Trends in the utilization of a peer-supported youth hotline.

BACKGROUND: Peer-supported youth hotlines have been in operation for many years but studies on the acceptance of this hotline model or on the demographics of the callers are lacking. This study was performed to examine the utilization of a metropolitan peer-supported youth hotline between 2010 and 2016. METHOD: The change in demographics, contact channels, and the reasons for contact were analyzed with standard linear regression analysis in 67,478 contacts over 7 years. RESULTS: The data revealed a significant increase in hotline utilization from 8008 annual contacts in 2010 to 12,409 contacts in 2016 (p = .03). The majority of contacts were made by 15-year-old and 16-year-old girls, but contacts by children aged 13 years old and younger have also increased significantly over the years (p = .003). In 2016, anxiety and stress were among the leading reasons for contact (20.14%), followed by sadness and depression (17.21%), suicidal ideation (14.18%), and self-harm (8.15%). Recommendations for follow-up with outside mental health resources were made in 56.22% of contacts. More than 60% of contacts had found information about the hotline on the Internet. More than 30% used text messaging to reach out to the hotline. CONCLUSIONS: Our data indicate that adolescents increasingly utilize a peer-supported youth hotline to get help for mental health concerns. Therefore, it should be explored whether this hotline model could also be used for prevention and early intervention. KEY PRACTITIONER MESSAGE: Peer-supported youth hotlines are well accepted and frequently utilized by adolescents to get help for mental health issues. Our data indicate that peer-supported youth hotlines could be utilized to identify youth at risk for depression and suicide. Further research should evaluate whether peer-supported youth hotlines could serve in the prevention and in early mental health intervention, and how they could be effectively linked to other mental health resources in the community.

Comprehensive comparison of monotherapies for psychiatric hospitalization risk in bipolar disorders.

OBJECTIVES: This study compared 29 drugs for risk of psychiatric hospitalization in bipolar disorders, addressing the evidence gap on the >50 drugs used by US patients for treatment. METHODS: The Truven Health Analytics MarketScan® database was used to identify 190 894 individuals with bipolar or schizoaffective disorder who filled a prescription for one of 29 drugs of interest: lithium, first- or second-generation antipsychotics, mood-stabilizing anticonvulsants, and antidepressants. Competing risks regression survival analysis was used to compare drugs for risk of psychiatric hospitalization, adjusting for patient age, sex, comorbidities, and pretreatment medications. Other competing risks were ending monotherapy and non-psychiatric hospitalization. RESULTS: Three drugs were associated with significantly lower risk of psychiatric hospitalization than lithium: valproate (relative risk [RR] = 0.80, P = 3.20 × 10-4 ), aripiprazole (RR = 0.80, P = 3.50 × 10-4 ), and bupropion (RR = 0.80, P = 2.80 × 10-4 ). Eight drugs were associated with significantly higher risk of psychiatric hospitalization: haloperidol (RR = 1.57, P = 9.40 × 10-4 ), clozapine (RR = 1.52, P = .017), fluoxetine (RR = 1.17, P = 3.70 × 10-3 ), sertraline (RR = 1.17, P = 3.20 × 10-3 ), citalopram (RR = 1.14, P = .013), duloxetine (RR = 1.24, P = 5.10 × 10-4 ), venlafaxine (RR = 1.33; P = 1.00 × 10-6 ), and ziprasidone (RR = 1.25; P = 6.20 × 10-3 ). CONCLUSIONS: This largest reported retrospective observational study on bipolar disorders pharmacotherapy to date demonstrates that the majority of patients end monotherapy within 2 months after treatment start. The risk of psychiatric hospitalization varied almost two-fold across individual medications. The data add to the evidence favoring lithium and mood stabilizer use in short-term bipolar disorder management. The findings that the dopaminergic drugs aripiprazole and bupropion had better outcomes than other members of their respective classes and that antidepressant outcomes may vary by baseline mood polarity merit further investigation.

Systemic challenges in bipolar disorder management: A patient-centered approach.

