Serum neurofilament light is superior to glial fibrillary acidic protein to distinguish sporadic frontotemporal dementia from late-onset primary psychiatric disorders: a retrospective DIPPA-FTD study
Bibliographic record
Abstract
Background: Sporadic behavioural variant frontotemporal dementia (bvFTD) is often misdiagnosed as late-onset primary psychiatric disorder (PPD). Previous research in small sample sizes has shown that neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) are promising biomarkers to distinguish FTD from PPD. We aimed to investigate the discriminative value of NfL and GFAP in a multicentre cohort of sporadic bvFTD and late-onset PPD. Methods: In total, n=275 sporadic bvFTD and n=82 PPD were included from our DIPPA-FTD study. Baseline serum NfL and GFAP levels were measured using Simoa. Biomarker levels were compared between groups. The effect of age and sex on NfL and GFAP was measured using linear regression models. Discriminative accuracies were assessed using logistic regression models and receiver operating characteristic curves, corrected for age and sex. Within a subset of bvFTD patients who were deceased, the prognostic value of biomarkers was assessed by correlating disease duration (age at death minus age at blood sampling) with biomarker levels. Results: Significantly higher serum median NfL and GFAP levels were found in sporadic bvFTD (NfL 33.3 pg/mL, IQR (19.6-49.6); GFAP 124.5 pg/mL, IQR (83.5-181.6)) compared with PPD (NfL 12.2 pg/mL, IQR (9.8-17.9); GFAP 68.9 pg/mL, IQR (50.6-95.0), both p<0.001). Discriminative performance was AUC=0.872 for NfL, AUC=0.787 for GFAP and AUC=0.878 for NfL+GFAP (DeLong's p for NfL+GFAP versus NfL AUCs: p=0.286). A shorter disease duration was significantly correlated with higher NfL, but not GFAP. Conclusion: Our study found that serum GFAP does not provide additional value as a discriminative marker compared with serum NfL alone when differentiating sporadic bvFTD from late-onset PPD.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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 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".