Insulin Modulates <i>In Vitro Secretion</i> of Cytokines and Cytotoxins by Human Glial Cells
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common form of dementia worldwide. Type 2 diabetes (T2D) has been implicated as a risk factor for AD. Since T2D is a peripheral inflammatory condition, and AD brains exhibit exacerbated neuroinflammation, we hypothesized that inflammatory mechanisms could contribute to the observed link between T2D and AD. Abnormal peripheral and brain insulin concentrations have been reported in both T2D and AD. The neurotrophic role of insulin has been described; however, this hormone can also regulate inflammatory responses in the periphery. Therefore we used in vitro human cell culture systems to elucidate the possible effects of insulin on neuroinflammation. We show that human astrocytes and microglia express both isoforms of the insulin receptor as well as the insulin-like growth factor (IGF)-1 receptor. They also express insulin receptor substrate (IRS)-1 and IRS-2, which are required for propagation of insulin/IGF- 1 signaling. We show that at low nanomolar concentrations, insulin could be pro-inflammatory by upregulating secretion of interleukin (IL)-6 and IL-8 from stimulated human astrocytes and secretion of IL-8 from stimulated human microglia. This effect dissipates at higher insulin concentrations. In contrast, insulin at a broader concentration range (10 pM - 1 μM) reduces the toxicity of stimulated human microglia and THP-1 monocytic cells towards SH-SY5Y neuronal cells. These data show that insulin may regulate the inflammatory status of glial cells by modulating their select functions, which in turn can influence the survival of neurons contributing to the observed link between T2D and AD.
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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 | 0.001 |
| 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".