OBJECTIVES: As part of a series of Patient-Centered Outcomes Research Institute-funded large-scale retrospective observational studies on bipolar disorder (BD) treatments and outcomes, we sought the input of patients with BD and their family members to develop research questions. We aimed to identify systemic root causes of patient-reported challenges with BD management in order to guide subsequent studies and initiatives. METHODS: Three focus groups were conducted where patients and their family members (total n = 34) formulated questions around the central theme, "What do you wish you had known in advance or over the course of treatment for BD?" In an affinity mapping exercise, participants clustered their questions and ranked the resulting categories by importance. The research team and members of our patient partner advisory council further rated the questions by expected impact on patients. Using a Theory of Constraints systems thinking approach, several causal models of BD management challenges and their potential solution were developed with patients using the focus group data. RESULTS: A total of 369 research questions were mapped to 33 categories revealing 10 broad themes. The top priorities for patient stakeholders involved pharmacotherapy and treatment alternatives. Analysis of causal relationships underlying 47 patient concerns revealed two core conflicts: for patients, whether or not to take pharmacotherapy, and for mental health services, the dilemma of care quality vs quantity. CONCLUSIONS: To alleviate the core conflicts identified, BD management requires a coordinated multidisciplinary approach including: improved access to mental health services, objective diagnostics, sufficient provider visit time, evidence-based individualized treatment, and psychosocial support.

Hypothyroidism risk compared among nine common bipolar disorder therapies in a large US cohort.

OBJECTIVES: Thyroid abnormalities in patients with bipolar disorder (BD) have been linked to lithium treatment for decades, yet other drugs have been less well studied. Our objective was to compare hypothyroidism risk for lithium versus the anticonvulsants and second-generation antipsychotics commonly prescribed for BD. METHODS: Administrative claims data on 24,574 patients with BD were analyzed with competing risk survival analysis. Inclusion criteria were (i) one year of no prior hypothyroid diagnosis nor BD drug treatment, (ii) followed by at least one thyroid test during BD monotherapy on lithium carbonate, mood-stabilizing anticonvulsants (lamotrigine, valproate, oxcarbazepine, or carbamazepine) or antipsychotics (aripiprazole, olanzapine, risperidone, or quetiapine). The outcome was cumulative incidence of hypothyroidism per drug, in the presence of the competing risk of ending monotherapy, adjusted for age, sex, physician visits, and thyroid tests. RESULTS: Adjusting for covariates, the four-year cumulative risk of hypothyroidism for lithium (8.8%) was 1.39-fold that of the lowest risk therapy, oxcarbazepine (6.3%). Lithium was non-statistically significantly different from quetiapine. While lithium conferred a higher risk when compared to all other treatments combined as a group, hypothyroidism risk error bars overlapped for all drugs. Treatment (p = 3.86e-3), age (p = 6.91e-10), sex (p = 3.93e-7), and thyroid testing (p = 2.79e-87) affected risk. Patients taking lithium were tested for hypothyroidism 2.26-3.05 times more frequently than those on other treatments. CONCLUSIONS: Thyroid abnormalities occur frequently in patients with BD regardless of treatment. Therefore, patients should be regularly tested for clinical or subclinical thyroid abnormalities on all therapies and treated as indicated to prevent adverse effects of hormone imbalances on mood.

Getting ready for the Human Phenome Project: the 2012 forum of the Human Variome Project.

A forum of the Human Variome Project (HVP) was held as a satellite to the 2012 Annual Meeting of the American Society of Human Genetics in San Francisco, California. The theme of this meeting was "Getting Ready for the Human Phenome Project." Understanding the genetic contribution to both rare single-gene "Mendelian" disorders and more complex common diseases will require integration of research efforts among many fields and better defined phenotypes. The HVP is dedicated to bringing together researchers and research populations throughout the world to provide the resources to investigate the impact of genetic variation on disease. To this end, there needs to be a greater sharing of phenotype and genotype data. For this to occur, many databases that currently exist will need to become interoperable to allow for the combining of cohorts with similar phenotypes to increase statistical power for studies attempting to identify novel disease genes or causative genetic variants. Improved systems and tools that enhance the collection of phenotype data from clinicians are urgently needed. This meeting begins the HVP's effort toward this important goal.

Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study.

