{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:31:21Z","timestamp":1787027481018,"version":"build-2736575974"},"reference-count":60,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"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":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To investigate whether common disturbances of glucose and lipid metabolism can be automatically identified from magnetic resonance signatures of the pancreas and liver.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this proof\u2010of\u2010principle study, 100 individuals with a history of pancreatitis\u2014a relatively homogeneous population at risk for metabolic derangements\u2014underwent magnetic resonance assessment on the same 3.0 Tesla scanner. Automated measurements of fat fraction and water proton transverse relaxation time (R2 water) in the pancreas and liver were obtained. Fasting blood samples were analysed for high\u2010density lipoprotein (HDL) cholesterol, low\u2010density lipoprotein (LDL) cholesterol, triglycerides, glucose and insulin. Associations between magnetic resonance signatures and blood metabolic measures were assessed using generalised additive models adjusted for age, sex and body mass index.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In fully adjusted models, HDL dyslipidaemia was significantly associated with intra\u2010pancreatic fat (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.015) and intra\u2010hepatic fat (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.047), LDL dyslipidaemia with pancreas R2 water (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.009), and triglyceride dyslipidaemia with intra\u2010hepatic fat (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.046). Lower HOMA\u2010\u03b2 was significantly associated with intra\u2010pancreatic fat (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.001), intra\u2010hepatic fat (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.004), pancreas R2 water (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.031) and liver R2 water (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.014). Higher HOMA\u2010IR was significantly associated with pancreas R2 water (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.016).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Automated magnetic resonance signatures of pancreatic and hepatic tissue composition were significantly associated with clinically relevant disturbances in lipid metabolism and indices of glucose homeostasis. These findings support the feasibility of opportunistic, automated detection of abnormal blood metabolic parameters using high\u2010resolution cross\u2010sectional imaging.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.70606","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T16:58:49Z","timestamp":1772989129000},"page":"4162-4175","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Identification of Common Blood Metabolic Derangements Using Magnetic Resonance Signatures of the Pancreas and Liver"],"prefix":"10.1111","volume":"28","author":[{"given":"Wandia","family":"Kimita","sequence":"first","affiliation":[{"name":"School of Medicine University of Auckland  Auckland New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juyeon","family":"Ko","sequence":"additional","affiliation":[{"name":"College of Medicine Yonsei University  Seoul Republic 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