Cerebrospinal fluid analysis detects cerebral amyloid-β accumulation earlier than positron emission tomography
Bibliographic record
Abstract
See Rabinovici (doi: 10.1093/brain/aww025 ) for a scientific commentary on this article. Cerebral accumulation of amyloid-β is thought to be the starting mechanism in Alzheimer’s disease. Amyloid-β can be detected by analysis of cerebrospinal fluid amyloid-β 42 or amyloid positron emission tomography, but it is unknown if any of the methods can identify an abnormal amyloid accumulation prior to the other. Our aim was to determine whether cerebrospinal fluid amyloid-β 42 change before amyloid PET during preclinical stages of Alzheimer’s disease. We included 437 non-demented subjects from the prospective, longitudinal Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. All underwent 18 F-florbetapir positron emission tomography and cerebrospinal fluid amyloid-β 42 analysis at baseline and at least one additional positron emission tomography after a mean follow-up of 2.1 years (range 1.1–4.4 years). Group classifications were based on normal and abnormal cerebrospinal fluid and positron emission tomography results at baseline. We found that cases with isolated abnormal cerebrospinal fluid amyloid-β and normal positron emission tomography at baseline accumulated amyloid with a mean rate of 1.2%/year, which was similar to the rate in cases with both abnormal cerebrospinal fluid and positron emission tomography (1.2%/year, P = 0.86). The mean accumulation rate of those with isolated abnormal cerebrospinal fluid was more than three times that of those with both normal cerebrospinal fluid and positron emission tomography (0.35%/year, P = 0.018). The group differences were similar when analysing yearly change in standardized uptake value ratio of florbetapir instead of percentage change. Those with both abnormal cerebrospinal fluid and positron emission tomography deteriorated more in memory and hippocampal volume compared with the other groups ( P < 0.001), indicating that they were closer to Alzheimer’s disease dementia. The results were replicated after adjustments of different factors and when using different cut-offs for amyloid-β abnormality including a positron emission tomography classification based on the florbetapir uptake in regions where the initial amyloid-β accumulation occurs in Alzheimer’s disease. This is the first study to show that individuals who have abnormal cerebrospinal amyloid-β 42 but normal amyloid-β positron emission tomography have an increased cortical amyloid-β accumulation rate similar to those with both abnormal cerebrospinal fluid and positron emission tomography and higher rate than subjects where both modalities are normal. The results indicate that cerebrospinal fluid amyloid-β 42 becomes abnormal in the earliest stages of Alzheimer’s disease, before amyloid positron emission tomography and before neurodegeneration starts. See Rabinovici (doi: 10.1093/brain/aww025 ) for a scientific commentary on this article. The success of disease-modifying therapies in Alzheimer’s disease may depend on detecting the earliest signs of abnormal amyloid-β load. Palmqvist et al . show that CSF amyloid-β 42 becomes abnormal before amyloid PET and before neurodegeneration onset in preclinical Alzheimer’s disease. This may have implications for risk factor interventions and future therapies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".