Plasma Neurofilament Light: A Marker of Neurodegeneration in Mild Behavioral Impairment
Bibliographic record
Abstract
BACKGROUND: Assessing neuropsychiatric symptoms (NPS) in older adults is important for determining dementia risk. Mild behavioral impairment (MBI) is an at-risk state for cognitive decline and dementia, characterized by emergent NPS in later life. MBI has significantly higher dementia incidence than late life psychiatric conditions. However, its utility as a proxy for neurodegeneration has not been demonstrated. Plasma neurofilament light (NfL) is a validated biomarker of axonal damage, and has been shown to associate with hallmarks of neurodegeneration. OBJECTIVE: The purpose of this investigation was to identify associations between NfL rate of change and the presence of MBI symptomatology. METHODS: We evaluated the association of MBI with changes in NfL in a cohort (n = 584; MBI + n = 190, MBI- n = 394) of non-demented participants from the Alzheimer's Disease Neuroimaging Initiative. MBI was determined by transforming Neuropsychiatric Inventory Questionnaire items using a published algorithm. Change in NfL was calculated over 2 years. RESULTS: Time*MBI status was the only significant interaction to predict change in NfL concentrations (F(1,574) = 4.59, p = 0.032), even after controlling for age, mild cognitive impairment, and demographics. Analyses reclassifying 64 participants with new onset MBI over 2 years similarly demonstrated greater increases in NfL (F(1,574) = 5.82, p = 0.016). CONCLUSION: These findings suggest MBI is a clinical proxy of early phase neurodegeneration with putative utility in identifying those at dementia risk. MBI can be used as a case ascertainment approach to capture those at high risk for cognitive decline and dementia, and is an important construct for clinicians dealing with cognitive and neuropsychiatric symptomatology in older adults.
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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 | 0.001 |
| 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".