{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T14:21:39Z","timestamp":1788358899801,"version":"build-2803163510"},"reference-count":50,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Magnetic resonance imaging (MRI) provides the most accurate assessments of site\u2010specific fat accumulation, but is not readily available. Less\u2010accurate methods, such as Dual\u2010energy x\u2010ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and anthropometry, have been widely used to measure overall and regional adiposity. However, the extent to which these approaches reflect MRI\u2010derived fat depots is not fully understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We included 18\u2009622 White participants from the UK Biobank imaging visit. A total of 27 indices from DXA, BIA, and anthropometry were selected to characterise overall and regional adiposity, and all indices were standardised. In subgroups stratified by age and sex, Pearson correlation coefficients were used to assess the degree of linear association between these indices and MRI\u2010determined subcutaneous, visceral, liver, and pancreas fat. Intraclass correlation coefficients (ICC) were further calculated to evaluate the absolute agreement between the corresponding fat measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Generally, correlations of adiposity indices from DXA (total, trunk, android, gynoid, arm, and leg fat), BIA (total, trunk, arm, and leg fat), and anthropometry (body mass index (BMI), waist and hip circumference) with MRI measures were strongest for subcutaneous fat, followed by visceral fat, and weakest for liver and pancreas fat. The correlation coefficients with MRI\u2010based visceral fat were larger for indices reflecting abdominal adiposity (e.g., DXA\u2010based visceral, android, and trunk fat, BIA\u2010based trunk fat, and waist circumference) and for measures of total adiposity (e.g., DXA\u2010 and BIA\u2010derived total fat). The absolute agreement with MRI\u2010based visceral fat was highest for DXA\u2010based visceral, trunk, and android fat (ICC\u2009=\u20090.73\u20130.94), followed by waist circumference (ICC\u2009=\u20090.73\u20130.77), and then the remaining indices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Indices of abdominal adiposity (as indicated by DXA\u2010derived visceral, trunk, and android fat and waist circumference) appeared highly reflective of MRI\u2010determined visceral fat. Considering cost and accessibility, waist circumference may serve as the most appropriate surrogate for assessing visceral fat.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.70456","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T03:48:45Z","timestamp":1767671325000},"page":"2795-2805","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Comparison of\n                    <scp>DXA<\/scp>\n                    ,\n                    <scp>BIA<\/scp>\n                    , and anthropometry for assessing subcutaneous, visceral, liver, and pancreas fat measured by\n                    <scp>MRI<\/scp>"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2768-4667","authenticated-orcid":false,"given":"Danyang","family":"Wang","sequence":"first","affiliation":[{"name":"Diabetes and Population Health Laboratory Baker Heart and Diabetes Institute  Melbourne Australia"},{"name":"School of Public Health and Preventive Medicine Monash University  Melbourne Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jedidiah I.","family":"Morton","sequence":"additional","affiliation":[{"name":"Diabetes and Population Health Laboratory Baker Heart and Diabetes Institute  Melbourne Australia"},{"name":"School of Public Health and Preventive Medicine Monash University  Melbourne Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agus","family":"Salim","sequence":"additional","affiliation":[{"name":"Diabetes and Population Health Laboratory Baker Heart and Diabetes Institute  Melbourne Australia"},{"name":"Melbourne School of Population and Global Health The University of Melbourne  Melbourne Australia"},{"name":"School of Mathematics and Statistics The University of Melbourne  Melbourne Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dianna J.","family":"Magliano","sequence":"additional","affiliation":[{"name":"Diabetes and Population Health Laboratory Baker Heart and Diabetes Institute  Melbourne Australia"},{"name":"School of Public Health and Preventive Medicine Monash University  Melbourne Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan E.","family":"Shaw","sequence":"additional","affiliation":[{"name":"Diabetes and Population Health Laboratory Baker Heart and Diabetes Institute  Melbourne Australia"},{"name":"School of Public Health and Preventive Medicine Monash University  Melbourne Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrdp.2017.34"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmet.2021.12.012"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41580-023-00680-1"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11886-023-01969-5"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/nu16071015"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/dom.70021"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/ijo.2017.289"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpediatrics.2009.148"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001779"},{"key":"e_1_2_11_11_1","unstructured":"The UK Biobank.DXA assessment. 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