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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
- 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
- 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.
Evaluation of Discrepancies in Carbapenem Minimum Inhibitory Concentrations Obtained at Clinical Laboratories Compared to a Public Health Laboratory
- Julian E. Grass, Shelley S. Magill, Isaac See, Uzma Ansari, Lucy E. Wilson, Elisabeth Vaeth, Paula Snippes Vagnone, Brittany Pattee, Jesse T. Jacob, Georgia Emerging Infections Program, Chris Bower, Atlanta Veterans Affairs Medical Center, Foundation for Atlanta Veterans Education and Research, Sarah W. Satola, Sarah J. Janelle, Kyle Schutz, Rebecca Tsay, Marion A. Kainer, Daniel Muleta, P. Maureen Cassidy, Vivian H. Leung, Meghan Maloney, Erin C. Phipps, New Mexico Emerging Infections Program, Kristina G. Flores, New Mexico Emerging Infections Program, Erin Epson, Joelle Nadle, Maria Karlsson, Joseph D. Lutgring
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s474-s476
- Print publication:
- October 2020
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Background: Automated testing instruments (ATIs) are commonly used by clinical microbiology laboratories to perform antimicrobial susceptibility testing (AST), whereas public health laboratories may use established reference methods such as broth microdilution (BMD). We investigated discrepancies in carbapenem minimum inhibitory concentrations (MICs) among Enterobacteriaceae tested by clinical laboratory ATIs and by reference BMD at the CDC. Methods: During 2016–2018, we conducted laboratory- and population-based surveillance for carbapenem-resistant Enterobacteriaceae (CRE) through the CDC Emerging Infections Program (EIP) sites (10 sites by 2018). We defined an incident case as the first isolation of Enterobacter spp (E. cloacae complex or E. aerogenes), Escherichia coli, Klebsiella pneumoniae, K. oxytoca, or K. variicola resistant to doripenem, ertapenem, imipenem, or meropenem from normally sterile sites or urine identified from a resident of the EIP catchment area in a 30-day period. Cases had isolates that were determined to be carbapenem-resistant by clinical laboratory ATI MICs (MicroScan, BD Phoenix, or VITEK 2) or by other methods, using current Clinical and Laboratory Standards Institute (CLSI) criteria. A convenience sample of these isolates was tested by reference BMD at the CDC according to CLSI guidelines. Results: Overall, 1,787 isolates from 112 clinical laboratories were tested by BMD at the CDC. Of these, clinical laboratory ATI MIC results were available for 1,638 (91.7%); 855 (52.2%) from 71 clinical laboratories did not confirm as CRE at the CDC. Nonconfirming isolates were tested on either a MicroScan (235 of 462; 50.9%), BD Phoenix (249 of 411; 60.6%), or VITEK 2 (371 of 765; 48.5%). Lack of confirmation was most common among E. coli (62.2% of E. coli isolates tested) and Enterobacter spp (61.4% of Enterobacter isolates tested) (Fig. 1A), and among isolates testing resistant to ertapenem by the clinical laboratory ATI (52.1%, Fig. 1B). Of the 1,388 isolates resistant to ertapenem in the clinical laboratory, 1,006 (72.5%) were resistant only to ertapenem. Of the 855 nonconfirming isolates, 638 (74.6%) were resistant only to ertapenem based on clinical laboratory ATI MICs. Conclusions: Nonconfirming isolates were widespread across laboratories and ATIs. Lack of confirmation was most common among E. coli and Enterobacter spp. Among nonconfirming isolates, most were resistant only to ertapenem. These findings may suggest that ATIs overcall resistance to ertapenem or that isolate transport and storage conditions affect ertapenem resistance. Further investigation into this lack of confirmation is needed, and CRE case identification in public health surveillance may need to account for this phenomenon.
