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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
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- 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
- 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.
Impact of a cis-associated gene expression SNP on chromosome 20q11.22 on bipolar disorder susceptibility, hippocampal structure and cognitive performance
- Ming Li, Xiong-jian Luo, Mikael Landén, Sarah E. Bergen, Christina M. Hultman, Xiao Li, Wen Zhang, Yong-Gang Yao, Chen Zhang, Jiewei Liu, Manuel Mattheisen, Sven Cichon, Thomas W. Mühleisen, Franziska A. Degenhardt, Markus M. Nöthen, Thomas G. Schulze, Maria Grigoroiu-Serbanescu, Hao Li, Chris K. Fuller, Chunhui Chen, Qi Dong, Chuansheng Chen, Stéphane Jamain, Marion Leboyer, Frank Bellivier, Bruno Etain, Jean-Pierre Kahn, Chantal Henry, Martin Preisig, Zoltán Kutalik, Enrique Castelao, Adam Wright, Philip B. Mitchell, Janice M. Fullerton, Peter R. Schofield, Grant W. Montgomery, Sarah E. Medland, Scott D. Gordon, Nicholas G. Martin, MooDS Consortium, The Swedish Bipolar Study Group, Marcella Rietschel, Chunyu Liu, Joel E. Kleinman, Thomas M. Hyde, Daniel R. Weinberger, Bing Su
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- Journal:
- The British Journal of Psychiatry / Volume 208 / Issue 2 / February 2016
- Published online by Cambridge University Press:
- 02 January 2018, pp. 128-137
- Print publication:
- February 2016
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Background
Bipolar disorder is a highly heritable polygenic disorder. Recent enrichment analyses suggest that there may be true risk variants for bipolar disorder in the expression quantitative trait loci (eQTL) in the brain.
AimsWe sought to assess the impact of eQTL variants on bipolar disorder risk by combining data from both bipolar disorder genome-wide association studies (GWAS) and brain eQTL.
MethodTo detect single nucleotide polymorphisms (SNPs) that influence expression levels of genes associated with bipolar disorder, we jointly analysed data from a bipolar disorder GWAS (7481 cases and 9250 controls) and a genome-wide brain (cortical) eQTL (193 healthy controls) using a Bayesian statistical method, with independent follow-up replications. The identified risk SNP was then further tested for association with hippocampal volume (n = 5775) and cognitive performance (n = 342) among healthy individuals.
ResultsIntegrative analysis revealed a significant association between a brain eQTL rs6088662 on chromosome 20q11.22 and bipolar disorder (log Bayes factor = 5.48; bipolar disorder P = 5.85×10–5). Follow-up studies across multiple independent samples confirmed the association of the risk SNP (rs6088662) with gene expression and bipolar disorder susceptibility (P = 3.54×10–8). Further exploratory analysis revealed that rs6088662 is also associated with hippocampal volume and cognitive performance in healthy individuals.
ConclusionsOur findings suggest that 20q11.22 is likely a risk region for bipolar disorder; they also highlight the informative value of integrating functional annotation of genetic variants for gene expression in advancing our understanding of the biological basis underlying complex disorders, such as bipolar disorder.
