Sub‐phenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large Data Sets

Linge, J., Whitcher, B., Borga, M. and Dahlqvist Leinhard, O. 2019. Sub‐phenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large Data Sets. Obesity. 27 (7), pp. 1190-1199. https://doi.org/10.1002/oby.22510

TitleSub‐phenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large Data Sets
TypeJournal article
AuthorsLinge, J., Whitcher, B., Borga, M. and Dahlqvist Leinhard, O.
Abstract

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 consequently enable optimization of treatment.

JournalObesity
Journal citation27 (7), pp. 1190-1199
ISSN1930-7381
Year2019
PublisherWiley
Publisher's version
Digital Object Identifier (DOI)https://doi.org/10.1002/oby.22510
Publication dates
Published16 May 2019
LicenseCC BY-NC-ND 4.0

Related outputs

Snacking on whole almonds for 6 weeks improves endothelial function and lowers LDL cholesterol but does not affect liver fat and other cardiometabolic risk factors in healthy adults: the ATTIS study, a randomized controlled trial
Dikariyanto, V., Smith, L., Francis, L., Robertson, M., Kusaslan, E., O’Callaghan-Latham, M., Palanche, C., D’Annibale, M., Christodoulou, D., Basty, N., Whitcher, B., Shuaib, H., Charles-Edwards, G., Chowienczyk, P.J., Ellis, P. R., Berry, S.E. and Hall, W. 2020. Snacking on whole almonds for 6 weeks improves endothelial function and lowers LDL cholesterol but does not affect liver fat and other cardiometabolic risk factors in healthy adults: the ATTIS study, a randomized controlled trial. American Journal of Clinical Nutrition. 111 (6), p. 1178–1189. https://doi.org/10.1093/ajcn/nqaa100

Ethnic differences in adiposity and diabetes risk – insights from genetic studies
Yaghootkar, H., Bell, J.D., Whitcher, B. and Thomas, E.L. 2020. Ethnic differences in adiposity and diabetes risk – insights from genetic studies. Journal of Internal Medicine. 288 (3), pp. 271-283. https://doi.org/10.1111/joim.13082

Neuroconductor: an R platform for medical imaging analysis
Muschelli, J., Gherman, A., Fortin, J.P., Avants, B., Whitcher, B., Clayden, J.D., Caffo, B.S. and Crainiceanu, C.M. 2019. Neuroconductor: an R platform for medical imaging analysis. Biostatistics. 20 (2), pp. 218-239. https://doi.org/10.1093/biostatistics/kxx068

Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases
Aldraimli, M., Soria, D., Parkinson, J., Whitcher, B., Thomas, E.L., Bell, J.D., Chaussalet, T.J. and Dwek, M. 2019. Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases. MEDICON 2019: XV Mediterranean Conference on Medical and Biological Engineering and Computing. Coimbra, Portugal 26 - 28 Sep 2019 Springer. https://doi.org/10.1007/978-3-030-31635-8_81

Ethnic differences in intrahepatic lipid and its association with hepatic insulin sensitivity and insulin clearance between men of Black and White ethnicity with early type 2 diabetes
Hakim, O., Bello, O., Bonadonna, O.C., Mohandas, C., Shojaee-Moradie, F., Jackson, N., Boselli, L., Whitcher, B., Shuaib, H., Alberti, K.G.M.M., Peacock, J.L., Umpleby, A.M., Charles-Edwards, E., Amiel, S.A. and Goff, L.M. 2019. Ethnic differences in intrahepatic lipid and its association with hepatic insulin sensitivity and insulin clearance between men of Black and White ethnicity with early type 2 diabetes. Diabetes, Obesity and Metabolism. 21 (9), pp. 2163-2168. https://doi.org/10.1111/dom.13771

Permalink - https://westminsterresearch.westminster.ac.uk/item/qv31x/sub-phenotyping-metabolic-disorders-using-body-composition-an-individualized-nonparametric-approach-utilizing-large-data-sets


Share this
Tweet
Email

Usage statistics

18 total views
16 total downloads
0 views this month
0 downloads this month
These values are for the period from September 2nd 2018, when this repository was created

Export as