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Association of Birth Weight With Type 2 Diabetes and Glycemic Traits: A Mendelian Randomization Study
Genetical Statistics and Systems Biology

Abstract (Expand)

Importance Observational studies have shown associations of birth weight with type 2 diabetes (T2D) and glycemic traits, but it remains unclear whether these associations represent causal associations. …. Objective To test the association of birth weight with T2D and glycemic traits using a mendelian randomization analysis. Design, Setting, and Participants This mendelian randomization study used a genetic risk score for birth weight that was constructed with 7 genome-wide significant single-nucleotide polymorphisms. The associations of this score with birth weight and T2D were tested in a mendelian randomization analysis using study-level data. The association of birth weight with T2D was tested using both study-level data (7 single-nucleotide polymorphisms were used as an instrumental variable) and summary-level data from the consortia (43 single-nucleotide polymorphisms were used as an instrumental variable). Data from 180 056 participants from 49 studies were included. Main Outcomes and Measures Type 2 diabetes and glycemic traits. Results This mendelian randomization analysis included 49 studies with 41 155 patients with T2D and 80 008 control participants from study-level data and 34 840 patients with T2D and 114 981 control participants from summary-level data. Study-level data showed that a 1-SD decrease in birth weight due to the genetic risk score was associated with higher risk of T2D among all participants (odds ratio [OR], 2.10; 95% CI, 1.69-2.61; P = 4.03 \times 10-5), among European participants (OR, 1.96; 95% CI, 1.42-2.71; P = .04), and among East Asian participants (OR, 1.39; 95% CI, 1.18-1.62; P = .04). Similar results were observed from summary-level analyses. In addition, each 1-SD lower birth weight was associated with 0.189 SD higher fasting glucose concentration (\textgreekb = 0.189; SE = 0.060; P = .002), but not with fasting insulin, 2-hour glucose, or hemoglobin A1c concentration. Conclusions and Relevance In this study, a genetic predisposition to lower birth weight was associated with increased risk of T2D and higher fasting glucose concentration, suggesting genetic effects on retarded fetal growth and increased diabetes risk that either are independent of each other or operate through alterations of integrated biological mechanisms.

Authors: Tao Huang, Tiange Wang, Yan Zheng, Christina Ellervik, Xiang Li, Meng Gao, Zhe Fang, Jin-Fang Chai, Tarun Veer S. Ahluwalia, Yujie Wang, Trudy Voortman, Raymond Noordam, Alexis Frazier-Wood, Markus Scholz, Emily Sonestedt, Masato Akiyama, Rajkumar Dorajoo, Ang Zhou, Tuomas O. Kilpeläinen, Marcus E. Kleber, Sarah R. Crozier, Keith M. Godfrey, Rozenn Lemaitre, Janine F. Felix, Yuan Shi, Preeti Gupta, Chiea-Chuen Khor, Terho Lehtimäki, Carol A. Wang, Carla M. T. Tiesler, Elisabeth Thiering, Marie Standl, Peter Rzehak, Eirini Marouli, Meian He, Cécile Lecoeur, Dolores Corella, Chao-Qiang Lai, Luis A. Moreno, Niina Pitkänen, Colin A. Boreham, Tao Zhang, Seang Mei Saw, Paul M. Ridker, Mariaelisa Graff, Frank J. A. van Rooij, Andre G. Uitterlinden, Albert Hofman, Diana van Heemst, Frits R. Rosendaal, Renée de Mutsert, Ralph Burkhardt, Christina-Alexandra Schulz, Ulrika Ericson, Yoichiro Kamatani, Jian-Min Yuan, Chris Power, Torben Hansen, Thorkild I. A. Sørensen, Anne Tjønneland, Kim Overvad, Graciela Delgado, Cyrus Cooper, Luc Djousse, Fernando Rivadeneira, Karen Jameson, Wanting Zhao, Jianjun Liu, Nanette R. Lee, Olli Raitakari, Mika Kähönen, Jorma Viikari, Veit Grote, Jean-Paul Langhendries, Berthold Koletzko, Joaquin Escribano, Elvira Verduci, George Dedoussis, Caizheng Yu, Yih Chung Tham, Blanche Lim, Sing Hui Lim, Philippe Froguel, Beverley Balkau, Nadia R. Fink, Rebecca K. Vinding, Astrid Sevelsted, Hans Bisgaard, Oscar Coltell, Jean Dallongeville, Frédéric Gottrand, Katja Pahkala, Harri Niinikoski, Elina Hyppönen, Oluf Pedersen, Winfried März, Hazel Inskip, Vincent W. V. Jaddoe, Elaine Dennison, Tien Yin Wong, Charumathi Sabanayagam, E-Shyong Tai, Karen L. Mohlke, David A. Mackey, Dariusz Gruszfeld, Panagiotis Deloukas, Katherine L. Tucker, Frédéric Fumeron, Klaus Bønnelykke, Peter Rossing, Ramon Estruch, Jose M. Ordovas, Donna K. Arnett, Aline Meirhaeghe, Philippe Amouyel, Ching-Yu Cheng, Xueling Sim, Yik Ying Teo, Rob M. van Dam, Woon-Puay Koh, Marju Orho-Melander, Markus Loeffler, Michiaki Kubo, Joachim Thiery, Dennis O. Mook-Kanamori, Dariush Mozaffarian, Bruce M. Psaty, Oscar H. Franco, Tangchun Wu, Kari E. North, George Davey Smith, Jorge E. Chavarro, Daniel I. Chasman, Lu Qi

