Reference proteins to improve Core 1 and Core 2 Alzheimer’s disease CSF and plasma biomarkers
Bibliographic record
Abstract
Concentration-based fluid biomarkers represent an informative and cost-effective way to detect and monitor Alzheimer's disease (AD) pathology. However, non-AD-related interindividual variation in biofluids can also affect biomarker concentrations. Here, we investigated whether normalization of CSF and plasma biomarkers to reference proteins, such as amyloid-β40 (Aβ40) and non-phosphorylated mid-region tau (np-tau), improves their robustness and reliability of representing AD pathology load. Using the Swedish BioFINDER-2 cohort [n = 1702, 50.7% male, mean (standard deviation) age 68.4 (12.2) years], we compared the associations between tau/Aβ-PET load and fluid biomarkers alone versus biomarkers in a ratio with a reference protein (Aβ40 or np-tau) in univariate linear regression models. Fluid biomarkers included CSF and plasma measures of p-tau217, p-tau181, p-tau205, np-tau181-190, np-tau195-210, np-tau212-221, Aβ42 and Aβ40; CSF MTBR-tau243, SNAP-25, neurogranin, YKL-40 and sTREM2; and plasma eMTBR-tau243. Biomarkers were measured with mass spectrometry assays and/or immunoassays. In addition, we performed validation and extended analyses, comparing, for example, group-level diagnostic differences and longitudinal biomarker trajectories, in three independent prospective cohorts [BioFINDER-1, Knight Alzheimer Disease Research Center (ADRC) and Translational Biomarkers in Aging and Dementia (TRIAD)] and in an Italian multiple sclerosis cohort. CSF Aβ40 normalization significantly strengthened the associations of several core CSF AD biomarkers, including CSF MTBR-tau243, p-tau isoforms and synaptic biomarkers, with tau-PET (ΔR2 = 0.064-0.24) and Aβ-PET (ΔR2 = 0.016-0.28). Normalization to CSF np-tau mainly improved concordance with Aβ-PET (ΔR2 = -0.0059 to 0.19). The strongest association with tau-PET was observed for MTBR-tau243/Aβ40 (R2 = 0.78, compared with 0.65 for non-normalized MTBR-tau243), and with Aβ-PET for p-tau217/np-tau (R2 = 0.65, compared with 0.46 for non-normalized p-tau217). Plasma biomarker associations with tau-PET improved when using normalization to plasma Aβ40 or np-tau (ΔR2 = 0.004-0.14), with the strongest effect for eMTBR-tau243/np-tau (R2 = 0.72 versus 0.60). Associations with Aβ-PET were enhanced with np-tau normalization (ΔR2 = 0.018-0.16, strongest for p-tau217/np-tau: R2 = 0.62 versus 0.53). The results were replicated in Knight ADRC and TRIAD. Furthermore, longitudinal analyses showed that Aβ40 normalization typically reduced interindividual rather than intra-individual variability over time. Normalization did not enhance group-level differences in inflammatory CSF biomarkers in AD, nor did it improve biomarker associations in the multiple sclerosis cohort. In conclusion, normalization of CSF and plasma biomarkers to reference proteins, such as Aβ40 or np-tau, enhances their association with brain tau and Aβ pathology, making already high-performing AD fluid biomarkers even more accurate.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".