The Effects of Diet, Body Composition and Exercise on The Serum Metabolome in Health and Disease
Bibliographic record
Abstract
The serum metabolite profile reflects a great variety of factors including age, gender, diet, exercise, gut microbial metabolism and the presence of disease. Importantly, changes in the serum metabolome may appear prior to the clinical manifestation of disease, provide insight into underlying biological mechanisms and be predictive of disease progression and/or amelioration. Using an animal model and human participants, the serum metabolome of obesity was studied in relations to diet and physical activity. In brief, obese rats consuming coffee had a favorable body composition, lower liver triglycerides and decreased serum concentrations of branched-chain amino acids, which are thought to cause diabetes when present at higher concentrations, compared to controls. In contrast, aspartame consuming rats showed impairments in glucoregulation. Our findings suggested that this might have been a result of aspartame causing an increase in the proportion of gut bacteria that produce propionate, a metabolite known to stimulate hepatic gluconeogenesis. In human subjects, obesity and metabolic syndrome risk factors were associated with lower concentrations of the sphingolipid precursors serine and glycine. Higher activity energy expenditure and physical activity levels showed the opposite association. Physical activity may thus improve on insulin sensitivity by reducing de novo synthesis of sphingolipids and their subsequent accumulation in insulin-sensitive tissues. Exercise also associated with improvements in body weight, lean mass, physical performance and symptom severity, following cancer treatment in head and neck cancer patients. However, none of these factors correlated with their 2-year survival. Instead, the baseline serum metabolite profile differentiated between survivors and nonsurvivors, despite matching for patient characteristics. Thus, serum metabolites show potential as prognostic biomarkers for head and neck cancer patients. Lastly, we found that combining three metabolomics approaches resulted in the most comprehensive coverage of metabolite classes and the most complete description of the phenotype, for women with ovarian cancer. This Chapter also highlighted the need to address the influence of common risk factors on the serum metabolome. Taken together, the work presented in this thesis has provided further insight into the serum metabolite profile of metabolic disease and cancer in the context of diet and physical activity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".