{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T23:25:45Z","timestamp":1768951545611,"version":"3.49.0"},"posted":{"date-parts":[[2026,1,16]]},"group-title":"Gastroenterology","reference-count":34,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,1,16]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>The pancreas is essential for metabolic homeostasis. Alterations in morphology and parenchymal integrity may impact proper function but are not routinely used for risk stratification. Here, we propose an AI-pipeline to quantify pancreas volume and fat content from MRI to identify individuals at high-risk for cardiometabolic disease in the general population.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We quantified pancreas volume (milliliters, mL) and intrapancreatic fat content (defined as fat fraction; FF, %) from MRI of UK Biobank (UKB) and German National Cohort (NAKO) participants using deep learning. We 1) analyzed differences in volume and FF across age and sex, 2) computed percentile-curves and z-scores adjusted for age and sex to identify high-risk volumes\/FF, and 3) conducted Cox regression to assess associations between z-score categories (volume: reference, z=\u22121 to 1; low, z=&lt;\u22121; high, z&gt;1; FF: low, z&lt;1; moderate, z=0-1; high, z&gt;1) and incident outcomes (diabetes, major adverse cardiovascular events (MACE), all-cause mortality) after adjustment for risk factors.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    Among 63,548 UKB and NAKO-participants (57.7\u00b112.8 years; BMI: 26.3\u00b14.4 kg\/m\n                    <jats:sup>2<\/jats:sup>\n                    , 46.9% female), automated pancreas analysis revealed a positive association between both volume and FF and age. In 33,099 UKB-participants (median 4.8 years follow-up), z-score categories were associated with incident diabetes (low volume, aHR:1.59, 95%CI[1.20-2.11]; high FF, aHR:1.70, 95%CI[1.31-2.19]), MACE (high volume, aHR: 0.79, 95%CI[0.61-1.01]; high FF, aHR: 1.32, 95%CI[1.01-1.73]), and all-cause mortality (low volume, aHR: 1.48, 95%CI[1.16-1.90]) beyond risk factors. Adding z-score categories to a baseline model including risk factors improved discrimination of future diabetes (volume:0.781 to 0.784, p=0.004; FF:0.781 to 0.787, p&lt;0.001) and mortality (volume:0.781 to 0.787, p&lt;0.001)\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Deviations from normalized pancreas volume and FF predicted cardiometabolic outcomes beyond known risk factors and alcohol intake. This automated approach identifies high-risk individuals who may benefit from cardiometabolic\/endocrinology referral.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.01.15.26344167","type":"posted-content","created":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T03:50:16Z","timestamp":1768621816000},"source":"Crossref","is-referenced-by-count":0,"title":["Refining cardiometabolic risk assessment using MRI-derived pancreas volume and fat content: insights from the NAKO and UK Biobank"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1124-4284","authenticated-orcid":false,"given":"Matthias","family":"Jung","sequence":"first","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Germany"},{"name":"Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeynep","family":"Berkarda","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Reisert","sequence":"additional","affiliation":[{"name":"Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany"},{"name":"Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susanne","family":"Rospleszcz","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Germany"},{"name":"Institute of Epidemiology, Helmholtz Zentrum M\u00fcnchen, German Research Center for Environmental Health, Neuherberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1568-767X","authenticated-orcid":false,"given":"Tobias","family":"Pischon","sequence":"additional","affiliation":[{"name":"Molecular Epidemiology Research Group, Max Delbr\u00fcck Center for Molecular Medicine in the Helmholtz Association (MDC),13125 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thoralf","family":"Niendorf","sequence":"additional","affiliation":[{"name":"Berlin Ultrahigh Field Facility, Max Delbr\u00fcck Center for Molecular Medicine in the Helmholtz Association (MDC),13125 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hans-Ulrich","family":"Kauczor","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, member of the German Center of Lung Research, University Hospital Heidelberg, 69120 Heidelberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henry","family":"V\u00f6lzke","sequence":"additional","affiliation":[{"name":"Institute for Community Medicine, University Medicine Greifswald, 17475 Greifswald, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katharina","family":"Laubner","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Diabetology, Department of Medicine II, Medical Center - 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