Metabolomic profiling of dietary‐induced insulin resistance in the high fat–fed C57BL/6J mouse
Why this work is in the frame
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Bibliographic record
Abstract
The predictive ability of metabolic profiling to detect obesity-induced perturbations in metabolism has not been clearly established. Complex aetiologies interacting with environmental factors highlight the need to understand how specific manipulations alter metabolite profiles in this state. The aim of this study was to determine if targeted metabolomic profiling could be employed as a reliable tool to detect dietary-induced insulin resistance in a small subset of experimental animals (n = 10/treatment). Following weaning, male C57BL/6J littermates were randomly divided into two dietary groups: chow and high fat. Following 12 weeks of dietary manipulation, mice were fasted for 5 h prior to serum collection. The resultant high fat-fed animals were obese and insulin resistant as shown by a euglycaemic-hyperinsulinaemic clamp. Sera were analysed by proton nuclear magnetic resonance spectroscopy, and 46 known compounds were identified and quantified. Multivariate analysis by orthogonal partial least squares discriminant analysis, a projection method for class separation, was then used to establish models of each treatment. Models were able to predict class separation between diets with 90% accuracy. Variable importance plots revealed the most important metabolites in this discrimination to include lysine, glycine, citrate, leucine, suberate and acetate. These metabolites are involved in energy metabolism and may be representative of the perturbations taking place with insulin resistance. Results show metabolomics to reliably describe the metabolic effects of insulin resistance in a small subset of samples and are an initial step in establishing metabolomics as a tool to understand the biochemical signature of insulin resistance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it