10 results
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.
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
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- 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.
O-25 - Variations in Pdyn Sequence are Associated With Negative Craving in Alcohol Dependent Subjects
- V. Karpyak, S. Winham, J. Biernacka, J. Cunningham, D. Walker, K. Lewis, J. Geske, C. Colby, O. Abulseoud, D. Hall-Flavin, L. Loukianova, T. Schneekloth, M. Frye, G. Bakalkin, D. Mrazek
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- Journal:
- European Psychiatry / Volume 27 / Issue S1 / 2012
- Published online by Cambridge University Press:
- 15 April 2020, p. 1
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Introduction
Craving in negative emotional situations (negative craving) is commonly associated with relapse and heavy alcohol use. Elevated dynorphin levels were associated with negative emotions, while variations in the OPRK1 and PDYN genes encoding OPRK1 receptor and dynorphins were associated with alcohol dependence.
ObjectivesTo investigate potential overlap in the genetic factors underlying, negative craving and alcohol dependence.
AimsExamine the association of the negative craving and genetic variation in the OPRK1 and PDYN genes.
Methods13 PDYN and 10 OPRK1 Single Nucleotide Polymorphisms (SNPs), including those previously reported to be associated with alcohol dependence were genotyped in 196 alcohol dependent subjects. The raw scores of the negative subscale of Inventory of Drug Taking Situations (IDTS) were utilized as a quantitative measure of negative craving. Logistic regression models were used to test for associations after controlling for age and gender.
ResultsGene-level haplotype testing demonstrated significant association of negative craving with variation in PDYN (p < 0.05) but not OPRK1 gene. The rs2281285 - rs199794 haplotype showed significant association (p = 0.0236) with negative craving, while rs2235749 - rs10485703 haplotype showed marginally significant association (p = 0.055). This replicates previous findings of association between these haplotypes and alcohol dependence. Negative craving was also associated with PDYN rs2281285 variant (p = 0.012) with estimated effect size of 6.95 (SE = 2.75). This new association finding was not significant after correction for multiple testing (p = 0.18).
ConclusionsOur findings support association of PDYN sequence variation with negative craving in alcohol dependent subjects. Future studies should investigate functional mechanisms of this association.
Abstinence Length in Acamprosate-treated Alcoholics and Variability in Glycine and Glutamate Signaling Gene Sets
- V.M. Karpyak, J.M. Biernacka, J. Geske, G. Jenkins, J.M. Cunningham, M. Skime, R. Weinshilboum, M.A. Frye, D.S. Choi
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- Journal:
- European Psychiatry / Volume 30 / Issue S1 / March 2015
- Published online by Cambridge University Press:
- 15 April 2020, p. 1
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Background
We recently identified association between GRIN2B rs2058878 variant and abstinence length in acamprosate-treated alcoholics (Karpyak et al. 2014). Here we present results of additional analyses exploring associations in the same sample (225 alcoholics treated with acamprosate for three months) at the gene and gene-set levels, for 12 genes involved in glycine signaling, 4 genes involved in glutamate reuptake, synthesis and degradation and 7 genes encoding NMDA receptor subunits.
MethodsAfter adjustment for relevant covariates, gene-level tests were performed using principal components (PC) analysis. Gene-set analyses were performed using the PC-Gamma approach with varying soft truncation threshold (STT) for the Gamma method for combining gene-level p-values.
ResultsShorter abstinence was associated with increased intensity of alcohol craving and lower number of days between last drink and initiation of acamprosate treatment. After adjustment for covariates, we observed nominally significant association of abstinence length with variation in the AMT (p=0.024), GRIN3A (p=0.016) and SHMT2 (p=0.039) genes, and marginally significant evidence for association with the GRIN2B (p=0.067) and GLRB (p=0.060) genes. At the gene-set level, association of abstinence length with variation in the glycine pathway was nominally significant (p=0.042 with STT=0.37). Marginal evidence of association with abstinence length was also observed for variation in the NMDA-receptor subunits (p<0.1 for STT<0.15).
