Clustering of antipsychotic-naïve patients with schizophrenia based on functional connectivity from resting-state electroencephalography.

Schizophrenia is associated with aberrations in the Default Mode Network (DMN), but the clinical implications remain unclear. We applied data-driven, unsupervised machine learning based on resting-state electroencephalography (rsEEG) functional connectivity within the DMN to cluster antipsychotic-naïve patients with first-episode schizophrenia. The identified clusters were investigated with respect to psychopathological profile and cognitive deficits. Thirty-seven antipsychotic-naïve, first-episode patients with schizophrenia (mean age 24.4 (5.4); 59.5% males) and 97 matched healthy controls (mean age 24.0 (5.1); 52.6% males) underwent assessments of rsEEG, psychopathology, and cognition. Source-localized, frequency-dependent functional connectivity was estimated using Phase Lag Index (PLI). The DMN-PLI was factorized for each frequency band using principal component analysis. Clusters of patients were identified using a Gaussian mixture model and neurocognitive and psychopathological profiles of identified clusters were explored. We identified two clusters of patients based on the theta band (4-8 Hz), and two clusters based on the beta band (12-30 Hz). Baseline psychopathology could predict theta clusters with an accuracy of 69.4% (p = 0.003), primarily driven by negative symptoms. Five a priori selected cognitive functions conjointly predicted the beta clusters with an accuracy of 63.6% (p = 0.034). The two beta clusters displayed higher and lower DMN connectivity, respectively, compared to healthy controls. In conclusion, the functional connectivity within the DMN provides a novel, data-driven means to stratify patients into clinically relevant clusters. The results support the notion of biological subgroups in schizophrenia and endorse the application of data-driven methods to recognize pathophysiological patterns at earliest stage of this syndrome.

Strong Sex-Dependent Effects of Malnutrition on Life- and Healthspan in Drosophila melanogaster.

Insufficient intake of essential nutrients, malnutrition is a major issue for millions of people and has a strong impact on the distribution and abundance of species in nature. In this study, we investigated the effect of malnutrition on several fitness components in the vinegar fly Drosophila melanogaster. Four diets with different nutritional values, including three diluted diets of an optimal nutritional balanced diet, were used as feed sources. The effect of malnutrition on fitness components linked to healthspan, the period of life spent in good health conditions, was evaluated by quantifying the flies' lifespan, locomotor activity, heat stress tolerance, lipid content, and dry weight. The results showed that malnutrition had severe negative impact, such as reduced lifespan, locomotor activity, heat stress tolerance, fat content, and dry weight. The negative phenotypic effects were highly sex-dependent, with males being more negatively impacted by malnutrition compared to females. These findings highlight important detrimental and sex-specific effects of malnutrition not only on lifespan but also on traits related to healthspan.

Clinical correlates of early onset antipsychotic treatment resistance.

BACKGROUND: There is evidence of heterogeneity within treatment-resistant schizophrenia (TRS), with some people not responding to antipsychotic treatment from illness onset and others becoming treatment-resistant after an initial response period. These groups may have different aetiologies. AIM: This study investigates sociodemographic and clinical correlates of early onset of TRS. METHOD: Employing a retrospective cohort design, we do a secondary analysis of data from a cohort of people with TRS attending the South London and Maudsley. Regression analyses were conducted to identify the correlates of the length of treatment to TRS. Predictors included the following: gender, age, ethnicity, problems with positive symptoms, problems with activities of daily living, psychiatric comorbidities, involuntary hospitalisation and treatment with long-acting injectable antipsychotics. RESULTS: In a cohort of 164 people with TRS (60% were men), the median length of treatment to TRS was 3 years and 8 months. We observed no cut-off on the length of treatment until TRS presentation differentiating between early and late TRS (i.e. no bimodal distribution). Having mild to very severe problems with hallucinations and delusions at the treatment start was associated with earlier TRS (~19 months earlier). In sensitivity analyses, including only complete cases (subject to selection bias), treatment with a long-acting injectable antipsychotic was additionally associated with later TRS (~15 months later). CONCLUSION: Our findings do not support a clear separation between early and late TRS but rather a continuum of the length of treatment before TRS onset. Having mild to very severe problems with positive symptoms at treatment start predicts earlier onset of TRS.

A predictor model of treatment resistance in schizophrenia using data from electronic health records.

