Assessment of a Person-Level Risk Calculator to Predict New-Onset Bipolar Spectrum Disorder in Youth at Familial Risk

Clinical prediction models in children that use repeated measurements with time-varying covariates: a scoping review

BACKGROUND

Emerging evidence suggests that clinical prediction models that use repeated (time-varying) measurements within each patient may have higher predictive accuracy than models that use patient information from a single measurement.

OBJECTIVE

To determine the breadth of the published literature reporting the development of clinical prediction models in children that use time-varying predictors.

DATA SOURCES

MEDLINE, EMBASE and Cochrane databases.

ELIGIBILITY CRITERIA

We included studies reporting the development of a multivariable clinical prediction model in children, with or without validation, to predict a repeatedly measured binary or time-to-event outcome and utilizing at least one repeatedly measured predictor.

SYNTHESIS METHODS

We categorized included studies by the method used to model time-varying predictors.

RESULTS

Of 99 clinical prediction model studies that had a repeated measurements data structure, only 27 (27%) used methods that incorporated the repeated measurements as time-varying predictors in a single model. Among these 27 time-varying prediction model studies, we grouped model types into nine categories: time-dependent Cox regression, generalized estimating equations, random effects model, landmark model, joint model, neural network, K-nearest neighbor, support vector machine and tree-based algorithms. Where there was comparison of time-varying models to single measurement models, using time-varying predictors improved predictive accuracy.

CONCLUSIONS

Various methods have been used to develop time-varying prediction models in children, but there is a paucity of pediatric time-varying models in the literature. Incorporating time-varying covariates in pediatric prediction models may improve predictive accuracy. Future research in pediatric prediction model development should further investigate whether incorporation of time-varying covariates improves predictive accuracy.

Beyond the Window of Risk? The Dutch Bipolar Offspring Study: 22-Year Follow-up

OBJECTIVE

Adolescent offspring of parents with bipolar disorder (BD) are at high risk to develop BD and other psychopathology, yet how this risk continues into middle adulthood remains unknown. This study aimed to determine the window of risk for BD and other psychopathology in offspring of parents with BD followed from adolescence into adulthood.

METHOD

This study reported on the 22-year follow-up assessment of the Dutch Bipolar Offspring Study, a fixed cohort study of 140 participants established in 1997. Offspring (n = 100; mean [SD] age = 38.28 [2.74] years) of parents with bipolar I disorder or bipolar II disorder were assessed at baseline and 1-, 5-, 12-, and 22-year follow-up.

RESULTS

No new BD onsets occurred since the 12-year follow-up (lifetime prevalence = 11%-13%; bipolar I disorder = 4%; bipolar II disorder = 7%). Lifetime prevalence of any mood disorder was 65%; for major depressive disorder, prevalence was 36%; and for recurrent mood episodes, prevalence was 37%. Prevalence of major depressive disorder more than doubled in the past decade. Point prevalence of any psychopathology peaked between 20 and 25 years (38%-46%), subsiding to 29% to 35% per year after age 30. Overall, 71% of offspring contacted mental health services since the last assessment.

CONCLUSION

The risk for homotypic transmission of BD in offspring of parents with BD is highest during adolescence. The heterotypic risk for mood disorder onset and recurrences continues over the life course. Severe mood disorders are often preceded by milder psychopathology, emphasizing the need for early identification and interventions. This study allows for better understanding of the onset and course of mood disorders and specific windows of risk in a familial high-risk population.

Putative Risk Biomarkers of Bipolar Disorder in At-risk Youth

Bipolar disorder is a highly heritable and functionally impairing disease. The recognition and intervention of BD especially that characterized by early onset remains challenging. Risk biomarkers for predicting BD transition among at-risk youth may improve disease prognosis. We reviewed the more recent clinical studies to find possible pre-diagnostic biomarkers in youth at familial or (and) clinical risk of BD. Here we found that putative biomarkers for predicting conversion to BD include findings from multiple sample sources based on different hypotheses. Putative risk biomarkers shown by perspective studies are higher bipolar polygenetic risk scores, epigenetic alterations, elevated immune parameters, front-limbic system deficits, and brain circuit dysfunction associated with emotion and reward processing. Future studies need to enhance machine learning integration, make clinical detection methods more objective, and improve the quality of cohort studies.

Longitudinal studies of bipolar patients and their families: translating findings to advance individualized risk prediction, treatment and research

BACKGROUND

Bipolar disorder is a broad diagnostic construct associated with significant phenotypic and genetic heterogeneity challenging progress in clinical practice and discovery research. Prospective studies of well-characterized patients and their family members have identified lithium responsive (LiR) and lithium non-responsive (LiNR) subtypes that hold promise for advancement.

METHOD

In this narrative review, relevant observations from published longitudinal studies of well-characterized bipolar patients and their families spanning six decades are highlighted. DSM diagnoses based on SADS-L interviews were decided in blind consensus reviews by expert clinicians. Genetic, neurobiological, and psychosocial factors were investigated in subsets of well-characterized probands and adult relatives. Systematic maintenance trials of lithium, antipsychotics, and lamotrigine were carried out. Clinical profiles that included detailed histories of the clinical course, symptom sets and disorders segregating in families were documented. Offspring of LiR and LiNR families were repeatedly assessed up to 20 years using KSADS-PL format interviews and DSM diagnoses and sub-threshold symptoms were decided by expert clinicians in blind consensus reviews using all available clinical and research data.

RESULTS

A characteristic clinical profile differentiated bipolar patients who responded to lithium stabilization from those who did not. The LiR subtype was characterized by a recurrent fully remitting course predominated by depressive episodes and a positive family history of episodic remitting mood disorders, and not schizophrenia. Response to lithium clustered in families and the characteristic clinical profile predicted lithium response, with the episodic remitting course being a strong correlate. There is accumulating evidence that genetic and neurobiological markers differ between LiR and LiNR subtypes. Further, offspring of bipolar parents subdivided by lithium response differed in developmental history, clinical antecedents and early course of mood disorders. Moreover, the nature of the emergent course bred true from parent to offspring, independent of the nature of emergent psychopathology.

CONCLUSIONS

Bipolar disorders are heterogeneous and response to long-term lithium is associated with a familial subtype with characteristic course, treatment response, family history and likely pathogenesis. Incorporating distinctive clinical profiles that index valid bipolar subtypes into routine practice and research will improve patient outcomes and advance the development and translation of novel treatment targets to improve prevention and early intervention.

Validation of a youth suicide risk calculator in an adult sample with bipolar disorder

BACKGROUND

Bipolar disorder (BD) conveys the highest risk of suicide of all mental disorders. We sought to externally validate a risk calculator (RC) of suicide attempts developed in youth with BD from the Course and Outcome of Bipolar Youth (COBY) study in an adult sample.

METHODS

A prospective cohort of adults with BD from the National Institute of Mental Health Collaborative Depression Study (CDS; N = 427; mean (+/- SD) age at intake (36 +/- 13 years)) was secondarily analyzed to validate the COBY RC for one-year risk of suicide attempts/deaths. Nine of the ten predictor variables from the COBY RC were available in the CDS and used: age, age of mood disorder onset, first and second (partial) degree family history of suicide, history of psychotic symptoms, substance use disorder, prior suicide attempt, socioeconomic status, and non-suicidal self-injury (prospectively, incompletely at baseline).

RESULTS

Over a mean (SD) follow-up of 19 (10) years, 29 % of the CDS sample attempted suicide. The RC predicted suicide attempts/deaths over one-year follow-up with an area under the receiver operating characteristic curve (AUC) of 0.78 (95 % CI 0.75-0.80). The RC performed slightly better in those with a younger age of mood disorder onset.

LIMITATIONS

Clinical samples may limit generalizability; the RC does not assess more acute suicide risk.

CONCLUSIONS

One-year risk of suicide attempts/deaths can be predicted with acceptable accuracy in youth and adults with BD, comparable to commonly used RCs to predict cardiovascular risk. This RC may help identify higher-risk individuals with BD for personalized treatment and research. https://cobysuicideattemptsrc.shinyapps.io/Shiny.

