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- Jonsson, Lina, 1982, et al.
(author)
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Characterisation of age and polarity at onset in bipolar disorder
- 2021
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In: British Journal of Psychiatry. - : Royal College of Psychiatrists. - 0007-1250 .- 1472-1465. ; 219:6, s. 659-669
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Journal article (peer-reviewed)abstract
- 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 (beta = -0.34 years, s.e. = 0.08), major depression (beta = -0.34 years, s.e. = 0.08), schizophrenia (beta = -0.39 years, s.e. = 0.08), and educational attainment (beta = -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.
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- Nunes, A, et al.
(author)
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Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
- 2020
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In: Molecular psychiatry. - : Springer Science and Business Media LLC. - 1476-5578 .- 1359-4184. ; 25:9, s. 2130-2143
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Journal article (peer-reviewed)abstract
- Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
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- Elvsashagen, T, et al.
(author)
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The genetic architecture of human brainstem structures and their involvement in common brain disorders
- 2020
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1, s. 4016-
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Journal article (peer-reviewed)abstract
- Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson’s disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders.
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| 7. |
- Scott, J., et al.
(author)
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Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative
- 2019
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In: International Journal of Bipolar Disorders. - : Springer Science and Business Media LLC. - 2194-7511. ; 7:1
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Journal article (peer-reviewed)abstract
- Background Lithium is recommended as a first line treatment for bipolar disorders. However, only 30% of patients show an optimal outcome and variability in lithium response and tolerability is poorly understood. It remains difficult for clinicians to reliably predict which patients will benefit without recourse to a lengthy treatment trial. Greater precision in the early identification of individuals who are likely to respond to lithium is a significant unmet clinical need. Structure The H2020-funded Response to Lithium Network (R-LiNK; ) will undertake a prospective cohort study of over 300 individuals with bipolar-I-disorder who have agreed to commence a trial of lithium treatment following a recommendation by their treating clinician. The study aims to examine the early prediction of lithium response, non-response and tolerability by combining systematic clinical syndrome subtyping with examination of multi-modal biomarkers (or biosignatures), including omics, neuroimaging, and actigraphy, etc. Individuals will be followed up for 24 months and an independent panel will assess and classify each participants' response to lithium according to predefined criteria that consider evidence of relapse, recurrence, remission, changes in illness activity or treatment failure (e.g. stopping lithium; new prescriptions of other mood stabilizers) and exposure to lithium. Novel elements of this study include the recruitment of a large, multinational, clinically representative sample specifically for the purpose of studying candidate biomarkers and biosignatures; the application of lithium-7 magnetic resonance imaging to explore the distribution of lithium in the brain; development of a digital phenotype (using actigraphy and ecological momentary assessment) to monitor daily variability in symptoms; and economic modelling of the cost-effectiveness of introducing biomarker tests for the customisation of lithium treatment into clinical practice. Also, study participants with sub-optimal medication adherence will be offered brief interventions (which can be delivered via a clinician or smartphone app) to enhance treatment engagement and to minimize confounding of lithium non-response with non-adherence. Conclusions The paper outlines the rationale, design and methodology of the first study being undertaken by the newly established R-LiNK collaboration and describes how the project may help to refine the clinical response phenotype and could translate into the personalization of lithium treatment.
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- Lagerberg, T, et al.
(author)
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Selective serotonin reuptake inhibitors and suicidal behaviour: a population-based cohort study
- 2022
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In: Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology. - : Springer Science and Business Media LLC. - 1740-634X. ; 4647:134, s. 817-823
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Journal article (peer-reviewed)abstract
- There is concern that selective serotonin reuptake inhibitor (SSRI) treatment may increase the risk of suicide attempts or deaths, particularly among children and adolescents. However, debate remains regarding the nature of the relationship. Using nationwide Swedish registers, we identified all individuals aged 6–59 years with an incident SSRI dispensation (N = 538,577) from 2006 to 2013. To account for selection into treatment, we used a within-individual design to compare the risk of suicide attempts or deaths (suicidal behaviour) in time periods before and after SSRI-treatment initiation. Within-individual incidence rate ratios (IRRs) of suicidal behaviour were estimated. The 30 days before SSRI-treatment initiation was associated with the highest risk of suicidal behaviour compared with the 30 days 1 year before SSRI initiation (IRR = 7.35, 95% CI 6.60–8.18). Compared with the 30 days before SSRI initiation, treatment periods after initiation had a reduced risk—the IRR in the 30 days after initiation was 0.62 (95% CI 0.58–0.65). The risk then declined over treatment time. These patterns were similar across age strata, and when stratifying on history of suicide attempts. Initiation with escitalopram was associated with the greatest risk reduction, though CIs for the IRRs of the different SSRI types were overlapping. The results do not suggest that SSRI-treatment increases the risk for suicidal behaviour in either youths or adults; rather, it may reduce the risk. Further research with different study designs and in different populations is warranted.
