Brain-derived neurotrophic factor but not beta-secretase 1, vascular endothelial growth factor, glial fibrillary acidic protein and interleukin-1β correlate with cognitive impairment in adult persons with epilepsy: a cross-sectional single-center study from India
Bibliographic record
Abstract
Objectives This study aims to evaluate cognitive impairment utilizing the Montreal Cognitive Assessment (MoCA) scale, while also exploring the correlation between cognitive impairment and various serum biomarkers, including Brain-derived neurotrophic factor (BDNF), Beta Secretase-1 (BACE1), Vascular Endothelial Growth Factors (VEGF), Glial fibrillary acidic protein (GFAP), and Interleukin-1 (IL-1β) in adults living with epilepsy. Methods In this study, 74 participants aged between 18 and 50 years, who were visiting neurology outpatient consultations, were included. The cognitive assessment was executed using the MoCA test. Serum levels of BDNF, BACE1, VEGF, GFAP, and IL-1β were evaluated through ELISA in patients with and without cognitive impairments. To determine the association between MoCA scores and the biomarkers, both Spearman and Pearson correlation analyses, as well as linear regression, were conducted. Results Among the 74 PWE, 61 exhibited cognitive impairment as determined by the MoCA assessment. Noteworthy alterations were detected across various MoCA subscales, encompassing visuospatial and executive functions, attention, language, abstraction, and delayed recall, with statistical significance established (p < 0.05). Furthermore, it was revealed that those in the cognitively impaired group presented with reduced serum BDNF levels (p < 0.05). It is important to highlight that no substantial differences were identified in the serum concentrations of BACE-1, VEGF, GFAP, and IL-1β. A moderate and statistically significant correlation was established between BDNF and the Total MoCA score (p < 0.05), in addition to BDNF’s relationship with Visuospatial & Executive function (p < 0.05). In the context of regression analysis, BDNF demonstrated a significant association to the Total MoCA score (p < 0.05), a connection that persisted as significant even when adjusted for confounding factors. Conclusion We conclude that adult PWE in India demonstrate a significant cognitive impairment. Further, our findings indicate that BDNF may serve as a potential biomarker for evaluating cognitive impairment in adult PWE. Further longitudinal, prospective and multi-center studies are required to confirm the same.
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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 | 0.001 |
| 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 | 0.000 | 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".