BACKGROUND: Incomplete suicidality coding in administrative claims data is a known obstacle for observational studies. With most of the negative outcomes missing from the data, it is challenging to assess the evidence on treatment strategies for the prevention of self-harm in bipolar disorder (BD), including pharmacotherapy and psychotherapy. There are conflicting data from studies on the drug-dependent risk of self-harm, and there is major uncertainty regarding the preventive effect of monotherapy and drug combinations. OBJECTIVE: The aim of this study was to compare all commonly used BD pharmacotherapies, as well as psychotherapy for the risk of self-harm, in a large population of commercially insured individuals, using self-harm imputation to overcome the known limitations of this outcome being underrecorded within US electronic health care records. METHODS: The IBM MarketScan administrative claims database was used to compare self-harm risk in patients with BD following 65 drug regimens and drug-free periods. Probable but uncoded self-harm events were imputed via machine learning, with different probability thresholds examined in a sensitivity analysis. Comparators included lithium, mood-stabilizing anticonvulsants (MSAs), second-generation antipsychotics (SGAs), first-generation antipsychotics (FGAs), and five classes of antidepressants. Cox regression models with time-varying covariates were built for individual treatment regimens and for any pharmacotherapy with or without psychosocial interventions ("psychotherapy"). RESULTS: Among 529,359 patients, 1.66% (n=8813 events) had imputed and/or coded self-harm following the exposure of interest. A higher self-harm risk was observed during adolescence. After multiple testing adjustment (P≤.012), the following six regimens had higher risk of self-harm than lithium: tri/tetracyclic antidepressants + SGA, FGA + MSA, FGA, serotonin-norepinephrine reuptake inhibitor (SNRI) + SGA, lithium + MSA, and lithium + SGA (hazard ratios [HRs] 1.44-2.29), and the following nine had lower risk: lamotrigine, valproate, risperidone, aripiprazole, SNRI, selective serotonin reuptake inhibitor (SSRI), "no drug," bupropion, and bupropion + SSRI (HRs 0.28-0.74). Psychotherapy alone (without medication) had a lower self-harm risk than no treatment (HR 0.56, 95% CI 0.52-0.60; P=8.76×10-58). The sensitivity analysis showed that the direction of drug-outcome associations did not change as a function of the self-harm probability threshold. CONCLUSIONS: Our data support evidence on the effectiveness of antidepressants, MSAs, and psychotherapy for self-harm prevention in BD. TRIAL REGISTRATION: ClinicalTrials.gov NCT02893371; https://clinicaltrials.gov/ct2/show/NCT02893371.

Use of longitudinal data in genetic studies in the genome-wide association studies era: summary of Group 14.

Participants analyzed actual and simulated longitudinal data from the Framingham Heart Study for various metabolic and cardiovascular traits. The genetic information incorporated into these investigations ranged from selected single-nucleotide polymorphisms to genome-wide association arrays. Genotypes were incorporated using a broad range of methodological approaches including conditional logistic regression, linear mixed models, generalized estimating equations, linear growth curve estimation, growth modeling, growth mixture modeling, population attributable risk fraction based on survival functions under the proportional hazards models, and multivariate adaptive splines for the analysis of longitudinal data. The specific scientific questions addressed by these different approaches also varied, ranging from a more precise definition of the phenotype, bias reduction in control selection, estimation of effect sizes and genotype associated risk, to direct incorporation of genetic data into longitudinal modeling approaches and the exploration of population heterogeneity with regard to longitudinal trajectories. The group reached several overall conclusions: (1) The additional information provided by longitudinal data may be useful in genetic analyses. (2) The precision of the phenotype definition as well as control selection in nested designs may be improved, especially if traits demonstrate a trend over time or have strong age-of-onset effects. (3) Analyzing genetic data stratified for high-risk subgroups defined by a unique development over time could be useful for the detection of rare mutations in common multifactorial diseases. (4) Estimation of the population impact of genomic risk variants could be more precise. The challenges and computational complexity demanded by genome-wide single-nucleotide polymorphism data were also discussed.

Imputation and characterization of uncoded self-harm in major mental illness using machine learning.

