Personalized management of bipolar disorder

Integrated proteomic and genomic analysis to identify predictive biomarkers for valproate response in bipolar disorder: a 6-month follow-up study

BACKGROUND

Several genetic studies have been undertaken to elucidate the intricate interplay between genetics and drug responses in bipolar disorder (BD). However, there has been notably limited research on biomarkers specifically linked to valproate, with only a few studies investigating integrated proteomic and genomic factors in response to valproate treatment. Therefore, this study aimed to identify biological markers for the therapeutic response to valproate treatment in BD. Patients with BD in remission were assessed only at baseline, whereas those experiencing acute mood episodes were evaluated at three points (baseline, 8 ± 2 weeks, and 6 ± 1 months). The response to valproate treatment was measured using the Alda scale, with individuals scoring an Alda A score ≥ 5 categorized into the acute-valproate responder (acute-VPAR) group. We analyzed 158 peptides (92 proteins) from peripheral blood samples using multiple reaction monitoring mass spectrometry, and proteomic result-guided candidate gene association analyses, with 1,627 single nucleotide variants (SNVs), were performed using the Korean chip.

RESULTS

The markers of 37 peptides (27 protein) showed temporal upregulation, indicating possible association with response to valproate treatment. A total of 58 SNVs in 22 genes and 37 SNVs in 16 genes showed nominally significant associations with the Alda A continuous score and the acute-VPAR group, respectively. No SNVs reached the genome-wide significance threshold; however, three SNVs (rs115788299, rs11563197, and rs117669164) in the secreted phosphoprotein 2 gene reached a gene-based false discovery rate-corrected significance threshold with response to valproate treatment. Significant markers were associated with the pathophysiological processes of bipolar disorders, including the immune response, acute phase reaction, and coagulation cascade. These results suggest that valproate effectively suppresses mechanisms associated with disease progression.

CONCLUSIONS

The markers identified in this study could be valuable indicators of the underlying mechanisms associated with response to valproate treatment.

Melatonin MT1 receptors as a target for the psychopharmacology of bipolar disorder: A translational study

The treatment of bipolar disorder (BD) still remains a challenge. Melatonin (MLT), acting through its two receptors MT1 and MT2, plays a key role in regulating circadian rhythms which are dysfunctional in BD. Using a translational approach, we examined the implication and potential of MT1 receptors in the pathophysiology and psychopharmacology of BD. We employed a murine model of the manic phase of BD (Clock mutant (ClockΔ19) mice) to study the activation of MT1 receptors by UCM871, a selective partial agonist, in behavioral pharmacology tests and in-vivo electrophysiology. We then performed a high-resolution Nuclear Magnetic Resonance study on isolated membranes to characterize the molecular mechanism of interaction of UCM871. Finally, in a cohort of BD patients, we investigated the link between clinical measures of BD and genetic variants located in the MT1 receptor and CLOCK genes. We demonstrated that: 1) UCM871 can revert behavioral and electrophysiological abnormalities of ClockΔ19 mice; 2) UCM871 promotes the activation state of MT1 receptors; 3) there is a significant association between the number of severe manic episodes and MLT levels, depending on the genetic configuration of the MT1 rs2165666 variant. Overall, this work lends support to the potentiality of MT1 receptors as target for the treatment of BD.

Suicide Assessment and Prevention in Bipolar Disorder: How Current Evidence Can Inform Clinical Practice

Bipolar disorder is associated with a considerable risk of suicide, and this fact must be incorporated into management of all patients with the condition. This article highlights the importance of a more nuanced understanding of the factors associated with the increased risk of suicidal behavior in people diagnosed as having bipolar disorder and interventions that could mitigate it. Several sociodemographic, clinical, environmental, and other variables have been associated with suicide attempts or deaths in bipolar disorder. Youths with bipolar disorder are a particularly vulnerable group, and their trajectory of illness could be modified by early interventions. Several medications have been studied regarding their relationship to suicide risk in bipolar disorder, and interventional psychiatry is a newer area of research focus. Finally, community-based approaches can be incorporated into a comprehensive approach to suicide prevention. This article summarizes the current understanding of key variables that can help inform a clinical risk assessment of individuals and interventions that can be employed in suicide prevention in bipolar disorder.

