Review of serum prolactin levels as an antipsychotic-response biomarker

Prolactin and morning cortisol concentrations in antipsychotic naïve first episode psychosis: A systematic review and meta-analysis

IMPORTANCE

Alterations in prolactin and cortisol levels have been reported in antipsychotic naïve patients with first episode psychosis (FEP). However, it has been studied in very small samples, and inter-group variability has never been studied before.

OBJECTIVE

To provide estimates of standardized mean differences (SMD) and inter-group variability for prolactin, cortisol awakening response (CAR) and morning cortisol concentrations in antipsychotic naïve FEP (AN-FEP) patients and healthy controls (HC).

DATA SOURCES

BIOSIS, KCI, MEDLINE, Russian Science Citation Index, SciELO, Cochrane, PsycINFO, Web of Science were searched from inception to February 28, 2022.

STUDY SELECTION

Peer-reviewed cohort studies that reported on prolactin or cortisol blood concentrations in AN- FEP patients and HC were included.

DATA EXTRACTION AND SYNTHESIS

Study characteristics, means and standard deviations (SD) were extracted from each article. Inter group differences in magnitude of effect were estimated using Hedges g. Inter-group variability was estimated with the coefficient of variation ratio (CVR). In both cases estimates were pooled using random-effects meta-analysis. Differences by study-level characteristics were estimated using meta-regression. PRISMA guideline was followed (No. CRD42022303555).

MAIN OUTCOMES AND MEASURES

Prolactin, CAR and morning cortisol blood concentrations in AN-FEP group in relation to HC group.

RESULTS

Fourteen studies for prolactin (N = 761 for AN-FEP group, N = 687 for HC group) and twelve studies for morning cortisol (N = 434 for AN-FEP group, N = 528 for HC group) were included. No studies were found in CAR in AN-FEP patients. Mean SMD for prolactin blood concentration was 0.88 (95% CI 0.57, 1.20) for male and 0.56 (95% CI 0.26, 0.87) for female. As a group, AN-FEP presented greater inter-group variability for prolactin levels than HC (CVR=1.28, 95% CI 1.02, 1.62). SMD for morning cortisol concentrations was non-significant: 0.34 (95% CI -0.01, 0.69) and no inter-group variability significant differences were detected: CVR= 1.05 (95% CI 0.91, 1.20). Meta-regression analyses for age and quality were non-significant. Funnel plots did not suggest a publication bias.

CONCLUSIONS AND RELEVANCE

Increased prolactin levels were found in AN-FEP patients. A greater inter-group variability in the AN-FEP group suggests the existence of patient subgroups with different prolactin levels. No significant abnormalities were found in morning cortisol levels. Further research is needed to clarify whether prolactin concentrations could be used as an illness biomarker.

Predicting psychotic relapse following randomised discontinuation of paliperidone in individuals with schizophrenia or schizoaffective disorder: an individual participant data analysis

BACKGROUND

Predicting relapse for individuals with psychotic disorders is not well established, especially after discontinuation of antipsychotic treatment. We aimed to identify general prognostic factors of relapse for all participants (irrespective of treatment continuation or discontinuation) and specific predictors of relapse for treatment discontinuation, using machine learning.

METHODS

For this individual participant data analysis, we searched the Yale University Open Data Access Project's database for placebo-controlled, randomised antipsychotic discontinuation trials with participants with schizophrenia or schizoaffective disorder (aged ≥18 years). We included studies in which participants were treated with any antipsychotic study drug and randomly assigned to continue the same antipsychotic drug or to discontinue it and receive placebo. We assessed 36 prespecified baseline variables at randomisation to predict time to relapse, using univariate and multivariate proportional hazard regression models (including multivariate treatment group by variable interactions) with machine learning to categorise the variables as general prognostic factors of relapse, specific predictors of relapse, or both.