Date Published: 4th Sep 2019

Publication Type: Journal article

Design and Concept of the SMITH Phenotyping Pipeline.
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Phenotyping means the determination of clinical relevant phenotypes, e.g. by classification or calculation based on EHR data. Within the German Medical Informatics Initiative, the SMITH consortium is … working on the implementation of a phenotyping pipeline. to extract, structure and normalize information from the EHR data of the hospital information systems of the participating sites; to automatically apply complex algorithms and models and to enrich the data within the research data warehouses of the distributed data integration centers with the computed results. Here we present the overall picture and essential building blocks and workflows of this concept.

Authors: F. A. Meineke, S. Staubert, M. Lobe, A. Uciteli, M. Loffler

Date Published: 3rd Sep 2019

Publication Type: Journal article

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification
GC-HBOC - German Consortium for Hereditary Breast and Ovarian Cancer

Abstract (Expand)

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated … in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.

Authors: Michael T. Parsons, Emma Tudini, Hongyan Li, Eric Hahnen, Barbara Wappenschmidt, Lidia Feliubadaló, Cora M. Aalfs, Simona Agata, Kristiina Aittomäki, Elisa Alducci, María Concepción Alonso-Cerezo, Norbert Arnold, Bernd Auber, Rachel Austin, Jacopo Azzollini, Judith Balmaña, Elena Barbieri, Claus R. Bartram, Ana Blanco, Britta Blümcke, Sandra Bonache, Bernardo Bonanni, Åke Borg, Beatrice Bortesi, Joan Brunet, Carla Bruzzone, Karolin Bucksch, Giulia Cagnoli, Trinidad Caldés, Almuth Caliebe, Maria A. Caligo, Mariarosaria Calvello, Gabriele L. Capone, Sandrine M. Caputo, Ileana Carnevali, Estela Carrasco, Virginie Caux-Moncoutier, Pietro Cavalli, Giulia Cini, Edward M. Clarke, Paola Concolino, Elisa J. Cops, Laura Cortesi, Fergus J. Couch, Esther Darder, Miguel de La Hoya, Michael Dean, Irmgard Debatin, Jesús Del Valle, Capucine Delnatte, Nicolas Derive, Orland Diez, Nina Ditsch, Susan M. Domchek, Véronique Dutrannoy, Diana M. Eccles, Hans Ehrencrona, Ute Enders, D. Gareth Evans, Ulrike Faust, Ute Felbor, Irene Feroce, Miriam Fine, Henrique C. R. Galvao, Gaetana Gambino, Andrea Gehrig, Francesca Gensini, Anne-Marie Gerdes, Aldo Germani, Jutta Giesecke, Viviana Gismondi, Carolina Gómez, Encarna B. Gómez Garcia, Sara González, Elia Grau, Sabine Grill, Eva Gross, Aliana Guerrieri-Gonzaga, Marine Guillaud-Bataille, Sara Gutiérrez-Enríquez, Thomas Haaf, Karl Hackmann, Thomas v. O. Hansen, Marion Harris, Jan Hauke, Tilman Heinrich, Heide Hellebrand, Karen N. Herold, Ellen Honisch, Judit Horvath, Claude Houdayer, Verena Hübbel, Silvia Iglesias, Angel Izquierdo, Paul A. James, Linda A. M. Janssen, Udo Jeschke, Silke Kaulfuß, Katharina Keupp, Marion Kiechle, Alexandra Kölbl, Sophie Krieger, Torben A. Kruse, Anders Kvist, Fiona Lalloo, Mirjam Larsen, Vanessa L. Lattimore, Charlotte Lautrup, Susanne Ledig, Elena Leinert, Alexandra L. Lewis, Joanna Lim, Markus Loeffler, Adrià López-Fernández, Emanuela Lucci-Cordisco, Nicolai Maass, Siranoush Manoukian, Monica Marabelli, Laura Matricardi, Alfons Meindl, Rodrigo D. Michelli, Setareh Moghadasi, Alejandro Moles-Fernández, Marco Montagna, Gemma Montalban, Alvaro N. Monteiro, Eva Montes, Luigi Mori, Lidia Moserle, Clemens R. Müller, Christoph Mundhenke, Nadia Naldi, Katherine L. Nathanson, Matilde Navarro, Heli Nevanlinna, Cassandra B. Nichols, Dieter Niederacher, Henriette R. Nielsen, Kai-Ren Ong, Nicholas Pachter, Edenir I. Palmero, Laura Papi, Inge Sokilde Pedersen, Bernard Peissel, Pedro Pérez-Segura, Katharina Pfeifer, Marta Pineda, Esther Pohl-Rescigno, Nicola K. Poplawski, Berardino Porfirio, Anne S. Quante, Juliane Ramser, Rui M. Reis, Françoise Revillion, Kerstin Rhiem, Barbara Riboli, Julia Ritter, Daniela Rivera, Paula Rofes, Andreas Rump, Monica Salinas, Ana María Sánchez de Abajo, Gunnar Schmidt, Ulrike Schoenwiese, Jochen Seggewiß, Ares Solanes, Doris Steinemann, Mathias Stiller, Dominique Stoppa-Lyonnet, Kelly J. Sullivan, Rachel Susman, Christian Sutter, Sean V. Tavtigian, Soo H. Teo, Alex Teulé, Mads Thomassen, Maria Grazia Tibiletti, Silvia Tognazzo, Amanda E. Toland, Eva Tornero, Therese Törngren, Sara Torres-Esquius, Angela Toss, Alison H. Trainer, Christi J. van Asperen, Marion T. van Mackelenbergh, Liliana Varesco, Gardenia Vargas-Parra, Raymonda Varon, Ana Vega, Ángela Velasco, Anne-Sophie Vesper, Alessandra Viel, Maaike P. G. Vreeswijk, Sebastian A. Wagner, Anke Waha, Logan C. Walker, Rhiannon J. Walters, Shan Wang-Gohrke, Bernhard H. F. Weber, Wilko Weichert, Kerstin Wieland, Lisa Wiesmüller, Isabell Witzel, Achim Wöckel, Emma R. Woodward, Silke Zachariae, Valentina Zampiga, Christine Zeder-Göß, Conxi Lázaro, Arcangela de Nicolo, Paolo Radice, Christoph Engel, Rita K. Schmutzler, David E. Goldgar, Amanda B. Spurdle