Development of a diverse osteoporosis screening tool for older US adults from the health and retirement study

Existing osteoporosis screening tools have limitations, including using race as a predictor, and development on homogeneous samples. This biases risk assessment of osteoporosis in diverse populations and increases health inequities. We develop a tool that relies on variables easily learned during point-of-care, known by individuals, and with negligible racial bias. Data from the 2012-2016 waves of the population-based cohort Health and Retirement Study (HRS) were used to build a predictive model of osteoporosis diagnosis on a 75 % training sample of adults ages 50-90. The model was validated on a 25 % holdout sample and a cross-sectional sample of American individuals ages 50-80 from the National Health and Nutrition Examination Survey (NHANES). Sensitivity and specificity were compared across sex and race/ethnicity. The model has high sensitivity in the HRS holdout sample (89.9 %), which holds for those identifying as female and across racial/ethnic groups. Specificity is 57.9 %, and area under the curve (AUC) is approximately 0.81. Validation in the NHANES sample using empirically measured osteoporosis produced relatively good values of sensitivity, specificity, and consistency across groups. The model was used to create a publicly-available, open-source tool called the Osteoporosis Health Equality (& Equity) Evaluation (OsteoHEE). The model provided high sensitivity for osteoporosis diagnosis, with consistently high results for those identifying as female, and across racial/ethnic groups. Use of this tool is expected to improve equity in screening and increase access to bone density scans for those at risk of osteoporosis. Validation on alternative samples is encouraged.

Young people at risk for developing bipolar disorder: Two-year findings from the multicenter prospective, naturalistic Early-BipoLife study

Early identification and intervention of individuals with an increased risk for bipolar disorder (BD) may improve the course of illness and prevent long‑term consequences. Early-BipoLife, a multicenter, prospective, naturalistic study, examined risk factors of BD beyond family history in participants aged 15-35 years. At baseline, positively screened help-seeking participants (screenBD at-risk) were recruited at Early Detection Centers and in- and outpatient depression and attention-deficit/hyperactivity disorder (ADHD) settings, references (Ref) drawn from a representative cohort. Participants reported sociodemographics and medical history and were repeatedly examined regarding psychopathology and the course of risk factors. N = 1,083 screenBD at-risk and n = 172 Ref were eligible for baseline assessment. Within the first two years, n = 31 screenBD at-risk (2.9 %) and none of Ref developed a manifest BD. The cumulative transition risk was 0.0028 at the end of multistep assessment, 0.0169 at 12 and 0.0317 at 24 months (p = 0.021). The transition rate with a BD family history was 6.0 %, 4.7 % in the Early Phase Inventory for bipolar disorders (EPIbipolar), 6.6 % in the Bipolar Prodrome Interview and Symptom Scale-Prospective (BPSS-FP) and 3.2 % with extended Bipolar At-Risk - BARS criteria). In comparison to help-seeking young patients from psychosis detection services, transition rates in screenBD at-risk participants were lower. The findings of Early-BipoLife underscore the importance of considering risk factors beyond family history in order to improved early detection and interventions to prevent/ameliorate related impairment in the course of BD. Large long-term cohort studies are crucial to understand the developmental pathways and long-term course of BD, especially in people at- risk.

Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features

BACKGROUND

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

METHODS

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

RESULTS

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11-0.361) and a balanced accuracy of 63.1% (95% CI 55.9-70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI -0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6-67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

CONCLUSIONS

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

Does the apple fall far from the tree? Commentary on Hafeman et al., early indicators of bipolar risk in preschool offspring of parents with bipolar disorder

Comorbid externalizing and internalizing disorders are common in offspring of a parent with bipolar I or II disorder. In some cases, the symptoms are harbingers of future bipolar spectrum disorder. Even when they are not, they are likely to be impairing to the child. Clinicians need to be better informed about how the history leading up to mania/hypomania unfolds, and what comorbid disorders are impairing in and of themselves. More information is needed about the parents' psychopathology, course of illness and response to treatment. Until we have data on how to prevent bipolar disorder, the best course of action is to treat the child's current impairing symptoms and render the parent as asymptomatic as possible.

Early indicators of bipolar risk in preschool offspring of parents with bipolar disorder

BACKGROUND

Offspring of parents with bipolar disorder (BD-I/II) are at increased risk to develop the disorder. Previous work indicates that bipolar spectrum disorder (BPSD) is often preceded by mood/anxiety symptoms. In school-age offspring of parents with BD, we previously built a risk calculator to predict BPSD onset, which generates person-level risk scores. Here, we test whether preschool symptoms predict school-age BPSD risk.

METHODS

We assessed 113 offspring of parents with BD 1-3 times during preschool years (2-5 years old) and then approximately every 2 years for a mean of 10.6 years. We used penalized (lasso) regression with linear mixed models to assess relationships between preschool mood, anxiety, and behavioral symptoms (parent-reported) and school-age predictors of BPSD onset (i.e., risk score, subthreshold manic symptoms, and mood lability), adjusting for demographics and parental symptomatology. Finally, we conducted survival analyses to assess associations between preschool symptoms and school-age onset of BPSD and mood disorder.

RESULTS

Of 113 preschool offspring, 33 developed new-onset mood disorder, including 19 with new-onset BPSD. Preschool irritability, sleep problems, and parental factors were lasso-selected predictors of school-age risk scores. After accounting for demographic and parental factors, preschool symptoms were no longer significant. Lasso regressions to predict mood lability and subthreshold manic symptoms yielded similar predictors (irritability, sleep problems, and parental affective lability), but preschool symptoms remained predictive even after adjusting for parental factors (ps < .005). Exploratory analyses indicated that preschool irritability univariately predicted new-onset BPSD (p = .02) and mood disorder (p = .02).

CONCLUSIONS

These results provide initial prospective evidence that, as early as preschool, youth who will develop elevated risk scores, mood lability, and subthreshold manic symptoms are already showing symptomatology; these preschool symptoms also predict new-onset BPSD. While replication of findings in larger samples is warranted, results point to the need for earlier assessment of risk and development of early interventions.

Bipolar At-Risk Criteria and Risk of Bipolar Disorder Over 10 or More Years

Importance

Predicting the onset of bipolar disorder (BD) could facilitate preventive treatments. Among risk measures, bipolar at-risk (BAR) criteria have shown promise in predicting onset of bipolar disorder in the first year in clinical cohorts; however, it is not known whether BAR criteria are associated with the onset of BD in the longer term.

Objective

To assess the association of BAR criteria with onset of BD over 10 to 13 years follow-up.

Design, Setting, and Participants

This prospective cohort study, completed between May 1, 2020, and November 7, 2022, included consenting people seeking help for nonpsychotic major mental health difficulties, including mood, personality, and substance use disorders, who were originally recruited at ages 15 to 25 years from a tertiary youth mental health setting in metropolitan Melbourne, Victoria, Australia, from May 1, 2008, to September 30, 2010.

Exposure

Meeting BAR criteria at baseline. Criteria included subthreshold mania, cyclothymic features, subthreshold depression, and family history of BD. A matched clinical comparison group was recruited from the same help-seeking population.

Main Outcomes and Measures

The primary outcome was expert consensus diagnosis of BD I or II based on the Mini International Neuropsychiatric Interview, self-reported information collected through online assessments, and linked data on mental health service utilization in Victoria over 10 to 13 years of follow-up.

Results

Among 69 eligible participants, follow-up data were available for 60 (88.2%). The mean (SD) age at the end of follow-up was 32.9 (2.8) years, and 49 (81.7%) were women. A total of 28 participants met BAR criteria, and 32 were in the comparison group. In the BAR group, 8 patients (28.6%) developed BD over a mean (SD) of 11.1 (0.7) years of follow-up, and no patients in the comparison group developed BD. The risk of developing BD was higher in the BAR group than in the non-BAR group (χ21 = 70.0; P < .001). The proportions of transitions to BD were equal in the first and second halves of the follow-up period.

Conclusions and relevance

In this cohort study of participants seeking care for mental health difficulties, patients meeting the BAR criteria were significantly more likely to transition to BD over a decade after ascertainment compared with patients not meeting the BAR criteria. The findings suggest that those meeting BAR criteria may benefit from longer-term monitoring and support. Evaluation of predictive properties in longer-term studies using a risk measure will help with implementation of BAR criteria in clinical settings.

Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes

The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve upon the early detection of this serious disorder. Patients with manifest BD display reduced volumes of the hippocampal subfields and amygdala nuclei. In this pre-registered analysis, we used structural MRI (n = 271, 7 sites) to compare volumes of hippocampus, amygdala and their subfields/nuclei between help-seeking subjects divided into risk groups for BD as estimated by BPSS-P, BARS and EPIbipolar. We performed between-group comparisons using linear mixed effects models for all three risk assessment tools. Additionally, we aimed to differentiate the risk groups using a linear support vector machine. We found no significant volume differences between the risk groups for all limbic structures during the main analysis. However, the SVM could still classify subjects at risk according to BPSS-P criteria with a balanced accuracy of 66.90% (95% CI 59.2-74.6) for 10-fold cross-validation and 61.9% (95% CI 52.0-71.9) for leave-one-site-out. Structural alterations of the hippocampus and amygdala may not be as pronounced in young people at risk; nonetheless, machine learning can predict the estimated risk for BD above chance. This suggests that neural changes may not merely be a consequence of BD and may have prognostic clinical value.