Chronotype is associated with affective temperaments, clinical severity and worse treatment outcomes in bipolar disorders: results from a two-center, cross-sectional study

OBJECTIVE

The present study was aimed at investigating the clinical correlates of evening chronotype in a population of subjects suffering from bipolar disorders (BD).

METHODS

We assessed chronotype using the Morningness-Eveningness Questionnaire. We administered the brief Temperament Evaluation of Memphis, Pisa, and San Diego, the Barratt Impulsiveness Scale, and the Alda Scale to evaluate affective temperaments, impulsiveness, and response to mood stabilisers. We performed bivariate analyses and ran a logistic regression model to analyse clinical variables associated with evening chronotype.

RESULTS

In our sample (n = 178), subjects with an evening chronotype (n = 56, 31.5%) more often suffered from BD type I and reported higher prevalence of seasonality, antidepressant-induced mood switches, psychotic, aggressive, mixed, and anxiety features, and substance use disorders. The number of lifetime suicide attempts and mood episodes was higher in this subgroup. Depressive, cyclothymic, irritable, and anxious temperament scores were higher among evening-chronotype subjects, who also displayed greater levels of impulsiveness and worse treatment response. At the logistic regression, evening chronotype was associated with depressive and irritable temperaments.

CONCLUSIONS

Subjects with evening chronotype display higher clinical severity and worse BD course. Clinicians should evaluate the presence of evening chronotype in BD subjects, especially in those with irritable or depressive temperament.Key pointsEvening chronotype is a frequent clinical feature in subjects suffering from bipolar disorders (BD);Affective temperaments, particularly depressive and irritable, are associated with evening chronotype in BD;Evening chronotype underpins higher severity of the clinical picture in BD, as well as a worse response to mood stabiliser treatment;Circadian preferences should be systematically assessed in subjects suffering from BD, with particular attention to evening preference.

The Minimum Feature Set for Designing Mobile Apps to Support Bipolar Disorder-Affected Patients: Proposal of Essential Functions and Requirements

Research conducted on mobile apps providing mental health services has concluded that patients with mental disorders tend to use such apps to maintain mental health balance technology may help manage and monitor issues like bipolar disorder (BP). This study was conducted in four steps to identify the features of designing a mobile application for BP-affected patients including (1) a literature search, (2) analyzing existing mobile apps to examine their efficiency, (3) interviewing patients affected with BP to discover their needs, and 4) exploring the points of view of experts using a dynamic narrative survey. Literature search and mobile app analysis resulted in 45 features, which were later reduced to 30 after the experts were surveyed about the project. The features included the following: mood monitoring, sleep schedule, energy level evaluation, irritability, speech level, communication, sexual activity, self-confidence level, suicidal thoughts, guilt, concentration level, aggressiveness, anxiety, appetite, smoking or drug abuse, blood pressure, the patient's weight and the side effects of medication, reminders, mood data scales, diagrams or charts of the collected data, referring the collected data to a psychologist, educational information, sending feedbacks to patients using the application, and standard tests for mood assessment. The first phase of analysis should consider an expert and patient view survey, mood and medication tracking, as well as communication with other people in the same situation are the most features to be considered. The present study has identified the necessity of apps intended to manage and monitor bipolar patients to maximize efficiency and minimize relapse and side effects.

Polygenic Risk and Episode Polarity Among Individuals With Bipolar Disorder

OBJECTIVE

The authors investigated associations between polygenic liabilities for bipolar disorder, major depression, and schizophrenia and episode polarity among individuals with bipolar disorder.

