@article{WuttkeLiLietal.2019,
  author    = {Wuttke, Matthias and Li, Yong and Li, Man and Sieber, Karsten B. and Feitosa, Mary F. and Gorski, Mathias and Tin, Adrienne and Wang, Lihua and Chu, Audrey Y. and Hoppmann, Anselm and Kirsten, Holger and Giri, Ayush and Chai, Jin-Fang and Sveinbjornsson, Gardar and Tayo, Bamidele O. and Nutile, Teresa and Fuchsberger, Christian and Marten, Jonathan and Cocca, Massimiliano and Ghasemi, Sahar and Xu, Yizhe and Horn, Katrin and Noce, Damia and Van der Most, Peter J. and Sedaghat, Sanaz and Yu, Zhi and Akiyama, Masato and Afaq, Saima and Ahluwalia, Tarunveer Singh and Almgren, Peter and Amin, Najaf and Arnlov, Johan and Bakker, Stephan J. L. and Bansal, Nisha and Baptista, Daniela and Bergmann, Sven and Biggs, Mary L. and Biino, Ginevra and Boehnke, Michael and Boerwinkle, Eric and Boissel, Mathilde and B{\"o}ttinger, Erwin and Boutin, Thibaud S. and Brenner, Hermann and Brumat, Marco and Burkhardt, Ralph and Butterworth, Adam S. and Campana, Eric and Campbell, Archie and Campbell, Harry and Canouil, Mickael and Carroll, Robert J. and Catamo, Eulalia and Chambers, John C. and Chee, Miao-Ling and Chee, Miao-Li and Chen, Xu and Cheng, Ching-Yu and Cheng, Yurong and Christensen, Kaare and Cifkova, Renata and Ciullo, Marina and Concas, Maria Pina and Cook, James P. and Coresh, Josef and Corre, Tanguy and Sala, Cinzia Felicita and Cusi, Daniele and Danesh, John and Daw, E. Warwick and De Borst, Martin H. and De Grandi, Alessandro and De Mutsert, Renee and De Vries, Aiko P. J. and Degenhardt, Frauke and Delgado, Graciela and Demirkan, Ayse and Di Angelantonio, Emanuele and Dittrich, Katalin and Divers, Jasmin and Dorajoo, Rajkumar and Eckardt, Kai-Uwe and Ehret, Georg and Elliott, Paul and Endlich, Karlhans and Evans, Michele K. and Felix, Janine F. and Foo, Valencia Hui Xian and Franco, Oscar H. and Franke, Andre and Freedman, Barry I. and Freitag-Wolf, Sandra and Friedlander, Yechiel and Froguel, Philippe and Gansevoort, Ron T. and Gao, He and Gasparini, Paolo and Gaziano, J. Michael and Giedraitis, Vilmantas and Gieger, Christian and Girotto, Giorgia and Giulianini, Franco and Gogele, Martin and Gordon, Scott D. and Gudbjartsson, Daniel F. and Gudnason, Vilmundur and Haller, Toomas and Hamet, Pavel and Harris, Tamara B. and Hartman, Catharina A. and Hayward, Caroline and Hellwege, Jacklyn N. and Heng, Chew-Kiat and Hicks, Andrew A. and Hofer, Edith and Huang, Wei and Hutri-Kahonen, Nina and Hwang, Shih-Jen and Ikram, M. Arfan and Indridason, Olafur S. and Ingelsson, Erik and Ising, Marcus and Jaddoe, Vincent W. V. and Jakobsdottir, Johanna and Jonas, Jost B. and Joshi, Peter K. and Josyula, Navya Shilpa and Jung, Bettina and Kahonen, Mika and Kamatani, Yoichiro and Kammerer, Candace M. and Kanai, Masahiro and Kastarinen, Mika and Kerr, Shona M. and Khor, Chiea-Chuen and Kiess, Wieland and Kleber, Marcus E. and Koenig, Wolfgang and Kooner, Jaspal S. and Korner, Antje and Kovacs, Peter and Kraja, Aldi T. and Krajcoviechova, Alena and Kramer, Holly and Kramer, Bernhard K. and Kronenberg, Florian and Kubo, Michiaki and Kuhnel, Brigitte and Kuokkanen, Mikko and Kuusisto, Johanna and La Bianca, Martina and Laakso, Markku and Lange, Leslie A. and Langefeld, Carl D. and Lee, Jeannette Jen-Mai and Lehne, Benjamin and Lehtimaki, Terho and Lieb, Wolfgang and Lim, Su-Chi and Lind, Lars and Lindgren, Cecilia M. and Liu, Jun and Liu, Jianjun and Loeffler, Markus and Loos, Ruth J. F. and Lucae, Susanne and Lukas, Mary Ann and Lyytikainen, Leo-Pekka and Magi, Reedik and Magnusson, Patrik K. E. and Mahajan, Anubha and Martin, Nicholas G. and Martins, Jade and Marz, Winfried and Mascalzoni, Deborah and Matsuda, Koichi and Meisinger, Christa and Meitinger, Thomas and Melander, Olle and Metspalu, Andres and Mikaelsdottir, Evgenia K. and Milaneschi, Yuri and Miliku, Kozeta and Mishra, Pashupati P. and Program, V. A. Million Veteran and Mohlke, Karen