Hepatic function is associated with cognitive function in patients with stable schizophrenia
Bibliographic record
Abstract
BACKGROUND: The liver-brain axis, mediated by inflammatory cytokines, metabolic byproducts, and oxidative stress, shares pathological pathways with schizophrenia neurobiology. Despite this overlap, direct clinical evidence linking hepatic function indicators to psychiatric symptoms or cognitive function in schizophrenia remains limited. Therefore, this study investigated associations between clinical hepatic function indicators and both psychiatric symptoms and cognitive performance in individuals with stable schizophrenia. METHODS: A cross-sectional study was conducted among 170 inpatients with clinically stable schizophrenia. Liver function indicators were obtained from fasting venous blood samples. Psychiatric symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS), and cognitive function was assessed with the Montreal Cognitive Assessment–Chinese version (MoCA-C). Multiple linear regression models, adjusted for covariates, were used to examine associations between hepatic indicators and outcomes (PANSS total/subscale scores; MoCA-C total score). RESULTS: After covariate adjustment, total bilirubin (TBil) (β = 0.29, 95% CI: 0.08–0.51; P = 0.008) and indirect bilirubin (IDBil) (β = 0.48, 95% CI: 0.17–0.80; P = 0.003) were significantly positively associated with MoCA-C scores. No significant associations were observed between any hepatic indicators and PANSS total/subscale scores. CONCLUSIONS: In this cross-sectional study of clinically stable schizophrenia, higher TBil and IDBil levels were associated with better cognitive performance. These findings suggest a potential role of bilirubin metabolism in cognitive function in schizophrenia, highlighting the need for further investigation into its relevance for cognitive impairment management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".