OBJECTIVE: We aimed to impute uncoded self-harm in administrative claims data of individuals with major mental illness (MMI), characterize self-harm incidence, and identify factors associated with coding bias. MATERIALS AND METHODS: The IBM MarketScan database (2003-2016) was used to analyze visit-level self-harm in 10 120 030 patients with ≥2 MMI codes. Five machine learning (ML) classifiers were tested on a balanced data subset, with XGBoost selected for the full dataset. Classification performance was validated via random data mislabeling and comparison with a clinician-derived "gold standard." The incidence of coded and imputed self-harm was characterized by year, patient age, sex, U.S. state, and MMI diagnosis. RESULTS: Imputation identified 1 592 703 self-harm events vs 83 113 coded events, with areas under the curve >0.99 for the balanced and full datasets, and 83.5% agreement with the gold standard. The overall coded and imputed self-harm incidence were 0.28% and 5.34%, respectively, varied considerably by age and sex, and was highest in individuals with multiple MMI diagnoses. Self-harm undercoding was higher in male than in female individuals and increased with age. Substance abuse, injuries, poisoning, asphyxiation, brain disorders, harmful thoughts, and psychotherapy were the main features used by ML to classify visits. DISCUSSION: Only 1 of 19 self-harm events was coded for individuals with MMI. ML demonstrated excellent performance in recovering self-harm visits. Male individuals and seniors with MMI are particularly vulnerable to self-harm undercoding and may be at risk of not getting appropriate psychiatric care. CONCLUSIONS: ML can effectively recover unrecorded self-harm in claims data and inform psychiatric epidemiological and observational studies.

Diabetes mellitus risk for 102 drugs and drug combinations used in patients with bipolar disorder.

OBJECTIVE: To compare the largest set of bipolar disorder pharmacotherapies to date (102 drugs and drug combinations) for risk of diabetes mellitus (DM). METHODS: The IBM MarketScan® database was used to retrospectively analyze data on 565,253 adults with bipolar disorder without prior glucose metabolism-related diagnoses. The pharmacotherapies compared were lithium, mood-stabilizing anticonvulsants, antipsychotics, and antidepressants (monotherapy and multi-class polypharmacy). Cox regression modeling included fixed pre-treatment covariates and time-varying drug exposure covariates to estimate the hazard ratio (HR) of each treatment versus "No drug". RESULTS: The annual incidence of new-onset diabetes during the exposure period was 3.09 % (22,951 patients). The HR of drug-dependent DM ranged from 0.79 to 2.37. One-third of the studied pharmacotherapies, including most of the antipsychotic-containing regimens, had a significantly higher risk of DM compared to "No drug". A significantly lower DM risk was associated with lithium, lamotrigine, oxcarbazepine and bupropion monotherapies, selective serotonin reuptake inhibitors (SSRI) mono-class therapy and several drug combinations containing bupropion and an SSRI. As additional drugs were combined in more complex polypharmacy, higher HRs were consistently observed. CONCLUSIONS: There is an increased risk of diabetes mellitus associated with antipsychotic and psychotropic polypharmacy use in bipolar disorder. The evidence of a lower-than-baseline risk of DM with lamotrigine, oxcarbazepine, lithium, and bupropion monotherapy should be further investigated.

Polymorphisms in the GRIA1 gene region in psychotic bipolar disorder.

We reported previously a significant linkage signal between psychotic bipolar disorder (BP) and microsatellite markers on chromosome 5q31-34 in the National Institute of Mental Health Bipolar Genetics Initiative (NIMH-BPGI) data set, Wave 1. In an attempt to fine-map this linkage signal we genotyped 1,134 single nucleotide polymorphisms (SNPs) under the linkage peak in 23 informative families (131 individuals) with evidence of linkage. We tested family based association in the presence of linkage with the computer software package FBAT. The most significant association in these families was with a SNP in the second intron of GRIA1 (alpha-amino-3-hydroxy-5-methyl-4-isoxazole proprionic acid (AMPA) subunit 1 receptor gene) (rs490922, Z-score = 3.3, P = 0.001). The analysis of 37 additional families with psychotic BP from NIMH-BPGI data sets, Waves 2, 3, and 4 revealed a signal at a SNP in intron 5 of the GRIA1 gene (rs4385264, Z-score = 3.2, P-value = 0.002). A combined analysis of all 60 families continued to support evidence for association of GRIA1 with psychotic BP; however, individual SNPs could not be replicated across datasets. The AMPA1 receptor has been shown to influence cognitive function, such as working memory and reward learning. Our findings suggest that variations in this receptor may contribute to the pathophysiology of BP with psychotic features in some families.