Detection of bipolar disorder in the prodromal phase: A systematic review of assessment instruments

BACKGROUND

Early detection of prodromal symptoms may contribute to improving the prognosis of patients with bipolar disorder (BD). The main objective of this systematic review is to present the different procedures for the identification of initial and relapse prodromes in these patients.

METHODS

PsycINFO, Web of Science and PubMed databases were searched using a predetermined strategy, until January 4, 2022. Then, by means of a regulated process, studies that used a BD prodrome detection procedure, in English-language and all ages participants were selected. Quantitative and qualitative studies were assessed using a modified version of the Newcastle-Ottawa Scale and by Critical Appraisals Skills Programme checklist, respectively.

RESULTS

Forty-five studies were selected. Of these, 26 used procedures for identifying initial prodromes (n = 8014) and 19 used procedures for detecting relapse prodromes (n = 1136). The interview was the most used method in the detection of both types of prodromes (k = 30 papers, n = 4068). It was variable in its degree of structure. Mobile applications and digital technologies are gaining importance in the detection of the relapse prodromes.

LIMITATIONS

A retrospective design in most papers, small samples sizes, existence of persistent subsyndromal symptoms and difficulty to identify the end of the prodrome and the onset of the disorder.

CONCLUSIONS

There is a wide variety of assessment instruments to detect prodromes in BD, among which the clinical interview is most frequently used. Future research should consider development of a brief tool to be applied in different formats to patients and family members.

Predicting bipolar disorder I/II in individuals at clinical high-risk: Results from a systematic review

INTRODUCTION

No systematic review has estimated the consistency and the magnitude of the risk of developing bipolar disorder I-II (BD-I/II) in individuals at clinical high risk for bipolar disorder (CHR-BD).

METHODS

PubMed and Web of Science databases were searched until April 2022 in this pre-registered (PROSPERO CRD42022346515) PRISMA-compliant systematic review to identify longitudinal studies in individuals meeting pre-defined CHR-BD criteria. The risk of bias was assessed using the Newcastle-Ottawa Scale, and results were systematically synthesized around CHR-BD criteria across follow-up periods and different subgroups.

RESULTS

Altogether, 13 studies were included reporting on nine prospective independent cohorts (n = 678 individuals at CHR-BD). The mean age of participants was 15.7 years (range 10.1-22.6 years), and 54.2 % were females. The most common CHR-BD subgroup was subthreshold mania (55.5 %), followed by BD-Not Otherwise Specified (BD-NOS: 33.3 %). Development of BD I/II ranged from 7.1 % to 23.4 % after 2 years. Development of BD-I ranged from 3.4 % at 4 years to 23 % at 8 years. Development of BD-II ranged from 10 % at 2 years to 63.8 % at 4 years. The risk of developing BD-I appeared highest in those meeting BD-NOS criteria (23 % at eight years). Predictors of development of BD were identified but remained mostly unreplicated. The quality of the included studies was moderate (NOS = 5.2 ± 1.1).

CONCLUSIONS

Emerging data from research studies point towards the promising utility of CHR-BD criteria. These studies may pave the way to the next generation of research, implementing detection, prognostication, and preventive interventions in individuals at CHR-BD identified and followed in clinical practice.

Anxiety onset in adolescents: a machine-learning prediction

Recent longitudinal studies in youth have reported MRI correlates of prospective anxiety symptoms during adolescence, a vulnerable period for the onset of anxiety disorders. However, their predictive value has not been established. Individual prediction through machine-learning algorithms might help bridge the gap to clinical relevance. A voting classifier with Random Forest, Support Vector Machine and Logistic Regression algorithms was used to evaluate the predictive pertinence of gray matter volumes of interest and psychometric scores in the detection of prospective clinical anxiety. Participants with clinical anxiety at age 18-23 (N = 156) were investigated at age 14 along with healthy controls (N = 424). Shapley values were extracted for in-depth interpretation of feature importance. Prospective prediction of pooled anxiety disorders relied mostly on psychometric features and achieved moderate performance (area under the receiver operating curve = 0.68), while generalized anxiety disorder (GAD) prediction achieved similar performance. MRI regional volumes did not improve the prediction performance of prospective pooled anxiety disorders with respect to psychometric features alone, but they improved the prediction performance of GAD, with the caudate and pallidum volumes being among the most contributing features. To conclude, in non-anxious 14 year old adolescents, future clinical anxiety onset 4-8 years later could be individually predicted. Psychometric features such as neuroticism, hopelessness and emotional symptoms were the main contributors to pooled anxiety disorders prediction. Neuroanatomical data, such as caudate and pallidum volume, proved valuable for GAD and should be included in prospective clinical anxiety prediction in adolescents.

A systematic review and meta-analysis of global and social functioning among people at risk of bipolar disorder

BACKGROUND

Functional status could predict development of bipolar disorder (BD) or have clinical significance. The relationship between BD risk and functioning is poorly understood. We undertook a systematic review examining the global and social functioning of those at risk for BD.

METHODS

We examined observational studies comparing a risk sample with healthy controls or full-threshold BD participants, using measures of global or social functioning. Risk status included family history of BD, meeting risk criteria, or having prodromal symptomatology, or premorbid functioning of persons with BD. Medline, PsycINFO, and Embase were searched. The Newcastle-Ottawa Scale for Cross-Sectional Studies was used to assess quality. Meta-analyses were performed where possible.

RESULTS

7215 studies were screened and 40 studies were included (8474 participants). Risk samples had poorer functioning than controls, and superior functioning to participants with BD. Meta-analysis indicated poorer global functioning among persons with familial risk compared to healthy controls (mean global functional difference: 5.92; 95 % confidence interval: 7.90, 3.95; mean premorbid functioning difference: 2.31; 95 % confidence interval: 0.70, 3.92). Studies with higher proportions of female participants had slightly poorer global functioning. High heterogeneity was attributable functional measures and potentially functionally differential subgroups within the risk samples.

LIMITATIONS

Broader measures of functioning, such as neurocognition and behavioural measures, were excluded. Measures of global functioning are limited by conflating functioning and symptoms.

CONCLUSIONS

Functioning in the BD risk populations is intermediate to that of healthy controls and persons with BD, indicating their value in definitions of BD risk, in itself a likely heterogeneous state.

Elevation of C-reactive protein in adolescent bipolar disorder vs. anxiety disorders

Bipolar disorder (BD) largely begins in adolescence, but diagnosis lags for years, causing significant morbidity and mortality, and demonstrating the need for better diagnostic tools. Suggesting an association between BD and immune activity, elevated levels of peripheral inflammatory markers, including C-reactive protein (CRP), have been found in adults with BD. As similar data are extremely limited in adolescents, this study examined CRP levels in adolescents with BD (n = 37) compared to those with anxiety disorders (ADs, n = 157) and healthy controls with no psychiatric diagnoses (HCs, n = 2760). CRP blood levels for patients aged 12-17 years were retrieved from a nationwide repository of deidentified clinical data. After excluding patients with inflammatory conditions, differences in CRP were examined using multivariate and weighted regressions (covariates: demographics and BMI). Mean CRP levels were significantly elevated in adolescents with BD relative to those with ADs and HCs. Mean CRP levels were lower in the ADs cohort versus HCs. Although CRP levels were significantly higher in males and younger patients, the significant between-cohort differences in CRP remained after controlling for multiple confounders. To our knowledge, our study is the first to compare CRP levels between adolescent BD, ADs, and HCs, comprising a novel and essential contribution. Our results suggest the presence of a unique immune process in adolescents with BD and indicate that CRP may represent a biomarker with a crucial role in the diagnostic assessment of adolescent BD.

Staging the bipolar disorders: Are early stages at too early a stage for intervention?

OBJECTIVES

A number of staging models have been generated for the bipolar disorders, which include pre-onset as well as post-onset stages. Some models propose treatments for those at the pre-onset stage, a recommendation which is critiqued here.

METHODS

Several exemplar staging models are overviewed, and a critique is provided.

RESULTS

The critique argues against intervention at a pre-onset stage, in light of there being limited risk factors, unquantified sensitivity and specificity data for most putative onset illness risk factors, and thus there is the risk of overtreatment. Also, it is possible that many of the recommended interventions for those at risk of a bipolar disorder may have general non-specific benefits for those at risk.

CONCLUSIONS

While retaining a pre-onset phase in the staging model, it would appear wiser for it to not be populated with recommended interventions until they have a firmer empirical base.