OBJECTIVES: To develop a prognostic tool of treatment resistant schizophrenia (TRS) in a large and diverse clinical cohort, with comprehensive coverage of patients using mental health services in four London boroughs. METHODS: We used the Least Absolute Shrinkage and Selection Operator (LASSO) for time-to-event data, to develop a risk prediction model from the first antipsychotic prescription to the development of TRS, using data from electronic health records. RESULTS: We reviewed the clinical records of 1,515 patients with a schizophrenia spectrum disorder and observed that 253 (17%) developed TRS. The Cox LASSO survival model produced an internally validated Harrel's C index of 0.60. A Kaplan-Meier curve indicated that the hazard of developing TRS remained constant over the observation period. Predictors of TRS were: having more inpatient days in the three months before and after the first antipsychotic, more community face-to-face clinical contact in the three months before the first antipsychotic, minor cognitive problems, and younger age at the time of the first antipsychotic. CONCLUSIONS: Routinely collected information, readily available at the start of treatment, gives some indication of TRS but is unlikely to be adequate alone. These results provide further evidence that earlier onset is a risk factor for TRS.

Using a statistical learning approach to identify sociodemographic and clinical predictors of response to clozapine.

BACKGROUND: A proportion of people with treatment-resistant schizophrenia fail to show improvement on clozapine treatment. Knowledge of the sociodemographic and clinical factors predicting clozapine response may be useful in developing personalised approaches to treatment. METHODS: This retrospective cohort study used data from the electronic health records of the South London and Maudsley (SLaM) hospital between 2007 and 2011. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression statistical learning approach, we examined 35 sociodemographic and clinical factors' predictive ability of response to clozapine at 3 months of treatment. Response was assessed by the level of change in the severity of the symptoms using the Clinical Global Impression (CGI) scale. RESULTS: We identified 242 service-users with a treatment-resistant psychotic disorder who had their first trial of clozapine and continued the treatment for at least 3 months. The LASSO regression identified three predictors of response to clozapine: higher severity of illness at baseline, female gender and having a comorbid mood disorder. These factors are estimated to explain 18% of the variance in clozapine response. The model's optimism-corrected calibration slope was 1.37, suggesting that the model will underfit when applied to new data. CONCLUSIONS: These findings suggest that women, people with a comorbid mood disorder and those who are most ill at baseline respond better to clozapine. However, the accuracy of the internally validated and recalibrated model was low. Therefore, future research should indicate whether a prediction model developed by including routinely collected data, in combination with biological information, presents adequate predictive ability to be applied in clinical settings.

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data.

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.

Rasch analysis of the PANSS negative subscale and exploration of negative symptom trajectories in first-episode schizophrenia - data from the OPTiMiSE trial.

The observed heterogeneity in negative symptom treatment response may be partly attributable to inadequate measurement tools or limitations in methods of analysis. Previous Item Response Theory models of the Positive and Negative Syndrome Scale (PANSS) have only examined samples of chronic patients and with mixed results. We examined the scalability of the negative subscale embedded in the PANSS and subsequently explored negative symptom trajectories across four weeks of amisulpride treatment. Data were derived from the OPTiMiSE trial comprising 446 patients with first-episode schizophrenia or schizophreniform disorder. Using the Rasch Model to examine psychometric properties of the PANSS negative subscale, we found that the composite score across items was not an adequate measure of negative symptom severity. Consequently, we chose an exploratory statistical approach involving Principal Component Analysis which yielded one significant component clustering into two significant symptom trajectories: 1) Subtle but constant decrease in negative symptom severity (N = 323; 72%), and 2) symptom instability across visits (N = 19; 4%). Explorative analytic methods as presented here may pave the way for more efficient and sensitive methods of analyzing negative symptom response in research and in clinical practice.

Sexual dysfunction and hyperprolactinemia in schizophrenia before and after six weeks of D2/3 receptor blockade - An exploratory study.

Sexual side-effects along with antipsychotic treatment may be linked to hyperprolactinemia and dopamine D2 receptor blockade. High prevalence of sexual dysfunction in un-medicated patients challenges the notion of sexual dysfunction as merely a side-effect of antipsychotic medication. Sexual dysfunction was assessed in fifty-six initially antipsychotic-naïve patients with schizophrenia using the UKU (Udvalget for Kliniske Undersøgelser) questionnaire. Serum-prolactin was obtained before and after six weeks of D2/3 receptor blockade with amisulpride. At baseline 68% of patients reported one or more items of sexual dysfunction (males > females,), but the cumulative load of sexual dysfunction was similar in males and females. After 6 weeks treatment with amisulpride (mean dose 279 mg/day), 65% of patients reported one or more items of sexual dysfunctions (females > males). There was a significant sex*time interaction on mean sexual dysfunction load. All patients developed hyperprolactinaemia, and a significant effect of time and sex was found on s-prolactin (females > males). The results support that patients with schizophrenia report high levels of sexual dysfunction before antipsychotic exposure. After treatment, sexual side-effects were more frequent in females, coinciding with pronounced serum-prolactin increases. These findings suggest sex differences in sexual dysfunction before and after antipsychotic treatment.

