Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Van Meter AR

PROVIDER: S-EPMC8075632 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Van Meter Anna R AR Hafeman Danella M DM Merranko John J Youngstrom Eric A EA Birmaher Boris B BB Fristad Mary A MA Horwitz Sarah M SM Arnold L Eugene LE Findling Robert L RL

Journal of the American Academy of Child and Adolescent Psychiatry 20201008 8

<h4>Objective</h4>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.<h4>Method</h4>Participants (aged 6-12 years at baseline) from the Lon ...[more]

PMID: 33038454

Similar Datasets

Project description:ObjectiveIn this study, we performed a candidate genetic risk score (GRS) analysis of early-onset bipolar disorder (BD).MethodsTreatment of Early Age Mania (TEAM) study enrollment and sample collection took place from 2003 to 2008. Mayo Clinic Bipolar Biobank samples were collected from 2009 to 2013. Genotyping and analyses for the present study took place from 2013 to 2014. The diagnosis of BD was based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision criteria. Eight single-nucleotide polymorphisms (SNPs), previously reported in genome-wide association studies to be associated with BD, were chosen for GRS analysis in early-onset bipolar disease. These SNPs map to 3 genes: CACNA1C (calcium channel, voltage-dependent, L type, alpha 1C subunit), ANK3 (ankyrin-3, node of Ranvier [ankyrin G]), and ODZ4 (teneurin transmembrane protein 4 [formerly "odz, odd Oz/10-m homolog 4 {Drosophila}, ODZ4"]). The 8 candidate SNPs were genotyped in patients from the TEAM study (n = 69); adult patients with BD (n = 732), including a subset with early-onset illness (n = 192); and healthy controls (n = 776). GRS analyses were performed to compare early-onset cases with controls. In addition, associations of early-onset BD with individual SNPs and haplotypes were explored.ResultsGRS analysis revealed associations of the risk score with early-onset BD (P = .01). Gene-level haplotype analysis comparing TEAM patients with controls suggested association of early-onset BD with a CACNA1C haplotype (global test, P = .01). At the level of individual SNPs, comparison of TEAM cases with healthy controls provided nominally significant evidence for association of SNP rs10848632 in CACNA1C with early-onset BD (P = .017), which did not remain significant after correction for multiple comparisons.ConclusionsThese preliminary analyses suggest that previously identified BD risk loci, especially CACNA1C, have a role in early-onset BD, possibly with stronger effects than for late-onset BD.

Project description:ObjectiveBipolar disorder (BD) is highly heritable. Neuroimaging studies comparing unaffected youth at high familial risk for BD (i.e., those with a first-degree relative with the disorder; termed "high-risk" [HR]) to "low-risk" (LR) youth (i.e., those without a first-degree relative with BD) and to patients with BD may help identify potential brain-based markers associated with risk (i.e., regions where HR+BD≠LR), resilience (HR≠BD+LR), or illness (BD≠HR+LR).MethodDuring functional magnetic resonance imaging (fMRI), 99 youths (i.e., adolescents and young adults) aged 9.8 to 24.8 years (36 BD, 22 HR, 41 LR) performed a task probing face emotion labeling, previously shown to be impaired behaviorally in youth with BD and HR youth.ResultsWe found three patterns of results. Candidate risk endophenotypes (i.e., where BD and HR shared deficits) included dysfunction in higher-order face processing regions (e.g., middle temporal gyrus, dorsolateral prefrontal cortex). Candidate resilience markers and disorder sequelae (where HR and BD, respectively, show unique alterations relative to the other two groups) included different patterns of neural responses across other regions mediating face processing (e.g., fusiform), executive function (e.g., inferior frontal gyrus), and social cognition (e.g., default network, superior temporal sulcus, temporo-parietal junction).ConclusionIf replicated in longitudinal studies and with additional populations, neural patterns suggesting risk endophenotypes could be used to identify individuals at risk for BD who may benefit from prevention measures. Moreover, information about risk and resilience markers could be used to develop novel treatments that recruit neural markers of resilience and attenuate neural patterns associated with risk. Clinical trial registration information-Studies of Brain Function and Course of Illness in Pediatric Bipolar Disorder and Child and Adolescent Bipolar Disorder Brain Imaging and Treatment Study; http://clinicaltrials.gov/; NCT00025935 and NCT00006177.

Project description:ImportanceEarly identification of individuals at high risk for the onset of bipolar spectrum disorder (BPSD) is key from both a clinical and research perspective. While previous work has identified the presence of a bipolar prodrome, the predictive implications for the individual have not been assessed, to date.ObjectiveTo build a risk calculator to predict the 5-year onset of BPSD in youth at familial risk for BPSD.Design, setting, and participantsThe Pittsburgh Bipolar Offspring Study is an ongoing community-based longitudinal cohort investigation of offspring of parents with bipolar I or II (and community controls), recruited between November 2001 and July 2007, with a median follow-up period of more than 9 years. Recruitment has ended, but follow-up is ongoing. The present analysis included offspring of parents with bipolar I or II (aged 6-17 years) who had not yet developed BPSD at baseline.Main outcomes and measuresThis study tested the degree to which a time-to-event model, including measures of mood and anxiety, general psychosocial functioning, age at mood disorder onset in the bipolar parent, and age at each visit, predicted new-onset BPSD. To fully use longitudinal data, the study assessed each visit separately, clustering within individuals. Discrimination was measured using the time-dependent area under the curve (AUC), predicting 5-year risk; internal validation was performed using 1000 bootstrapped resamples. Calibration was assessed by comparing observed vs predicted probability of new-onset BPSD.ResultsThere were 412 at-risk offspring (202 [49.0%] female), with a mean (SD) visit age of 12.0 (3.5) years and a mean (SD) age at new-onset BPSD of 14.2 (4.5) years. Among them, 54 (13.1%) developed BPSD during follow-up (18 with BD I or II); these participants contributed a total of 1058 visits, 67 (6.3%) of which preceded new-onset BPSD within the next 5 years. Using internal validation to account for overfitting, the model provided good discrimination between converting vs nonconverting visits (AUC, 0.76; bootstrapped 95% CI, 0.71-0.82). Important univariate predictors of outcome (AUC range, 0.66-0.70) were dimensional measures of mania, depression, anxiety, and mood lability; psychosocial functioning; and parental age at mood disorder.Conclusions and relevanceThis risk calculator provides a practical tool for assessing the probability that a youth at familial risk for BPSD will develop new-onset BPSD within the next 5 years. Such a tool may be used by clinicians to inform frequency of monitoring and treatment options and for research studies to better identify potential participants at ultra high risk of conversion.

Dataset Information

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

Objective

Method

Results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets