Characterization of specific Gut Bacteria and bacterial Metabolites in Human Obesity and Type 2 Diabetes
Bibliographic record
Abstract
In the present thesis, we aimed to characterize the gut bacterium Parasutterella in our cross-sectional FoCus cohort (n=1,544), an independent Canadian cohort (n=438) and additionally in a weight loss intervention cohort (n=55). Parasutterella was positively associated with BMI (body mass index), fasting insulin and type 2 diabetes independently of low-grade inflammation. Dietary analysis revealed a positive association between Parasutterella sp. with the dietary intake of carbohydrates but not with fat or protein consumption. MS-metabolomics analysis appointed L-cysteine as strongly reduced in subjects with high Parasutterella abundance. Additionally, metabolic network enrichment analysis identified an association of high Parasutterella abundance with the activation of the human fatty acid biosynthesis pathway suggesting a mechanism for body weight gain. This was supported by a reduction of Parasutterella excrementihominis abundance during a low-carb diet within the weight loss intervention. Together, these data indicate a role of Parasutterella in human obesity and type 2 diabetes. Secondly, the present thesis shows by biostatistical modeling that agmatine, measured by MS-metabolomics, was elevated in FoCus subjects with an increased BMI (n=1,704) coupled with reduced microbial α-diversity. In addition, cell culture models provided insight into the effect of agmatine on decelerating adipogenesis and activating inflammatory processes in human macrophages. These results suggest that agmatine has effects on metabolic processes and these should be validated in future clinical interventions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".