DiscussionOur findings suggest association of abstinence length in acamprosate-treated alcoholics with variation in the glycine signaling pathway and genes encoding NMDA receptor subunits. Investigation of the mechanisms underlying these associations and their usefulness for individualized treatment selection should follow.
Influence of birth cohort on age of onset cluster analysis in bipolar I disorder
- M. Bauer, T. Glenn, M. Alda, O.A. Andreassen, E. Angelopoulos, R. Ardau, C. Baethge, R. Bauer, F. Bellivier, R.H. Belmaker, M. Berk, T.D. Bjella, L. Bossini, Y. Bersudsky, E.Y.W. Cheung, J. Conell, M. Del Zompo, S. Dodd, B. Etain, A. Fagiolini, M.A. Frye, K.N. Fountoulakis, J. Garneau-Fournier, A. Gonzalez-Pinto, H. Harima, S. Hassel, C. Henry, A. Iacovides, E.T. Isometsä, F. Kapczinski, S. Kliwicki, B. König, R. Krogh, M. Kunz, B. Lafer, E.R. Larsen, U. Lewitzka, C. Lopez-Jaramillo, G. MacQueen, M. Manchia, W. Marsh, M. Martinez-Cengotitabengoa, I. Melle, S. Monteith, G. Morken, R. Munoz, F.G. Nery, C. O’Donovan, Y. Osher, A. Pfennig, D. Quiroz, R. Ramesar, N. Rasgon, A. Reif, P. Ritter, J.K. Rybakowski, K. Sagduyu, A.M. Scippa, E. Severus, C. Simhandl, D.J. Stein, S. Strejilevich, A. Hatim Sulaiman, K. Suominen, H. Tagata, Y. Tatebayashi, C. Torrent, E. Vieta, B. Viswanath, M.J. Wanchoo, M. Zetin, P.C. Whybrow
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- Journal:
- European Psychiatry / Volume 30 / Issue 1 / January 2015
- Published online by Cambridge University Press:
- 15 April 2020, pp. 99-105
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Purpose:
Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database.
Methods:The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared.
Results:There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups.
Conclusion:These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.
Molecular analysis of imipenem-resistant Acinetobacter baumannii isolated from US service members wounded in Iraq, 2003–2008
- X.-Z. HUANG, M. A. CHAHINE, J. G. FRYE, D. M. CASH, E. P. LESHO, D. W. CRAFT, L. E. LINDLER, M. P. NIKOLICH
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- Journal:
- Epidemiology & Infection / Volume 140 / Issue 12 / December 2012
- Published online by Cambridge University Press:
- 25 January 2012, pp. 2302-2307
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Global dissemination of imipenem-resistant (IR) clones of Acinetobacter baumannii–A. calcoaceticus complex (ABC) have been frequently reported but the molecular epidemiological features of IR-ABC in military treatment facilities (MTFs) have not been described. We characterized 46 IR-ABC strains from a dataset of 298 ABC isolates collected from US service members hospitalized in different US MTFs domestically and overseas during 2003–2008. All IR strains carried the blaOXA-51 gene and 40 also carried blaOXA-23 on plasmids and/or chromosome; one carried blaOXA-58 and four contained ISAbal located upstream of blaOXA-51. Strains tended to cluster by pulsed-field gel electrophoresis profiles in time and location. Strains from two major clusters were identified as international clone I by multilocus sequence typing.
Mood switch in bipolar depression: comparison of adjunctive venlafaxine, bupropion and sertraline
- R. M. Post, L. L. Altshuler, G. S. Leverich, M. A. Frye, W A. Nolen, R. W. Kupka, T. Suppes, S. McElroy, P. E. Keck, K. D. Denicoff, H. Grunze, J. Walden, C. M. R. Kitchen, J. Mintz
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- Journal:
- The British Journal of Psychiatry / Volume 189 / Issue 2 / August 2006
- Published online by Cambridge University Press:
- 02 January 2018, pp. 124-131
- Print publication:
- August 2006
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Background
Few studies have examined the relative risks of switching into hypomania or mania associated with second-generation antidepressant drugs in bipolar depression.
