Prevalence and correlates of low-grade systemic inflammation in adult psychiatric inpatients: An electronic health record-based study.

Low-grade inflammation is a risk factor for depression, psychosis and other major psychiatric disorders. It is associated with poor response to antidepressant and antipsychotics, and could potentially be a treatment target. However, there is limited data on the prevalence of low-grade inflammation in major psychiatric disorders, and on the characteristics of patients who show evidence of inflammation. We examined the prevalence of low-grade inflammation and associated socio-demographic and clinical factors in acute psychiatric inpatients. An anonymised search of the electronic patient records of Cambridgeshire and Peterborough NHS Foundation Trust was used to identify patients aged 18-65 years who were hospitalised between 2013 and 2016 (inclusive). We excluded patients on antibiotics or oral steroids, or with missing data. Inflammation was defined using serum C-reactive protein (>3 mg/L) or total white cell count (>9.4 × 109/L) as measured within 14 days of admission. Out of all 599 admissions, the prevalence of inflammation (serum CRP >3 mg/L) in the ICD-10 diagnostic groups of psychotic disorders (F20-29), mood disorders (F30-39), neurotic disorders (F40-48) and personality disorders (F60-69) was 32%, 21%, 22% and 42%, respectively. In multivariable analyses, low-grade inflammation was associated with older age, black ethnicity, being single, self-harm, diagnoses of schizophrenia, bipolar disorder, current treatments with antidepressants, benzodiazepines, and with current treatment for medical comorbidities. A notable proportion of acutely unwell psychiatric patients from all ICD-10 major diagnostic groups show evidence of low-grade inflammation, suggesting inflammation may be relevant for all psychiatric disorders

A cross-sectional MR study of body fat volumes and distribution in chronic schizophrenia

Funder: DH | National Institute for Health Research (NIHR); doi: https://doi.org/10.13039/501100000272Funder: RCUK | Medical Research Council (MRC); doi: https://doi.org/10.13039/501100000265AbstractPeople with schizophrenia show higher risk for abdominal obesity than the general population, which could contribute to excess mortality. However, it is unclear whether this is driven by alterations in abdominal fat partitioning. Here, we test the hypothesis that individuals with schizophrenia show a higher proportion of visceral to total body fat measured using magnetic resonance imaging (MRI). We recruited 38 participants with schizophrenia and 38 healthy controls matched on age, sex, ethnicity, and body mass index. We found no significant differences in body fat distribution between groups, suggesting that increased abdominal obesity in schizophrenia is not associated with altered fat distribution.</jats:p

Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC): a cardiometabolic risk prediction algorithm for young people with psychosis.

BACKGROUND: Young people with psychosis are at high risk of developing cardiometabolic disorders; however, there is no suitable cardiometabolic risk prediction algorithm for this group. We aimed to develop and externally validate a cardiometabolic risk prediction algorithm for young people with psychosis. METHODS: We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up to 6-year risk of incident metabolic syndrome in young people (aged 16-35 years) with psychosis from commonly recorded information at baseline. We developed two PsyMetRiC versions using the forced entry method: a full model (including age, sex, ethnicity, body-mass index, smoking status, prescription of a metabolically active antipsychotic medication, HDL concentration, and triglyceride concentration) and a partial model excluding biochemical results. PsyMetRiC was developed using data from two UK psychosis early intervention services (Jan 1, 2013, to Nov 4, 2020) and externally validated in another UK early intervention service (Jan 1, 2012, to June 3, 2020). A sensitivity analysis was done in UK birth cohort participants (aged 18 years) who were at risk of developing psychosis. Algorithm performance was assessed primarily via discrimination (C statistic) and calibration (calibration plots). We did a decision curve analysis and produced an online data-visualisation app. FINDINGS: 651 patients were included in the development samples, 510 in the validation sample, and 505 in the sensitivity analysis sample. PsyMetRiC performed well at internal (full model: C 0·80, 95% CI 0·74-0·86; partial model: 0·79, 0·73-0·84) and external validation (full model: 0·75, 0·69-0·80; and partial model: 0·74, 0·67-0·79). Calibration of the full model was good, but there was evidence of slight miscalibration of the partial model. At a cutoff score of 0·18, in the full model PsyMetRiC improved net benefit by 7·95% (sensitivity 75%, 95% CI 66-82; specificity 74%, 71-78), equivalent to detecting an additional 47% of metabolic syndrome cases. INTERPRETATION: We have developed an age-appropriate algorithm to predict the risk of incident metabolic syndrome, a precursor of cardiometabolic morbidity and mortality, in young people with psychosis. PsyMetRiC has the potential to become a valuable resource for early intervention service clinicians and could enable personalised, informed health-care decisions regarding choice of antipsychotic medication and lifestyle interventions. FUNDING: National Institute for Health Research and Wellcome Trust