Contributors
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- By Mowaffaq Almikhlafi, Osama Al-muslim, Robert Arntfield, Ian M Ball, Sue Berney, Mohit Bhutani, Clay A Block, Ken Blonde, Rudi Brits, Ron Butler, Lois Champion, Chris Clarke, Linda Denehy, Joseph Dreier, A Ebersohn, Shane W English, Ari Ercole, Darren H Freed, John Fuller, Julio P Zavala Georffino, RT Noel Gibney, Jeff Granton, Donald EG Griesdale, Arun K Gupta, Wael Haddara, Ahmed F Hegazy, Umjeet Singh Jolly, Philip M Jones, Ilya Kagan, Kala Kathirgamanathan, Harneet Kaur, John Kellett, Bhupesh Khadka, Biniam Kidane, Carlos Kidel, Anand Kumar, Alejandro Lazo-Langner, David Leasa, W Robert Leeper, Stephen Y Liang, Tania Ligori, Jaimie Manlucu, Janet Martin, Ian McConachie, Alan McGlennan, Lauralyn McIntyre, Tina Mele, MJ Naisbitt, Raj Nichani, Daniel H Ovakim, Neil Parry, Daniel Castro Pereira, Thomas Piraino, Brian Pollard, Valerie Schulz, Michael D Sharpe, Rohit K Singal, Pierre Singer, Mark Soth, Christian P Subbe, Jaffer Syed, Ravi Taneja, Tom Varughese, Jennifer Vergel Del Dios, Jessie R Welbourne, Christopher W White, Rebecca P Winsett, Titus C Yeung, G Bryan Young, Shelley R Zieroth
- Edited by John Fuller, University of Western Ontario, Jeff Granton, University of Western Ontario, Ian McConachie, University of Western Ontario
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- Handbook of ICU Therapy
- Published online:
- 05 February 2015
- Print publication:
- 04 December 2014, pp vii-xii
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Contributors
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- By Michael H. Allen, Leora Amira, Victoria Arango, David W. Ayer, Helene Bach, Christopher R. Bailey, Ross J. Baldessarini, Kelsey Ball, Alan L. Berman, Marian E. Betz, Emily A. Biggs, R. Warwick Blood, Kathleen T. Brady, David A. Brent, Jeffrey A. Bridge, Gregory K. Brown, Anat Brunstein Klomek, A. Jacqueline Buchanan, Michelle J. Chandley, Tim Coffey, Jessica Coker, Yeates Conwell, Scott J. Crow, Collin L. Davidson, Yogesh Dwivedi, Stacey Espaillat, Jan Fawcett, Steven J. Garlow, Robert D. Gibbons, Catherine R. Glenn, Deborah Goebert, Erica Goldstein, Tina R. Goldstein, Madelyn S. Gould, Kelly L. Green, Alison M. Greene, Philip D. Harvey, Robert M. A. Hirschfeld, Donna Holland Barnes, Andres M. Kanner, Gary J. Kennedy, Stephen H. Koslow, Benoit Labonté, Alison M. Lake, William B. Lawson, Steve Leifman, Adam Lesser, Timothy W. Lineberry, Amanda L. McMillan, Herbert Y. Meltzer, Michael Craig Miller, Michael J. Miller, James A. Naifeh, Katharine J. Nelson, Charles B. Nemeroff, Alexander Neumeister, Matthew K. Nock, Jennifer H. Olson-Madden, Gregory A. Ordway, Michael W. Otto, Ghanshyam N. Pandey, Giampaolo Perna, Jane Pirkis, Kelly Posner, Anne Rohs, Pedro Ruiz, Molly Ryan, Alan F. Schatzberg, S. Charles Schulz, M. Katherine Shear, Morton M. Silverman, April R. Smith, Marcus Sokolowski, Barbara Stanley, Zachary N. Stowe, Sarah A. Struthers, Leonardo Tondo, Gustavo Turecki, Robert J. Ursano, Kimberly Van Orden, Anne C. Ward, Danuta Wasserman, Jerzy Wasserman, Melinda K. Westlund, Tracy K. Witte, Kseniya Yershova, Alexandra Zagoloff, Sidney Zisook
- Edited by Stephen H. Koslow, University of Miami, Pedro Ruiz, University of Miami, Charles B. Nemeroff, University of Miami
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- A Concise Guide to Understanding Suicide
- Published online:
- 05 October 2014
- Print publication:
- 18 September 2014, pp vii-x
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Nanotextured Material for Applications in CSF Sample Screening and Characterization
- Krishna Vattipalli, Savindra Brandigampala, Claire McGraw, Gaurav Chatterjee, Srinath Kasturirangan, Philip Schulz, Michael Sierks, Shalini Prasad
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- Journal:
- MRS Online Proceedings Library Archive / Volume 1466 / 2012
- Published online by Cambridge University Press:
- 20 July 2012, mrss12-1466-tt02-04
- Print publication:
- 2012
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Neurodegenerative disease is primarily characterized by protein misfolding and the resultant protein aggregation. Presence of soluble oligomeric aggregates of proteins including various Aβ and α-syn aggregate species can be correlated to the onset and progression of many neurodegenerative diseases. The ability to detect protein misfolding requires the design of a diagnostics assay the will enable molecular level probing. The use of nanoporous ceramic templates enables size based immobilization of the target proteins and by leveraging the principle of “macromolecular crowding” protein association can be mapped with a high degree of resolution. By tailoring the surface functionalization within nanoporous ceramic templates, macromolecular immobilization can be selectively controlled, which in turn significantly enhances the perturbation to the electrical double layer/. The changes to the electrical double layer are measured with a high degree of sensitivity through impedance spectroscopy.