Comparison of 71 bipolar disorder pharmacotherapies for kidney disorder risk: The potential hazards of polypharmacy.

BACKGROUND: This study compared the largest set of bipolar disorder pharmacotherapies to date (71 drugs and drug combinations) for risk of kidney disorders (KDs). METHODS: This retrospective observational study used the IBM MarketScan® database to analyze data on 591,052 adults with bipolar disorder without prior nephropathy, for onset of KDs (of "moderate" or "high" severity) following psychopharmacotherapy (lithium, mood stabilizing anticonvulsants [MSAs], antipsychotics, antidepressants), or "No drug". Cox regression models included fixed pre-treatment covariates and time-varying drug exposure covariates to estimate the hazard ratio (HR) of each treatment versus "No drug". RESULTS: Newly observed KD occurred in 14,713 patients. No regimen had significantly lower risk of KDs than "No drug". The HR estimates ranged 0.86-2.66 for "all" KDs and 0.87-5.30 for "severe" KDs. As additional drugs were combined to compare more complex polypharmacies, higher HRs were consistently observed. Most regimens containing lithium, MSAs, or antipsychotics had a higher risk than "No drug" (p < 0.05). The risk for "all" and "severe" KDs was highest respectively on monoamine oxidase inhibitors (MAOIs) (HR = 2.66, p = 5.73 × 10-5), and a lithium-containing four-class combination (HR = 5.30, p = 2.46 × 10-9). The HR for lithium monotherapy was 1.82 (p = 4.73 × 10-17) for "severe" KDs. LIMITATIONS: The limitations inherent for an observational study were non-randomized assignment of patients to treatment groups, non-standardization of diagnostic decisions, and non-uniform quality of data collection. No correction was made for medication dosage. CONCLUSIONS: The findings support literature concerns about lithium nephrotoxicity and highlight the potential risks of MAOIs, MSAs, antipsychotics and psychotropic polypharmacy.

Preferences of Information Dissemination on Treatment for Bipolar Disorder: Patient-Centered Focus Group Study.

BACKGROUND: Patient education has taken center stage in successfully shared decision making between patients and health care providers. However, little is known about how patients with bipolar disorder typically obtain information on their illness and the treatment options available to them. OBJECTIVE: This study aimed to obtain the perspectives of patients with bipolar disorder and their family members on the preferred and most effectively used information channels on bipolar disorder and the available treatment options. METHODS: We conducted nine focus groups in Montana, New Mexico, and California, in which we surveyed 84 individuals including patients with bipolar disorder and family members of patients with bipolar disorder. The participants were recruited using National Alliance on Mental Illness mailing lists and websites. Written verbatim responses to semistructured questionnaires were analyzed using summative content analysis based on grounded theory. Two annotators coded and analyzed the data on the sentence or phrase level to create themes. Relationships between demographics and information channel were also examined using the Chi-square and Fisher exact tests. RESULTS: The focus group participants mentioned a broad range of information channels that were successfully used in the past and could be recommended for future information dissemination. The majority of participants used providers (74%) and internet-based resources (75%) as their main information sources. There was no association between internet use and basic demographics such as age or geographical region of the focus groups. Patients considered time constraints and the fast pace in which an overwhelming amount of information is often presented by the provider as major barriers to successful provider-patient interactions. If Web-based channels were used, the participants perceived information obtained through Web-based channels as more helpful than information received in the provider's office (P<.05). CONCLUSIONS: Web-based resources are increasingly used by patients with bipolar disorder and their family members to educate themselves about the disease and its treatment. Although provider-patient interactions are frequently perceived to be burdened with time constraints, Web-based information sources are considered reliable and helpful. Future research should explore how high-quality websites could be used to empower patients and improve provider-patient interactions with the goal of enhancing shared decision making between patients and providers.

