Blood‐based markers of neurodegeneration linked with brain atrophy and cognition in aging
Bibliographic record
Abstract
Abstract Background Neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) have been linked to aging‐related brain tissue loss and cognitive decline, both related to neurodegeneration. Recently, NfL and GFAP have become quantifiable in blood; little is known about their association with brain structure in aging adults. Method Single‐Molecule Array (SIMOA) technology was used for ultra‐sensitive detection and quantification of NfL and GFAP in blood plasma at baseline in healthy volunteers (N = 102, 52 female, 52‐79 years old). These individuals also underwent magnetic resonance imaging twice, 3 years apart (N = 72 at follow‐up), and cognitive testing. We examined the relationships among NfL, GFAP, age, brain atrophy parameters (gray‐matter volume ‐ GMV, cortical thickness ‐ CT) and cognitive performance (composite score of episodic and working memory, executive functions and perceptual speed). The relationships between blood markers, GMV and CT were assessed using voxel‐ and surface‐based morphometry, cross‐sectionally and longitudinally, adjusting for age, sex and education. Result Older age was related to higher levels of NfL and GFAP (ps < .001; Figure 1). Cross‐sectionally, older age was related to lower GMV in the hippocampi and lateral middle temporal regions, and lower CT in lateral frontoparietal areas. Longitudinally, atrophy was located in fronto‐parieto‐temporal regions, more strongly in the left hemisphere (all ps < .05 Family‐Wise Error corrected). Controlling for age, sex, education and total intracranial volume, higher NfL levels were associated with lower volume in inferior temporal regions (cross‐sectionally), and with atrophy in right hippocampus (longitudinally) (p < .001 uncorrected; Figure 2). Higher GFAP levels were associated with lower volume and thinner cortex in frontoparietal regions, and atrophy in right frontal cortex (p < .001; Figure 2). After splitting the individuals as decliners and non‐decliners based on their cognitive‐performance changes, it was found that the negative associations between NfL and inferior‐temporal volume, and between GFAP and fronto‐temporal volume, were driven by the decliners (Figure 3). Conclusion Higher NfL or GFAP levels are related to atrophy, beyond age, in key regions known to be affected in neurodegenerative disorders, possibly indicating early neuropathological changes.
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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 | 0.000 | 0.001 |
| 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".