Conceptualizing eating disorder recovery research: Current perspectives and future research directions

BACKGROUND

How we research eating disorder (ED) recovery impacts what we know (perceive as fact) about it. Traditionally, research has focused more on the "what" of recovery (e.g., establishing criteria for recovery, reaching consensus definitions) than the "how" of recovery research (e.g., type of methodologies, triangulation of perspectives). In this paper we aim to provide an overview of the ED field's current perspectives on recovery, discuss how our methodologies shape what is known about recovery, and suggest a broadening of our methodological "toolkits" in order to form a more complete picture of recovery. BODY: This paper examines commonly used methodologies in research, and explores how incorporating different perspectives can add to our understanding of the recovery process. To do this, we (1) provide an overview of commonly used methodologies (quantitative, qualitative), (2) consider their benefits and limitations, (3) explore newer approaches, including mixed-methods, creative methods (e.g., Photovoice, digital storytelling), and multi-methods (e.g., quantitative, qualitative, creative methods, psycho/physiological, behavioral, laboratory, online observations), and (4) suggest that broadening our methodological "toolkits" could spur more nuanced and specific insights about ED recoveries. We propose a potential future research model that would ideally have a multi-methods design, incorporate different perspectives (e.g., expanding recruitment of diverse participants, including supportive others, in study co-creation), and a longitudinal course (e.g., capturing cognitive and emotional recovery, which often comes after physical). In this way, we hope to move the field towards different, more comprehensive, perspectives on ED recovery.

CONCLUSION

Our current perspectives on studying ED recovery leave critical gaps in our knowledge about the process. The traditional research methodologies impact our conceptualization of recovery definitions, and in turn limit our understanding of the phenomenon. We suggest that we expand our range of methodologies, perspectives, and timeframes in research, in order to form a more complete picture of what is possible in recovery; the multiple aspects of an individual's life that can improve, the greater number of people who can recover than previously believed, and the reaffirmation of hope that, even after decades, individuals can begin, and successfully continue, their ED recovery process.

A risk calculator to predict suicide attempts among individuals with early-onset bipolar disorder

OBJECTIVES

To build a one-year risk calculator (RC) to predict individualized risk for suicide attempt in early-onset bipolar disorder.

METHODS

Youth numbering 394 with bipolar disorder who completed ≥2 follow-up assessments (median follow-up length = 13.1 years) in the longitudinal Course and Outcome of Bipolar Youth (COBY) study were included. Suicide attempt over follow-up was assessed via the A-LIFE Self-Injurious/Suicidal Behavior scale. Predictors from the literature on suicidal behavior in bipolar disorder that are readily assessed in clinical practice were selected and trichotomized as appropriate (presence past 6 months/lifetime history only/no lifetime history). The RC was trained via boosted multinomial classification trees; predictions were calibrated via Platt scaling. Half of the sample was used to train, and the other half to independently test the RC.

RESULTS

There were 249 suicide attempts among 106 individuals. Ten predictors accounted for >90% of the cross-validated relative influence in the model (AUC = 0.82; in order of relative influence): (1) age of mood disorder onset; (2) non-suicidal self-injurious behavior (trichotomized); (3) current age; (4) psychosis (trichotomized); (5) socioeconomic status; (6) most severe depressive symptoms in past 6 months (trichotomized none/subthreshold/threshold); (7) history of suicide attempt (trichotomized); (8) family history of suicidal behavior; (9) substance use disorder (trichotomized); (10) lifetime history of physical/sexual abuse. For all trichotomized variables, presence in the past 6 months reliably predicted higher risk than lifetime history.

CONCLUSIONS

This RC holds promise as a clinical and research tool for prospective identification of individualized high-risk periods for suicide attempt in early-onset bipolar disorder.

Mood Instability in Youth at High Risk for Bipolar Disorder

OBJECTIVE

Mood instability is associated with the onset of bipolar disorder (BD) in youth with a family history of the illness. In a clinical trial with youth at high risk for BD, we examined the association between mood instability and symptomatic, psychosocial, and familial functioning over an average of 2 years.

METHOD

Youth (aged 9-17 years) with major depressive disorder or other specified BD, current mood symptoms, and a family history of BD were rated by parents on a mood instability scale. Participants were randomly assigned to 4 months of family-focused therapy or enhanced care psychoeducation, both with medication management as needed. Independent evaluators rated youth every 4-6 months for up to 4 years on symptom severity and psychosocial functioning, whereas parents rated mood instability of the youth and levels of family conflict.

RESULTS

High-risk youth (N = 114; mean age 13.3 ± 2.6 years; 72 female) were followed for an average of 104.3 ± 65.8 weeks (range, 0-255 weeks) after randomization. Youth with other specified BD (vs major depressive disorder), younger age, earlier symptom onset, more severe mood symptoms, lower psychosocial functioning, and more familial conflict over time had higher mood instability ratings throughout the study period. Mood instability mediated the association between baseline diagnosis and mother/offspring conflict at follow-up (Z = 2.88, p = .004, αβ = 0.19, 95% CI = 0.06-0.32). Psychosocial interventions did not moderate these associations.

CONCLUSION

A questionnaire measure of mood instability tracked closely with symptomatic, psychosocial, and family functioning in youth at high risk for BD. Interventions that are successful in reducing mood instability may enhance long-term outcomes among high-risk youth.

CLINICAL TRIAL REGISTRATION INFORMATION

Early Intervention for Youth at Risk for Bipolar Disorder; https://clinicaltrials.gov/; NCT01483391.

Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review

OBJECTIVES

Magnetic resonance imaging (MRI) studies comparing bipolar and unipolar depression characterize pathophysiological differences between these conditions. However, it is difficult to interpret the current literature due to differences in MRI modalities, analysis methods, and study designs.

METHODS

We conducted a systematic review of publications using MRI to compare individuals with bipolar and unipolar depression. We grouped studies according to MRI modality and task design. Within the discussion, we critically evaluated and summarized the functional MRI research and then further complemented these findings by reviewing the structural MRI literature.

RESULTS

We identified 88 MRI publications comparing participants with bipolar depression and unipolar depressive disorder. Compared to individuals with unipolar depression, participants with bipolar disorder exhibited heightened function, increased within network connectivity, and reduced grey matter volume in salience and central executive network brain regions. Group differences in default mode network function were less consistent but more closely associated with depressive symptoms in participants with unipolar depression but distractibility in bipolar depression.

CONCLUSIONS

When comparing mood disorder groups, the neuroimaging evidence suggests that individuals with bipolar disorder are more influenced by emotional and sensory processing when responding to their environment. In contrast, depressive symptoms and neurofunctional response to emotional stimuli were more closely associated with reduced central executive function and less adaptive cognitive control of emotionally oriented brain regions in unipolar depression. Researchers now need to replicate and refine network-level trends in these heterogeneous mood disorders and further characterize MRI markers associated with early disease onset, progression, and recovery.

Development of a risk calculator to predict attention-deficit/hyperactivity disorder in very preterm/very low birth weight newborns

BACKGROUND

Very preterm/very low birth weight (VP/VLBW) newborns can have lifelong morbidities, as attention-deficit/hyperactivity disorder (ADHD). Clinicians have no markers to discriminate which among those individuals will develop later ADHD, based only on the clinical presentation at birth. Our aim was to develop an individualized risk calculator for ADHD in VP/VLBW newborns.

METHODS

This retrospective prognostic study included a consecutive sample of all VP/VLBW children (gestational age <32 weeks and/or birth weight <1.5 kg) born between 2010 and 2012 from a clinical cohort in a Brazilian tertiary care hospital. Children were clinically assessed at 6 years of age for ADHD using the Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS). The least absolute shrinkage and selection operator (LASSO) method was used for model-building.

RESULTS

Ninety-six VP/VLBW children were assessed at 6 years of age (92% follow-up), of whom 32 (33%) were diagnosed with ADHD. The area under the ROC curve (AUC) for ADHD prediction based on seven parameters (late-onset sepsis confirmed by blood culture, necrotizing enterocolitis, neonatal seizures, periventricular leukomalacia, respiratory distress syndrome, length of hospital stay, and number of maternal ADHD symptoms) was .875 (CI, 0.800-0.942, p < .001; AUC corrected for optimism with bootstrapping: .806), a performance that is comparable to other medical risk calculators. Compared to approaches that would offer early intervention to all, or intervention to none, the risk calculator will be more useful in selecting VP/VLBW newborns, with statistically significant net benefits at cost:benefits of around 1:2 to around 10:6 (range of ADHD risk thresholds of 32%-62%, respectively). It also showed specificity for ADHD compared to other prevalent child psychopathologies.

CONCLUSIONS

The risk calculator showed good performance for early identification of VP/VLBW newborns at high risk of future ADHD diagnosis. External validity in population-based samples is needed to extend clinical usefulness.

