Omega-3 polyunsaturated fatty acids modify glucose metabolism in THP-1 monocytes
Bibliographic record
Abstract
Abstract Chronic inflammation is a driving factor in diseases like obesity and type 2 diabetes. Enhanced glucose metabolism, including via oxidative phosphorylation, may contribute to heightened immune activation. A recent clinical trial showed that supplementation with the n-3 fatty acid α-linolenic acid (ALA) reduced oxidative phosphorylation rates in human monocytes. However, the mechanism remains unknown. Therefore, our objective was to explore the direct effects of ALA and docosahexaenoic acid (DHA) on glucose metabolism in a cell culture model and to explore the molecular mechanism. THP-1 monocytes were treated for 48h with 10-40 μM of ALA or DHA and compared with vehicle and oleic acid controls. The Seahorse XFe24 system was used to approximate catabolic rates in the presence of glucose, including glycolysis and oxidative phosphorylation. The latter was validated by respirometry using an Oroboros O2k Oxygraph. Both ALA and DHA treatments reduced oxidative phosphorylation and increased glycolytic rates relative to control conditions. We identified pyruvate dehydrogenase kinase 4 (PDK4), an enzyme that inhibits the conversion of pyruvate to acetyl-CoA, as a possible mechanistic candidate. This gene was significantly upregulated by ALA and, to a greater extent, by DHA. Using fluorescent indicators, we also found that DHA increased reactive oxygen species while ALA had no effect. Our data suggest that ALA and DHA trigger a re-wiring of bioenergetic pathways in monocytes, possibly via the upregulation of PKD4. Given the close relationship between cell metabolism and immune cell activation, this may represent a novel mechanism by which n-3 fatty acids modulate immune function and inflammation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".