METHODS

The sample consisted of 2,705 individuals diagnosed with bipolar disorder at Danish psychiatric hospitals between January 1995 and March 2017. DNA was obtained from dried blood spots collected at birth as part of routine screening. Polygenic risk scores (PRSs) for bipolar disorder, major depression, and schizophrenia were generated using a meta-PRS method combining internally and externally trained components. Associations between PRS and polarity at first episode, polarity at any episode, and number of episodes with a given polarity were evaluated for each disorder-specific PRS using logistic and negative binominal regressions adjusted for the other two PRSs, age, sex, genotype platform, and five ancestral principal components.

RESULTS

PRS for bipolar disorder was positively associated with any manic episodes (odds ratio=1.23, 95% CI=1.09-1.38). PRS for depression was positively associated with any depressive (odds ratio=1.11, 95% CI=1.01-1.23) and mixed (odds ratio=1.15, 95% CI=1.03-1.28) episodes and negatively associated with any manic episodes (odds ratio=0.76, 95% CI=0.69-0.84). PRS for schizophrenia was positively associated with any manic episodes (odds ratio=1.13, 95% CI=1.01-1.27), but only when psychotic symptoms were present (odds ratio for psychotic mania: 1.27, 95% CI=1.05-1.54; odds ratio for nonpsychotic mania: 1.06, 95% CI=0.93-1.20). These patterns were similar for first-episode polarity and for the number of episodes within each pole.

CONCLUSIONS

PRSs for bipolar disorder, major depression, and schizophrenia are associated with episode polarity and psychotic symptoms in a congruent manner among individuals with bipolar disorder.

Investigation of Genetic Variants Associated with Tryptophan Metabolite Levels via Serotonin and Kynurenine Pathways in Patients with Bipolar Disorder

The kynurenine pathway (KP) may play a role in the pathophysiology of bipolar disorder (BD). We conducted a genome-wide association study (GWAS) to identify genetic variants associated with the plasma levels of the metabolites of tryptophan (TRP) via the serotonin (5-HT) and kynurenine (KYN) pathways in 44 patients with BD and 45 healthy controls. We assessed whether variants that were differentially associated with metabolite levels based on the diagnostic status improved the prediction accuracy of BD using penalized regression approaches. We identified several genetic variants that were significantly associated with metabolites (5-HT, 5-hydroxytryptophan (5-HTP), TRP, and quinolinic acid (QA) or metabolite ratios (5-HTP/TRP and KYN/TRP) and for which the diagnostic status exerted a significant effect. The inclusion of genetic variants led to increased accuracy in the prediction of the BD diagnostic status. Specifically, we obtained an accuracy of 0.77 using Least Absolute Shrinkage and Selection Operator (LASSO) regression. The predictors retained as informative in this model included body mass index (BMI), the levels of TRP, QA, and 5-HT, the 5-HTP/TRP ratio, and genetic variants associated with the levels of QA (rs6827515, rs715692, rs425094, rs4645874, and rs77048355) and TRP (rs292212) or the 5-HTP/TRP ratio (rs7902231). In conclusion, our study identified statistically significant associations between metabolites of TRP via the 5-HT and KYN pathways and genetic variants at the genome-wide level. The discriminative performance of penalized regression models incorporating clinical, genetic, and metabolic predictors warrants a follow-up analysis of this panel of determinants.

Why lithium should be used in patients with bipolar disorder? A scoping review and an expert opinion paper

INTRODUCTION

Lithium treatment is considered the gold standard for the long-term management of bipolar disorder and recurrent unipolar depression. It is also extremely effective in other psychiatric conditions characterized by impulsivity and aggression, and for the prevention of suicidal behaviours.

AREAS COVERED

This paper provides a scoping review and an expert commentary regarding the use of lithium in adult patients. Available information about efficacy, tolerability, dosing, and switching is analyzed, and the strategies that may be most useful in real-world clinical settings are highlighted.

