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Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining

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Kirk, IK, Simon, C, Banasik, K, Holm, PC, Haue, AD, Jensen, PB, Juhl Jensen, L, Rodríguez, CL, Pedersen, MK, Eriksson, R, Andersen, HU, Almdal, T, Bork-Jensen, J, Grarup, N, Borch-Johnsen, K, Pedersen, O, Pociot, F, Hansen, T, Bergholdt, R, Rossing, P & Brunak, S 2019, 'Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining' eLife, bind 8, e44941. https://doi.org/10.7554/eLife.44941

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Author

Kirk, Isa Kristina ; Simon, Christian ; Banasik, Karina ; Holm, Peter Christoffer ; Haue, Amalie Dahl ; Jensen, Peter Bjødstrup ; Juhl Jensen, Lars ; Rodríguez, Cristina Leal ; Pedersen, Mette Krogh ; Eriksson, Robert ; Andersen, Henrik Ullits ; Almdal, Thomas ; Bork-Jensen, Jette ; Grarup, Niels ; Borch-Johnsen, Knut ; Pedersen, Oluf ; Pociot, Flemming ; Hansen, Torben ; Bergholdt, Regine ; Rossing, Peter ; Brunak, Søren. / Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining. I: eLife. 2019 ; Bind 8.

Bibtex

@article{ad36afd541fa48e58625c92d10a26fbb,
title = "Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining",
abstract = "Diabetes is a diverse and complex disease, with considerable variation in phenotypic manifestation and severity. This variation hampers the study of etiological differences and reduces the statistical power of analyses of associations to genetics, treatment outcomes, and complications. We address these issues through deep, fine-grained phenotypic stratification of a diabetes cohort. Text mining the electronic health records of 14,017 patients, we matched two controlled vocabularies (ICD-10 and a custom vocabulary developed at the clinical center Steno Diabetes Center Copenhagen) to clinical narratives spanning a 19 year period. The two matched vocabularies comprise over 20,000 medical terms describing symptoms, other diagnoses, and lifestyle factors. The cohort is genetically homogeneous (Caucasian diabetes patients from Denmark) so the resulting stratification is not driven by ethnic differences, but rather by inherently dissimilar progression patterns and lifestyle related risk factors. Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum. The clusters display both distinct and shared longitudinal glycemic dysregulation patterns, temporal co-occurrences of comorbidities, and associations to single nucleotide polymorphisms in or near genes relevant for diabetes comorbidities.",
author = "Kirk, {Isa Kristina} and Christian Simon and Karina Banasik and Holm, {Peter Christoffer} and Haue, {Amalie Dahl} and Jensen, {Peter Bj{\o}dstrup} and {Juhl Jensen}, Lars and Rodr{\'i}guez, {Cristina Leal} and Pedersen, {Mette Krogh} and Robert Eriksson and Andersen, {Henrik Ullits} and Thomas Almdal and Jette Bork-Jensen and Niels Grarup and Knut Borch-Johnsen and Oluf Pedersen and Flemming Pociot and Torben Hansen and Regine Bergholdt and Peter Rossing and S{\o}ren Brunak",
note = "{\circledC} 2019, Kirk et al.",
year = "2019",
month = "12",
day = "10",
doi = "10.7554/eLife.44941",
language = "English",
volume = "8",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications",

}

RIS

TY - JOUR

T1 - Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining

AU - Kirk, Isa Kristina

AU - Simon, Christian

AU - Banasik, Karina

AU - Holm, Peter Christoffer

AU - Haue, Amalie Dahl

AU - Jensen, Peter Bjødstrup

AU - Juhl Jensen, Lars

AU - Rodríguez, Cristina Leal

AU - Pedersen, Mette Krogh

AU - Eriksson, Robert

AU - Andersen, Henrik Ullits

AU - Almdal, Thomas

AU - Bork-Jensen, Jette

AU - Grarup, Niels

AU - Borch-Johnsen, Knut

AU - Pedersen, Oluf

AU - Pociot, Flemming

AU - Hansen, Torben

AU - Bergholdt, Regine

AU - Rossing, Peter

AU - Brunak, Søren

N1 - © 2019, Kirk et al.

PY - 2019/12/10

Y1 - 2019/12/10

N2 - Diabetes is a diverse and complex disease, with considerable variation in phenotypic manifestation and severity. This variation hampers the study of etiological differences and reduces the statistical power of analyses of associations to genetics, treatment outcomes, and complications. We address these issues through deep, fine-grained phenotypic stratification of a diabetes cohort. Text mining the electronic health records of 14,017 patients, we matched two controlled vocabularies (ICD-10 and a custom vocabulary developed at the clinical center Steno Diabetes Center Copenhagen) to clinical narratives spanning a 19 year period. The two matched vocabularies comprise over 20,000 medical terms describing symptoms, other diagnoses, and lifestyle factors. The cohort is genetically homogeneous (Caucasian diabetes patients from Denmark) so the resulting stratification is not driven by ethnic differences, but rather by inherently dissimilar progression patterns and lifestyle related risk factors. Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum. The clusters display both distinct and shared longitudinal glycemic dysregulation patterns, temporal co-occurrences of comorbidities, and associations to single nucleotide polymorphisms in or near genes relevant for diabetes comorbidities.

AB - Diabetes is a diverse and complex disease, with considerable variation in phenotypic manifestation and severity. This variation hampers the study of etiological differences and reduces the statistical power of analyses of associations to genetics, treatment outcomes, and complications. We address these issues through deep, fine-grained phenotypic stratification of a diabetes cohort. Text mining the electronic health records of 14,017 patients, we matched two controlled vocabularies (ICD-10 and a custom vocabulary developed at the clinical center Steno Diabetes Center Copenhagen) to clinical narratives spanning a 19 year period. The two matched vocabularies comprise over 20,000 medical terms describing symptoms, other diagnoses, and lifestyle factors. The cohort is genetically homogeneous (Caucasian diabetes patients from Denmark) so the resulting stratification is not driven by ethnic differences, but rather by inherently dissimilar progression patterns and lifestyle related risk factors. Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum. The clusters display both distinct and shared longitudinal glycemic dysregulation patterns, temporal co-occurrences of comorbidities, and associations to single nucleotide polymorphisms in or near genes relevant for diabetes comorbidities.

U2 - 10.7554/eLife.44941

DO - 10.7554/eLife.44941

M3 - Journal article

VL - 8

JO - eLife

JF - eLife

SN - 2050-084X

M1 - e44941

ER -

ID: 58624288