Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study

AbstractMany patients with bipolar disorder (BD) are initially misdiagnosed with major depressive disorder (MDD) and are treated with antidepressants, whose potential iatrogenic effects are widely discussed. It is unknown whether MDD is a comorbidity of BD or its earlier stage, and no consensus exists on individual conversion predictors, delaying BD’s timely recognition and treatment. We aimed to build a predictive model of MDD to BD conversion and to validate it across a multi-national network of patient databases using the standardization afforded by the Observational Medical Outcomes Partnership (OMOP) common data model. Five “training” US databases were retrospectively analyzed: IBM MarketScan CCAE, MDCR, MDCD, Optum EHR, and Optum Claims. Cyclops regularized logistic regression models were developed on one-year MDD-BD conversion with all standard covariates from the HADES PatientLevelPrediction package. Time-to-conversion Kaplan-Meier analysis was performed up to a decade after MDD, stratified by model-estimated risk. External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. The model’s area under the curve (AUC) varied 0.633–0.745 (µ = 0.689) across the five US training databases. Nine variables predicted one-year MDD-BD transition. Factors that increased risk were: younger age, severe depression, psychosis, anxiety, substance misuse, self-harm thoughts/actions, and prior mental disorder. AUCs of the validation datasets ranged 0.570–0.785 (µ = 0.664). An assessment algorithm was built for MDD to BD conversion that allows distinguishing as much as 100-fold risk differences among patients and validates well across multiple international data sources.

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The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically different groups of individuals

Scientific Reports ◽

10.1038/s41598-020-79964-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rona J. Strawbridge ◽

Keira J. A. Johnston ◽

Mark E. S. Bailey ◽

Damiano Baldassarre ◽

Breda Cullen ◽

...

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

European Ancestry ◽

Cardiometabolic Disease ◽

Metabolic Profiles ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Major Depressive ◽

Increased Risk

AbstractUnderstanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researchers. For individuals with European ancestry we explored whether genetic variation could identify sub-groups with different metabolic profiles. Loci associated with schizophrenia, bipolar disorder and major depressive disorder from previous genome-wide association studies and loci that were also implicated in cardiometabolic processes and diseases were selected. In the IMPROVE study (a high cardiovascular risk sample) and UK Biobank (general population sample) multidimensional scaling was applied to genetic variants implicated in both psychiatric and cardiometabolic disorders. Visual inspection of the resulting plots used to identify distinct clusters. Differences between these clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both schizophrenia and cardiometabolic disease (but not bipolar disorder or major depressive disorder) identified three groups of individuals with distinct metabolic profiles. This grouping was replicated within UK Biobank, with somewhat less distinction between metabolic profiles. This work focused on individuals of European ancestry and is unlikely to apply to more genetically diverse populations. Overall, this study provides proof of concept that common biology underlying mental and physical illness may help to stratify subsets of individuals with different cardiometabolic profiles.

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The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically different groups of individuals

10.1101/2020.06.23.20138271 ◽

2020 ◽

Author(s):

Rona J. Strawbridge ◽

Keira J. A. Johnston ◽

Mark E. S. Bailey ◽

Damiano Baldasarre ◽

Breda Cullen ◽

...

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Depressive Disorders ◽

Cardiometabolic Disease ◽

Metabolic Profiles ◽

Uk Biobank ◽

Major Depressive ◽

Increased Risk ◽

Chi Squared

AbstractUnderstanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researcher. We explored whether genetic variation could identify individuals with different metabolic profiles. Loci previously associated with schizophrenia, bipolar disorder and major depressive disorder were identified from literature and those overlapping loci genotyped on the Illumina CardioMetabo and Immuno chips (representing cardiometabolic processes and diseases) were selected. In the IMPROVE study (high cardiovascular risk) and UK Biobank (general population) multidimensional scaling was applied to genetic variants implicated in both mental and cardiometabolic illness. Visual inspection of the resulting plots used to identify distinct clusters. Differences between clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both cardiometabolic disease and schizophrenia (but not bipolar or major depressive disorders) identified three groups of individuals with distinct metabolic profiles. The grouping was replicated in UK Biobank, albeit with less distinction between metabolic profiles. This study provides proof of concept that common biology underlying mental and physical illness can identify subsets of individuals with different cardiometabolic profiles.

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Disruptive mood dysregulation disorder in offspring of parents with depression and bipolar disorder

The British Journal of Psychiatry ◽

10.1192/bjp.bp.117.198754 ◽

2017 ◽

Vol 210 (6) ◽

pp. 408-412 ◽

Cited By ~ 7

Author(s):

Lukas Propper ◽

Jill Cumby ◽

Victoria C. Patterson ◽

Vladislav Drobinin ◽

Jacqueline M. Glover ◽

...