EXPERT OPINION

Lithium is effective on different domains of bipolar disorder, including the long-term prevention of recurrences of affective episodes, management of acute mania as well as in the prophylaxis of all affective episodes. Lithium has been defined a 'forgotten drug,' since its use in routine clinical practice has been declined over the last 20 or 30 years. Reasons for this trend include lack of adequate training on the management of lithium side effects. Considering its efficacy, use of lithium in ordinary clinical practice should be promoted. Several strategies, such as using slow-release formulations, can be easily implemented in order to minimize lithium side effects and improve its tolerability profile.

Substance use disorders in bipolar disorders: Clinical correlates and treatment response to mood stabilizers

BACKGROUND

Substance use disorders (SUD) in bipolar disorders (BD) present relevant impact on psychopathological features and illness course. The present study was aimed at analyzing the clinical correlates of this comorbidity.

METHODS

In- and outpatients suffering from BD were recruited. Socio-demographic and clinical characteristics were collected. Subjects underwent a psychopathological assessment evaluating affective temperaments and impulsiveness. The appraisal of treatment response to mood stabilizers was conducted with the Alda Scale. Bivariate analyses were used to compare subjects suffering from BD with (SUD-BD) or without comorbid SUD (nSUD-BD) (p<0.05). A logistic regression model was performed to identify specific correlates of SUD in BD.

RESULTS

Among the 161 included subjects, 63 (39.1%) were diagnosed with comorbid SUD. SUD-BD subjects showed younger age at onset (p = 0.003) and higher prevalence of BD type I diagnosis (BDI) (p<0.001). Furthermore, lifetime mixed features (p<0.001), psychotic symptoms (p<0.001), suicide attempts (p = 0.002), aggression (p = 0.003), antidepressant-induced manic switch (p = 0.003), and poor treatment response (p<0.001) were more frequent in the SUD-BD subgroup. At the logistic regression, SUD revealed a positive association with BD type I diagnosis (Odds Ratio (OR) 4.77, 95% CI 1.66-13.71, p = 0.004) and mixed features (OR 2.54, 95% CI 1.17-5.53, p = 0.019).

LIMITATIONS

The cross-sectional study design and the relatively small sample size may limit the generalizability of the findings. The retrospective evaluation of comorbid SUD could have biased the outcome assessment.

CONCLUSIONS

Subjects with BD and SUD are characterized by higher clinical severity and require careful assessment of treatment response.

Predominant Polarity and Polarity Index of Maintenance Treatments for Bipolar Disorder: A Validation Study in a Large Naturalistic Sample in Italy

Background and Objectives: Predominant polarity (PP) may be a useful course specifier in at least a significant proportion of patients with Bipolar Disorder (BD), being associated with several clinically relevant correlates. Emerging evidence suggests that the concept of PP might influence the selection of maintenance treatments, based on a drug polarity index (PI) which measures the greater antidepressive vs. antimanic preventive efficacy of mood stabilizers over long-term maintenance treatment. In this study, we aimed to validate the PI in a large sample of Italian BD patients with accurate longitudinal characterization of the clinical course, which ensured a robust definition of the PP. Materials and Methods: Our sample is comprised of 653 patients with BD, divided into groups based on the predominant polarity (manic/hypomanic predominant polarity—MPP, depressive predominant polarity—DPP and no predominant polarity). Subsequently we calculated the mean total polarity index for each group, and we compared the groups. Results: When we examined the mean PI of treatments prescribed to individuals with DPP, MPP and no predominant polarity, calculated using two different methods, we failed to find significant differences, with the exception of the PI calculated with the Popovic method and using the less stringent criterion for predominant polarity (PP_50%). Conclusions: Future prospective studies are needed in order to determine whether the predominant polarity is indeed one clinical factor that might guide the clinician in choosing the right mood stabilizer for BD maintenance treatment.

