Compartment-specific Metabolic Alterations to Insulin Reflect Adiposity-driven Variation and Predict Type 2 Diabetes
Bibliographic record
Abstract
CONTEXT: The metabolic mechanisms underlying insulin resistance are not fully understood. Metabolomic profiling can reveal compartment-specific variations and identify individuals at risk for type 2 diabetes (T2D). OBJECTIVE: To characterize insulin-induced metabolomic changes during a hyperinsulinemic-euglycemic clamp and evaluate a derived risk score's predictive value for T2D. DESIGN, SETTING, AND PARTICIPANTS: Clamp studies were conducted in 80 adults (38 with T2D, 42 without) to measure plasma and muscle metabolites in fasting and hyperinsulinemic states. An insulin resistance metabolomic score was developed and tested in a prospective case-control study (367 cases, 910 controls) from the Women's Health Initiative (28.5-year follow-up). MAIN OUTCOME MEASURES: Metabolite changes during hyperinsulinemia and incident T2D. RESULTS: Hyperinsulinemia altered 79.5% of plasma metabolites (notably fatty acids, lactate, and pyruvate) and 15.8% of muscle metabolites (eg, branched-chain and aromatic amino acids). T2D was associated with higher triglycerides and lower tricarboxylic acid intermediates during clamp. Adiposity amplified insulin-induced increases in plasma lipids. The risk score predicted incident T2D (hazard ratio, 1.20 per SD; 95% CI, 1.09-1.32; P = 7.4 × 10-4). CONCLUSION: Compartment-specific metabolic responses to insulin are shaped by adiposity and predict future T2D risk, supporting use of metabolomic signatures for early identification and prevention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".