Plasma Aβ42:Aβ40 ratio as a biomarker for cognitive impairment in haemodialysis patients: a multicentre study
Bibliographic record
Abstract
ABSTRACT Background Mild cognitive impairment (MCI) and dementia are more prevalent in patients undergoing haemodialysis (HD). Although the cerebrospinal fluid amyloid beta (Aβ) and tau (τ) have proven to be valid biomarkers for the diagnosis of Alzheimer's disease (AD) in the general population, the roles of plasma Aβ and τ for the diagnosis of cognitive impairment in HD patients remain unknown. Methods We conducted a cross-sectional study including patients receiving HD in three hospitals in Shanghai. All patients completed the Montreal Cognitive Assessment–Basic (MoCA-B). To validate the effectiveness of the MoCA-B score for screening MCI, a subset group underwent neuropsychological batteries. Serum proteomes were compared in HD patients with normal cognitive function and dementia. Plasma Aβ42, Aβ40 and total τ were measured using a single molecule array. Results A total of 311 HD patients were enrolled (mean age 63 years, 55% male). The best cut-off score of MoCA-B for differentiating MCI and normal cognition was 24, with an area under the curve of 0.94. Serum proteomics revealed that neurodegenerative pathways related to AD were enriched in HD patients with dementia. The plasma Aβ42:Aβ40 ratio was significantly reduced in patients with MCI and dementia and was independently associated with cognitive function after adjusting for age, sex and education levels. Conclusions We validated the MoCA-B as an optimal cognitive function screening instrument for MCI in HD patients. The plasma Aβ42:Aβ40 ratio was a potential biomarker in distinguishing normal cognition, MCI and dementia in HD populations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".