Biomarkers for the detection of progressive early Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Background We investigated the ability of MRI, plasma, and cognitive biomarkers in detecting preclinical or prodromal Alzheimer’s Disease (AD) who will progress to the next syndrome stage of the cognitive continuum. Method 589 subjects with longitudinal cognitive data were recruited from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which included 227 cognitive unimpaired (CU) and 362 mild cognitive impairment (MCI) subjects. Preclinical or prodromal AD was defined by a low amyloid beta (Aβ)42 (A+) and high phosphorylated ‐tau (p‐tau) (T+) on cerebrospinal fluid (CSF) assessment at baseline. The following baseline MRI biomarkers were derived by an automatic segmentation tool (AccuBrain®): AD‐resemblance atrophy index (AD‐RAI), quantitative medial temporal lobe atrophy (QMTA) and hippocampus volume (HV). Plasma biomarkers included p‐tau 181, neurofilament (NFL) and APOE ε4. Montreal Cognitive Assessment (MoCA) score was collected for all subjects at baseline. Conversion (C+) was defined as subjects progressed from CU to MCI and from MCI to dementia within 4 years. Results Of the 589 subjects (mean [SD] age, 72.2 [6.9] years; 314 men [53.3%]), 96 (16.3%) were A+T+C+ and 180 (30.6%) were A+T+C‐. In the ROC analysis, AD‐RAI achieved the best detection ability than other individual biomarker with a sensitivity of 83.4%, a specificity of 69.4% and an AUC of 87.8%. A combination of AD‐RAI, plasma p‐tau 181, APOE ε4 and MoCA score achieved the best detection ability with AUC of 89.8%, sensitivity of 85.1% and specificity of 80.2%. In the subgroup analysis, the combination of AD‐RAI, plasma p‐tau 181, APOE ε4 and MoCA score also showed good accuracy in identifying A+T+C+ subjects in the CU group (AUC = 87.6%) and in the MCI group (AUC = 89.3%). Conclusion A panel of MRI, plasma and cognitive biomarkers might help to detect progressive early AD subjects.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".