9 results
Expansion of an Australian food composition database to estimate plant and animal intakes
- Jordan Stanford, Sarah McMahon, Kelly Lambert, Karen E. Charlton, Anita Stefoska-Needham
-
- Journal:
- British Journal of Nutrition / Volume 130 / Issue 11 / 14 December 2023
- Published online by Cambridge University Press:
- 09 May 2023, pp. 1950-1960
- Print publication:
- 14 December 2023
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
Despite evidence for favourable health outcomes associated with plant-based diets, a database containing the plant and animal content of all foods eaten is required to undertake a reliable assessment of plant-based diets within a population. This study aimed to expand an existing Australian food database to include the plant and animal content of all whole foods, beverages, multi-ingredient products and mixed dishes. Twenty-three plant- and animal-based food group classifications were first defined. The food servings per 100 g of each product were then systematically calculated using either a recipe-based approach, a food label-based approach, estimates based on similar products or online recipes. Overall, 4687 (83·5 %) foods and beverages were identified as plant or plant-containing products, and 3701 (65·9 %) were animal or animal-containing products. Results highlighted the versatility of plant and animal ingredients as they were found in various foods across many food categories, including savoury and sweet foods, as well as discretionary and core foods. For example, over 97 % of animal fat-containing foods were found in major food groups outside the AUSNUT 2011–2013 ‘fats and oils’ group. Surprisingly, fruits, nuts and seeds were present in a greater percentage of discretionary products than in core foods and beverages. This article describes a systematic approach that is suitable for the development of other novel food databases. This database allows more accurate quantitative estimates of plant and animal intakes, which is significant for future epidemiological and clinical research aiming to investigate plant-based diets and their related health outcomes.
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
-
- 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
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
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
-
- 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
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
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.
Latent classes of oppositional defiant disorder in adolescence and prediction to later psychopathology
- Sarah J. Racz, Robert J. McMahon, Gretchen Gudmundsen, Elizabeth McCauley, Ann Vander Stoep
-
- Journal:
- Development and Psychopathology / Volume 35 / Issue 2 / May 2023
- Published online by Cambridge University Press:
- 25 January 2022, pp. 730-748
-
- Article
- Export citation
-
Current conceptualizations of oppositional defiant disorder (ODD) place the symptoms of this disorder within three separate but related dimensions (i.e., angry/irritable mood, argumentative/defiant behavior, vindictiveness). Variable-centered models of these dimensions have yielded discrepant findings, limiting their clinical utility. The current study utilized person-centered latent class analysis based on self and parent report of ODD symptomatology from a community-based cohort study of 521 adolescents. We tested for sex, race, and age differences in the identified classes and investigated their ability to predict later symptoms of depression and conduct disorder (CD). Diagnostic information regarding ODD, depression, and CD were collected annually from adolescents (grades 6–9; 51.9% male; 48.7% White, 28.2% Black, 18.5% Asian) and a parent. Results provided evidence for three classes of ODD (high, medium, and low endorsement of symptoms), which demonstrated important developmental differences across time. Based on self-report, Black adolescents were more likely to be in the high and medium classes, while according to parent report, White adolescents were more likely to be in the high and medium classes. Membership in the high and medium classes predicted later increases in symptoms of depression and CD, with the high class showing the greatest risk for later psychopathology.
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
-
- 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
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
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.
Does high-variation training facilitate transfer of training in paediatric transthoracic echocardiography?
- Colin J. McMahon, Sarah Gallagher, Adam James, Aoife Deery, Mark Rhodes, Jeroen J. G. van Merriënboer
-
- Journal:
- Cardiology in the Young / Volume 31 / Issue 4 / April 2021
- Published online by Cambridge University Press:
- 10 December 2020, pp. 602-608
-
- Article
- Export citation
-
Background:
Factors that facilitate transfer of training in paediatric echocardiography remain poorly understood. This study assessed whether high-variation training facilitated successful transfer in paediatric echocardiography.
Methods:A mixed-methods study of transfer of technical and interpretive skill application amongst postgraduate trainees. Trainees were randomised to a low or high-variation training group. After a period of 8 weeks intensive echocardiography training, we video-recorded how trainees completed an echocardiogram in a complex cardiac lesion not previously encountered. Blinded quantitative analysis and scoring of trainee performance (echocardiogram performance, report, and technical proficiency) were performed using a validated assessment tool by a blinded cardiologist and senior cardiac physiologist. Qualitative interviews of the trainees were recorded to ascertain trainee experiences during the training and transfer process.
Results:Sixteen trainees were enrolled in the study. For the cumulative score for all three components tested (echocardiogram performance, report, and technical proficiency), χ2 = 8.223, p = .016, which showed the high-variation group outperformed the low-variation group. Two common themes which assisted in the transfer emerged from interviews are as follows: (1) use of strategies described in variation theory to describe abnormal hearts, (2) the use of formative live feedback from trainers during hands-on training.
Conclusion:Training strategies exposing trainees to high-variation training may aid transfer of paediatric echocardiography skills.