L. and Mononen, Nina and Montgomery, Grant W. and Mook-Kanamori, Dennis O. and Mychaleckyj, Josyf C. and Nadkarni, Girish N. and Nalls, Mike A. and Nauck, Matthias and Nikus, Kjell and Ning, Boting and Nolte, Ilja M. and Noordam, Raymond and Olafsson, Isleifur and Oldehinkel, Albertine J. and Orho-Melander, Marju and Ouwehand, Willem H. and Padmanabhan, Sandosh and Palmer, Nicholette D. and Palsson, Runolfur and Penninx, Brenda W. J. H. and Perls, Thomas and Perola, Markus and Pirastu, Mario and Pirastu, Nicola and Pistis, Giorgio and Podgornaia, Anna I. and Polasek, Ozren and Ponte, Belen and Porteous, David J. and Poulain, Tanja and Pramstaller, Peter P. and Preuss, Michael H. and Prins, Bram P. and Province, Michael A. and Rabelink, Ton J. and Raffield, Laura M. and Raitakari, Olli T. and Reilly, Dermot F. and Rettig, Rainer and Rheinberger, Myriam and Rice, Kenneth M. and Ridker, Paul M. and Rivadeneira, Fernando and Rizzi, Federica and Roberts, David J. and Robino, Antonietta and Rossing, Peter and Rudan, Igor and Rueedi, Rico and Ruggiero, Daniela and Ryan, Kathleen A. and Saba, Yasaman and Sabanayagam, Charumathi and Salomaa, Veikko and Salvi, Erika and Saum, Kai-Uwe and Schmidt, Helena and Schmidt, Reinhold and Ben Schottker, and Schulz, Christina-Alexandra and Schupf, Nicole and Shaffer, Christian M. and Shi, Yuan and Smith, Albert V. and Smith, Blair H. and Soranzo, Nicole and Spracklen, Cassandra N. and Strauch, Konstantin and Stringham, Heather M. and Stumvoll, Michael and Svensson, Per O. and Szymczak, Silke and Tai, E-Shyong and Tajuddin, Salman M. and Tan, Nicholas Y. Q. and Taylor, Kent D. and Teren, Andrej and Tham, Yih-Chung and Thiery, Joachim and Thio, Chris H. L. and Thomsen, Hauke and Thorleifsson, Gudmar and Toniolo, Daniela and Tonjes, Anke and Tremblay, Johanne and Tzoulaki, Ioanna and Uitterlinden, Andre G. and Vaccargiu, Simona and Van Dam, Rob M. and Van der Harst, Pim and Van Duijn, Cornelia M. and Edward, Digna R. Velez and Verweij, Niek and Vogelezang, Suzanne and Volker, Uwe and Vollenweider, Peter and Waeber, Gerard and Waldenberger, Melanie and Wallentin, Lars and Wang, Ya Xing and Wang, Chaolong and Waterworth, Dawn M. and Bin Wei, Wen and White, Harvey and Whitfield, John B. and Wild, Sarah H. and Wilson, James F. and Wojczynski, Mary K. and Wong, Charlene and Wong, Tien-Yin and Xu, Liang and Yang, Qiong and Yasuda, Masayuki and Yerges-Armstrong, Laura M. and Zhang, Weihua and Zonderman, Alan B. and Rotter, Jerome I. and Bochud, Murielle and Psaty, Bruce M. and Vitart, Veronique and Wilson, James G. and Dehghan, Abbas and Parsa, Afshin and Chasman, Daniel I. and Ho, Kevin and Morris, Andrew P. and Devuyst, Olivier and Akilesh, Shreeram and Pendergrass, Sarah A. and Sim, Xueling and Boger, Carsten A. and Okada, Yukinori and Edwards, Todd L. and Snieder, Harold and Stefansson, Kari and Hung, Adriana M. and Heid, Iris M. and Scholz, Markus and Teumer, Alexander and Kottgen, Anna and Pattaro, Cristian},
  title     = {A catalog of genetic loci associated with kidney function from analyses of a million individuals},
  series = {Nature genetics},
  volume    = {51},
  journal   = {Nature genetics},
  number    = {6},
  publisher = {Nature Publ. Group},
  address   = {New York},
  organization    = {Lifelines COHort Study},
  issn      = {1061-4036},
  doi       = {10.1038/s41588-019-0407-x},
  pages     = {957 -- +},
  year      = {2019},
  abstract  = {Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.},
  language  = {en}
}
@article{ChanJaladankiSomanietal.2021,
  author    = {Chan, Lili and Jaladanki, Suraj K. and Somani, Sulaiman and Paranjpe, Ishan and Kumar, Arvind and Zhao, Shan and Kaufman, Lewis and Leisman, Staci and Sharma, Shuchita and He, John Cijiang and Murphy, Barbara and Fayad, Zahi A. and Levin, Matthew A. and B{\"o}ttinger, Erwin and Charney, Alexander W. and Glicksberg, Benjamin and Coca, Steven G. and Nadkarni, Girish N.},