Psychosocial markers of age at onset in bipolar disorder: a machine learning approach

BACKGROUND

Bipolar disorder is a chronic and severe mental health disorder. Early stratification of individuals into subgroups based on age at onset (AAO) has the potential to inform diagnosis and early intervention. Yet, the psychosocial predictors associated with AAO are unknown.

AIMS

We aim to identify psychosocial factors associated with bipolar disorder AAO.

METHOD

Using data from the Bipolar Disorder Research Network UK, we employed least absolute shrinkage and selection operator regression to identify psychosocial factors associated with bipolar disorder AAO. Twenty-eight factors were entered into our model, with AAO as our outcome measure.

RESULTS

We included 1022 participants with bipolar disorder (μ = 23.0, s.d. ± 9.86) in our model. Six variables predicted an earlier AAO: childhood abuse (β = -0.2855), regular cannabis use in the year before onset (β = -0.2765), death of a close family friend or relative in the 6 months before onset (β = -0.2435), family history of suicide (β = -0.1385), schizotypal personality traits (β = -0.1055) and irritable temperament (β = -0.0685). Five predicted a later AAO: the average number of alcohol units consumed per week in the year before onset (β = 0.1385); birth of a child in the 6 months before onset (β = 0.2755); death of parent, partner, child or sibling in the 6 months before onset (β = 0.3125); seeking work without success for 1 month or more in the 6 months before onset (β = 0.3505) and a major financial crisis in the 6 months before onset (β = 0.4575).

CONCLUSIONS

The identified predictor variables have the potential to help stratify high-risk individuals into likely AAO groups, to inform treatment provision and early intervention.

Characteristics prior to and at time of diagnosis in pediatric bipolar disorder

OBJECTIVES

Describe symptoms before and at time of register-diagnosis in children and adolescents.

METHODS

A random sample was selected for chart-review from a Danish nationwide cohort of patients <18 years registered with an incident ICD-10 register-diagnosis of single hypomanic/manic episode or bipolar disorder between 1995 and 2014. Patients with symptoms which adequately documented a BD diagnosis in the chart were included for analysis.

RESULTS

521 were diagnosed in the study period. A random sample of 25% were selected, and 106 charts were possible to retrieve, with 48 chart reviews resulting in confirmation of diagnosis. Time from first reported affective symptoms to diagnosis was 2.6 ± 2.7 years for depressive symptoms, 2.5 ± 2.9 years for mixed symptoms, 1.4 ± 1.6 years for hypomanic symptoms, and 0.4 ± 0.5 years for manic symptoms. A hierarchical clustering analysis revealed three patient-profiles: primarily hypomanic/manic, primarily depressive, and more rare, primarily mixed profile. Frequently reported symptoms prior to diagnosis include anhedonia (79%), irritability (71%), hyperactivity (62.5%), decreased energy (62.5%), and psychotic symptoms (52%).Symptoms of ADHD (19%), comorbid ADHD (15%), symptoms of anxiety (52%), comorbid anxiety (4%), suicidal thoughts (50%), suicide attempts (8%), cutting (23%), substance misuse (21%), and criminal activity (10%) were reported before incident BD diagnosis.

CONCLUSION

The observed patient-profiles leading to diagnosis were primarily manic or depressive, resembling presentations in adults. The presence of ADHD, anxiety, suicide attempts, cutting, and criminal activity prior to diagnosis emphasizes the need for treatment of children and adolescents with affective symptoms. The gap from appearance of the symptoms to diagnosis suggests a window for earlier treatment.

Predicting the emergence of full-threshold bipolar I, bipolar II and psychotic disorders in young people presenting to early intervention mental health services

BACKGROUND

Predictors of new-onset bipolar disorder (BD) or psychotic disorder (PD) have been proposed on the basis of retrospective or prospective studies of 'at-risk' cohorts. Few studies have compared concurrently or longitudinally factors associated with the onset of BD or PDs in youth presenting to early intervention services. We aimed to identify clinical predictors of the onset of full-threshold (FT) BD or PD in this population.

METHOD

Multi-state Markov modelling was used to assess the relationships between baseline characteristics and the likelihood of the onset of FT BD or PD in youth (aged 12-30) presenting to mental health services.

RESULTS

Of 2330 individuals assessed longitudinally, 4.3% (n = 100) met criteria for new-onset FT BD and 2.2% (n = 51) met criteria for a new-onset FT PD. The emergence of FT BD was associated with older age, lower social and occupational functioning, mania-like experiences (MLE), suicide attempts, reduced incidence of physical illness, childhood-onset depression, and childhood-onset anxiety. The emergence of a PD was associated with older age, male sex, psychosis-like experiences (PLE), suicide attempts, stimulant use, and childhood-onset depression.

CONCLUSIONS

Identifying risk factors for the onset of either BD or PDs in young people presenting to early intervention services is assisted not only by the increased focus on MLE and PLE, but also by recognising the predictive significance of poorer social function, childhood-onset anxiety and mood disorders, and suicide attempts prior to the time of entry to services. Secondary prevention may be enhanced by greater attention to those risk factors that are modifiable or shared by both illness trajectories.

White matter predictors of worsening of subthreshold hypomania severity in non-bipolar young adults parallel abnormalities in individuals with bipolar disorder

BACKGROUND

Identifying neural predictors of worsening subthreshold hypomania severity can help identify risk of progression to BD. While diffusion Magnetic Resonance Imaging (dMRI) studies reported white matter microstructural abnormalities in tracts supporting emotional regulation in individuals with BD, it remains unknown whether similar patterns of white matter microstructure predict worsening of subthreshold hypomania severity in non-BD individuals.

METHODS

dMRI data were collected in: 81 non-BD individuals recruited across a range of subthreshold depression and hypomania, and followed for six months; and independent samples of 75 BD and 58 healthy individuals. All individuals were assessed using standardized diagnostic assessments, mood and anxiety symptom rating scales. Global probabilistic tractography and a tract-profile approach examined fractional anisotropy (FA), a measure of fiber collinearity, in tracts supporting emotional regulation shown to have abnormalities in BD: forceps minor (FMIN), anterior thalamic radiation (ATR), cingulum bundle (CB), and uncinate fasciculus (UF).

RESULTS

Lower FA in left CB (middle, β = -0.22, P = 0.022; posterior, β = -0.32, P < 0.001), right CB (anterior, β = -0.30, P = 0.003; posterior, β = -0.27, P = 0.005), and right UF (frontal, β = -0.29, P = 0.002; temporal, β = -0.40, P < 0.001) predicted worsening of subthreshold hypomania severity in non-BD individuals. BD versus healthy individuals showed lower FA in several of these segments: middle left CB (F = 8.7, P = 0.004), anterior right CB (F = 9.8, P = 0.002), and frontal right UF (F = 7.0, P = 0.009). Non-BD individuals with worsening 6-month hypomania had lower FA in these three segments versus HC and non-BD individuals without worsening hypomania, but similar FA to BD individuals.

LIMITATIONS

Relatively short follow-up.

CONCLUSIONS

White matter predictors of worsening subthreshold hypomania in non-BD individuals parallel abnormalities in BD individuals, and can guide early risk identification and interventions.

Overview of CAPICE-Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe-an EU Marie Skłodowska-Curie International Training Network

The Roadmap for Mental Health and Wellbeing Research in Europe (ROAMER) identified child and adolescent mental illness as a priority area for research. CAPICE (Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe) is a European Union (EU) funded training network aimed at investigating the causes of individual differences in common childhood and adolescent psychopathology, especially depression, anxiety, and attention deficit hyperactivity disorder. CAPICE brings together eight birth and childhood cohorts as well as other cohorts from the EArly Genetics and Life course Epidemiology (EAGLE) consortium, including twin cohorts, with unique longitudinal data on environmental exposures and mental health problems, and genetic data on participants. Here we describe the objectives, summarize the methodological approaches and initial results, and present the dissemination strategy of the CAPICE network. Besides identifying genetic and epigenetic variants associated with these phenotypes, analyses have been performed to shed light on the role of genetic factors and the interplay with the environment in influencing the persistence of symptoms across the lifespan. Data harmonization and building an advanced data catalogue are also part of the work plan. Findings will be disseminated to non-academic parties, in close collaboration with the Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN-Europe).

Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data

BACKGROUND

Adolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level.

METHODS

A subsample of a multisite longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into the following: 1) those developing a diagnosis of major depressive disorder or subthreshold major depressive disorder and 2) healthy control subjects. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental, and structural magnetic resonance imaging) at age 14 years were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (n = 407). The features contributing the highest to the prediction were validated in an independent hold-out sample (three independent IMAGEN sites; n = 137).

RESULTS

The area under the receiver operating characteristic curve for predicting depression onset ranged between 0.70 and 0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events, and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression, with an area under the receiver operating characteristic curve between 0.68 and 0.72 in the independent validation sample.

