Distinguishing differences between intrinsic aerobic capacity and age: a 1H‐NMR metabolomics approach (884.19)
Bibliographic record
Abstract
Differences in intrinsic aerobic capacity play a critical role in the development of perturbed metabolism, chronic disease and all‐cause mortality. Aims were to employ metabolomics to examine differences in age vs. aerobic capacity in young and old rats selectively bred for low (LCR) or high (HCR) aerobic capacity. Proton nuclear magnetic resonance spectroscopy (1H‐NMR) evaluated the metabolic profile of plasma samples obtained from fasted LCR and HCR. Multivariate statistical analysis was employed, with individual features judged based on variability R2 and predictive ability Q2 in unsupervised and supervised models built using the most significant metabolites. Taurine, pyruvate, acetone, valine, amongst others were key metabolites that contributed to distinct separation based on age (R2=0.83, Q2=0.65). In contrast, weaker predictive models were observed for LCR vs. HCR with scores of R2=0.53 and Q2=0.35 respectively. Key metabolites that decreased in HCR compared to LCR included isopropanol, o‐acetylcarnithine, sarcosine and proline. Pathway analysis highlighted changes in methionine, purine and TCA cycle intermediates. In conclusion, metabolomics analysis was a better predictor and age rather than aerobic capacity in LCR and HCR rats. This observation highlights the importance of age when attempting to isolate metabolic changes in aerobic capacity and their relation to chronic disease risk. Grant Funding Source : Supported by the Alberta Cancer Foundation
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".