Serum Irisin as a Potential Biomarker for Cognitive Decline in Vascular Dementia
Bibliographic record
Abstract
Background: Irisin, a new exercise-related myokine, has been shown to be associated with a variety of diseases including serious neurological disorders. However, whether irisin is involved in the pathogenesis of vascular dementia (VD) has not yet been reported. Our aim is to determine the serum irisin level in patients with VD and investigate its relationship with cognitive function. Methods: The subjects of the study were VD patients and controls with normal cognitive function who were hospitalized in the Neck-Shoulder and Lumbocrural Pain Hospital of Shandong First Medical University from July 2018 to June 2020. Upon admission, a cognitive function assessment was performed. Enzyme-linked immunosorbent assay (ELISA) was used to determine the concentration of irisin in serum. Results: During the study period, 187 subjects (82 controls and 105 VD patients) were included in the analysis. The serum irisin level of VD patients was significantly lower than that of the control group (p < 0.001). Spearman analysis showed that irisin was positively correlated with HLD-C and MoCA, and negatively correlated with all clinical characteristics except for HCY. Logistic regression analysis showed that after adjusting for all clinical characteristics, the serum irisin of VD patients still had a significant correlation with MoCA (β = 0.304, p = 0.029). According to receiver operating characteristic (ROC) curve analysis, the diagnostic accuracy for serum irisin levels on VD was 76% with the sensitivity and 71% with specificity respectively. Conclusions: These data indicate that a decrease in serum irisin levels is a powerful biological marker for cognitive decline in patients with VD, even after adjustment for risk factors. Further multi-center studies need to confirm this connection, which may pave the way for new treatment options.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".