Funding: None
Disclosures: None
Effect of fibre additions to flatbread flour mixes on glucose kinetics: a randomised controlled trial
- Hanny M. Boers, Theo H. van Dijk, Harry Hiemstra, Anne-Roos Hoogenraad, David J. Mela, Harry P. F. Peters, Roel J. Vonk, Marion G. Priebe
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- Journal:
- British Journal of Nutrition / Volume 118 / Issue 10 / 28 November 2017
- Published online by Cambridge University Press:
- 07 November 2017, pp. 777-787
- Print publication:
- 28 November 2017
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We previously found that guar gum (GG) and chickpea flour (CPF) added to flatbread wheat flour lowered postprandial blood glucose (PPG) and insulin responses dose dependently. However, rates of glucose influx cannot be determined from PPG, which integrates rates of influx, tissue disposal and hepatic glucose production. The objective was to quantify rates of glucose influx and related fluxes as contributors to changes in PPG with GG and CPF additions to wheat-based flatbreads. In a randomised cross-over design, twelve healthy males consumed each of three different 13C-enriched meals: control flatbreads (C), or C incorporating 15 % CPF with either 2 % (GG2) or 4 % (GG4) GG. A dual isotope technique was used to determine the time to reach 50 % absorption of exogenous glucose (T50 %abs, primary objective), rate of appearance of exogenous glucose (RaE), rate of appearance of total glucose (RaT), endogenous glucose production (EGP) and rate of disappearance of total glucose (RdT). Additional exploratory outcomes included PPG, insulin, glucose-dependent insulinotropic peptide and glucagon-like peptide 1, which were additionally measured over 4 h. Compared with C, GG2 and GG4 had no significant effect on T50 %abs. However, GG4 significantly reduced 4-h AUC values for RaE, RaT, RdT and EGP, by 11, 14, 14 and 64 %, respectively, whereas GG2 showed minor effects. Effect sizes over 2 and 4 h were similar except for significantly greater reduction in EGP for GG4 at 2 h. In conclusion, a soluble fibre mix added to flatbreads only slightly reduced rates of glucose influx, but more substantially affected rates of postprandial disposal and hepatic glucose production.
Contributors
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- By Rony A. Adam, Gloria Bachmann, Nichole M. Barker, Randall B. Barnes, John Bennett, Inbar Ben-Shachar, Jonathan S. Berek, Sarah L. Berga, Monica W. Best, Eric J. Bieber, Frank M. Biro, Shan Biscette, Anita K. Blanchard, Candace Brown, Ronald T. Burkman, Joseph Buscema, John E. Buster, Michael Byas-Smith, Sandra Ann Carson, Judy C. Chang, Annie N. Y. Cheung, Mindy S. Christianson, Karishma Circelli, Daniel L. Clarke-Pearson, Larry J. Copeland, Bryan D. Cowan, Navneet Dhillon, Michael P. Diamond, Conception Diaz-Arrastia, Nicole M. Donnellan, Michael L. Eisenberg, Eric Eisenhauer, Sebastian Faro, J. Stuart Ferriss, Lisa C. Flowers, Susan J. Freeman, Leda Gattoc, Claudine Marie Gayle, Timothy M. Geiger, Jennifer S. Gell, Alan N. Gordon, Victoria L. Green, Jon K. Hathaway, Enrique Hernandez, S. Paige Hertweck, Randall S. Hines, Ira R. Horowitz, Fred M. Howard, William W. Hurd, Fidan Israfilbayli, Denise J. Jamieson, Carolyn R. Jaslow, Erika B. Johnston-MacAnanny, Rohna M. Kearney, Namita Khanna, Caroline C. King, Jeremy A. King, Ira J. Kodner, Tamara Kolev, Athena P. Kourtis, S. Robert Kovac, Ertug Kovanci, William H. Kutteh, Eduardo Lara-Torre, Pallavi Latthe, Herschel W. Lawson, Ronald L. Levine, Frank W. Ling, Larry I. Lipshultz, Steven D. McCarus, Robert McLellan, Shruti Malik, Suketu M. Mansuria, Mohamed K. Mehasseb, Pamela J. Murray, Saloney Nazeer, Farr R. Nezhat, Hextan Y. S. Ngan, Gina M. Northington, Peggy A. Norton, Ruth M. O'Regan, Kristiina Parviainen, Resad P. Pasic, Tanja Pejovic, K. Ulrich Petry, Nancy A. Phillips, Ashish Pradhan, Elizabeth E. Puscheck, Suneetha Rachaneni, Devon M. Ramaeker, David B. Redwine, Robert L. Reid, Carla P. Roberts, Walter Romano, Peter G. Rose, Robert L. Rosenfield, Shon P. Rowan, Mack T. Ruffin, Janice M. Rymer, Evis Sala, Ritu Salani, Joseph S. Sanfilippo, Mahmood I. Shafi, Roger P. Smith, Meredith L. Snook, Thomas E. Snyder, Mary D. Stephenson, Thomas G. Stovall, Richard L. Sweet, Philip M. Toozs-Hobson, Togas Tulandi, Elizabeth R. Unger, Denise S. Uyar, Marion S. Verp, Rahi Victory, Tamara J. Vokes, Michelle J. Washington, Katharine O'Connell White, Paul E. Wise, Frank M. Wittmaack, Miya P. Yamamoto, Christine Yu, Howard A. Zacur
