Assessing Amyloid Biomarkers in Alzheimer's Disease: Comparing the Sensitivity of PET, CSF, and Plasma to Alzheimer‐Related Brain and Cognitive Changes
Bibliographic record
Abstract
Abstract Background With the recent availability of the new amyloid‐beta directed treatments for Alzheimer's disease (AD), accurate assessment of amyloid burden has gained increasing importance. However, limited data exists examining the sensitivity of PET, CSF, and plasma amyloid biomarkers to AD‐related brain and cognitive changes. Methods 248 participants who had MRI as well as amyloid measurements based on CSF, PET, and plasma were included from the ADNI database. Correlation analyses and linear regression models were performed to assess the intercorrelations of the three biomarker levels (i.e., CSF, PET, plasma) as well as their association with cognitive performance and brain measurements, with age, sex, and education added as covariates. Results CSF and PET had the strongest correlation ( r = ‐0.565, p < .001), followed by PET and plasma ( r = ‐0.390, p < .001), and CSF and plasma ( r = 0.338, p < .001, Figure 1a.). Amyloid progressively increased from CN, to MCI, to dementia ( p < .001) when using both PET and CSF. Using plasma, however, CN participants were not significantly different from MCI participants ( p = .10), nor did MCI participants differ from dementia participants, p = .36 (Figure 1b). Structural brain changes differed in their association with biomarker measurements (Table 1). For example, hippocampal volume was significantly associated with CSF amyloid ( p < .001), PET amyloid ( p < .001), and plasma amyloid ( p = .01), while ventricular volume was significantly associated with CSF amyloid, ( p = .01), but not PET ( p = .78) or plasma ( p = .78, Figure 2a). Global cognitive functioning (measured by the MoCA) and functional status (measured by the functional activities questionnaire) were significantly associated with CSF and PET ( p < .001), but not plasma ( p > .05, Figure 2b). Conclusion Research in AD and dementia often uses CSF, PET, or plasma to discuss findings related to amyloid, which may lead to contradictory findings. These findings highlight how different measurement tools for quantifying amyloid are associated with brain and cognitive outcomes. Our findings suggest that current plasma measures of amyloid might not be sufficiently sensitive to AD‐related pathology, brain, and cognitive changes to replace PET and CSF measures.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.001 |
| 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".