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| 29. |
- Lagerberg, T, et al.
(author)
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Use of central nervous system drugs in combination with selective serotonin reuptake inhibitor treatment: A Bayesian screening study for risk of suicidal behavior
- 2022
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In: Frontiers in psychiatry. - : Frontiers Media SA. - 1664-0640. ; 13, s. 1012650-
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Journal article (peer-reviewed)abstract
- Using other central nervous system (CNS) medications in combination with selective serotonin reuptake inhibitor (SSRI) treatment is common. Despite this, there is limited evidence on the impact on suicidal behavior of combining specific medications. We aim to provide evidence on signals for suicidal behavior risk when initiating CNS drugs during and outside of SSRI treatment.Materials and methodsUsing a linkage of Swedish national registers, we identified a national cohort of SSRI users aged 6–59 years residing in Sweden 2006–2013. We used a two-stage Bayesian Poisson model to estimate the incidence rate ratio (IRR) of suicidal behavior in periods up to 90 days before and after a CNS drug initiation during SSRI treatment, while accounting for multiple testing. For comparison, and to assess whether there were interactions between SSRIs and other CNS drugs, we also estimated the IRR of initiating the CNS drug without SSRI treatment.ResultsWe identified 53 common CNS drugs initiated during SSRI treatment, dispensed to 262,721 individuals. We found 20 CNS drugs with statistically significant IRRs. Of these, two showed a greater risk of suicidal behavior after versus before initiating the CNS drug (alprazolam, IRR = 1.39; flunitrazepam, IRR = 1.83). We found several novel signals of drugs that were statistically significantly associated with a reduction in the suicidal behavior risk. We did not find evidence of harmful interactions between SSRIs and the selected CNS drugs.ConclusionSeveral of the detected signals for reduced risk correspond to drugs where there is previous evidence of benefit for antidepressant augmentation (e.g., olanzapine, quetiapine, lithium, buspirone, and mirtazapine). Novel signals of reduced suicidal behavior risk, including for lamotrigine, valproic acid, risperidone, and melatonin, warrant further investigation.
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| 31. |
- Tønnesen, Siren, et al.
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Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder : A Multisample Diffusion Tensor Imaging Study
- 2020
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In: Biological Psychiatry. - : Elsevier BV. - 2451-9022 .- 2451-9030. ; 5:12, s. 1095-1103
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Journal article (peer-reviewed)abstract
- BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts.METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics.CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.
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| 35. |
- Zhu, N., et al.
(author)
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Kidney function and prescribed dose in middle-aged and older patients starting selective serotonin reuptake inhibitors
- 2022
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In: Pharmacoepidemiology and Drug Safety. - : Wiley. - 1053-8569 .- 1099-1557. ; 31:10, s. 1091-1101
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Journal article (peer-reviewed)abstract
- Purpose: To avoid adverse drug reactions, dose reductions are recommended when prescribing selective serotonin reuptake inhibitors (SSRIs) to patients with impaired kidney function. The extent of this practice in routine clinical care is however unknown. We aimed to evaluate the starting and maintenance SSRI doses prescribed to patients stratified by levels of kidney function in real-world practice. Methods: Using data from the Stockholm CREAtinine Measurements (SCREAM) project, we identified 101 409 new users of antidepressants (including 52 286 SSRI users) in the region of Stockholm during 2006–2019, who were ≥50 years of age and had a recent creatinine test taken in order to estimate glomerular filtration rate (eGFR). SSRI dose reduction was defined as a prescribed SSRI dose of ≤0.5 defined daily doses, according to current recommendations. We examined the associations between eGFR and reductions in initial dose and maintenance dose of SSRIs using logistic regression models. Results: Overall, reductions in initial and maintenance dose were observed among 54.1% and 34.1% of new SSRI users. Nevertheless, about 40% of individuals with an eGFR <30 ml/min/1.73 m2 were prescribed an SSRI without dose reduction. After adjusting for age and other covariates, lower eGFR was associated with moderately higher odds of dose reduction, for both initial and maintenance dose. Compared to individuals with an eGFR of 90–104 ml/min/1.73 m2, the adjusted odds ratios for those with an eGFR <30 ml/min/1.73 m2 were 1.18 (95% CI: 1.03, 1.36) for initial dose reduction, and 1.49 (1.29, 1.72) for maintenance dose reduction. Stratified analyses showed stronger associations between lower eGFR and SSRI dose reduction among individuals aged 50–64 years and in those receiving prescriptions from psychiatric care. Conclusions: Lower kidney function was moderately associated with a reduced SSRI dose, independently of age. Prescribing SSRIs to middle-aged and older patients should not only consider patients' age but also their kidney function.
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