The Experts below are selected from a list of 5229 Experts worldwide ranked by ideXlab platform
Manuel Fuentes - One of the best experts on this subject based on the ideXlab platform.
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Association of schizophrenia polygenic risk score with manic and Depressive Psychosis in bipolar disorder.
Translational psychiatry, 2018Co-Authors: Matej Markota, Brandon Coombes, Beth R. Larrabee, Susan L. Mcelroy, Colin L. Colby, David J. Bond, Marin Veldic, Mohit Chauhan, Alfredo B. Cuellar-barboza, Manuel FuentesAbstract:Bipolar disorder (BD) is highly heterogeneous in symptomatology. Narrowing the clinical phenotype may increase the power to identify risk genes that contribute to particular BD subtypes. This study was designed to test the hypothesis that genetic overlap between schizophrenia (SZ) and BD is higher for BD with a history of manic Psychosis. Analyses were conducted using a Mayo Clinic Bipolar Biobank cohort of 957 bipolar cases (including 333 with history of Psychosis during mania, 64 with history of Psychosis only during depression, 547 with no history of Psychosis, and 13 with unknown history of Psychosis) and 778 controls. Polygenic risk score (PRS) analysis was performed by calculating a SZ-PRS for the BD cases and controls, and comparing the calculated SZ risk between different Psychosis subgroups and bipolar types. The SZ-PRS was significantly higher for BD-I cases with manic Psychosis than BD-I cases with Depressive Psychosis (Nagelkerke’s R2 = 0.021; p = 0.045), BD-I cases without Psychosis (R2 = 0.015; p = 0.007), BD-II cases without Psychosis (R2 = 0.014; p = 0.017), and controls (R2 = 0.065; p = 2 × 10−13). No other significant differences were found. Our results show that BD-I with manic Psychosis is genetically more similar to SZ than any other tested BD subgroup. Further investigations on genetics of distinct clinical phenotypes composing major psychoses may help refine the current diagnostic classification system.
Matej Markota - One of the best experts on this subject based on the ideXlab platform.
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Association of schizophrenia polygenic risk score with manic and Depressive Psychosis in bipolar disorder.
Translational psychiatry, 2018Co-Authors: Matej Markota, Brandon Coombes, Beth R. Larrabee, Susan L. Mcelroy, Colin L. Colby, David J. Bond, Marin Veldic, Mohit Chauhan, Alfredo B. Cuellar-barboza, Manuel FuentesAbstract:Bipolar disorder (BD) is highly heterogeneous in symptomatology. Narrowing the clinical phenotype may increase the power to identify risk genes that contribute to particular BD subtypes. This study was designed to test the hypothesis that genetic overlap between schizophrenia (SZ) and BD is higher for BD with a history of manic Psychosis. Analyses were conducted using a Mayo Clinic Bipolar Biobank cohort of 957 bipolar cases (including 333 with history of Psychosis during mania, 64 with history of Psychosis only during depression, 547 with no history of Psychosis, and 13 with unknown history of Psychosis) and 778 controls. Polygenic risk score (PRS) analysis was performed by calculating a SZ-PRS for the BD cases and controls, and comparing the calculated SZ risk between different Psychosis subgroups and bipolar types. The SZ-PRS was significantly higher for BD-I cases with manic Psychosis than BD-I cases with Depressive Psychosis (Nagelkerke’s R2 = 0.021; p = 0.045), BD-I cases without Psychosis (R2 = 0.015; p = 0.007), BD-II cases without Psychosis (R2 = 0.014; p = 0.017), and controls (R2 = 0.065; p = 2 × 10−13). No other significant differences were found. Our results show that BD-I with manic Psychosis is genetically more similar to SZ than any other tested BD subgroup. Further investigations on genetics of distinct clinical phenotypes composing major psychoses may help refine the current diagnostic classification system.
N Tarrier - One of the best experts on this subject based on the ideXlab platform.
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prodromal symptoms in manic Depressive Psychosis
Social Psychiatry and Psychiatric Epidemiology, 1992Co-Authors: J A Smith, N TarrierAbstract:Twenty patients suffering from manic Depressive Psychosis were interviewed about the prodromes to both manic and Depressive episodes. These prodromal periods were compared with a recent control period during which the patient was in remission. It was possible for 85% of patients to identify a Depressive prodrome and 75% a manic prodrome. Prodromal periods were characterised by a significant increase in the number and magnitude of symptoms compared with those present during remission. The mean duration of manic prodromes was slightly longer than that of Depressive prodromes (28.9 days and 18.8 days respectively). The majority of patients could identify a time sequence to the retainment of insight during their prodromes and could also identify idiosyncratic symptoms.
Jeffrey Aronson - One of the best experts on this subject based on the ideXlab platform.
