Serum Adiponectin in Alzheimer’s Disease (AD): Association with AD Biomarkers and Cognitive Outcome
Bibliographic record
Abstract
BACKGROUND: The association between dementia and serum adiponectin has been evaluated in many studies; however, conclusions remain mixed. OBJECTIVE: We investigated the cross-sectional associations of adiponectin with cognitive function and Alzheimer's disease (AD) biomarkers and whether serum adiponectin levels can predict cognitive outcomes. METHODS: This study included 496 participants from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) with available serum adiponectin levels at baseline and ≥65 years of age. Subjects were stratified based on sex and apolipoprotein ɛ4 (APOE4) carrier status to determine associations between adiponectin and cognitive function. The linear mixed model was used to analyze associations between adiponectin level and cognitive outcome in amnestic mild cognitive impairment (aMCI) patients. RESULTS: Serum adiponectin levels were higher in aMCI and AD than in CN subjects among APOE4 non-carrier males (adiponectin in CN, aMCI, and AD: 0.54±0.24, 0.74±0.25, and 0.85±0.25, respectively, p < 0.001). In this group, serum adiponectin levels were associated with age (p = 0.001), ADAS13 (p < 0.001), memory function (p < 0.001), executive function (p < 0.001), total tau (p < 0.001), and phosphorylated tau (p < 0.001) measures in cerebrospinal fluid (CSF). Higher adiponectin level was not associated with cognitive outcome in aMCI patients in the linear mixed model analysis over 5.3±2.6 years of mean follow-up. CONCLUSION: Serum adiponectin level was associated with cognitive function and CSF AD biomarkers among APOE4 non-carrier males. However, serum adiponectin level was not associated with longitudinal cognitive function outcome in aMCI.
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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 | 0.000 | 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".