- Edited by Eric J. Bieber, Joseph S. Sanfilippo, University of Pittsburgh, Ira R. Horowitz, Emory University, Atlanta, Mahmood I. Shafi
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- Book:
- Clinical Gynecology
- Published online:
- 05 April 2015
- Print publication:
- 23 April 2015, pp viii-xiv
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Diet and glycaemia: the markers and their meaning. A report of the Unilever Nutrition Workshop
- Marjan Alssema, Hanny M. Boers, Antonio Ceriello, Eric S. Kilpatrick, David J. Mela, Marion G. Priebe, Patrick Schrauwen, Bruce H. Wolffenbuttel, Andreas F. H. Pfeiffer
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- Journal:
- British Journal of Nutrition / Volume 113 / Issue 2 / 28 January 2015
- Published online by Cambridge University Press:
- 11 December 2014, pp. 239-248
- Print publication:
- 28 January 2015
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Consumption of carbohydrate-containing foods leads to transient postprandial rises in blood glucose concentrations that vary between food types. Higher postprandial glycaemic exposures have particularly been implicated in the development of chronic cardiometabolic diseases. Reducing such diet-related exposures may be beneficial not only for diabetic patients but also for the general population. A variety of markers have been used to track different aspects of glycaemic exposures, with most of the relevant knowledge derived from diabetic patients. The assessment of glycaemic exposures among the non-diabetic population may require other, more sensitive markers. The present report summarises key messages of presentations and related discussions from a workshop organised by Unilever intended to consider currently applied markers of glycaemic exposure. The particular focus of the meeting was to identify the potential applicability of glycaemic exposure markers for studying dietary effects in the non-diabetic population. Workshop participants concluded that markers of glycaemic exposures are sparsely used in intervention studies among non-diabetic populations. Continuous glucose monitoring remains the optimal approach to directly assess glycaemic exposure. Markers of glycaemic exposure such as glycated Hb, fructosamine, glycated albumin, 1,5-anhydroglucitol and advanced glycation end products can be preferred dependent on the aspect of interest (period of exposure and glucose variability). For all the markers of glycaemia, the responsiveness to interventions will probably be smaller among the non-diabetic than among the diabetic population. Further validation and acceptance of existing glycaemic exposure markers applied among the non-diabetic population would aid food innovation and better design of dietary interventions targeting glycaemic exposure.
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- By Michael F. Azari, Michael S. Beattie, Michael J. Bell, David M. Benglis, Anat Biegon, Jacqueline C. Bresnahan, A. Ross Bullock, D. James Cooper, Frances Corrigan, Kallol K. Dey, W. Dalton Dietrich, Volker Dietz, Per Enblad, Michael G. Fehlings, Julio C. Furlan, John C. Gensel, Gerald A. Grant, Gopalakrishna Gururaj, Ronald L. Hayes, Lars T. Hillered, John Houle, Jimmy W. Huh, Pavla Jendelová, Theresa A. Jones, Patrick M. Kochanek, Thomas Kossmann, Dorothy A. Kozlowski, Laura Krisa, Andrew Maas, Lawrence F. Marshall, Ankit I. Mehta, David K. Menon, Cristina Morganti-Kossmann, Marion Murray, Virginia F.J. Newcombe, Alistair D. Nichol, Linda Papa, Steven Petratos, Jennie Ponsford, Phillip G. Popovich, Gourikumar K. Prusty, Ramesh Raghupathi, Ricky Rasschaert, Peter L. Reilly, Nataliya Romanyuk, Bob Roozenbeek, Jeffrey V. Rosenfeld, Kathryn E. Saatman, Bridgette D. Semple, Esther Shohami, Eva Syková, Charles H. Tator, Brett Trimble, Robert Vink, Kevin K.W. Wang, Jefferson R. Wilson, Wise Young, Jenna M. Ziebell
- Edited by Cristina Morganti-Kossmann, Ramesh Raghupathi, Andrew Maas
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- Book:
- Traumatic Brain and Spinal Cord Injury
- Published online:
- 05 August 2012
- Print publication:
- 19 July 2012, pp ix-xii