Predictors and outcomes of somatization in bipolar I disorder: A latent class mixture modeling approach.

BACKGROUND: Mood disorders are often associated with somatic symptoms. The role of somatic symptoms on disease progression in unipolar depression is substantially better characterized than that role in bipolar disorder. Moreover, the contribution of comorbid anxiety disorders and medical illness is not well understood. METHOD: We investigated 527 patients with bipolar I disorder clustered within 102 families using a latent class approach. Predictors were added stepwise into the model. Anxiety and commonly associated medical illnesses were added as covariates. RESULTS: The rate of somatic symptoms in this sample was 73% (mean 1.7 symptoms), and 27.3% had a comorbid anxiety disorder. A two-class model, with a subgroup at high-risk for somatization, gave the best fit to the data. Multilevel mixture modeling accounted for family clusters. Somatic symptoms were independently associated with disease severity, defined as earlier age of first seeking psychiatric help (x = 21.7 vs x = 24.7, p = 0.005) and first psychiatric hospitalization (x = 25.7 vs x = 28.2, p = 0.03), greater probability of attempting suicide (x = 0.41 vs x = 0.32, p = 0.047), and rapid-cycling disease course (x = 0.57 vs x = 0.36, p < 0.001). Persons with few or no somatic symptoms were more likely to be hospitalized for severe mania (x = 0.63 vs x = 0.51; p = 0.013), but did not significantly differ in hospitalization for severe depression. LIMITATIONS: The study is correlational. Information on pharmacologic interventions and comorbid diseases was limited. CONCLUSIONS: Somatic symptoms in bipolar disorder could be an independent indicator for disease severity, suicidality, and rapid-cycling disease course. In severe mental illness, somatic and psychological symptoms must be jointly addressed.

Bipolar disorder and diabetes mellitus: evidence for disease-modifying effects and treatment implications.

BACKGROUND: Bipolar disorder refers to a group of chronic psychiatric disorders of mood and energy levels. While dramatic psychiatric symptoms dominate the acute phase of the diseases, the chronic course is often determined by an increasing burden of co-occurring medical conditions. High rates of diabetes mellitus in patients with bipolar disorder are particularly striking, yet unexplained. Treatment and lifestyle factors could play a significant role, and some studies also suggest shared pathophysiology and risk factors. OBJECTIVE: In this systematic literature review, we explored data around the relationship between bipolar disorder and diabetes mellitus in recently published population-based cohort studies with special focus on the elderly. METHODS: A systematic search in the PubMed database for the combined terms "bipolar disorder" AND "elderly" AND "diabetes" in papers published between January 2009 and December 2015 revealed 117 publications; 7 studies were large cohort studies, and therefore, were included in our review. RESULTS: We found that age- and gender- adjusted risk for diabetes mellitus was increased in patients with bipolar disorder and vice versa (odds ratio range between 1.7 and 3.2). DISCUSSION: Our results in large population-based cohort studies are consistent with the results of smaller studies and chart reviews. Even though it is likely that heterogeneous risk factors may play a role in diabetes mellitus and in bipolar disorder, growing evidence from cell culture experiments and animal studies suggests shared disease mechanisms. Furthermore, disease-modifying effects of bipolar disorder and diabetes mellitus on each other appear to be substantial, impacting both treatment response and outcomes. CONCLUSIONS: The risk of diabetes mellitus in patients with bipolar disorder is increased. Our findings add to the growing literature on this topic. Increasing evidence for shared disease mechanisms suggests new disease models that could explain the results of our study. A better understanding of the complex relationship between bipolar disorder and diabetes mellitus could lead to novel therapeutic approaches and improved outcomes.

Medically unexplained somatic symptoms and bipolar spectrum disorders: A systematic review and meta-analysis.

