Serum IL-6, sAXL, and YKL-40 as systemic correlates of reduced brain structure and function in Alzheimer’s disease: results from the DELCODE study
Bibliographic record
Abstract
Abstract Background Neuroinflammation constitutes a pathological hallmark of Alzheimer’s disease (AD). Still, it remains unresolved if peripheral inflammatory markers can be utilized for research purposes similar to blood-based beta-amyloid and neurodegeneration measures. We investigated experimental inflammation markers in serum and analyzed interrelations towards AD pathology features in a cohort with a focus on at-risk stages of AD. Methods Data of 74 healthy controls (HC), 99 subjective cognitive decline (SCD), 75 mild cognitive impairment (MCI), 23 AD relatives, and 38 AD subjects were obtained from the DELCODE cohort. A panel of 20 serum biomarkers was determined using immunoassays. Analyses were adjusted for age, sex, APOE status, and body mass index and included correlations between serum and CSF marker levels and AD biomarker levels. Group-wise comparisons were based on screening diagnosis and routine AD biomarker-based schematics. Structural imaging data were combined into composite scores representing Braak stage regions and related to serum biomarker levels. The Preclinical Alzheimer’s Cognitive Composite (PACC5) score was used to test for associations between the biomarkers and cognitive performance. Results Each experimental marker displayed an individual profile of interrelations to AD biomarkers, imaging, or cognition features. Serum-soluble AXL (sAXL), IL-6, and YKL-40 showed the most striking associations. Soluble AXL was significantly elevated in AD subjects with pathological CSF beta-amyloid/tau profile and negatively related to structural imaging and cognitive function. Serum IL-6 was negatively correlated to structural measures of Braak regions, without associations to corresponding IL-6 CSF levels or other AD features. Serum YKL-40 correlated most consistently to CSF AD biomarker profiles and showed the strongest negative relations to structure, but none to cognitive outcomes. Conclusions Serum sAXL, IL-6, and YKL-40 relate to different AD features, including the degree of neuropathology and cognitive functioning. This may suggest that peripheral blood signatures correspond to specific stages of the disease. As serum markers did not reflect the corresponding CSF protein levels, our data highlight the need to interpret serum inflammatory markers depending on the respective protein’s specific biology and cellular origin. These marker-specific differences will have to be considered to further define and interpret blood-based inflammatory profiles for AD research.
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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) | 0.001 | 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 | 0.001 |
| 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".