Metabolomic profiling as a useful tool for diagnosis and treatment of chronic disease: focus on obesity, diabetes and cardiovascular diseases
Bibliographic record
Abstract
There have been considerable improvements in therapeutics for chronic diseases. However, the maximum benefit of these or other options are hard to achieve in practice, due in part to the difficulties associated with determining optimal targets for such interventions. Recent developments have suggested that understanding changes in metabolite profiles will confer a high degree of predictive accuracy in terms of understanding the fundamental mechanisms resulting in perturbations of the metabolic state. Metabolomics involves the establishment of relationships between phenotype and a metabolic signature, which are key aspects of biological function. These approaches have been applied to the identification of serum/plasma metabolic markers involved in obesity, diabetes and vascular disease using animal models or in humans. Different kinds of metabolite profiling techniques using nuclear magnetic resonance spectroscopy, mass spectrometry, ultraperformance liquid chromatography and so on are currently employed to generate global metabolic profiles. Scientific information derived from these techniques can be applied to provide accurate and clinically useful prognostic/diagnostic capability for the management of major chronic diseases. One current consideration limiting the widespread use of metabolomic profiling is the analysis of its cost-effectiveness. In summary, it is hoped that the information derived from metabolite profiling will make it possible to suggest individualized therapies that more effectively treat disease.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".