CONCLUSIONS

This study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical characteristics, life events, personality traits, and brain structure variables.

Clinical precursors of tics: an EMTICS study

BACKGROUND

Children with Tourette syndrome (TS) often have comorbid disorders, particularly attention-deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD). While subtle premorbid symptoms have been described in various psychiatric disorders, the presence of clinical precursors that may exist before the onset of tics is unknown. This longitudinal study aimed to find clinical precursors of tics by assessing a range of clinical characteristics prior to tic onset in comparison with children without onset of tics.

METHODS

A sample of 187 3- to 10-year-old first-degree unaffected relatives of children with TS were followed up to 7 years in the European Multicentre Tics in Children Study (EMTICS). We investigated whether clinical characteristics assessed at baseline predicted tic onset, comparing 126 children without tic onset to 61 children who developed tics. We used the least absolute shrinkage and selection operator (LASSO) method, a penalised logistic regression approach. We also explored sex differences and repeated our analyses in an age- and sex-matched subsample.

RESULTS

Children with tic onset were more frequently male (β = -0.36), had higher baseline severity of conduct problems (β = 0.23), autism spectrum disorder symptoms (ASD; β = 0.08), compulsions (β = 0.02) and emotional problems (β = 0.03) compared to children without tic onset. Conduct and ASD problems were male-specific predictors, whereas severity of compulsions and oppositional (β = 0.39) and emotional problems were female-specific predictors.

CONCLUSION

This study supports the presence of clinical precursors prior to tic onset and highlights the need of sex-specific monitoring of children at risk of developing tics. This may aid in the earlier detection of tics, particularly in females. We moreover found that tics most often persisted one year after tic onset, in contrast to the common belief that tics are mostly transient.

Psychosocial Treatments for Bipolar Disorder in Children and Adolescents

Evidence suggests that adjunctive psychosocial intervention for the treatment of pediatric bipolar spectrum disorders (BPSDs) is effective, feasible, and highly accepted as both an acute and maintenance treatment for youth with BPSD diagnoses as well as a preventive treatment for high-risk youth who are either asymptomatic or exhibit subsyndromal mood symptoms. Here, we provide a comprehensive review of all known evidence-based interventions, including detailed descriptions of treatment targets and core components, results of clinical trials, and updated research on mediators and moderators of treatment efficacy. Treatments are presented systematically according to level of empirical support (i.e., well established, probably efficacious, possibly efficacious, experimental, or questionable); upcoming and ongoing trials are included when possible. In line with a staging approach, preventive interventions are presented separately. Recommendations for best practices based on age, stage, and additional evidence-based child and family factors shown to affect treatment outcomes are provided. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Retinal function and preclinical risk traits in children and adolescents at genetic risk of schizophrenia and bipolar disorder

BACKGROUND

The millions of children having a parent affected by a major psychiatric disorder may carry, as vulnerability indicators, electroretinographic (ERG) anomalies resembling those seen in adult patients. Our goal was to determine whether ERG anomalies in high-risk youths are related to clinical precursors of a later transition to illness such as the presence of childhood DSM-IV diagnoses, bouts of psychotic like experiences, lower global IQ and social functioning deterioration.

METHODS

The 99 youths (53% males) aged 5-27 years had one parent affected by schizophrenia, bipolar disorder or major depressive disorder. They were assessed with a best-estimate DSM-IV diagnoses based on review of medical charts and a structured interview (K-SADS or SCID), global IQ (WISC-V and WAIS-IV), global functioning (GAF scale) and psychotic-like experiences using interviews and a review of medical records. The electroretinogram of rods and cones was recorded.

RESULTS

Cone Vmax latency was longer in offspring having psychotic-like experiences, respective adjusted mean [SE] ms of 31.59 [0.27] and of 30.96 [0.14]; P = 0.018). The cone Vmax delayed latency was associated with a lower global IQ (R = -0.18; P = 0.045) and with deteriorated global functioning (GAF; R = -0.25; P = 0.008). In contrast, rods had decreased b-wave amplitude only in offspring with a non-psychotic non-affective DSM diagnoses, respective means [SE] μV of 170.18 [4.90] and of 184.01 [6.12]; P = 0.044).

CONCLUSIONS

ERG may mark neurodevelopmental pathways leading to adult illness and have an effect on early pre-clinical traits, giving clues to clinicians for the surveillance of sibling differences in high-risk families.

Transitioning to bipolar disorder: A systematic review of prospective high-risk studies

PURPOSE OF REVIEW

Bipolar disorder is a highly heritable condition, which can progress from an asymptomatic period in at-risk individuals to a potentially debilitating illness. Identifying individuals who are at a high risk of developing bipolar disorder may provide an opportunity for early intervention to improve outcomes. The main objective of this systematic review is to provide an overview of prospective studies that evaluated the incidence and predictors of transitioning to bipolar disorder among high-risk individuals.

RECENT FINDINGS

Twenty-three publications from 16 cohorts were included in the final review. Most studies focused on familial high-risk groups, while others either used clinical or a combination of clinical and genetic risk factors. The follow-up length was from 1 to 21 years and the rate of conversion to bipolar disorder was between 8 and 25% among different studies. Overall, the results suggest that a combination of genetic and clinical risk factors; namely, subthreshold (hypo)manic symptoms and elevated depressive symptoms, may be required to optimally predict conversion to bipolar disorder.

SUMMARY

The concept of high-risk for bipolar disorder is still in its infancy. Further discussions are needed to work towards an expert consensus on the high-risk criteria for bipolar disorder, taking into account both clinical and genetic risk factors.

Validation of the youth mood recurrences risk calculator in an adult sample with bipolar disorder

BACKGROUND

The ability to predict an individual's risk of mood episode recurrence can facilitate personalized medicine in bipolar disorder (BD). We sought to externally validate, in an adult sample, a risk calculator of mood episode recurrence developed in youth/young adults with BD from the Course and Outcome of Bipolar Youth (COBY) study.

METHODS

Adult participants from the National Institute of Mental Health Collaborative Depression Study (CDS; N=258; mean(SD) age=35.5(12.0) years; mean follow-up=24.9 years) were utilized as a sample to validate the youth COBY risk calculator for onset of depressive, manic, or any mood episodes.

RESULTS

In this older validation sample, the risk calculator predicted recurrence of any episode over 1, 2, 3, or 5-year follow-up intervals, with Area Under the Curves (AUCs) approximating 0.77. The AUC for prediction of depressive episodes was about 0.81 for each of the time windows, which was higher than for manic or hypomanic episodes (AUC=0.72). While the risk calculator was well-calibrated across the range of risk scores, it systematically underestimated risk in the CDS sample by about 20%. The length of current remission was a highly significant predictor of recurrence risk in the CDS sample.

LIMITATIONS

Predominantly self-reported White samples may limit generalizability; the risk calculator does not assess more proximal risk (e.g., 1 month).

CONCLUSIONS

Risk of mood episode recurrence can be predicted with good accuracy in youth and adults with BD in remission. The risk calculators may help identify higher risk BD subgroups for treatment and research.

Characterisation of age and polarity at onset in bipolar disorder

BACKGROUND

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

AIMS

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

METHOD

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

RESULTS

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = -0.34 years, s.e. = 0.08), major depression (β = -0.34 years, s.e. = 0.08), schizophrenia (β = -0.39 years, s.e. = 0.08), and educational attainment (β = -0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

CONCLUSIONS

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Consensus on nomenclature for clinical staging models in bipolar disorder: A narrative review from the International Society for Bipolar Disorders (ISBD) Staging Task Force

OBJECTIVES

Clinical staging is widely used in medicine to map disease progression, inform prognosis, and guide treatment decisions; in psychiatry, however, staging remains a hypothetical construct. To facilitate future research in bipolar disorders (BD), a well-defined nomenclature is needed, especially since diagnosis is often imprecise with blurred boundaries, and a full understanding of pathophysiology is lacking.

METHODS

Under the auspices of the International Society of Bipolar Disorders, a Task Force of international experts was convened to review, discuss, and integrate findings from the scientific literature relevant to the development of a consensus staging model and standardize a terminology that could be used to advance future research including staging of BD and related disorders.

RESULTS

Consensus opinion and areas of uncertainty or difference were identified in regard to terms referring to staging as it may apply to BD, to at-risk status and subthreshold stages, and to various clinical stages of BD as it is currently diagnosed.

CONCLUSION

The use of a standardized nomenclature about the clinical stages of BD will facilitate communication about research on clinical and pathological components of this heterogeneous group of disorders. The concepts presented are based on current evidence, but the template provided allows for further refinements as etiological advances come to light.