Date Published: 1st Sep 2019

Publication Type: Journal article

Human Diseases: hereditary breast ovarian cancer syndrome

Design and concept of the SMITH phenotyping pipeline
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Phenotyping means the determination of clinical relevant phenotypes, e.g. by classification or calculation based on EHR data. Within the German Medical Informatics Initiative, the SMITH consortium is … working on the implementation of a phenotyping pipeline. to extract, structure and normalize information from the EHR data of the hospital information systems of the participating sites; to automatically apply complex algorithms and models and to enrich the data within the research data warehouses of the distributed data integration centers with the computed results. Here we present the overall picture and essential building blocks and workflows of this concept.

Authors: Frank A Meineke, Sebastian Stäubert, Matthias Löbe, Alexandr Uciteli, Markus Löffler

Date Published: 1st Sep 2019

Publication Type: Journal article

Design of Metadata Services for Clinical Data Interoperability in Germany.
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Secondary use of electronic health record (EHR) data requires a detailed description of metadata, especially when data collection and data re-use are organizationally and technically far apart. This … paper describes the concept of the SMITH consortium that includes conventions, processes, and tools for describing and managing metadata using common standards for semantic interoperability. It deals in particular with the chain of processing steps of data from existing information systems and provides an overview of the planned use of metadata, medical terminologies, and semantic services in the consortium.

Authors: M. Lobe, O. Beyan, S. Staubert, F. Meineke, D. Ammon, A. Winter, S. Decker, M. Loffler, T. Kirsten

Date Published: 21st Aug 2019

Publication Type: Journal article

Integrating Heterogeneous Data Sources for Cross-Institutional Data Sharing: Requirements Elicitation and Management in SMITH.
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

The digitization of health records and cross-institutional data sharing is a necessary precondition to improve clinical research and patient care. The SMITH project unites several university hospitals … and medical faculties in order to provide medical informatics solutions for health data integration and cross-institutional communication. In this paper, we focus on requirements elicitation and management for extracting clinical data from heterogeneous subsystems and data integration based on eHealth standards such as HL7 FHIR and IHE profiles.

Authors: K. Tahar, C. Muller, A. Durschmid, S. Haferkamp, K. Saleh, P. Jurs, S. Staubert, J. E. Gewehr, S. Zenker, D. Ammon, T. Wendt

Date Published: 21st Aug 2019

Publication Type: Journal article

Annotating German Clinical Documents for De-Identification.
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

We devised annotation guidelines for the de-identification of German clinical documents and assembled a corpus of 1,106 discharge summaries and transfer letters with 44K annotated protected health … information (PHI) items. After three iteration rounds, our annotation team finally reached an inter-annotator agreement of 0.96 on the instance level and 0.97 on the token level of annotation (averaged pair-wise F1 score). To establish a baseline for automatic de-identification on our corpus, we trained a recurrent neural network (RNN) and achieved F1 scores greater than 0.9 on most major PHI categories.

Authors: T. Kolditz, C. Lohr, J. Hellrich, L. Modersohn, B. Betz, M. Kiehntopf, U. Hahn

Date Published: 21st Aug 2019

Publication Type: InProceedings

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