Effect of acute hyperglycaemia and/or hyperinsulinaemia on proinflammatory gene expression, cytokine production and neutrophil function in humans
Bibliographic record
Abstract
AIMS: Type 2 diabetes is frequently associated with infectious complications. Swift activation of leucocytes is important for an adequate immune response. We determined the selective effects of hyperglycaemia and hyperinsulinaemia on lipopolysaccharide (LPS)-induced proinflammatory gene expression and cytokine production in leucocytes and on neutrophil functions. METHODS: Six healthy humans were studied on four occasions for 6 h during: (i) lower insulinaemic euglycaemic clamp, (ii) lower insulinaemic hyperglycaemic clamp, (iii) hyperinsulinaemic euglycaemic clamp, and (iv) hyperinsulinaemic hyperglycaemic clamp. Target levels of plasma glucose were 12.0 mmol/l (hyperglycaemic clamps) or 5.0 mmol/l (euglycaemic clamps). Target plasma insulin levels were 400 pmol/l (hyperinsulinaemic clamps) or 100 pmol/l (lower insulinaemic clamps). RESULTS: Hyperglycaemia reduced LPS-induced mRNA expression of nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor alpha (NFKBIA), interleukin-1 alpha (IL1A) and chemokine (C-C motif) ligand 3 (CCL3), whereas during hyperinsulinaemia enhanced mRNA levels occurred in six out of eight measured inflammation-related genes, irrespective of plasma glucose levels. Combined hyperglycaemia and hyperinsulinaemia led to enhanced IL1A, interleukin-1 beta (IL1B) and CCL3 mRNA levels upon LPS stimulation. Neither hyperglycaemia nor hyperinsulinaemia altered cytokine protein production, neutrophil migration, phagocytic capacity or oxidative burst activity. CONCLUSIONS: These results suggest that short-term hyperglycaemia and hyperinsulinaemia influence the expression of several inflammatory genes in an opposite direction, that the acute effects of hyperinsulinaemia on inflammatory mRNA levels may be stronger than those of hyperglycaemia, and that the effects of insulin, in particular, may be relevant in the concurrent presence of hyperglycaemia.
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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".