Reducing misdiagnosis of Alzheimer’s Disease pathology utilizing CSF and amyloid PET
Bibliographic record
Abstract
Abstract Background Utilizing cognitive tests alone, including the Mini‐Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), cannot detect the presence of amyloid plaques and tangles in Alzheimer’s disease (AD). While in late stages of AD clinical diagnosis relying primarily on cognitive testing is correct 70‐90% of the time, in MCI accuracy is reduced to 50‐60%. Although autopsy is the gold standard for AD diagnosis, amyloid PET imaging and more recently the Lumipulse Gβ‐Amyloid Ratio (Aβ1‐42/Aβ1‐40) are validated to determine amyloid pathology. With the upcoming availability of disease modifying therapies targeting amyloid in MCI and early AD, and anti‐tau drugs in the pipeline, it is necessary to have true measures of AD pathology for a clinical evaluation early in the disease process. Here we examine the performance of cognitive testing alone for identification of amyloid positivity in MCI patients from the ADNI study when compared to amyloid PET and CSF testing. Method A subset of 170 CSF samples from the ADNI biobank (all MCI; ≥ 50 years old; amyloid PET negative (n=74) or amyloid PET positive (n=96)) was used to examine correlation of PET, CSF and MMSE/MoCA scores. Amyloid PET images were derived from an FDA approved tracer (Florbetapir F18) and method. CSF Aβ1‐42 and Aβ1‐40 were measured using the LUMIPULSE G1200 (Fujirebio). Result At a set cutoff of 0.058, Aβ1‐42/Aβ1‐40 demonstrated a PPA and NPA of 85.9% and 93.5% respectively for identifying amyloid PET status in individuals suffering from cognitive complaints. When examining the Aβ1‐42/Aβ1‐40 ratio of the MCI patients compared to amyloid PET in ADNI, Aβ1‐42/Aβ1‐40 achieved an AUC of 0.90. Alternatively, the MMSE score and MOCA score had AUCs with amyloid PET of 0.61 and 0.63 respectively and exhibited poor correlation with CSF. Conclusion CSF measurements or amyloid PET provide greater accuracy in determining amyloid status than cognitive screening tests. MMSE or MoCA as standalone tests give low confidence in a correct pathological diagnosis for MCI patients. Moving forward it will be important to consider PET or CSF results to determine underlying pathology in patients with MCI to ensure proper treatments and avoid unwanted side effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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".