Reference Intervals for Plasma Biomarkers of Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Background Several blood‐based biomarkers are emerging as potential screening, diagnostic, and prognostic tools for Alzheimer’s disease (AD), including: the ratio of amyloid beta 42 over 40 (Aβ42/40), reflecting the pathology of amyloid plaques; phosphorylated tau‐181 (p‐tau‐181), reflecting the formation of neurofibrillary tangles; neurofilament light (NF‐L), reflecting axonal damage observed in neurodegeneration; and glial fibrillary acidic protein (GFAP), reflecting neuroinflammation through astrocyte activation. A critical step in the translation of these biomarkers from research to clinical implementation is establishment of reference intervals derived from an appropriate population. Methods 900 plasma samples from male and female participants aged 3‐79 years old were analyzed for Aβ42/40, p‐tau‐181, NF‐L and GFAP to create reference intervals (RI). Samples are obtained from the Canadian Health Measures Survey (CHMS), a national study that collects demographics, health questionnaires, clinical data and biospecimens from participants. Analysis was preformed on the Quanterix Simoa HD‐X analyzer using the Neurology 4‐plex E and p‐tau‐181 assay. Discrete and continuous RIs were produced for each biomarker. Discrete RIs are produced according to the Clinical Laboratory Standards Institute guidelines (EP20‐A3c). Continuous RIs are created using quantile regression. Results Participant samples were selected from the following age bins designated by the CHMS biobank: 3 to 5 years (N=56), 6 to 11 years (N=58), 12 to 19 years (N=58), 20 to 39 years (102), 40 to 59 years (N= 102), 60 to 79 years (N=140). After analysis, age partitions for discrete RI were created, resulting in two to five age‐bins per biomarker which reference intervals were generated for. Continuous RIs produced smooth centile curves across all ages, from which point estimates for any age can be calculated. Conclusions Both discrete and continuous RIs for plasma Aβ42/40, p‐tau‐181, NF‐L and GFAP may help refine normative cut offs for each biomarker across the lifespan.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".