Plasma biomarkers in neuropsychiatric syndromes: A narrative review
Bibliographic record
Abstract
Neuropsychiatric symptoms (NPS) are common features of neurodegenerative disease (NDD) but are relatively understudied compared to cognition, especially regarding biomarkers. Further, emerging evidence describes the utility of systematic assessment of NPS across the cognitive continuum, even in advance of dementia. In this narrative review, we discuss the role of plasma biomarkers in relation to NPS across the cognitive continuum of unimpaired, subjective cognitive decline, mild cognitive impairment, and dementia. While Alzheimer's disease is the primary focus, vascular, Lewy body, and frontotemporal dementia etiologies are also discussed. Literature searches included NPS and dementia-related search terms with additional literature identified based on the author group's subject area expertise. We found that plasma biomarkers are a burgeoning field, and scalability and accessibility make them well-suited for the study of NPS across the disease continuum. In early-stage NDD, diagnostic biomarkers are best suited for discriminating NDD-related NPS from non-NDD psychiatric syndromes and/or NPS due to other causes. In those with dementia, monitoring and prognostic biomarkers may enable the assessment of treatment response or help predict the risk of worsening symptoms. We conclude that plasma amyloid-β and tau show great promise in assessing NPS, especially during early-stage disease, but inflammatory and genetic biomarkers may also play a role across the disease course. Systematic research is required, keeping in mind the ethical considerations of knowing biomarker status in early-stage 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".