Cortical structures and their clinical correlates in antipsychotic-naïve schizophrenia patients before and after 6 weeks of dopamine D2/3 receptor antagonist treatment.

BACKGROUND: Schizophrenia has been associated with changes in both cortical thickness and surface area, but antipsychotic exposure, illness progression and substance use may confound observations. In antipsychotic-naïve schizophrenia patients, we investigated cortical thickness and surface area as well as mean curvature before and after monotherapy with amisulpride, a relatively selective dopamine D2/3 receptor antagonist. METHODS: Fifty-six patients and 59 matched healthy controls (HCs) underwent T1-weighted 3T magnetic resonance imaging. Forty-one patients and 51 HCs were re-scanned. FreeSurfer-processed baseline, follow-up values and symmetrized percentage changes (SPC) in cortical structures were analysed using univariate analysis of variance. Clinical measures comprised psychopathology ratings, assessment of functioning and tests of premorbid and current intelligence. We applied false discovery rate correction to account for multiple comparisons. RESULTS: At baseline, groups did not differ in cortical thickness or surface area; however, curvature in the left hemisphere was higher in patients (p = 0.015). In both patients and HCs, higher curvature was associated with lower premorbid (p = 0.009) and current intelligence (p 0.43). Cortical thickness SPC was negatively associated with symptom improvement (p = 0.002). CONCLUSIONS: Schizophrenia appears associated with subtle, yet clinically relevant aberrations in cortical structures. Mean curvature holds promise as a sensitive supplement to cortical thickness and surface area to detect complex structural brain abnormalities.

Glucagon-like peptide-1 receptor agonists for antipsychotic-associated cardio-metabolic risk factors: A systematic review and individual participant data meta-analysis.

AIMS: To evaluate if glucagon-like peptide-1 receptor agonists (GLP-1RAs) reduce antipsychotic-associated body weight gain in patients with schizophrenia, when compared to controls. MATERIALS AND METHODS: We systematically searched PubMed/EMBASE/PsycINFO/Cochrane using the search terms '(antipsychotic and GLP-1RA)'. Individual participant data from studies randomizing patients to GLP-1RA or control were meta-analysed. The primary outcome was difference in body weight between GLP-1RA and control; secondary outcomes included cardio-metabolic variables and adverse drug reactions (ADRs). Multiple linear regression was conducted including sex, age, psychosis severity, metabolic variable, ADRs, and GLP-1RA agent. RESULTS: Three studies (exenatide once-weekly = 2; liraglutide once-daily = 1) provided participant-level data (n = 164, age = 40.0 ± 11.1 years, body weight = 105.8 ± 20.8 kg). After 16.2 ± 4.0 weeks of treatment, body weight loss was 3.71 kg (95% CI = 2.44-4.99 kg) greater for GLP-1RA versus control (p < 0.001), number-needed-to-treat ≥5% body weight loss = 3.8 (95% CI = 2.6-7.2). Waist circumference, body mass index, HbA1c, fasting glucose and visceral adiposity were each significantly lower with GLP-1RA. Sex, age, psychosis severity, nausea, any ADR, and GLP-1RA agent did not significantly impact outcomes. Body weight loss with GLP-1RAs was greater for clozapine/olanzapine-treated patients (n = 141) than other antipsychotics (n = 27) (4.70 kg, 95% CI = 3.13-6.27 vs. 1.5 kg, 95% CI = -1.47-4.47) (p < 0.001). Nausea was more common with GLP-1RAs than control (53.6% vs. 27.5%, p = 0.002, number-needed-to-harm = 3.8). CONCLUSION: GLP-1RAs are effective and tolerable for antipsychotic-associated body weight gain, particularly clozapine/olanzapine-treated patients. With few included patients, further studies are required before making routine use recommendations for GLP-1RAs.

The impact of schizophrenia and intelligence on the relationship between age and brain volume.

