Glial and neuronal cell-free DNA in plasma of sporadic bvFTD and late onset primary psychiatric disease patients
Bibliographic record
Abstract
Behavioral variant frontotemporal dementia (bvFTD) and primary psychiatric disorders (PPD) have symptomatic overlap that leads to diagnostic challenges. Cell-free DNA (cfDNA) tests have revolutionized prenatal non-invasive testing and cancer diagnostics. This study investigated the diagnostic potential of brain-derived cfDNA in plasma to differentiate sporadic bvFTD from PPD subjects. Targeted bisulfite sequencing was conducted to quantify glial and neuronal cfDNA levels in plasma samples from 179 bvFTD and 102 PPD subjects of the multi-center DIPPA-FTD study. No significant differences were observed in the absolute levels of glial or neuronal cfDNA between the groups. However, the neuronal-to-glial cfDNA ratio (NGR) was significantly higher in PPD cases (p = 0.0002), suggesting a relative increase in neuronal cfDNA in PPD compared to bvFTD. Diagnostic performance analysis revealed that neuronal cfDNA and NGR achieved an area under the curve (AUC) of 0.74, with a sensitivity of 90% but a specificity of 44% in distinguishing bvFTD from PPD. While increased serum levels of neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) have been shown to effectively differentiate bvFTD from PPD, the addition of brain-derived cfDNA did not provide any incremental diagnostic benefit over these established biomarkers.
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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 | 0.001 | 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 | 0.000 |
| 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".