Metabolomic profiling of dietary‐induced insulin resistance in the high fat–fed C57BL/6J mouse
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".