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Sub-phenotyping Met...
Sub-phenotyping Metabolic Disorders Using Body Composition : An Individualized, Nonparametric Approach Utilizing Large Data Sets
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- Linge, Jennifer (författare)
- AMRA Medical AB, Linköping, Sweden
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- Whitcher, Brandon (författare)
- AMRA Medical AB, Linköping, Sweden
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- Borga, Magnus, 1965- (författare)
- Linköpings universitet,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Avdelningen för medicinsk teknik,Tekniska fakulteten,AMRA Medical AB, Linköping, Sweden
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- Dahlqvist Leinhard, Olof, 1978- (författare)
- Linköpings universitet,Avdelningen för radiologiska vetenskaper,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Medicinsk strålningsfysik,AMRA Medical AB, Linköping, Sweden
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(creator_code:org_t)
- 2019-05-16
- 2019
- Engelska.
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Ingår i: Obesity. - : John Wiley & Sons. - 1930-7381 .- 1930-739X. ; 27:7, s. 1190-1199
- Relaterad länk:
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https://doi.org/10.1...
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https://liu.diva-por... (primary) (Raw object)
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Endokrinologi och diabetes (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Endocrinology and Diabetes (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- Body composition
- magnetic resonance imaging
- UK Biobank
- coronary heart disease
- type two diabetes
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Obesity
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