Soluble receptor of advanced glycation end product as a biomarker in neurocognitive and neuropsychiatric disorders: A meta‐analysis of controlled studies
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Currently, there is a significant focus on the decrease of soluble receptor of advanced glycation end products (sRAGE) in neurocognitive and neuropsychiatric disorders. sRAGE plays a decoy role against the inflammatory response of advanced glycation end products (AGE), which has led to increased interest in its role in these disorders. This meta-analysis aimed to investigate the significant differences in sRAGE levels between neurocognitive and neuropsychiatric disorders compared to control groups. METHOD: , followed by subgroup analysis and meta-regression tests. Quality assessment was performed using the Newcastle-Ottawa Quality Assessment Scale. RESULTS: In total, 16 studies were included in the present meta-analysis. Subjects with neurocognitive (n = 1444) and neuropsychiatric (n = 444) disorders had lower sRAGE levels in case-control (WMD: -0.21, 95% CI: -0.33, -0.10; p <.001) and cross-sectional (WMD: -0.29, 95% CI = -0.44, -0.13, p <.001) studies with high heterogeneity and no publication bias. In subgroup analysis, subjects with cognitive impairment (WMD: -0.87, 95% CI: -1.61, -0.13, p =.000), and age >50 years (WMD: -0.39, 95% CI: -0.74, -0.05, p =.000), had lower sRAGE levels in case-control studies. Also, dementia patients (WMD: -0.41, 95% CI: -0.72, -0.10, p =.014) with age >50 years (WMD: -0.33, 95% CI: -0.54, -0.13, p = 0.000) and in Asian countries (WMD: -0.28, 95% CI: -0.42, -0.13, p =.141) had lower sRAGE levels in cross-sectional studies. CONCLUSION: This meta-analysis revealed a significant reduction in sRAGE in neurocognitive and neuropsychiatric disorders particularly in Asians and moderate age.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.071 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".