Pre‐amyloid stage of Alzheimer's disease in cognitively normal individuals
Bibliographic record
Abstract
Abstract Objective To study risk factors for decreasing aβ1–42 concentrations in cerebrospinal fluid (CSF) in cognitively unimpaired individuals with initially normal amyloid and tau markers, and to investigate whether such aβ1–42 decreases are associated with subsequent decline in cognition and other biomarkers of Alzheimer's disease. Methods Cognitively normal subjects (n = 83, 75 ± 5 years, 35(42%) female) with normal CSF aβ1–42 and tau and repeated CSF sampling were selected from ADNI. Subject level slopes of aβ1–42 decreases were estimated with mixed models. We tested associations of baseline APP processing markers (BACE1 activity, aβ1–40, aβ1–38 and sAPPβ) and decreasing aβ1–42 levels by including an interaction term between time and APP marker. Associations between decreasing aβ1–42 levels and clinical decline (i.e., progression to mild cognitive impairment or dementia, MMSE, memory functioning) and biological decline (tau, hippocampal volume, glucose processing and amyloid PET) over a time period of 8–10 years were assessed. Results Aβ1–42 levels decreased annually with −4.6 ± 1 pg/mL. Higher baseline BACE1 activity (β(se) = −0.06(0.03), P < 0.05), aβ1–40 (β(se)= −0.11(.03), P < 0.001), and aβ1–38 levels (β(se) = −0.11(0.03), P < 0.001) predicted faster decreasing aβ1–42. The fastest tertile of decreasing aβ1–42 rates was associated with subsequent pathophysiological processes: 11(14%) subjects developed abnormal amyloid levels after 3 ± 1.7 years, showed increased risk for clinical progression (Hazard Ratio[95CI] = 4.8[1.1–21.0]), decreases in MMSE, glucose metabolism and hippocampal volume, and increased CSF tau and amyloid aggregation on PET (all P < 0.05). Interpretation Higher APP processing and fast decreasing aβ1–42 could be among the earliest, pre‐amyloid, pathological changes in Alzheimer's disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".