Pre symptomatic diagnosis and distinction between Alzheimer’s and Parkinson’s diseases can be achieved by the specific detection and quantification of levels of each of these different toxic protein species in cerebrospinal fluid (CSF). Detection using highly selective morphology specific reagents in conjunction with the ultrasensitive nanoporous electronic biosensor showed the presence of different protein morphologies in human CSF samples. Detection is primarily achieved by identifying the specific association of the protein with its receptor using electrochemical impedance spectroscopy. Furthermore, we show that these morphology specific reagents can readily classify between post-mortem CSF samples from AD, PD and cognitively normal sources. These studies suggest that detection of specific oligomeric aggregate species holds great promise as sensitive biomarkers for neurodegenerative disease.
Identifying target groups for the prevention of depression among caregivers of dementia patients
- Karlijn J. Joling, Filip Smit, Harm W. J. van Marwijk, Henriëtte E. van der Horst, Philip Scheltens, Richard Schulz, Hein P. J. van Hout
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- Journal:
- International Psychogeriatrics / Volume 24 / Issue 2 / February 2012
- Published online by Cambridge University Press:
- 01 September 2011, pp. 298-306
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Background: Depression in informal caregivers of persons with dementia is a major, costly and growing problem. However, it is not yet clear which caregivers are at increased risk of developing depression. With this knowledge preventive strategies could focus on these groups to maximize health gain and minimize effort.
Methods: The onset of clinically relevant depression was measured with the Center for Epidemiologic Studies - Depression Scale in 725 caregivers who were not depressed at baseline and who were providing care for a relative with dementia. Caregivers were followed over 18 months. The indices calculated to identify the most important risk indicators were: odds ratio, attributable fraction, exposure rate and number needing to be treated.
Results: The following significant indicators of depression onset were identified: increased initial depressive symptoms, poor self-rated health status and white or Hispanic race/ethnicity. The incidence of depression would decrease by 72.3% (attributive fraction) if these risk indicators together are targeted by a completely effective intervention. Race/ethnicity was not a significant predictor if caregivers of patients who died or were institutionalized were left out of the analyses.
Conclusion: Detection of only a few characteristics makes it possible to identify high-risk groups in an efficient way. Focusing on these easy-to-assess characteristics might contribute to a cost-effective prevention of depression in caregivers.