BACKGROUND: Patients with bipolar spectrum disorders (BSD) frequently report medically unexplained somatic symptoms. However, the prevalence and the consequences for treatment and outcome are currently unknown. METHODS: To estimate the prevalence of somatic symptoms in BSD, we conducted a systematic review and meta-analysis of empirical studies published between 1980 and 2015. The odds for somatic symptoms in BSD were compared with unipolar depression (UPD) and general population or mixed psychiatric controls. Studies were retrieved from four electronic databases utilizing Boolean operations and reference list searches. Pooled data estimates were derived using random-effects methods. RESULTS: Out of 2634 studies, 23 were eligible for inclusion, yielding an N of 106,785 patients. The estimated prevalence of somatic symptoms in BSD was 47.8%. The estimated prevalence of BSD in persons with somatic symptoms was 1.4%. Persons with BSD had a higher prevalence of somatic symptoms compared with population or mixed psychiatric controls (OR 1.82, 95% CI 1.14-2.92). Persons with BSD had a similar prevalence of somatic symptoms compared with UPD controls (OR 0.99, 95% CI 0.68-1.44). LIMITATIONS: This study is correlational; thus causal inferences cannot be made. Reporting of somatic symptoms likely varies with BSD severity and subtype. Some studies reported insufficient information regarding comorbid medical conditions and medications. CONCLUSIONS: Persons with BSD suffer from somatic symptoms at a rate nearly double that of the general population, a rate similar to persons with UPD. Our results suggest the utility of an integrated care model in which primary care and specialist physicians collaborate with mental health professionals to jointly address psychological and bodily symptoms.

Toward a Deeper Understanding of the Genetics of Bipolar Disorder.

Bipolar disorder is a common, complex psychiatric disorder characterized by mania and depression. The disease aggregates in families, but despite much effort, it has been difficult to delineate the basic genetic model or identify specific genetic risk factors. Not only single gene Mendelian transmission and common variant hypotheses but also multivariate threshold models and oligogenic quasi-Mendelian modes of inheritance have dominated the discussion at times. Almost complete sequence information of the human genome and falling sequencing costs now offer the opportunity to test these models in families in which the disorder is transmitted over several generations. Exome-wide sequencing studies have revealed an astonishing number of rare and potentially damaging mutations in brain-expressed genes that could have contributed to the disease manifestation. However, the statistical analysis of these data has been challenging, because genetic risk factors displayed a high degree of dissimilarity across families. This scenario is not unique to bipolar disorder, but similar results have also been found in schizophrenia, a potentially related psychiatric disorder. Recently, our group has published data which supported an oligogenic genetic model of transmission in a family with bipolar disorder. In this family, three affected siblings shared rare, damaging mutations in multiple genes, which were linked to stress response pathways. These pathways are also the target for drugs frequently used to treat bipolar disorder. This article discusses these findings in the context of previously proclaimed disease models and suggests future research directions, including biological confirmation and phenotype stratification as an approach to disease heterogeneity.

Comorbid substance use disorders in schizophrenia: a latent class approach.

Schizophrenia is a complex psychiatric disorder with a characteristic disease course and heterogeneous etiology. While substance use disorders and a family history of psychosis have individually been identified as risk factors for schizophrenia, it is less well understood if and how these factors are related. To address this deficiency, we examined the relationship between substance use disorders and family history of psychosis in a sample of 1219 unrelated patients with schizophrenia. The lifetime rate of substance use disorders in this sample was 50%, and 30% had a family history of psychosis. Latent class mixture modeling identified three distinct patient subgroups: (1) individuals with low probability of substance use disorders; (2) patients with drug and alcohol abuse, but no symptoms of dependence; and (3) patients with substance dependence. Substance use was related to being male, to a more severe disease course, and more acute symptoms at assessment, but not to an earlier age of onset of schizophrenia or a specific pattern of positive and negative symptoms. Furthermore, substance use in schizophrenia was not related to a family history of psychosis. The results suggest that substance use in schizophrenia is an independent risk factor for disease severity and onset.

Genetics of bipolar disorder.

