Metabolomic biomarkers of psychotic conversion in ultra-high-risk subjects: a pilot study
Bibliographic record
Abstract
Psychosis is a psychiatric condition that can become a chronic and severe psychiatric disorder affecting more than 1% of the population. The ultra-high risk (UHR) patients have a transition rate to psychosis of 25% after three years. We aimed to identify circulating metabolomic biomarkers for psychotic conversion in UHR patients using nuclear magnetic resonance (NMR) spectroscopy. We used samples from 35 UHR patients: 14 converters (UHR-C) and 21 non-converters (UHR-NC) at inclusion from the ICAAR cohort. Serum samples were analysed using the high-throughput screening IVDr NMR method. R and SIMCA were used for statistical analysis. Several lipoprotein parameters related to HDL and LDL metabolism were downregulated in UHR-C compared to UHR-NC at the time of inclusion. The 3 best lipoproteins to predict psychotic conversion at baseline were H4A1, H4FC, and L4FC (Area under the Curve (AUC) values were 0.81, 0.81, and 0.78, respectively). These lipoproteins were also negatively correlated with PANSS scores. Our study is the first to use NMR technology to identify biomarkers to predict the risk of psychotic transition in UHR subjects. This pilot study found lipoprotein parameters related to ApoA-1 and HDL-cholesterol (subclass 4) as potential biomarkers. These results need to be replicated on a larger sample. This study highlights the importance of the detailed analysis of circulant lipoproteins related to the brain using NMR technology in early psychosis to identify biomarkers of psychotic transitions and perhaps to better understand the physiopathology of psychosis.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".