The bidirectional association of nonalcoholic fatty liver disease with depression, bipolar disorder, and schizophrenia
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is a complex metabolic-inflammatory disease associated with poor outcomes and decreased quality of life. NAFLD is overrepresented in patients with psychiatric disorders like depression, bipolar disorder, and schizophrenia; however, a comprehensive review on NAFLD and psychiatric disorders remains to be delineated. This review endeavors to investigate the association of NAFLD with psychiatric disorders, including shared pathogenesis and future clinical derivatives. Extant literature suggests that patients with psychiatric disorders (in particular, mood disorders) are more susceptible to the development of NAFLD due to multiple reasons, including but not limited to hypothalamic-pituitary-adrenal axis dysregulation, metabolic syndrome, and chronic perceived stress. Moreover, the clinical manifestations of mood disorders (e.g., anhedonia, psychomotor retardation, lifestyle modification, etc.), and potentially long-term treatment with weight-gaining agents, differentially affect these patients, making them more prone to NAFLD. Considering the increased morbidity associated with both mood disorders and NAFLD, our review recommends regular screenings for NAFLD in select patients with mood disorders exhibiting signs of increased risk (i.e., obesity, metabolic syndrome, diabetes, or family history of NAFLD) for better diagnosis and holistic care of both potentially interrelated conditions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".