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Sub-phenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large Data Sets
AMRA Medical AB, Linköping, Sweden.ORCID iD: 0000-0001-7399-8375
AMRA Medical AB, Linköping, Sweden.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. AMRA Medical AB, Linköping, Sweden.ORCID iD: 0000-0002-9267-2191
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics. AMRA Medical AB, Linköping, Sweden.ORCID iD: 0000-0002-6189-0807
2019 (English)In: Obesity, ISSN 1930-7381, E-ISSN 1930-739X, Vol. 27, no 7, p. 1190-1199Article in journal (Refereed) Published
Abstract [en]

Objective: This study performed individual-centric, data-driven calculations of propensity for coronary heart disease (CHD) and type 2 diabetes (T2D), utilizing magnetic resonance imaging-acquired body composition measurements, for sub-phenotyping of obesity and nonalcoholic fatty liver disease (NAFLD).Methods: A total of 10,019 participants from the UK Biobank imaging substudy were included and analyzed for visceral and abdominal subcutaneous adipose tissue, muscle fat infiltration, and liver fat. An adaption of the k-nearest neighbors algorithm was applied to the imaging variable space to calculate individualized CHD and T2D propensity and explore metabolic sub-phenotyping within obesity and NAFLD.

Results: The ranges of CHD and T2D propensity for the whole cohort were 1.3% to 58.0% and 0.6% to 42.0%, respectively. The diagnostic performance, area under the receiver operating characteristic curve (95% CI), using disease propensities for CHD and T2D detection was 0.75 (0.73-0.77) and 0.79 (0.77-0.81). Exploring individualized disease propensity, CHD phenotypes, T2D phenotypes, comorbid phenotypes, and metabolically healthy phenotypes were found within obesity and NAFLD.

Conclusions: The adaptive k-nearest neighbors algorithm allowed an individual-centric assessment of each individual’s metabolic phenotype moving beyond discrete categorizations of body composition. Within obesity and NAFLD, this may help in identifying which comorbidities a patient may develop and conse- quently enable optimization of treatment.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019. Vol. 27, no 7, p. 1190-1199
Keywords [en]
Body composition, magnetic resonance imaging, UK Biobank, coronary heart disease, type two diabetes
National Category
Medical Image Processing Endocrinology and Diabetes Cardiac and Cardiovascular Systems Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-156958DOI: 10.1002/oby.22510ISI: 000472669700022PubMedID: 31094076OAI: oai:DiVA.org:liu-156958DiVA, id: diva2:1316204
Note

Funding agencies: Pfizer Inc.

Available from: 2019-05-16 Created: 2019-05-16 Last updated: 2019-07-19Bibliographically approved

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Linge, JenniferBorga, MagnusDahlqvist Leinhard, Olof
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