Inflammation PET and plasma neurofilament light predict survival in people with progressive supranuclear palsy
Bibliographic record
Abstract
Abstract Progressive supranuclear palsy (PSP) is a primary tauopathy characterized by atrophy and neuroinflammation of the brainstem, the basal ganglia and, to a lesser degree, the cortex. This study investigates the association of regional atrophy (structural MRI), neuroinflammation ([11C]-PK11195 PET), peripheral markers of neurodegeneration [plasma neurofilament light chain (NfL)] and clinical severity [PSP rating scale (PSPRS)] with survival in people with PSP. Fifty-nine people with PSP underwent longitudinal structural MRI, surviving on average 3.2 years from the first scan (MRI cohort). Sixteen participants (PET cohort) within this cohort underwent cross-sectional [11C]-PK11195 PET and blood sampling for plasma NfL. We applied modality-specific principal component analyses on imaging data and ran partial correlations, multivariate regressions and Bayesian models to evaluate the association between survival and imaging patterns, clinical severity and plasma NfL. In the PET cohort, higher levels of localized inflammation in subcortical regions [rho = −0.49, P = 0.02, Bayes factor (BF) = 8.07] and plasma NfL (rho = −0.57, P = 0.01, BF = 4.63) were associated with shorter survival, while PSPRS scores were not significant predictors of survival. Subcortical atrophy was associated with shorter survival in the larger cohort (r = −0.38, P = 0.001; β = −0.66, P = 0.001). Spearman’s correlations, multivariate regressions and Bayesian models converged to the same results. Regional subcortical atrophy is a robust biomarker associated with survival in people with PSP that can be utilized in large-scale clinical trials. Translocator protein (TSPO) PET and plasma NfL offer promising complementary markers for smaller-scale trials, where they may prove more sensitive than clinical scores or structural MRI alone. By linking neuroinflammation to survival, our results also highlight immunotherapy as a promising avenue for disease-modifying treatment in PSP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".