  title     = {Outcomes of patients on maintenance dialysis hospitalized with COVID-19},
  series = {Clinical journal of the American Society of Nephrology : CJASN},
  volume    = {16},
  journal   = {Clinical journal of the American Society of Nephrology : CJASN},
  number    = {3},
  publisher = {American Society of Nephrology},
  address   = {Washington},
  organization    = {Mount Sinai Covid I},
  issn      = {1555-9041},
  doi       = {10.2215/CJN.12360720},
  pages     = {452 -- 455},
  year      = {2021},
  language  = {en}
}
@article{VaidChanChaudharyetal.2021,
  author    = {Vaid, Akhil and Chan, Lili and Chaudhary, Kumardeep and Jaladanki, Suraj K. and Paranjpe, Ishan and Russak, Adam J. and Kia, Arash and Timsina, Prem and Levin, Matthew A. and He, John Cijiang and B{\"o}ttinger, Erwin and Charney, Alexander W. and Fayad, Zahi A. and Coca, Steven G. and Glicksberg, Benjamin S. and Nadkarni, Girish N.},
  title     = {Predictive approaches for acute dialysis requirement and death in COVID-19},
  series = {Clinical journal of the American Society of Nephrology : CJASN},
  volume    = {16},
  journal   = {Clinical journal of the American Society of Nephrology : CJASN},
  number    = {8},
  publisher = {American Society of Nephrology},
  address   = {Washington},
  organization    = {MSCIC},
  issn      = {1555-9041},
  doi       = {10.2215/CJN.17311120},
  pages     = {1158 -- 1168},
  year      = {2021},
  abstract  = {Background and objectives AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited. Design, setting, participants, \& measurements Using data from adult patients hospitalized with COVID-19 from five hospitals from theMount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to theMount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission. Results A total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93-0.98) and area under the precisionrecall curve (AUPRC; range of 0.78-0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85-0.87, and AUPRC range of 0.27-0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model's prediction. Conclusions An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models.},
  language  = {en}
}
@article{VaidSomaniRussaketal.2020,
  author    = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.},
  title     = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation},
  series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  volume    = {22},
  journal   = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  number    = {11},
  publisher = {Healthcare World},
  address   = {Richmond, Va.},
  issn      = {1439-4456},
  doi       = {10.2196/24018},
  pages     = {19},
  year      = {2020},
  abstract  = {Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.},
  language  = {en}
}
@article{ChanChaudharySahaetal.2021,
  author    = {Chan, Lili and Chaudhary, Kumardeep and Saha, Aparna and Chauhan, Kinsuk and Vaid, Akhil and Zhao, Shan and Paranjpe, Ishan and Somani, Sulaiman and Richter, Felix and Miotto, Riccardo and Lala, Anuradha and Kia, Arash and Timsina, Prem and Li, Li and Freeman, Robert and Chen, Rong and Narula, Jagat and Just, Allan C. and Horowitz, Carol and Fayad, Zahi and Cordon-Cardo, Carlos and Schadt, Eric and Levin, Matthew A. and Reich, David L. and Fuster, Valentin and Murphy, Barbara and He, John C. and Charney, Alexander W. and B{\"o}ttinger, Erwin and Glicksberg, Benjamin and Coca, Steven G. and Nadkarni, Girish N.},
  title     = {AKI in hospitalized patients with COVID-19},
  series = {Journal of the American Society of Nephrology : JASN},
  volume    = {32},
  journal   = {Journal of the American Society of Nephrology : JASN},
  number    = {1},
  publisher = {American Society of Nephrology},
  address   = {Washington},
  organization    = {Mt Sinai COVID Informatics Ct},
  issn      = {1046-6673},
  doi       = {10.1681/ASN.2020050615},
  pages     = {151 -- 160},