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the upregulation of na k atpase pump numbers in lymphocytes from the first degree unaffected relatives of patients with manic Depressive Psychosis in response to in vitro lithium and sodium ethacrynate
Journal of Affective Disorders, 1995Co-Authors: I J Antia, A J Wood, C E Smith, Jeffrey AronsonAbstract:Patients with manic Depressive disorder (DSM-III-R bipolar disorder) have an abnormality of the Na+,K(+)-ATPase pumps in their lymphocytes: the pump numbers do not upregulate to stimulation with lithium and ethacrynate. We have now investigated the in vitro adaptive responses of lymphocyte Na+,K(+)-ATPase pumps in the first-degree unaffected relatives of patients with a clear history of manic Depressive disorder. The lymphocytes of the healthy relatives upregulated their Na+,K(+)-ATPase pumps normally, suggesting that the abnormal response that we have previously observed in patients with the disorder reflects a complex relation between the biochemical phenotype and the development of clinical symptoms.
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altered in vitro adaptive responses of lymphocyte na k atpase in patients with manic Depressive Psychosis
Journal of Affective Disorders, 1991Co-Authors: A J Wood, C E Smith, E E Clarke, P J Cowen, Jeffrey Aronson, D G GrahamesmithAbstract:When lymphocytes from healthy subjects are incubated in lithium (8 mM) or ethacrynate (1 microM) they show a time-dependent adaptive response, which consists of a significant increase in the number of Na+,K(+)-ATPase molecules in the lymphocyte membrane. We have studied the lymphocytes from nine euthymic drug-free patients with a history of manic Depressive Psychosis, and have found that this normal adaptive response was absent. It was also absent from the lymphocytes of euthymic patients taking lithium. We conclude that this altered in vitro adaptive response of lymphocyte Na+,K(+)-ATPase represents an enduring trait marker in manic Depressive Psychosis.
Anderson-schmidt H. - One of the best experts on this subject based on the ideXlab platform.
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An investigation of Psychosis subgroups with prognostic validation and exploration of genetic underpinnings: The PsyCourse study.
2020Co-Authors: Dwyer D.b., Kalman J.l., Budde M., Kambeitz J., Ruef A., Antonucci L.a., Kambeitz-ilankovic L., Hasan A., Anderson-schmidt H.Abstract:Importance: Identifying Psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle illness trajectories, and investigate genetic associations. Objective: To detect Psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement. Design, Setting, and Participants: This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective disorder (I/II), schizoaffective disorder, schizophreniform disorder, and brief psychotic disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019. Main Outcomes and Measures: A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables. Results: Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective Psychosis (n = 252), suicidal Psychosis (n = 44), Depressive Psychosis (n = 131), high-functioning Psychosis (n = 252), and severe Psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for Psychosis symptoms (R2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R2 = 0.28; 95% CI, 0.25-0.32), global functioning (R2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R2 = 0.20; 95% CI, 0.17-0.23). The Depressive and severe Psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean [SD] partial η2 = 0.014 [0.003]) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort. Conclusions and Relevance: Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future Psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the Depressive and severe Psychosis subgroups.
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An investigation of Psychosis subgroups with prognostic validation and exploration of genetic underpinnings: The PsyCourse study.
'American Medical Association (AMA)', 2020Co-Authors: Dwyer D.b., Kalman J.l., Budde M., Kambeitz J., Ruef A., Antonucci L.a., Kambeitz-ilankovic L., Hasan A., Anderson-schmidt H.Abstract:This cohort study aims to detect Psychosis subgroups and examine their illness courses over 1.5 years and their polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement.Question Will data-driven clustering using high-dimensional clinical data reveal Psychosis subgroups with relevance to prognoses and polygenic risk? Findings In this cohort study including 1223 individuals, in the discovery sample of 765 individuals with predominantly bipolar and schizophrenia diagnoses, 5 subgroups were detected with different clinical signatures, illness trajectories, and genetic scores for educational attainment. Results were validated in a sample of 458 individuals. Meaning New data-driven clustering paired with rigorous validation may offer a means to extend symptom-based Psychosis taxonomies toward functional outcomes, genetic markers, and trajectory-based stratifications.Importance Identifying Psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle illness trajectories, and investigate genetic associations. Objective To detect Psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement. Design, Setting, and Participants This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective disorder (I/II), schizoaffective disorder, schizophreniform disorder, and brief psychotic disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019. Main Outcomes and Measures A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables. Results Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective Psychosis (n = 252), suicidal Psychosis (n = 44), Depressive Psychosis (n = 131), high-functioning Psychosis (n = 252), and severe Psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for Psychosis symptoms (R-2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R-2 = 0.28; 95% CI, 0.25-0.32), global functioning (R-2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R-2 = 0.20; 95% CI, 0.17-0.23). The Depressive and severe Psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean [SD] partial eta(2) = 0.014 [0.003]) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort. Conclusions and Relevance Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future Psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the Depressive and severe Psychosis subgroups