Longitudinal association between CRP levels and risk of psychosis: a meta-analysis of population-based cohort studies.

Funder: MQ: Transforming Mental Health; Grant(s): MQDS17/40Meta-analyses of cross-sectional studies suggest that patients with psychosis have higher circulating levels of C-reactive protein (CRP) compared with healthy controls; however, cause and effect is unclear. We examined the prospective association between CRP levels and subsequent risk of developing a psychotic disorder by conducting a systematic review and meta-analysis of population-based cohort studies. Databases were searched for prospective studies of CRP and psychosis. We obtained unpublished results, including adjustment for age, sex, body mass index, smoking, alcohol use, and socioeconomic status and suspected infection (CRP > 10 mg/L). Based on random effect meta-analysis of 89,792 participants (494 incident cases of psychosis at follow-up), the pooled odds ratio (OR) for psychosis for participants with high (>3 mg/L), as compared to low (≤3 mg/L) CRP levels at baseline was 1.50 (95% confidence interval [CI], 1.09-2.07). Evidence for this association remained after adjusting for potential confounders (adjusted OR [aOR] = 1.31; 95% CI, 1.03-1.66). After excluding participants with suspected infection, the OR for psychosis was 1.36 (95% CI, 1.06-1.74), but the association attenuated after controlling for confounders (aOR = 1.23; 95% CI, 0.95-1.60). Using CRP as a continuous variable, the pooled OR for psychosis per standard deviation increase in log(CRP) was 1.11 (95% CI, 0.93-1.34), and this association further attenuated after controlling for confounders (aOR = 1.07; 95% CI, 0.90-1.27) and excluding participants with suspected infection (aOR = 1.07; 95% CI, 0.92-1.24). There was no association using CRP as a categorical variable (low, medium or high). While we provide some evidence of a longitudinal association between high CRP (>3 mg/L) and psychosis, larger studies are required to enable definitive conclusions

Longitudinal association between CRP levels and risk of psychosis: a meta-analysis of population-based cohort studies.

Funder: MQ: Transforming Mental Health; Grant(s): MQDS17/40Meta-analyses of cross-sectional studies suggest that patients with psychosis have higher circulating levels of C-reactive protein (CRP) compared with healthy controls; however, cause and effect is unclear. We examined the prospective association between CRP levels and subsequent risk of developing a psychotic disorder by conducting a systematic review and meta-analysis of population-based cohort studies. Databases were searched for prospective studies of CRP and psychosis. We obtained unpublished results, including adjustment for age, sex, body mass index, smoking, alcohol use, and socioeconomic status and suspected infection (CRP > 10 mg/L). Based on random effect meta-analysis of 89,792 participants (494 incident cases of psychosis at follow-up), the pooled odds ratio (OR) for psychosis for participants with high (>3 mg/L), as compared to low (≤3 mg/L) CRP levels at baseline was 1.50 (95% confidence interval [CI], 1.09-2.07). Evidence for this association remained after adjusting for potential confounders (adjusted OR [aOR] = 1.31; 95% CI, 1.03-1.66). After excluding participants with suspected infection, the OR for psychosis was 1.36 (95% CI, 1.06-1.74), but the association attenuated after controlling for confounders (aOR = 1.23; 95% CI, 0.95-1.60). Using CRP as a continuous variable, the pooled OR for psychosis per standard deviation increase in log(CRP) was 1.11 (95% CI, 0.93-1.34), and this association further attenuated after controlling for confounders (aOR = 1.07; 95% CI, 0.90-1.27) and excluding participants with suspected infection (aOR = 1.07; 95% CI, 0.92-1.24). There was no association using CRP as a categorical variable (low, medium or high). While we provide some evidence of a longitudinal association between high CRP (>3 mg/L) and psychosis, larger studies are required to enable definitive conclusions