  year      = {2021},
  abstract  = {Background: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associatedwith worse outcomes. However, AKI among hospitalized patients with COVID19 in the United States is not well described. Methods: This retrospective, observational study involved a review of data from electronic health records of patients aged >= 18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. Results: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46\%) patients; 347 (19\%) of the patientswith AKI required dialysis. The proportionswith stages 1, 2, or 3 AKIwere 39\%, 19\%, and 42\%, respectively. A total of 976 (24\%) patients were admitted to intensive care, and 745 (76\%) experienced AKI. Of the 435 patients with AKI and urine studies, 84\% had proteinuria, 81\% had hematuria, and 60\% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50\% among patients with AKI versus 8\% among those without AKI (aOR, 9.2; 95\% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35\% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36\%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. Conclusions: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30\% survived with recovery of kidney function by the time of discharge.},
  language  = {en}
}
@misc{KonigorskiWernickeSlosareketal.2021,
  author    = {Konigorski, Stefan and Wernicke, Sarah and Slosarek, Tamara and Zenner, Alexander Maximilian and Strelow, Nils and Ruether, Darius Ferenc and Henschel, Florian and Manaswini, Manisha and Pottb{\"a}cker, Fabian and Edelman, Jonathan Antonio and Owoyele, Babajide and Danieletto, Matteo and Golden, Eddye and Zweig, Micol and Nadkarni, Girish N. and Bottinger, Erwin},
  title     = {StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  number    = {12},
  doi       = {10.25932/publishup-58037},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-580370},
  pages     = {12},
  year      = {2021},
  abstract  = {N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.},
  language  = {en}
}
@article{DeFreitasJohnsonGoldenetal.2021,
  author    = {De Freitas, Jessica K. and Johnson, Kipp W. and Golden, Eddye and Nadkarni, Girish N. and Dudley, Joel T. and B{\"o}ttinger, Erwin and Glicksberg, Benjamin S. and Miotto, Riccardo},
  title     = {Phe2vec},
  series = {Patterns},
  volume    = {2},
  journal   = {Patterns},
  number    = {9},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2666-3899},
  doi       = {10.1016/j.patter.2021.100337},
  pages     = {9},
  year      = {2021},
  abstract  = {Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.},
  language  = {en}
}
@article{KonigorskiWernickeSlosareketal.2021,
  author    = {Konigorski, Stefan and Wernicke, Sarah and Slosarek, Tamara and Zenner, Alexander Maximilian and Strelow, Nils and Ruether, Darius Ferenc Ruether and Henschel, Florian and Manaswini, Manisha and Pottb{\"a}cker, Fabian and Edelman, Jonathan Antonio and Owoyele, Babajide and Danieletto, Matteo and Golden, Eddye and Zweig, Micol and Nadkarni, Girish N. and Bottinger, Erwin},
  title     = {StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials},
  series = {Journal of Medical Internet Research},
  volume    = {24},
  journal   = {Journal of Medical Internet Research},
  edition   = {7},
  publisher = {JMIR Publications},
  address   = {Richmond, Virginia, USA},
  issn      = {1438-8871},
  doi       = {10.2196/35884},
  pages     = {12},
  year      = {2021},
  abstract  = {N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.},
  language  = {en}
}
@misc{GorskiJungLietal.2020,
