 120 minutes. The homeostatic model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), insulin area-under-the-curve (AUC), and sum-of-insulin were calculated as indices of IR. Pearson correlations revealed significant, but moderate, associations between the 13C-glucose breath test and fasting insulin (r = -0.50; p < 0.01), 2-hour insulin (r = plots showed acceptable levels of agreement between indices of IR. In obese prepubertal children, the 13C-glucose breath test can provide a proxy estimate of IR when gold-standard techniques are either unavailable or impractical.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".