Pharmacological treatment profiles in the FACE-BD cohort: An unsupervised machine learning study, applied to a nationwide bipolar cohort✰

BACKGROUND

Despite thorough and validated clinical guidelines based on bipolar disorders subtypes, large pharmacological treatment heterogeneity remains in these patients. There is limited knowledge about the different treatment combinations used and their influence on patient outcomes. We attempted to determine profiles of patients based on their treatments and to understand the clinical characteristics associated with these treatment profiles.

METHODS

This multicentre longitudinal study was performed on a French nationwide bipolar cohort database. We performed hierarchical agglomerative clustering to search for clusters of individuals based on their treatments during the first year following inclusion. We then compared patient clinical characteristics according to these clusters.

RESULTS

Four groups were identified among the 1795 included patients: group 1 ("heterogeneous" n = 1099), group 2 ("lithium" n = 265), group 3 ("valproate" n = 268), and group 4 ("lamotrigine" n = 163). Proportion of bipolar 1 disorder, in groups 1 to 4 were: 48.2%, 57.0%, 48.9% and 32.5%. Groups 1 and 4 had greater functional impact at baseline and a less favorable clinical and functioning evolution at one-year follow-up, especially on GAF and FAST scales.

LIMITATIONS

The one-year period used for the analysis of mood stabilizing treatments remains short in the evolution of bipolar disorder.

CONCLUSIONS

Treatment profiles are associated with functional evolution of patients and were not clearly determined by bipolar subtypes. These profiles seem to group together common patient phenotypes. These findings do not seem to be influenced by the duration of disease prior to inclusion and neither by the number of treatments used during the follow-up period.

Manic-Depressive Cycles in Bipolar Disorder: Clinical and Treatment Implications

INTRODUCTION

Cycle patterns of bipolar disorders (BDs) have been previously shown to be associated with clinical characteristics and response to lithium salts. Here, we evaluated the distribution of different types of manic-depressive cycles in a large sample of patients with BD. The associations between a mania-depression-interval (MDI) course and depression-mania-interval (DMI) course with sociodemographic/clinical factors were also assessed in order to define specific clinical profiles.

METHODS

In this cross-sectional study, 806 patients with BD admitted to the Psychiatric Unit of San Luigi Gonzaga Hospital in Orbassano and Molinette Hospital in Turin, Italy, were recruited. Patients were grouped according to the following course patterns: MDI, DMI, continuous cycling (CC, <4 episodes/year without intervals), rapid cycling (RC, ≥4 episodes/year), and irregular (IRR) cycling. We compared several sociodemographic and clinical variables in an MDI versus DMI course by means of ANOVA and Pearson χ2 with Bonferroni correction.

RESULTS

Bipolar cycles were distributed as follows: 50.2% IRR course, 31.5% MDI course, 16% DMI course, 1.2% CC, and 1% RC. Compared to DMI course, patients with an MDI course were more often men, younger, with an earlier onset, a manic polarity onset, and more lifetime compulsory admissions. They were more frequently treated with lithium and antipsychotics. Patients with a DMI course had older age at diagnosis and at first mood-stabilizer treatment and were more often misdiagnosed with a major depressive disorder. These patients were more commonly treated with anticonvulsants, and they had more frequently failed treatment trials with lithium salts in the past.

CONCLUSION

This study supports the utility of classifying BD according to their course patterns. This classification holds prognostic as well as therapeutic implications.

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics

There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

OBJECTIVES

The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement.

METHOD

We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019.

RESULT

We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%.

CONCLUSIONS

Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.

Clinical correlates associated with the long-term response of bipolar disorder patients to lithium, valproate or lamotrigine: A retrospective study

Background

Although mood stabilizers such as lithium (LIT), valproate (VAL), and lamotrigine (LMT) appear to be efficacious treatments for bipolar disorder (BD) in research settings, the long-term response to these mood stabilizers in clinical practice is highly variable among individuals. Thus, the present study examined the characteristics associated with good or insufficient responses to long-term treatment with LIT, VAL, or LMT for BD.

