Associations between subjective cognitive decline (SCD) diagnosis and severity with plasma tau and NfL levels using SIMOA technology
Bibliographic record
Abstract
Abstract Background Subjective cognitive decline (SCD) is thought to be a preclinical stage of Alzheimer’s disease (AD) and is based largely on subjective report data in the context of normal neuropsychological functioning. There is, however, an unclear relationship between SCD and potential plasma markers along the AD spectrum. Method 213 older adults (53 cognitively normal, [NL], 52 SCD, 54 mild cognitive impairment [MCI] and 54 AD), mean age 74.9±9.6, completed the UDS 3.0 battery, the Global Deterioration Scale (GDS) and a blood draw. Plasma levels of Aβ40, Aβ42, neurofilament light (NfL) and tau were measured using ultra‐sensitive, single‐molecule array (SIMOA) technology. Result Plasma NfL levels were high in MCI patients and higher still in those with AD over cognitively unimpaired people (NL and SCD) (F(2,198)=22, p<.001) before and after correction for age, sex and apoE4 status. Both plasma tau and Aβ42 levels were higher in SCD over NL subjects (F(1,95)=7.8 and F(1,96)=6.5; both p<0.05). Importantly, plasma tau but not Aβ42, Aβ42/ Aβ40 ratio or NfL was correlated with self‐reported Cognitive Change Index (CCI) scores both in the overall sample (r154=2.4, p<0.01) and in the NL and SCD only (r89=2.15, p<0.5). Conclusion Plasma NfL is a potential biomarker of cognitive decline to MCI and AD, while plasma tau may have higher discriminatory value in the preclinical stages of the disease and is associated with SCD.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".