Forskning
Udskriv Udskriv
Switch language
Region Hovedstaden - en del af Københavns Universitetshospital
Udgivet

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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

DOI

  1. Arginine-vasopressin mediates counter-regulatory glucagon release and is diminished in type 1 diabetes

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Cortical signatures of precision grip force control in children, adolescents, and adults

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Vis graf over relationer

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.

OriginalsprogEngelsk
Artikelnummere44941
TidsskrifteLife
Vol/bind8
Antal sider19
ISSN2050-084X
DOI
StatusUdgivet - 10 dec. 2019

ID: 58624288