Age has been shown to have an impact on both grey (GM) and white matter (WM) volume, with a steeper slope of age-related decline in schizophrenia compared to healthy controls. In schizophrenia, the relation between age and brain volume is further complicated by factors such as lower intelligence, antipsychotic medication, and cannabis use, all of which have been shown to have independent effects on brain volume. In a study of first-episode, antipsychotic-naïve schizophrenia patients (N = 54) and healthy controls (N = 56), we examined the effects of age on whole brain measures of GM and WM volume, and whether these relationships were moderated by schizophrenia and intelligence (IQ). Secondarily, we examined lifetime cannabis use as a moderator of the relationship between age and brain volume. Schizophrenia patients had lower GM volumes than healthy controls but did not differ on WM volume. We found an age effect on GM indicating that increasing age was associated with lower GM volumes, which did not differ between groups. IQ did not have a direct effect on GM, but showed a trend-level interaction with age, suggesting a greater impact of age with lower IQ. There were no age effects on WM volume, but a direct effect of IQ, with higher IQ showing an association with larger WM volume. Lifetime cannabis use did not alter these findings significantly. This study points to effects of schizophrenia on GM early in the illness, before antipsychotic treatment is initiated, suggesting that WM changes may occur later in the disease process.

Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients.

BACKGROUND: A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation. METHODS: Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission. RESULTS: Cognitive data significantly classified patients from controls (accuracies = 60-69%; p values = 0.0001-0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission. CONCLUSIONS: In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.

Bone Status in Obese, Non-diabetic, Antipsychotic-Treated Patients, and Effects of the Glucagon-Like Peptide-1 Receptor Agonist Exenatide on Bone Turnover Markers and Bone Mineral Density.

Background: Low bone mineral density (BMD) may constitute an underestimated comorbidity in schizophrenia patients undergoing long-term antipsychotic treatment. Glucagon-like peptide 1 (GLP-1) receptor agonists are antidiabetic drugs, which may also affect bone turnover. Methods: In planned secondary analyses of a 3 months, double-blind, randomized, placebo-controlled trial (n = 45), we explored effects of the GLP-1 receptor agonist exenatide 2 mg once-weekly (n = 23), or placebo (n = 22) on bone turnover markers (BTMs) and BMD in chronic, obese, antipsychotic-treated patients with schizophrenia spectrum disorder. Baseline BTMs were compared to sex- and age-adjusted reference values from a Danish population cohort, and T- and Z-scores were calculated for BMD. Results: In women (n = 24), all baseline BTM measurements of procollagen type I N-terminal propeptide (PINP) and C-terminal cross-linking telopeptide of type I collagen (CTX) were within reference values. In men (n = 21), 5% displayed lower PINP and 14% displayed lower CTX. One patient displayed BMD Z-score < -2, and 23% of patients (17% of women and 29% of men) displayed -2.5 < T-scores < -1 indicating osteopenia, but none had osteoporosis. After treatment, PINP decreased at trend level significance (P = 0.05), and body mass index BMD increased for L2-L4 (P = 0.016). No changes in bone markers were significant after correction for mean prolactin levels. Conclusions: Sex- and age-adjusted measures of bone status in chronic, obese, antipsychotic-treated patients appeared comparable to the reference population. Subtle changes in bone markers during 3 months exenatide treatment may suggest beneficial effects of GLP-1 receptor agonists on bone status in antipsychotic-treated patients, and further studies should consider the potential influence of prolactin.

Deficient prepulse inhibition of the startle reflex in schizophrenia using a cross-modal paradigm.

OBJECTIVES: To investigate whether the typically reported deficient sensorimotor gating in patients with schizophrenia using unimodal paradigms can also be detected by a cross-modal paradigm which made use of an electrocutaneous-acoustic coupling of stimuli. METHODS: Twenty-one male schizophrenia patients took part in a prepulse inhibition (PPI) paradigm with an electrocutaneous prepulse and an acoustic startle-eliciting pulse. Their results were compared with those from nineteen healthy males. RESULTS: As expected, the patients showed significantly lower PPI than controls. No associations were found between measures of illness severity and PPI. DISCUSSION: To the best of our knowledge, this is the first study showing reduced PPI in patients with schizophrenia by using an electrocutaneous-acoustic prepulse-pulse combination. Hence, this study gives further evidence of a modality-independent sensorimotor gating deficit in schizophrenia. Furthermore, as PPI was also lower than usual in controls using unimodal paradigms, results are interpreted in favour of longer processing times of the electrocutaneous prepulse, which probably led to a shorter perceived stimulus onset asynchrony (SOA) in the brain.

