{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T04:49:18Z","timestamp":1791434958581,"version":"4.3.3"},"reference-count":21,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"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":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiometabolic outcomes. Artificial intelligence (AI)\u2010assisted ultrasound of the rectus femoris (RF) offers a non\u2010invasive approach for quantifying IMF. This study evaluated the association of IMF with diabetes\u2010related complications (particularly diabetic nephropathy) and metabolic risk factors in patients with diabetes mellitus (DM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>In this cross\u2010sectional study, outpatients from a tertiary Endocrinology and Nutrition Department underwent anthropometric assessment, bioimpedance analysis, and muscular ultrasound. Ultrasound images were analysed using the PIIXMED AI\u2010system (DAWAKO MedTech; Valencia, Spain) to quantify muscle (Mi) and fat (FATi) percentages (the latter indicating the IMF). Associations between IMF, clinical characteristics, metabolic biomarkers, and vascular complications were examined.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 120 patients were included (57.5% men), with a mean age of 70.8\u2009\u00b1\u200910.3\u2009years and diabetes duration of 13.8\u2009\u00b1\u20099.6\u2009years. Most participants had type 2 DM (79.2%), with suboptimal glycaemic control (HbA1c 8.1%\u2009\u00b1\u20091.5%). Individuals in the highest FATi quartile (&gt;\u200943%) showed higher prevalence of microvascular complications, particularly diabetic nephropathy (44.0 vs. 14.7%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.001), and lower estimated glomerular filtration rate (63.6\u2009\u00b1\u200921.3 vs. 72.8\u2009\u00b1\u200920.1\u2009mL\/min\/1.73m\n                      <jats:sup>2<\/jats:sup>\n                      ,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.045). Multivariate analysis showed that FATi remained independently associated with nephropathy (OR 6.01, 95% CI 1.99\u201318.14;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01). ROC analysis identified a FATi threshold of 43.5% with modest discriminative ability (AUC\u2009=\u20090.668).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI\u2010assisted ultrasound\u2010derived FATi is associated with adverse metabolic profiles and diabetic nephropathy. Assessment of IMF may provide an accessible biomarker related to microvascular complications. Longitudinal and multicentre studies are needed to determine the role of IMF in diabetes\u2010related complications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.71249","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T04:12:47Z","timestamp":1787631167000},"page":"10409-10417","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intramuscular Fat Assessed by AI\u2010Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7473-5216","authenticated-orcid":false,"given":"Paloma","family":"P\u00e9rez\u2010L\u00f3pez","sequence":"first","affiliation":[{"name":"Hospital Cl\u00ednico Universitario de Valladolid  Valladolid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Jos\u00e9","family":"L\u00f3pez\u2010G\u00f3mez","sequence":"additional","affiliation":[{"name":"Hospital Cl\u00ednico Universitario de Valladolid  Valladolid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1lvaro","family":"de la Calzada\u2010Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Hospital Cl\u00ednico Universitario de Valladolid  Valladolid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaime","family":"Gonz\u00e1lez\u2010Guti\u00e9rrez","sequence":"additional","affiliation":[{"name":"Hospital Cl\u00ednico Universitario de Valladolid  Valladolid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beatriz","family":"Ramos\u2010Bachiller","sequence":"additional","affiliation":[{"name":"Hospital Cl\u00ednico Universitario de Valladolid  Valladolid 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