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Family History ◽

Depressive Disorder ◽

Control Group ◽

Major Depressive ◽

Disruptive Mood Dysregulation Disorder ◽

Increased Risk ◽

History Of ◽

Mood Dysregulation

BackgroundIt has been suggested that offspring of parents with bipolar disorder are at increased risk for disruptive mood dysregulation disorder (DMDD), but the specificity of this association has not been established.AimsWe examined the specificity of DMDD to family history by comparing offspring of parents with (a) bipolar disorder, (b) major depressive disorder and (c) a control group with no mood disorders.MethodWe established lifetime diagnosis of DMDD using the Schedule for Affective Disorders and Schizophrenia for School Aged Children for DSM-5 in 180 youth aged 6–18 years, including 58 offspring of parents with bipolar disorder, 82 offspring of parents with major depressive disorder and 40 control offspring.ResultsDiagnostic criteria for DMDD were met in none of the offspring of parents with bipolar disorder, 6 of the offspring of parents with major depressive disorder and none of the control offspring. DMDD diagnosis was significantly associated with family history of major depressive disorder.ConclusionsOur results suggest that DMDD is not specifically associated with a family history of bipolar disorder and may be associated with parental depression.

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Distinguishing bipolar disorder from major depressive disorder: does an increased risk of substance use account for other differences?

Bipolar Disorders ◽

10.1111/bdi.12031 ◽

2012 ◽

Vol 15 (1) ◽

pp. 114-114

Author(s):

Thomas Richardson

Keyword(s):

Bipolar Disorder ◽

Substance Use ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Major Depressive ◽

Increased Risk

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Predictors of Time to Relapse/Recurrence after Electroconvulsive Therapy in Patients with Major Depressive Disorder: A Population-Based Cohort Study

Depression Research and Treatment ◽

10.1155/2011/470985 ◽

2011 ◽

Vol 2011 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Axel Nordenskjöld ◽

Lars von Knorring ◽

Ingemar Engström

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Electroconvulsive Therapy ◽

Substance Dependence ◽

Population Based ◽

Major Depressive ◽

Quality Register ◽

Increased Risk ◽

One Year ◽

Risk Of Relapse

Objective. The aim of the study is to define predictors of relapse/recurrence after electroconvulsive therapy, ECT, for patients with major depressive disorder.Methods. A study of all patients (n=486) treated by means of ECT for major depressive disorder was performed. The data were derived from a regional quality register in Sweden. Psychiatric hospitalisation or suicide was used as a marker for relapse/recurrence.Results. The relapse/recurrence rate within one year after ECT was 34%. Factors associated with increased risk of relapse/recurrence included comorbid substance dependence and treatment with benzodiazepines or antipsychotics during the follow-up period.Conclusions. Within the first years after ECT, relapses/recurrences leading to hospitalisation or suicide are common. Treatment with lithium might be beneficial, while benzodiazepines, antipsychotics, or continuation ECT does not seem to significantly reduce the risk of relapse/recurrence.

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117 Impact of Antipsychotic Treatment Switching in Patients with Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

CNS Spectrums ◽

10.1017/s1092852920000358 ◽

2020 ◽

Vol 25 (2) ◽

pp. 276-276

Author(s):

Rajeev Ayyagari ◽

Darren Thomason ◽

Fan Mu ◽

Michael Philbin ◽

Benjamin Carroll

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Side Effects ◽

Depressive Disorder ◽

Antipsychotic Treatment ◽

Disease Relapse ◽

Major Depressive ◽

Study Objective ◽

Increased Risk ◽

Risk Of Relapse

Abstract:Study Objective:To evaluate the risk of relapse for patients with schizophrenia (SZ), bipolar disorder (BP), and major depressive disorder (MDD) who switched antipsychotics compared with those who did not switch.Background:Antipsychotics are commonly used for maintenance treatment of SZ, BP, and MDD but can have significant side effects, such as extrapyramidal symptoms (EPS). Adherence to treatment is important for reducing the risk of relapse, but fear of side effects may prompt medication switching.Methods:Medicaid claims from 6 US states spanning 6 years were retrospectively analyzed for antipsychotic switching versus non-switching. For all patients with SZ, BD or MDD and for the subset of patients who also had ≥1 EPS diagnosis during the baseline period, times to the following outcomes, during a 2-year study period were analyzed: underlying disease relapse, psychiatric relapse, all-cause emergency room (ER) visit, all-cause inpatient (IP) admission and EPS diagnosis.Results:Switchers (N=10,548) had a shorter time to disease relapse, other psychiatric relapse, IP admissions, ER visits, and EPS diagnosis (all, log-rank P<0.001) than non-switchers (N=31,644). Switchers reached the median for IP admission (21.50 months) vs non-switchers (not reached) and for ER visits (switchers, 9.07 months; non-switchers, 13.35 months). For disease relapse, other psychiatric relapse, and EPS diagnosis, <50% of patients had an event during the 2-year study period. Comparisons in a subgroup of patients with ≥1 EPS diagnosis revealed similar outcomes.Conclusions:These results show that disease and other psychiatric relapse, all-cause ER visits, IP admissions, and EPS diagnosis occurred earlier for switchers than for non-switchers, suggesting that switching is associated with an increased risk of relapse in patients with SZ, BP and MDD.Funding Acknowledgements:This study was supported by Teva Pharmaceuticals, Petach Tikva, Israel.

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