Human Paneth cell α-defensin-5 treatment reverses dyslipidemia and improves glucoregulatory capacity in diet-induced obese mice
Bibliographic record
Abstract
Overnutrition is the principal cause of insulin resistance (IR) and dyslipidemia, which drive nonalcoholic fatty liver disease (NAFLD). Overnutrition is further linked to disrupted bowel function, microbiota alterations, and change of function in gut-lining cell populations, including Paneth cells of the small intestine. Paneth cells regulate microbial diversity through expression of antimicrobial peptides, particularly human α-defensin-5 (HD-5), and have shown repressed secretory capacity in human obesity. Mice were fed a 60% high-fat diet for 13 wk and subsequently treated with physiologically relevant amounts of HD-5 (0.001%) or vehicle for 10 wk. The glucoregulatory capacity was determined by glucose tolerance tests and measurements of corresponding insulin concentrations both before and during intervention. Gut microbiome composition was examined by 16S rRNA gene amplicon sequencing. HD-5-treated mice exhibited improved glucoregulatory capacity along with an ameliorated plasma and liver lipid profile. This was accompanied by specific decrease in jejunal inflammation and gut microbiota alterations including increased Bifidobacterium abundances, which correlated inversely with metabolic dysfunctions. This study provides proof of concept for the use of human defensins to improve host metabolism by mitigating the triad cluster of dyslipidemia, IR, and NAFLD.
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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".