165-LB: Circulating Metabolomic Biomarkers of Glycemic Control in Youth with Type 2 Diabetes (T2D)
Bibliographic record
Abstract
There are few biomarkers of glycemic response among youth with T2D, despite increasing disease prevalence and suboptimal response to approved therapies. We hypothesized that plasma metabolites may predict glycemic outcomes in youth-onset T2D. We measured 480 metabolites in fasting plasma samples in the Treatment options for T2D in Adolescents and Youth (TODAY) study (n=391), in which youth with T2D aged 10-17 years were randomized to metformin alone, metformin + rosiglitazone, or metformin + intensive lifestyle intervention. Metabolite associations with loss of glycemic control (defined as HbA1c ≥8% for 6 months or need for insulin therapy) were modeled using Cox proportional hazards regression adjusted for baseline age, sex, race/ethnicity, BMI, treatment group, and fasting glucose. Loss of glycemic control was observed in 150 of 391 youth (mean 2.6 years). Baseline levels of 11 metabolites were associated with loss of glycemic control (FDR<0.05, Fig. 1A). Treatment group modified the association of hexose and xanthurenic acid levels with glycemic control. For both compounds, youth with higher baseline levels had a lower risk of treatment failure when randomized to metformin therapy alone (Fig. 1B). Thus, metabolomics provides insight into circulating analytes associated with loss of glycemic control, and may highlight different effects of specific treatments in youth with T2D. Disclosure Z. Chen: None. C. Lu: None. X. Shi: None. S. Zheng: None. D. Wolfs: None. P. Bjornstad: Advisory Panel; AstraZeneca, Novo Nordisk, Lilly, Horizon Therapeutics plc, Boehringer Ingelheim (Canada) Ltd., LG Chem, Consultant; Bayer Inc., Bristol-Myers Squibb Company. R. E. Gerszten: None. E. M. Isganaitis: None. Funding National Institutes of Health (K23DK127073)
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".