Methods

This study retrospectively analyzed the medical records of patients who visited an outpatient clinic with a diagnosis of BD I or II. Data from patients who were treated with one of three mood stabilizing medications (LIT, VAL, or LMT) for more than 6 months were selected, and the long-term treatment responses were evaluated using the Alda scale. For the purposes of this study, two response categories were formed: insufficient response (ISR), including non-response or poor response (Alda total score ≤ 6), and good response (GR; Alda total score ≥ 7).

Results

Of the 645 patients included in the present study, 172 were prescribed LIT, 320 were prescribed VAL, and 153 were prescribed LMT for at least 6 months. A binary logistic regression analysis revealed that a diagnosis of BD II (odds ratio [OR], 8.868; 95% confidence interval [CI], 1.123–70.046; p = 0.038), comorbid alcohol/substance use disorder (OR, 4.238; 95% CI, 1.154–15.566; p = 0.030), and a history of mixed episodes (OR, 4.363; 95% CI, 1.191–15.985; p = 0.026) were significant predictors of LIT-ISR. Additionally, a depressive-predominant polarity significantly predicted LMT-GR (OR, 8.586; 95% CI, 2.767–26.644; p < 0.001).

Conclusion

The present findings demonstrated that patients with a diagnosis of BD II, a comorbid alcohol/substance problem, or a history of mixed episodes were not likely to respond to LIT treatment. Additionally, LMT might be a better treatment choice for patients with a depressive-predominant polarity.

Biomarkers in aggression

Aggressive behavior exerts an enormous impact on society remaining among the main causes of worldwide premature death. Effective primary interventions, relying on predictive models of aggression that show adequate sensitivity and specificity are currently lacking. One strategy to increase the accuracy and precision of prediction would be to include biological data in the predictive models. Clearly, to be included in such models, biological markers should be reliably associated with the specific trait under study (i.e., diagnostic biomarkers). Aggression, however, is phenotypically highly heterogeneous, an element that has hindered the identification of reliable biomarkers. However, current research is trying to overcome these challenges by focusing on more homogenous aggression subtypes and/or by studying large sample size of aggressive individuals. Further advance is coming by bioinformatics approaches that are allowing the integration of inter-species biological data as well as the development of predictive algorithms able to discriminate subjects on the basis of the propensity toward aggressive behavior. In this review we first present a brief summary of the available evidence on neuroimaging of aggression. We will then treat extensively the data on genetic determinants, including those from hypothesis-free genome-wide association studies (GWAS) and candidate gene studies. Transcriptomic and neurochemical biomarkers will then be reviewed, and we will dedicate a section on the role of metabolomics in aggression. Finally, we will discuss how biomarkers can inform the development of new pharmacological tools as well as increase the efficacy of preventive strategies.

Psychotic versus non-psychotic bipolar disorder: Socio-demographic and clinical profiles in an Italian nationwide study

OBJECTIVE

Psychotic versus non-psychotic patients with bipolar disorder have been traditionally associated with different unfavorable clinical features. In this study on bipolar Italian patients, we aimed to compare clinical and demographic differences between psychotic and non-psychotic individuals, exploring clinical factors that may favor early diagnosis and personalized treatment.

METHODS

A total of 1671 patients (males: n = 712 and females: n = 959; bipolar type 1: n = 1038 and bipolar type 2: n = 633) from different psychiatric departments were compared according to the lifetime presence of psychotic symptoms in terms of socio-demographic and clinical variables. Chi-square tests for qualitative variables and Student's t-tests for quantitative variables were performed for group comparison, and a multivariable logistic regression was performed, considering the lifetime psychotic symptoms as dependent variables and socio-demographic/clinical characteristics as independent variables.

