Association of Interleukin-6 and Interleukin-8 with Cognitive Decline in an Asian Memory Clinic Population
Bibliographic record
Abstract
BACKGROUND: Neuroinflammation has been postulated to play an important role in cognitive impairment, cognitive decline, and dementia. Inflammatory biomarkers such as interleukin-6 (IL-6) and IL-8 are found to be associated with the neuro-inflammatory process and worse cognitive function. However, it is unknown whether these interleukins are associated with long-term cognitive function. OBJECTIVE: To investigate the association of baseline IL-6 and IL-8 with cognitive function at baseline as well as its association with cognitive decline over five-year follow-up. METHODS: 387 patients were recruited from an ongoing memory clinic-based study who underwent comprehensive physical, medical, neuropsychological and blood assessments together with brain MRI. IL-6 and IL-8 were measured using LUMINEX assays. The National Institute of Neurological Disorders and Stroke-Canadian Stroke Network neuropsychological battery was used to assess cognitive decline across multiple domains. RESULTS: Among the 387 (mean age = 72.9 years and 53.7% males) participants, 322 had at least two follow-up assessments and were included in the longitudinal analysis. Negative linear trend associations were found between tertiles of IL-8 with baseline global cognition (p-trend< 0.001), attention (p-trend = 0.005), executive function (p-trend< 0.001), and visuospatial function (p-trend = 0.002) domains. No association was found between baseline IL-8 and cognitive decline. IL-6 was not associated with both baseline and follow-up cognition. CONCLUSION: IL-8 was associated with worse cognition especially in attention, executive function, and visuospatial function, suggesting the role of neuroinflammation in cognitive impairment. Hence, blood inflammatory biomarkers may be useful indicators in identifying patients at risk of cognitive impairment and warrant consideration for inclusion in treatment trials.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".