Resolvin E1 derived from eicosapentaenoic acid prevents hyperinsulinemia and hyperglycemia in a host genetic manner
Bibliographic record
Abstract
Eicosapentaenoic acid (EPA) has garnered attention after the success of the REDUCE-IT trial, which contradicted previous conclusions on EPA for cardiovascular disease risk. Here we first investigated EPA's preventative role on hyperglycemia and hyperinsulinemia. EPA ethyl esters prevented obesity-induced glucose intolerance, hyperinsulinemia, and hyperglycemia in C57BL/6J mice. Supporting NHANES analyses showed that fasting glucose levels of obese adults were inversely related to EPA intake. We next investigated how EPA improved murine hyperinsulinemia and hyperglycemia. EPA overturned the obesity-driven decrement in the concentration of 18-hydroxyeicosapentaenoic acid (18-HEPE) in white adipose tissue and liver. Treatment of obese inbred mice with RvE1, the downstream immunoresolvant metabolite of 18-HEPE, but not 18-HEPE itself, reversed hyperinsulinemia and hyperglycemia through the G-protein coupled receptor ERV1/ChemR23. To translate the findings, we determined if the effects of RvE1 were dependent on host genetics. RvE1's effects on hyperinsulinemia and hyperglycemia were divergent in diversity outbred mice that model human genetic variation. Secondary SNP analyses further confirmed extensive genetic variation in human RvE1/EPA-metabolizing genes. Collectively, the data suggest EPA prevents hyperinsulinemia and hyperglycemia, in part, through RvE1's activation of ERV1/ChemR23 in a host genetic manner. The studies underscore the need for personalized administration of RvE1 based on genetic/metabolic enzyme profiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".