Affective lability as a prospective predictor of subsequent bipolar disorder diagnosis: a systematic review

Objectives

The early pathogenesis and precursors of Bipolar Disorder (BD) are poorly understood. There is some cross-sectional and retrospective evidence of affective lability as a predictor of BD, but this is subject to recall biases. The present review synthesises the prospective evidence examining affective lability and the subsequent development of BD at follow-up.

Methods

The authors performed a systematic search of PubMed, PsycInfo and Embase (1960–June 2020) and conducted hand searches to identify studies assessing affective lability (according to a conceptually-inclusive definition) at baseline assessment in individuals without a BD diagnosis, and a longitudinal follow-up assessment of bipolar (spectrum) disorders. Results are reported according to the PRISMA guidelines, and the synthesis without meta-analysis (SWiM) reporting guidelines were used to strengthen the narrative synthesis. The Newcastle–Ottawa Scale was used to assess risk of bias (ROB).

Results

11 articles describing 10 studies were included. Being identified as having affective lability at baseline was associated with an increased rate of bipolar diagnoses at follow-up; this association was statistically significant in six of eight studies assessing BD type I/II at follow-up and in all four studies assessing for bipolar spectrum disorder (BSD) criteria. Most studies received a ‘fair’ or ‘poor’ ROB grade.

Conclusions

Despite a paucity of studies, an overall association between prospectively-identified affective lability and a later diagnosis of BD or BSD is apparent with relative consistency between studies. This association and further longitudinal studies could inform future clinical screening of those who may be at risk of BD, with the potential to improve diagnostic accuracy and facilitate early intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40345-021-00237-1.

Differentiating white matter measures that protect against vs. predispose to bipolar disorder and other psychopathology in at-risk youth

Bipolar disorder (BD) is highly heritable. Identifying objective biomarkers reflecting pathophysiological processes predisposing to, versus protecting against BD, can help identify BD risk in offspring of BD parents. We recruited 21 BD participants with a first-degree relative with BD, 25 offspring of BD parents, 27 offspring of comparison parents with non-BD psychiatric disorders, and 32 healthy offspring of healthy parents. In at-risk groups, 23 had non-BD diagnoses and 29, no Axis-I diagnoses(healthy). Five at-risk offspring who developed BD post scan(Converters) were included. Diffusion imaging(dMRI) analysis with tract segmentation identified between-group differences in the microstructure of prefrontal tracts supporting emotional regulation relevant to BD: forceps minor, anterior thalamic radiation(ATR), cingulum bundle(CB), and uncinate fasciculus(UF). BD participants showed lower fractional anisotropy (FA) in the right CB (anterior portion) than other groups (q < 0.05); and in bilateral ATR (posterior portion) versus at-risk groups (q < 0.001). Healthy, but not non-BD, at-risk participants showed significantly higher FA in bilateral ATR clusters than healthy controls (qs < 0.05). At-risk groups showed higher FA in these clusters than BD participants (qs < 0.05). Non-BD versus healthy at-risk participants, and Converters versus offspring of BD parents, showed lower FA in the right ATR cluster (qs < 0.05). Low anterior right CB FA in BD participants versus other groups might result from having BD. High bilateral ATR FA in at-risk groups, and in healthy at-risk participants, versus healthy controls might protect against BD/other psychiatric disorders. Absence of elevated right ATR FA in non-BD versus healthy at-risk participants, and in Converters versus non-converter offspring of BD parents, might lower protection against BD in at-risk groups.

Using density of antecedent events and trajectory path analysis to investigate family-correlated patterns of onset of bipolar I disorder: a comparison of cohorts from Europe and USA

BACKGROUND

Major contributors to the global burden of bipolar disorders (BD) are the early age at onset (AAO) and the co-occurrence of non-mood disorders before and after the onset of BD. Using data from two independent cohorts from Europe and the USA, we investigated whether the trajectories of BD-I onset and patterns of psychiatric comorbidities differed in (a) individuals with or without a family history (FH) of BD, or (b) probands and parents who both had BD-I.

METHODS

First, we estimated cumulative probabilities and AAO of comorbid mental disorders in familial and non-familial cases of BD-I (Europe, n = 573), and sex-matched proband-parent pairs of BD-I cases (USA, n = 194). Then we used time to onset analyses to compare overall AAO of BD-I and AAO according to onset polarity. Next, we examined associations between AAO and polarity of onset of BD-I according to individual experiences of comorbidities. This included analysis of the density of antecedent events (defined as the number of antecedent comorbidities per year of exposure to mental illness per individual) and time trend analysis of trajectory paths plotted for the subgroups included in each cohort (using R² goodness of fit analysis).

RESULTS

Earlier AAO of BD-I was found in FH versus non-FH cases (log rank test = 7.63; p = 0.006) and in probands versus parents with BD-I (log rank test = 15.31; p = 0.001). In the European cohort, AAO of BD-I was significantly associated with factors such as: FH of BD (hazard ratio [HR]: 0.60), earlier AAO of first non-mood disorder (HR: 0.93) and greater number of comorbidities (HR: 0.74). In the USA cohort, probands with BD-I had an earlier AAO for depressive and manic episodes and AAO was also associated with e.g., number of comorbidities (HR: 0.65) and year of birth (HR: 2.44). Trajectory path analysis indicated significant differences in density of antecedents between subgroups within each cohort. However, the time trend R² analysis was significantly different for the European cohort only.

CONCLUSIONS

Estimating density of antecedent events and comparing trajectory plots for different BD subgroups are informative adjuncts to established statistical approaches and may offer additional insights that enhance understanding of the evolution of BD-I.

Individuals at increased risk for development of bipolar disorder display structural alterations similar to people with manifest disease

In psychiatry, there has been a growing focus on identifying at-risk populations. For schizophrenia, these efforts have led to the development of early recognition and intervention measures. Despite a similar disease burden, the populations at risk of bipolar disorder have not been sufficiently characterized. Within the BipoLife consortium, we used magnetic resonance imaging (MRI) data from a multicenter study to assess structural gray matter alterations in N = 263 help-seeking individuals from seven study sites. We defined the risk using the EPIbipolar assessment tool as no-risk, low-risk, and high-risk and used a region-of-interest approach (ROI) based on the results of two large-scale multicenter studies of bipolar disorder by the ENIGMA working group. We detected significant differences in the thickness of the left pars opercularis (Cohen's d = 0.47, p = 0.024) between groups. The cortex was significantly thinner in high-risk individuals compared to those in the no-risk group (p = 0.011). We detected no differences in the hippocampal volume. Exploratory analyses revealed no significant differences in other cortical or subcortical regions. The thinner cortex in help-seeking individuals at risk of bipolar disorder is in line with previous findings in patients with the established disorder and corresponds to the region of the highest effect size in the ENIGMA study of cortical alterations. Structural alterations in prefrontal cortex might be a trait marker of bipolar risk. This is the largest structural MRI study of help-seeking individuals at increased risk of bipolar disorder.

Development and validation of a risk calculator for major mood disorders among the offspring of bipolar parents using information collected in routine clinical practice

BACKGROUND

Family history is a significant risk factor for bipolar disorders (BD), but the magnitude of risk varies considerably between individuals within and across families. Accurate risk estimation may increase motivation to reduce modifiable risk exposures and identify individuals appropriate for monitoring over the peak risk period. Our objective was to develop and independently replicate an individual risk calculator for bipolar spectrum disorders among the offspring of BD parents using data collected in routine clinical practice.

METHODS

Data from the longitudinal Canadian High-Risk Offspring cohort study collected from 1996 to 2020 informed the development of a 5 and 10-year risk calculator using parametric time-to-event models with a cure fraction and a generalized gamma distribution. The calculator was then externally validated using data from the Lausanne-Geneva High-Risk Offspring cohort study collected from 1996 to 2020. A time-varying C-index by age in years was used to estimate the probability that the model correctly classified risk. Bias corrected estimates and 95% confidence limits were derived using a jackknife resampling approach.

FINDINGS

The primary outcome was age of onset of a major mood disorder. The risk calculator was most accurate at classifying risk in mid to late adolescence in the Canadian cohort (n = 285), and a similar pattern was replicated in the Swiss cohort (n = 128). Specifically, the time-varying C-index indicated that there was approximately a 70% chance that the model would correctly predict which of two 15-year-olds would be more likely to develop the outcome in the future. External validation within a smaller Swiss cohort showed mixed results.

INTERPRETATION

Findings suggest that this model may be a useful clinical tool in routine practice for improved individualized risk estimation of bipolar spectrum disorders among the adolescent offspring of a BD parent; however, risk estimation in younger high-risk offspring is less accurate, perhaps reflecting the evolving nature of psychopathology in early childhood. Based on external validation with a Swiss cohort, the risk calculator may not be as predictive in more heterogenous high-risk populations.