Bipolar disorder is a common, complex genetic disorder, but the mode of transmission remains to be discovered. Many researchers assume that common genomic variants carry some risk for manifesting the disease. The research community has celebrated the first genome-wide significant associations between common single nucleotide polymorphisms (SNPs) and bipolar disorder. Currently, attempts are under way to translate these findings into clinical practice, genetic counseling, and predictive testing. However, some experts remain cautious. After all, common variants explain only a very small percentage of the genetic risk, and functional consequences of the discovered SNPs are inconclusive. Furthermore, the associated SNPs are not disease specific, and the majority of individuals with a "risk" allele are healthy. On the other hand, population-based genome-wide studies in psychiatric disorders have rediscovered rare structural variants and mutations in genes, which were previously known to cause genetic syndromes and monogenic Mendelian disorders. In many Mendelian syndromes, psychiatric symptoms are prevalent. Although these conditions do not fit the classic description of any specific psychiatric disorder, they often show nonspecific psychiatric symptoms that cross diagnostic boundaries, including intellectual disability, behavioral abnormalities, mood disorders, anxiety disorders, attention deficit, impulse control deficit, and psychosis. Although testing for chromosomal disorders and monogenic Mendelian disorders is well established, testing for common variants is still controversial. The standard concept of genetic testing includes at least three broad criteria that need to be fulfilled before new genetic tests should be introduced: analytical validity, clinical validity, and clinical utility. These criteria are currently not fulfilled for common genomic variants in psychiatric disorders. Further work is clearly needed before genetic testing for common variants in psychiatric disorders should be established.

Rare Genomic Variants Link Bipolar Disorder with Anxiety Disorders to CREB-Regulated Intracellular Signaling Pathways.

Bipolar disorder is a common, complex, and severe psychiatric disorder with cyclical disturbances of mood and a high suicide rate. Here, we describe a family with four siblings, three affected females and one unaffected male. The disease course was characterized by early-onset bipolar disorder and co-morbid anxiety spectrum disorders that followed the onset of bipolar disorder. Genetic risk factors were suggested by the early onset of the disease, the severe disease course, including multiple suicide attempts, and lack of adverse prenatal or early life events. In particular, drug and alcohol abuse did not contribute to the disease onset. Exome sequencing identified very rare, heterozygous, and likely protein-damaging variants in eight brain-expressed genes: IQUB, JMJD1C, GADD45A, GOLGB1, PLSCR5, VRK2, MESDC2, and FGGY. The variants were shared among all three affected family members but absent in the unaffected sibling and in more than 200 controls. The genes encode proteins with significant regulatory roles in the ERK/MAPK and CREB-regulated intracellular signaling pathways. These pathways are central to neuronal and synaptic plasticity, cognition, affect regulation and response to chronic stress. In addition, proteins in these pathways are the target of commonly used mood-stabilizing drugs, such as tricyclic antidepressants, lithium, and valproic acid. The combination of multiple rare, damaging mutations in these central pathways could lead to reduced resilience and increased vulnerability to stressful life events. Our results support a new model for psychiatric disorders, in which multiple rare, damaging mutations in genes functionally related to a common signaling pathway contribute to the manifestation of bipolar disorder.

Propensity score analysis in the Genetic Analysis Workshop 17 simulated data set on independent individuals.

Genetic Analysis Workshop 17 provided simulated phenotypes and exome sequence data for 697 independent individuals (209 case subjects and 488 control subjects). The disease liability in these data was influenced by multiple quantitative traits. We addressed the lack of statistical power in this small data set by limiting the genomic variants included in the study to those with potential disease-causing effect, thereby reducing the problem of multiple testing. After this adjustment, we could readily detect two common variants that were strongly associated with the quantitative trait Q1 (C13S523 and C13S522). However, we found no significant associations with the affected status or with any of the other quantitative traits, and the relationship between disease status and genomic variants remained obscure. To address the challenge of the multivariate phenotype, we used propensity scores to combine covariates with genetic risk factors into a single risk factor and created a new phenotype variable, the probability of being affected given the covariates. Using the propensity score as a quantitative trait in the case-control analysis, we again could identify the two common single-nucleotide polymorphisms (C13S523 and C13S522). In addition, this analysis captured the correlation between Q1 and the affected status and reduced the problem of multiple testing. Although the propensity score was useful for capturing and clarifying the genetic contributions of common variants to the disease phenotype and the mediating role of the quantitative trait Q1, the analysis did not increase power to detect rare variants.