Predisposition to Type 2 Diabetes in Aspects of the Glycemic Curve and Glycated Hemoglobin in Healthy, Young Adults: A Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVES: Our aim in this study was to identify predictors for diabetes among the characteristics of the glycemic curve and glycated hemoglobin (A1C) in healthy, young adults. METHODS: We used a cross-sectional study to establish predictors for diabetes based on earlier studies and evaluated occurrence of the condition in 81 healthy, young adult subjects. These volunteers underwent analysis of fasting plasma glucose, oral glucose tolerance test plasma glucose, A1C, and inflammatory markers (leukocytes, monocytes, and C-reactive protein). The nonparametric Mann-Whitney U test, Fisher's exact test, chi-square test, Kruskal-Wallis test, and multiple-comparisons test were used to analyze the data. RESULTS: ). The older group had a higher incidence of predictors (p=0.0005) and was associated with the predictors 30-minute blood glucose ≥164 mg/dL (p=0.0190), 60-minute blood glucose ≥125 mg/dL (p=0.0346), and A1C ≥5.5% (p=0.0162), with a monophasic glycemic curve (p=0.007). The younger group was associated with the 2-hour plasma glucose predictor ≥140 mg/dL (p=0.014). All subjects had fasting glucose in the normal range. CONCLUSIONS: Healthy, young adults may already have predictors of diabetes, identified mainly by aspects of the glycemic curve and A1C, but at more modest levels than those with prediabetes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".