Pathophysiological Links Between Stroke and Prediabetes: A Systematic Review
Bibliographic record
Abstract
Prediabetes is an intermediate stage between normoglycaemia and type 2 diabetes mellitus (T2DM), affecting over 425 million people globally and contributing to vascular damage and increased stroke risk. Despite the severity of both conditions, their association remains underexplored. This review examines the literature on stroke-related biomarkers in normoglycaemia, prediabetes and T2DM to identify potential links between prediabetes and stroke. This systematic review followed PRISMA-2020 guidelines. PubMed, Google Scholar, Scopus, Web of Science and Science Direct were searched for studies (2003–2023) on stroke biomarkers in prediabetes. Eligible studies were original human research in English, with defined diagnostic criteria (ADA or WHO) for glycaemic status and reported biomarker associations or stroke risk. Studies with major comorbidities were excluded. Data were extracted and bias was assessed using the Newcastle–Ottawa Scale. Meta-analysis was not performed due to limited studies per biomarker. Eight studies (n = 3003) were included. NSE was examined in three studies, all reporting significant elevations in hyperglycaemic individuals. Interleukin-6 (IL-6) was assessed in two studies; one showed a significant increase in diabetes, while the other found a non-significant upward trend. D-dimer and GFAP were each reported in separate single studies, both showing significant elevations in hyperglycaemic individuals with stroke or neurocognitive impairment. S100B was investigated in two studies, with divergent findings: one showed a positive association with glycaemic status, while the other reported lower levels in hyperglycaemia. Findings indicate biomarker alterations in T2DM, suggesting that early changes may occur in prediabetes. Our review suggests that individuals with prediabetes may show alterations in inflammatory (IL-6), coagulation (D-dimer), and neurovascular (S100B, GFAP, NSE) markers, though some findings are inconsistent, reflecting early pathophysiological changes that may increase stroke risk. Further well-designed studies are needed to clarify these associations and establish biomarker-based tools for earlier stroke risk detection and prevention in individuals with prediabetes.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".