Severe Mental Illness as a Multisystem Metabolic Disorder From Brain to Body and Back Again
Bibliographic record
Abstract
Severe mental illnesses (SMIs), such as schizophrenia and bipolar disorder, are traditionally conceptualized as disorders of the central nervous system (CNS). Growing evidence, however, suggests that SMIs may be better understood as multisystem disorders, characterized by metabolic and immune dysregulation affecting both brain and body. In this chapter, the hypothesis that psychiatric symptoms may emerge because of systemic metabolic dysfunction is explored. Evidence is presented to show that metabolic abnormalities—including insulin resistance, dysglycemia, and immune activation—are present from onset of SMI, and that these abnormalities may contribute to core psychiatric symptoms such as amotivation, cognitive impairment, anhedonia, and neurovegetative alterations. The role of insulin and cytokine signaling pathways is discussed, with a focus on their reciprocal influence across the CNS and peripheral organs, and the potential intrinsic and extrinsic modulators of these pathways, including sex, stress, and nutrition, are outlined. A conceptual framework is proposed in which SMIs are understood as manifestations of dysregulated energy allocation and metabolic stress. Finally, a research strategy is outlined to investigate peripheral metabolic signatures as tools for stratification, prognosis, and treatment in SMIs, with the goal of advancing a precision medicine approach to psychiatric care.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".