9 results
Phenotypic distinctions in depression and anxiety: a comparative analysis of comorbid and isolated cases
- Y. Nina Gao, Brandon Coombes, Euijung Ryu, Vanessa Pazdernik, Gregory Jenkins, Richard Pendegraft, Joanna Biernacka, Mark Olfson
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- Journal:
- Psychological Medicine / Volume 53 / Issue 16 / December 2023
- Published online by Cambridge University Press:
- 05 July 2023, pp. 7766-7774
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Background
Anxiety and depression are frequently comorbid yet phenotypically distinct. This study identifies differences in the clinically observable phenome across a wide variety of physical and mental disorders comparing patients with diagnoses of depression without anxiety, anxiety without depression, or both depression and anxiety.
MethodsUsing electronic health records for 14 994 participants with depression and/or anxiety in the Mayo Clinic Biobank, a phenotype-based phenome-wide association study (Phe2WAS) was performed to test for differences between these groups across a broad range of clinical diagnoses observed in the electronic health record. Additional analyses were performed to determine the temporal sequencing of diagnoses.
ResultsCompared to patients diagnosed only with anxiety, those diagnosed only with depression were more likely to have diagnoses of obesity (OR 1.75; p = 1 × 10−27), sleep apnea (OR 1.71; p = 1 × 10−22), and type II diabetes (OR 1.74; p = 9 × 10−18). Compared to those diagnosed only with depression, those diagnosed only with anxiety were more likely to have diagnoses of palpitations (OR 1.91; p = 2 × 10−25), benign skin neoplasms (OR 1.61; p = 2 × 10−17), and cardiac dysrhythmias (OR 1.45; p = 2 × 10−12). Patients with comorbid depression and anxiety were more likely to have diagnoses of other mental health disorders, substance use disorders, sleep problems, and gastroesophageal reflux relative to isolated depression.
ConclusionsWhile depression and anxiety are closely related, this study suggests that phenotypic distinctions exist between depression and anxiety. Improving phenotypic characterization within the broad categories of depression and anxiety could improve the clinical assessment of depression and anxiety.
The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people
- Brandon J Coombes, Isotta Landi, Karmel W Choi, Kritika Singh, Brian Fennessy, Greg D Jenkins, Anthony Batzler, Richard Pendegraft, Nicolas A Nunez, Y Nina Gao, Euijung Ryu, Priya Wickramaratne, Myrna M Weissman, Regeneron Genetics Center, Jyotishman Pathak, J John Mann, Jordan W Smoller, Lea K Davis, Mark Olfson, Alexander W Charney, Joanna M Biernacka
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- Journal:
- Psychological Medicine / Volume 53 / Issue 15 / November 2023
- Published online by Cambridge University Press:
- 05 May 2023, pp. 7368-7374
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Background
Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation.
MethodsDiagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry.
ResultsIn total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18–1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05–1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06–1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11–1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02–1.09), showing a genetic risk gradient across the conditions and the comorbidity.
ConclusionsThis study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.
Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach – CORRIGENDUM
- Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, JeanMichel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, HsiChung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O'Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil TekolaAyele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark, Bernhard T. Baune
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- Journal:
- The British Journal of Psychiatry / Volume 221 / Issue 2 / August 2022
- Published online by Cambridge University Press:
- 04 May 2022, p. 494
- Print publication:
- August 2022
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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach
- Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, Jean-Michel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, Hsi-Chung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O'Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil Tekola-Ayele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark, Bernhard T. Baune
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- Journal:
- The British Journal of Psychiatry / Volume 220 / Issue 4 / April 2022
- Published online by Cambridge University Press:
- 28 February 2022, pp. 219-228
- Print publication:
- April 2022
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Background
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.
AimsTo use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
MethodThis 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.
ResultsThe 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.
ConclusionsUsing 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.
The importance of social activity to risk of major depression in older adults
- Euijung Ryu, Gregory D. Jenkins, Yanshan Wang, Mark Olfson, Ardesheer Talati, Lauren Lepow, Brandon J. Coombes, Alexander W. Charney, Benjamin S. Glicksberg, J. John Mann, Myrna M. Weissman, Priya Wickramaratne, Jyotishman Pathak, Joanna M. Biernacka
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- Journal:
- Psychological Medicine / Volume 53 / Issue 6 / April 2023
- Published online by Cambridge University Press:
- 12 November 2021, pp. 2634-2642
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Background
Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults.
MethodsWe used self-reported health-related survey data from 41 174 older adults (50–89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis (N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis.
ResultsFollowing biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00–2.50) for highest v. lowest level].
ConclusionAcross a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.