AimsTo examine the relative acute effects of bupropion, sertraline and venlafaxine as adjuncts to mood stabilisers.
MethodIn a 10-week trial, participants receiving out-patient treatment for bipolar disorder (stratified for rapid cycling) were randomly treated with a flexible dose of one of the antidepressants, or their respective matching placebos, as adjuncts to mood stabilisers.
ResultsA total of 174 adults with bipolar disorder I, II or not otherwise specified, currently in the depressed phase, were included. All three antidepressants were associated with a similar range of acute response (49–53%) and remission (34–41%). There was a significantly increased risk of switches into hypomania or mania in participants treated with venlafaxine compared with bupropion or sertraline.
ConclusionsMore caution appears indicated in the use of venlafaxine rather than bupropion or sertraline in the adjunctive treatment of bipolar depression, especially if there is a prior history of rapid cycling.
Validation of the prospective NIMH-Life-Chart Method (NIMH-LCMTM-p) for longitudinal assessment of bipolar illness
- K. D. DENICOFF, G. S. LEVERICH, W. A. NOLEN, A. J. RUSH, S. L. McELROY, P. E. KECK, T. SUPPES, L. L. ALTSHULER, R. KUPKA, M. A. FRYE, J. HATEF, M. A. BROTMAN, R. M. POST
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- Journal:
- Psychological Medicine / Volume 30 / Issue 6 / November 2000
- Published online by Cambridge University Press:
- 16 November 2000, pp. 1391-1397
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Background. Systematic and accurate depiction of a patient's course of illness is crucial for assessing the efficacy of maintenance treatments for bipolar disorder. This need to rate the long-term prospective course of illness led to the development of the National Institute of Mental Health prospective Life Chart Methodology (NIMH-LCMTM-p or LCM). The NIMH-LCMTM-p allows for the daily assessment of mood and episode severity based on the degree of mood associated functional impairment. We have previously presented preliminary evidence of the reliability and validity of the LCM, and its utility in clinical trials. This study is a further and more extensive validation of the clinician rated NIMH-LCMTM-p.
Methods. Subjects included 270 bipolar patients from the five sites participating in the Stanley Foundation Bipolar Network. Daily prospective LCM ratings on the clinician form were initiated upon entry, in addition to at least monthly ratings with the Inventory of Depressive Symptomatology-clinician rated (IDS-C), the Young Mania Rating Scale (YMRS) and the Global Assessment of Functioning (GAF). We correlated appropriate measures and time domains of the LCM with the IDS-C, YMRS and GAF.
Results. Severity of depression on the LCM and on the IDS-C were highly correlated in 270 patients (r = −0·785, P < 0·001). Similarly, a strong correlation was found between LCM mania and the YMRS (r = 0·656, P < 0·001) and between the LCM average severity of illness and the GAF (r = −0·732, P < 0·001).
Conclusions. These data further demonstrate the validity and potential utility of the NIMH- LCMTM-p for the detailed daily longitudinal assessment of manic and depressive severity and course, and response to treatment.
Laser Processing of Porous Silicon
- H. Baumgart, R. C. Frye, L. E. Trimble, H. J. Leamy, G. K. Celler
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- Journal:
- MRS Online Proceedings Library Archive / Volume 4 / 1981
- Published online by Cambridge University Press:
- 15 February 2011, 609
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- 1981
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Silicon can be prepared in a unique morphological form by anodic dissolution in HF. The resultant material contains pores of 10–100Å diameter in numbers sufficient toproduce a high surface area, low density single crystal. We have investigated the potential of both pulsed and CW laser processing in this material for dielectric isolation applications. Pulsed processing at λ = 532 nm yields fused surface layer structures that are epitaxial. The porous Si that underlies this surface film is undisturbed and can beoxidized to produce vertical isolation from the substrate.