The Role of Peripheral Inflammation in Clinical Outcome and Brain Imaging Abnormalities in Psychosis: A Systematic Review
Bibliographic record
Abstract
Promising research investigating the association between inflammatory biomarkers and response to antipsychotic and/or adjunctive therapy, observed by improvement in psychiatric assessment, is emerging. Increased inflammation has been suggested to contribute to higher severity of symptoms/treatment resistance through the effects that this has on brain structure and function. The present systematic review aims to clarify the potential role of peripheral inflammatory markers as predictors of clinical outcomes and their association with neuroimaging markers in patients with psychosis. Systematic searches of the literature using the databases PsychInfo, OVID Medline, and Embase were conducted to collate studies investigating the association of inflammatory biomarkers with clinical outcome in patients with psychotic disorders and studies examining the relationships between inflammatory biomarkers and neuroimaging data. Seventeen studies on predictors of clinical outcome and 14 on associations between neuroimaging data and inflammatory biomarkers in psychosis were identified, and risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). The main inflammatory markers associated with clinical outcome in psychosis were interleukin (IL)-6, IL-10, and C-reactive protein (CRP). High levels of CRP and IL-6 were associated with worse clinical outcome and deterioration of symptoms over time; in contrast, increased levels of IL-10 were associated with greater symptoms improvement. Smaller hippocampal volume and reduced cortical thickness were the main neuroimaging markers associated with increased peripheral inflammation. The heterogeneity across the studies (i.e., treatments strategies, duration) suggests that potential prediction power of inflammatory biomarkers could partially depend on the methodologies, supported by the overall NOS ratings of the studies. Future studies may need to consider whether a combination of these inflammatory and neuroimaging markers could further improve our ability of predicting clinical outcome in patients with psychosis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".