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- By Giustino Albanese, Andrew Amaranto, Brandon H. Backlund, Alexander Baxter, Abraham Berger, Mark Bernstein, Marian E. Betz, Omar Bholat, Suzanne Bigelow, Carl Bonnett, Elizabeth Borock, Christopher B. Colwell, Alasdair Conn, Moira Davenport, David Dreitlein, Aaron Eberhardt, Ugo A. Ezenkwele, Diana Felton, Spiros G. Frangos, John E. Frank, Jonathan S. Gates, Lewis Goldfrank, Pinchas Halpern, Jean Hammel, Kristin E. Harkin, Jason S. Haukoos, E. Parker Hays, Aaron Hexdall, James F. Holmes, Debra Houry, Jennifer Isenhour, Andy Jagoda, John L. Kendall, Erica Kreisman, Nancy Kwon, Eric Legome, Matthew R. Levine, Phillip D. Levy, Charles Little, Marion Machado, Heather Mahoney, Vincent J. Markovchick, Nancy Martin, John Marx, Julie Mayglothling, Ron Medzon, Maurizio A. Miglietta, Elizabeth L. Mitchell, Ernest Moore, Maria E. Moreira, Sassan Naderi, Salvatore Pardo, Sajan Patel, David Peak, Christine Preblick, Niels K. Rathlev, Charles Ray, Phillip L. Rice, Carlo L. Rosen, Peter Rosen, Livia Santiago-Rosado, Tamara A. Scerpella, David Schwartz, Fred Severyn, Kaushal Shah, Lee W. Shockley, Mari Siegel, Matthew Simons, Michael Stern, D. Matthew Sullivan, Carrie D. Tibbles, Knox H. Todd, Shawn Ulrich, Neil Waldman, Kurt Whitaker, Stephen J. Wolf, Daniel Zlogar
- Edited by Eric Legome, Lee W. Shockley
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- Book:
- Trauma
- Published online:
- 07 September 2011
- Print publication:
- 16 June 2011, pp ix-xiv
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- By G. David Adamson, Majed Al Hudhud, Baris Ata, Pedro N. Barri, Christopher L. R. Barratt, Elisabet Clua, C. Dechanet, H. Déchaud, Didier Dewailly, Marion Dewailly, David K. Gardner, Linda Hammer Burns, B. Hédon, Wayland Hsiao, Vanessa J. Kay, Gab Kovacs, Robert I. McLachlan, Vicki Nisenblat, Robert J. Norman, W. Ombelet, Edouard Poncelet, Shauna Reinblatt, Anthony J. Rutherford, Peter N. Schlegel, Wendy B. Shelly, F. Shenfield, Joe Leigh Simpson, Anna Smirnova, Seang Lin Tan, George A. Thouas, Geoffrey Trew, P. C. Wong, Cheng Toh Yeong
- Edited by Gab Kovacs
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- Book:
- The Subfertility Handbook
- Published online:
- 06 December 2010
- Print publication:
- 11 November 2010, pp ix-xii
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Use of milk epithelial cells to study regulation of cell activity and apoptosis during once-daily milking in goats
- H. Ben Chedly, P. Lacasse, P.-G. Marnet, M. Komara, S. Marion, M. Boutinaud
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Generally, once-daily milking (ODM) decreases milk yield. This effect may be the consequence of a decrease in mammary epithelial cell (MEC) activity or a reduction in their number. The aim of this study was to determine the effect of ODM on the synthetic activity and rate of apoptosis of MEC using a non-invasive method. Eight Alpine goats were subjected to ODM or twice-daily milking for two 5-week periods. MECs were purified by centrifugation and immunocytochemical binding in milk after 1 and 5 weeks of each period. mRNA levels of some proteins involved in lactose and milk protein synthesis and in apoptosis were evaluated using real-time PCR. Isolation of MEC from milk was a useful method to investigate transcriptional regulation in a timeline study. ODM induced greater decreases in milk, lactose and protein yields after 1 week than after 5 weeks. This suggests an adaptation of the mammary gland to ODM, which reduces the inhibitory effect of this practice. Reductions in milk component yields were associated with lower α-lactalbumin transcripts, suggesting a transcriptional decrease of lactose synthesis during ODM. Glucose transporter GLUT1 transcripts were downregulated under ODM, suggesting that lactose precursor uptake by MEC might be involved in the regulation of lactose synthesis. κ-Casein mRNA levels tended to be lower during ODM. ODM increased levels of the pro-apoptotic transcript Bax after both 1 and 5 weeks, but no variation was observed in the Bax/Bcl-2 ratio. ODM affected cell synthetic activity through transcriptional regulation and may have induced apoptosis. The reduction of the negative effect of ODM on milk yield suggests that Alpine goats are able to adapt to ODM. Further studies are needed to investigate the effect of ODM on MEC turnover.
