Review of Major Social Determinants of Health in Schizophrenia-Spectrum Psychotic Disorders: III. Biology
Bibliographic record
Abstract
BACKGROUND: Social determinants of health (SDoHs) are nonmedical factors that significantly impact health and longevity. We found no published reviews on the biology of SDoHs in schizophrenia-spectrum psychotic disorders (SSPD). STUDY DESIGN: We present an overview of pathophysiological mechanisms and neurobiological processes plausibly involved in the effects of major SDoHs on clinical outcomes in SSPD. STUDY RESULTS: This review of the biology of SDoHs focuses on early-life adversities, poverty, social disconnection, discrimination including racism, migration, disadvantaged neighborhoods, and food insecurity. These factors interact with psychological and biological factors to increase the risk and worsen the course and prognosis of schizophrenia. Published studies on the topic are limited by cross-sectional design, variable clinical and biomarker assessments, heterogeneous methods, and a lack of control for confounding variables. Drawing on preclinical and clinical studies, we propose a biological framework to consider the likely pathogenesis. Putative systemic pathophysiological processes include epigenetics, allostatic load, accelerated aging with inflammation (inflammaging), and the microbiome. These processes affect neural structures, brain function, neurochemistry, and neuroplasticity, impacting the development of psychosis, quality of life, cognitive impairment, physical comorbidities, and premature mortality. Our model provides a framework for research that could lead to developing specific strategies for prevention and treatment of the risk factors and biological processes, thereby improving the quality of life and increasing the longevity of people with SSPD. CONCLUSIONS: Biology of SDoHs in SSPD is an exciting area of research that points to innovative multidisciplinary team science for improving the course and prognosis of these serious psychiatric disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".