Metabolic Abnormalities in Psychiatric Disorders A Transdiagnostic, Whole-Body Approach
Bibliographic record
Abstract
Psychiatric disorders have traditionally been conceptualized as conditions primarily affecting the brain. However, mounting evidence challenges this view, revealing complex interactions between systemic energy metabolism, immune function, and brain physiology. This chapter examines the critical role of metabolic abnormalities in psychiatric disorders, advocating for a “whole-body” framework of investigation. Recent research demonstrates that individuals with severe psychiatric disorders experience significantly reduced life expectancy, largely due to metabolic and cardiovascular conditions that are not fully attributable to medication side effects. Evidence from genetic as well as peripheral and central nervous system studies reveals multifaceted metabolic dysregulation across psychiatric disorders, including altered glucose metabolism, mitochondrial function, and oxidative stress. Notably, specific metabolic signatures may correspond to distinct clinical phenotypes that cut across traditional diagnostic boundaries, suggesting the need for a transdiagnostic approach to both research and treatment strategies. The relationship between metabolic dysfunction and psychiatric symptoms is bidirectional and complex. Although metabolic abnormalities may not directly cause psychiatric disorders, they likely represent significant risk factors that interact with genetic, physiological, environmental, and socioeconomic conditions. Understanding these interactions is crucial for developing more effective, personalized therapeutic strategies. Future investigations focused on identifying biology-based symptom constructs may be better able to guide individualized treatment approaches than traditional diagnostic categories.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".