Effect of GLP-1 receptor agonist treatment on body weight in obese antipsychotic-treated patients with schizophrenia: a randomized, placebo-controlled trial.

AIMS: Schizophrenia is associated with cardiovascular co-morbidity and a reduced life-expectancy of up to 20 years. Antipsychotics are dopamine D2 receptor antagonists and are the standard of medical care in schizophrenia, but the drugs are associated with severe metabolic side effects such as obesity and diabetes. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are registered for treatment of both obesity and type 2 diabetes. We investigated metabolic effects of the GLP-1RA, exenatide once-weekly, in non-diabetic, antipsychotic-treated, obese patients with schizophrenia. MATERIAL AND METHODS: Antipsychotic-treated, obese, non-diabetic, schizophrenia spectrum patients were randomized to double-blinded adjunctive treatment with once-weekly subcutaneous exenatide (n = 23) or placebo (n = 22) injections for 3 months. The primary outcome was loss of body weight after treatment and repeated measures analysis of variance was used as statistical analysis. RESULTS: Between March 2013 and June 2015, 40 patients completed the trial. At baseline, mean body weight was 118.3 ± 16.0 kg in the exenatide group and 111.7 ± 18.0 kg in the placebo group, with no group differences ( P = .23). The exenatide and placebo groups experienced significant ( P = .004), however similar ( P = .98), weight losses of 2.24 ± 3.3 and 2.23 ± 4.4 kg, respectively, after 3 months of treatment. CONCLUSIONS: Treatment with exenatide once-weekly did not promote weight loss in obese, antipsychotic-treated patients with schizophrenia compared to placebo. Our results could suggest that the body weight-lowering effect of GLP-1RAs involves dopaminergic signaling, but blockade of other receptor systems may also play a role. Nevertheless, anti-obesity regimens effective in the general population may not be readily implemented in antipsychotic-treated patients with schizophrenia.

Data Driven Estimation of Imputation Error-A Strategy for Imputation with a Reject Option.

Missing data is a common problem in many research fields and is a challenge that always needs careful considerations. One approach is to impute the missing values, i.e., replace missing values with estimates. When imputation is applied, it is typically applied to all records with missing values indiscriminately. We note that the effects of imputation can be strongly dependent on what is missing. To help make decisions about which records should be imputed, we propose to use a machine learning approach to estimate the imputation error for each case with missing data. The method is thought to be a practical approach to help users using imputation after the informed choice to impute the missing data has been made. To do this all patterns of missing values are simulated in all complete cases, enabling calculation of the "true error" in each of these new cases. The error is then estimated for each case with missing values by weighing the "true errors" by similarity. The method can also be used to test the performance of different imputation methods. A universal numerical threshold of acceptable error cannot be set since this will differ according to the data, research question, and analysis method. The effect of threshold can be estimated using the complete cases. The user can set an a priori relevant threshold for what is acceptable or use cross validation with the final analysis to choose the threshold. The choice can be presented along with argumentation for the choice rather than holding to conventions that might not be warranted in the specific dataset.

Social cognition in patients at ultra-high risk for psychosis: What is the relation to social skills and functioning?

OBJECTIVE: Patients at ultra-high risk (UHR) for psychosis show significant impairments in functioning. It is essential to determine which factors influence functioning, as it may have implications for intervention strategies. This study examined whether social cognitive abilities and clinical symptoms are associated with functioning and social skills. METHODS: The study included 65 UHR patients and 30 healthy controls. Social cognitive function, social skills, and a broad range of functioning measures were assessed. RESULTS: The UHR patients demonstrated significant decrements on The Awareness of Social Inferences Task total score (p = .046, d = .51), and on the CANTAB emotion recognition task total percent correct (p = .023, d = .54) displaying particular difficulties in negative affect recognition. The patients exhibited significant impairments in social skills measured with the High Risk Social Challenge (p˂.001, d = 1.05). Aspects of emotion recognition were associated with role functioning and social skill performance. The level of attributional bias was associated with overall functioning, and theory of mind ability was associated with self-reported functioning. Negative symptoms were associated with all measures of functioning (p ≤ .05). CONCLUSION: Significant impairments in social cognition and social skills were found in UHR patients. The patients' social cognitive function was associated with overall functioning and social skills. Negative symptoms appear to play an important role for functioning. Research is needed to investigate how the relations between social cognition, social skills and functioning develop from the UHR state to the stage of manifest illness. Research into how deficits in social cognition and social skills can be ameliorated in UHR patients is warranted.