Sepsis and obesity: a scoping review of diet-induced obesity murine models
Bibliographic record
Abstract
BACKGROUND: Sepsis, the life-threatening host response to infection, is a major cause of mortality. Obesity increases vulnerability to sepsis; however, some degree of obesity may be protective, called the "obesity paradox". This scoping review systematically maps the literature on outcomes associated with diet-induced obesity and sepsis-induced organ injury, focusing on non-transgenic murine models. METHODS: A literature search of primary articles was conducted from database inception to June 2023. Eligible articles compared diet-induced obesity to non-obese mice in sepsis models involving live pathogens. Two reviewers screened articles and extracted data on obesogenic and sepsis models utilized, and organ injury outcomes, including physiological dysfunction, histological alterations, and biochemical changes. RESULTS: Seventeen studies met eligibility criteria; 82% used male C57BL/6 mice, and 88% used cecal ligation and puncture to induce sepsis. Most studies used 60% high-fat diets compared to 10-16% fat in controls. Seven (64%) studies reported increased mortality in obese septic mice, one (9%) observed a decrease, and three (37%) found no significant difference. The liver, lungs, and kidneys were the most studied organs. Alanine transaminase results were inconclusive. Myeloperoxidase levels were increased in the livers of two studies and inconclusive in the lungs of obese septic mice. Creatinine and neutrophil gelatinase-associated lipocalin were elevated in obese septic mice. CONCLUSIONS: There is variability in the methodology and measured outcomes in murine models of diet-induced obesity and sepsis and a lack of studies in female mice. The absence of standardized models has produced conflicting findings on the impact of obesity on sepsis outcomes.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".