ORCID Profile
0000-0002-1966-5120
Current Organisation
UNSW Sydney
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Publisher: Wiley
Date: 02-11-2021
DOI: 10.1002/AJMG.B.32879
Abstract: Bipolar disorder (BD) is associated with a 20–30‐fold increased suicide risk compared to the general population. First‐degree relatives of BD patients show inflated rates of psychopathology including suicidal behaviors. As reliable biomarkers of suicide attempts (SA) are lacking, we examined associations between suicide‐related polygenic risk scores (PRSs)—a quantitative index of genomic risk—and variability in brain structures implicated in SA. Participants ( n = 206 aged 12–30 years) were unrelated in iduals of European ancestry and comprised three groups: 41 BD cases, 96 BD relatives (“high risk”), and 69 controls. Genotyping employed PsychArray, followed by imputation. Three PRSs were computed using genome‐wide association data for SA in BD (SA‐in‐BD), SA in major depressive disorder (SA‐in‐MDD) (Mullins et al., 2019, The American Journal of Psychiatry , 176 (8), 651–660), and risky behavior (Karlsson Linnér et al., 2019, Nature Genetics , 51 (2), 245–257). Structural magnetic resonance imaging processing employed FreeSurfer v5.3.0. General linear models were constructed using 32 regions‐of‐interest identified from suicide neuroimaging literature, with false‐discovery‐rate correction. SA‐in‐MDD and SA‐in‐BD PRSs negatively predicted parahippoc al thickness, with the latter association modified by group membership. SA‐in‐BD and Risky Behavior PRSs inversely predicted rostral and caudal anterior cingulate structure, respectively, with the latter effect driven by the “high risk” group. SA‐in‐MDD and SA‐in‐BD PRSs positively predicted cuneus structure, irrespective of group. This study demonstrated associations between PRSs for suicide‐related phenotypes and structural variability in brain regions implicated in SA. Future exploration of extended PRSs, in conjunction with a range of biological, phenotypic, environmental, and experiential data in high risk populations, may inform predictive models for suicidal behaviors.
Publisher: Cambridge University Press (CUP)
Date: 07-09-2020
DOI: 10.1017/S0033291720003153
Abstract: Bipolar disorder (BD) is a familial psychiatric disorder associated with frontotemporal and subcortical brain abnormalities. It is unclear whether such abnormalities are present in relatives without BD, and little is known about structural brain trajectories in those at risk. Neuroimaging was conducted at baseline and at 2-year follow-up interval in 90 high-risk in iduals with a first-degree BD relative (HR), and 56 participants with no family history of mental illness who could have non-BD diagnoses. All 146 subjects were aged 12–30 years at baseline. We examined longitudinal change in gray and white matter volume, cortical thickness, and surface area in the frontotemporal cortex and subcortical regions. Compared to controls, HR participants showed accelerated cortical thinning and volume reduction in right lateralised frontal regions, including the inferior frontal gyrus, lateral orbitofrontal cortex, frontal pole and rostral middle frontal gyrus. Independent of time, the HR group had greater cortical thickness in the left caudal anterior cingulate cortex, larger volume in the right medial orbitofrontal cortex and greater area of right accumbens, compared to controls. This pattern was evident even in those without the new onset of psychopathology during the inter-scan interval. This study suggests that differences previously observed in BD are developing prior to the onset of the disorder. The pattern of pathological acceleration of cortical thinning is likely consistent with a disturbance of molecular mechanisms responsible for normal cortical thinning. We also demonstrate that neuroanatomical differences in HR in iduals may be progressive in some regions and stable in others.
Publisher: Wiley
Date: 12-2021
DOI: 10.1002/AJMG.B.32803
Publisher: Cold Spring Harbor Laboratory
Date: 14-09-2021
DOI: 10.1101/2021.09.06.21262817
Abstract: Bipolar Disorder (BD) is associated with a 20-30 fold increased suicide risk compared to the general population. First-degree relatives of BD patients show inflated rates of psychopathology including suicidal behaviors. As reliable biomarkers of suicide attempts (SA) are lacking, we examined associations between suicide-related polygenic risk scores (PRS) – a quantitative index of genomic risk – and variability in brain structures implicated in SA. Participants (n=206 aged 12-30 years) were unrelated in iduals of European ancestry and comprised three groups: 41 BD cases, 96 BD relatives (‘high-risk’), and 69 controls. Genotyping employed PsychArray, followed by imputation. Three PRS were computed using genome-wide association data for SA in BD (SA-in-BD), SA in Major Depressive Disorder (SA-in-MDD) [Mullins et al., 2019], and risky behavior [Karlsson Linnér et al., 2019]. Structural MRI processing employed FreeSurfer v5.3.0. General linear models were constructed using 32 regions-of-interest identified from suicide neuroimaging literature, with false-discovery-rate correction. SA-in-MDD and SA-in-BD PRS negatively predicted parahippoc al thickness, with the latter association modified by group membership. SA-in-BD and Risky Behavior PRS inversely predicted rostral and caudal anterior cingulate structure, respectively, with the latter effect driven by the ‘high-risk’ group. SA-in-MDD and SA-in-BD PRS positively predicted cuneus structure, irrespective of group. This study demonstrated associations between PRS for suicide-related phenotypes and structural variability in brain regions implicated in SA. Future exploration of extended PRS, in conjunction with a range of biological, phenotypic, environmental and experiential data in high-risk populations, may inform predictive models for suicidal behaviors.
Publisher: Royal College of Psychiatrists
Date: 28-02-2022
DOI: 10.1192/BJP.2022.28
Abstract: Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment. To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder. This study utilised genetic and clinical data ( n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi + Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework. The best performing linear model explained 5.1% ( P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% ( P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% ( P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data. Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
Publisher: Wiley
Date: 16-12-2021
DOI: 10.1111/BDI.13172
Abstract: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under‐researched in psychiatry. We obtained body mass index (BMI) and magnetic resonance imaging‐derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control in iduals from 14 sites within the ENIGMA‐BD Working Group. We identified regionally specific profiles of cortical thickness using K‐means clustering and studied clinical characteristics associated with in idual cortical profiles. We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the s le) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD in iduals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in in iduals with higher BMI, which was additive and similar to the BD‐associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.
Publisher: Cold Spring Harbor Laboratory
Date: 28-09-2023
No related grants have been discovered for Gloria Roberts.