The Mediating Role of Inflammation in the Relationship Between α-Synuclein and Cognitive Functioning
Bibliographic record
Abstract
Accumulating evidence suggests that α-synuclein plays a role in the pathophysiology of Alzheimer's disease (AD). This study examined whether α-synuclein level in cerebrospinal fluid (CSF) was associated with cognitive functioning among older adults. We also explored whether this relationship was mediated by proinflammatory cytokines TNF-α and IL-6, along with sIL-6R and vascular endothelial growth factor (VEGF). Using a cross-sectional Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 148) sample, we examined the relationship between α-synuclein and participants' performance on Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS-Cog 13) at baseline. Mediation analyses were utilized, adjusting for age, education, APOEe4, and Geriatric Depression Scale scores. All biological markers were measured in CSF. Participants in the current sample were 58.3% males, 41.7% females, and Caucasian (95.5%); their average education and age were 15.5 (standard deviation [SD] = 2.97) and 74.4 (SD = 7.51) years, respectively. Higher accumulation of α-synuclein was associated with poorer MMSE scores (β = -0.41, standard error [SE] = 1.54, p < .001). This relationship appeared to be mediated by VEGF (β = 0.27, SE = 2.15, p = .025) and IL-6r (β = 0.22, SE = 1.66, p < .026). In addition, α-synuclein was associated with poorer performance on the ADAS-Cog 13 (β = 0.34, p = .005) and mediated by VEGF (β = -0.19, SE = 4.13, p = .025) after adjusting for age, education, APOEe4, and depressive symptoms. α-Synuclein may serve as an additional biomarker for determining poor cognitive functioning. VEGF and IL-6 soluble receptors were significant mediators of the relationship between α-synuclein and cognitive functioning. If confirmed in prospective analyses, these findings can further inform the pathologic cascade and early diagnosis of AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".