Effect of different phosphatidylcholines on high fat diet-induced insulin resistance in mice
Bibliographic record
Abstract
Phosphatidylcholine (PC) is the most abundant phospholipid in natural products and exhibits various bioactivities in vivo. However, the beneficial effects of PC on IR and the potential mechanisms are rarely reported. PCs from different biological sources vary greatly in their fatty acid compositions. The present study aimed to investigate the effect of EPA/DHA-PC derived from Sthenototeuthis oualaniensis on high fat diet (HFD)-induced IR in comparison with terrestrial soybean PC (Soy-PC) and egg yolk PC (Egg-PC) in C57BL/6J mice. The results indicated that EPA/DHA-PC, but not Soy-PC or Egg-PC, reversed HFD-induced obesity, IR and hyperglycemia. This improvement was accompanied by enhanced IRS/PI3K/AKT insulin signaling pathway in peripheral tissue, ameliorated JNK and NF-κB inflammatory pathway in white adipose tissue, and changes in the gut microbial composition. Microbiological analysis showed that EPA/DHA-PC treatment prevented the loss of Bacteroidetes, Verrucomicrobia, Akkermansia, Coriobacteriaceae_UCG-002, Lactobacillus, Clostridium_sensu_stricto_1, Bacteroides and inhibited the increase of Firmicutes, Actinobacteria, Proteobacteria, and Ileibacterium. The gut microbiota-derived metabolites LPS and TMAO were also reduced by EPA/DHA-PC. In summary, the improvement effect of PCs on IR is largely related to their fatty acid composition. EPA/DHA-PC prevented IR probably by modulating the gut microbiota composition, ameliorating the chronic inflammation in the adipose tissue and promoting transduction of insulin signaling pathways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".