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1.
Am J Med Genet B Neuropsychiatr Genet ; 183(6): 309-330, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32681593

RESUMO

It is imperative to understand the specific and shared etiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression. Polygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic etiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in 11 data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank. All cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease, and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression. The phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic etiology irrespective of the age depression first presents.

2.
Mol Psychiatry ; 2020 Mar 16.
Artigo em Inglês | MEDLINE | ID: mdl-32203155

RESUMO

Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18-2.01) and European sample: OR = 1.75 (95% CI: 1.30-2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61-4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.

4.
Transl Psychiatry ; 9(1): 285, 2019 11 11.
Artigo em Inglês | MEDLINE | ID: mdl-31712550

RESUMO

Machine learning methods show promise to translate univariate biomarker findings into clinically useful multivariate decision support systems. At current, works in major depressive disorder have predominantly focused on neuroimaging and clinical predictor modalities, with genetic, blood-biomarker, and cardiovascular modalities lacking. In addition, the prediction of rehospitalization after an initial inpatient major depressive episode is yet to be explored, despite its clinical importance. To address this gap in the literature, we have used baseline clinical, structural imaging, blood-biomarker, genetic (polygenic risk scores), bioelectrical impedance and electrocardiography predictors to predict rehospitalization within 2 years of an initial inpatient episode of major depression. Three hundred and eighty patients from the ongoing 12-year Bidirect study were included in the analysis (rehospitalized: yes = 102, no = 278). Inclusion criteria was age ≥35 and <66 years, a current or recent hospitalisation for a major depressive episode and complete structural imaging and genetic data. Optimal performance was achieved with a multimodal panel containing structural imaging, blood-biomarker, clinical, medication type, and sleep quality predictors, attaining a test AUC of 67.74 (p = 9.99-05). This multimodal solution outperformed models based on clinical variables alone, combined biomarkers, and individual data modality prognostication for rehospitalization prediction. This finding points to the potential of predictive models that combine multimodal clinical and biomarker data in the development of clinical decision support systems.


Assuntos
Transtorno Depressivo Maior/fisiopatologia , Transtorno Depressivo Maior/terapia , Aprendizado de Máquina , Readmissão do Paciente , Adulto , Idoso , Antidepressivos/uso terapêutico , Área Sob a Curva , Biomarcadores , Encéfalo/diagnóstico por imagem , Encéfalo/patologia , Transtorno Depressivo Maior/diagnóstico por imagem , Feminino , Seguimentos , Humanos , Imagem por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Resultado do Tratamento
5.
Transl Psychiatry ; 9(1): 271, 2019 10 22.
Artigo em Inglês | MEDLINE | ID: mdl-31641106

RESUMO

Machine learning methods hold promise for personalized care in psychiatry, demonstrating the potential to tailor treatment decisions and stratify patients into clinically meaningful taxonomies. Subsequently, publication counts applying machine learning methods have risen, with different data modalities, mathematically distinct models, and samples of varying size being used to train and test models with the promise of clinical translation. Consequently, and in part due to the preliminary nature of such works, many studies have reported largely varying degrees of accuracy, raising concerns over systematic overestimation and methodological inconsistencies. Furthermore, a lack of procedural evaluation guidelines for non-expert medical professionals and funding bodies leaves many in the field with no means to systematically evaluate the claims, maturity, and clinical readiness of a project. Given the potential of machine learning methods to transform patient care, albeit, contingent on the rigor of employed methods and their dissemination, we deem it necessary to provide a review of current methods, recommendations, and future directions for applied machine learning in psychiatry. In this review we will cover issues of best practice for model training and evaluation, sources of systematic error and overestimation, model explainability vs. trust, the clinical implementation of AI systems, and finally, future directions for our field.


Assuntos
Psiquiatria/métodos , Aprendizado de Máquina Supervisionado/normas , Aprendizado de Máquina Supervisionado/tendências , Humanos , Transtornos Mentais/diagnóstico , Transtornos Mentais/terapia , Guias de Prática Clínica como Assunto
6.
J Psychiatry Neurosci ; 44(6): 423-431, 2019 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-31304733

RESUMO

Background: Preliminary research suggests that major depressive disorder (MDD) is associated with structural alterations in the brain; as well as with low-grade peripheral inflammation. However, even though a link between inflammatory processes and altered brain structural integrity has been purported by experimental research, well-powered studies to confirm this hypothesis in patients with MDD have been lacking. We aimed to investigate the potential association between structural brain alterations and low-grade inflammation as interrelated biological correlates of MDD. Methods: In this cross-sectional study, 514 patients with MDD and 359 healthy controls underwent structural MRI. We used voxel-based morphometry to study local differences in grey matter volume. We also assessed serum levels of high-sensitivity C-reactive protein (hsCRP) in each participant. Results: Compared with healthy controls (age [mean ± standard deviation] 52.57 ± 7.94 yr; 50% male), patients with MDD (49.14 ± 7.28 yr, 39% male) exhibited significantly increased hsCRP levels (Z = −5.562, p < 0.001) and significantly decreased grey matter volume in the prefrontal cortex and the insula. Prefrontal grey matter volume reductions were significantly associated with higher hsCRP levels in patients with MDD (x = 50, y = 50, z = 8; t1,501 = 5.15; k = 92; pFWE < 0.001). In the MDD sample, the significant negative association between hsCRP and grey matter appeared independent of age, sex, body mass index, current smoking status, antidepressant load, hospitalization and medical comorbidities. Limitations: This study had a cross-sectional design. Conclusion: The present study highlights the role of reduced grey matter volume and low-grade peripheral inflammation as interrelated biological correlates of MDD. The reported inverse association between peripheral low-grade inflammation and brain structural integrity in patients with MDD translates current knowledge from experimental studies to the bedside.


Assuntos
Proteína C-Reativa/metabolismo , Transtorno Depressivo Maior/diagnóstico por imagem , Transtorno Depressivo Maior/metabolismo , Substância Cinzenta/diagnóstico por imagem , Inflamação/metabolismo , Adulto , Encéfalo/diagnóstico por imagem , Encéfalo/patologia , Estudos de Casos e Controles , Córtex Cerebral/diagnóstico por imagem , Córtex Cerebral/patologia , Estudos Transversais , Feminino , Substância Cinzenta/patologia , Humanos , Masculino , Pessoa de Meia-Idade , Tamanho do Órgão , Córtex Pré-Frontal/diagnóstico por imagem , Córtex Pré-Frontal/patologia
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