Sofia Christakoudi
Imperial College London
63 Papers
64 Citations
Sofia Christakoudi is an academic researcher from Imperial College London. The author has contributed to research in topics: Medicine & European Prospective Investigation into Cancer and Nutrition. The author has an hindex of 14, co-authored 35 publications. Previous affiliations of Sofia Christakoudi include University of London & University of Cambridge.
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Papers
A Body Shape Index (ABSI) achieves better mortality risk stratification than alternative indices of abdominal obesity : results from a large European cohort
Sofia Christakoudi,Konstantinos K. Tsilidis,Konstantinos K. Tsilidis,David C. Muller,Heinz Freisling,Elisabete Weiderpass,Kim Overvad,Kim Overvad,Stefan Söderberg,Christel Häggström,Christel Häggström,Tobias Pischon,Tobias Pischon,Christina C. Dahm,Jie Zhang,Anne Tjønneland,Jytte Halkjær,Conor James MacDonald,Conor James MacDonald,Marie-Christine Boutron-Ruault,Marie-Christine Boutron-Ruault,Francesca Mancini,Francesca Mancini,Tilman Kühn,Rudolf Kaaks,Matthias B. Schulze,Antonia Trichopoulou,Anna Karakatsani,Eleni Peppa,Giovanna Masala,Valeria Pala,Salvatore Panico,Rosario Tumino,Carlotta Sacerdote,J. Ramón Quirós,Antonio Agudo,María José Sánchez,Lluís Cirera,Aurelio Barricarte-Gurrea,Pilar Amiano,Ensieh Memarian,Emily Sonestedt,Bas Bueno-de-Mesquita,Anne M. May,Kay-Tee Khaw,Nicholas J. Wareham,Tammy Y.N. Tong,Inge Huybrechts,Hwayoung Noh,Elom K. Aglago,Merete Ellingjord-Dale,Heather Ward,Dagfinn Aune,Dagfinn Aune,Elio Riboli +54 more
TL;DR: Only a waist index independent of BMI by design, such as ABSI, complements BMI and enables efficient risk stratification, which could facilitate personalisation of screening, treatment and monitoring.
Prospective analysis of circulating metabolites and breast cancer in EPIC
Mathilde His,Vivian Viallon,Laure Dossus,Audrey Gicquiau,David Achaintre,Augustin Scalbert,Pietro Ferrari,Isabelle Romieu,N. Charlotte Onland-Moret,Elisabete Weiderpass,Christina C. Dahm,Kim Overvad,Kim Overvad,Anja Olsen,Anne Tjønneland,Agnès Fournier,Agnès Fournier,Joseph A. Rothwell,Joseph A. Rothwell,Gianluca Severi,Gianluca Severi,Tilman Kühn,Renée T. Fortner,Heiner Boeing,Antonia Trichopoulou,Anna Karakatsani,Georgia Martimianaki,Giovanna Masala,Sabina Sieri,Rosario Tumino,Paolo Vineis,Salvatore Panico,Carla H. van Gils,Therese Haugdahl Nøst,Torkjel M. Sandanger,Guri Skeie,Guri Skeie,J. Ramón Quirós,Antonio Agudo,María José Sánchez,Pilar Amiano,José María Huerta,Eva Ardanaz,Julie A. Schmidt,Ruth C. Travis,Elio Riboli,Konstantinos K. Tsilidis,Konstantinos K. Tsilidis,Sofia Christakoudi,Sofia Christakoudi,Marc J. Gunter,Sabina Rinaldi +51 more
TL;DR: These findings point to potentially novel pathways and biomarkers of breast cancer development, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity.
Biomarkers of Tolerance in Kidney Transplantation: Are We Predicting Tolerance or Response to Immunosuppressive Treatment?
Irene Rebollo-Mesa,Irene Rebollo-Mesa,Estefania Nova-Lamperti,Paula Mobillo,Manohursingh Runglall,Sofia Christakoudi,S. Norris,S. Norris,N. Smallcombe,Yogesh Kamra,Rachel Hilton,Sunil Bhandari,Richard J. Baker,David Berglund,Sue Carr,David Game,Sian Griffin,Philip A Kalra,Robert Lewis,Patrick B. Mark,Stephen D. Marks,Iain MacPhee,William McKane,Markus G. Mohaupt,Ravi Pararajasingam,Sui Phin Kon,Daniel Serón,Manish D. Sinha,Beatriz Tucker,Ondrej Viklický,Robert I. Lechler,Robert I. Lechler,Graham M. Lord,Graham M. Lord,Graham M. Lord,Maria P. Hernandez-Fuentes,Maria P. Hernandez-Fuentes,Maria P. Hernandez-Fuentes +37 more
TL;DR: A validated and highly accurate gene expression signature is reported that can be reliably used to identify patients suitable for IS reduction (approximately 12% of stable patients), irrespective of the IS drugs they are receiving.
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Machine Learning Predicts Accurately Mycobacterium tuberculosis Drug Resistance From Whole Genome Sequencing Data
Wouter Deelder,Sofia Christakoudi,Sofia Christakoudi,Jody Phelan,Ernest Diez Benavente,Susana Campino,Ruth McNerney,Luigi Palla,Taane G. Clark +8 more
TL;DR: The utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability is demonstrated, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation.
Nutrient-wide association study of 92 foods and nutrients and breast cancer risk
Alicia K Heath,David C. Muller,Piet A. van den Brandt,Nikos Papadimitriou,Nikos Papadimitriou,Elena Critselis,Elena Critselis,Marc J. Gunter,Marc J. Gunter,Paolo Vineis,Elisabete Weiderpass,Guy Fagherazzi,Heiner Boeing,Pietro Ferrari,Anja Olsen,Anne Tjønneland,Patrick Arveux,Marie-Christine Boutron-Ruault,Francesca Mancini,Tilman Kühn,Renée Turzanski-Fortner,Matthias B. Schulze,Anna Karakatsani,Paschalis Thriskos,Antonia Trichopoulou,Giovanna Masala,Paolo Contiero,Fulvio Ricceri,Salvatore Panico,Bas Bueno-de-Mesquita,Marije F. Bakker,Carla H. van Gils,Karina Standahl Olsen,Guri Skeie,Cristina Lasheras,Antonio Agudo,Miguel Rodríguez-Barranco,María José Sánchez,Pilar Amiano,María Dolores Chirlaque,Aurelio Barricarte,Isabel Drake,Ulrika Ericson,Ingegerd Johansson,Anna Winkvist,Anna Winkvist,Timothy J. Key,Heinz Freisling,Mathilde His,Inge Huybrechts,Sofia Christakoudi,Sofia Christakoudi,Merete Ellingjord-Dale,Elio Riboli,Konstantinos K. Tsilidis,Konstantinos K. Tsilidis,Ioanna Tzoulaki,Ioanna Tzoulaki +57 more
TL;DR: A positive association of alcohol consumption is confirmed and an inverse association of dietary fibre and possibly fruit intake with breast cancer risk is suggested, which is similar in magnitude and direction to that seen in the NLCS.