Glucagon-like Peptide-1 Secretion Is Inhibited by Lysophosphatidic Acid
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) potentiates glucose-stimulated insulin secretion (GSIS). While dozens of compounds stimulate GLP-1 secretion, few inhibit. Reduced GLP-1 secretion and impaired GSIS occur in chronic inflammation. Lysophosphatidic acids (LPAs) are bioactive phospholipids elevated in inflammation. The aim of this study was to test whether LPA inhibits GLP-1 secretion in vitro and in vivo. GLUTag L-cells were treated with various LPA species, with or without LPA receptor (LPAR) antagonists, and media GLP-1 levels, cellular cyclic AMP and calcium ion concentrations, and DPP4 activity levels were analyzed. Mice were injected with LPA, with or without LPAR antagonists, and serum GLP-1 and DPP4 activity were measured. GLUTag GLP-1 secretion was decreased ~70–90% by various LPAs. GLUTag expression of Lpar1, 2, and 3 was orders of magnitude higher than Lpar4, 5, and 6, implicating the former group in this effect. In agreement, inhibition of GLP-1 secretion was reversed by the LPAR1/3 antagonist Ki16425, the LPAR1 antagonists AM095 and AM966, or the LPAR2 antagonist LPA2-antagonist 1. We hypothesized involvement of Gαi-mediated LPAR activity, and found that intracellular cyclic AMP and calcium ion concentrations were decreased by LPA, but restored by Ki16425. Mouse LPA injection caused an ~50% fall in circulating GLP-1, although only LPAR1 or LPAR1/3 antagonists, but not LPAR2 antagonism, prevented this. GLUTag L-cell and mouse serum DPP4 activity was unchanged by LPA or LPAR antagonists. LPA therefore impairs GLP-1 secretion in vitro and in vivo through Gαi-coupled LPAR1/3 signaling, providing a new mechanism linking inflammation with impaired GSIS.
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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 | 0.001 |
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".