Food intake patterns associated with carotid artery atherosclerosis in the Insulin Resistance Atherosclerosis Study
- Angela D. Liese, Michele Nichols, Denise Hodo, Philip B. Mellen, Mandy Schulz, David C. Goff, Jr, Ralph B. D'Agostino, Jr
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- Journal:
- British Journal of Nutrition / Volume 103 / Issue 10 / 28 May 2010
- Published online by Cambridge University Press:
- 22 January 2010, pp. 1471-1479
- Print publication:
- 28 May 2010
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We aimed to identify food intake patterns that operate via haemostatic and inflammatory pathways on progression of atherosclerosis among 802 middle-aged adults with baseline and 5-year follow-up ultrasound measurements of common (CCA) and internal carotid artery (ICA) intimal medial thickness (IMT). Food intake was ascertained with an FFQ. We derived food patterns using reduced rank regression (RRR) with plasminogen activator inhibitor 1 and fibrinogen as response variables. We explored the impact of various food pattern simplification approaches. We identified a food pattern characterised by higher intakes of less healthful foods (low-fibre bread and cereal, red and processed meat, cottage cheese, tomato foods, regular soft drinks and sweetened beverages) and lower intakes of more healthful foods (wine, rice and pasta, meal replacements and poultry). The pattern was positively associated with mean CCA IMT at follow-up (P = 0·0032), a 1 sd increase corresponding to an increase of 13 μm higher CCA IMT at follow-up, adjusted for demographic and cardiovascular risk factors. With increasing pattern quartile (Q), the percentage change in CCA IMT increased significantly: Q1 0·8 %; Q2 3·2 %; Q3 8·6 %; Q4 7·9 % (P = 0·0045). No clear association with ICA IMT was observed. All simplification methods yielded similar results. The present results support the contention that a pro-inflammatory and pro-thrombotic dietary pattern increases the rate of coronary artery atherosclerosis progression, independent of traditional cardiovascular risk factors. RRR is a promising and robust tool for moving beyond the previous focus on nutrients or foods into research on the health effects of broader dietary patterns.
High-Permeability Particles for Magnetic Composites
- Robert Sailer, Pamela J Jeppson, Eric L Jarabek, Joseph A Sandstrom, Zoha Al-Badri, Dean G Grier, Anthony N Caruso, Philip R Boudjouk, Pete Eames, Mark Tondra, Douglas L Schulz
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- Journal:
- MRS Online Proceedings Library Archive / Volume 906 / 2005
- Published online by Cambridge University Press:
- 26 February 2011, 0906-HH01-06
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- 2005
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Electromagnetic shields and flux concentrators for magnetic sensors could utilize flexible and insulating composites applied using simple thin film deposition methods such as dip-coating, spin-coating, spraying, etc. As the first step towards development of composites with superior performance, efforts focused on isolating nanoparticles with large magnetizations under low fields. In this paper, we provide the results of proof-of-concept studies for two systems: metal-functionalized silicone-based materials (metal-silicone); and, Co-ferrite (Co2+1−xFe2+xFe3+2O4) nanoparticles. The metal-silicone materials studied included a polysiloxane that contained a pendant ferrocene where an optimum saturization magnetization of 5.9 emu/g (coercivity = 11 Oe) was observed. Co-ferrite nanoparticle samples prepared in this study showed unprecendented saturation magnetization (i.e., Ms > 150 emu/g) with low coercivity (Hc ∼ 10 Oe) at room temperature and offer potential application as flux concentrators.
Pulsed Laser Deposition of Cadmium Stannate, a Spinel-Type Transparent Conducting Oxide
- Jeanne M. Mcgraw, Philip A. Parilla, Douglas L. Schulz, Jeffrey Alleman, Xuanzhi Wu, William P. Mulligan, David S. Ginley, Timothy J. Coutts
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- Journal:
- MRS Online Proceedings Library Archive / Volume 388 / 1995
- Published online by Cambridge University Press:
- 21 February 2011, 51
- Print publication:
- 1995
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We present the first report of the synthesis of Cd2SnO4 films by pulsed laser deposition. Controlling the substrate temperature and the ambient atmosphere allowed for the synthesis of films ranging from amorphous to crystalline with some crystalline films exhibiting strong texture. Highly transparent films with large mobilities were obtained for both the amorphous and crystalline films. Sheet resistances of 15.5 Ω/square and mobilities of up to 44.7 cm2/V.s were observed. Typical carrier concentrations showed the crystalline films to be degenerate with carrier concentrations of 5 х 1020cm-3 while amorphous films had carrier concentrations lower by about half. Band gaps for the films ranged between 3.1-3.8 eV. these films are attractive candidates for TCO applications in thin film photovoltaic devices, flat panel displays, electrochromic windows, and as plasma filters for thermophotovoltaic devices.