FINDINGS

We identified 414 trials, of which five trials with 700 participants (304 [43%] women and 396 [57%] men) were eligible for the continuation group and 692 participants (292 [42%] women and 400 [58%] men) were eligible for the discontinuation group (median age 37 [IQR 28-47] years for continuation group and 38 [28-47] years for discontinuation group). Out of the 36 baseline variables, general prognostic factors of increased risk of relapse for all participants were drug-positive urine; paranoid, disorganised, and undifferentiated types of schizophrenia (lower risk for schizoaffective disorder); psychiatric and neurological adverse events; higher severity of akathisia (ie, difficulty or inability to sit still); antipsychotic discontinuation; lower social performance; younger age; lower glomerular filtration rate; benzodiazepine comedication (lower risk for anti-epileptic comedication). Out of the 36 baseline variables, predictors of increased risk specifically after antipsychotic discontinuation were increased prolactin concentration, higher number of hospitalisations, and smoking. Both prognostic factors and predictors with increased risk after discontinuation were oral antipsychotic treatment (lower risk for long-acting injectables), higher last dosage of the antipsychotic study drug, shorter duration of antipsychotic treatment, and higher score on the Clinical Global Impression (CGI) severity scale The predictive performance (concordance index) for participants who were not used to train the model was 0·707 (chance level is 0·5).

INTERPRETATION

Routinely available general prognostic factors of psychotic relapse and predictors specific for treatment discontinuation could be used to support personalised treatment. Abrupt discontinuation of higher dosages of oral antipsychotics, especially for individuals with recurring hospitalisations, higher scores on the CGI severity scale, and increased prolactin concentrations, should be avoided to reduce the risk of relapse.

FUNDING

German Research Foundation and Berlin Institute of Health.

Protein Succinylation and Malonylation as Potential Biomarkers in Schizophrenia

Two protein post-translational modifications, lysine succinylation and malonylation, are implicated in protein regulation, glycolysis, and energy metabolism. The precursors of these modifications, succinyl-CoA and malonyl-CoA, are key players in central metabolic processes. Both modification profiles have been proven to be responsive to metabolic stimuli, such as hypoxia. As mitochondrial dysfunction and metabolic dysregulation are implicated in schizophrenia and other psychiatric illnesses, these modification profiles have the potential to reveal yet another layer of protein regulation and can furthermore represent targets for biomarkers that are indicative of disease as well as its progression and treatment. In this work, data from shotgun mass spectrometry-based quantitative proteomics were compiled and analyzed to probe the succinylome and malonylome of postmortem brain tissue from patients with schizophrenia against controls and the human oligodendrocyte precursor cell line MO3.13 with the dizocilpine chemical model for schizophrenia, three antipsychotics, and co-treatments. Several changes in the succinylome and malonylome were seen in these comparisons, revealing these modifications to be a largely under-studied yet important form of protein regulation with broad potential applications.

Risperidone Induced Hyperprolactinemia: From Basic to Clinical Studies

Risperidone is one of the most commonly used antipsychotics (AP), due to its safety and efficacy in reducing psychotic symptoms. Despite the favorable side effect profile, the therapy is accompanied by side effects due to the non-selectivity of this medicine. This review will briefly highlight the most important basic and clinical findings in this area, consider the clinical effects of AP-induced hyperprolactinemia (HPL), and suggest different approaches to the treatment.The route of application of this drug primarily affects the daily variation and the total concentration of drug levels in the blood, which consequently affects the appearance of side effects, either worsening or even reducing them. Our attention has been drawn to HPL, a frequent but neglected adverse effect observed in cases treated with Risperidone and its secondary manifestations. An increase in prolactin levels above the reference values result in impairment of other somatic functions (lactation, irregular menses, fertility) as well as a significant reduction in quality of life. It has been frequently shown that the side effects of the Risperidone are the most common cause of non-compliance with therapy, resulting in worsening of psychiatric symptoms and hospitalization. However, the mechanism of Risperidone-induced HPL is complicated and still far from fully understood. Most of the preclinical and clinical studies described in this study show that hyperprolactinemia is one of the most common if not the leading side effect of Risperidone therefore to improve the quality of life of these patients, clinicians must recognize and treat HPL associated with the use of these drugs.

Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients

BACKGROUND AND AIM

Available evidence suggests elevated serum prolactin (PRL) levels in olanzapine (OLZ)-treated patients with schizophrenia. However, machine learning (ML)-based comprehensive evaluations of the influence of pathophysiological and pharmacological factors on PRL levels in OLZ-treated patients are rare. We aimed to forecast the PRL level in OLZ-treated patients and mine pharmacovigilance information on PRL-related adverse events by integrating ML and electronic health record (EHR) data.

METHODS

Data were extracted from an EHR system to construct an ML dataset in 672×384 matrix format after preprocessing, which was subsequently randomly divided into a derivation cohort for model development and a validation cohort for model validation (8:2). The eXtreme gradient boosting (XGBoost) algorithm was used to build the ML models, the importance of the features and predictive behaviors of which were illustrated by SHapley Additive exPlanations (SHAP)-based analyses. The sequential forward feature selection approach was used to generate the optimal feature subset. The co-administered drugs that might have influenced PRL levels during OLZ treatment as identified by SHAP analyses were then compared with evidence from disproportionality analyses by using OpenVigil FDA.

RESULTS

The 15 features that made the greatest contributions, as ranked by the mean (|SHAP value|), were identified as the optimal feature subset. The features were gender_male, co-administration of risperidone, age, co-administration of aripiprazole, concentration of aripiprazole, concentration of OLZ, progesterone, co-administration of sulpiride, creatine kinase, serum sodium, serum phosphorus, testosterone, platelet distribution width, α-L-fucosidase, and lipoprotein (a). The XGBoost model after feature selection delivered good performance on the validation cohort with a mean absolute error of 0.046, mean squared error of 0.0036, root-mean-squared error of 0.060, and mean relative error of 11%. Risperidone and aripiprazole exhibited the strongest associations with hyperprolactinemia and decreased blood PRL according to the disproportionality analyses, and both were identified as co-administered drugs that influenced PRL levels during OLZ treatment by SHAP analyses.

CONCLUSIONS

Multiple pathophysiological and pharmacological confounders influence PRL levels associated with effective treatment and PRL-related side-effects in OLZ-treated patients. Our study highlights the feasibility of integration of ML and EHR data to facilitate the detection of PRL levels and pharmacovigilance signals in OLZ-treated patients.

Antipsychotics-induced hyperprolactinemia and screening for macroprolactin

Introduction

High prolactin (PRL) concentrations are found in laboratory test results of patients on majority of antipsychotic drugs. Prevalence rates and degrees of severity of hyperprolactinemia (HPRL) based on PRL concentration may depend on the presence of macroprolactin in the serum. The aim of the study was to investigate the difference between PRL concentrations before and after precipitation of macroprolactin and to examine if there were any changes in the categorization of HPRL between samples prior and after precipitation.

Materials and methods

Total of 98 female patients (median age 33; range 19-47 years) diagnosed with a psychotic disorder, proscribed antipsychotic drugs, and with HPRL were included. Total PRL concentration and PRL concentration after macroprolactin precipitation with polyethylene glycol (postPEG-PRL) were determined by the chemiluminometric method on the Beckman Coulter Access2 analyser.

Results

Total PRL concentrations (median 1471; IQC: 1064-2016 mlU/L) and postPEG-PRL concentrations (median 1453; IQC: 979-1955 mlU/L) were significantly correlated using intraclass correlation coefficient for single measurements (mean estimation 0.96; 95%CI 0.93-0.97) and average measurement (mean estimation 0.98; 95%CI 0.96-0.99), and all investigated female patient had HPRL according to PRL and postPEG-PRL concentration. The median PRL recovery following PEG precipitation was 95; IQC: 90-100%. There was substantial agreement (kappa test = 0.859, 95% CI: 0.764-0.953) between the categories of HPRL severity based on total PRL concentrations and postPEG-PRL concentrations.

Conclusion

The study demonstrated that HPRL was present in all subjects using the reference interval for total PRL concentration and postPEG-PRL concentration with no significant impact of macroprolactin presence in the serum on the categorization of patients according to severity of HPRL.