RESULTS

Psychotic versus non-psychotic bipolar subjects resulted to: be more frequently unemployed (p < 0.01) and never married/partnered (p < 0.01); have an earlier age at onset (p < 0.01); more frequently receive a first diagnosis different from a mood disorder (p < 0.01); have a shorter duration of untreated illness (p < 0.01); have a more frequently hypomanic/manic prevalent polarity (p < 0.01) and a prevalent manic-depressive type of cycling (p < 0.01); present a lower lifetime number of depressive episodes (p < 0.01), but have more manic episodes (p < 0.01); and less insight (p < 0.01) and more hospitalizations in the last year (p < 0.01). Multivariable regression analysis showed that psychotic versus non-psychotic bipolar patients received more frequently a first diagnosis different from bipolar disorder (odds ratio = 0.64, 95% confidence interval = [0.46, 0.90], p = 0.02) or major depressive disorder (odds ratio = 0.66, 95% confidence interval = [0.48, 0.91], p = 0.02), had more frequently a prevalent manic polarity (odds ratio = 1.84, 95% confidence interval = [1.14, 2.98], p < 0.01) and had a higher number of lifetime manic episodes (more than six) (odds ratio = 8.79, 95% confidence interval = [5.93, 13.05], p < 0.01).

CONCLUSION

Lifetime psychotic symptoms in bipolar disorder are associated with unfavorable socio-demographic and clinical features as well as with a more frequent initial misdiagnosis.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

Personalized and precision medicine as informants for treatment management of bipolar disorder

DSM-5 diagnostic categories, defined by a set of psychopathological symptoms are heterogeneous conditions that may include different biological entities, with distinct etiopathogenesis, different courses and requiring different treatment management. For bipolar disorder the major evidences for this lack of validity are the long paths before a proper diagnosis, the inconsistence of treatment guidelines, the long phases of pharmacological adjustment and the low average of long-term treatment response rates. Personalized medicine for mental disorders aims to couple established clinical-pathological indexes with new molecular profiling to create diagnostic, prognostic and therapeutic strategies precisely tailored to each patient. Regarding bipolar disorder, the clinical history and presentation are still the most reliable markers in stratifying patients and guiding therapeutic management, despite the research goes to great lengths to develop new neuropsychological or biological markers that can reliably predict individual therapy effectiveness. We provide an overview of the advancements in personalized medicine in bipolar disorder, with particular attention to how psychopathology, age at onset, comorbidity, course and staging, genetic and epigenetic, imaging and biomarkers can influence treatment management and provide an integration to the conventional treatment guidelines. This approach may offer a new and rational path for the development of treatments for targeted subgroups of patients with bipolar disorder.

Significant increasing of DISC2 long non-coding RNA expression as a potential biomarker in bipolar disorder

Bipolar disorder (BD) is a mental disorder that is often misdiagnosed with ineffective treatment. It has strong genetic component but unknown pathophysiology. Long non-coding RNAs (lncRNAs) have been recently recognized as one of the important genetic factors and are considered as one of the regulatory mechanisms of nervous system. Given that lncRNAs may be diagnostic biomarkers for BD, we aimed to quantify the levels of DISC1 and DISC2 lncRNA transcripts. The levels of DISC1 and DISC2 lncRNA were tested in peripheral blood mononuclear cells (PBMCs) of 50 BD and 50 controls by real-time PCR. In addition, we performed ROC curve analysis as well as correlation analysis between the gene expression and some clinical features of BD cases. Computational analysis of miRNAs binding sites and CpG Islands on DISC1 and DISC2 lncRNA was performed as well. Significant down-regulation of DISC1 and up-regulation of DISC2 were observed in BD cases compared with controls. The areas under the ROC curve (AUC) for DISC1 and DISC2 lncRNA were 0.76 and 0.68 respectively. There was no significant correlation between the levels of mRNA expression in PBMCs of BD patients and clinical features. These data demonstrated that DISC1 and DISC2 lncRNA expression was potentially associated with an increased risk of bipolar disorder and might involve several molecular mechanisms. Our results revealed that the transcript levels of DISC1 and DISC2 lncRNA could be considered as a good putative biomarker for individuals with bipolar disorder.