  author    = {Gorski, Mathias and Jung, Bettina and Li, Yong and Matias-Garcia, Pamela R. and Wuttke, Matthias and Coassin, Stefan and Thio, Chris H. L. and Kleber, Marcus E. and Winkler, Thomas W. and Wanner, Veronika and Chai, Jin-Fang and Chu, Audrey Y. and Cocca, Massimiliano and Feitosa, Mary F. and Ghasemi, Sahar and Hoppmann, Anselm and Horn, Katrin and Li, Man and Nutile, Teresa and Scholz, Markus and Sieber, Karsten B. and Teumer, Alexander and Tin, Adrienne and Wang, Judy and Tayo, Bamidele O. and Ahluwalia, Tarunveer S. and Almgren, Peter and Bakker, Stephan J. L. and Banas, Bernhard and Bansal, Nisha and Biggs, Mary L. and Boerwinkle, Eric and B{\"o}ttinger, Erwin and Brenner, Hermann and Carroll, Robert J. and Chalmers, John and Chee, Miao-Li and Chee, Miao-Ling and Cheng, Ching-Yu and Coresh, Josef and de Borst, Martin H. and Degenhardt, Frauke and Eckardt, Kai-Uwe and Endlich, Karlhans and Franke, Andre and Freitag-Wolf, Sandra and Gampawar, Piyush and Gansevoort, Ron T. and Ghanbari, Mohsen and Gieger, Christian and Hamet, Pavel and Ho, Kevin and Hofer, Edith and Holleczek, Bernd and Foo, Valencia Hui Xian and Hutri-Kahonen, Nina and Hwang, Shih-Jen and Ikram, M. Arfan and Josyula, Navya Shilpa and Kahonen, Mika and Khor, Chiea-Chuen and Koenig, Wolfgang and Kramer, Holly and Kraemer, Bernhard K. and Kuehnel, Brigitte and Lange, Leslie A. and Lehtimaki, Terho and Lieb, Wolfgang and Loos, Ruth J. F. and Lukas, Mary Ann and Lyytikainen, Leo-Pekka and Meisinger, Christa and Meitinger, Thomas and Melander, Olle and Milaneschi, Yuri and Mishra, Pashupati P. and Mononen, Nina and Mychaleckyj, Josyf C. and Nadkarni, Girish N. and Nauck, Matthias and Nikus, Kjell and Ning, Boting and Nolte, Ilja M. and O'Donoghue, Michelle L. and Orho-Melander, Marju and Pendergrass, Sarah A. and Penninx, Brenda W. J. H. and Preuss, Michael H. and Psaty, Bruce M. and Raffield, Laura M. and Raitakari, Olli T. and Rettig, Rainer and Rheinberger, Myriam and Rice, Kenneth M. and Rosenkranz, Alexander R. and Rossing, Peter and Rotter, Jerome and Sabanayagam, Charumathi and Schmidt, Helena and Schmidt, Reinhold and Schoettker, Ben and Schulz, Christina-Alexandra and Sedaghat, Sanaz and Shaffer, Christian M. and Strauch, Konstantin and Szymczak, Silke and Taylor, Kent D. and Tremblay, Johanne and Chaker, Layal and van der Harst, Pim and van der Most, Peter J. and Verweij, Niek and Voelker, Uwe and Waldenberger, Melanie and Wallentin, Lars and Waterworth, Dawn M. and White, Harvey D. and Wilson, James G. and Wong, Tien-Yin and Woodward, Mark and Yang, Qiong and Yasuda, Masayuki and Yerges-Armstrong, Laura M. and Zhang, Yan and Snieder, Harold and Wanner, Christoph and Boger, Carsten A. and Kottgen, Anna and Kronenberg, Florian and Pattaro, Cristian and Heid, Iris M.},
  title     = {Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  number    = {19},
  doi       = {10.25932/publishup-56537},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-565379},
  pages     = {14},
  year      = {2020},
  abstract  = {Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m(2)/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25\% or more and eGFRcrea under 60 mL/min/1.73m(2) at follow-up among those with eGFRcrea 60 mL/min/1.73m(2) or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or (LARP4B). Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95\% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.},
  language  = {en}
}
@article{GorskiJungLietal.2020,
  author    = {Gorski, Mathias and Jung, Bettina and Li, Yong and Matias-Garcia, Pamela R. and Wuttke, Matthias and Coassin, Stefan and Thio, Chris H. L. and Kleber, Marcus E. and Winkler, Thomas W. and Wanner, Veronika and Chai, Jin-Fang and Chu, Audrey Y. and Cocca, Massimiliano and Feitosa, Mary F. and Ghasemi, Sahar and Hoppmann, Anselm and Horn, Katrin and Li, Man and Nutile, Teresa and Scholz, Markus and Sieber, Karsten B. and Teumer, Alexander and Tin, Adrienne and Wang, Judy and Tayo, Bamidele O. and Ahluwalia, Tarunveer S. and Almgren, Peter and Bakker, Stephan J. L. and Banas, Bernhard and Bansal, Nisha and Biggs, Mary L. and Boerwinkle, Eric and B{\"o}ttinger, Erwin and Brenner, Hermann and Carroll, Robert J. and Chalmers, John and Chee, Miao-Li and