Kindergarten antecedents of the developmental course of active and passive parental monitoring strategies during middle childhood and adolescence
- Sarah J. Racz, Robert J. McMahon, Kevin M. King, Ellen E. Pinderhughes, Jason J. Bendezú
-
- Journal:
- Development and Psychopathology / Volume 31 / Issue 5 / December 2019
- Published online by Cambridge University Press:
- 13 November 2019, pp. 1675-1694
-
- Article
- Export citation
-
Decades of research have highlighted the significance of parenting in children's development, yet few studies have focused specifically on the development of parental monitoring strategies in diverse families living in at-risk neighborhoods. The current study investigated the development of active (i.e., parental discussions and curfew rules) and passive (i.e., child communication with parents) parental monitoring strategies across different developmental periods (middle childhood and adolescence; Grades 4–5 and 7–11) as well as individual (child, parent), family, and contextual antecedents (measured in kindergarten) of this parenting behavior. Using an ecological approach, this study evaluated longitudinal data from 753 participants in the Fast Track Project, a multisite study directed at the development and prevention of conduct problems in at-risk children. Latent trajectory modeling results identified little to no mean growth in these monitoring strategies over time, suggesting that families living in at-risk environments may engage in consistent levels of monitoring strategies to ensure children's safety and well-being. Findings also identified several kindergarten antecedents of the growth factors of these parental monitoring strategies including (a) early child conduct problems; (b) parental warmth/involvement, satisfaction, and efficacy; and (c) parent–child relationship quality. These predictive effects largely highlighted the important role of early parenting behaviors on later levels of and growth in parental monitoring strategies. These findings have important implications for potential prevention and intervention targets to promote the development of parental monitoring strategies among families living in more at-risk contexts.
List of contributors
-
- By Jimmy N. Avari, Joshua Berman, David A. Brent, Benjamin D. Brody, Carolyn Broudy, Gerard E. Bruder, Deborah L. Cabaniss, Megan S. Chesin, Melissa P. DelBello, Davangere P. Devanand, Jordan W. Eipper, Jean Endicott, Eric A. Fertuck, Michael B. First, Benicio N. Frey, Emily Gastelum, Lucas Giner, Barbara L. Gracious, David J. Hellerstein, Aerin M. Hyun, David A. Kahn, Jürgen Kayser, S. Aiden Kelly, James H. Kocsis, Robert A. Kowatch, Gonzalo Laje, Martin J. Lan, Kyle A. B. Lapidus, Frances R. Levin, Sarah H. Lisanby, J. John Mann, Sanjay J. Mathew, Patrick J. McGrath, Francis J. McMahon, Barnett S. Meyers, Luciano Minuzzi, Diana E. Moga, Philip R. Muskin, Edward V. Nunes, Maria A. Oquendo, Ramin V. Parsey, Joan Prudic, Annie E. Rabinovitch, Drew Ramsey, Steven P. Roose, Moacyr A. Rosa, Bret R. Rutherford, Roberto Sassi, Peter A. Shapiro, Margaret G. Spinelli, Barbara H. Stanley, Meir Steiner, Jonathan W. Stewart, M. Elizabeth Sublette, Craig E. Tenke, Jiuan Su Terman, Michael Terman, Michael E. Thase, Helen Verdeli, Myrna M. Weissman
- Edited by J. John Mann, Columbia University, New York
- Edited in association with Patrick J. McGrath, Columbia University, New York, Steven P. Roose, Columbia University, New York
-
- Book:
- Clinical Handbook for the Management of Mood Disorders
- Published online:
- 05 May 2013
- Print publication:
- 09 May 2013, pp vii-x
-
- Chapter
- Export citation
Alzheimer's Disease Risk Gene, GAB2, is Associated with Regional Brain Volume Differences in 755 Young Healthy Twins
- Derrek P. Hibar, Neda Jahanshad, Jason L. Stein, Omid Kohannim, Arthur W. Toga, Sarah E. Medland, Narelle K. Hansell, Katie L. McMahon, Greig I. de Zubicaray, Grant W. Montgomery, Nicholas G. Martin, Margaret J. Wright, Paul M. Thompson
-
- Journal:
- Twin Research and Human Genetics / Volume 15 / Issue 3 / June 2012
- Published online by Cambridge University Press:
- 15 June 2012, pp. 286-295
-
- Article
-
- You have access Access
- HTML
- Export citation
-
The development of late-onset Alzheimer's disease (LOAD) is under strong genetic control and there is great interest in the genetic variants that confer increased risk. The Alzheimer's disease risk gene, growth factor receptor bound protein 2-associated protein (GAB2), has been shown to provide a 1.27–1.51 increased odds of developing LOAD for rs7101429 major allele carriers, in case-control analysis. GAB2 is expressed across the brain throughout life, and its role in LOAD pathology is well understood. Recent studies have begun to examine the effect of genetic variation in the GAB2 gene on differences in the brain. However, the effect of GAB2 on the young adult brain has yet to be considered. Here we found a significant association between the GAB2 gene and morphological brain differences in 755 young adult twins (469 females) (M = 23.1, SD = 3.1 years), using a gene-based test with principal components regression (PCReg). Detectable differences in brain morphology are therefore associated with variation in the GAB2 gene, even in young adults, long before the typical age of onset of Alzheimer's disease.