Longitudinal association between CRP levels and risk of psychosis: a meta-analysis of population-based cohort studies

Abstract: Meta-analyses of cross-sectional studies suggest that patients with psychosis have higher circulating levels of C-reactive protein (CRP) compared with healthy controls; however, cause and effect is unclear. We examined the prospective association between CRP levels and subsequent risk of developing a psychotic disorder by conducting a systematic review and meta-analysis of population-based cohort studies. Databases were searched for prospective studies of CRP and psychosis. We obtained unpublished results, including adjustment for age, sex, body mass index, smoking, alcohol use, and socioeconomic status and suspected infection (CRP > 10 mg/L). Based on random effect meta-analysis of 89,792 participants (494 incident cases of psychosis at follow-up), the pooled odds ratio (OR) for psychosis for participants with high (>3 mg/L), as compared to low (≤3 mg/L) CRP levels at baseline was 1.50 (95% confidence interval [CI], 1.09–2.07). Evidence for this association remained after adjusting for potential confounders (adjusted OR [aOR] = 1.31; 95% CI, 1.03–1.66). After excluding participants with suspected infection, the OR for psychosis was 1.36 (95% CI, 1.06–1.74), but the association attenuated after controlling for confounders (aOR = 1.23; 95% CI, 0.95–1.60). Using CRP as a continuous variable, the pooled OR for psychosis per standard deviation increase in log(CRP) was 1.11 (95% CI, 0.93–1.34), and this association further attenuated after controlling for confounders (aOR = 1.07; 95% CI, 0.90–1.27) and excluding participants with suspected infection (aOR = 1.07; 95% CI, 0.92–1.24). There was no association using CRP as a categorical variable (low, medium or high). While we provide some evidence of a longitudinal association between high CRP (>3 mg/L) and psychosis, larger studies are required to enable definitive conclusions

Using Electronic Health Records to Facilitate Precision Psychiatry

The use of clinical prediction models to produce individualised risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implementing them in routine clinical care. The present review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number-needed-to-test). We review four externally validated clinical prediction models designed to predict, respectively: psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models, and the potential added value of integrating data from evidence syntheses, standardised psychometric assessments, and biological data into EHRs. Clinical prediction models can utilise routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g. meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve performance of clinical prediction models. [Abstract copyright: Copyright © 2024. Published by Elsevier Inc.

Can typical and atypical antipsychotics show differential effectiveness in treating paranoia and hallucinations in schizophrenia?

A dopamine excess is thought to be involved in positive psychotic symptoms in schizophrenia. All current antipsychotics show a degree of dopamine receptor antagonism. Little is known about the differential effectiveness of different antipsychotics in treating specific sets of symptoms. We report the case of a 35-year-old man with schizophrenia who presented with prominent hallucinatory symptoms (Positive and Negative Syndrome Scale [PANSS] P1=5, P3=5, P6=5) resistant to high doses of a dopamine, serotonin receptor antagonist, olanzapine. Switching from olanzapine to zuclopenthixol, a dopamine D2 receptor antagonist, led to a complete shift of his symptomatology: his hallucinations abated, however, he presented as very highly paranoid (PANSS P1=6, P3=2, P6=7). On a combination of both antipsychotics, his symptoms subsided (PANSS P1=3, P3=2, P6=2). We discuss the potential for differential effectiveness of different antipsychotic medications in treating hallucinations and paranoia. We argue that future studies could address this question by stratifying patients based on symptoms