Direct enhancement of any solution NMR signal using the distant dipolar fields created by highly polarized and concentrated nuclear spin systems
- H. Desvaux, D. J. Marion, G. Huber, L. Dubois, P. Berthault
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- Journal:
- The European Physical Journal - Applied Physics / Volume 36 / Issue 1 / October 2006
- Published online by Cambridge University Press:
- 06 October 2006, pp. 25-34
- Print publication:
- October 2006
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Peculiar nuclear spin systems can be polarized at a level of thousands times the value obtained at thermal equilibrium, for instance by optical pumping. When concentrated, these systems create a sizeable average dipolar field which is experienced by any nuclear spin. We propose to use these distant dipolar fields for performing a polarization transfer in the Hartmann-Hahn conditions. We report the maximum enhancement value calculated using the spin temperature approach and first theoretical insights on the polarization transfer rate. Using, as an example, dissolved laser-polarized xenon, we show that by spin-locking both xenon spins and a proton spin of a solute, the polarization of the latter is enhanced. This is obtained without the existence of chemical interaction between the two entities and with characteristic rising time not directly correlated to the proton self-relaxation time. By its generality and its non-local feature, this approach could make possible nuclear magnetic resonance spectroscopy on very dilute systems.
6 - Near-infrared spectroscopy of stripped-envelope supernovae
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- By C. L. Gerardy, W. J. McDonald Postdoctoral Fellow, University of Texas at Austin, R. A. Fesen, Dartmouth College, G. H. Marion, University of Taxas at Austin, P. Höflich, University of Taxas at Austin, J. C. Wheeler, University of Taxas at Austin, K. Nomoto, University of Tokyo, K. Motohara, University of Tokyo
- Edited by Peter Höflich, University of Texas, Austin, Pawan Kumar, University of Texas, Austin, J. Craig Wheeler, University of Texas, Austin
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- Book:
- Cosmic Explosions in Three Dimensions
- Published online:
- 11 August 2009
- Print publication:
- 16 December 2004, pp 57-63
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Summary
Abstract
Near-infrared (NIR) spectroscopy of several stripped-envelope core-collapse supernovae (SNe) are presented. NIR spectra of these objects are quite rich, exhibiting a large number of emission features. Particularly important are strong lines of He I and C I, which probe the outermost ejecta and constrain the pre-collapse mass-loss. Interestingly, the SN 1998bw-like broad-line Type Ic SN 2002ap does not exhibit the strong C I features seen in other Type Ic SNe. NIR spectra also exhibit strong, relatively isolated lines of Mg I, Si I, Ca II, and O I that provide clues into the kinematics and mixing in the ejecta. Finally, late-time NIR spectra of two Type Ic events: SN 2000ew and SN 2002ap show strong first-overtone carbon monoxide (CO) emission, providing the first observational evidence that molecule formation may not only be common in Type II SNe, but perhaps in all core-collapse events.
Introduction
Near-infrared (NIR) spectroscopy is a powerful tool for the study of supernovae (SNe), offering new insights into the kinematic, chemical, and evolutionary properties of these events. Here we present applications of NIR spectroscopy for the study of three stripped-envelope supernovae, the Type Ib SN 2001B, the Type Ic SN 2000ew and the broad-line Type Ic SN 2002ap. All of the data presented here were obtained using TIFKAM on the 2.4 m Hiltner telescope at MDM Observatory, except for the SN 2002ap data set which also includes spectra obtained at Lick Observatory, IRTF, and Subaru. The reduced spectra are presented in Figures 6.1–6.3.
Algorithms with polynomial interpretation termination proof
- G. BONFANTE, A. CICHON, J.-Y. MARION, H. TOUZET
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- Journal:
- Journal of Functional Programming / Volume 11 / Issue 1 / January 2001
- Published online by Cambridge University Press:
- 26 March 2001, pp. 33-53
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We study the effect of polynomial interpretation termination proofs of deterministic (resp. non-deterministic) algorithms defined by con uent (resp. non-con uent) rewrite systems over data structures which include strings, lists and trees, and we classify them according to the interpretations of the constructors. This leads to the definition of six function classes which turn out to be exactly the deterministic (resp. non-deterministic) polynomial time, linear exponential time and linear doubly exponential time computable functions when the class is based on con uent (resp. non-con uent) rewrite systems. We also obtain a characterisation of the linear space computable functions. Finally, we demonstrate that functions with exponential interpretation termination proofs are super-elementary.