Metabolic endotoxemia is dictated by the type of lipopolysaccharide
Bibliographic record
Abstract
Abstract Lipopolysaccharides (LPS) can promote metabolic endotoxemia, which is considered inflammatory and metabolically detrimental based on Toll-like receptor (TLR)4 agonists such as Escherichia coli -derived LPS. LPS from certain bacteria antagonize TLR4 yet contribute to endotoxemia measured by Endotoxin Units (EU). We found that E. coli LPS impaired gut barrier function and worsened glycemic control in mice, but equal doses of LPS from other bacteria did not. Matching the LPS dose from R. sphaeroides and E. coli by EU revealed that only E. coli LPS promoted dysglycemia, adipose inflammation, delayed intestinal glucose absorption, and augmented insulin and GLP-1 secretion. Metabolically beneficial endotoxemia promoted by R. sphaeroides LPS counteracted dysglycemia caused by an equal dose of E. coli LPS and promoted insulin sensitivity in obese mice. The concept of metabolic endotoxemia should be expanded beyond LPS load (EU) to include LPS characteristics, where the balance of deleterious and beneficial endotoxemia regulates host metabolism. Highlights Type of LPS dictates gut barrier function, inflammation, insulin, GLP-1, intestinal glucose absorption and blood glucose Endotoxin Units (EU) do not reflect how LPS influences blood glucose or hormones LPS derived from certain types of bacteria are insulin sensitizers R. sphaeroides LPS promotes metabolically beneficial endotoxemia LPS characteristics dictate metabolically beneficial versus deleterious endotoxemia
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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 | 0.000 | 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 | 0.001 |
| 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".