Evaluation of clarity of presentation and applicability of monitoring instructions for patients using lithium in clinical practice guidelines for treatment of bipolar disorder

OBJECTIVES

Clinical practice guidelines (CPGs) for treatment of bipolar disorder (BD) aim to provide guidance to health care professionals on monitoring of patients using lithium. The aim was to assess the clarity of presentation and applicability of monitoring instructions for patients using lithium in CPGs for treatment of BD.

METHODS

CPGs for treatment of BD were selected from acknowledged professional organizations from multiple continents. CPGs were rated on the clarity of presentation and applicability of lithium monitoring instructions using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool. The applicability of monitoring instructions was assessed according to the Systematic Information for Monitoring (SIM) score. Monitoring instructions were considered applicable when a SIM score of ≥3 was found.

RESULTS

The clarity of presentation for six out of the nine CPGs was good (>70%) using the AGREE II tool. Only one CPG scored >70% on applicability. Descriptions of the resource implications and facilitators of and barriers to monitoring were most often missing. All CPGs contained instructions for monitoring of lithium serum levels and renal and thyroid function. Information provided in monitoring instructions (n = 247) was in general applicable to clinical practice (77%) based on the SIM score. Overall, a median SIM score of 3 (interquartile range 3-4) was found.

CONCLUSIONS

Improvement of the applicability of CPGs is recommended, and can be achieved by describing the resource implications and facilitators of and barriers to monitoring. In addition, information on critical values and instructions on how to respond to aberrant monitoring parameters are needed. With such improvements, CPGs may better aid health care professionals to monitor patients using lithium.

Biomarkers for bipolar disorder: current status and challenges ahead

INTRODUCTION

Bipolar disorder (BD) is a chronic psychiatric disorder marked by clinical and pathophysiological heterogeneity. There is a high expectation that personalized approaches can improve the management of patients with BD. For that, identification and validation of potential biomarkers are fundamental. Areas covered: This manuscript will critically review the current status of different biomarkers for BD, including peripheral, genetic, neuroimaging, and neurophysiological candidates, discussing the challenges to move the field forward. Expert commentary: There are no lab or complementary tests currently recommended for the diagnosis or management of patients with BD. Panels composed by multiple biomarkers will probably contribute to stratifying patients according to their clinical stage, therapeutic response, and prognosis.

Can an Integrated Science Approach to Precision Medicine Research Improve Lithium Treatment in Bipolar Disorders?

Clinical practice guidelines identify lithium as a first line treatment for mood stabilization and reduction of suicidality in bipolar disorders (BD); however, most individuals show sub-optimal response. Identifying biomarkers for lithium response could enable personalization of treatment and refine criteria for stratification of BD cases into treatment-relevant subgroups. Existing systematic reviews identify potential biomarkers of lithium response, but none directly address the conceptual issues that need to be addressed to enhance translation of research into precision prescribing of lithium. For example, although clinical syndrome subtyping of BD has not led to customized individual treatments, we emphasize the importance of assessing clinical response phenotypes in biomarker research. Also, we highlight the need to give greater consideration to the quality of prospective longitudinal monitoring of illness activity and the differentiation of non-response from partial or non-adherence with medication. It is unlikely that there is a single biomarker for lithium response or tolerability, so this review argues that more research should be directed toward the exploration of biosignatures. Importantly, we emphasize that an integrative science approach may improve the likelihood of discovering the optimal combination of clinical factors and multimodal biomarkers (e.g., blood omics, neuroimaging, and actigraphy derived-markers). This strategy could uncover a valid lithium response phenotype and facilitate development of a composite prediction algorithm. Lastly, this narrative review discusses how these strategies could improve eligibility criteria for lithium treatment in BD, and highlights barriers to translation to clinical practice including the often-overlooked issue of the cost-effectiveness of introducing biomarker tests in psychiatry.