FUNDING

The Canadian High-Risk Study has been funded by consecutive operating grants from the Canadian Institutes for Health Research, currently CIHR PJT Grant 152796 he Lausanne-Geneva high-risk study was and is supported by five grants from the Swiss National Foundation (#3200-040,677, #32003B-105,969, #32003B-118,326, #3200-049,746 and #3200-061,974), three grants from the Swiss National Foundation for the National Centres of Competence in Research project "The Synaptic Bases of Mental Diseases" (#125,759, #158,776, and #51NF40 - 185,897), and a grant from GlaxoSmithKline Clinical Genetics.

Generalizing the Prediction of Bipolar Disorder Onset Across High-Risk Populations

OBJECTIVE

Risk calculators (RC) to predict clinical outcomes are gaining interest. An RC to estimate risk of bipolar spectrum disorders (BPSD) could help reduce the duration of undiagnosed BPSD and improve outcomes. Our objective was to adapt an RC previously validated in the Pittsburgh Bipolar Offspring Study (BIOS) sample to achieve adequate predictive ability in both familial high-risk and clinical high-risk youths.

METHOD

Participants (aged 6-12 years at baseline) from the Longitudinal Assessment of Manic Symptoms (LAMS) study (N = 473) were evaluated semi-annually. Evaluations included a Kiddie Schedule for Affective Disorders (K-SADS) interview. After testing an RC that closely approximated the original, we made modifications to improve model prediction. Models were trained in the BIOS data, which included biennial K-SADS assessments, and tested in LAMS. The final model was then trained in LAMS participants, including family history of BPSD as a predictor, and tested in the familial high-risk sample.

RESULTS

Over follow-up, 65 youths newly met criteria for BPSD. The original RC identified youths who developed BPSD only moderately well (area under the curve [AUC] = 0.67). Eliminating predictors other than the K-SADS screening items for mania and depression improved accuracy (AUC = 0.73) and generalizability. The model trained in LAMS, including family history as a predictor, performed well in the BIOS sample (AUC = 0.74).

CONCLUSION

The clinical circumstances under which the assessment of symptoms occurs affects RC accuracy; focusing on symptoms related to the onset of BPSD improved generalizability. Validation of the RC under clinically realistic circumstances will be an important next step.

Editorial: It's Difficult To Make Predictions, Especially About the Future: Risk Calculators Come of Age in Child Psychiatry

A quote attributed to many people, from the Nobel prize-winning Quantum physicist Niels Bohr to legendary baseball player (and philosopher) Yogi Berra states: "It is difficult to make predictions, especially about the future." As though any other prediction would matter; but this is exactly what parents want when they bring their child to the doctor for any concern, ranging from a bump or bruise to whether the child has bipolar disorder. They want the doctor to use both the science and art of medicine to answer key questions: What is wrong with my child? What tests or workup is needed to figure this out? What is the best treatment for this problem? Will my child get better?

Clinical Risk Constellations for the Development of Bipolar Disorders

The early recognition of psychiatric disorders has been a focus of research in the last decades and has led to improvements in clinical care, especially in the area of early psychosis. Like non-affective psychosis, bipolar disorders are often diagnosed with a delay that can lead to long periods of untreated illness and impact long-term outcomes. This article presents the rationale for early recognition in bipolar disorder and presents the current evidence for the identification of risk factors, their assessment and validity in predicting the onset of bipolar disorder.

Predicting the risk and timing of major mood disorder in offspring of bipolar parents: exploring the utility of a neural network approach

Background

Bipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. However, penetrance within families varies and most children of bipolar parents will not develop the illness. Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. Using prospectively collected data from the Canadian Flourish High-risk Offspring cohort study available in routine practice, we explored the use of a neural network, known as the Partial Logistic Artificial Neural Network (PLANN) to predict the time to diagnosis of major mood disorders in 1, 3 and 5-year intervals.

Results

Overall, for predictive performance, PLANN outperformed the more traditional discrete survival model for 3-year and 5-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing a major mood disorder, better able to predict the probability of developing a major mood disorder and better able to identify individuals who would be diagnosed in future time intervals. The average AUC achieved by PLANN for 5-year prediction was 0.74, which indicates good discrimination.

Conclusions

This evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of mood disorders in at-risk individuals and the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk offspring sample.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40345-021-00228-2.

Phenoscreening: a developmental approach to research domain criteria-motivated sampling

BACKGROUND

To advance early identification efforts, we must detect and characterize neurodevelopmental sequelae of risk among population-based samples early in development. However, variability across the typical-to-atypical continuum and heterogeneity within and across early emerging psychiatric/neurodevelopmental disorders represent fundamental challenges to overcome. Identifying multidimensionally determined profiles of risk, agnostic to DSM categories, via data-driven computational approaches represents an avenue to improve early identification of risk.

METHODS

Factor mixture modeling (FMM) was used to identify subgroups and characterize phenotypic risk profiles, derived from multiple parent-report measures of typical and atypical behaviors common to autism spectrum disorder, in a community-based sample of 17- to 25-month-old toddlers (n = 1,570). To examine the utility of risk profile classification, a subsample of toddlers (n = 107) was assessed on a distal, independent outcome examining internalizing, externalizing, and dysregulation at approximately 30 months.

RESULTS

FMM results identified five asymmetrically sized subgroups. The putative high- and moderate-risk groups comprised 6% of the sample. Follow-up analyses corroborated the utility of the risk profile classification; the high-, moderate-, and low-risk groups were differentially stratified (i.e., HR > moderate-risk > LR) on outcome measures and comparison of high- and low-risk groups revealed large effect sizes for internalizing (d = 0.83), externalizing (d = 1.39), and dysregulation (d = 1.19).

CONCLUSIONS

This data-driven approach yielded five subgroups of toddlers, the utility of which was corroborated by later outcomes. Data-driven approaches, leveraging multiple developmentally appropriate dimensional RDoC constructs, hold promise for future efforts aimed toward early identification of at-risk-phenotypes for a variety of early emerging neurodevelopmental disorders.

Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents

Knowledge about early risk factors for major depressive disorder (MDD) is critical to identify those who are at high risk. A multivariable model to predict adolescents' individual risk of future MDD has recently been developed however its performance in a UK sample was far from perfect. Given the potential role of air pollution in the aetiology of depression, we investigate whether including childhood exposure to air pollution as an additional predictor in the risk prediction model improves the identification of UK adolescents who are at greatest risk for developing MDD. We used data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally representative UK birth cohort of 2232 children followed to age 18 with 93% retention. Annual exposure to four pollutants - nitrogen dioxide (NO₂), nitrogen oxides (NO_X), particulate matter <2.5 μm (PM_2.5) and <10 μm (PM₁₀) - were estimated at address-level when children were aged 10. MDD was assessed via interviews at age 18. The risk of developing MDD was elevated most for participants with the highest (top quartile) level of annual exposure to NO_X (adjusted OR = 1.43, 95% CI = 0.96-2.13) and PM_2.5 (adjusted OR = 1.35, 95% CI = 0.95-1.92). The separate inclusion of these ambient pollution estimates into the risk prediction model improved model specificity but reduced model sensitivity - resulting in minimal net improvement in model performance. Findings indicate a potential role for childhood ambient air pollution exposure in the development of adolescent MDD but suggest that inclusion of risk factors other than this may be important for improving the performance of the risk prediction model.

Youth Experience Tracker Instrument: A self-report measure of developmental antecedents to severe mental illness

AIM

We sought to examine the structure, internal consistency, convergent and criterion validity of the Youth Experience Tracker Instrument (YETI), a new brief self-report measure designed to facilitate early identification of risk for severe forms of mental illness, including major depressive disorder, bipolar disorder, and schizophrenia.

METHODS

We collected 716 YETIs from 315 individuals aged 8 to 27 with and without familial risk of severe mental illness. The YETI measures six developmental antecedents that precede and predict serious forms of mental illness: affective lability, anxiety, basic symptoms, depressive symptoms, psychotic-like experiences, and sleep. A battery of concurrent questionnaires and interviews measured the same constructs.

RESULTS

The best-fitting bifactor model supported the validity of both total score and antecedent-specific subscales. Internal consistency was high for the total score (ω = 0.94) and subscales (ω = 0.80-0.92; ρ = 0.72). The total score captured the majority of information from the 26 YETI items (hierarchical omega ω_h = 0.74). Correlations of YETI subscales with established measures of the same constructs (r = 0.45-0.80) suggested adequate convergent validity. We propose cut-offs with high negative predictive values to facilitate efficient risk screening.

CONCLUSION

The YETI, a brief self-report measure of antecedents, provides an alternative to using multiple longer instruments. Future research may examine the predictive validity of the YETI for the onset of major mood and psychotic disorders.