Characterisation of age and polarity at onset in bipolar disorder
- Janos L. Kalman, Loes M. Olde Loohuis, Annabel Vreeker, Andrew McQuillin, Eli A. Stahl, Douglas Ruderfer, Maria Grigoroiu-Serbanescu, Georgia Panagiotaropoulou, Stephan Ripke, Tim B. Bigdeli, Frederike Stein, Tina Meller, Susanne Meinert, Helena Pelin, Fabian Streit, Sergi Papiol, Mark J. Adams, Rolf Adolfsson, Kristina Adorjan, Ingrid Agartz, Sofie R. Aminoff, Heike Anderson-Schmidt, Ole A. Andreassen, Raffaella Ardau, Jean-Michel Aubry, Ceylan Balaban, Nicholas Bass, Bernhard T. Baune, Frank Bellivier, Antoni Benabarre, Susanne Bengesser, Wade H Berrettini, Marco P. Boks, Evelyn J. Bromet, Katharina Brosch, Monika Budde, William Byerley, Pablo Cervantes, Catina Chillotti, Sven Cichon, Scott R. Clark, Ashley L. Comes, Aiden Corvin, William Coryell, Nick Craddock, David W. Craig, Paul E. Croarkin, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Udo Dannlowski, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Srdjan Djurovic, Howard J. Edenberg, Mariam Al Eissa, Torbjørn Elvsåshagen, Bruno Etain, Ayman H. Fanous, Frederike Fellendorf, Alessia Fiorentino, Andreas J. Forstner, Mark A. Frye, Janice M. Fullerton, Katrin Gade, Julie Garnham, Elliot Gershon, Michael Gill, Fernando S. Goes, Katherine Gordon-Smith, Paul Grof, Jose Guzman-Parra, Tim Hahn, Roland Hasler, Maria Heilbronner, Urs Heilbronner, Stephane Jamain, Esther Jimenez, Ian Jones, Lisa Jones, Lina Jonsson, Rene S. Kahn, John R. Kelsoe, James L. Kennedy, Tilo Kircher, George Kirov, Sarah Kittel-Schneider, Farah Klöhn-Saghatolislam, James A. Knowles, Thorsten M. Kranz, Trine Vik Lagerberg, Mikael Landen, William B. Lawson, Marion Leboyer, Qingqin S. Li, Mario Maj, Dolores Malaspina, Mirko Manchia, Fermin Mayoral, Susan L. McElroy, Melvin G. McInnis, Andrew M. McIntosh, Helena Medeiros, Ingrid Melle, Vihra Milanova, Philip B. Mitchell, Palmiero Monteleone, Alessio Maria Monteleone, Markus M. Nöthen, Tomas Novak, John I. Nurnberger, Niamh O'Brien, Kevin S. O'Connell, Claire O'Donovan, Michael C. O'Donovan, Nils Opel, Abigail Ortiz, Michael J. Owen, Erik Pålsson, Carlos Pato, Michele T. Pato, Joanna Pawlak, Julia-Katharina Pfarr, Claudia Pisanu, James B. Potash, Mark H Rapaport, Daniela Reich-Erkelenz, Andreas Reif, Eva Reininghaus, Jonathan Repple, Hélène Richard-Lepouriel, Marcella Rietschel, Kai Ringwald, Gloria Roberts, Guy Rouleau, Sabrina Schaupp, William A Scheftner, Simon Schmitt, Peter R. Schofield, K. Oliver Schubert, Eva C. Schulte, Barbara Schweizer, Fanny Senner, Giovanni Severino, Sally Sharp, Claire Slaney, Olav B. Smeland, Janet L. Sobell, Alessio Squassina, Pavla Stopkova, John Strauss, Alfonso Tortorella, Gustavo Turecki, Joanna Twarowska-Hauser, Marin Veldic, Eduard Vieta, John B. Vincent, Wei Xu, Clement C. Zai, Peter P. Zandi, Psychiatric Genomics Consortium (PGC) Bipolar Disorder Working Group, International Consortium on Lithium Genetics (ConLiGen), Colombia-US Cross Disorder Collaboration in Psychiatric Genetics, Arianna Di Florio, Jordan W. Smoller, Joanna M. Biernacka, Francis J. McMahon, Martin Alda, Bertram Müller-Myhsok, Nikolaos Koutsouleris, Peter Falkai, Nelson B. Freimer, Till F.M. Andlauer, Thomas G. Schulze, Roel A. Ophoff
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- Journal:
- The British Journal of Psychiatry / Volume 219 / Issue 6 / December 2021
- Published online by Cambridge University Press:
- 25 August 2021, pp. 659-669
- Print publication:
- December 2021
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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.
AimsTo examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
MethodGenome-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.
ResultsEarlier 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 (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −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.
ConclusionsAAO 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.
