Effects of diabetes on microglial physiology: a systematic review of in vitro, preclinical and clinical studies
Bibliographic record
Abstract
Diabetes mellitus is a heterogeneous chronic metabolic disorder characterized by the presence of hyperglycemia, commonly preceded by a prediabetic state. The excess of blood glucose can damage multiple organs, including the brain. In fact, cognitive decline and dementia are increasingly being recognized as important comorbidities of diabetes. Despite the largely consistent link between diabetes and dementia, the underlying causes of neurodegeneration in diabetic patients remain to be elucidated. A common factor for almost all neurological disorders is neuroinflammation, a complex inflammatory process in the central nervous system for the most part orchestrated by microglial cells, the main representatives of the immune system in the brain. In this context, our research question aimed to understand how diabetes affects brain and/or retinal microglia physiology. We conducted a systematic search in PubMed and Web of Science to identify research items addressing the effects of diabetes on microglial phenotypic modulation, including critical neuroinflammatory mediators and their pathways. The literature search yielded 1327 records, including 18 patents. Based on the title and abstracts, 830 papers were screened from which 250 primary research papers met the eligibility criteria (original research articles with patients or with a strict diabetes model without comorbidities, that included direct data about microglia in the brain or retina), and 17 additional research papers were included through forward and backward citations, resulting in a total of 267 primary research articles included in the scoping systematic review. We reviewed all primary publications investigating the effects of diabetes and/or its main pathophysiological traits on microglia, including in vitro studies, preclinical models of diabetes and clinical studies on diabetic patients. Although a strict classification of microglia remains elusive given their capacity to adapt to the environment and their morphological, ultrastructural and molecular dynamism, diabetes modulates microglial phenotypic states, triggering specific responses that include upregulation of activity markers (such as Iba1, CD11b, CD68, MHC-II and F4/80), morphological shift to amoeboid shape, secretion of a wide variety of cytokines and chemokines, metabolic reprogramming and generalized increase of oxidative stress. Pathways commonly activated by diabetes-related conditions include NF-κB, NLRP3 inflammasome, fractalkine/CX3CR1, MAPKs, AGEs/RAGE and Akt/mTOR. Altogether, the detailed portrait of complex interactions between diabetes and microglia physiology presented here can be regarded as an important starting point for future research focused on the microglia-metabolism interface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".