Plasma neurofilament light protein provides evidence of accelerated brain ageing in treatment-resistant schizophrenia
Bibliographic record
Abstract
Abstract Background Accelerated brain aging has been observed across multiple psychiatric disorders. Blood markers of neuronal injury such as Neurofilament Light (NfL) protein may therefore represent biomarkers of accelerated brain aging in these disorders. The current study aimed to examine whether relationships between age and plasma NfL were increased in individuals with primary psychiatric disorders compared to healthy individuals. Methods Plasma NfL was analysed in major depressive disorder (MDD, n = 42), bipolar affective disorder (BPAD, n = 121), treatment-resistant schizophrenia (TRS, n = 82), a large reference normative healthy control (HC) group (n= 1,926) and a locally-acquired HC sample (n = 59). A general linear model (GLM) was used to examine diagnosis by age interactions on NfL z-scores using the large normative HC sample as a reference group. Significant results were then validated using the locally-acquired HC sample. Results a GLM identified a significant age by diagnosis interaction for TRS vs HCs and BPAD vs HCs. Post hoc analyses revealed a positive correlation between NfL levels and age among individuals with TRS, whereas a negative correlation was found among individuals with BPAD. However, only the TRS findings were replicated using the locally-acquired HC sample. Post hoc analyses revealed that individuals with TRS aged <40 had lower NfL levels compared to same-age HCs, whereas individuals with TRS aged >40 had higher NfL levels compared to same-age HCs. Conclusions These findings add to the growing literature supporting the notion of accelerated brain ageing in schizophrenia-spectrum disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".