4363 TCF7L2 variants in African American patients with bipolar disorder and high BMI
- Linsey Jackson, Brandon Coombes, Joyce E. Balls-Berry, Joanna Biernacka
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- Journal:
- Journal of Clinical and Translational Science / Volume 4 / Issue s1 / June 2020
- Published online by Cambridge University Press:
- 29 July 2020, p. 52
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OBJECTIVES/GOALS: In a previous genome-wide association study of European Americans, rs12772424, a variant in the gene encoding transcription factor 7-like 2 (TCF7L2), was shown to have a SNP-BMI interaction association with bipolar disorder (BD). The objective of this study was to replicate this finding in an African American (AA) sample. METHODS/STUDY POPULATION: Using a sample of 659 controls and 323 BD cases from the Genetic Association Information (GAIN) Study of BD, we conducted analyses to assess association between BD and the interaction of BMI with genetic variation in TCF7L2. For this study we identified 4572 single nucleotide polymorphisms (SNPs) in a 1Mb region around the candidate SNP rs1272424. Based on variants identified in the prior analysis of the larger European American dataset we selected SNPs for analysis in the AA data. This allowed for a smaller AA sample, and still maintained adequate power for statistical significance. RESULTS/ANTICIPATED RESULTS: We anticipate observing an effect of TCF7L2 variation on the relationship between BD and BMI in the AA data. We also anticipate that combining results from the European and African American patients may help narrow down potentially functional variants in TCF7L2 that influence the association between BD and BMI. DISCUSSION/SIGNIFICANCE OF IMPACT: Psychiatric genetics studies lack ancestral diversity among participants, decreasing their generalizability, and possibly increasing health disparities, especially for diseases like BD, which is often misdiagnosed and untreated in AAs. This work propels us towards understanding the genetics of BD and obesity in this underrepresented population.
2040 Symptom endorsement in bipolar patients of African Versus European ancestry
- Margaret Akinhanmi, Joyce E. Balls-Berry, Suliman El-Amin, Jennifer Geske, Colin Colby, Christi Patten, Joanna Biernacka, Mark A. Frye
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- Journal:
- Journal of Clinical and Translational Science / Volume 2 / Issue S1 / June 2018
- Published online by Cambridge University Press:
- 21 November 2018, p. 74
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OBJECTIVES/SPECIFIC AIMS: Learning Objectives of this session: Identify possible reasons for misdiagnosis of bipolar patients of African ancestry by reviewing differences in symptom presentation between African American (AA) and European American (EA) bipolar individuals. Introduction: Bipolar disorder is a chronic mental illness with a prevalence rate up to 5.5% of the US population and is associated with substantial personal and economic morbidity/mortality. Misdiagnosis is common in bipolar disorder, which can impact treatment and outcome. Misdiagnosis disproportionally affects racial/ethnic minorities; in particular, AAs are often misdiagnosed with schizophrenia. There is interest in better understanding the contribution of differential illness presentation and/or racial bias to misdiagnosis. METHODS/STUDY POPULATION: Patients and Methods Utilizing the Genetic Association Information Network (GAIN) public database, this study compared clinical phenomenology between bipolar patients of African Versus European ancestry (AA=415 vs. EA=1001). The semi-structured Diagnostic Interview for Genetic Studies (DIGS) was utilized to evaluate individual symptom endorsement contributing to diagnostic confirmation. A χ2 test was used to compare group differences in DIGS harvested mania and psychosis sections, and overview of psychiatric medications. RESULTS/ANTICIPATED RESULTS: Results: The symptom of auditory hallucination was significantly more endorsed in AA bipolar patients than EA bipolar patients (57.9% AA vs. 36.1% EA, p≤0.0001). Conversely, the symptom of elevated or euphoric mood was significantly less endorsed in AA bipolar patients than in EA patients (94.6% AA vs. 97.5% EA, p=0.027). AA, in comparison to EA bipolar patients, had a significantly higher prevalence of lifetime exposure to haloperidol (36.9% AA vs. 29.4% EA, p=0.017) and fluphenazine (12.3% AA vs. 6.7% EA, p=0.004). In contrast, AA, in comparison to EA bipolar patients, had a significantly lower prevalence rate of lifetime exposure to lithium (52.5% AA vs. 74.2% EA, p<0.0001), and lamotrigine (13.7% AA vs. 35.6% EA, p<0.0001). DISCUSSION/SIGNIFICANCE OF IMPACT: Conclusion: The higher rate of psychotic symptom endorsement and lower rate of core manic symptom endorsement represent differential illness presentation that may contribute to misdiagnosis in African-American bipolar patients. The higher rate of high potency typical antipsychotic treatment and lower rate of classic mood stabilizing treatment may also contribute poorer bipolar treatment outcome. While structured diagnostic interviews are the gold standard in diagnostic confirmation, this study is limited by lack of knowledge of clinician/expert interviewer interpretation of symptom endorsement which may contribute to symptom misattribution and misdiagnosis. Incorporation of additional African American participants in research is a critical future direction to further delineate symptom presentation and diagnosis to serve as validation for these results.
Construction of luminosity function for galaxy clusters
- Włodzimierz Godłowski, Joanna Popiela, Katarzyna Bajan, Monika Biernacka, Piotr Flin, Elena Panko
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- Journal:
- Proceedings of the International Astronomical Union / Volume 10 / Issue S309 / July 2014
- Published online by Cambridge University Press:
- 09 February 2015, p. 316
- Print publication:
- July 2014
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The luminosity function is an important quantity for analysis of large scale structure statistics, interpretation of galaxy counts (Lin & Kirshner 1996). We investigate the luminosity function of galaxy clusters. This is performed by counting the brightness of galaxies belonging to clusters in PF Catalogue. The obtained luminosity function is significantly different than that obtained both for optical and radiogalaxies (Machalski & Godowski 2000). The implications of this result for theories of galaxy formation are discussed as well.