Omega-3 polyunsaturated fatty acids modify glucose metabolism in THP-1 monocytes
Bibliographic record
Abstract
Chronic inflammation is a driving factor in diseases like obesity and type 2 diabetes. Enhanced cellular glucose metabolism may contribute to heightened immune activation. A human supplementation trial showed that the n-3 PUFA α-linolenic acid (ALA) reduced oxidative phosphorylation in monocytes. Our objective here is to assess the direct effects of ALA and docosahexaenoic acid (DHA) on glucose metabolism in a cell culture model and to explore possible molecular mechanisms. THP-1 monocytes were treated with 10–40 µmol/L of ALA or DHA and compared with vehicle and oleic acid controls. The Seahorse XFe24 and Oroboros O 2 k Oxygraph systems were used to approximate catabolic rates in the presence of glucose. Both ALA and DHA reduced oxidative phosphorylation. We identified pyruvate dehydrogenase kinase 4 (PDK4) as a possible mechanistic candidate explaining the effect of DHA. Additionally, both n-3 PUFAs reduced lipopolysaccharides-induced IL-1β production, while only DHA increased reactive oxygen species to a small but significant extent. Our data suggest that ALA and DHA trigger a re-wiring of bioenergetic pathways in monocytes, possibly via the upregulation of PDK4. 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) | 0.001 | 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".