High Glucose Enhances Neurotoxicity and Inflammatory Cytokine Secretion by Stimulated Human Astrocytes
Bibliographic record
Abstract
BACKGROUND: Chronic neuroinflammation caused by activation of microglia and astrocytes in the brain contributes to neuronal loss and disease progression in Alzheimer's disease (AD). Recent research has identified type 2 diabetes mellitus (T2DM) as a risk factor for AD. High blood glucose (hyperglycemia) and the phenomenon of insulin resistance are being considered as the major factors contributing to an increased risk of AD. However, the mechanisms involved in this interaction remain unclear. OBJECTIVE: High glucose has been shown to increase release of pro-inflammatory mediators from various immune cells, including microglia. Since astrocytes are the most abundant glial cell type in the brain, we investigated the effects of elevated glucose concentrations (5.5-30.5 mM) on selected functions of cultured human astrocytes in the presence of inflammatory stimuli. METHOD: Experiments were conducted using primary human astrocytes and U-118 MG astrocytoma cells. RESULTS: High glucose (30.5 mM) increased mRNA expression of interleukin (IL)-6 and secretion of both IL-6 and IL-8 by astrocytes. This astrocytic inflammatory response to high glucose did not appear to be mediated by augmented p38 or p44/42 mitogen activated protein kinase (MAPK) signaling pathways. In addition, high glucose increased the susceptibility of undifferentiated human SH-SY5Y neuronal cells and retinoic-acid differentiated SH-SY5Y cells to injury by hydrogen peroxide (H2O2) and fibrillar amyloid beta-42 protein (Aβ42), respectively. CONCLUSION: Our data indicate that hyperglycemia in T2DM may be one of the factors contributing to the observed increased risk of AD by exacerbating astrocyte-mediated neuroinflammation and neuronal injury caused by disease-associated agents.
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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".