Reference proteins to improve performance of core 1 and core 2 Alzheimer's disease CSF and plasma biomarkers
Bibliographic record
Abstract
Abstract Background Concentration‐based fluid biomarkers represent an informative and cost‐effective way to detect and monitor Alzheimer's disease (AD) pathology. However, non‐AD‐related inter‐individual variation in biofluids can also affect biomarker concentrations. We previously identified several reference proteins that, in the AT(N) classification framework, improved concordance between CSF Aβ42 and Aβ‐positron emission tomography (PET), as well as between CSF p ‐tau181 and tau‐PET. 1 However, it is still unclear what effect reference proteins have on the relationship between CSF AD biomarkers and the load of AD pathology. It is also unclear if plasma AD biomarkers can be improved by accounting for reference proteins in a similar manner. Methods Using the Swedish BioFINDER‐2 cohort ( n = 1702, 50.7% male, mean [SD] age 68.4 [12.2] years), we compared the associations between tau/Aβ‐PET load and CSF biomarkers (MTBR‐tau243, p ‐tau217, p ‐tau181, p ‐tau205, Aβ42, SNAP‐25, neurogranin) alone versus in a ratio with a reference protein (e.g. CSF Aβ40 or non‐phosphorylated tau [np‐tau]) in univariate linear regression models. We repeated this analysis for plasma biomarkers. Results 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 (ΔR 2 =0.064‐0.24) and Aβ‐PET (ΔR 2 =0.016‐0.28), Figure 1. CSF np‐tau normalization mainly improved concordance between CSF biomarkers and Aβ‐PET (ΔR 2 =‐0.0059‐0.19). The strongest association with tau‐PET was observed for MTBR‐tau243/Aβ40 (R‐squared=0.78, compared to 0.65 for non‐normalized MTBR‐tau243), and with Aβ‐PET for p ‐tau217/np‐tau (R‐squared= 0.65, compared to 0.46 for non‐normalized p ‐tau217). For core plasma AD biomarkers, including MTBR‐tau243 and p ‐tau isoforms, associations with tau‐PET were enhanced by using plasma Aβ40 or np‐tau as references (ΔR 2 =0.0019‐0.14), while associations with Aβ‐PET mainly improved with np‐tau (ΔR 2 =0.018‐0.16), Figure 2. The findings were successfully replicated in Knight ADRC and TRIAD for improved biomarker associations with both tau‐PET (Table 1) and Aβ‐PET. Conclusions Normalization to reference proteins (i.e., Aβ40 or np‐tau) enhances the associations between CSF and plasma biomarkers with the load of tau and Aβ pathology in the brain, making already high‐performing AD and synaptic fluid biomarkers even more precise. Reference 1. Karlsson, L. et al. Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases. Nat Commun 15 , (2024).
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".