Chee, Miao-Ling and Cheng, Ching-Yu and Coresh, Josef and de Borst, Martin H. and Degenhardt, Frauke and Eckardt, Kai-Uwe and Endlich, Karlhans and Franke, Andre and Freitag-Wolf, Sandra and Gampawar, Piyush and Gansevoort, Ron T. and Ghanbari, Mohsen and Gieger, Christian and Hamet, Pavel and Ho, Kevin and Hofer, Edith and Holleczek, Bernd and Foo, Valencia Hui Xian and Hutri-Kahonen, Nina and Hwang, Shih-Jen and Ikram, M. Arfan and Josyula, Navya Shilpa and Kahonen, Mika and Khor, Chiea-Chuen and Koenig, Wolfgang and Kramer, Holly and Kraemer, Bernhard K. and Kuehnel, Brigitte and Lange, Leslie A. and Lehtimaki, Terho and Lieb, Wolfgang and Loos, Ruth J. F. and Lukas, Mary Ann and Lyytikainen, Leo-Pekka and Meisinger, Christa and Meitinger, Thomas and Melander, Olle and Milaneschi, Yuri and Mishra, Pashupati P. and Mononen, Nina and Mychaleckyj, Josyf C. and Nadkarni, Girish N. and Nauck, Matthias and Nikus, Kjell and Ning, Boting and Nolte, Ilja M. and O'Donoghue, Michelle L. and Orho-Melander, Marju and Pendergrass, Sarah A. and Penninx, Brenda W. J. H. and Preuss, Michael H. and Psaty, Bruce M. and Raffield, Laura M. and Raitakari, Olli T. and Rettig, Rainer and Rheinberger, Myriam and Rice, Kenneth M. and Rosenkranz, Alexander R. and Rossing, Peter and Rotter, Jerome and Sabanayagam, Charumathi and Schmidt, Helena and Schmidt, Reinhold and Schoettker, Ben and Schulz, Christina-Alexandra and Sedaghat, Sanaz and Shaffer, Christian M. and Strauch, Konstantin and Szymczak, Silke and Taylor, Kent D. and Tremblay, Johanne and Chaker, Layal and van der Harst, Pim and van der Most, Peter J. and Verweij, Niek and Voelker, Uwe and Waldenberger, Melanie and Wallentin, Lars and Waterworth, Dawn M. and White, Harvey D. and Wilson, James G. and Wong, Tien-Yin and Woodward, Mark and Yang, Qiong and Yasuda, Masayuki and Yerges-Armstrong, Laura M. and Zhang, Yan and Snieder, Harold and Wanner, Christoph and Boger, Carsten A. and Kottgen, Anna and Kronenberg, Florian and Pattaro, Cristian and Heid, Iris M.},
  title     = {Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline},
  series = {Kidney international : official journal of the International Society of Nephrology},
  volume    = {99},
  journal   = {Kidney international : official journal of the International Society of Nephrology},
  number    = {4},
  publisher = {Elsevier},
  address   = {New York},
  organization    = {Lifelines Cohort Study<br /> Regeneron Genetics Ctr},
  issn      = {0085-2538},
  doi       = {10.1016/j.kint.2020.09.030},
  pages     = {926 -- 939},
  year      = {2020},
  abstract  = {Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m(2)/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25\% or more and eGFRcrea under 60 mL/min/1.73m(2) at follow-up among those with eGFRcrea 60 mL/min/1.73m(2) or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or (LARP4B). Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95\% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.},
  language  = {en}
}
@misc{KonigorskiWernickeSlosareketal.2022,
  author    = {Konigorski, Stefan and Wernicke, Sarah and Slosarek, Tamara and Zenner, Alexander M. and Strelow, Nils and Ruether, Darius F. and Henschel, Florian and Manaswini, Manisha and Pottb{\"a}cker, Fabian and Edelman, Jonathan A. and Owoyele, Babajide and Danieletto, Matteo and Golden, Eddye and Zweig, Micol and Nadkarni, Girish N. and B{\"o}ttinger, Erwin},
  title     = {StudyU: a platform for designing and conducting innovative digital N-of-1 trials},
  series = {Journal of medical internet research},
  volume    = {24},
  journal   = {Journal of medical internet research},
  number    = {7},
  publisher = {Healthcare World},
  address   = {Richmond, Va.},
  issn      = {1439-4456},
  doi       = {10.2196/35884},
  pages     = {12},
  year      = {2022},
  abstract  = {N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.},
  language  = {en}
}