A head-to-head comparison of Alzheimer’s disease plasma biomarkers as predictors of one-year decline in cognitive subdomains
Bibliographic record
Abstract
Objective\nThere is scarce evidence how plasma biomarkers of Alzheimer’s disease (AD) relate cross-sectionally and longitudinally to cognitive subdomains. In this study, we investigated these features in a one-year prospective single-center memory clinic research cohort.\nMethods\nIndividuals with AD dementia, mild cognitive impairment (MCI), non-AD neurodegenerative diseases (Non-AD) and community-dwelling cognitively unimpaired (CU) controls from the Translational Biomarkers of Aging and Dementia (TRIAD) cohort (McGill University, Canada) were included. Plasma and cerebrospinal fluid (CSF) (in a subset) biomarkers were measured at baseline. Positron emission tomography (PET) was used stratify groups for β-amyloid pathology. Measures of memory, language, executive function and global cognition were obtained at baseline and after one year (in a subset).\nResults\n210 individuals (median age, % female) were included in the study, and comprised CU (n = 127; 71, 66), MCI (n = 48; 71, 54), AD (n = 18; 64, 61), and 17 (69; 47) individuals with non-AD neurodegenerative dementias. Phosphorylated (p)-tau 181 and p-tau231 in both CSF and\nSAHLGRENSKA ACADEMY\nplasma, and glial fibrillary acidic protein (GFAp) in plasma, increases along the AD continuum (defined as β-amyloid positive by PET) were seen compared to non-AD and CU without β-amyloid pathology. CU performed better on several neuropsychological measures after one year, whereas most were unaltered in cognitively impaired individuals. In CU, neuropsychological performance largely associated with age and years of education. However, for cognitively impaired individuals with β-amyloid pathology, associations were seen with plasma p-tau181, p-tau231 and GFAp in memory and global cognition. No associations were seen with baseline biomarker levels and subsequent cognitive decline in any of the measures.\nConclusion\nBiomarkers in plasma reflect AD-specific pathophysiology, and significantly associate with severity of global cognitive and memory impairment. Furthermore, to investigate the prognostic capabilities of biomarker levels in cognitive subdomains, larger and longer studies are warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".